Tokyo, Japan – Scientists from Tokyo Metropolitan University have used machine learning to automate the identification of defects in sister chromatid cohesion.\nThey trained a convolutional neural network (CNN) with microscopy images of individual stained chromosomes, identified by researchers as having or not having cohesion defects.\nAutomation promises better statistics, and more insight into the wide range of disorders which cause cohesion defects.\nThe study of cohesion defects in chromosomes has been largely carried out by researchers observing the chromosomes under the microscope.\nThey used the same technology that powers facial recognition and machine vision to analyze microscopy images of chromosomes with and without cohesion defects.
"}}}},{"node":{"postTitle":"Dems Call For Moratorium On Federal Agencies Spying On Your Face","slug":"dems-call-for-moratorium-on-federal-agencies-spying-on-your-face","publishDate":"2023-03-10 16:50:00+00:00","createdLocal":"2023-03-11 14:30:47.909771","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"Robyn Pennacchia is a brilliant, fabulously talented and visually stunning angel of a human being, who shrugged off what she is pretty sure would have been a Tony Award-winning career in musical theater in order to write about stuff on the internet.\nFollow her on Twitter at @RobynElyse
"}}}},{"node":{"postTitle":"Breaking Through the Haze: A New Method for Clearer Images","slug":"breaking-through-the-haze-a-new-method-for-clearer-images","publishDate":"2023-03-10 22:52:48-08:00","createdLocal":"2023-03-11 14:30:47.407557","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"A new technique that generates a sharp image through the use of a low-cost, random scattering medium in real-time could offer a solution to the challenges posed by blurred images.\n“We have developed a strategy of calibration-free, reconstruction-free, real-time imaging of static and moving objects with their actual orientation information,” explains Yang.\n“This novel technique only requires simple or low-cost devices, without the post-computational reconstruction”The team passed light from a small standardized test object through a thin diffusing material.\nThey could also find the lost orientation information and the location of the test object.\n“Our work presents a significant step in the field of scattering imaging and will shed light on new avenues for imaging through diffusive media,” says Yang.
"}}}},{"node":{"postTitle":"Wireless AGV and AMR Charging Solution Provider Wiferion Enters North American Market","slug":"wireless-agv-and-amr-charging-solution-provider-wiferion-enters-north-american-market","publishDate":"2023-03-10 11:00:00","createdLocal":"2023-03-11 14:30:47.261546","feedName":"Autonomous Trucks","postSummary":{"childMarkdownRemark":{"html":""}}}},{"node":{"postTitle":"March prod. sanity test-1 by shilpa Embark Partners with Ryder to Launch Nationwide Network of Up to 100 Freight Transfer Points for Autonomous Fleets","slug":"march-prod-sanity-test-1-by-shilpa-embark-partners-with-ryder-to-launch-nationwide-network-of-up-to-100-freight-transfer-points-for-autonomous-fleets","publishDate":"None","createdLocal":"2023-03-11 14:30:46.963579","feedName":"Autonomous Trucks","postSummary":{"childMarkdownRemark":{"html":"Over the next five years, working with a network of real estate operators, the two companies aim to open as many as 100 Embark transfer points nationwide.\n'A fully developed transfer point has to include autonomous truck maintenance services and efficient yard operations,' says Alex Rodrigues , CEO of Embark.\nNote Regarding Forward-Looking Statements: Certain statements and information included in this news release are 'forward-looking statements' within the meaning of the Federal Private Securities Litigation Reform Act of 1995.\nThese forward-looking statements are based on our current plans and expectations and are subject to risks, uncertainties and assumptions.\nAccordingly, we undertake no obligation to publicly update or revise any forward-looking statements, whether as a result of new information, future events, or otherwise.
"}}}},{"node":{"postTitle":"Abdullah bin Zayed launches Smart Mission project","slug":"abdullah-bin-zayed-launches-smart-mission-project","publishDate":"2023-03-11 11:58:30+04:00","createdLocal":"2023-03-11 14:30:46.806263","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"Dear Reader,This section is about Living in UAE and essential information you cannot live without.\nRegister to read and get full access to gulfnews.com
"}}}},{"node":{"postTitle":"You are being redirected...","slug":"you-are-being-redirected","publishDate":"None","createdLocal":"2023-03-11 14:30:46.046058","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":""}}}},{"node":{"postTitle":"A New AI Research Proposes A Novel Approach To Compact And Optimal Deep Learning By Decoupling Model DoF And Model Parameters","slug":"a-new-ai-research-proposes-a-novel-approach-to-compact-and-optimal-deep-learning-by-decoupling-model-dof-and-model-parameters","publishDate":"2023-03-11 00:00:00","createdLocal":"2023-03-11 14:30:45.945866","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"This has led to a growing need for more efficient deep learning models that can be trained and deployed on resource-constrained devices such as smartphones, embedded systems, and Internet of Things (IoT) devices.\nAdditionally, reducing computational and memory requirements can also help reduce the environmental impact of deep learning by lowering energy consumption and carbon footprint.\nTherefore, there is a need for new techniques and approaches to reduce the computational and memory requirements of deep learning models while maintaining or even improving accuracy.\nThe researchers found a log-linear relationship between model DoF and accuracy, which means that reducing the number of DoF required for a deep learning model does not necessarily result in a loss of accuracy.\nOverall, the proposed method presents a significant potential for efficient and practical deployment of deep learning models by reducing the number of DoF required without sacrificing accuracy.
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