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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <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/2309.11526">arXiv:2309.11526</a> <span> [<a href="https://arxiv.org/pdf/2309.11526">pdf</a>, <a href="https://arxiv.org/format/2309.11526">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 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/JSEN.2023.3341503">10.1109/JSEN.2023.3341503 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Likelihood-based Sensor Calibration using Affine Transformation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Machhamer%2C+R">R眉diger Machhamer</a>, <a href="/search/cs?searchtype=author&query=Fazlic%2C+L+B">Lejla Begic Fazlic</a>, <a href="/search/cs?searchtype=author&query=Guven%2C+E">Eray Guven</a>, <a href="/search/cs?searchtype=author&query=Junk%2C+D">David Junk</a>, <a href="/search/cs?searchtype=author&query=Kurt%2C+G+K">Gunes Karabulut Kurt</a>, <a href="/search/cs?searchtype=author&query=Naumann%2C+S">Stefan Naumann</a>, <a href="/search/cs?searchtype=author&query=Didas%2C+S">Stephan Didas</a>, <a href="/search/cs?searchtype=author&query=Gollmer%2C+K">Klaus-Uwe Gollmer</a>, <a href="/search/cs?searchtype=author&query=Bergmann%2C+R">Ralph Bergmann</a>, <a href="/search/cs?searchtype=author&query=Timm%2C+I+J">Ingo J. Timm</a>, <a href="/search/cs?searchtype=author&query=Dartmann%2C+G">Guido Dartmann</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="2309.11526v4-abstract-short" style="display: inline;"> An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation between different systems, which can be improved by the knowledge of experts. This paper presents an improved solution from Glacier Research that was publishe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.11526v4-abstract-full').style.display = 'inline'; document.getElementById('2309.11526v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.11526v4-abstract-full" style="display: none;"> An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation between different systems, which can be improved by the knowledge of experts. This paper presents an improved solution from Glacier Research that was published back in 1973. The results demonstrate the adaptability of this solution for various applications, including software calibration of sensors, implementation of expert-based adaptation, and paving the way for future advancements such as distributed learning methods. One idea here is to use the knowledge of experts for estimating an affine transformation between different systems. We evaluate our research with simulations and also with real measured data of a multi-sensor board with 8 identical sensors. Both data set and evaluation script are provided for download. The results show an improvement for both the simulation and the experiments with real data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.11526v4-abstract-full').style.display = 'none'; document.getElementById('2309.11526v4-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 20 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.06032">arXiv:2303.06032</a> <span> [<a href="https://arxiv.org/pdf/2303.06032">pdf</a>, <a href="https://arxiv.org/format/2303.06032">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> <span class="tag is-small is-grey 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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Exploring Adversarial Attacks on Neural Networks: An Explainable Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Renkhoff%2C+J">Justus Renkhoff</a>, <a href="/search/cs?searchtype=author&query=Tan%2C+W">Wenkai Tan</a>, <a href="/search/cs?searchtype=author&query=Velasquez%2C+A">Alvaro Velasquez</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+i+Y">illiam Yichen Wang</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yongxin Liu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jian Wang</a>, <a href="/search/cs?searchtype=author&query=Niu%2C+S">Shuteng Niu</a>, <a href="/search/cs?searchtype=author&query=Fazlic%2C+L+B">Lejla Begic Fazlic</a>, <a href="/search/cs?searchtype=author&query=Dartmann%2C+G">Guido Dartmann</a>, <a href="/search/cs?searchtype=author&query=Song%2C+H">Houbing Song</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="2303.06032v1-abstract-short" style="display: inline;"> Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure the robustness of these methods and thus counteract uncertain behaviors caused by adversarial attacks. In this paper, we use gradient heatmaps to analyze the response characteristics of the VGG-16 model when the input ima… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.06032v1-abstract-full').style.display = 'inline'; document.getElementById('2303.06032v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.06032v1-abstract-full" style="display: none;"> Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure the robustness of these methods and thus counteract uncertain behaviors