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    Data from Nanjing University of Science and Technology Advance Knowledge in Robo tics (State-dependent Maximum Entropy Reinforcement Learning for Robot Long-hori zon Task Learning)

    57-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Robotics are presented in a new rep ort. According to news reporting originating in Nanjing, People's Republic of Ch ina, by NewsRx journalists, research stated, "Task-oriented robot learning has s hown significant potential with the development of Reinforcement Learning (RL) a lgorithms. However, the learning of long-horizon tasks for robots remains a form idable challenge due to the inherent complexity of tasks, typically comprising m ultiple diverse stages." Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of China (NSFC), Primary Research & Development Plan of Jiangsu Province, Six talent peaks project in Jiangsu Provin ce.
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    Study Findings on Artificial Intelligence Detailed by Researchers at Faculty of Economics and Social Sciences (Artificial Intelligence in The Corporate Sector)

    58-59页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting from the Faculty of Economics and Social Sciences by NewsRx journalists, research stated, "Humanity has made huge progress over the past millennia." Our news correspondents obtained a quote from the research from Faculty of Econo mics and Social Sciences: "We are working with technologies, robots that not onl y help us to work accurately, efficiently and quickly, but they work in a simila r way to the human brain: they perceive, think, learn and solve problems. In my research, I will focus on artificial intelligence, which is becoming more and mo re popular nowadays, looking at its past, present and future, its main trends in the corporate sector, and how it threatens people's job opportunities." According to the news reporters, the research concluded: "At the same time, one of my research objectives is to investigate how much the development of a countr y is related to the uptake of AI in the European Union, which I will test with c orrelation analysis, taking into account indicators of artificial intelligence p enetration in the corporate sector from one side and the various AI indicators s uch as digital penetration, internet usage, computer culture, and economic indic ators as GDP per capita from the other side."
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    Investigators at University of Tubingen Describe Findings in Machine Learning [Virtual Reality (Vr) As a Testing Bench for Consumer Optical Solutions: a Machin e Learning Approach (Gbr) To Visual Comfort Under Simulated Progressive Addition ...]

    59-60页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news originating from Tubingen, Germany, by NewsRx correspondents, research stated, "For decades, manufacturers have attemp ted to reduce or eliminate the optical aberrations that appear on the progressiv e addition lens' surfaces during manufacturing. Besides every effort made, some of these distortions are inevitable given how lenses are fabricated, where in fa ct, astigmatism appears on the surface and cannot be entirely removed, or where non-uniform magnification becomes inherent to the power change across the lens." Financial support for this research came from European Grant PLATYPUS, Marie Skl odowska-Curie RISE initiative. Our news journalists obtained a quote from the research from the University of T ubingen, "Some presbyopes may refer to certain discomfort when wearing these len ses for the first time, and a subset of them might never adapt. Developing, prot otyping, testing and purveying those lenses into the market come at a cost, whic h is usually reflected in the retail price. This study aims to test the feasibil ity of virtual reality (VR) for testing customers' satisfaction with these lense s, even before getting them onto production. VR offers a controlled environment where different parameters affecting progressive lens comforts, such as distorti ons, image displacement or optical blurring, can be inspected separately. In thi s study, the focus was set on the distortions and image displacement, not taking blur into account. Behavioural changes (head and eye movements) were recorded u sing the built-in eye tracker. We found participants were significantly more dis pleased in the presence of highly distorted lens simulations." According to the news editors, the research concluded: "In addition, a gradient boosting regressor was fitted to the data, so predictors of discomfort could be unveiled, and ratings could be predicted without performing additional measureme nts."
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    Cleveland Clinic Reports Findings in Artificial Intelligence (Artificial intelli gence and open science in discovery of disease-modifying medicines for Alzheimer 's disease)

    60-61页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Cleveland, Ohio , by NewsRx editors, research stated, "The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of u nderstanding of the pathophysiology of disease, and this deficit may be addresse d by applying artificial intelligence (AI) to ‘big data' to rapidly and effectiv ely expand therapeutic development efforts. Recent accelerations in computing po wer and availability of big data, including electronic health records and multi- omics profiles, have converged to provide opportunities for scientific discovery and treatment development." Our news journalists obtained a quote from the research from Cleveland Clinic, " Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative e fforts among neurologists, gerontologists, geneticists, pharmacologists, medicin al chemists, and computational scientists." According to the news editors, the research concluded: "AI and open data science expedite drug discovery and development of disease-modifying therapeutics for A D/ADRD and other neurodegenerative diseases."
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    Research Conducted at Nanjing University of Posts and Telecommunications Has Pro vided New Information about Robotics (A Heterogeneous Attention Fusion Mechanism for the Cross-environment Scene Classification of the Home Service Robot)

