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 from Rome, Italy, by NewsRx journalists, research stated, "Generative AI is disrupting the creative process(es) of intel lectual works on an unparalleled scale. Algorithmic tools are increasing users' production capacity for literary and artistic works to almost infinite levels." The news correspondents obtained a quote from the research from LUISS Guido Carl i University, "However, the quality of the outputs is strictly dependent on the quantity and quality of the inputs, some of which are protected by copyright. Th is scenario gave raise to tensions between copyright holders and generative AI c ompanies. While the formers claim control over this new kind of exploitation of their works, the latters wish to train their algorithms freely with as many cont ents as possible. This contribution suggests exploring the idea of introducing a statutory license for machine learning purposes as a compromise solution to ens ure an attractive environment for artificial intelligence without marginalizing the role played by human authors." According to the news reporters, the research concluded: "This remuner-ation pro posal is rooted in a fundamental rights analysis that balances i.e., the right t o science and culture and freedom of artistic expression (Arts. 11 and 13 EUCF, 19 UDHR, 27.1 UDHR, 15.1 a and b ICESCR) vis-‘a-vis the right for creators to be nefit from the protection of the moral and material interests resulting from the ir scientific, literary or artistic production (Arts. 17.2 EUCF, 27.2 UDHR, and 15.1 c ICESCR)."
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 out of Valparaiso, Chile, by NewsRx editors, research stated, "The subsea exploration of complex and challenging ar eas has increased the need for advanced robotic frameworks, such as cable -based parallel manipulators (CPMs). Known for their flexibility and precision, CPMs a re essential for performing detailed tasks underwater." Financial supporters for this research include CONICYT FONDECYT, National Agency for Research and Development (ANID) of the Chilean government under the Ministr y of Science, Technology, Knowledge, and Innovation, CMAT-Research Centre of Mat hematics of the University of Minho, Portugal.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Neurodegenerative Dise ases and Conditions - Alzheimer Disease is the subject of a report. According to news reporting originating from Atlanta, United States, by NewsRx correspondent s, research stated, "For successful biomarker discovery, it is essential to deve lop computational frameworks that summarize high-dimensional neuroimaging data i n terms of involved sub-systems of the brain, while also revealing underlying he terogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate cli nical assessment information, while supervised approaches extract only individua l feature importance, thereby impeding qualitative interpretation at the level o f subspaces." Our news editors obtained a quote from the research from the Georgia Institute o f Technology and Emory University, "We present a novel framework to extract robu st multimodal brain subspaces associated with changes in a given cognitive or bi ological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summar ize the most salient and consistent subspaces associated with the target variabl e. Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also re tains predictive performance in standard machine learning algorithms. We also sh ow that the salient active subspace directions occur consistently across randoml y sub-sampled repetitions of the analysis. Compared to existing unsupervised dec ompositions based on principle component analysis, the subspace components in ou r framework retain higher predictive information."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Nanotechnology - Nanomec hanical have been published. According to news originating from Jacksonville, Fl orida, by NewsRx correspondents, research stated, "Fibrosis characterized by exc ess accumulation of extracellular matrix (ECM) due to complex cell-ECM interacti ons plays a pivotal role in pathogenesis. Herein, we employ the pancreatic ducta l adenocarcinoma (PDAC) model to investigate dynamic alterations in nanomechanic al attributes arising from the cell-ECM interactions to study the fibrosis parad igm." Financial supporters for this research include National Institutes of Health (NI H) - USA, NIH National Heart Lung & Blood Institute (NHLBI), Flori da Department of Health (Cancer Research Chair Fund), Florida Department of Heal th, Mayo Clinic Pancreatic Cancer SPORE Career Enhancement Award, Jay and Deanie Stein Career Development Award for Cancer Research at Mayo Clinic Jacksonville, Eagles fifth District Cancer Telethon-Cancer Research Fund, Benefactor Funded C hampions for Hope Pancreatic Cancer.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on artificial intelligence are present ed in a new report. According to news originating from Vaasa, Finland, by NewsRx correspondents, research stated, "The growing impact and importance of artifici al intelligence in society has led to an increasing interest for the potential o f artificial intelligence as an educational tool in schools to aid both students and teachers." The news editors obtained a quote from the research from Novia University of App lied Sciences: "In this study we investigate digitally skilled K-12 mathematics teachers' (N=85) attitudes towards and expectations on the role of artificial in telligence in the classroom. The study was done by conducting and analyzing the results of a web-based survey among Swedish and Finnish speaking mathematics tea chers using a mixed methods strategy. The Will, Skill and Tool framework was use d for the analysis. The survey was done before the introduction of ChatGPT-3. Th e results indicate that the teachers' attitudes toward AI tools in school are ch aracterized by interest, openness, and awareness. Teachers have a balanced view on the possibilities and risks of AI use in school."
