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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/2409.12640">arXiv:2409.12640</a> <span> [<a href="https://arxiv.org/pdf/2409.12640">pdf</a>, <a href="https://arxiv.org/format/2409.12640">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vodrahalli%2C+K">Kiran Vodrahalli</a>, <a href="/search/cs?searchtype=author&query=Ontanon%2C+S">Santiago Ontanon</a>, <a href="/search/cs?searchtype=author&query=Tripuraneni%2C+N">Nilesh Tripuraneni</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+K">Kelvin Xu</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+S">Sanil Jain</a>, <a href="/search/cs?searchtype=author&query=Shivanna%2C+R">Rakesh Shivanna</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+J">Jeffrey Hui</a>, <a href="/search/cs?searchtype=author&query=Dikkala%2C+N">Nishanth Dikkala</a>, <a href="/search/cs?searchtype=author&query=Kazemi%2C+M">Mehran Kazemi</a>, <a href="/search/cs?searchtype=author&query=Fatemi%2C+B">Bahare Fatemi</a>, <a href="/search/cs?searchtype=author&query=Anil%2C+R">Rohan Anil</a>, <a href="/search/cs?searchtype=author&query=Dyer%2C+E">Ethan Dyer</a>, <a href="/search/cs?searchtype=author&query=Shakeri%2C+S">Siamak Shakeri</a>, <a href="/search/cs?searchtype=author&query=Vij%2C+R">Roopali Vij</a>, <a href="/search/cs?searchtype=author&query=Mehta%2C+H">Harsh Mehta</a>, <a href="/search/cs?searchtype=author&query=Ramasesh%2C+V">Vinay Ramasesh</a>, <a href="/search/cs?searchtype=author&query=Le%2C+Q">Quoc Le</a>, <a href="/search/cs?searchtype=author&query=Chi%2C+E">Ed Chi</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+Y">Yifeng Lu</a>, <a href="/search/cs?searchtype=author&query=Firat%2C+O">Orhan Firat</a>, <a href="/search/cs?searchtype=author&query=Lazaridou%2C+A">Angeliki Lazaridou</a>, <a href="/search/cs?searchtype=author&query=Lespiau%2C+J">Jean-Baptiste Lespiau</a>, <a href="/search/cs?searchtype=author&query=Attaluri%2C+N">Nithya Attaluri</a>, <a href="/search/cs?searchtype=author&query=Olszewska%2C+K">Kate Olszewska</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="2409.12640v2-abstract-short" style="display: inline;"> We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation is derived via a novel, unifying framework for evaluations over arbitrarily long contexts which measure the model's ability to do more than retrieve a single piece of information from its context. The central idea of th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.12640v2-abstract-full').style.display = 'inline'; document.getElementById('2409.12640v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.12640v2-abstract-full" style="display: none;"> We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation is derived via a novel, unifying framework for evaluations over arbitrarily long contexts which measure the model's ability to do more than retrieve a single piece of information from its context. The central idea of the Latent Structure Queries framework (LSQ) is to construct tasks which require a model to ``chisel away'' the irrelevant information in the context, revealing a latent structure in the context. To verify a model's understanding of this latent structure, we query the model for details of the structure. Using LSQ, we produce three diagnostic long-context evaluations across code and natural-language domains intended to provide a stronger signal of long-context language model capabilities. We perform evaluations on several state-of-the-art models and demonstrate both that a) the proposed evaluations are high-signal and b) that there is significant room for improvement in synthesizing long-context information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.12640v2-abstract-full').style.display = 'none'; document.getElementById('2409.12640v2-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.11409">arXiv:2406.11409</a> <span> [<a href="https://arxiv.org/pdf/2406.11409">pdf</a>, <a href="https://arxiv.org/format/2406.11409">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> <p class="title is-5 mathjax"> CodeGemma: Open Code Models Based on Gemma </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=CodeGemma+Team"> CodeGemma Team</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+H">Heri Zhao</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+J">Jeffrey Hui</a>, <a href="/search/cs?searchtype=author&query=Howland%2C+J">Joshua Howland</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+N">Nam Nguyen</a>, <a href="/search/cs?searchtype=author&query=Zuo%2C+S">Siqi Zuo</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+A">Andrea