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name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <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/2312.04546">arXiv:2312.04546</a> <span> [<a href="https://arxiv.org/pdf/2312.04546">pdf</a>, <a href="https://arxiv.org/format/2312.04546">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="Applications">stat.AP</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"> Adversarial Learning for Feature Shift Detection and Correction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barrabes%2C+M">Miriam Barrabes</a>, <a href="/search/cs?searchtype=author&query=Montserrat%2C+D+M">Daniel Mas Montserrat</a>, <a href="/search/cs?searchtype=author&query=Geleta%2C+M">Margarita Geleta</a>, <a href="/search/cs?searchtype=author&query=Giro-i-Nieto%2C+X">Xavier Giro-i-Nieto</a>, <a href="/search/cs?searchtype=author&query=Ioannidis%2C+A+G">Alexander G. Ioannidis</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="2312.04546v1-abstract-short" style="display: inline;"> Data shift is a phenomenon present in many real-world applications, and while there are multiple methods attempting to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth. Feature shifts can occur in many datasets, including in multi-sensor data, where some sensors are malfunctioning, or in tabular and structured data, including b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.04546v1-abstract-full').style.display = 'inline'; document.getElementById('2312.04546v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.04546v1-abstract-full" style="display: none;"> Data shift is a phenomenon present in many real-world applications, and while there are multiple methods attempting to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth. Feature shifts can occur in many datasets, including in multi-sensor data, where some sensors are malfunctioning, or in tabular and structured data, including biomedical, financial, and survey data, where faulty standardization and data processing pipelines can lead to erroneous features. In this work, we explore using the principles of adversarial learning, where the information from several discriminators trained to distinguish between two distributions is used to both detect the corrupted features and fix them in order to remove the distribution shift between datasets. We show that mainstream supervised classifiers, such as random forest or gradient boosting trees, combined with simple iterative heuristics, can localize and correct feature shifts, outperforming current statistical and neural network-based techniques. The code is available at https://github.com/AI-sandbox/DataFix. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.04546v1-abstract-full').style.display = 'none'; document.getElementById('2312.04546v1-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 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/2309.16177">arXiv:2309.16177</a> <span> [<a href="https://arxiv.org/pdf/2309.16177">pdf</a>, <a href="https://arxiv.org/format/2309.16177">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Atmospheric and Oceanic Physics">physics.ao-ph</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"> Sampling Hybrid Climate Simulation at Scale to Reliably Improve Machine Learning Parameterization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lin%2C+J">Jerry Lin</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+S">Sungduk Yu</a>, <a href="/search/cs?searchtype=author&query=Peng%2C+L">Liran Peng</a>, <a href="/search/cs?searchtype=author&query=Beucler%2C+T">Tom Beucler</a>, <a href="/search/cs?searchtype=author&query=Wong-Toi%2C+E">Eliot Wong-Toi</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+Z">Zeyuan Hu</a>, <a href="/search/cs?searchtype=author&query=Gentine%2C+P">Pierre Gentine</a>, <a href="/search/cs?searchtype=author&query=Geleta%2C+M">Margarita Geleta</a>, <a href="/search/cs?searchtype=author&query=Pritchard%2C+M">Mike Pritchard</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="2309.16177v2-abstract-short" style="display: inline;"> Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-resolution physics without the cost of explicit simulation. However, their development has been stymied by uncertainty surrounding whether or not improved offline performance translates to improved online performance (i.e.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.16177v2-abstract-full').style.display = 'inline'; document.getElementById('2309.16177v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.16177v2-abstract-full" style="display: none;"> Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-resolution physics without the cost of explicit simulation. However, their development has been stymied by uncertainty surrounding whether or not improved offline performance translates to improved online performance (i.e., when coupled to a large-scale general circulation model (GCM)). A key barrier has been the limited sampling of the online effects of the ML design decisions and tuning due to the complexity of performing large ensembles of hybrid physics-ML climate simulations. Our work examines the coupled behavior of full-physics ML parameterizations using large ensembles of hybrid simulations, totalling 2,970 in our case. With extensive sampling, we statistically confirm that lowering offline error lowers online error (given certain constraints). However, we also reveal that decisions decreasing online error, like removing dropout, can trade off against hybrid model stability and vice versa. Nevertheless, we are able to identify design decisions that yield unambiguous improvements to offline and online performance, namely incorporating memory and training on multiple climates. We also find that converting moisture input from specific to relative humidity enhances online stability and that using a Mean Absolute Error (MAE) loss breaks the aforementioned offline/online error relationship. By enabling rapid online experimentation at scale, we empirically answer previously unresolved questions regarding subgrid ML parameterization design. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.16177v2-abstract-full').style.display = 'none'; document.getElementById('2309.16177v2-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">16 pages, 4 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/2306.08238">arXiv:2306.08238</a> <span> [<a href="https://arxiv.org/pdf/2306.08238">pdf</a>, <a href="https://arxiv.org/format/2306.08238">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</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"> Maestro: A Gamified Platform for Teaching AI Robustness </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Geleta%2C+M">Margarita Geleta</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jiacen Xu</a>, <a href="/search/cs?searchtype=author&query=Loya%2C+M">Manikanta Loya</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Junlin Wang</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+S">Sameer Singh</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Z">Zhou Li</a>, <a href="/search/cs?searchtype=author&query=Gago-Masague%2C+S">Sergio Gago-Masague</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="2306.08238v1-abstract-short" style="display: inline;"> Although the prevention of AI vulnerabilities is critical to preserve the safety and privacy of users and businesses, educational tools for robust AI are still underdeveloped worldwide. We present the design, implementation, and assessment of Maestro. Maestro is an effective open-source game-based platform that contributes to the advancement of robust AI education. Maestro provides goal-based scen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.08238v1-abstract-full').style.display = 'inline'; document.getElementById('2306.08238v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.08238v1-abstract-full" style="display: none;"> Although the prevention of AI vulnerabilities is critical to preserve the safety and privacy of users and businesses, educational tools for robust AI are still underdeveloped worldwide. We present the design, implementation, and assessment of Maestro. Maestro is an effective open-source game-based platform that contributes to the advancement of robust AI education. Maestro provides goal-based scenarios where college students are exposed to challenging life-inspired assignments in a competitive programming environment. We assessed Maestro's influence on students' engagement, motivation, and learning success in robust AI. This work also provides insights into the design features of online learning tools that promote active learning opportunities in the robust AI domain. We analyzed the reflection responses (measured with Likert scales) of 147 undergraduate students using Maestro in two quarterly college courses in AI. According to the results, students who felt the acquisition of new skills in robust AI tended to appreciate highly Maestro and scored highly on material consolidation, curiosity, and mastery in robust AI. Moreover, the leaderboard, our key gamification element in Maestro, has effectively contributed to students' engagement and learning. Results also indicate that Maestro can be effectively adapted to any course length and depth without losing its educational quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.08238v1-abstract-full').style.display = 'none'; document.getElementById('2306.08238v1-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 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">9 pages, 6 figures, published at the Thirteenth Symposium on Educational Advances in Artificial Intelligence (EAAI-23) in the Association for the Advancement of Artificial Intelligence Conference (AAAI), 2023</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68U35 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> H.5.2; I.2.m; J.m </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.05007">arXiv:2303.05007</a> <span> [<a href="https://arxiv.org/pdf/2303.05007">pdf</a>, <a href="https://arxiv.org/format/2303.05007">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="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</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"> Towards Robust Image-in-Audio Deep Steganography </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ros%2C+J">Jaume Ros</a>, <a href="/search/cs?searchtype=author&query=Geleta%2C+M">Margarita Geleta</a>, <a href="/search/cs?searchtype=author&query=Pons%2C+J">Jordi Pons</a>, <a href="/search/cs?searchtype=author&query=Giro-i-Nieto%2C+X">Xavier Giro-i-Nieto</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="2303.05007v2-abstract-short" style="display: inline;"> The field of steganography has experienced a surge of interest due to the recent advancements in AI-powered techniques, particularly in the context of multimodal setups that enable the concealment of signals within signals of a different nature. The primary objectives of all steganographic methods are to achieve perceptual transparency, robustness, and large embedding capacity - which often presen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.05007v2-abstract-full').style.display = 'inline'; document.getElementById('2303.05007v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.05007v2-abstract-full" style="display: none;"> The field of steganography has experienced a surge of interest due to the recent advancements in AI-powered techniques, particularly in the context of multimodal setups that enable the concealment of signals within signals of a different nature. The primary objectives of all steganographic methods are to achieve perceptual transparency, robustness, and large embedding capacity - which often present conflicting goals that classical methods have struggled to reconcile. This paper extends and enhances an existing image-in-audio deep steganography