caused by adversarial attacks. In this paper, we use gradient heatmaps to analyze the response characteristics of the VGG-16 model when the input images are mixed with adversarial noise and statistically similar Gaussian random noise. In particular, we compare the network response layer by layer to determine where errors occurred. Several interesting findings are derived. First, compared to Gaussian random noise, intentionally generated adversarial noise causes severe behavior deviation by distracting the area of concentration in the networks. Second, in many cases, adversarial examples only need to compromise a few intermediate blocks to mislead the final decision. Third, our experiments revealed that specific blocks are more vulnerable and easier to exploit by adversarial examples. Finally, we demonstrate that the layers $Block4\_conv1$ and $Block5\_cov1$ of the VGG-16 model are more susceptible to adversarial attacks. Our work could provide valuable insights into developing more reliable Deep Neural Network (DNN) models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.06032v1-abstract-full').style.display = 'none'; document.getElementById('2303.06032v1-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 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/2007.04725">arXiv:2007.04725</a> <span> [<a href="https://arxiv.org/pdf/2007.04725">pdf</a>, <a href="https://arxiv.org/format/2007.04725">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="Neural and Evolutionary Computing">cs.NE</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"> EVO-RL: Evolutionary-Driven Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hallawa%2C+A">Ahmed Hallawa</a>, <a href="/search/cs?searchtype=author&query=Born%2C+T">Thorsten Born</a>, <a href="/search/cs?searchtype=author&query=Schmeink%2C+A">Anke Schmeink</a>, <a href="/search/cs?searchtype=author&query=Dartmann%2C+G">Guido Dartmann</a>, <a href="/search/cs?searchtype=author&query=Peine%2C+A">Arne Peine</a>, <a href="/search/cs?searchtype=author&query=Martin%2C+L">Lukas Martin</a>, <a href="/search/cs?searchtype=author&query=Iacca%2C+G">Giovanni Iacca</a>, <a href="/search/cs?searchtype=author&query=Eiben%2C+A+E">A. E. Eiben</a>, <a href="/search/cs?searchtype=author&query=Ascheid%2C+G">Gerd Ascheid</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="2007.04725v2-abstract-short" style="display: inline;"> In this work, we propose a novel approach for reinforcement learning driven by evolutionary computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (evo-RL), embeds the reinforcement learning algorithm in an evolutionary cycle, where we distinctly differentiate between purely evolvable (instinctive) behaviour versus purely learnable behaviour. Furthermore, we propose that… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.04725v2-abstract-full').style.display = 'inline'; document.getElementById('2007.04725v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.04725v2-abstract-full" style="display: none;"> In this work, we propose a novel approach for reinforcement learning driven by evolutionary computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (evo-RL), embeds the reinforcement learning algorithm in an evolutionary cycle, where we distinctly differentiate between purely evolvable (instinctive) behaviour versus purely learnable behaviour. Furthermore, we propose that this distinction is decided by the evolutionary process, thus allowing evo-RL to be adaptive to different environments. In addition, evo-RL facilitates learning on environments with rewardless states, which makes it more suited for real-world problems with incomplete information. To show that evo-RL leads to state-of-the-art performance, we present the performance of different state-of-the-art reinforcement learning algorithms when operating within evo-RL and compare it with the case when these same algorithms are executed independently. Results show that reinforcement learning algorithms embedded within our evo-RL approach significantly outperform the stand-alone versions of the same RL algorithms on OpenAI Gym control problems with rewardless states constrained by the same computational budget. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.04725v2-abstract-full').style.display = 'none'; document.getElementById('2007.04725v2-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 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">9 pages, 7 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/1308.1876">arXiv:1308.1876</a> <span> [<a href="https://arxiv.org/pdf/1308.1876">pdf</a>, <a href="https://arxiv.org/ps/1308.1876">ps</a>, <a href="https://arxiv.org/format/1308.1876">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> A Non-Alternating Algorithm for Joint BS-RS Precoding Design in Two-Way Relay Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zandi%2C+E">Ehsan Zandi</a>, <a href="/search/cs?searchtype=author&query=Dartmann%2C+G">Guido Dartmann</a>, <a