Hu</a>, <a href="/search/cs?searchtype=author&query=Choquette-Choo%2C+C+A">Christopher A. Choquette-Choo</a>, <a href="/search/cs?searchtype=author&query=Shen%2C+J">Jingyue Shen</a>, <a href="/search/cs?searchtype=author&query=Kelley%2C+J">Joe Kelley</a>, <a href="/search/cs?searchtype=author&query=Bansal%2C+K">Kshitij Bansal</a>, <a href="/search/cs?searchtype=author&query=Vilnis%2C+L">Luke Vilnis</a>, <a href="/search/cs?searchtype=author&query=Wirth%2C+M">Mateo Wirth</a>, <a href="/search/cs?searchtype=author&query=Michel%2C+P">Paul Michel</a>, <a href="/search/cs?searchtype=author&query=Choy%2C+P">Peter Choy</a>, <a href="/search/cs?searchtype=author&query=Joshi%2C+P">Pratik Joshi</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+R">Ravin Kumar</a>, <a href="/search/cs?searchtype=author&query=Hashmi%2C+S">Sarmad Hashmi</a>, <a href="/search/cs?searchtype=author&query=Agrawal%2C+S">Shubham Agrawal</a>, <a href="/search/cs?searchtype=author&query=Gong%2C+Z">Zhitao Gong</a>, <a href="/search/cs?searchtype=author&query=Fine%2C+J">Jane Fine</a>, <a href="/search/cs?searchtype=author&query=Warkentin%2C+T">Tris Warkentin</a>, <a href="/search/cs?searchtype=author&query=Hartman%2C+A+J">Ale Jakse Hartman</a>, <a href="/search/cs?searchtype=author&query=Ni%2C+B">Bin Ni</a>, <a href="/search/cs?searchtype=author&query=Korevec%2C+K">Kathy Korevec</a> , et al. (2 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="2406.11409v2-abstract-short" style="display: inline;"> This paper introduces CodeGemma, a collection of specialized open code models built on top of Gemma, capable of a variety of code and natural language generation tasks. We release three model variants. CodeGemma 7B pretrained (PT) and instruction-tuned (IT) variants have remarkably resilient natural language understanding, excel in mathematical reasoning, and match code capabilities of other open… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11409v2-abstract-full').style.display = 'inline'; document.getElementById('2406.11409v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.11409v2-abstract-full" style="display: none;"> This paper introduces CodeGemma, a collection of specialized open code models built on top of Gemma, capable of a variety of code and natural language generation tasks. We release three model variants. CodeGemma 7B pretrained (PT) and instruction-tuned (IT) variants have remarkably resilient natural language understanding, excel in mathematical reasoning, and match code capabilities of other open models. CodeGemma 2B is a state-of-the-art code completion model designed for fast code infilling and open-ended generation in latency-sensitive settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11409v2-abstract-full').style.display = 'none'; document.getElementById('2406.11409v2-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">v1: 11 pages, 4 figures, 5 tables. v2: Update metadata</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.01636">arXiv:2406.01636</a> <span> [<a href="https://arxiv.org/pdf/2406.01636">pdf</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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> COVID-19: post infection implications in different age groups, mechanism, diagnosis, effective prevention, treatment, and recommendations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Raheem%2C+M+A">Muhammad Akmal Raheem</a>, <a href="/search/cs?searchtype=author&query=Rahim%2C+M+A">Muhammad Ajwad Rahim</a>, <a href="/search/cs?searchtype=author&query=Gul%2C+I">Ijaz Gul</a>, <a href="/search/cs?searchtype=author&query=Reyad-ul-Ferdous%2C+M">Md. Reyad-ul-Ferdous</a>, <a href="/search/cs?searchtype=author&query=Le%2C+L">Liyan Le</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+J">Junguo Hui</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Shuiwei Xia</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+M">Minjiang Chen</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+D">Dongmei Yu</a>, <a href="/search/cs?searchtype=author&query=Pandey%2C+V">Vijay Pandey</a>, <a href="/search/cs?searchtype=author&query=Qin%2C+P">Peiwu Qin</a>, <a href="/search/cs?searchtype=author&query=Ji%2C+J">Jiansong Ji</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="2406.01636v1-abstract-short" style="display: inline;"> SARS-CoV-2, the highly contagious pathogen responsible for the COVID-19 pandemic, has persistent effects that begin four weeks after initial infection and last for an undetermined duration. These chronic effects are more harmful than acute ones. This review explores the long-term impact of the virus