method by focusing on improving its robustness. The proposed enhancements include modifications to the loss function, utilization of the Short-Time Fourier Transform (STFT), introduction of redundancy in the encoding process for error correction, and buffering of additional information in the pixel subconvolution operation. The results demonstrate that our approach outperforms the existing method in terms of robustness and perceptual transparency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.05007v2-abstract-full').style.display = 'none'; document.getElementById('2303.05007v2-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 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 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">8 pages, 5 figures, 2 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T99 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.4.9; I.2.m </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.09814">arXiv:2106.09814</a> <span> [<a href="https://arxiv.org/pdf/2106.09814">pdf</a>, <a href="https://arxiv.org/format/2106.09814">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</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"> PixInWav: Residual Steganography for Hiding Pixels in Audio </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Geleta%2C+M">Margarita Geleta</a>, <a href="/search/cs?searchtype=author&query=Punti%2C+C">Cristina Punti</a>, <a href="/search/cs?searchtype=author&query=McGuinness%2C+K">Kevin McGuinness</a>, <a href="/search/cs?searchtype=author&query=Pons%2C+J">Jordi Pons</a>, <a href="/search/cs?searchtype=author&query=Canton%2C+C">Cristian Canton</a>, <a href="/search/cs?searchtype=author&query=Giro-i-Nieto%2C+X">Xavier Giro-i-Nieto</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="2106.09814v1-abstract-short" style="display: inline;"> Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images, or hiding audio in audio), PixInWav targets the multimodal case of hiding images in audio. To this end, we propose a novel residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.09814v1-abstract-full').style.display = 'inline'; document.getElementById('2106.09814v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.09814v1-abstract-full" style="display: none;"> Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images, or hiding audio in audio), PixInWav targets the multimodal case of hiding images in audio. To this end, we propose a novel residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. Among our results, we find that the residual audio steganography setup we propose allows independent encoding of the hidden image from the host audio without compromising quality. Accordingly, while previous works require both host and hidden signals to hide a signal, PixInWav can encode images offline -- which can be later hidden, in a residual fashion, into any audio signal. Finally, we test our scheme in a lab setting to transmit images over airwaves from a loudspeaker to a microphone verifying our theoretical insights and obtaining promising results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.09814v1-abstract-full').style.display = 'none'; document.getElementById('2106.09814v1-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, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">Extended abstract presented in CVPR 2021 Women in Computer Vision Workshop</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.13156">arXiv:2005.13156</a> <span> [<a href="https://arxiv.org/pdf/2005.13156">pdf</a>, <a href="https://arxiv.org/format/2005.13156">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> </div> </div> <p class="title is-5 mathjax"> MT-Adapted Datasheets for Datasets: Template and Repository </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Costa-juss%C3%A0%2C+M+R">Marta R. Costa-juss脿</a>, <a href="/search/cs?searchtype=author&query=Creus%2C+R">Roger Creus</a>, <a href="/search/cs?searchtype=author&query=Domingo%2C+O">Oriol Domingo</a>, <a href="/search/cs?searchtype=author&query=Dom%C3%ADnguez%2C+A">Albert Dom铆nguez</a>, <a href="/search/cs?searchtype=author&query=Escobar%2C+M">Miquel Escobar</a>, <a href="/search/cs?searchtype=author&query=L%C3%B3pez%2C+C">Cayetana L贸pez</a>, <a href="/search/cs?searchtype=author&query=Garcia%2C+M">Marina Garcia</a>, <a href="/search/cs?searchtype=author&query=Geleta%2C+M">Margarita Geleta</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="2005.13156v1-abstract-short" style="display: inline;"> In this report we are taking the standardized model proposed by Gebru et al. (2018) for documenting the popular machine translation datasets of the EuroParl (Koehn, 2005) and News-Commentary (Barrault et al., 2019). Within this documentation process, we have adapted the original datasheet to the particular case of data consumers within the Machine Translation area. We are also proposing a reposito… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.13156v1-abstract-full').style.display = 'inline'; document.getElementById('2005.13156v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.13156v1-abstract-full" style="display: none;"> In this report we are taking the standardized model proposed by Gebru et al. (2018) for documenting the popular machine translation datasets of the EuroParl (Koehn, 2005) and News-Commentary (Barrault et al., 2019). Within this documentation process, we have adapted the original datasheet to the particular case of data consumers within the Machine Translation area. We are also proposing a repository for collecting the adapted datasheets in this research area <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.13156v1-abstract-full').style.display = 'none'; document.getElementById('2005.13156v1-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> 27 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </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 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