href="/search/cs?searchtype=author&query=Ascheid%2C+G">Gerd Ascheid</a>, <a href="/search/cs?searchtype=author&query=Mathar%2C+R">Rudolf Mathar</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="1308.1876v1-abstract-short" style="display: inline;"> Cooperative relay systems have become an active area of research during recent years since they help cellular networks to enhance data rate and coverage. In this paper we develop a method to jointly optimize precoding matrices for amplify-and-forward relay station and base station. Our objective is to increase max-min SINR fairness within co-channel users in a cell. The main achievement of this wo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1308.1876v1-abstract-full').style.display = 'inline'; document.getElementById('1308.1876v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1308.1876v1-abstract-full" style="display: none;"> Cooperative relay systems have become an active area of research during recent years since they help cellular networks to enhance data rate and coverage. In this paper we develop a method to jointly optimize precoding matrices for amplify-and-forward relay station and base station. Our objective is to increase max-min SINR fairness within co-channel users in a cell. The main achievement of this work is avoiding any tedious alternating optimization for joint design of RS/BS precoders, in order to save complexity. Moreover, no convex solver is required in this method. RS precoding is done by transforming the underlying non-convex problem into a system of nonlinear equations which is then solved using Levenberg-Marquardt algorithm. This method for RS precoder design is guaranteed to converge to a local optimum. For the BS precoder a low-complexity iterative method is proposed. The efficiency of the joint optimization method is verified by simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1308.1876v1-abstract-full').style.display = 'none'; document.getElementById('1308.1876v1-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 August, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2013. </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 at IEEE TVT</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1307.3121">arXiv:1307.3121</a> <span> [<a href="https://arxiv.org/pdf/1307.3121">pdf</a>, <a href="https://arxiv.org/format/1307.3121">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</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/TVT.2014.2306204">10.1109/TVT.2014.2306204 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Modified Levenberg-Marquardt Method for the Bidirectional Relay Channel </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dartmann%2C+G">Guido Dartmann</a>, <a href="/search/cs?searchtype=author&query=Zandi%2C+E">Ehsan Zandi</a>, <a href="/search/cs?searchtype=author&query=Ascheid%2C+G">Gerd Ascheid</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="1307.3121v3-abstract-short" style="display: inline;"> This paper presents an optimization approach for a system consisting of multiple bidirectional links over a two-way amplify-and-forward relay. It is desired to improve the fairness of the system. All user pairs exchange information over one relay station with multiple antennas. Due to the joint transmission to all users, the users are subject to mutual interference. A mitigation of the interferenc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1307.3121v3-abstract-full').style.display = 'inline'; document.getElementById('1307.3121v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1307.3121v3-abstract-full" style="display: none;"> This paper presents an optimization approach for a system consisting of multiple bidirectional links over a two-way amplify-and-forward relay. It is desired to improve the fairness of the system. All user pairs exchange information over one relay station with multiple antennas. Due to the joint transmission to all users, the users are subject to mutual interference. A mitigation of the interference can be achieved by max-min fair precoding optimization where the relay is subject to a sum power constraint. The resulting optimization problem is non-convex. This paper proposes a novel iterative and low complexity approach based on a modified Levenberg-Marquardt method to find near optimal solutions. The presented method finds solutions close to the standard convex-solver based relaxation approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1307.3121v3-abstract-full').style.display = 'none'; document.getElementById('1307.3121v3-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 February, 2014; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 July, 2013; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2013. </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 to IEEE Transactions on Vehicular Technology We corrected small mistakes in the proof of Lemma 2 and Proposition 2</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" 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