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is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Sahu%2C+A&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Sahu%2C+A&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Sahu%2C+A&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.10541">arXiv:2503.10541</a> <span> [<a href="https://arxiv.org/pdf/2503.10541">pdf</a>, <a href="https://arxiv.org/format/2503.10541">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Discrete Mathematics">cs.DM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Combinatorics">math.CO</span> </div> </div> <p class="title is-5 mathjax"> Towards Transitive-free Digraphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abhinav%2C+A">Ankit Abhinav</a>, <a href="/search/cs?searchtype=author&query=Jana%2C+S">Satyabrata Jana</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhishek Sahu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.10541v1-abstract-short" style="display: inline;"> In a digraph $D$, an arc $e=(x,y) $ in $D$ is considered transitive if there is a path from $x$ to $y$ in $D- e$. A digraph is transitive-free if it does not contain any transitive arc. In the Transitive-free Vertex Deletion (TVD) problem, the goal is to find at most $k$ vertices $S$ such that $D-S$ has no transitive arcs. In our work, we study a more general version of the TVD problem, denoted by… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.10541v1-abstract-full').style.display = 'inline'; document.getElementById('2503.10541v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.10541v1-abstract-full" style="display: none;"> In a digraph $D$, an arc $e=(x,y) $ in $D$ is considered transitive if there is a path from $x$ to $y$ in $D- e$. A digraph is transitive-free if it does not contain any transitive arc. In the Transitive-free Vertex Deletion (TVD) problem, the goal is to find at most $k$ vertices $S$ such that $D-S$ has no transitive arcs. In our work, we study a more general version of the TVD problem, denoted by $\ell$-Relaxed Transitive-free Vertex Deletion ($\ell$-RTVD), where we look for at most $k$ vertices $S$ such that $D-S$ has no more than $\ell$ transitive arcs. We explore $\ell$-RTVD on various well-known graph classes of digraphs such as directed acyclic graphs (DAGs), planar DAGs, $伪$-bounded digraphs, tournaments, and their multiple generalizations such as in-tournaments, out-tournaments, local tournaments, acyclic local tournaments, and obtain the following results. Although the problem admits polynomial-time algorithms in tournaments, $伪$-bounded digraphs, and acyclic local tournaments for fixed values of $\ell$, it remains NP-hard even in planar DAGs with maximum degree 6. In the parameterized realm, for $\ell$-RTVD on in-tournaments and out-tournaments, we obtain polynomial kernels parameterized by $k+\ell$ for bounded independence number. But the problem remains fixed-parameter intractable on DAGs when parameterized by $k$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.10541v1-abstract-full').style.display = 'none'; document.getElementById('2503.10541v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.14249">arXiv:2501.14249</a> <span> [<a href="https://arxiv.org/pdf/2501.14249">pdf</a>, <a href="https://arxiv.org/format/2501.14249">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Humanity's Last Exam </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Phan%2C+L">Long Phan</a>, <a href="/search/cs?searchtype=author&query=Gatti%2C+A">Alice Gatti</a>, <a href="/search/cs?searchtype=author&query=Han%2C+Z">Ziwen Han</a>, <a href="/search/cs?searchtype=author&query=Li%2C+N">Nathaniel Li</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+J">Josephina Hu</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+H">Hugh Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+C+B+C">Chen Bo Calvin Zhang</a>, <a href="/search/cs?searchtype=author&query=Shaaban%2C+M">Mohamed Shaaban</a>, <a href="/search/cs?searchtype=author&query=Ling%2C+J">John Ling</a>, <a href="/search/cs?searchtype=author&query=Shi%2C+S">Sean Shi</a>, <a href="/search/cs?searchtype=author&query=Choi%2C+M">Michael Choi</a>, <a href="/search/cs?searchtype=author&query=Agrawal%2C+A">Anish Agrawal</a>, <a href="/search/cs?searchtype=author&query=Chopra%2C+A">Arnav Chopra</a>, <a href="/search/cs?searchtype=author&query=Khoja%2C+A">Adam Khoja</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+R">Ryan Kim</a>, <a href="/search/cs?searchtype=author&query=Ren%2C+R">Richard Ren</a>, <a href="/search/cs?searchtype=author&query=Hausenloy%2C+J">Jason Hausenloy</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+O">Oliver Zhang</a>, <a href="/search/cs?searchtype=author&query=Mazeika%2C+M">Mantas Mazeika</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+T">Tung Nguyen</a>, <a href="/search/cs?searchtype=author&query=Anderson%2C+D">Daron Anderson</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+I+A">Imad Ali Shah</a>, <a href="/search/cs?searchtype=author&query=Doroshenko%2C+M">Mikhail Doroshenko</a>, <a href="/search/cs?searchtype=author&query=Stokes%2C+A+C">Alun Cennyth Stokes</a>, <a href="/search/cs?searchtype=author&query=Mahmood%2C+M">Mobeen Mahmood</a> , et al. (709 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.14249v5-abstract-short" style="display: inline;"> Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.14249v5-abstract-full').style.display = 'inline'; document.getElementById('2501.14249v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.14249v5-abstract-full" style="display: none;"> Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.14249v5-abstract-full').style.display = 'none'; document.getElementById('2501.14249v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">27 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.03919">arXiv:2411.03919</a> <span> [<a href="https://arxiv.org/pdf/2411.03919">pdf</a>, <a href="https://arxiv.org/format/2411.03919">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> A Causal Framework for Precision Rehabilitation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cotton%2C+R+J">R. James Cotton</a>, <a href="/search/cs?searchtype=author&query=Seamon%2C+B+A">Bryant A. Seamon</a>, <a href="/search/cs?searchtype=author&query=Segal%2C+R+L">Richard L. Segal</a>, <a href="/search/cs?searchtype=author&query=Davis%2C+R+D">Randal D. Davis</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Amrita Sahu</a>, <a href="/search/cs?searchtype=author&query=McLeod%2C+M+M">Michelle M. McLeod</a>, <a href="/search/cs?searchtype=author&query=Celnik%2C+P">Pablo Celnik</a>, <a href="/search/cs?searchtype=author&query=Ramey%2C+S+L">Sharon L. Ramey</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.03919v1-abstract-short" style="display: inline;"> Precision rehabilitation offers the promise of an evidence-based approach for optimizing individual rehabilitation to improve long-term functional outcomes. Emerging techniques, including those driven by artificial intelligence, are rapidly expanding our ability to quantify the different domains of function during rehabilitation, other encounters with healthcare, and in the community. While this s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03919v1-abstract-full').style.display = 'inline'; document.getElementById('2411.03919v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03919v1-abstract-full" style="display: none;"> Precision rehabilitation offers the promise of an evidence-based approach for optimizing individual rehabilitation to improve long-term functional outcomes. Emerging techniques, including those driven by artificial intelligence, are rapidly expanding our ability to quantify the different domains of function during rehabilitation, other encounters with healthcare, and in the community. While this seems poised to usher rehabilitation into the era of big data and should be a powerful driver of precision rehabilitation, our field lacks a coherent framework to utilize these data and deliver on this promise. We propose a framework that builds upon multiple existing pillars to fill this gap. Our framework aims to identify the Optimal Dynamic Treatment Regimens (ODTR), or the decision-making strategy that takes in the range of available measurements and biomarkers to identify interventions likely to maximize long-term function. This is achieved by designing and fitting causal models, which extend the Computational Neurorehabilitation framework using tools from causal inference. These causal models can learn from heterogeneous data from different silos, which must include detailed documentation of interventions, such as using the Rehabilitation Treatment Specification System. The models then serve as digital twins of patient recovery trajectories, which can be used to learn the ODTR. Our causal modeling framework also emphasizes quantitatively linking changes across levels of the functioning to ensure that interventions can be precisely selected based on careful measurement of impairments while also being selected to maximize outcomes that are meaningful to patients and stakeholders. We believe this approach can provide a unifying framework to leverage growing big rehabilitation data and AI-powered measurements to produce precision rehabilitation treatments that can improve clinical outcomes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03919v1-abstract-full').style.display = 'none'; document.getElementById('2411.03919v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">keywords: rehabilitation; precision rehabilitation; causal inference; international classification of functioning; rehabilitation treatment specification system; computational neurorehabilitation</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.00361">arXiv:2411.00361</a> <span> [<a href="https://arxiv.org/pdf/2411.00361">pdf</a>, <a href="https://arxiv.org/format/2411.00361">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Hierarchical Preference Optimization: Learning to achieve goals via feasible subgoals prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Singh%2C+U">Utsav Singh</a>, <a href="/search/cs?searchtype=author&query=Chakraborty%2C+S">Souradip Chakraborty</a>, <a href="/search/cs?searchtype=author&query=Suttle%2C+W+A">Wesley A. Suttle</a>, <a href="/search/cs?searchtype=author&query=Sadler%2C+B+M">Brian M. Sadler</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+M">Mubarak Shah</a>, <a href="/search/cs?searchtype=author&query=Namboodiri%2C+V+P">Vinay P. Namboodiri</a>, <a href="/search/cs?searchtype=author&query=Bedi%2C+A+S">Amrit Singh Bedi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.00361v1-abstract-short" style="display: inline;"> This work introduces Hierarchical Preference Optimization (HPO), a novel approach to hierarchical reinforcement learning (HRL) that addresses non-stationarity and infeasible subgoal generation issues when solving complex robotic control tasks. HPO leverages maximum entropy reinforcement learning combined with token-level Direct Preference Optimization (DPO), eliminating the need for pre-trained re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00361v1-abstract-full').style.display = 'inline'; document.getElementById('2411.00361v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.00361v1-abstract-full" style="display: none;"> This work introduces Hierarchical Preference Optimization (HPO), a novel approach to hierarchical reinforcement learning (HRL) that addresses non-stationarity and infeasible subgoal generation issues when solving complex robotic control tasks. HPO leverages maximum entropy reinforcement learning combined with token-level Direct Preference Optimization (DPO), eliminating the need for pre-trained reference policies that are typically unavailable in challenging robotic scenarios. Mathematically, we formulate HRL as a bi-level optimization problem and transform it into a primitive-regularized DPO formulation, ensuring feasible subgoal generation and avoiding degenerate solutions. Extensive experiments on challenging robotic navigation and manipulation tasks demonstrate impressive performance of HPO, where it shows an improvement of up to 35% over the baselines. Furthermore, ablation studies validate our design choices, and quantitative analyses confirm the ability of HPO to mitigate non-stationarity and infeasible subgoal generation issues in HRL. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00361v1-abstract-full').style.display = 'none'; document.getElementById('2411.00361v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20900">arXiv:2410.20900</a> <span> [<a href="https://arxiv.org/pdf/2410.20900">pdf</a>, <a href="https://arxiv.org/format/2410.20900">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> Parameterized Approximation for Capacitated $d$-Hitting Set with Hard Capacities </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lokshtanov%2C+D">Daniel Lokshtanov</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhishek Sahu</a>, <a href="/search/cs?searchtype=author&query=Saurabh%2C+S">Saket Saurabh</a>, <a href="/search/cs?searchtype=author&query=Surianarayanan%2C+V">Vaishali Surianarayanan</a>, <a href="/search/cs?searchtype=author&query=Xue%2C+J">Jie Xue</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.20900v1-abstract-short" style="display: inline;"> The \textsc{Capacitated $d$-Hitting Set} problem involves a universe $U$ with a capacity function $\mathsf{cap}: U \rightarrow \mathbb{N}$ and a collection $\mathcal{A}$ of subsets of $U$, each of size at most $d$. The goal is to find a minimum subset $S \subseteq U$ and an assignment $蠁: \mathcal{A} \rightarrow S$ such that for every $A \in \mathcal{A}$, $蠁(A) \in A$, and for each $x \in U$,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20900v1-abstract-full').style.display = 'inline'; document.getElementById('2410.20900v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20900v1-abstract-full" style="display: none;"> The \textsc{Capacitated $d$-Hitting Set} problem involves a universe $U$ with a capacity function $\mathsf{cap}: U \rightarrow \mathbb{N}$ and a collection $\mathcal{A}$ of subsets of $U$, each of size at most $d$. The goal is to find a minimum subset $S \subseteq U$ and an assignment $蠁: \mathcal{A} \rightarrow S$ such that for every $A \in \mathcal{A}$, $蠁(A) \in A$, and for each $x \in U$, $|蠁^{-1}(x)| \leq \mathsf{cap}(x)$. For $d=2$, this is known as \textsc{Capacitated Vertex Cover}. In the weighted variant, each element of $U$ has a positive integer weight, with the objective of finding a minimum-weight capacitated hitting set. Chuzhoy and Naor [SICOMP 2006] provided a factor-3 approximation for \textsc{Capacitated Vertex Cover} and showed that the weighted case lacks an $o(\log n)$-approximation unless $P=NP$. Kao and Wong [SODA 2017] later independently achieved a $d$-approximation for \textsc{Capacitated $d$-Hitting Set}, with no $d - 蔚$ improvements possible under the Unique Games Conjecture. Our main result is a parameterized approximation algorithm with runtime $\left(\frac{k}蔚\right)^k 2^{k^{O(kd)}}(|U|+|\mathcal{A}|)^{O(1)}$ that either concludes no solution of size $\leq k$ exists or finds $S$ of size $\leq 4/3 \cdot k$ and weight at most $2+蔚$ times the minimum weight for solutions of size $\leq k$. We further show that no FPT-approximation with factor $c > 1$ exists for unweighted \textsc{Capacitated $d$-Hitting Set} with $d \geq 3$, nor with factor $2 - 蔚$ for the weighted version, assuming the Exponential Time Hypothesis. These results extend to \textsc{Capacitated Vertex Cover} in multigraphs. Additionally, a variant of multi-dimensional \textsc{Knapsack} is shown hard to FPT-approximate within $2 - 蔚$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20900v1-abstract-full').style.display = 'none'; document.getElementById('2410.20900v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to SODA 2025, Abstract is shortened due to space requirement</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16724">arXiv:2410.16724</a> <span> [<a href="https://arxiv.org/pdf/2410.16724">pdf</a>, <a href="https://arxiv.org/format/2410.16724">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Efficient Scheduling of Vehicular Tasks on Edge Systems with Green Energy and Battery Storage </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sarkar%2C+S">Suvarthi Sarkar</a>, <a href="/search/cs?searchtype=author&query=Ray%2C+A+K">Abinash Kumar Ray</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Aryabartta Sahu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.16724v2-abstract-short" style="display: inline;"> The autonomous vehicle industry is rapidly expanding, requiring significant computational resources for tasks like perception and decision-making. Vehicular edge computing has emerged to meet this need, utilizing roadside computational units (roadside edge servers) to support autonomous vehicles. Aligning with the trend of green cloud computing, these roadside edge servers often get energy from so… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16724v2-abstract-full').style.display = 'inline'; document.getElementById('2410.16724v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16724v2-abstract-full" style="display: none;"> The autonomous vehicle industry is rapidly expanding, requiring significant computational resources for tasks like perception and decision-making. Vehicular edge computing has emerged to meet this need, utilizing roadside computational units (roadside edge servers) to support autonomous vehicles. Aligning with the trend of green cloud computing, these roadside edge servers often get energy from solar power. Additionally, each roadside computational unit is equipped with a battery for storing solar power, ensuring continuous computational operation during periods of low solar energy availability. In