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    Patent Application Titled 'Three dimensional radio controlled targettraining de vice and methods of use' Published Online (USPTO20240328761)

    144-147页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting originatin g from Washington, D.C., by NewsRx journalists,a patent application by the inve ntor Bonilla, Edgar (Chino HIlls, CA, US), filed on February 15, 2024, wasmade available online on October 3, 2024.No assignee for this patent application has been made.Reporters obtained the following quote from the background information supplied by the inventors: ““Field of Invention“This invention relates generally to robotics, more specifically, the present in vention is a remotecontrollable robotic device for use in target training purpo ses.“Background of the Invention“The field of law enforcement and security training evolved to incorporate advan ced technologies thatsimulate real-life scenarios, thereby enhancing the prepar edness and response capabilities of officers andsecurity personnel. In this con text, the development of training tools that accurately mimic the dynamicsof li ve situations, particularly those involving potential threats and hostages, is o f paramount importance.Traditional training methods often fall short in replica ting the unpredictability and complexity of suchscenarios, leading to a gap in the training efficacy.“The use of firearms by law enforcement officers, given their potential to cause harm, mandates a highlevel of precision and discernment. Officers are frequent ly faced with high-stakes situations where theymust make split-second decisions that could mean the difference between life and death. The challenge isfurther compounded when suspects use innocent bystanders as shields, creating a scenari o where the riskof collateral damage is significantly high. The ability to accu rately differentiate between a threat and aninnocent bystander, and to take app ropriate action, is thus a critical skill that needs to be honed throughrigorou s and realistic training.
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    Researchers Submit Patent Application, 'Vacuum Control SystemsAnd Methods For U se In Object Processing', for Approval (USPTO20240326264)

    147-150页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – From Washington, D.C., NewsRx journali sts report that a patent application by theinventors ANDERSON, Bretton (Westfor d, MA, US); BEST, Joshua (Raleigh, NC, US); DEELEY, AdamJames (Youngstown, OH, US); FREEMAN, Dominick (Boston, MA, US); O’NEIL, Conor (Boston, MA,US), filed o n March 28, 2024, was made available online on October 3, 2024.No assignee for this patent application has been made.News editors obtained the following quote from the background information suppli ed by the inventors:“The invention generally relates to automated, robotic and other object processing systems such as sortationsystems, and relates in partic ular to automated and robotic systems intended for use in environmentsrequiring , for example, that a variety of objects (e.g., parcels, packages, and articles, etc.) be processedand distributed to several output destinations.“Many parcel distribution systems receive parcels from a vehicle, such as a trai ler of a tractor trailer.The parcels are unloaded and delivered to a processing station in a disorganized stream that may beprovided as individual parcels or parcels aggregated in groups such as in bags, and may be provided to anyof seve ral different conveyances, such as a conveyor, or one or more pallets, Gaylords, or bins. Each parcelmust then be distributed to the correct destination contai ner, as determined by identification informationassociated with the parcel, whi ch is commonly determined by a label printed on the parcel or on a stickerappli ed to the parcel. The destination container may take many forms, such as a bag o r a bin.
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    Patent Issued for Automatic content generation (USPTO12105747)

    150-153页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – From Alexandria, Virginia, NewsRx jour nalists report that a patent by the inventorsAldred, Warren A. (Redmond, WA, US ), Brockett, Christopher J. (Kirkland, WA, US), Cai, Weixin(Bothell, WA, US), C hen, Si-Qing (Bellevue, WA, US), Dolan, William Brennan (Kirkland, WA, US), Freitas, Jesse Alexander (Seattle, WA, US), Galley, Michel (Seattle, WA, US), He, Xi nyu (Lynnwood, WA,US), Li, Zhang (Bellevue, WA, US), Narayanan, Kaushik Ramaiah (Bellevue, WA, US), filed on June 9,2022, was published online on October 1, 2 024.The patent’s assignee for patent number 12105747 is Microsoft Technology Licensi ng LLC (Redmond,Washington, United States).News editors obtained the following quote from the background information suppli ed by the inventors:“Oftentimes, a user may get writer’s block and cannot fluen tly compose content that reflects what the userwants to convey. Unfortunately, conventional content generation systems are limited in their capabilities.Typic ally, these conventional systems only allow a user to provide a few keyworks or a few phrases togenerate content (e.g., a paragraph). As a result, these conven tional systems do not take context intoconsideration resulting in generated con tent that may only be marginally relevant to a user’s original intentand that m ay not be in a style of the user.”
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    Machine learning sparse reaction-diffusion models from stochasticdynamics and s patiotemporal patterns

    153-154页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from bi orxiv.org:“In recent years, live-cell imaging has generated detailed spatiotemporal datase ts of biochemical networkswithin cells. These networks often exhibit characteri stics of spatially-distributed excitable systems,with propagating waves of sign aling activity that govern processes such as cell migration, division, andother essential physiological functions. Traditionally, these reaction-diffusion syst ems have been modeledusing stochastic partial differential equations incorporat ing spatial Langevin-type dynamics. Although these knowledge-based models have p rovided valuable insights, they are typically not directly inferredfrom experim ental data.
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    High-dimensional Biomarker Identification for Scalable and InterpretableDisease Prediction via Machine Learning Models

    154-155页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from bi orxiv.org:“Omics data generated from high-throughput technologies and clinical features jo intly impact manycomplex human diseases. Identifying key biomarkers and clinica l risk factors is essential for understandingdisease mechanisms and advancing e arly disease diagnosis and precision medicine.“However, the high-dimensionality and intricate associations between disease out comes and omicsprofiles present significant analytical challenges.“To address these, we propose an ensemble data-driven biomarker identification t ool, Hybrid FeatureScreening (HFS), to construct a candidate feature set for do wnstream advanced machine learningmodels. The pre-screened candidate features f rom HFS are further refined using a computationally efficientpermutation-based feature importance test, forming the comprehensive High-dimensional FeatureImpo rtance Test (HiFIT) framework. Through extensive numerical simulations and real- world applications,we demonstrate HiFITs superior performance in both outcome p rediction and feature importanceidentification. An R package implementing HiFIT is available on GitHub.”
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