on various human organs, including the pulmonary, cardiovascular, neurological, reproductive, gastr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.01636v1-abstract-full').style.display = 'inline'; document.getElementById('2406.01636v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.01636v1-abstract-full" style="display: none;"> SARS-CoV-2, the highly contagious pathogen responsible for the COVID-19 pandemic, has persistent effects that begin four weeks after initial infection and last for an undetermined duration. These chronic effects are more harmful than acute ones. This review explores the long-term impact of the virus on various human organs, including the pulmonary, cardiovascular, neurological, reproductive, gastrointestinal, musculoskeletal, endocrine, and lymphoid systems, particularly in older adults. Regarding diagnosis, RT-PCR is the gold standard for detecting COVID-19, though it requires specialized equipment, skilled personnel, and considerable time to produce results. To address these limitations, artificial intelligence in imaging and microfluidics technologies offers promising alternatives for diagnosing COVID-19 efficiently. Pharmacological and non-pharmacological strategies are effective in mitigating the persistent impacts of COVID-19. These strategies enhance immunity in post-COVID-19 patients by reducing cytokine release syndrome, improving T cell response, and increasing the circulation of activated natural killer and CD8 T cells in blood and tissues. This, in turn, alleviates symptoms such as fever, nausea, fatigue, muscle weakness, and pain. Vaccines, including inactivated viral, live attenuated viral, protein subunit, viral vectored, mRNA, DNA, and nanoparticle vaccines, significantly reduce the adverse long-term effects of the virus. However, no vaccine has been reported to provide lifetime protection against COVID-19. Consequently, protective measures such as physical distancing, mask usage, and hand hygiene remain essential strategies. This review offers a comprehensive understanding of the persistent effects of COVID-19 on individuals of varying ages, along with insights into diagnosis, treatment, vaccination, and future preventative measures against the spread of SARS-CoV-2. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.01636v1-abstract-full').style.display = 'none'; document.getElementById('2406.01636v1-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.11805">arXiv:2312.11805</a> <span> [<a href="https://arxiv.org/pdf/2312.11805">pdf</a>, <a href="https://arxiv.org/format/2312.11805">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> <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"> Gemini: A Family of Highly Capable Multimodal Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gemini+Team"> Gemini Team</a>, <a href="/search/cs?searchtype=author&query=Anil%2C+R">Rohan Anil</a>, <a href="/search/cs?searchtype=author&query=Borgeaud%2C+S">Sebastian Borgeaud</a>, <a href="/search/cs?searchtype=author&query=Alayrac%2C+J">Jean-Baptiste Alayrac</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+J">Jiahui Yu</a>, <a href="/search/cs?searchtype=author&query=Soricut%2C+R">Radu Soricut</a>, <a href="/search/cs?searchtype=author&query=Schalkwyk%2C+J">Johan Schalkwyk</a>, <a href="/search/cs?searchtype=author&query=Dai%2C+A+M">Andrew M. Dai</a>, <a href="/search/cs?searchtype=author&query=Hauth%2C+A">Anja Hauth</a>, <a href="/search/cs?searchtype=author&query=Millican%2C+K">Katie Millican</a>, <a href="/search/cs?searchtype=author&query=Silver%2C+D">David Silver</a>, <a href="/search/cs?searchtype=author&query=Johnson%2C+M">Melvin Johnson</a>, <a href="/search/cs?searchtype=author&query=Antonoglou%2C+I">Ioannis Antonoglou</a>, <a href="/search/cs?searchtype=author&query=Schrittwieser%2C+J">Julian Schrittwieser</a>, <a href="/search/cs?searchtype=author&query=Glaese%2C+A">Amelia Glaese</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Jilin Chen</a>, <a href="/search/cs?searchtype=author&query=Pitler%2C+E">Emily Pitler</a>, <a href="/search/cs?searchtype=author&query=Lillicrap%2C+T">Timothy Lillicrap</a>, <a href="/search/cs?searchtype=author&query=Lazaridou%2C+A">Angeliki Lazaridou</a>, <a href="/search/cs?searchtype=author&query=Firat%2C+O">Orhan Firat</a>, <a href="/search/cs?searchtype=author&query=Molloy%2C+J">James Molloy</a>, <a href="/search/cs?searchtype=author&query=Isard%2C+M">Michael Isard</a>, <a href="/search/cs?searchtype=author&query=Barham%2C+P+R">Paul R. Barham</a>, <a href="/search/cs?searchtype=author&query=Hennigan%2C+T">Tom Hennigan</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+B">Benjamin