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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/2306.10656">arXiv:2306.10656</a> <span> [<a href="https://arxiv.org/pdf/2306.10656">pdf</a>, <a href="https://arxiv.org/format/2306.10656">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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Oono%2C+K">Kenta Oono</a>, <a href="/search/cs?searchtype=author&query=Charoenphakdee%2C+N">Nontawat Charoenphakdee</a>, <a href="/search/cs?searchtype=author&query=Bito%2C+K">Kotatsu Bito</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+Z">Zhengyan Gao</a>, <a href="/search/cs?searchtype=author&query=Igata%2C+H">Hideyoshi Igata</a>, <a href="/search/cs?searchtype=author&query=Yoshikawa%2C+M">Masashi Yoshikawa</a>, <a href="/search/cs?searchtype=author&query=Ota%2C+Y">Yoshiaki Ota</a>, <a href="/search/cs?searchtype=author&query=Okui%2C+H">Hiroki Okui</a>, <a href="/search/cs?searchtype=author&query=Akita%2C+K">Kei Akita</a>, <a href="/search/cs?searchtype=author&query=Yamaguchi%2C+S">Shoichiro Yamaguchi</a>, <a href="/search/cs?searchtype=author&query=Sugawara%2C+Y">Yohei Sugawara</a>, <a href="/search/cs?searchtype=author&query=Maeda%2C+S">Shin-ichi Maeda</a>, <a href="/search/cs?searchtype=author&query=Miyoshi%2C+K">Kunihiko Miyoshi</a>, <a href="/search/cs?searchtype=author&query=Saito%2C+Y">Yuki Saito</a>, <a href="/search/cs?searchtype=author&query=Tsuda%2C+K">Koki Tsuda</a>, <a href="/search/cs?searchtype=author&query=Maruyama%2C+H">Hiroshi Maruyama</a>, <a href="/search/cs?searchtype=author&query=Hayashi%2C+K">Kohei Hayashi</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="2306.10656v3-abstract-short" style="display: inline;"> Identifying the relationship between healthcare attributes, lifestyles, and personality is vital for understanding and improving physical and mental well-being. Machine learning approaches are promising for modeling their relationships and offering actionable suggestions. In this paper, we propose Virtual Human Generative Model (VHGM), a machine learning model for estimating healthcare, lifestyles… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.10656v3-abstract-full').style.display = 'inline'; document.getElementById('2306.10656v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.10656v3-abstract-full" style="display: none;"> Identifying the relationship between healthcare attributes, lifestyles, and personality is vital for understanding and improving physical and mental well-being. Machine learning approaches are promising for modeling their relationships and offering actionable suggestions. In this paper, we propose Virtual Human Generative Model (VHGM), a machine learning model for estimating healthcare, lifestyles, and personality attributes. VHGM is a deep generative model trained with masked modeling to learn the joint distribution of attributes conditioned on known ones. Using heterogeneous tabular datasets, VHGM learns more than 2,000 attributes efficiently. We numerically evaluate the performance of VHGM and its training techniques and have deployed VHGM as a Web service, enabling various healthcare applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.10656v3-abstract-full').style.display = 'none'; document.getElementById('2306.10656v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 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">19 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/2205.12756">arXiv:2205.12756</a> <span> [<a href="https://arxiv.org/pdf/2205.12756">pdf</a>, <a href="https://arxiv.org/format/2205.12756">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> </div> </div> <p class="title is-5 mathjax"> Development of a Stereo-Vision Based High-Throughput Robotic System for Mouse Tail Vein Injection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ko%2C+T">Tianyi Ko</a>, <a href="/search/cs?searchtype=author&query=Nishiwaki%2C+K">Koichi Nishiwaki</a>, <a href="/search/cs?searchtype=author&query=Terada%2C+K">Koji Terada</a>, <a href="/search/cs?searchtype=author&query=Tanaka%2C+Y">Yusuke Tanaka</a>, <a href="/search/cs?searchtype=author&query=Mitsumata%2C+S">Shun Mitsumata</a>, <a href="/search/cs?searchtype=author&query=Katagiri%2C+R">Ryuichi Katagiri</a>, <a href="/search/cs?searchtype=author&query=Junko%2C+T">Taketo Junko</a>, <a href="/search/cs?searchtype=author&query=Horiba%2C+N">Naoshi Horiba</a>, <a href="/search/cs?searchtype=author&query=Igata%2C+H">Hideyoshi Igata</a>, <a href="/search/cs?searchtype=author&query=Mizuno%2C+K">Kazue Mizuno</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="2205.12756v1-abstract-short" style="display: inline;"> In this paper, we present a robotic device for mouse tail vein injection. We propose a mouse holding mechanism to realize vein injection without anesthetizing the mouse, which consists of a tourniquet, vacuum port, and adaptive tail-end fixture. The position of the target vein in 3D space is reconstructed from a high-resolution stereo vision. The vein is detected by a simple but robust vein line d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.12756v1-abstract-full').style.display = 'inline'; document.getElementById('2205.12756v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.12756v1-abstract-full" style="display: none;"> In this paper, we present a robotic device for mouse tail vein injection. We propose a mouse holding mechanism to realize vein injection without anesthetizing the mouse, which consists of a tourniquet, vacuum port, and adaptive tail-end fixture. The position of the target vein in 3D space is reconstructed from a high-resolution stereo vision. The vein is detected by a simple but robust vein line detector. Thanks to the proposed two-staged calibration process, the total time for the injection process is limited to 1.5 minutes, despite that the position of needle and tail vein varies for each trial. We performed an injection experiment targeting 40 mice and succeeded to inject saline to 37 of them, resulting 92.5% success ratio. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.12756v1-abstract-full').style.display = 'none'; document.getElementById('2205.12756v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted to ICRA2022 (7 pages, 11 figures, 2 tables)</span> </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> 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