    61-62页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics is the subjec t of a report. According to news originating from Jiangsu, People's Republic of China, by NewsRx correspondents, research stated, "There have been many methods to improve the capacity of scene classification of service robots. However, most of them are proposed from a technical standpoint but without reference to any c ognitive principle of the brain, and furthermore, from design to evaluation, the particularity of the robot task is still not fully considered, such as cross -e nvironment generalization, explicit semantic preservation and interpretation." Our news journalists obtained a quote from the research from the Nanjing Univers ity of Posts and Telecommunications, "Thus, the scene cognitive behavior of robo ts is far from humans, and their environmental adaptability is still poor. It is difficult to complete learning place concepts from discrete fragments and then continuously perceiving them with a limited view in unvisited spaces. Inspired b y the recent findings from neuroscience, an attention -based global and object a ttribute fusion mechanism (AGOFM for short) constructed by three parts is propos ed to overcome these deficiencies. In the global attribute part, a global featur e extractor and a sequence context extractor are used to generate the holistic f eature. The involved context integrates limited views to form an overall impress ion of a scene for guiding attention. In the object attribute part, a novel obje ct vector is proposed. It simultaneously involves the detected object quantity, category and confidence information, which are all related to the vector index a nd high-level semantics. In the attention generation part, two sorted top -X cha racteristics deriving from the above two parts are fed into a fully connected (F C) network with batch normalization to generate effective attention. The attenti on weights are then applied to the batch normalized global and object vectors re spectively, and subsequently, the two heterogeneous information are directly fus ed by another FC network to achieve scene classification. The policies for multi -learner fusion and frame rejection are also provided. Finally, a novel evaluat ion paradigm is proposed that the model is trained on a discrete prior dataset, and then the inference is tested on a traditional dataset and two robot view dat asets. This simulates the cross -environment situation."
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    Researchers from Chongqing University Report Recent Findings in Robotics (A New Calculation Method for Instantaneous Efficiency and Torque Fluctuation of Spur G ears)

    62-63页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news reporting from Chongqing, People's Republic o f China, by NewsRx journalists, research stated, "As a critical component of the joint gearbox, spur gear pairs play a crucial role in energy conversion, limiti ng the performance of a collaborative robot. Accurately assessing their instanta neous efficiency and torque fluctuation is essential for developing high-precisi on robot joint control models." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Chongqing Univer sity, "This study proposes a computational model to predict the instantaneous ef ficiency and torque fluctuation of spur gears under typical operating conditions . The model incorporates a torque balance model, a load distribution model, and a friction model to reflect the relationship between gear meshing position and e fficiency. The instantaneous efficiency and torque fluctuation of gear pairs wer e compared with the Coulomb friction model with an average friction coefficient and the elastohydrodynamic lubrication model with a time-varying friction coeffi cient. The effect of gear contact ratio on efficiency is analysed, while the ins tantaneous efficiency and torque fluctuation of gears are studied under varying operating conditions. The results indicate a maximum efficiency difference of 1. 86 % between the two friction coefficient models. Under specific o perating conditions, the instantaneous efficiency variation of the gear pair can reach 3.34 %, and the torque fluctuation can reach 5.19 Nm."
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    Researchers from University of Roma Tre Describe Findings in Machine Learning (K inematic Variables and Feature Engineering for Particle Phenomenology)

    63-63页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Rome, Italy, by NewsRx correspondents, research stated, "Kinematic variables play an importa nt role in collider phenomenology, as they expedite discoveries of new particles by separating signal events from unwanted background events and allow for measu rements of particle properties such as masses, couplings, and spins. For the pas t ten years, an enormous number of kinematic variables have been designed and pr oposed, primarily for the experiments at the CERN Large Hadron Collider, allowin g for a drastic reduction of highdimensional experimental data to lower-dimensio nal observables, from which one can readily extract underlying features of phase space and develop better-optimized data-analysis strategies." Funders for this research include National Science Foundation (NSF), United Stat es Department of Energy (DOE), United States Department of Energy (DOE), Nationa l Research Foundation of Korea, United States Department of Energy (DOE), United States Department of Energy (DOE). Our news editors obtained a quote from the research from the University of Roma Tre, "Recent developments in the area of phase-space kinematics are reviewd, and new kinematic variables with important phenomenological implications and physic s applications are summarized. Recently proposed analysis methods and techniques specifically designed to leverage new kinematic variables are also reviewed. As machine learning is currently percolating through many fields of particle physi cs, including collider phenomenology, the interconnection and mutual complementa rity of kinematic variables and machine-learning techniques are discussed."
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    Researchers from University of Wisconsin Detail New Studies and Findings in the Area of Machine Learning (Machine Learning Design of Perovskite Catalytic Proper ties)

