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 out of Jiangsu, People’s Repu blic of China, by NewsRx editors, research stated, “Vehicular travel time distri butions (TTDs) are of great importance for traffic management and control, and v arious probability distributions have been used for TTD prediction in previous s tudies. However, it is difficult to determine a generalized probability distribu tion of vehicular travel times on urban roads that is applicable to all traffic conditions in real situations.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Postgraduate Research and Practice Innovation Program of Jia ngsu Province.
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 ro botics. According to news originating from Qingdao, People’s Republic of China, by NewsRx correspondents, research stated, “Task allocation plays an important r ole in multi-robot systems regarding team efficiency.” Financial supporters for this research include National Natural Science Foundati on of China; Key Research And Development Program of Shandong Province; Project of Natural Science Foundation of Shandong Province; China University Innovation Fund; Qingdao Natural Science Foundation; Postdoctoral Innovation Project of Sha ndong Province; Qingdao Postdoctoral Funding Project.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Robotics is the subject of a repo rt. According to news reporting from Reggio Emilia, Italy, by NewsRx journalists , research stated, “The present study addresses the challenge of effectively dep loying a multi-robot team to optimally cover a domain with unknown density distr ibution. Specifically, we propose a distribute coverage-based control algorithm that enables a group of autonomous robots to simultaneously learn and estimate a spatial field over the domain.” Financial support for this research came from Ministry of Education, Universitie s and Research (MIUR).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting originating from Palmerston, New Z ealand, by NewsRx correspondents, research stated, “Surprisingly little is known about how the home environment influences the behaviour of pet cats.” The news journalists obtained a quote from the research from Massey University: “This study aimed to determine how factors in the home environment (e.g., with o r without outdoor access, urban vs. rural, presence of a child) and the season i nfluences the daily behaviour of cats. Using accelerometer data and a validated machine learning model, behaviours including being active, eating, grooming, lit tering, lying, scratching, sitting, and standing were quantified for 28 pet cats . Generalized estimating equation models were used to determine the effects of d ifferent environmental conditions. Increasing cat age was negatively correlated with time spent active (p <0.05). Cats with outdoor access (n = 18) were less active in winter than in summer (p <0. 05), but no differences were observed between seasons for indoor-only (n = 10) c ats. Cats living in rural areas (n = 7) spent more time eating than cats in urba n areas (n = 21; p <0.05).”
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 - Ro botics and Automation have been published. According to news originating from At lanta, Georgia, by NewsRx correspondents, research stated, “Humans are required to maintain balance during locomotion in challenging environments, which present an even bigger challenge for individuals with balance impairments. Exoskeleton- driven balance augmentation is a promising avenue to assist users in these envir onments, but there has been little work in developing exoskeleton devices for th ese applications.” Financial support for this research came from National Science#x00A 0;Foundation Graduate Research Fellowship.
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 Austin, Texas, by NewsRx edito rs, research stated, “Model-based control for robots has increasingly depended o n optimization-based methods, such as differential dynamic programming (DDP) and iterative LQR (iLQR). These methods can form the basis of model-predictive cont rol, which is commonly used for controlling legged robots.” Financial support for this research came from National Science Foundation (NSF).
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 - Ro botics and Automation have been published. According to news reporting out of Ha ngzhou, People’s Republic of China, by NewsRx editors, research stated, “Pinchin g and grasping are the key fundamental actions for manipulator to handle objects , while achieving high-quality execution of these actions has always been an int ense attention in robotics field. In this letter, a novel underactuated manipula tor with composed multiple-linkage mechanism is proposed, which enables both pre cise pinching and powerful grasping.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Adenocarcin oma is the subject of a report. According to news reporting originating in Sheny ang, People’s Republic of China, by NewsRx journalists, research stated, “Distan t metastasis of cancer is a significant contributor to cancer-related complicati ons, and early identification of unidentified stomach adenocarcinoma is crucial for a positive prognosis. Changes inDNA methylation are being increasingly recog nized as a crucial factor in predicting cancer progression.” The news reporters obtained a quote from the research from the First Hospital of China Medical University, “Within this research, we developed machine learning and deep learning models for distinguishing distant metastasis in samples of sto mach adenocarcinoma based on DNA methylation profile. Employing deep neural netw orks (DNN), support vector machines (SVM), random forest (RF), Naive Bayes (NB) and decision tree (DT), and models for forecasting distant metastasis in stomach adenocarcinoma. The results show that the performance of DNN is better than tha t of other models, AUC and AUPR achieving 99.9 % and 99.5 % respectively.”
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 in Rovereto, Ital y, by NewsRx journalists, research stated, “Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primari ly because of methodological limitations of the previous studies, such as the lo w sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsuper vised and supervised machine learning methods, to identify the joint contributio ns of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT).” The news reporters obtained a quote from the research from the University of Tre nto, “After preprocessing, the brain scans of 135 participants were decomposed i nto eight independent networks of covarying GM and WM via parallel ICA. Subseque ntly, stepwise regression and Random Forest were used to predict NPT. We hypothe sized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined w hether the brain network predicting narcissism could also predict other personal ity traits (i.e., histrionic, paranoid and avoidant personalities). Notably, thi s network did not predict such personality traits. Additionally, a supervised ma chine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predi ct new cases.”
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 Machine Learning is now available. According to news reporting originating from Durham, North Carolina, by NewsRx correspondents, research stated, “Social media has become a dominant educational resource for breast reconstruction patients. Rather than passively c onsuming information, patients interact di- rectly with other users and healthca re professionals.” Our news editors obtained a quote from the research from Duke University Medical Center, “While online information for breast re- construction has been analyzed previously, a robust analysis of patient questions on online forums has not bee n conducted. In this study, the authors used a machine learning approach to anal yze and categorize online patient questions regarding breast reconstruction. Rea lself.com was accessed and questions pertaining to breast reconstruction were ex tracted. Data collected included the date of question, poster’s location, questi on header, question text, and available tags. Questions were analyzed and catego rized by two in- dependent reviewers. 522 preoperative questions were analyzed. Geographic analysis is displayed in Figure 1. Questions were often asked in the pre -mastectomy period (38.3%); however, patients with tissue expan ders currently in place made up 28.5%. Questions were often related to re- constructive methods (23.2%), implant selection (19.5% ), and tissue expander concerns (16.7%). Questions asked in the pos t -lumpectomy period were significantly more likely to be related to insurance/c ost and reconstructive candidacy (p <0.01). The ‘Top 6 “ p atient questions were determined by machine learning analysis, and the most comm on of which was ‘Can I get good results going direct to implant after mastectomy ?’ Conclusions: Analysis of online questions provides valuable insights and may help inform our educational approach toward our breast reconstruction patients. Our findings suggest that questions are common throughout the reconstructive pro cess and do not end after the initial consultation.”
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