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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Someya, T"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" 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/2411.13916">arXiv:2411.13916</a> <span> [<a href="https://arxiv.org/pdf/2411.13916">pdf</a>, <a href="https://arxiv.org/format/2411.13916">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Joint-repositionable Inner-wireless Planar Snake Robot </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kanada%2C+A">Ayato Kanada</a>, <a href="/search/cs?searchtype=author&query=Takahashi%2C+R">Ryo Takahashi</a>, <a href="/search/cs?searchtype=author&query=Hayashi%2C+K">Keito Hayashi</a>, <a href="/search/cs?searchtype=author&query=Hosaka%2C+R">Ryusuke Hosaka</a>, <a href="/search/cs?searchtype=author&query=Yukita%2C+W">Wakako Yukita</a>, <a href="/search/cs?searchtype=author&query=Nakashima%2C+Y">Yasutaka Nakashima</a>, <a href="/search/cs?searchtype=author&query=Yokota%2C+T">Tomoyuki Yokota</a>, <a href="/search/cs?searchtype=author&query=Someya%2C+T">Takao Someya</a>, <a href="/search/cs?searchtype=author&query=Kamezaki%2C+M">Mitsuhiro Kamezaki</a>, <a href="/search/cs?searchtype=author&query=Kawahara%2C+Y">Yoshihiro Kawahara</a>, <a href="/search/cs?searchtype=author&query=Yamamoto%2C+M">Motoji Yamamoto</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13916v1-abstract-short" style="display: inline;"> Bio-inspired multi-joint snake robots offer the advantages of terrain adaptability due to their limbless structure and high flexibility. However, a series of dozens of motor units in typical multiple-joint snake robots results in a heavy body structure and hundreds of watts of high power consumption. This paper presents a joint-repositionable, inner-wireless snake robot that enables multi-joint-li… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13916v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13916v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13916v1-abstract-full" style="display: none;"> Bio-inspired multi-joint snake robots offer the advantages of terrain adaptability due to their limbless structure and high flexibility. However, a series of dozens of motor units in typical multiple-joint snake robots results in a heavy body structure and hundreds of watts of high power consumption. This paper presents a joint-repositionable, inner-wireless snake robot that enables multi-joint-like locomotion using a low-powered underactuated mechanism. The snake robot, consisting of a series of flexible passive links, can dynamically change its joint coupling configuration by repositioning motor-driven joint units along rack gears inside the robot. Additionally, a soft robot skin wirelessly powers the internal joint units, avoiding the risk of wire tangling and disconnection caused by the movable joint units. The combination of the joint-repositionable mechanism and the wireless-charging-enabled soft skin achieves a high degree of bending, along with a lightweight structure of 1.3 kg and energy-efficient wireless power transmission of 7.6 watts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13916v1-abstract-full').style.display = 'none'; document.getElementById('2411.13916v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.12691">arXiv:2402.12691</a> <span> [<a href="https://arxiv.org/pdf/2402.12691">pdf</a>, <a href="https://arxiv.org/format/2402.12691">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"> Tree-Planted Transformers: Unidirectional Transformer Language Models with Implicit Syntactic Supervision </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yoshida%2C+R">Ryo Yoshida</a>, <a href="/search/cs?searchtype=author&query=Someya%2C+T">Taiga Someya</a>, <a href="/search/cs?searchtype=author&query=Oseki%2C+Y">Yohei Oseki</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="2402.12691v2-abstract-short" style="display: inline;"> Syntactic Language Models (SLMs) can be trained efficiently to reach relatively high performance; however, they have trouble with inference efficiency due to the explicit generation of syntactic structures. In this paper, we propose a new method dubbed tree-planting: instead of explicitly generating syntactic structures, we "plant" trees into attention weights of unidirectional Transformer LMs to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.12691v2-abstract-full').style.display = 'inline'; document.getElementById('2402.12691v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.12691v2-abstract-full" style="display: none;"> Syntactic Language Models (SLMs) can be trained efficiently to reach relatively high performance; however, they have trouble with inference efficiency due to the explicit generation of syntactic structures. In this paper, we propose a new method dubbed tree-planting: instead of explicitly generating syntactic structures, we "plant" trees into attention weights of unidirectional Transformer LMs to implicitly reflect syntactic structures of natural language. Specifically, unidirectional Transformer LMs trained with tree-planting will be called Tree-Planted Transformers (TPT), which inherit the training efficiency from SLMs without changing the inference efficiency of their underlying Transformer LMs. Targeted syntactic evaluations on the SyntaxGym benchmark demonstrated that TPTs, despite the lack of explicit generation of syntactic structures, significantly outperformed not only vanilla Transformer LMs but also various SLMs that generate hundreds of syntactic structures in parallel. This result suggests that TPTs can learn human-like syntactic knowledge as data-efficiently as SLMs while maintaining the modeling space of Transformer LMs unchanged. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.12691v2-abstract-full').style.display = 'none'; document.getElementById('2402.12691v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by ACL 2024 (Findings)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.12676">arXiv:2309.12676</a> <span> [<a href="https://arxiv.org/pdf/2309.12676">pdf</a>, <a href="https://arxiv.org/format/2309.12676">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"> JCoLA: Japanese Corpus of Linguistic Acceptability </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Someya%2C+T">Taiga Someya</a>, <a href="/search/cs?searchtype=author&query=Sugimoto%2C+Y">Yushi Sugimoto</a>, <a href="/search/cs?searchtype=author&query=Oseki%2C+Y">Yohei Oseki</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="2309.12676v1-abstract-short" style="display: inline;"> Neural language models have exhibited outstanding performance in a range of downstream tasks. However, there is limited understanding regarding the extent to which these models internalize syntactic knowledge, so that various datasets have recently been constructed to facilitate syntactic evaluation of language models across languages. In this paper, we introduce JCoLA (Japanese Corpus of Linguist… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.12676v1-abstract-full').style.display = 'inline'; document.getElementById('2309.12676v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.12676v1-abstract-full" style="display: none;"> Neural language models have exhibited outstanding performance in a range of downstream tasks. However, there is limited understanding regarding the extent to which these models internalize syntactic knowledge, so that various datasets have recently been constructed to facilitate syntactic evaluation of language models across languages. In this paper, we introduce JCoLA (Japanese Corpus of Linguistic Acceptability), which consists of 10,020 sentences annotated with binary acceptability judgments. Specifically, those sentences are manually extracted from linguistics textbooks, handbooks and journal articles, and split into in-domain data (86 %; relatively simple acceptability judgments extracted from textbooks and handbooks) and out-of-domain data (14 %; theoretically significant acceptability judgments extracted from journal articles), the latter of which is categorized by 12 linguistic phenomena. We then evaluate the syntactic knowledge of 9 different types of Japanese language models on JCoLA. The results demonstrated that several models could surpass human performance for the in-domain data, while no models were able to exceed human performance for the out-of-domain data. Error analyses by linguistic phenomena further revealed that although neural language models are adept at handling local syntactic dependencies like argument structure, their performance wanes when confronted with long-distance syntactic dependencies like verbal agreement and NPI licensing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.12676v1-abstract-full').style.display = 'none'; document.getElementById('2309.12676v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 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/2309.06006">arXiv:2309.06006</a> <span> [<a href="https://arxiv.org/pdf/2309.06006">pdf</a>, <a href="https://arxiv.org/ps/2309.06006">ps</a>, <a href="https://arxiv.org/format/2309.06006">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> SoccerNet 2023 Challenges Results </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cioppa%2C+A">Anthony Cioppa</a>, <a href="/search/cs?searchtype=author&query=Giancola%2C+S">Silvio Giancola</a>, <a href="/search/cs?searchtype=author&query=Somers%2C+V">Vladimir Somers</a>, <a href="/search/cs?searchtype=author&query=Magera%2C+F">Floriane Magera</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+X">Xin Zhou</a>, <a href="/search/cs?searchtype=author&query=Mkhallati%2C+H">Hassan Mkhallati</a>, <a href="/search/cs?searchtype=author&query=Deli%C3%A8ge%2C+A">Adrien Deli猫ge</a>, <a href="/search/cs?searchtype=author&query=Held%2C+J">Jan Held</a>, <a href="/search/cs?searchtype=author&query=Hinojosa%2C+C">Carlos Hinojosa</a>, <a href="/search/cs?searchtype=author&query=Mansourian%2C+A+M">Amir M. Mansourian</a>, <a href="/search/cs?searchtype=author&query=Miralles%2C+P">Pierre Miralles</a>, <a href="/search/cs?searchtype=author&query=Barnich%2C+O">Olivier Barnich</a>, <a href="/search/cs?searchtype=author&query=De+Vleeschouwer%2C+C">Christophe De Vleeschouwer</a>, <a href="/search/cs?searchtype=author&query=Alahi%2C+A">Alexandre Alahi</a>, <a href="/search/cs?searchtype=author&query=Ghanem%2C+B">Bernard Ghanem</a>, <a href="/search/cs?searchtype=author&query=Van+Droogenbroeck%2C+M">Marc Van Droogenbroeck</a>, <a href="/search/cs?searchtype=author&query=Kamal%2C+A">Abdullah Kamal</a>, <a href="/search/cs?searchtype=author&query=Maglo%2C+A">Adrien Maglo</a>, <a href="/search/cs?searchtype=author&query=Clap%C3%A9s%2C+A">Albert Clap茅s</a>, <a href="/search/cs?searchtype=author&query=Abdelaziz%2C+A">Amr Abdelaziz</a>, <a href="/search/cs?searchtype=author&query=Xarles%2C+A">Artur Xarles</a>, <a href="/search/cs?searchtype=author&query=Orcesi%2C+A">Astrid Orcesi</a>, <a href="/search/cs?searchtype=author&query=Scott%2C+A">Atom Scott</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+B">Bin Liu</a>, <a href="/search/cs?searchtype=author&query=Lim%2C+B">Byoungkwon Lim</a> , et al. (77 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="2309.06006v1-abstract-short" style="display: inline;"> The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, fo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.06006v1-abstract-full').style.display = 'inline'; document.getElementById('2309.06006v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.06006v1-abstract-full" style="display: none;"> The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.06006v1-abstract-full').style.display = 'none'; document.getElementById('2309.06006v1-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 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </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 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