    64-64页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting from Madison, Wisconsin, by NewsRx journalists, research stated, "Discovering new materials that efficiently catal yze the oxygen reduction and evolution reactions is critical for facilitating th e widespread adoption of solid oxide fuel cell and electrolyzer (SOFC/SOEC) tech nologies. Here, machine learning (ML) models are developed to predict perovskite catalytic properties critical for SOFC/SOEC applications, including oxygen surf ace exchange, oxygen diffusivity, and area specific resistance (ASR)." Funders for this research include United States Department of Energy (DOE), Unit ed States Department of Energy (DOE). The news correspondents obtained a quote from the research from the University o f Wisconsin, "The models are based on trivial-to-calculate elemental features an d are more accurate and dramatically faster than the best models based on ab ini tio-derived features, potentially eliminating the need for ab initio calculation s in descriptor-based screening. The model of ASR enables temperature-dependent predictions, has well calibrated uncertainty estimates and online accessibility. Use of temporal cross-validation reveals the model to be effective at discoveri ng new promising materials prior to their initial discovery, demonstrating the m odel can make meaningful predictions. Using the SHapley Additive ExPlanations (S HAP) approach, detailed discussion of different approaches of model featurizatio n is provided for ML property prediction."
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    New Robotics Study Results from Federal University of Goias (UFG) Described (Dee p-q-network Hybridization With Extended Kalman Filter for Accelerate Learning In Autonomous Navigation With Auxiliary Security Module)

    65-66页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Robotics have been publi shed. According to news originating from Goiania, Brazil, by NewsRx corresponden ts, research stated, "This article proposes an algorithm for autonomous navigati on of mobile robots that mixes reinforcement learning with extended Kalman filte r (EKF) as a localization technique, namely EKF-DQN, aiming to accelerate the ma ximization of the learning curve and improve the reward values obtained in the l earning process. More specifically, Deep-Q-Networks (DQN) are used to control th e trajectory of an autonomous robot in an environment with many obstacles." Our news journalists obtained a quote from the research from the Federal Univers ity of Goias (UFG), "To improve navigation capability in this environment, we al so propose a fusion of visual and nonvisual sensors. Due to the ability of EKF t o predict states, this algorithm is used as a learning accelerator for the DQN n etwork, predicting future states and inserting this information into the memory replay. Aiming to increase the safety of the navigation process, a visual safety system is also proposed to avoid collisions between the mobile robot and people circulating in the environment. The efficiency of the proposed control system i s verified through computational simulations using the CoppeliaSIM simulator wit h code insertion in Python. The simulation results show that the EKF-DQN algorit hm accelerates the maximization of rewards obtained and provides a higher succes s rate in fulfilling the mission assigned to the robot when compared to other va lue-based and policy-based algorithms. A demo video of the navigation system can be seen at:. This article proposes an algorithm for autonomous navigation of mo bile robots that merges reinforcement learning with extended Kalman filter (EKF) as a localization technique, namely, EKF-DQN, aiming to accelerate learning and improve the reward values obtained in the process of apprenticeship. More speci fically, deep neural networks (DQN-Deep-Q-Networks) are used to control the traj ectory of an autonomous vehicle in an indoor environment. Due to the ability of EKF to predict states, this algorithm is proposed to be used as a learning accel erator of the DQN network, predicting states ahead and inserting this informatio n in the memory replay."
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    Findings in the Area of Machine Learning Reported from Rush University (Machine Learning-based Prediction of Hip Joint Moment In Healthy Subjects, Patients and Post-operative Subjects)

    66-66页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Chicago, Il linois, by NewsRx correspondents, research stated, "The application of machine l earning in the field of motion capture research is growing rapidly." Financial support for this research came from Michael and Jacqueline Newman orth opaedic research fund. Our news editors obtained a quote from the research from Rush University, "The p urpose of the study is to implement a long-short term memory (LSTM) model able t o predict sagittal plane hip joint moment (HJM) across three distinct cohorts (h ealthy controls, patients and post-operative patients) starting from 3D motion c apture and force data. Statistical parametric mapping with paired samples t-test was performed to compare machine learning and inverse dynamics HJM predicted va lues, with the latter used as gold standard." According to the news editors, the research concluded: "The results demonstrated favorable model performance on each of the three cohorts, showcasing its abilit y to successfully generalize predictions across diverse cohorts." This research has been peer-reviewed.
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