查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been pr esented.According to news originating from Beijing,People's Republic of China,by NewsRx correspondents,research stated,"Safety is essential for reinforceme nt learning (RL) applied in the real world.Adding chance constraints (or probab ilistic constraints) is a suitable way to enhance RL safety under uncertainty." Funders for this research include International Science and Technology Cooperati on Program of China,National Natural Science Foundation of China (NSFC).Our news journalists obtained a quote from the research from Tsinghua University,"Existing chanceconstrained RL methods,such as the penalty methods and the L agrangian methods,either exhibit periodic oscillations or learn an overconserva tive or unsafe policy.In this article,we address these shortcomings by proposi ng a separated proportional-integral Lagrangian (SPIL) algorithm.We first revie w the constrained policy optimization process from a feedback control perspectiv e,which regards the penalty weight as the control input and the safe probabilit y as the control output.Based on this,the penalty method is formulated as a pr oportional controller,and the Lagrangian method is formulated as an integral co ntroller.We then unify them and present a proportional-integral Lagrangian meth od to get both their merits with an integral separation technique to limit the i ntegral value to a reasonable range.To accelerate training,the gradient of saf e probability is computed in a model-based manner.The convergence of the overal l algorithm is analyzed.We demonstrate that our method can reduce the oscillati ons and conservatism of RL policy in a car-following simulation."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on artificial intelligence is the su bject of a new report.According to news reporting originating from Warsaw,Pola nd,by NewsRx correspondents,research stated,"Recently,artificial intelligenc e (AI)-based algorithms have revolutionized the medical image segmentation proce sses." Financial supporters for this research include National Centre For Research And Development.Our news editors obtained a quote from the research from Polish Academy of Scien ces:"Thus,the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted t herapies,as well as increasing the effectiveness of the training process.In th is context,AI may contribute to the automatization of the image scan segmentati on process and increase the quality of the resulting 3D objects,which may lead to the generation of more realistic virtual objects.In this paper,we focus on the AI-based solutions applied in medical image scan segmentation and intelligen tvisual content generation,i.e.,computer-generated three-dimensional (3D) ima ges in the context of extended reality (XR).We consider different types of neur al networks used with a special emphasis on the learning rules applied,taking i nto account algorithm accuracy and performance,as well as open data availabilit y.This paper attempts to summarize the current development of AI-based segmenta tion methods in medical imaging and intelligent visual content generation that a re applied in XR.It concludes with possible developments and open challenges in AI applications in extended reality-based solutions."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Machine Learning have be en published.According to news reporting originating from Amsterdam,Netherland s,by NewsRx correspondents,research stated,"As radio telescopes increase in s ensitivity and flexibility,so do their complexity and data rates.For this reas on,automated system health management approaches are becoming increasingly crit ical to ensure nominal telescope operations." Financial support for this research came from Dutch Research Council (NWO) domai n Applied and Engineering Sciences (TTW).Our news editors obtained a quote from the research from the University of Amste rdam,"We propose a new machine-learning anomaly detection framework for classif ying both commonly occurring anomalies in radio telescopes as well as detecting unknown rare anomalies that the system has potentially not yet seen.To evaluate our method,we present a dataset consisting of 6708 autocorrelation-based spect rograms from the Low Frequency Array (LOFAR) telescope and assign ten different labels relating to the system-wide anomalies from the perspective of telescope o perators.This includes electronic failures,miscalibration,solar storms,netwo rk and compute hardware errors,among many more.We demonstrate how a novel self -supervised learning (SSL) paradigm,that utilises both context prediction and r econstruction losses,is effective in learning normal behaviour of the LOFAR tel escope.We present the Radio Observatory Anomaly Detector (ROAD),a framework th at combines both SSL-based anomaly detection and a supervised classification,th ereby enabling both classification of both commonly occurring anomalies and dete ction of unseen anomalies.We demonstrate that our system works in real time in the context of the LOFAR data processing pipeline,requiring <1ms to process a single spectrogram."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on robotics have bee n published.According to news originating from Porto,Portugal,by NewsRx corre spondents,research stated,"Autonomous vehicles are a continuously rising techn ology in several industry sectors." Our news reporters obtained a quote from the research from University of Porto:"Examples of these technologies lie in the advances in self-driving cars and can be linked to extraterrestrial exploration,such as NASA's Mars Exploration Rove rs.These systems present a leading methodology allowing for increased task perf ormance and capabilities,which are no longer limited to active human support.H owever,these robotic systems may vary in shape,size,locomotion capabilities,and applications.As such,this report presents a systematic literature review ( SLR) regarding hybrid autonomous robotic vehicles focusing on leg-wheel locomoti on.During this systematic review of the literature,a considerable number of ar ticles were extracted from four different databases."