Lee</a> , et al. (1325 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="2312.11805v4-abstract-short" style="display: inline;"> This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.11805v4-abstract-full').style.display = 'inline'; document.getElementById('2312.11805v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.11805v4-abstract-full" style="display: none;"> This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.11805v4-abstract-full').style.display = 'none'; document.getElementById('2312.11805v4-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.10403">arXiv:2305.10403</a> <span> [<a href="https://arxiv.org/pdf/2305.10403">pdf</a>, <a href="https://arxiv.org/format/2305.10403">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> <p class="title is-5 mathjax"> PaLM 2 Technical Report </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Anil%2C+R">Rohan Anil</a>, <a href="/search/cs?searchtype=author&query=Dai%2C+A+M">Andrew M. Dai</a>, <a href="/search/cs?searchtype=author&query=Firat%2C+O">Orhan Firat</a>, <a href="/search/cs?searchtype=author&query=Johnson%2C+M">Melvin Johnson</a>, <a href="/search/cs?searchtype=author&query=Lepikhin%2C+D">Dmitry Lepikhin</a>, <a href="/search/cs?searchtype=author&query=Passos%2C+A">Alexandre Passos</a>, <a href="/search/cs?searchtype=author&query=Shakeri%2C+S">Siamak Shakeri</a>, <a href="/search/cs?searchtype=author&query=Taropa%2C+E">Emanuel Taropa</a>, <a href="/search/cs?searchtype=author&query=Bailey%2C+P">Paige Bailey</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Z">Zhifeng Chen</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+E">Eric Chu</a>, <a href="/search/cs?searchtype=author&query=Clark%2C+J+H">Jonathan H. Clark</a>, <a href="/search/cs?searchtype=author&query=Shafey%2C+L+E">Laurent El Shafey</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+Y">Yanping Huang</a>, <a href="/search/cs?searchtype=author&query=Meier-Hellstern%2C+K">Kathy Meier-Hellstern</a>, <a href="/search/cs?searchtype=author&query=Mishra%2C+G">Gaurav Mishra</a>, <a href="/search/cs?searchtype=author&query=Moreira%2C+E">Erica Moreira</a>, <a href="/search/cs?searchtype=author&query=Omernick%2C+M">Mark Omernick</a>, <a href="/search/cs?searchtype=author&query=Robinson%2C+K">Kevin Robinson</a>, <a href="/search/cs?searchtype=author&query=Ruder%2C+S">Sebastian Ruder</a>, <a href="/search/cs?searchtype=author&query=Tay%2C+Y">Yi Tay</a>, <a href="/search/cs?searchtype=author&query=Xiao%2C+K">Kefan Xiao</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+Y">Yuanzhong Xu</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yujing Zhang</a>, <a href="/search/cs?searchtype=author&query=Abrego%2C+G+H">Gustavo Hernandez Abrego</a> , et al. (103 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="2305.10403v3-abstract-short" style="display: inline;"> We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10403v3-abstract-full').style.display = 'inline'; document.getElementById('2305.10403v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.10403v3-abstract-full" style="display: none;"> We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10403v3-abstract-full').style.display = 'none'; document.getElementById('2305.10403v3-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 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.01973">arXiv:2302.01973</a> <span> [<a href="https://arxiv.org/pdf/2302.01973">pdf</a>, <a href="https://arxiv.org/format/2302.01973">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="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> </div> </div> <p class="title is-5 mathjax"> Measuring The Impact Of Programming Language Distribution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Orlanski%2C+G">Gabriel Orlanski</a>, <a href="/search/cs?searchtype=author&query=Xiao%2C+K">Kefan Xiao</a>, <a href="/search/cs?searchtype=author&query=Garcia%2C+X">Xavier Garcia</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+J">Jeffrey Hui</a>, <a href="/search/cs?searchtype=author&query=Howland%2C+J">Joshua Howland</a>, <a href="/search/cs?searchtype=author&query=Malmaud%2C+J">Jonathan Malmaud</a>, <a href="/search/cs?searchtype=author&query=Austin%2C+J">Jacob Austin</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+R">Rishabh Singh</a>, <a href="/search/cs?searchtype=author&query=Catasta%2C+M">Michele Catasta</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="2302.01973v3-abstract-short" style="display: inline;"> Current benchmarks for evaluating neural code models focus on only a small subset of programming languages, excluding