our research, we address the scheduling of computational tasks generated by autonomous vehicles to roadside units with power consumption proportional to the cube of the computational load of the server. Each computational task is associated with a revenue, dependent on its computational needs and deadline. Our objective is to maximize the total revenue of the system of roadside computational units. We propose an offline heuristics approach based on predicted solar energy and incoming task patterns for different time slots. Additionally, we present heuristics for real-time adaptation to varying solar energy and task patterns from predicted values for different time slots. Our comparative analysis shows that our methods outperform state-of-the-art approaches upto 40\% for real-life datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16724v2-abstract-full').style.display = 'none'; document.getElementById('2410.16724v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.04609">arXiv:2409.04609</a> <span> [<a href="https://arxiv.org/pdf/2409.04609">pdf</a>, <a href="https://arxiv.org/format/2409.04609">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhijeet Sahu</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+T">Truc Nguyen</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+K">Kejun Chen</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+X">Xiangyu Zhang</a>, <a href="/search/cs?searchtype=author&query=Hassanaly%2C+M">Malik Hassanaly</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.04609v1-abstract-short" style="display: inline;"> With the deeper penetration of inverter-based resources in power systems, false data injection attacks (FDIA) are a growing cyber-security concern. They have the potential to disrupt the system's stability like frequency stability, thereby leading to catastrophic failures. Therefore, an FDIA detection method would be valuable to protect power systems. FDIAs typically induce a discrepancy between t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.04609v1-abstract-full').style.display = 'inline'; document.getElementById('2409.04609v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.04609v1-abstract-full" style="display: none;"> With the deeper penetration of inverter-based resources in power systems, false data injection attacks (FDIA) are a growing cyber-security concern. They have the potential to disrupt the system's stability like frequency stability, thereby leading to catastrophic failures. Therefore, an FDIA detection method would be valuable to protect power systems. FDIAs typically induce a discrepancy between the desired and the effective behavior of the power system dynamics. A suitable detection method can leverage power dynamics predictions to identify whether such a discrepancy was induced by an FDIA. This work investigates the efficacy of temporal and spatio-temporal state prediction models, such as Long Short-Term Memory (LSTM) and a combination of Graph Neural Networks (GNN) with LSTM, for predicting frequency dynamics in the absence of an FDIA but with noisy measurements, and thereby identify FDIA events. For demonstration purposes, the IEEE 39 New England Kron-reduced model simulated with a swing equation is considered. It is shown that the proposed state prediction models can be used as a building block for developing an effective FDIA detection method that can maintain high detection accuracy across various attack and deployment settings. It is also shown how the FDIA detection should be deployed to limit its exposure to detection inaccuracies and mitigate its computational burden. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.04609v1-abstract-full').style.display = 'none'; document.getElementById('2409.04609v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Under review</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.16958">arXiv:2408.16958</a> <span> [<a href="https://arxiv.org/pdf/2408.16958">pdf</a>, <a href="https://arxiv.org/format/2408.16958">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Prasad%2C+R">Romesh Prasad</a>, <a href="/search/cs?searchtype=author&query=Hassanaly%2C+M">Malik Hassanaly</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+X">Xiangyu Zhang</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhijeet Sahu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.16958v1-abstract-short" style="display: inline;"> While inverter-based distributed energy resources (DERs) play a crucial role in integrating renewable energy into the power system, they concurrently diminish the grid's system inertia, elevating the risk of frequency instabilities. Furthermore, smart inverters, interfaced via communication networks, pose a potential vulnerability to cyber threats if not diligently managed. To proactively fortify… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.16958v1-abstract-full').style.display = 'inline'; document.getElementById('2408.16958v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.16958v1-abstract-full" style="display: none;"> While inverter-based distributed energy resources (DERs) play a crucial role in integrating renewable energy into the power system, they concurrently diminish the grid's system inertia, elevating the risk of frequency instabilities. Furthermore, smart inverters, interfaced via communication networks, pose a potential vulnerability to cyber threats if not diligently managed. To proactively fortify the power grid against sophisticated cyber attacks, we propose to employ reinforcement learning (RL) to identify potential threats and system vulnerabilities. This study concentrates on analyzing adversarial strategies for false data injection, specifically targeting smart inverters involved in primary frequency control. Our findings demonstrate that an RL agent can adeptly discern optimal false data injection methods to manipulate inverter settings, potentially causing catastrophic consequences. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.16958v1-abstract-full').style.display = 'none'; document.getElementById('2408.16958v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.01176">arXiv:2408.01176</a> <span> [<a href="https://arxiv.org/pdf/2408.01176">pdf</a>, <a href="https://arxiv.org/format/2408.01176">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Power Aware Container Placement in Cloud Computing with Affinity and Cubic Power Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sarkar%2C+S">Suvarthi Sarkar</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+N">Nandini Sharma</a>, <a href="/search/cs?searchtype=author&query=Mittal%2C+A">Akshat Mittal</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Aryabartta Sahu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.01176v1-abstract-short" style="display: inline;"> Modern data centres are increasingly adopting containers to enhance power and performance efficiency. These data centres consist of multiple heterogeneous machines, each equipped with varying amounts of resources such as CPU, I/O, memory, and network bandwidth. Data centers rent their resources to applications, which demand different amounts of resources and execute on machines for extended durati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.01176v1-abstract-full').style.display = 'inline'; document.getElementById('2408.01176v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.01176v1-abstract-full" style="display: none;"> Modern data centres are increasingly adopting containers to enhance power and performance efficiency. These data centres consist of multiple heterogeneous machines, each equipped with varying amounts of resources such as CPU, I/O, memory, and network bandwidth. Data centers rent their resources to applications, which demand different amounts of resources and execute on machines for extended durations if the machines provide the demanded resources to the applications. Certain applications run efficiently on specific machines, referred to as system affinity between applications and machines. In contrast, others are incompatible with specific machines, referred to as anti-affinity between applications and machines. We consider that there are multiple applications, and data centers need to execute as many applications as possible. Data centers incur electricity based on CPU usage due to the execution of applications, with the cost being proportional to the cube of the total CPU usage. It is a challenging problem to place applications on the machines they have an affinity for while keeping the electricity cost in check. Our work addresses the placement problem of matching applications to machines to minimize overall electricity costs while maximizing the number of affinity pairs of machines and applications. We propose three solution approaches: (a) Power-Aware Placement (PAP): applications are placed on machines where power usage is minimized, (b) Affinity-Aware Placement (AAP): applications are placed on machines where affinity is maximized, (c) Combined Power-Affinity Placement (CPAAP): this approach integrates the benefits of both PAP and AAP. Our proposed approach improves the affinity satisfaction ratio by up to 4% while reducing the total system cost by up to 26% and improving the affinity payoff ratio by up to 37% compared to state-of-the-art approaches for real-life datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.01176v1-abstract-full').style.display = 'none'; document.getElementById('2408.01176v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.14793">arXiv:2407.14793</a> <span> [<a href="https://arxiv.org/pdf/2407.14793">pdf</a>, <a href="https://arxiv.org/format/2407.14793">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> QoS Aware Mixed-Criticality Task Scheduling in Vehicular Edge Cloud System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sarkar%2C+S">Suvarthi Sarkar</a>, <a href="/search/cs?searchtype=author&query=Trivedi%2C+A">Aditya Trivedi</a>, <a href="/search/cs?searchtype=author&query=Bansal%2C+R">Ritish Bansal</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Aryabartta Sahu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.14793v1-abstract-short" style="display: inline;"> Modern-day cars are equipped with numerous cameras and sensors, typically integrated with advanced decision-control systems that enable the vehicle to perceive its surroundings and navigate autonomously. Efficient processing of data from sensors, lidars, radars and cameras is quite computationally intensive and can not be done with good accuracy using less capable onboard resources. In order to de… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.14793v1-abstract-full').style.display = 'inline'; document.getElementById('2407.14793v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.14793v1-abstract-full" style="display: none;"> Modern-day cars are equipped with numerous cameras and sensors, typically integrated with advanced decision-control systems that enable the vehicle to perceive its surroundings and navigate autonomously. Efficient processing of data from sensors, lidars, radars and cameras is quite computationally intensive and can not be done with good accuracy using less capable onboard resources. In order to deal with this problem, some computation requirements (also referred as tasks) are offloaded to infrastructure or executed in parallel in both autonomous vehicle (AV) and infrastructure to enhance accuracy. The infrastructure comprises base stations, a centralized cloud, and a CS. Base stations (BSs) execute tasks in collaboration with a significantly more powerful centralized cloud, while the centralised scheduler (CS) centrally schedules all the tasks. The base station receives tasks from multiple AVs, each with varying deadlines, criticality, and locations. Our main goal is to maximize the profit of the infrastructure by (a) minimizing the number of drop tasks, (b) minimizing the distance cost for task offloading, and (c) minimizing the energy usage of BSs. In this work, we proposed efficient approaches to schedule the collection of tasks to the BSs, by employing a hybrid scheduling approach where tasks from AVs get allocated to nearby base stations if the nearby BSs are lightly loaded, otherwise AVs send the task to CS for allocation. The CS maximizes the profit by following strategies: (a) selection of BS considering distance and energy consumption, (b) when task load is moderate or low, highly critical tasks run at favourable utilisation, and (c) low-critical tasks are dropped to free up resources for executing high-critical tasks. Based on our experiments, proposed approaches improved the QoS provided by up to 25% compared to the state-of-the-art approach in real-life datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.14793v1-abstract-full').style.display = 'none'; document.getElementById('2407.14793v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.14477">arXiv:2407.14477</a> <span> [<a href="https://arxiv.org/pdf/2407.14477">pdf</a>, <a href="https://arxiv.org/format/2407.14477">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Data-Centric Human Preference Optimization with Rationales </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Just%2C+H+A">Hoang Anh Just</a>, <a href="/search/cs?searchtype=author&query=Jin%2C+M">Ming Jin</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Anit Sahu</a>, <a href="/search/cs?searchtype=author&query=Phan%2C+H">Huy Phan</a>, <a href="/search/cs?searchtype=author&query=Jia%2C+R">Ruoxi Jia</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.14477v3-abstract-short" style="display: inline;"> Reinforcement learning from human feedback plays a crucial role in aligning language models towards human preferences, traditionally represented through comparisons between pairs or sets of responses within a given context. While many studies have enhanced algorithmic techniques to optimize learning from such data, this work shifts focus to improving preference learning through a data-centric appr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.14477v3-abstract-full').style.display = 'inline'; document.getElementById('2407.14477v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.14477v3-abstract-full" style="display: none;"> Reinforcement learning from human feedback plays a crucial role in aligning language models towards human preferences, traditionally represented through comparisons between pairs or sets of responses within a given context. While many studies have enhanced algorithmic techniques to optimize learning from such data, this work shifts focus to improving preference learning through a data-centric approach. Specifically, we propose enriching existing preference datasets with machine-generated rationales that explain the reasons behind choices. We develop a simple and principled framework to augment current preference learning methods with rationale information. Our comprehensive analysis highlights how rationales enhance learning efficiency. Extensive experiments reveal that rationale-enriched preference learning offers multiple advantages: it improves data efficiency, accelerates convergence to higher-performing models, and reduces verbosity bias and hallucination. Furthermore, this framework is versatile enough to integrate with various preference optimization algorithms. Overall, our findings highlight the potential of re-imagining data design for preference learning, demonstrating that even freely available machine-generated rationales can significantly boost performance across multiple dimensions. The code repository is available at https: //github.com/reds-lab/preference-learning-with-rationales <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.14477v3-abstract-full').style.display = 'none'; document.getElementById('2407.14477v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Data-Centric Human Preference Learning with Rationales</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.02774">arXiv:2405.02774</a> <span> [<a href="https://arxiv.org/pdf/2405.02774">pdf</a>, <a href="https://arxiv.org/format/2405.02774">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+F">Feiyang Kang</a>, <a href="/search/cs?searchtype=author&query=Just%2C+H+A">Hoang Anh Just</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+Y">Yifan Sun</a>, <a href="/search/cs?searchtype=author&query=Jahagirdar%2C+H">Himanshu Jahagirdar</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yuanzhi Zhang</a>, <a href="/search/cs?searchtype=author&query=Du%2C+R">Rongxing Du</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Jia%2C+R">Ruoxi Jia</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.02774v1-abstract-short" style="display: inline;"> This work focuses on leveraging and selecting from vast, unlabeled, open data to pre-fine-tune a pre-trained language model. The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performance levels. While many data selection algorithms have been designed for small-scale applications, rendering them unsuitable for our context, some emerg… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02774v1-abstract-full').style.display = 'inline'; document.getElementById('2405.02774v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.02774v1-abstract-full" style="display: none;"> This work focuses on leveraging and selecting from vast, unlabeled, open data to pre-fine-tune a pre-trained language model. The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performance levels. While many data selection algorithms have been designed for small-scale applications, rendering them unsuitable for our context, some emerging methods do cater to language data scales. However, they often prioritize data that aligns with the target distribution. While this strategy may be effective when training a model from scratch, it can yield limited results when the model has already been pre-trained on a different distribution. Differing from prior work, our key idea is to select data that nudges the pre-training distribution closer to the target distribution. We show the optimality of this approach for fine-tuning tasks under certain conditions. We demonstrate the efficacy of our methodology across a diverse array of tasks (NLU, NLG, zero-shot) with models up to 2.7B, showing that it consistently surpasses other selection methods. Moreover, our proposed method is significantly faster than existing techniques, scaling to millions of samples within a single GPU hour. Our code is open-sourced (Code repository: https://anonymous.4open.science/r/DV4LLM-D761/ ). While fine-tuning offers significant potential for enhancing performance across diverse tasks, its associated costs often limit its widespread adoption; with this work, we hope to lay the groundwork for cost-effective fine-tuning, making its benefits more accessible. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02774v1-abstract-full').style.display = 'none'; document.getElementById('2405.02774v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Published as a conference paper at ICLR 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.15487">arXiv:2404.15487</a> <span> [<a href="https://arxiv.org/pdf/2404.15487">pdf</a>, <a href="https://arxiv.org/format/2404.15487">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Geometry">cs.CG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> Minimum Consistent Subset in Trees and Interval Graphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Banik%2C+A">Aritra Banik</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S">Sayani Das</a>, <a href="/search/cs?searchtype=author&query=Maheshwari%2C+A">Anil Maheshwari</a>, <a href="/search/cs?searchtype=author&query=Manna%2C+B">Bubai Manna</a>, <a href="/search/cs?searchtype=author&query=Nandy%2C+S+C">Subhas C Nandy</a>, <a href="/search/cs?searchtype=author&query=M%2C+K+P+K">Krishna Priya K M</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+B">Bodhayan Roy</a>, <a href="/search/cs?searchtype=author&query=Roy%2C+S">Sasanka Roy</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhishek Sahu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.15487v1-abstract-short" style="display: inline;"> In the Minimum Consistent Subset (MCS) problem, we are presented with a connected simple undirected graph $G=(V,E)$, consisting of a vertex set $V$ of size $n$ and an edge set $E$. Each vertex in $V$ is assigned a color from the set $\{1,2,\ldots, c\}$. The objective is to determine a subset $V' \subseteq V$ with minimum possible cardinality, such that for every vertex $v \in V$, at least one of i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.15487v1-abstract-full').style.display = 'inline'; document.getElementById('2404.15487v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.15487v1-abstract-full" style="display: none;"> In the Minimum Consistent Subset (MCS) problem, we are presented with a connected simple undirected graph $G=(V,E)$, consisting of a vertex set $V$ of size $n$ and an edge set $E$. Each vertex in $V$ is assigned a color from the set $\{1,2,\ldots, c\}$. The objective is to determine a subset $V' \subseteq V$ with minimum possible cardinality, such that for every vertex $v \in V$, at least one of its nearest neighbors in $V'$ (measured in terms of the hop distance) shares the same color as $v$. The decision problem, indicating whether there exists a subset $V'$ of cardinality at most $l$ for some positive integer $l$, is known to be NP-complete even for planar graphs. In this paper, we establish that the MCS problem for trees, when the number of colors $c$ is considered an input parameter, is NP-complete. We propose a fixed-parameter tractable (FPT) algorithm for MCS on trees running in $O(2^{6c}n^6)$ time, significantly improving the currently best-known algorithm whose running time is $O(2^{4c}n^{2c+3})$. In an effort to comprehensively understand the computational complexity of the MCS problem across different graph classes, we extend our investigation to interval graphs. We show that it remains NP-complete for interval graphs, thus enriching graph classes where MCS remains intractable. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.15487v1-abstract-full').style.display = 'none'; document.getElementById('2404.15487v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.04623">arXiv:2404.04623</a> <span> [<a href="https://arxiv.org/pdf/2404.04623">pdf</a>, <a href="https://arxiv.org/format/2404.04623">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> An Automated Machine Learning Approach to Inkjet Printed Component Analysis: A Step Toward Smart Additive Manufacturing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhishek Sahu</a>, <a href="/search/cs?searchtype=author&query=Aaen%2C+P+H">Peter H. Aaen</a>, <a href="/search/cs?searchtype=author&query=Damacharla%2C+P">Praveen Damacharla</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.04623v1-abstract-short" style="display: inline;"> In this paper, we present a machine learning based architecture for microwave characterization of inkjet printed components on flexible substrates. Our proposed architecture uses several machine learning algorithms and automatically selects the best algorithm to extract the material parameters (ink conductivity and dielectric properties) from on-wafer measurements. Initially, the mutual dependence… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.04623v1-abstract-full').style.display = 'inline'; document.getElementById('2404.04623v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.04623v1-abstract-full" style="display: none;"> In this paper, we present a machine learning based architecture for microwave characterization of inkjet printed components on flexible substrates. Our proposed architecture uses several machine learning algorithms and automatically selects the best algorithm to extract the material parameters (ink conductivity and dielectric properties) from on-wafer measurements. Initially, the mutual dependence between material parameters of the inkjet printed coplanar waveguides (CPWs) and EM-simulated propagation constants is utilized to train the machine learning models. Next, these machine learning models along with measured propagation constants are used to extract the ink conductivity and dielectric properties of the test prototypes. To demonstrate the applicability of our proposed approach, we compare and contrast four heuristic based machine learning models. It is shown that eXtreme Gradient Boosted Trees Regressor (XGB) and Light Gradient Boosting (LGB) algorithms perform best for the characterization problem under study. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.04623v1-abstract-full').style.display = 'none'; document.getElementById('2404.04623v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">2024 IEEE Texas Symposium on Wireless & Micrwowave Circuits and Systems</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.07328">arXiv:2403.07328</a> <span> [<a href="https://arxiv.org/pdf/2403.07328">pdf</a>, <a href="https://arxiv.org/format/2403.07328">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> Satisfiability to Coverage in Presence of Fairness, Matroid, and Global Constraints </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Inamdar%2C+T">Tanmay Inamdar</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+P">Pallavi Jain</a>, <a href="/search/cs?searchtype=author&query=Lokshtanov%2C+D">Daniel Lokshtanov</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhishek Sahu</a>, <a href="/search/cs?searchtype=author&query=Saurabh%2C+S">Saket Saurabh</a>, <a href="/search/cs?searchtype=author&query=Upasana%2C+A">Anannya Upasana</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.07328v1-abstract-short" style="display: inline;"> In MaxSAT with Cardinality Constraint problem (CC-MaxSAT), we are given a CNF-formula $桅$, and $k \ge 0$, and the goal is to find an assignment $尾$ with at most $k$ variables set to true (also called a weight $k$-assignment) such that the number of clauses satisfied by $尾$ is maximized. MaxCov can be seen as a special case of CC-MaxSAT, where the formula $桅$ is monotone, i.e., does not contain any… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07328v1-abstract-full').style.display = 'inline'; document.getElementById('2403.07328v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.07328v1-abstract-full" style="display: none;"> In MaxSAT with Cardinality Constraint problem (CC-MaxSAT), we are given a CNF-formula $桅$, and $k \ge 0$, and the goal is to find an assignment $尾$ with at most $k$ variables set to true (also called a weight $k$-assignment) such that the number of clauses satisfied by $尾$ is maximized. MaxCov can be seen as a special case of CC-MaxSAT, where the formula $桅$ is monotone, i.e., does not contain any negative literals. CC-MaxSAT and MaxCov are extremely well-studied problems in the approximation algorithms as well as parameterized complexity literature. Our first contribution is that the two problems are equivalent to each other in the context of FPT-Approximation parameterized by $k$ (approximation is in terms of number of clauses satisfied/elements covered). We give a randomized reduction from CC-MaxSAT to MaxCov in time $O(1/蔚)^{k} \cdot (m+n)^{O(1)}$ that preserves the approximation guarantee up to a factor of $1-蔚$. Furthermore, this reduction also works in the presence of fairness and matroid constraints. Armed with this reduction, we focus on designing FPT-Approximation schemes (FPT-ASes) for MaxCov and its generalizations. Our algorithms are based on a novel combination of a variety of ideas, including a carefully designed probability distribution that exploits sparse coverage functions. These algorithms substantially generalize the results in Jain et al. [SODA 2023] for CC-MaxSAT and MaxCov for $K_{d,d}$-free set systems (i.e., no $d$ sets share $d$ elements), as well as a recent FPT-AS for Matroid-Constrained MaxCov by Sellier [ESA 2023] for frequency-$d$ set systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07328v1-abstract-full').style.display = 'none'; document.getElementById('2403.07328v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Abstract shortened due to arxiv restrictions</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.04265">arXiv:2403.04265</a> <span> [<a href="https://arxiv.org/pdf/2403.04265">pdf</a>, <a href="https://arxiv.org/format/2403.04265">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> Conflict and Fairness in Resource Allocation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bandopadhyay%2C+S">Susobhan Bandopadhyay</a>, <a href="/search/cs?searchtype=author&query=Banik%2C+A">Aritra Banik</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+S">Sushmita Gupta</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+P">Pallavi Jain</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhishek Sahu</a>, <a href="/search/cs?searchtype=author&query=Saurabh%2C+S">Saket Saurabh</a>, <a href="/search/cs?searchtype=author&query=Tale%2C+P">Prafullkumar Tale</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.04265v1-abstract-short" style="display: inline;"> In the standard model of fair allocation of resources to agents, every agent has some utility for every resource, and the goal is to assign resources to agents so that the agents' welfare is maximized. Motivated by job scheduling, interest in this problem dates back to the work of Deuermeyer et al. [SIAM J. on Algebraic Discrete Methods'82]. Recent works consider the compatibility between resource… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.04265v1-abstract-full').style.display = 'inline'; document.getElementById('2403.04265v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.04265v1-abstract-full" style="display: none;"> In the standard model of fair allocation of resources to agents, every agent has some utility for every resource, and the goal is to assign resources to agents so that the agents' welfare is maximized. Motivated by job scheduling, interest in this problem dates back to the work of Deuermeyer et al. [SIAM J. on Algebraic Discrete Methods'82]. Recent works consider the compatibility between resources and assign only mutually compatible resources to an agent. We study a fair allocation problem in which we are given a set of agents, a set of resources, a utility function for every agent over a set of resources, and a {\it conflict graph} on the set of resources (where an edge denotes incompatibility). The goal is to assign resources to the agents such that $(i)$ the set of resources allocated to an agent are compatible with each other, and $(ii)$ the minimum satisfaction of an agent is maximized, where the satisfaction of an agent is the sum of the utility of the assigned resources. Chiarelli et al. [Algorithmica'22] explore this problem from the classical complexity perspective to draw the boundary between the cases that are polynomial-time solvable and those that are \NP-hard. In this article, we study the parameterized complexity of the problem (and its variants) by considering several natural and structural parameters. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.04265v1-abstract-full').style.display = 'none'; document.getElementById('2403.04265v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: substantial text overlap with arXiv:2309.04995</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.03415">arXiv:2401.03415</a> <span> [<a href="https://arxiv.org/pdf/2401.03415">pdf</a>, <a href="https://arxiv.org/format/2401.03415">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Discrete Mathematics">cs.DM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Combinatorics">math.CO</span> </div> </div> <p class="title is-5 mathjax"> A Polynomial Kernel for Proper Helly Circular-arc Vertex Deletion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Agrawal%2C+A">Akanksha Agrawal</a>, <a href="/search/cs?searchtype=author&query=Jana%2C+S">Satyabrata Jana</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhishek Sahu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.03415v1-abstract-short" style="display: inline;"> A proper Helly circular-arc graph is an intersection graph of a set of arcs on a circle such that none of the arcs properly contains any other arc and every set of pairwise intersecting arcs has a common intersection. The Proper Helly Circular-arc Vertex Deletion problem takes as input a graph $G$ and an integer $k$, and the goal is to check if we can remove at most $k$ vertices from the graph to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03415v1-abstract-full').style.display = 'inline'; document.getElementById('2401.03415v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.03415v1-abstract-full" style="display: none;"> A proper Helly circular-arc graph is an intersection graph of a set of arcs on a circle such that none of the arcs properly contains any other arc and every set of pairwise intersecting arcs has a common intersection. The Proper Helly Circular-arc Vertex Deletion problem takes as input a graph $G$ and an integer $k$, and the goal is to check if we can remove at most $k$ vertices from the graph to obtain a proper Helly circular-arc graph; the parameter is $k$. Recently, Cao et al.~[MFCS 2023] obtained an FPT algorithm for this (and related) problem. In this work, we obtain a polynomial kernel for the problem. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03415v1-abstract-full').style.display = 'none'; document.getElementById('2401.03415v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 pages, 3 figures, In LATIN 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.10548">arXiv:2311.10548</a> <span> [<a href="https://arxiv.org/pdf/2311.10548">pdf</a>, <a href="https://arxiv.org/format/2311.10548">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Efficient Profit Maximization in Reliability Concerned Static Vehicular Cloud System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sarkar%2C+S">Suvarthi Sarkar</a>, <a href="/search/cs?searchtype=author&query=Arun%2C+A">Akshat Arun</a>, <a href="/search/cs?searchtype=author&query=Surekha%2C+H">Harshit Surekha</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Aryabartta Sahu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.10548v1-abstract-short" style="display: inline;"> Modern electric VUs are equipped with a variety of increasingly potent computing, communication, and storage resources, and with this tremendous computation power in their arsenal can be used to enhance the computing power of regular cloud systems, which is termed as vehicular cloud. Unlike in the traditional cloud computing resources, these vehicular cloud resource moves around and participates i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.10548v1-abstract-full').style.display = 'inline'; document.getElementById('2311.10548v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.10548v1-abstract-full" style="display: none;"> Modern electric VUs are equipped with a variety of increasingly potent computing, communication, and storage resources, and with this tremendous computation power in their arsenal can be used to enhance the computing power of regular cloud systems, which is termed as vehicular cloud. Unlike in the traditional cloud computing resources, these vehicular cloud resource moves around and participates in the vehicular cloud for a sporadic duration at parking places, shopping malls, etc. This introduces the dynamic nature of vehicular resource participation in the vehicular cloud. As the user-submitted task gets allocated on these vehicular units for execution and the dynamic stay nature of vehicular units, enforce the system to ensure the reliability of task execution by allocating multiple redundant vehicular units for the task. In this work, we are maximizing the profit of vehicular cloud by ensuring the reliability of task execution where user tasks come online manner with different revenue, execution, and deadline. We propose an efficient approach to solve this problem by considering (a) task classification based on the deadline and laxity of the task, (b) ordering of tasks for task admission based on the expected profit of the task, (c) classification of vehicular units based in expected residency time and reliability concerned redundant allocation of tasks of vehicular units considering this classification and (d) handing dynamic scenario of the vehicular unit leaving the cloud system by copying the maximum percentage of executed virtual machine of the task to the substitute unit. We compared our proposed profit maximization approach with the state of art approach and showed that our approach outperforms the state of art approach with an extra 10\% to 20\% profit margin. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.10548v1-abstract-full').style.display = 'none'; document.getElementById('2311.10548v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.13681">arXiv:2310.13681</a> <span> [<a href="https://arxiv.org/pdf/2310.13681">pdf</a>, <a href="https://arxiv.org/format/2310.13681">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Theoretical Economics">econ.TH</span> </div> </div> <p class="title is-5 mathjax"> Towards Realistic Mechanisms That Incentivize Federated Participation and Contribution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bornstein%2C+M">Marco Bornstein</a>, <a href="/search/cs?searchtype=author&query=Bedi%2C+A+S">Amrit Singh Bedi</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+F">Furqan Khan</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+F">Furong Huang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.13681v3-abstract-short" style="display: inline;"> Edge device participation in federating learning (FL) is typically studied through the lens of device-server communication (e.g., device dropout) and assumes an undying desire from edge devices to participate in FL. As a result, current FL frameworks are flawed when implemented in realistic settings, with many encountering the free-rider dilemma. In a step to push FL towards realistic settings, we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.13681v3-abstract-full').style.display = 'inline'; document.getElementById('2310.13681v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.13681v3-abstract-full" style="display: none;"> Edge device participation in federating learning (FL) is typically studied through the lens of device-server communication (e.g., device dropout) and assumes an undying desire from edge devices to participate in FL. As a result, current FL frameworks are flawed when implemented in realistic settings, with many encountering the free-rider dilemma. In a step to push FL towards realistic settings, we propose RealFM: the first federated mechanism that (1) realistically models device utility, (2) incentivizes data contribution and device participation, (3) provably removes the free-rider dilemma, and (4) relaxes assumptions on data homogeneity and data sharing. Compared to previous FL mechanisms, RealFM allows for a non-linear relationship between model accuracy and utility, which improves the utility gained by the server and participating devices. On real-world data, RealFM improves device and server utility, as well as data contribution, by over 3 and 4 magnitudes respectively compared to baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.13681v3-abstract-full').style.display = 'none'; document.getElementById('2310.13681v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">24 pages, 11 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.02013">arXiv:2308.02013</a> <span> [<a href="https://arxiv.org/pdf/2308.02013">pdf</a>, <a href="https://arxiv.org/format/2308.02013">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Federated Representation Learning for Automatic Speech Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ramesh%2C+G+V">Guruprasad V Ramesh</a>, <a href="/search/cs?searchtype=author&query=Chennupati%2C+G">Gopinath Chennupati</a>, <a href="/search/cs?searchtype=author&query=Rao%2C+M">Milind Rao</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Rastrow%2C+A">Ariya Rastrow</a>, <a href="/search/cs?searchtype=author&query=Droppo%2C+J">Jasha Droppo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.02013v2-abstract-short" style="display: inline;"> Federated Learning (FL) is a privacy-preserving paradigm, allowing edge devices to learn collaboratively without sharing data. Edge devices like Alexa and Siri are prospective sources of unlabeled audio data that can be tapped to learn robust audio representations. In this work, we bring Self-supervised Learning (SSL) and FL together to learn representations for Automatic Speech Recognition respec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.02013v2-abstract-full').style.display = 'inline'; document.getElementById('2308.02013v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.02013v2-abstract-full" style="display: none;"> Federated Learning (FL) is a privacy-preserving paradigm, allowing edge devices to learn collaboratively without sharing data. Edge devices like Alexa and Siri are prospective sources of unlabeled audio data that can be tapped to learn robust audio representations. In this work, we bring Self-supervised Learning (SSL) and FL together to learn representations for Automatic Speech Recognition respecting data privacy constraints. We use the speaker and chapter information in the unlabeled speech dataset, Libri-Light, to simulate non-IID speaker-siloed data distributions and pre-train an LSTM encoder with the Contrastive Predictive Coding framework with FedSGD. We show that the pre-trained ASR encoder in FL performs as well as a centrally pre-trained model and produces an improvement of 12-15% (WER) compared to no pre-training. We further adapt the federated pre-trained models to a new language, French, and show a 20% (WER) improvement over no pre-training. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.02013v2-abstract-full').style.display = 'none'; document.getElementById('2308.02013v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at ISCA SPSC Symposium 3rd Symposium on Security and Privacy in Speech Communication, 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.02460">arXiv:2307.02460</a> <span> [<a href="https://arxiv.org/pdf/2307.02460">pdf</a>, <a href="https://arxiv.org/format/2307.02460">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+F">Feiyang Kang</a>, <a href="/search/cs?searchtype=author&query=Just%2C+H+A">Hoang Anh Just</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Jia%2C+R">Ruoxi Jia</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.02460v1-abstract-short" style="display: inline;"> Traditionally, data selection has been studied in settings where all samples from prospective sources are fully revealed to a machine learning developer. However, in practical data exchange scenarios, data providers often reveal only a limited subset of samples before an acquisition decision is made. Recently, there have been efforts to fit scaling laws that predict model performance at any size a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.02460v1-abstract-full').style.display = 'inline'; document.getElementById('2307.02460v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.02460v1-abstract-full" style="display: none;"> Traditionally, data selection has been studied in settings where all samples from prospective sources are fully revealed to a machine learning developer. However, in practical data exchange scenarios, data providers often reveal only a limited subset of samples before an acquisition decision is made. Recently, there have been efforts to fit scaling laws that predict model performance at any size and data source composition using the limited available samples. However, these scaling functions are black-box, computationally expensive to fit, highly susceptible to overfitting, or/and difficult to optimize for data selection. This paper proposes a framework called <projektor>, which predicts model performance and supports data selection decisions based on partial samples of prospective data sources. Our approach distinguishes itself from existing work by introducing a novel *two-stage* performance inference process. In the first stage, we leverage the Optimal Transport distance to predict the model's performance for any data mixture ratio within the range of disclosed data sizes. In the second stage, we extrapolate the performance to larger undisclosed data sizes based on a novel parameter-free mapping technique inspired by neural scaling laws. We further derive an efficient gradient-based method to select data sources based on the projected model performance. Evaluation over a diverse range of applications demonstrates that <projektor> significantly improves existing performance scaling approaches in terms of both the accuracy of performance inference and the computation costs associated with constructing the performance predictor. Also, <projektor> outperforms by a wide margin in data selection effectiveness compared to a range of other off-the-shelf solutions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.02460v1-abstract-full').style.display = 'none'; document.getElementById('2307.02460v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">An extended abstract of this work appears in Data-centric Machine Learning Research (DMLR) Workshop at 40th International Conference on Machine Learning, Honolulu HI, USA. July 29, 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.00142">arXiv:2307.00142</a> <span> [<a href="https://arxiv.org/pdf/2307.00142">pdf</a>, <a href="https://arxiv.org/format/2307.00142">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Emami%2C+P">Patrick Emami</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhijeet Sahu</a>, <a href="/search/cs?searchtype=author&query=Graf%2C+P">Peter Graf</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.00142v3-abstract-short" style="display: inline;"> Short-term forecasting of residential and commercial building energy consumption is widely used in power systems and continues to grow in importance. Data-driven short-term load forecasting (STLF), although promising, has suffered from a lack of open, large-scale datasets with high building diversity. This has hindered exploring the pretrain-then-fine-tune paradigm for STLF. To help address this,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.00142v3-abstract-full').style.display = 'inline'; document.getElementById('2307.00142v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.00142v3-abstract-full" style="display: none;"> Short-term forecasting of residential and commercial building energy consumption is widely used in power systems and continues to grow in importance. Data-driven short-term load forecasting (STLF), although promising, has suffered from a lack of open, large-scale datasets with high building diversity. This has hindered exploring the pretrain-then-fine-tune paradigm for STLF. To help address this, we present BuildingsBench, which consists of: 1) Buildings-900K, a large-scale dataset of 900K simulated buildings representing the U.S. building stock; and 2) an evaluation platform with over 1,900 real residential and commercial buildings from 7 open datasets. BuildingsBench benchmarks two under-explored tasks: zero-shot STLF, where a pretrained model is evaluated on unseen buildings without fine-tuning, and transfer learning, where a pretrained model is fine-tuned on a target building. The main finding of our benchmark analysis is that synthetically pretrained models generalize surprisingly well to real commercial buildings. An exploration of the effect of increasing dataset size and diversity on zero-shot commercial building performance reveals a power-law with diminishing returns. We also show that fine-tuning pretrained models on real commercial and residential buildings improves performance for a majority of target buildings. We hope that BuildingsBench encourages and facilitates future research on generalizable STLF. All datasets and code can be accessed from https://github.com/NREL/BuildingsBench. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.00142v3-abstract-full').style.display = 'none'; document.getElementById('2307.00142v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">NeurIPS 2023 Datasets & Benchmarks Track camera-ready version. 35 pages. Code available at https://github.com/NREL/BuildingsBench/ and data available at https://data.openei.org/submissions/5859</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.12015">arXiv:2306.12015</a> <span> [<a href="https://arxiv.org/pdf/2306.12015">pdf</a>, <a href="https://arxiv.org/format/2306.12015">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Federated Self-Learning with Weak Supervision for Speech Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rao%2C+M">Milind Rao</a>, <a href="/search/cs?searchtype=author&query=Chennupati%2C+G">Gopinath Chennupati</a>, <a href="/search/cs?searchtype=author&query=Tiwari%2C+G">Gautam Tiwari</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Raju%2C+A">Anirudh Raju</a>, <a href="/search/cs?searchtype=author&query=Rastrow%2C+A">Ariya Rastrow</a>, <a href="/search/cs?searchtype=author&query=Droppo%2C+J">Jasha Droppo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.12015v1-abstract-short" style="display: inline;"> Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy. We study the problem of federated continual incremental learning for recurrent neural network-transducer (RNN-T) ASR models in the privacy-enhancing scheme of learning on-device, without access to ground truth human transcripts or machine t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.12015v1-abstract-full').style.display = 'inline'; document.getElementById('2306.12015v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.12015v1-abstract-full" style="display: none;"> Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy. We study the problem of federated continual incremental learning for recurrent neural network-transducer (RNN-T) ASR models in the privacy-enhancing scheme of learning on-device, without access to ground truth human transcripts or machine transcriptions from a stronger ASR model. In particular, we study the performance of a self-learning based scheme, with a paired teacher model updated through an exponential moving average of ASR models. Further, we propose using possibly noisy weak-supervision signals such as feedback scores and natural language understanding semantics determined from user behavior across multiple turns in a session of interactions with the conversational agent. These signals are leveraged in a multi-task policy-gradient training approach to improve the performance of self-learning for ASR. Finally, we show how catastrophic forgetting can be mitigated by combining on-device learning with a memory-replay approach using selected historical datasets. These innovations allow for 10% relative improvement in WER on new use cases with minimal degradation on other test sets in the absence of strong-supervision signals such as ground-truth transcriptions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.12015v1-abstract-full').style.display = 'none'; document.getElementById('2306.12015v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proceedings of ICASSP 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.12012">arXiv:2306.12012</a> <span> [<a href="https://arxiv.org/pdf/2306.12012">pdf</a>, <a href="https://arxiv.org/format/2306.12012">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.21437/Interspeech.2023-2205">10.21437/Interspeech.2023-2205 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Learning When to Trust Which Teacher for Weakly Supervised ASR </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Agrawal%2C+A">Aakriti Agrawal</a>, <a href="/search/cs?searchtype=author&query=Rao%2C+M">Milind Rao</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Chennupati%2C+G">Gopinath Chennupati</a>, <a href="/search/cs?searchtype=author&query=Stolcke%2C+A">Andreas Stolcke</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.12012v1-abstract-short" style="display: inline;"> Automatic speech recognition (ASR) training can utilize multiple experts as teacher models, each trained on a specific domain or accent. Teacher models may be opaque in nature since their architecture may be not be known or their training cadence is different from that of the student ASR model. Still, the student models are updated incrementally using the pseudo-labels generated independently by t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.12012v1-abstract-full').style.display = 'inline'; document.getElementById('2306.12012v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.12012v1-abstract-full" style="display: none;"> Automatic speech recognition (ASR) training can utilize multiple experts as teacher models, each trained on a specific domain or accent. Teacher models may be opaque in nature since their architecture may be not be known or their training cadence is different from that of the student ASR model. Still, the student models are updated incrementally using the pseudo-labels generated independently by the expert teachers. In this paper, we exploit supervision from multiple domain experts in training student ASR models. This training strategy is especially useful in scenarios where few or no human transcriptions are available. To that end, we propose a Smart-Weighter mechanism that selects an appropriate expert based on the input audio, and then trains the student model in an unsupervised setting. We show the efficacy of our approach using LibriSpeech and LibriLight benchmarks and find an improvement of 4 to 25\% over baselines that uniformly weight all the experts, use a single expert model, or combine experts using ROVER. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.12012v1-abstract-full').style.display = 'none'; document.getElementById('2306.12012v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proceedings of INTERSPEECH 2023</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proc. Interspeech, Aug. 2023, pp. 381-385 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.16469">arXiv:2305.16469</a> <span> [<a href="https://arxiv.org/pdf/2305.16469">pdf</a>, <a href="https://arxiv.org/format/2305.16469">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Bayesian Reinforcement Learning for Automatic Voltage Control under Cyber-Induced Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhijeet Sahu</a>, <a href="/search/cs?searchtype=author&query=Davis%2C+K">Katherine Davis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.16469v1-abstract-short" style="display: inline;"> Voltage control is crucial to large-scale power system reliable operation, as timely reactive power support can help prevent widespread outages. However, there is currently no built in mechanism for power systems to ensure that the voltage control objective to maintain reliable operation will survive or sustain the uncertainty caused under adversary presence. Hence, this work introduces a Bayesian… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.16469v1-abstract-full').style.display = 'inline'; document.getElementById('2305.16469v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.16469v1-abstract-full" style="display: none;"> Voltage control is crucial to large-scale power system reliable operation, as timely reactive power support can help prevent widespread outages. However, there is currently no built in mechanism for power systems to ensure that the voltage control objective to maintain reliable operation will survive or sustain the uncertainty caused under adversary presence. Hence, this work introduces a Bayesian Reinforcement Learning (BRL) approach for power system control problems, with focus on sustained voltage control under uncertainty in a cyber-adversarial environment. This work proposes a data-driven BRL-based approach for automatic voltage control by formulating and solving a Partially-Observable Markov Decision Problem (POMDP), where the states are partially observable due to cyber intrusions. The techniques are evaluated on the WSCC and IEEE 14 bus systems. Additionally, BRL techniques assist in automatically finding a threshold for exploration and exploitation in various RL techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.16469v1-abstract-full').style.display = 'none'; document.getElementById('2305.16469v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.10866">arXiv:2303.10866</a> <span> [<a href="https://arxiv.org/pdf/2303.10866">pdf</a>, <a href="https://arxiv.org/format/2303.10866">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> An Improved Exact Algorithm for Knot-Free Vertex Deletion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=S%2C+A+E">Ajaykrishnan E S</a>, <a href="/search/cs?searchtype=author&query=Maity%2C+S">Soumen Maity</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhishek Sahu</a>, <a href="/search/cs?searchtype=author&query=Saurabh%2C+S">Saket Saurabh</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.10866v1-abstract-short" style="display: inline;"> A knot $K$ in a directed graph $D$ is a strongly connected component of size at least two such that there is no arc $(u,v)$ with $u \in V(K)$ and $v\notin V(K)$. Given a directed graph $D=(V,E)$, we study Knot-Free Vertex Deletion (KFVD), where the goal is to remove the minimum number of vertices such that the resulting graph contains no knots. This problem naturally emerges from its application i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.10866v1-abstract-full').style.display = 'inline'; document.getElementById('2303.10866v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.10866v1-abstract-full" style="display: none;"> A knot $K$ in a directed graph $D$ is a strongly connected component of size at least two such that there is no arc $(u,v)$ with $u \in V(K)$ and $v\notin V(K)$. Given a directed graph $D=(V,E)$, we study Knot-Free Vertex Deletion (KFVD), where the goal is to remove the minimum number of vertices such that the resulting graph contains no knots. This problem naturally emerges from its application in deadlock resolution since knots are deadlocks in the OR-model of distributed computation. The fastest known exact algorithm in literature for KFVD runs in time $\mathcal{O}^\star(1.576^n)$. In this paper, we present an improved exact algorithm running in time $\mathcal{O}^\star(1.4549^n)$, where $n$ is the number of vertices in $D$. We also prove that the number of inclusion wise minimal knot-free vertex deletion sets is $\mathcal{O}^\star(1.4549^n)$ and construct a family of graphs with $惟(1.4422^n)$ minimal knot-free vertex deletion sets <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.10866v1-abstract-full').style.display = 'none'; document.getElementById('2303.10866v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.10624">arXiv:2303.10624</a> <span> [<a href="https://arxiv.org/pdf/2303.10624">pdf</a>, <a href="https://arxiv.org/format/2303.10624">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wadhwa%2C+M">Manas Wadhwa</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+G+R">Gagan Raj Gupta</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Ashutosh Sahu</a>, <a href="/search/cs?searchtype=author&query=Saini%2C+R">Rahul Saini</a>, <a href="/search/cs?searchtype=author&query=Mittal%2C+V">Vidhi Mittal</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.10624v1-abstract-short" style="display: inline;"> The traditional framework of federated learning (FL) requires each client to re-train their models in every iteration, making it infeasible for resource-constrained mobile devices to train deep-learning (DL) models. Split learning (SL) provides an alternative by using a centralized server to offload the computation of activations and gradients for a subset of the model but suffers from problems of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.10624v1-abstract-full').style.display = 'inline'; document.getElementById('2303.10624v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.10624v1-abstract-full" style="display: none;"> The traditional framework of federated learning (FL) requires each client to re-train their models in every iteration, making it infeasible for resource-constrained mobile devices to train deep-learning (DL) models. Split learning (SL) provides an alternative by using a centralized server to offload the computation of activations and gradients for a subset of the model but suffers from problems of slow convergence and lower accuracy. In this paper, we implement PFSL, a new framework of distributed split learning where a large number of thin clients perform transfer learning in parallel, starting with a pre-trained DL model without sharing their data or labels with a central server. We implement a lightweight step of personalization of client models to provide high performance for their respective data distributions. Furthermore, we evaluate performance fairness amongst clients under a work fairness constraint for various scenarios of non-i.i.d. data distributions and unequal sample sizes. Our accuracy far exceeds that of current SL algorithms and is very close to that of centralized learning on several real-life benchmarks. It has a very low computation cost compared to FL variants and promises to deliver the full benefits of DL to extremely thin, resource-constrained clients. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.10624v1-abstract-full').style.display = 'none'; document.getElementById('2303.10624v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To be published in : THE 23RD IEEE/ACM INTERNATIONAL SYMPOSIUM ON Cluster, Cloud and Internet Computing. Granted: Open Research Objects (ORO) and Research Objects Reviewed (ROR) badges. See https://www.niso.org/publications/rp-31-2021-badging for definitions of the badges. Code available at: https://github.com/mnswdhw/PFSL</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.16882">arXiv:2211.16882</a> <span> [<a href="https://arxiv.org/pdf/2211.16882">pdf</a>, <a href="https://arxiv.org/format/2211.16882">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> MVRackLay: Monocular Multi-View Layout Estimation for Warehouse Racks and Shelves </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pathre%2C+P">Pranjali Pathre</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Anurag Sahu</a>, <a href="/search/cs?searchtype=author&query=Rao%2C+A">Ashwin Rao</a>, <a href="/search/cs?searchtype=author&query=Prabhu%2C+A">Avinash Prabhu</a>, <a href="/search/cs?searchtype=author&query=Nigam%2C+M+S">Meher Shashwat Nigam</a>, <a href="/search/cs?searchtype=author&query=Karandikar%2C+T">Tanvi Karandikar</a>, <a href="/search/cs?searchtype=author&query=Pandya%2C+H">Harit Pandya</a>, <a href="/search/cs?searchtype=author&query=Krishna%2C+K+M">K. Madhava Krishna</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.16882v1-abstract-short" style="display: inline;"> In this paper, we propose and showcase, for the first time, monocular multi-view layout estimation for warehouse racks and shelves. Unlike typical layout estimation methods, MVRackLay estimates multi-layered layouts, wherein each layer corresponds to the layout of a shelf within a rack. Given a sequence of images of a warehouse scene, a dual-headed Convolutional-LSTM architecture outputs segmented… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.16882v1-abstract-full').style.display = 'inline'; document.getElementById('2211.16882v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.16882v1-abstract-full" style="display: none;"> In this paper, we propose and showcase, for the first time, monocular multi-view layout estimation for warehouse racks and shelves. Unlike typical layout estimation methods, MVRackLay estimates multi-layered layouts, wherein each layer corresponds to the layout of a shelf within a rack. Given a sequence of images of a warehouse scene, a dual-headed Convolutional-LSTM architecture outputs segmented racks, the front and the top view layout of each shelf within a rack. With minimal effort, such an output is transformed into a 3D rendering of all racks, shelves and objects on the shelves, giving an accurate 3D depiction of the entire warehouse scene in terms of racks, shelves and the number of objects on each shelf. MVRackLay generalizes to a diverse set of warehouse scenes with varying number of objects on each shelf, number of shelves and in the presence of other such racks in the background. Further, MVRackLay shows superior performance vis-a-vis its single view counterpart, RackLay, in layout accuracy, quantized in terms of the mean IoU and mAP metrics. We also showcase a multi-view stitching of the 3D layouts resulting in a representation of the warehouse scene with respect to a global reference frame akin to a rendering of the scene from a SLAM pipeline. To the best of our knowledge, this is the first such work to portray a 3D rendering of a warehouse scene in terms of its semantic components - Racks, Shelves and Objects - all from a single monocular camera. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.16882v1-abstract-full').style.display = 'none'; document.getElementById('2211.16882v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE International Conference on Robotics and Biomimetics (ROBIO) 2022 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.13884">arXiv:2208.13884</a> <span> [<a href="https://arxiv.org/pdf/2208.13884">pdf</a>, <a href="https://arxiv.org/format/2208.13884">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Toward a Mathematical Vulnerability Propagation and Defense Model in Smart Grid Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhijeet Sahu</a>, <a href="/search/cs?searchtype=author&query=Mai%2C+B">Bin Mai</a>, <a href="/search/cs?searchtype=author&query=Davis%2C+K">Katherine Davis</a>, <a href="/search/cs?searchtype=author&query=Goulart%2C+A">Ana Goulart</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.13884v1-abstract-short" style="display: inline;"> For reducing threat propagation within an inter-connected network, it is essential to distribute the defense investment optimally. Most electric power utilities are resource constrained, yet how to account for costs while designing threat reduction techniques is not well understood. Hence, in this work, a vulnerability propagation and a defense model is proposed based on an epidemic model. The new… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.13884v1-abstract-full').style.display = 'inline'; document.getElementById('2208.13884v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.13884v1-abstract-full" style="display: none;"> For reducing threat propagation within an inter-connected network, it is essential to distribute the defense investment optimally. Most electric power utilities are resource constrained, yet how to account for costs while designing threat reduction techniques is not well understood. Hence, in this work, a vulnerability propagation and a defense model is proposed based on an epidemic model. The new defense mechanism is then validated through sensitivity of the propagation parameters on the optimal investment with two-node and N-node cases. Further, the model efficacy is evaluated with implementation in one of the communication networks of a cyber-physical power system. Topological impact on the optimal nodal investment is also emphasized. Optimal investment of the neighbors with less degree were found to be highly sensitive to fluctuation in vulnerability exploitability probability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.13884v1-abstract-full').style.display = 'none'; document.getElementById('2208.13884v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 20 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.09078">arXiv:2207.09078</a> <span> [<a href="https://arxiv.org/pdf/2207.09078">pdf</a>, <a href="https://arxiv.org/format/2207.09078">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3534678.3539174">10.1145/3534678.3539174 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chennupati%2C+G">Gopinath Chennupati</a>, <a href="/search/cs?searchtype=author&query=Rao%2C+M">Milind Rao</a>, <a href="/search/cs?searchtype=author&query=Chadha%2C+G">Gurpreet Chadha</a>, <a href="/search/cs?searchtype=author&query=Eakin%2C+A">Aaron Eakin</a>, <a href="/search/cs?searchtype=author&query=Raju%2C+A">Anirudh Raju</a>, <a href="/search/cs?searchtype=author&query=Tiwari%2C+G">Gautam Tiwari</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Rastrow%2C+A">Ariya Rastrow</a>, <a href="/search/cs?searchtype=author&query=Droppo%2C+J">Jasha Droppo</a>, <a href="/search/cs?searchtype=author&query=Oberlin%2C+A">Andy Oberlin</a>, <a href="/search/cs?searchtype=author&query=Nandanoor%2C+B">Buddha Nandanoor</a>, <a href="/search/cs?searchtype=author&query=Venkataramanan%2C+P">Prahalad Venkataramanan</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Z">Zheng Wu</a>, <a href="/search/cs?searchtype=author&query=Sitpure%2C+P">Pankaj Sitpure</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.09078v2-abstract-short" style="display: inline;"> Incremental learning is one paradigm to enable model building and updating at scale with streaming data. For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge. Motivated by these challenges, in this paper we use a cloud based framework for production systems… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09078v2-abstract-full').style.display = 'inline'; document.getElementById('2207.09078v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.09078v2-abstract-full" style="display: none;"> Incremental learning is one paradigm to enable model building and updating at scale with streaming data. For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge. Motivated by these challenges, in this paper we use a cloud based framework for production systems to demonstrate insights from privacy preserving incremental learning for automatic speech recognition (ILASR). By privacy preserving, we mean, usage of ephemeral data which are not human annotated. This system is a step forward for production levelASR models for incremental/continual learning that offers near real-time test-bed for experimentation in the cloud for end-to-end ASR, while adhering to privacy-preserving policies. We show that the proposed system can improve the production models significantly(3%) over a new time period of six months even in the absence of human annotated labels with varying levels of weak supervision and large batch sizes in incremental learning. This improvement is 20% over test sets with new words and phrases in the new time period. We demonstrate the effectiveness of model building in a privacy-preserving incremental fashion for ASR while further exploring the utility of having an effective teacher model and use of large batch sizes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09078v2-abstract-full').style.display = 'none'; document.getElementById('2207.09078v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.10815">arXiv:2206.10815</a> <span> [<a href="https://arxiv.org/pdf/2206.10815">pdf</a>, <a href="https://arxiv.org/format/2206.10815">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bedi%2C+A+S">Amrit Singh Bedi</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+C">Chen Fan</a>, <a href="/search/cs?searchtype=author&query=Koppel%2C+A">Alec Koppel</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Sadler%2C+B+M">Brian M. Sadler</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+F">Furong Huang</a>, <a href="/search/cs?searchtype=author&query=Manocha%2C+D">Dinesh Manocha</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2206.10815v4-abstract-short" style="display: inline;"> In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as a constrained program. The objective of a device is its local objective, which it seeks to minimize while satisfying nonlinear constraints that quantify the proximity between the local and the global model. By considerin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.10815v4-abstract-full').style.display = 'inline'; document.getElementById('2206.10815v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.10815v4-abstract-full" style="display: none;"> In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as a constrained program. The objective of a device is its local objective, which it seeks to minimize while satisfying nonlinear constraints that quantify the proximity between the local and the global model. By considering the Lagrangian relaxation of this problem, we develop a novel primal-dual method called Federated Learning Beyond Consensus (\texttt{FedBC}). Theoretically, we establish that \texttt{FedBC} converges to a first-order stationary point at rates that matches the state of the art, up to an additional error term that depends on a tolerance parameter introduced to scalarize the multi-criterion formulation. Finally, we demonstrate that \texttt{FedBC} balances the global and local model test accuracy metrics across a suite of datasets (Synthetic, MNIST, CIFAR-10, Shakespeare), achieving competitive performance with state-of-the-art. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.10815v4-abstract-full').style.display = 'none'; document.getElementById('2206.10815v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.08069">arXiv:2204.08069</a> <span> [<a href="https://arxiv.org/pdf/2204.08069">pdf</a>, <a href="https://arxiv.org/format/2204.08069">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Self-Aware Personalized Federated Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+H">Huili Chen</a>, <a href="/search/cs?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/cs?searchtype=author&query=Tramel%2C+E">Eric Tramel</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+S">Shuang Wu</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Avestimehr%2C+S">Salman Avestimehr</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+T">Tao Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.08069v1-abstract-short" style="display: inline;"> In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned. Inspired by Bayesian hierarchical models, we develop a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global mo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.08069v1-abstract-full').style.display = 'inline'; document.getElementById('2204.08069v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.08069v1-abstract-full" style="display: none;"> In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned. Inspired by Bayesian hierarchical models, we develop a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients' training. Such a balance is derived from the inter-client and intra-client uncertainty quantification. A larger inter-client variation implies more personalization is needed. Correspondingly, our method uses uncertainty-driven local training steps and aggregation rule instead of conventional local fine-tuning and sample size-based aggregation. With experimental studies on synthetic data, Amazon Alexa audio data, and public datasets such as MNIST, FEMNIST, CIFAR10, and Sent140, we show that our proposed method can achieve significantly improved personalization performance compared with the existing counterparts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.08069v1-abstract-full').style.display = 'none'; document.getElementById('2204.08069v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.02593">arXiv:2204.02593</a> <span> [<a href="https://arxiv.org/pdf/2204.02593">pdf</a>, <a href="https://arxiv.org/format/2204.02593">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jakovetic%2C+D">Dusan Jakovetic</a>, <a href="/search/cs?searchtype=author&query=Bajovic%2C+D">Dragana Bajovic</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Kar%2C+S">Soummya Kar</a>, <a href="/search/cs?searchtype=author&query=Milosevic%2C+N">Nemanja Milosevic</a>, <a href="/search/cs?searchtype=author&query=Stamenkovic%2C+D">Dusan Stamenkovic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.02593v1-abstract-short" style="display: inline;"> We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails. The proposed framework subsumes several popular nonlinearity choices, like clipped, normalized, signed or quantized gradient, but we also consider novel nonlinearity choices. We establish for the considered class of methods strong convergence guarantees assum… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.02593v1-abstract-full').style.display = 'inline'; document.getElementById('2204.02593v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.02593v1-abstract-full" style="display: none;"> We introduce a general framework for nonlinear stochastic gradient descent (SGD) for the scenarios when gradient noise exhibits heavy tails. The proposed framework subsumes several popular nonlinearity choices, like clipped, normalized, signed or quantized gradient, but we also consider novel nonlinearity choices. We establish for the considered class of methods strong convergence guarantees assuming a strongly convex cost function with Lipschitz continuous gradients under very general assumptions on the gradient noise. Most notably, we show that, for a nonlinearity with bounded outputs and for the gradient noise that may not have finite moments of order greater than one, the nonlinear SGD's mean squared error (MSE), or equivalently, the expected cost function's optimality gap, converges to zero at rate~$O(1/t^味)$, $味\in (0,1)$. In contrast, for the same noise setting, the linear SGD generates a sequence with unbounded variances. Furthermore, for the nonlinearities that can be decoupled component wise, like, e.g., sign gradient or component-wise clipping, we show that the nonlinear SGD asymptotically (locally) achieves a $O(1/t)$ rate in the weak convergence sense and explicitly quantify the corresponding asymptotic variance. Experiments show that, while our framework is more general than existing studies of SGD under heavy-tail noise, several easy-to-implement nonlinearities from our framework are competitive with state of the art alternatives on real data sets with heavy tail noises. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.02593v1-abstract-full').style.display = 'none'; document.getElementById('2204.02593v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted for publication Nov 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.00807">arXiv:2202.00807</a> <span> [<a href="https://arxiv.org/pdf/2202.00807">pdf</a>, <a href="https://arxiv.org/format/2202.00807">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Federated Learning Challenges and Opportunities: An Outlook </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/cs?searchtype=author&query=Tramel%2C+E">Eric Tramel</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+S">Shuang Wu</a>, <a href="/search/cs?searchtype=author&query=Avestimehr%2C+S">Salman Avestimehr</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+T">Tao Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.00807v1-abstract-short" style="display: inline;"> Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs. Although many methods and applications have been developed for FL, several critical challenges for practical FL systems remain unaddressed. This paper provides an outlook on FL development, categorized into… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.00807v1-abstract-full').style.display = 'inline'; document.getElementById('2202.00807v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.00807v1-abstract-full" style="display: none;"> Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs. Although many methods and applications have been developed for FL, several critical challenges for practical FL systems remain unaddressed. This paper provides an outlook on FL development, categorized into five emerging directions of FL, namely algorithm foundation, personalization, hardware and security constraints, lifelong learning, and nonstandard data. Our unique perspectives are backed by practical observations from large-scale federated systems for edge devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.00807v1-abstract-full').style.display = 'none'; document.getElementById('2202.00807v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper provides an outlook on FL development as part of the ICASSP 2022 special session entitled "Frontiers of Federated Learning: Applications, Challenges, and Opportunities"</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.03789">arXiv:2201.03789</a> <span> [<a href="https://arxiv.org/pdf/2201.03789">pdf</a>, <a href="https://arxiv.org/format/2201.03789">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lee%2C+S">Sunwoo Lee</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=He%2C+C">Chaoyang He</a>, <a href="/search/cs?searchtype=author&query=Avestimehr%2C+S">Salman Avestimehr</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2201.03789v1-abstract-short" style="display: inline;"> Local Stochastic Gradient Descent (SGD) with periodic model averaging (FedAvg) is a foundational algorithm in Federated Learning. The algorithm independently runs SGD on multiple workers and periodically averages the model across all the workers. When local SGD runs with many workers, however, the periodic averaging causes a significant model discrepancy across the workers making the global loss c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.03789v1-abstract-full').style.display = 'inline'; document.getElementById('2201.03789v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.03789v1-abstract-full" style="display: none;"> Local Stochastic Gradient Descent (SGD) with periodic model averaging (FedAvg) is a foundational algorithm in Federated Learning. The algorithm independently runs SGD on multiple workers and periodically averages the model across all the workers. When local SGD runs with many workers, however, the periodic averaging causes a significant model discrepancy across the workers making the global loss converge slowly. While recent advanced optimization methods tackle the issue focused on non-IID settings, there still exists the model discrepancy issue due to the underlying periodic model averaging. We propose a partial model averaging framework that mitigates the model discrepancy issue in Federated Learning. The partial averaging encourages the local models to stay close to each other on parameter space, and it enables to more effectively minimize the global loss. Given a fixed number of iterations and a large number of workers (128), the partial averaging achieves up to 2.2% higher validation accuracy than the periodic full averaging. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.03789v1-abstract-full').style.display = 'none'; document.getElementById('2201.03789v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.06431">arXiv:2112.06431</a> <span> [<a href="https://arxiv.org/pdf/2112.06431">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=GM%2C+H">Harshvardhan GM</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Aanchal Sahu</a>, <a href="/search/cs?searchtype=author&query=Gourisaria%2C+M+K">Mahendra Kumar Gourisaria</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.06431v2-abstract-short" style="display: inline;"> While generative adversarial networks (GAN) are popular for their higher sample quality as opposed to other generative models like the variational autoencoders (VAE) and Boltzmann machines, they suffer from the same difficulty of the evaluation of generated samples. Various aspects must be kept in mind, such as the quality of generated samples, the diversity of classes (within a class and among cl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.06431v2-abstract-full').style.display = 'inline'; document.getElementById('2112.06431v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.06431v2-abstract-full" style="display: none;"> While generative adversarial networks (GAN) are popular for their higher sample quality as opposed to other generative models like the variational autoencoders (VAE) and Boltzmann machines, they suffer from the same difficulty of the evaluation of generated samples. Various aspects must be kept in mind, such as the quality of generated samples, the diversity of classes (within a class and among classes), the use of disentangled latent spaces, agreement of said evaluation metric with human perception, etc. In this paper, we propose a new score, namely, GM Score, which takes into various factors such as sample quality, disentangled representation, intra-class and inter-class diversity, and other metrics such as precision, recall, and F1 score are employed for discriminability of latent space of deep belief network (DBN) and restricted Boltzmann machine (RBM). The evaluation is done for different GANs (GAN, DCGAN, BiGAN, CGAN, CoupledGAN, LSGAN, SGAN, WGAN, and WGAN Improved) trained on the benchmark MNIST dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.06431v2-abstract-full').style.display = 'none'; document.getElementById('2112.06431v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">21 pages, 9 figures. Version 2: Added author names and affiliation</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.10484">arXiv:2111.10484</a> <span> [<a href="https://arxiv.org/pdf/2111.10484">pdf</a>, <a href="https://arxiv.org/format/2111.10484">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3390/s22062100">10.3390/s22062100 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhijeet Sahu</a>, <a href="/search/cs?searchtype=author&query=Davis%2C+K">Katherine Davis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2111.10484v1-abstract-short" style="display: inline;"> False alerts due to misconfigured/ compromised IDS in ICS networks can lead to severe economic and operational damage. To solve this problem, research has focused on leveraging deep learning techniques that help reduce false alerts. However, a shortcoming is that these works often require or implicitly assume the physical and cyber sensors to be trustworthy. Implicit trust of data is a major probl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.10484v1-abstract-full').style.display = 'inline'; document.getElementById('2111.10484v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.10484v1-abstract-full" style="display: none;"> False alerts due to misconfigured/ compromised IDS in ICS networks can lead to severe economic and operational damage. To solve this problem, research has focused on leveraging deep learning techniques that help reduce false alerts. However, a shortcoming is that these works often require or implicitly assume the physical and cyber sensors to be trustworthy. Implicit trust of data is a major problem with using artificial intelligence or machine learning for CPS security, because during critical attack detection time they are more at risk, with greater likelihood and impact, of also being compromised. To address this shortcoming, the problem is reframed on how to make good decisions given uncertainty. Then, the decision is detection, and the uncertainty includes whether the data used for ML-based IDS is compromised. Thus, this work presents an approach for reducing false alerts in CPS power systems by dealing uncertainty without the knowledge of prior distribution of alerts. Specifically, an evidence theoretic based approach leveraging Dempster Shafer combination rules are proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from various supervised-learning classifiers. Using this model, a location-cum-domain based fusion framework is proposed and evaluated with different combination rules, that fuse multiple evidence from inter-domain and intra-domain sensors. The approach is demonstrated in a cyber-physical power system testbed with Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, plausibility, belief, pignistic, etc. metrics as decision functions are considered. To improve the performance, a multi-objective based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.10484v1-abstract-full').style.display = 'none'; document.getElementById('2111.10484v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 21 Figures (out of which 17 sub-figures), 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> MDPI Sensors 2022 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.01108">arXiv:2111.01108</a> <span> [<a href="https://arxiv.org/pdf/2111.01108">pdf</a>, <a href="https://arxiv.org/format/2111.01108">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3552326.3567485">10.1145/3552326.3567485 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Resource-Efficient Federated Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abdelmoniem%2C+A+M">Ahmed M. Abdelmoniem</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+N">Atal Narayan Sahu</a>, <a href="/search/cs?searchtype=author&query=Canini%2C+M">Marco Canini</a>, <a href="/search/cs?searchtype=author&query=Fahmy%2C+S+A">Suhaib A. Fahmy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2111.01108v2-abstract-short" style="display: inline;"> Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device capabilities, and participant availability as deployments scale, which can impact both model convergence and bias. Existing FL schemes use random participant s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.01108v2-abstract-full').style.display = 'inline'; document.getElementById('2111.01108v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.01108v2-abstract-full" style="display: none;"> Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device capabilities, and participant availability as deployments scale, which can impact both model convergence and bias. Existing FL schemes use random participant selection to improve fairness; however, this can result in inefficient use of resources and lower quality training. In this work, we systematically address the question of resource efficiency in FL, showing the benefits of intelligent participant selection, and incorporation of updates from straggling participants. We demonstrate how these factors enable resource efficiency while also improving trained model quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.01108v2-abstract-full').style.display = 'none'; document.getElementById('2111.01108v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to appear in ACM EuroSys 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.00951">arXiv:2108.00951</a> <span> [<a href="https://arxiv.org/pdf/2108.00951">pdf</a>, <a href="https://arxiv.org/format/2108.00951">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Rethinking gradient sparsification as total error minimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sahu%2C+A+N">Atal Narayan Sahu</a>, <a href="/search/cs?searchtype=author&query=Dutta%2C+A">Aritra Dutta</a>, <a href="/search/cs?searchtype=author&query=Abdelmoniem%2C+A+M">Ahmed M. Abdelmoniem</a>, <a href="/search/cs?searchtype=author&query=Banerjee%2C+T">Trambak Banerjee</a>, <a href="/search/cs?searchtype=author&query=Canini%2C+M">Marco Canini</a>, <a href="/search/cs?searchtype=author&query=Kalnis%2C+P">Panos Kalnis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2108.00951v1-abstract-short" style="display: inline;"> Gradient compression is a widely-established remedy to tackle the communication bottleneck in distributed training of large deep neural networks (DNNs). Under the error-feedback framework, Top-$k$ sparsification, sometimes with $k$ as little as $0.1\%$ of the gradient size, enables training to the same model quality as the uncompressed case for a similar iteration count. From the optimization pers… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.00951v1-abstract-full').style.display = 'inline'; document.getElementById('2108.00951v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.00951v1-abstract-full" style="display: none;"> Gradient compression is a widely-established remedy to tackle the communication bottleneck in distributed training of large deep neural networks (DNNs). Under the error-feedback framework, Top-$k$ sparsification, sometimes with $k$ as little as $0.1\%$ of the gradient size, enables training to the same model quality as the uncompressed case for a similar iteration count. From the optimization perspective, we find that Top-$k$ is the communication-optimal sparsifier given a per-iteration $k$ element budget. We argue that to further the benefits of gradient sparsification, especially for DNNs, a different perspective is necessary -- one that moves from per-iteration optimality to consider optimality for the entire training. We identify that the total error -- the sum of the compression errors for all iterations -- encapsulates sparsification throughout training. Then, we propose a communication complexity model that minimizes the total error under a communication budget for the entire training. We find that the hard-threshold sparsifier, a variant of the Top-$k$ sparsifier with $k$ determined by a constant hard-threshold, is the optimal sparsifier for this model. Motivated by this, we provide convex and non-convex convergence analyses for the hard-threshold sparsifier with error-feedback. Unlike with Top-$k$ sparsifier, we show that hard-threshold has the same asymptotic convergence and linear speedup property as SGD in the convex case and has no impact on the data-heterogeneity in the non-convex case. Our diverse experiments on various DNNs and a logistic regression model demonstrated that the hard-threshold sparsifier is more communication-efficient than Top-$k$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.00951v1-abstract-full').style.display = 'none'; document.getElementById('2108.00951v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">33 pages, 31 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.09918">arXiv:2107.09918</a> <span> [<a href="https://arxiv.org/pdf/2107.09918">pdf</a>, <a href="https://arxiv.org/format/2107.09918">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Risk-Based Safety Envelopes for Autonomous Vehicles Under Perception Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bernhard%2C+J">Julian Bernhard</a>, <a href="/search/cs?searchtype=author&query=Hart%2C+P">Patrick Hart</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Amit Sahu</a>, <a href="/search/cs?searchtype=author&query=Sch%C3%B6ller%2C+C">Christoph Sch枚ller</a>, <a href="/search/cs?searchtype=author&query=Cancimance%2C+M+G">Michell Guzman Cancimance</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2107.09918v1-abstract-short" style="display: inline;"> Ensuring the safety of autonomous vehicles, given the uncertainty in sensing other road users, is an open problem. Moreover, separate safety specifications for perception and planning components raise how to assess the overall system safety. This work provides a probabilistic approach to calculate safety envelopes under perception uncertainty. The probabilistic envelope definition is based on a ri… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.09918v1-abstract-full').style.display = 'inline'; document.getElementById('2107.09918v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.09918v1-abstract-full" style="display: none;"> Ensuring the safety of autonomous vehicles, given the uncertainty in sensing other road users, is an open problem. Moreover, separate safety specifications for perception and planning components raise how to assess the overall system safety. This work provides a probabilistic approach to calculate safety envelopes under perception uncertainty. The probabilistic envelope definition is based on a risk threshold. It limits the cumulative probability that the actual safety envelope in a fully observable environment is larger than an applied envelope and is solved using iterative worst-case analysis of envelopes. Our approach extends non-probabilistic envelopes - in this work, the Responsibility-Sensitive Safety (RSS) - to handle uncertainties. To evaluate our probabilistic envelope approach, we compare it in a simulated highway merging scenario against several baseline safety architectures. Our evaluation shows that our model allows adjusting safety and performance based on a chosen risk level and the amount of perception uncertainty. We conclude with an outline of how to formally argue safety under perception uncertainty using our formulation of envelope violation risk. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.09918v1-abstract-full').style.display = 'none'; document.getElementById('2107.09918v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.9; G.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.02012">arXiv:2104.02012</a> <span> [<a href="https://arxiv.org/pdf/2104.02012">pdf</a>, <a href="https://arxiv.org/format/2104.02012">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/JSYST.2021.3109082">10.1109/JSYST.2021.3109082 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Boyaci%2C+O">Osman Boyaci</a>, <a href="/search/cs?searchtype=author&query=Umunnakwe%2C+A">Amarachi Umunnakwe</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhijeet Sahu</a>, <a href="/search/cs?searchtype=author&query=Narimani%2C+M+R">Mohammad Rasoul Narimani</a>, <a href="/search/cs?searchtype=author&query=Ismail%2C+M">Muhammad Ismail</a>, <a href="/search/cs?searchtype=author&query=Davis%2C+K">Katherine Davis</a>, <a href="/search/cs?searchtype=author&query=Serpedin%2C+E">Erchin Serpedin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2104.02012v2-abstract-short" style="display: inline;"> False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors' knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better de… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.02012v2-abstract-full').style.display = 'inline'; document.getElementById('2104.02012v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.02012v2-abstract-full" style="display: none;"> False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors' knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this paper to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a Graph Neural Network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern AC power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in F1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.02012v2-abstract-full').style.display = 'none'; document.getElementById('2104.02012v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 10 figures, journal</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.09174">arXiv:2103.09174</a> <span> [<a href="https://arxiv.org/pdf/2103.09174">pdf</a>, <a href="https://arxiv.org/format/2103.09174">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3490035.3490263">10.1145/3490035.3490263 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Monocular Multi-Layer Layout Estimation for Warehouse Racks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nigam%2C+M+S">Meher Shashwat Nigam</a>, <a href="/search/cs?searchtype=author&query=Prabhu%2C+A">Avinash Prabhu</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Anurag Sahu</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+P">Puru Gupta</a>, <a href="/search/cs?searchtype=author&query=Karandikar%2C+T">Tanvi Karandikar</a>, <a href="/search/cs?searchtype=author&query=Shankar%2C+N+S">N. Sai Shankar</a>, <a href="/search/cs?searchtype=author&query=Sarvadevabhatla%2C+R+K">Ravi Kiran Sarvadevabhatla</a>, <a href="/search/cs?searchtype=author&query=Krishna%2C+K+M">K. Madhava Krishna</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.09174v3-abstract-short" style="display: inline;"> Given a monocular colour image of a warehouse rack, we aim to predict the bird's-eye view layout for each shelf in the rack, which we term as multi-layer layout prediction. To this end, we present RackLay, a deep neural network for real-time shelf layout estimation from a single image. Unlike previous layout estimation methods, which provide a single layout for the dominant ground plane alone, Rac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.09174v3-abstract-full').style.display = 'inline'; document.getElementById('2103.09174v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.09174v3-abstract-full" style="display: none;"> Given a monocular colour image of a warehouse rack, we aim to predict the bird's-eye view layout for each shelf in the rack, which we term as multi-layer layout prediction. To this end, we present RackLay, a deep neural network for real-time shelf layout estimation from a single image. Unlike previous layout estimation methods, which provide a single layout for the dominant ground plane alone, RackLay estimates the top-view and front-view layout for each shelf in the considered rack populated with objects. RackLay's architecture and its variants are versatile and estimate accurate layouts for diverse scenes characterized by varying number of visible shelves in an image, large range in shelf occupancy factor and varied background clutter. Given the extreme paucity of datasets in this space and the difficulty involved in acquiring real data from warehouses, we additionally release a flexible synthetic dataset generation pipeline WareSynth which allows users to control the generation process and tailor the dataset according to contingent application. The ablations across architectural variants and comparison with strong prior baselines vindicate the efficacy of RackLay as an apt architecture for the novel problem of multi-layered layout estimation. We also show that fusing the top-view and front-view enables 3D reasoning applications such as metric free space estimation for the considered rack. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.09174v3-abstract-full').style.display = 'none'; document.getElementById('2103.09174v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Visit our project repository at https://github.com/Avinash2468/RackLay</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.11455">arXiv:2102.11455</a> <span> [<a href="https://arxiv.org/pdf/2102.11455">pdf</a>, <a href="https://arxiv.org/format/2102.11455">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1049/cps2.12014">10.1049/cps2.12014 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Man-in-The-Middle Attacks and Defense in a Power System Cyber-Physical Testbed </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wlazlo%2C+P">Patrick Wlazlo</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhijeet Sahu</a>, <a href="/search/cs?searchtype=author&query=Mao%2C+Z">Zeyu Mao</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+H">Hao Huang</a>, <a href="/search/cs?searchtype=author&query=Goulart%2C+A">Ana Goulart</a>, <a href="/search/cs?searchtype=author&query=Davis%2C+K">Katherine Davis</a>, <a href="/search/cs?searchtype=author&query=Zonouz%2C+S">Saman Zonouz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.11455v1-abstract-short" style="display: inline;"> Man-in-The-Middle (MiTM) attacks present numerous threats to a smart grid. In a MiTM attack, an intruder embeds itself within a conversation between two devices to either eavesdrop or impersonate one of the devices, making it appear to be a normal exchange of information. Thus, the intruder can perform false data injection (FDI) and false command injection (FCI) attacks that can compromise power s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.11455v1-abstract-full').style.display = 'inline'; document.getElementById('2102.11455v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.11455v1-abstract-full" style="display: none;"> Man-in-The-Middle (MiTM) attacks present numerous threats to a smart grid. In a MiTM attack, an intruder embeds itself within a conversation between two devices to either eavesdrop or impersonate one of the devices, making it appear to be a normal exchange of information. Thus, the intruder can perform false data injection (FDI) and false command injection (FCI) attacks that can compromise power system operations, such as state estimation, economic dispatch, and automatic generation control (AGC). Very few researchers have focused on MiTM methods that are difficult to detect within a smart grid. To address this, we are designing and implementing multi-stage MiTM intrusions in an emulation-based cyber-physical power system testbed against a large-scale synthetic grid model to demonstrate how such attacks can cause physical contingencies such as misguided operation and false measurements. MiTM intrusions create FCI, FDI, and replay attacks in this synthetic power grid. This work enables stakeholders to defend against these stealthy attacks, and we present detection mechanisms that are developed using multiple alerts from intrusion detection systems and network monitoring tools. Our contribution will enable other smart grid security researchers and industry to develop further detection mechanisms for inconspicuous MiTM attacks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.11455v1-abstract-full').style.display = 'none'; document.getElementById('2102.11455v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IET Cyber-Physical Systems: Theory & Applications 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.00029">arXiv:2102.00029</a> <span> [<a href="https://arxiv.org/pdf/2102.00029">pdf</a>, <a href="https://arxiv.org/format/2102.00029">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Willmott%2C+D">Devin Willmott</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Sheikholeslami%2C+F">Fatemeh Sheikholeslami</a>, <a href="/search/cs?searchtype=author&query=Condessa%2C+F">Filipe Condessa</a>, <a href="/search/cs?searchtype=author&query=Kolter%2C+Z">Zico Kolter</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.00029v1-abstract-short" style="display: inline;"> Researchers have repeatedly shown that it is possible to craft adversarial attacks on deep classifiers (small perturbations that significantly change the class label), even in the "black-box" setting where one only has query access to the classifier. However, all prior work in the black-box setting attacks the classifier by repeatedly querying the same image with minor modifications, usually thous… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.00029v1-abstract-full').style.display = 'inline'; document.getElementById('2102.00029v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.00029v1-abstract-full" style="display: none;"> Researchers have repeatedly shown that it is possible to craft adversarial attacks on deep classifiers (small perturbations that significantly change the class label), even in the "black-box" setting where one only has query access to the classifier. However, all prior work in the black-box setting attacks the classifier by repeatedly querying the same image with minor modifications, usually thousands of times or more, making it easy for defenders to detect an ensuing attack. In this work, we instead show that it is possible to craft (universal) adversarial perturbations in the black-box setting by querying a sequence of different images only once. This attack prevents detection from high number of similar queries and produces a perturbation that causes misclassification when applied to any input to the classifier. In experiments, we show that attacks that adhere to this restriction can produce untargeted adversarial perturbations that fool the vast majority of MNIST and CIFAR-10 classifier inputs, as well as in excess of $60-70\%$ of inputs on ImageNet classifiers. In the targeted setting, we exhibit targeted black-box universal attacks on ImageNet classifiers with success rates above $20\%$ when only allowed one query per image, and $66\%$ when allowed two queries per image. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.00029v1-abstract-full').style.display = 'none'; document.getElementById('2102.00029v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.06897">arXiv:2101.06897</a> <span> [<a href="https://arxiv.org/pdf/2101.06897">pdf</a>, <a href="https://arxiv.org/format/2101.06897">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ACCESS.2021.3106873">10.1109/ACCESS.2021.3106873 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Multi-Source Data Fusion for Cyberattack Detection in Power Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhijeet Sahu</a>, <a href="/search/cs?searchtype=author&query=Mao%2C+Z">Zeyu Mao</a>, <a href="/search/cs?searchtype=author&query=Wlazlo%2C+P">Patrick Wlazlo</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+H">Hao Huang</a>, <a href="/search/cs?searchtype=author&query=Davis%2C+K">Katherine Davis</a>, <a href="/search/cs?searchtype=author&query=Goulart%2C+A">Ana Goulart</a>, <a href="/search/cs?searchtype=author&query=Zonouz%2C+S">Saman Zonouz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.06897v1-abstract-short" style="display: inline;"> Cyberattacks can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the cyber-physical nature of the system coupled with the need for high reliability and resilience of the physical system. Conventional rule-based and anomaly-based intrusio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.06897v1-abstract-full').style.display = 'inline'; document.getElementById('2101.06897v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.06897v1-abstract-full" style="display: none;"> Cyberattacks can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the cyber-physical nature of the system coupled with the need for high reliability and resilience of the physical system. Conventional rule-based and anomaly-based intrusion detection system (IDS) tools are insufficient for detecting zero-day cyber intrusions in the industrial control system (ICS) networks. Hence, in this work, we show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. Specifically, we present how to recognize and address the barriers that can prevent the accurate use of multiple data sources for fusion-based detection. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle (MiTM) attacks. Post collection, the data fusion application uses time-synchronized merge and extracts features followed by pre-processing such as imputation and encoding before training supervised, semi-supervised, and unsupervised learning models to evaluate the performance of the IDS. A major finding is the improvement of detection accuracy by fusion of features from cyber, security, and physical domains. Additionally, we observed the co-training technique performs at par with supervised learning methods when fed with our features. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.06897v1-abstract-full').style.display = 'none'; document.getElementById('2101.06897v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Access 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.11406">arXiv:2012.11406</a> <span> [<a href="https://arxiv.org/pdf/2012.11406">pdf</a>, <a href="https://arxiv.org/format/2012.11406">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sagel%2C+A">Alexander Sagel</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Amit Sahu</a>, <a href="/search/cs?searchtype=author&query=Matthes%2C+S">Stefan Matthes</a>, <a href="/search/cs?searchtype=author&query=Pfeifer%2C+H">Holger Pfeifer</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+T">Tianming Qiu</a>, <a href="/search/cs?searchtype=author&query=Rue%C3%9F%2C+H">Harald Rue脽</a>, <a href="/search/cs?searchtype=author&query=Shen%2C+H">Hao Shen</a>, <a href="/search/cs?searchtype=author&query=W%C3%B6rmann%2C+J">Julian W枚rmann</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2012.11406v1-abstract-short" style="display: inline;"> Research in machine learning is at a turning point. While supervised deep learning has conquered the field at a breathtaking pace and demonstrated the ability to solve inference problems with unprecedented accuracy, it still does not quite live up to its name if we think of learning as the process of acquiring knowledge about a subject or problem. Major weaknesses of present-day deep learning mode… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.11406v1-abstract-full').style.display = 'inline'; document.getElementById('2012.11406v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.11406v1-abstract-full" style="display: none;"> Research in machine learning is at a turning point. While supervised deep learning has conquered the field at a breathtaking pace and demonstrated the ability to solve inference problems with unprecedented accuracy, it still does not quite live up to its name if we think of learning as the process of acquiring knowledge about a subject or problem. Major weaknesses of present-day deep learning models are, for instance, their lack of adaptability to changes of environment or their incapability to perform other kinds of tasks than the one they were trained for. While it is still unclear how to overcome these limitations, one can observe a paradigm shift within the machine learning community, with research interests shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks, and towards employing machine learning algorithms in highly diverse domains. This research question can be approached from different angles. For instance, the field of Informed AI investigates the problem of infusing domain knowledge into a machine learning model, by using techniques such as regularization, data augmentation or post-processing. On the other hand, a remarkable number of works in the recent years has focused on developing models that by themselves guarantee a certain degree of versatility and invariance with respect to the domain or problem at hand. Thus, rather than investigating how to provide domain-specific knowledge to machine learning models, these works explore methods that equip the models with the capability of acquiring the knowledge by themselves. This white paper provides an introduction and discussion of this emerging field in machine learning research. To this end, it reviews the role of knowledge in machine learning, and discusses its relation to the concept of invariance, before providing a literature review of the field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.11406v1-abstract-full').style.display = 'none'; document.getElementById('2012.11406v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.09602">arXiv:2012.09602</a> <span> [<a href="https://arxiv.org/pdf/2012.09602">pdf</a>, <a href="https://arxiv.org/format/2012.09602">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Application of the Neural Network Dependability Kit in Real-World Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Amit Sahu</a>, <a href="/search/cs?searchtype=author&query=V%C3%A1llez%2C+N">Noelia V谩llez</a>, <a href="/search/cs?searchtype=author&query=Rodr%C3%ADguez-Bobada%2C+R">Rosana Rodr铆guez-Bobada</a>, <a href="/search/cs?searchtype=author&query=Alhaddad%2C+M">Mohamad Alhaddad</a>, <a href="/search/cs?searchtype=author&query=Moured%2C+O">Omar Moured</a>, <a href="/search/cs?searchtype=author&query=Neugschwandtner%2C+G">Georg Neugschwandtner</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2012.09602v1-abstract-short" style="display: inline;"> In this paper, we provide a guideline for using the Neural Network Dependability Kit (NNDK) during the development process of NN models, and show how the algorithm is applied in two image classification use cases. The case studies demonstrate the usage of the dependability kit to obtain insights about the NN model and how they informed the development process of the neural network model. After int… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.09602v1-abstract-full').style.display = 'inline'; document.getElementById('2012.09602v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.09602v1-abstract-full" style="display: none;"> In this paper, we provide a guideline for using the Neural Network Dependability Kit (NNDK) during the development process of NN models, and show how the algorithm is applied in two image classification use cases. The case studies demonstrate the usage of the dependability kit to obtain insights about the NN model and how they informed the development process of the neural network model. After interpreting neural networks via the different metrics available in the NNDK, the developers were able to increase the NNs' accuracy, trust the developed networks, and make them more robust. In addition, we obtained a novel application-oriented technique to provide supporting evidence for an NN's classification result to the user. In the medical image classification use case, it was used to retrieve case images from the training dataset that were similar to the current patient's image and could therefore act as a support for the NN model's decision and aid doctors in interpreting the results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.09602v1-abstract-full').style.display = 'none'; document.getElementById('2012.09602v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 7 Figures including 2 appendices Main Content: 5 pages, 1 Figure</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.04205">arXiv:2010.04205</a> <span> [<a href="https://arxiv.org/pdf/2010.04205">pdf</a>, <a href="https://arxiv.org/format/2010.04205">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sahu%2C+A+K">Anit Kumar Sahu</a>, <a href="/search/cs?searchtype=author&query=Shukla%2C+S+N">Satya Narayan Shukla</a>, <a href="/search/cs?searchtype=author&query=Kolter%2C+J+Z">J. Zico Kolter</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2010.04205v1-abstract-short" style="display: inline;"> We study the problem of generating adversarial examples in a black-box setting, where we only have access to a zeroth order oracle, providing us with loss function evaluations. Although this setting has been investigated in previous work, most past approaches using zeroth order optimization implicitly assume that the gradients of the loss function with respect to the input images are \emph{unstruc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.04205v1-abstract-full').style.display = 'inline'; document.getElementById('2010.04205v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.04205v1-abstract-full" style="display: none;"> We study the problem of generating adversarial examples in a black-box setting, where we only have access to a zeroth order oracle, providing us with loss function evaluations. Although this setting has been investigated in previous work, most past approaches using zeroth order optimization implicitly assume that the gradients of the loss function with respect to the input images are \emph{unstructured}. In this work, we show that in fact substantial correlations exist within these gradients, and we propose to capture these correlations via a Gaussian Markov random field (GMRF). Given the intractability of the explicit covariance structure of the MRF, we show that the covariance structure can be efficiently represented using the Fast Fourier Transform (FFT), along with low-rank updates to perform exact posterior estimation under this model. We use this modeling technique to find fast one-step adversarial attacks, akin to a black-box version of the Fast Gradient Sign Method~(FGSM), and show that the method uses fewer queries and achieves higher attack success rates than the current state of the art. We also highlight the general applicability of this gradient modeling setup. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.04205v1-abstract-full').style.display = 'none'; document.getElementById('2010.04205v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.13978">arXiv:2009.13978</a> <span> [<a href="https://arxiv.org/pdf/2009.13978">pdf</a>, <a href="https://arxiv.org/ps/2009.13978">ps</a>, <a href="https://arxiv.org/format/2009.13978">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Anonymous proof-of-asset transactions using designated blind signatures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sharma%2C+N">Neetu Sharma</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+R+A">Rajeev Anand Sahu</a>, <a href="/search/cs?searchtype=author&query=Saraswat%2C+V">Vishal Saraswat</a>, <a href="/search/cs?searchtype=author&query=Garcia-Alfaro%2C+J">Joaquin Garcia-Alfaro</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.13978v2-abstract-short" style="display: inline;"> We propose a scheme to preserve the anonymity of users in proof-of-asset transactions. We assume bitcoin-like cryptocurrency systems in which a user must prove the strength of its assets (i.e., solvency), prior conducting further transactions. The traditional way of addressing such a problem is the use of blind signatures, i.e., a kind of digital signature whose properties satisfy the anonymity of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.13978v2-abstract-full').style.display = 'inline'; document.getElementById('2009.13978v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.13978v2-abstract-full" style="display: none;"> We propose a scheme to preserve the anonymity of users in proof-of-asset transactions. We assume bitcoin-like cryptocurrency systems in which a user must prove the strength of its assets (i.e., solvency), prior conducting further transactions. The traditional way of addressing such a problem is the use of blind signatures, i.e., a kind of digital signature whose properties satisfy the anonymity of the signer. Our work focuses on the use of a designated verifier signature scheme that limits to only a single authorized party (within a group of signature requesters) to verify the correctness of the transaction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.13978v2-abstract-full').style.display = 'none'; document.getElementById('2009.13978v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, extended conference version</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.07953">arXiv:2008.07953</a> <span> [<a href="https://arxiv.org/pdf/2008.07953">pdf</a>, <a href="https://arxiv.org/format/2008.07953">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Discrete Mathematics">cs.DM</span> </div> </div> <p class="title is-5 mathjax"> Parameterized Complexity of Maximum Edge Colorable Subgraph </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Agrawal%2C+A">Akanksha Agrawal</a>, <a href="/search/cs?searchtype=author&query=Kundu%2C+M">Madhumita Kundu</a>, <a href="/search/cs?searchtype=author&query=Sahu%2C+A">Abhishek Sahu</a>, <a href="/search/cs?searchtype=author&query=Saurabh%2C+S">Saket Saurabh</a>, <a href="/search/cs?searchtype=author&query=Tale%2C+P">Prafullkumar Tale</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2008.07953v1-abstract-short" style="display: inline;"> A graph $H$ is {\em $p$-edge colorable} if there is a coloring $蠄: E(H) \rightarrow \{1,2,\dots,p\}$, such that for distinct $uv, vw \in E(H)$, we have $蠄(uv) \neq 蠄(vw)$. The {\sc Maximum Edge-Colorable Subgraph} problem takes as input a graph $G$ and integers $l$ and $p$, and the objective is to find a subgraph $H$ of $G$ and a $p$-edge-coloring of $H$, such that $|E(H)| \geq l$. We study the ab… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.07953v1-abstract-full').style.display = 'inline'; document.getElementById('2008.07953v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.07953v1-abstract-full" style="display: none;"> A graph $H$ is {\em $p$-edge colorable} if there is a coloring $蠄: E(H) \rightarrow \{1,2,\dots,p\}$, such that for distinct $uv, vw \in E(H)$, we have $蠄(uv) \neq 蠄(vw)$. The {\sc Maximum Edge-Colorable Subgraph} problem takes as input a graph $G$ and integers $l$ and $p$, and the objective is to find a subgraph $H$ of $G$ and a $p$-edge-coloring of $H$, such that $|E(H)| \geq l$. We study the above problem from the viewpoint of Parameterized Complexity. We obtain \FPT\ algorithms when parameterized by: $(1)$ the vertex cover number of $G$, by using {\sc Integer Linear Programming}, and $(2)$ $l$, a randomized algorithm via a reduction to \textsc{Rainbow Matching}, and a deterministic algorithm by using color coding, and divide and color. With respect to the parameters $p+k$, where $k$ is one of the following: $(1)$ the solution size, $l$, $(2)$ the vertex cover number of $G$, and $(3)$ $l - {\mm}(G)$, where ${\mm}(G)$ is the size of a maximum matching in $G$; we show that the (decision version of the) problem admits a kernel with $\mathcal{O}(k \cdot p)$ vertices. Furthermore, we show that there is no kernel of size $\mathcal{O}(k^{1-蔚} \cdot f(p))$, for any $蔚> 0$ and computable function $f$, unless $\NP \subseteq \CONPpoly$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.07953v1-abstract-full').style.display = 'none'; document.getElementById('2008.07953v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Sahu%2C+A&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a 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