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 new report.According to news reporting originating from O saka,Japan,by NewsRx correspondents,research stated,"To complement classical methods for identifying Japanese,Chinese,and Western dietary styles,this stu dy aimed to develop a machine learning model." Our news editors obtained a quote from the research from National Institute of H ealth and Nutrition:"This study utilized 604 features from 8183 cooking recipes based on a Japanese recipe site.The data were randomly divided into training,validation,and test sets for each dietary style at a 60:20:20 ratio.Six machin e learning models were developed in this study to effectively classify cooking r ecipes according to dietary styles.The evaluation indicators were above 0.8 for all models in each dietary style.The top ten features were extracted from each model,and the features common to three or more models were employed as the bes t predictive features.Five well-predicted features were indicated for the follo wing seasonings:soy sauce,miso (fermented soy beans),and mirin (sweet cooking rice wine) in the Japanese diet; oyster sauce and doubanjiang (chili bean sauce ) in the Chinese diet; and olive oil in the Western diet."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence.According to news reporting out of Padova,Italy,by News Rx editors,research stated,"Assessing executive functions in individuals with disorders or clinical conditions can be challenging,as they may lack the abilit ies needed for conventional test formats." Funders for this research include Italian Ministry of Research And University.The news reporters obtained a quote from the research from University of Padua:"The use of more personalized test versions,such as adaptive assessments,might be helpful in evaluating individuals with specific needs.This paper introduces PsycAssist,a web-based artificial intelligence system designed for neuropsycho logical adaptive assessment and training.PsycAssist is a highly flexible and sc alable system based on procedural knowledge space theory and may be used potenti ally with many types of tests.We present the architecture and adaptive assessme nt engine of PsycAssist and the two currently available tests:Adap-ToL,an adap tive version of the Tower of London-like test to assess planning skills,and Mat riKS,a Raven-like test to evaluate fluid intelligence.Finally,we describe the results of an investigation of the usability of Adap-ToL and MatriKS:the evalu ators perceived these tools as appropriate and well-suited for their intended pu rposes,and the test-takers perceived the assessment as a positive experience."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report.According to news originating from Soran U niversity by NewsRx correspondents,research stated,"At Klang Valley,ground-le vel ozone is a significant source of air pollution." Our news editors obtained a quote from the research from Soran University:"Ozon e (O3) concentration is affected by meteorological conditions and air pollutants .Linear Regression Models (LRM),Regression Trees (RT),Support Vector Machines (SVM),Ensembles of Trees (ET),Gaussian Process Regression (GPR),and Neural N etworks (NN) are utilized in a thorough analysis to determine the accuracy of va rious machine learning in forecasting the ground level O3 concentration.The pri mary associated contributions from this research are comparisons of regression s tatistical model performance based on indicators of root mean squared error (RMS E),coefficient of determination (R2),mean squared error (MSE),mean absolute e rror (MAE),prediction speed,and training time of regression models.Overall,e xponential GPR outperformed other regression models in scenario 1 (S-1),scenari o 2 (S-2),scenario (S-3),and scenario 4 (S-4) by incorporating multiple number of lags into respective scenarios and new method of testing "re-substitution" p erformed more reliable and consistent than applying identical datasets to 20 % of model testing."
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 reporting out of Pisa,Italy,by NewsRx editors,research stated,"This data descriptor presents a novel dataset collected usin g the quadrupedal robot ANYmal C in the Mediterranean coastal dune environment o f the European Union (EU) habitats 2110 and 2120 in Sardinia,Italy.The dataset mainly consists of photos,videos,and point clouds of the coastal dunes,provi ding valuable information on the structure and composition of this habitat." Our news journalists obtained a quote from the research from the University of P isa,"The data was collected by a team of robotic engineers and plant scientists as result of a joint effort towards robotic habitat monitoring.The dataset is publicly available through Zenodo and can be used by researchers working in both the fields of robotics and habitat ecology and conservation.The availability o f this dataset has the potential to inform future research and conservation effo rts in the EU habitats 2110 and 2120,and it highlights the importance of interd isciplinary collaboration in the field of habitat monitoring." According to the news editors,the research concluded:"This paper serves as a c omprehensive description of the dataset and the methods used to collect it,maki ng it a valuable resource for the scientific community."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented.According to news originating from Shenyang,People's Re public of China,by NewsRx editors,the research stated,"Developing materials w ith multiple desired characteristics is a tremendous challenge,particularly in an elaborate material system." Financial supporters for this research include National Natural Science Foundati on of China.Our news journalists obtained a quote from the research from Northeastern Univer sity:"Herein,a machine learning assisted material design strategy was applied to simultaneously optimize dual target attributes by considering g' solvus tempe rature and alloy density of multi-component Co-based superalloys.To verify the soundness of our strategy,four alloys were selected and experimentally synthesi zed from > 510,000 candidates,each of them possessing g' solvus temperature exceeding 1200 °C and alloy density below 8.3 g/cm3.Of thos e,Co-35Ni-12Al-5Ti-3V-3Cr-2Ta-2Mo (at.%) possesses the highest g' solvus temperature of 1250 °C and lower density of 8.2 g/cm3."
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 originating from Huang gang,People's Republic of China,by NewsRx correspondents,research stated,"Gi ven the intricate and grave nature of trauma-related injuries in ICU settings,i t is imperative to develop and deploy reliable predictive tools that can aid in the early identification of high-risk patients who are at risk of early death.T he objective of this study is to create and validate an artificial intelligence (AI) model that can accurately predict early mortality among critical fracture p atients."
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