many popular languages such as Go or Rust. To ameliorate this issue, we present the BabelCode framework for execution-based evaluation of any benchmark in any language. BabelCode enables new investigations into the qualitative performance of models' memory, runtime, and individual… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.01973v3-abstract-full').style.display = 'inline'; document.getElementById('2302.01973v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.01973v3-abstract-full" style="display: none;"> Current benchmarks for evaluating neural code models focus on only a small subset of programming languages, excluding many popular languages such as Go or Rust. To ameliorate this issue, we present the BabelCode framework for execution-based evaluation of any benchmark in any language. BabelCode enables new investigations into the qualitative performance of models' memory, runtime, and individual test case results. Additionally, we present a new code translation dataset called Translating Python Programming Puzzles (TP3) from the Python Programming Puzzles (Schuster et al. 2021) benchmark that involves translating expert-level python functions to any language. With both BabelCode and the TP3 benchmark, we investigate if balancing the distributions of 14 languages in a training dataset improves a large language model's performance on low-resource languages. Training a model on a balanced corpus results in, on average, 12.34% higher $pass@k$ across all tasks and languages compared to the baseline. We find that this strategy achieves 66.48% better $pass@k$ on low-resource languages at the cost of only a 12.94% decrease to high-resource languages. In our three translation tasks, this strategy yields, on average, 30.77% better low-resource $pass@k$ while having 19.58% worse high-resource $pass@k$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.01973v3-abstract-full').style.display = 'none'; document.getElementById('2302.01973v3-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 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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 to ICML 2023, Code and data release: https://github.com/google-research/babelcode</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.12609">arXiv:2110.12609</a> <span> [<a href="https://arxiv.org/pdf/2110.12609">pdf</a>, <a href="https://arxiv.org/format/2110.12609">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> No News is Good News: A Critique of the One Billion Word Benchmark </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ngo%2C+H">Helen Ngo</a>, <a href="/search/cs?searchtype=author&query=Ara%C3%BAjo%2C+J+G+M">Jo茫o G. M. Ara煤jo</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+J">Jeffrey Hui</a>, <a href="/search/cs?searchtype=author&query=Frosst%2C+N">Nicholas Frosst</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="2110.12609v1-abstract-short" style="display: inline;"> The One Billion Word Benchmark is a dataset derived from the WMT 2011 News Crawl, commonly used to measure language modeling ability in natural language processing. We train models solely on Common Crawl web scrapes partitioned by year, and demonstrate that they perform worse on this task over time due to distributional shift. Analysis of this corpus reveals that it contains several examples of ha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.12609v1-abstract-full').style.display = 'inline'; document.getElementById('2110.12609v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.12609v1-abstract-full" style="display: none;"> The One Billion Word Benchmark is a dataset derived from the WMT 2011 News Crawl, commonly used to measure language modeling ability in natural language processing. We train models solely on Common Crawl web scrapes partitioned by year, and demonstrate that they perform worse on this task over time due to distributional shift. Analysis of this corpus reveals that it contains several examples of harmful text, as well as outdated references to current events. We suggest that the temporal nature of news and its distribution shift over time makes it poorly suited for measuring language modeling ability, and discuss potential impact and considerations for researchers building language models and evaluation datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.12609v1-abstract-full').style.display = 'none'; document.getElementById('2110.12609v1-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, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.06349">arXiv:2104.06349</a> <span> [<a href="https://arxiv.org/pdf/2104.06349">pdf</a>, <a