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 Ma chine Learning. According to news reporting from Tianjin, People's Republic of C hina, by NewsRx journalists, research stated, "Chromophoric dissolved organic ma tter (CDOM) in aquatic environments is an important component of the biogeochemi cal cycle and carbon cycle. The aim of this study is to investigate the long-ter m changes in CDOM in shallow and eutrophic Chaohu Lake, as well as its relations hip with climate, environment and social factors." Financial support for this research came from National Key R&D Prog ram of China. The news correspondents obtained a quote from the research from Nankai Universit y, "Using long time series Landsat image data and machine learning technology, t he spatiotemporal evolution of Chaohu CDOM since 1987 was reconstructed. A total of 180 samples were collected, which were divided into three parts based on reg ional and hydrological characteristics. The results show that the water quality in different regions were significantly different, and TN may be the key factor driving the change of CDOM in Chaohu Lake. Machine learning algorithms including random forest (RF), support vector regression (SVR), neural network (NN), multi modal deep learning (MDL) model and Extreme Gradient Boosting (XGBoost) were use d, among which XGBoost model performed best (R-2 = 0.955, mean absolute error [MAE] = 0.024 mg/L, root mean square error [RMSE] = 0.036 mg/L, bias = 1.005) and was used for CDOM spati otemporal variation retrieval. The change of CDOM was seasonal, highest in Augus t (0.67 m(-1)) and lowest in December (0.48 m(-1)), and the western lake is the main source of CDOM. Annual variability of the CDOM indicates that it began to d ecline after the completion of water pollution control in 2000. Temperature chan ges were closely related to CDOM (P <0.01) and agricultura l non-point source pollution plays an important role in Chaohu Lake."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on Robotics is now available. Accordi ng to news reporting originating in Jinan, People's Republic of China, by NewsRx journalists, research stated, "Because of many advantages, such as single-sided riveting and no welding, blind rivet nuts are widely used to fasten various met al sheets and pipes. However, fixing the blind rivet nut to the base material is mainly accomplished by manual riveting and robot riveting based on teaching-pla yback." Financial supporters for this research include National Key R&D Pro gram of China, Shandong Provincial Key Research and Development Program, Taishan Industry Leading Talent Project. The news reporters obtained a quote from the research from Shandong University, "In order to realize automatic riveting without teaching, this paper proposes a robot automatic riveting method based on machine vision. Firstly, with the help of the light source that increases the difference between the foreground and the background, a binarization method based on morphology is designed to remove lot s of useless information in the image. Then, an improved EDCircles algorithm bas ed on the geometric properties of circular arcs proposed to detect circles in th e binary image, which also realizes the detection of the head circles of the nut . Finally, in order to obtain the precise position of the nut, an optimization m ethod for the inner circle of the nut is designed."
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 originating from Tempe, Arizona, by NewsRx correspondents, research stated, "It has been recently demonstrated th at two machine-learning architectures, reservoir computing and time-delayed feed -forward neural networks, can be exploited for detecting the Earth's anomaly mag netic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment." Funders for this research include Air Force Office of Scientific Research. Our news correspondents obtained a quote from the research from Arizona State Un iversity: "The accuracy of the detected anomaly field corresponds to a positioni ng accuracy in the range of 10-40 m. To increase the accuracy and reduce the unc ertainty of weak signal detection as well as to directly obtain the position inf ormation, we exploit the machine-learning model of random forests that combines the output of multiple decision trees to give optimal values of the physical qua ntities of interest. In particular, from time-series data gathered from the cock pit of a flying airplane during various maneuvering stages, where strong backgro und complex signals are caused by other elements of the Earth's magnetic field a nd the fields produced by the electronic systems in the cockpit, we demonstrate that the random-forest algorithm performs remarkably well in detecting the weak anomaly field and in filtering the position of the aircraft." According to the news reporters, the research concluded: "With the aid of the co nventional inertial navigation system, the positioning error can be reduced to l ess than 10 m. We also find that, contrary to the conventional wisdom, the class ic Tolles-Lawson model for calibrating and removing the magnetic field generated by the body of the aircraft is not necessary and may even be detrimental for th e success of the random-forest method."
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 London, United Kingdom, by New sRx editors, research stated, "We show that a distributed network of robots or o ther devices which make measurements of each other can collaborate to globally l ocalize via efficient ad hoc peer-to-peer communication. Our Robot Web solution is based on Gaussian belief propagation £ on the fundamental nonlinear factor gr aph describing the probabilistic structure of all of the observations robots mak e internally or of each other, and is flexible for any type of robot, motion or sensor." Financial support for this research came from Engineering & Physic al Sciences Research Council (EPSRC). Our news journalists obtained a quote from the research from Imperial College Lo ndon, "We define a simple and efficient communication protocol which can be impl emented by the publishing and reading of web pages or other asynchronous communi cation technologies. We show in simulations with up to 1000 robots interacting i n arbitrary patterns that our solution convergently achieves global accuracy as accurate as a centralized nonlinear factor graph solver while operating with hig h distributed efficiency of computation and communication. Via the use of robust factors in GBP, our method is tolerant to a high percentage of faulty sensor me asurements or dropped communication packets."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics are disc ussed in a new report. According to news reporting originating in Zhengzhou, Peo ple's Republic of China, by NewsRx journalists, research stated, "By mapping the performances of the task requirement and the inherent physical characteristics of the robotic systems to the hybrid constraints, this article proposes a task-o riented adaptive position/force control (TOAPFC) scheme for the robotic systems to ensure the execution of the predefined tasks and the safety of robotic manipu lators and humans in the task workspace. In the proposed scheme, a reference tra jectory generation strategy and admittance model are regarded as the outer loop of TOAPFC to obtain and shape the robotic system's task trajectory that guarante es the safety of the interaction system." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news reporters obtained a quote from the research from Zhengzhou University, "An admittancebased adaptive position/force control scheme unifying the positi on and force into a control law is used as the inner loop of TOAPFC to track the shaped task trajectory, where a barrier Lyapunov function is utilized to constr ain the tracking errors within permitted ranges. Moreover, the system uncertaint ies and lumped disturbances are compensated by the radial basis function neural network and robust compensator, respectively. Meanwhile, the stability of the pr oposed admittance-based adaptive position/force control scheme is analyzed by us ing the Lyapunov stability theory."
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