href="https://arxiv.org/format/2104.06349">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Programming Languages">cs.PL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> Gleipnir: Toward Practical Error Analysis for Quantum Programs (Extended Version) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tao%2C+R">Runzhou Tao</a>, <a href="/search/cs?searchtype=author&query=Shi%2C+Y">Yunong Shi</a>, <a href="/search/cs?searchtype=author&query=Yao%2C+J">Jianan Yao</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+J">John Hui</a>, <a href="/search/cs?searchtype=author&query=Chong%2C+F+T">Frederic T. Chong</a>, <a href="/search/cs?searchtype=author&query=Gu%2C+R">Ronghui Gu</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="2104.06349v2-abstract-short" style="display: inline;"> Practical error analysis is essential for the design, optimization, and evaluation of Noisy Intermediate-Scale Quantum(NISQ) computing. However, bounding errors in quantum programs is a grand challenge, because the effects of quantum errors depend on exponentially large quantum states. In this work, we present Gleipnir, a novel methodology toward practically computing verified error bounds in quan… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.06349v2-abstract-full').style.display = 'inline'; document.getElementById('2104.06349v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.06349v2-abstract-full" style="display: none;"> Practical error analysis is essential for the design, optimization, and evaluation of Noisy Intermediate-Scale Quantum(NISQ) computing. However, bounding errors in quantum programs is a grand challenge, because the effects of quantum errors depend on exponentially large quantum states. In this work, we present Gleipnir, a novel methodology toward practically computing verified error bounds in quantum programs. Gleipnir introduces the $(\hat蟻,未)$-diamond norm, an error metric constrained by a quantum predicate consisting of the approximate state $\hat蟻$ and its distance $未$ to the ideal state $蟻$. This predicate $(\hat蟻,未)$ can be computed adaptively using tensor networks based on the Matrix Product States. Gleipnir features a lightweight logic for reasoning about error bounds in noisy quantum programs, based on the $(\hat蟻,未)$-diamond norm metric. Our experimental results show that Gleipnir is able to efficiently generate tight error bounds for real-world quantum programs with 10 to 100 qubits, and can be used to evaluate the error mitigation performance of quantum compiler transformations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.06349v2-abstract-full').style.display = 'none'; document.getElementById('2104.06349v2-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 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 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">typos corrected</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.07226">arXiv:2012.07226</a> <span> [<a href="https://arxiv.org/pdf/2012.07226">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Risk Assessment, Threat Modeling and Security Testing in SDLC </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kamal%2C+A+H+A">Alya Hannah Ahmad Kamal</a>, <a href="/search/cs?searchtype=author&query=Yen%2C+C+C+Y">Caryn Chuah Yi Yen</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+G+J">Gan Jia Hui</a>, <a href="/search/cs?searchtype=author&query=Ling%2C+P+S">Pang Sze Ling</a>, <a href="/search/cs?searchtype=author&query=Fatima-tuz-Zahra"> Fatima-tuz-Zahra</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="2012.07226v1-abstract-short" style="display: inline;"> The software development process is considered as one of the key guidelines in the creation of said software and this approach is necessary for providing a more efficient yet satisfactory output. Without separation of work into distinct stages, it may lead to many delays and inefficiency of the project process where this disorganization can directly affect the product quality and reliability. More… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.07226v1-abstract-full').style.display = 'inline'; document.getElementById('2012.07226v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.07226v1-abstract-full" style="display: none;"> The software development process is considered as one of the key guidelines in the creation of said software and this approach is necessary for providing a more efficient yet satisfactory output. Without separation of work into distinct stages, it may lead to many delays and inefficiency of the project process where this disorganization can directly affect the product quality and reliability. Moreover, with this methodology established as the standard for any project, there are bound to be missteps specifically in regard to the involvement of security due to the lack of awareness. Therefore, the aim of this research is to identify and elaborate the findings and understanding of the security integrated into the process of software development as well as the related individual roles in ensuring that this security is maintained. Through thorough analysis and review of literature, an effort has been made through this paper to showcase the correct processes and ways for securing the software development process. At the same time, certain issues that pertain to this subject have been discussed together with proposing appropriate solutions. Furthermore, in depth discussion is carried out regarding methods such as security testing, risk assessment, threat modeling and other techniques that are able to create a more secure environment and systematic approach in a software development process. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.07226v1-abstract-full').style.display = 'none'; document.getElementById('2012.07226v1-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 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/2004.06718">arXiv:2004.06718</a> <span> [<a href="https://arxiv.org/pdf/2004.06718">pdf</a>, <a href="https://arxiv.org/format/2004.06718">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="Graphics">cs.GR</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"> Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qian%2C+Z">Zhang Qian</a>, <a href="/search/cs?searchtype=author&query=Bo%2C+W">Wang Bo</a>, <a href="/search/cs?searchtype=author&query=Wei%2C+W">Wen Wei</a>, <a href="/search/cs?searchtype=author&query=Hai%2C+L">Li Hai</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+L+J">Liu Jun Hui</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="2004.06718v3-abstract-short" style="display: inline;"> Automatic animation line art colorization is a challenging computer vision problem, since the information of the line art is highly sparse and abstracted and there exists a strict requirement for the color and style consistency between frames. Recently, a lot of Generative Adversarial Network (GAN) based image-to-image translation methods for single line art colorization have emerged. They can gen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.06718v3-abstract-full').style.display = 'inline'; document.getElementById('2004.06718v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.06718v3-abstract-full" style="display: none;"> Automatic animation line art colorization is a challenging computer vision problem, since the information of the line art is highly sparse and abstracted and there exists a strict requirement for the color and style consistency between frames. Recently, a lot of Generative Adversarial Network (GAN) based image-to-image translation methods for single line art colorization have emerged. They can generate perceptually appealing results conditioned on line art images. However, these methods can not be adopted for the purpose of animation colorization because there is a lack of consideration of the in-between frame consistency. Existing methods simply input the previous colored frame as a reference to color the next line art, which will mislead the colorization due to the spatial misalignment of the previous colored frame and the next line art especially at positions where apparent changes happen. To address these challenges, we design a kind of correlation matching feature transfer model (called CMFT) to align the colored reference feature in a learnable way and integrate the model into an U-Net based generator in a coarse-to-fine manner. This enables the generator to transfer the layer-wise synchronized features from the deep semantic code to the content progressively. Extension evaluation shows that CMFT model can effectively improve the in-between consistency and the quality of colored frames especially when the motion is intense and diverse. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.06718v3-abstract-full').style.display = 'none'; document.getElementById('2004.06718v3-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 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">8pages,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/1801.06495">arXiv:1801.06495</a> <span> [<a href="https://arxiv.org/pdf/1801.06495">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="Computers and Society">cs.CY</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/ICACI.2018.8377579">10.1109/ICACI.2018.8377579 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gordienko%2C+Y">Yu. Gordienko</a>, <a href="/search/cs?searchtype=author&query=Kochura%2C+Y">Yu. Kochura</a>, <a href="/search/cs?searchtype=author&query=Alienin%2C+O">O. Alienin</a>, <a href="/search/cs?searchtype=author&query=Rokovyi%2C+O">O. Rokovyi</a>, <a href="/search/cs?searchtype=author&query=Stirenko%2C+S">S. Stirenko</a>, <a href="/search/cs?searchtype=author&query=Gang%2C+P">Peng Gang</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+J">Jiang Hui</a>, <a href="/search/cs?searchtype=author&query=Zeng%2C+W">Wei Zeng</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="1801.06495v1-abstract-short" style="display: inline;"> Efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest X-ray (CXR) 2D images by deep learning approach to help radiologists identify marks of lung cancer in CXR. Training and validation of the simple convolutional neural network (CNN)… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.06495v1-abstract-full').style.display = 'inline'; document.getElementById('1801.06495v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1801.06495v1-abstract-full" style="display: none;"> Efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest X-ray (CXR) 2D images by deep learning approach to help radiologists identify marks of lung cancer in CXR. Training and validation of the simple convolutional neural network (CNN) was performed on the open JSRT dataset (dataset #01), the JSRT after bone shadow exclusion - BSE-JSRT (dataset #02), JSRT after lung segmentation (dataset #03), BSE-JSRT after lung segmentation (dataset #04), and segmented BSE-JSRT after exclusion of outliers by t-SNE method (dataset #05). The results demonstrate that the pre-processed dataset obtained after lung segmentation, bone shadow exclusion, and filtering out the outliers by t-SNE (dataset #05) demonstrates the highest training rate and best accuracy in comparison to the other pre-processed datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.06495v1-abstract-full').style.display = 'none'; document.getElementById('1801.06495v1-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 January, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2018. </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">6 pages, 14 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI), Xiamen, 2018, pp. 878-883 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1712.07632">arXiv:1712.07632</a> <span> [<a href="https://arxiv.org/pdf/1712.07632">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="Computer Vision and Pattern Recognition">cs.CV</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/10.1007/978-3-319-91008-6_63">10.1007/978-3-319-91008-6_63 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gordienko%2C+Y">Yu. Gordienko</a>, <a href="/search/cs?searchtype=author&query=Gang%2C+P">Peng Gang</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+J">Jiang Hui</a>, <a href="/search/cs?searchtype=author&query=Zeng%2C+W">Wei Zeng</a>, <a href="/search/cs?searchtype=author&query=Kochura%2C+Y">Yu. Kochura</a>, <a href="/search/cs?searchtype=author&query=Alienin%2C+O">O. Alienin</a>, <a href="/search/cs?searchtype=author&query=Rokovyi%2C+O">O. Rokovyi</a>, <a href="/search/cs?searchtype=author&query=Stirenko%2C+S">S. Stirenko</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="1712.07632v1-abstract-short" style="display: inline;"> The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.07632v1-abstract-full').style.display = 'inline'; document.getElementById('1712.07632v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1712.07632v1-abstract-full" style="display: none;"> The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1712.07632v1-abstract-full').style.display = 'none'; document.getElementById('1712.07632v1-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 December, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2017. </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; The First International Conference on Computer Science, Engineering and Education Applications (ICCSEEA2018) (www.uacnconf.org/iccseea2018) (accepted)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> In: Hu Z., Petoukhov S., Dychka I., He M. (eds) Advances in Computer Science for Engineering and Education. ICCSEEA 2018. Advances in Intelligent Systems and Computing, vol 754, p. 638-647. Springer, Cham </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

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