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 Machine Learn ing - Computational Intelligence have been published. According to news reportin g from Hangzhou, People’s Republic of China, by NewsRx journalists, research sta ted, “Graph neural networks are easily deceived by adversarial attacks that inte ntionally modify the graph structure. Particularly, homophilous edges connecting similar nodes can be maliciously deleted when adversarial edges are inserted in to the graph.” 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 – Investigators publish new report on artificial in telligence. According to news reporting originating from Sichuan Agriculture Uni versity by NewsRx correspondents, research stated, “Sugar content is an essentia l indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction.” Our news reporters obtained a quote from the research from Sichuan Agriculture U niversity: “In this study, we paired a support vector machine (SVM) algorithm wi th genetic algorithm optimization to reliably estimate the sugar content in cris p pears. We evaluated the spectral data and actual sugar content in crisp pears, then applied three preprocessing methods to the spectral data: standard normal variable transformation (SNV), multivariate scattering correction (MSC), and con volution smoothing (SG). Support vector regression (SVR) models were built using processing approaches. According to the findings, the SVM model preprocessed wi th convolution smoothing (SG) was the most accurate, with a correlation coeffici ent 0.0742 higher than that of the raw spectral data. Based on this finding, we used competitive adaptive reweighting (CARS) and the continuous projection algor ithm (SPA) to select key representative wavelengths from the spectral data. Fina lly, we used the retrieved characteristic wavelength data to create a support ve ctor machine model (GASVR) that was genetically tuned.”
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 Hanoi, Vietnam, by NewsRx ed itors, research stated, “In recent years, robotics has experienced significant d evelopment and widespread application across various manufacturing sectors. This progress has been driven by the integration of breakthroughs in technology such as artificial intelligence and computer vision, enabling robots to become more intelligent and adaptable when performing specific tasks.” Financial support for this research came from Hanoi University of Science and Te chnology (HUST).
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 from the University of Utah b y NewsRx journalists, research stated, “This paper introduces a novel cable-driv en robotic platform that enables six degrees-of-freedom (DoF) natural head-neck movements. Poor postural control of the head-neck can be a debilitating symptom of neurological disorders such as amyotrophic lateral sclerosis and cerebral pal sy.” Funders for this research include National Science Foundation.
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 out of Kyiv, Ukraine, by NewsRx e ditors, research stated, “Aquatic ecosystems are crucial in maintaining environm ental equilibrium and sustaining human well-being. However, the traditional manu al methods used in hydrobiological research have limitations in providing a far- reaching understanding of these intricate ecosystems.” Our news journalists obtained a quote from the research from Adam Mickiewicz Uni versity in Poznan: “Data science, machine learning, and deep learning techniques offer a variety of opportunities to overcome these limitations and unlock new i nsights into aquatic environments. This study highlights the impact of computati onal tools in areas such as taxonomic identification, metagenomic sequence analy sis, and water quality prediction. Deep learning techniques have demonstrated su perior accuracy in classifying organisms, including those previously unidentifie d by conventional methods. In metagenomic sequence analysis, machine learning ai ds in effectively assembling DNA sequences, aligning them with known databases, and addressing challenges related to sequence repeats, errors, and missing data. Furthermore, predictive models have been developed to provide insights into wat er quality parameters, such as eutrophication events and heavy metal concentrati ons. These advancements lead to informed conservation measures and a deep unders tanding of the intricate relationships within aquatic ecosystems. However, chall enges persist, including data quality issues, model interpretability, and the ne ed for robust training datasets. Thus, data integration strategies designed spec ifically for environmental and genomic studies are necessary. Data fusion and im putation can help address data scarcity and provide a comprehensive view of hydr obiological processes.”
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 Trento, Italy, by NewsRx journalists, research stated, “There is an emerging requirement for p erforming data-intensive parallel computations, e.g., machine-learning inference , locally on batteryless sensors. These devices are resource-constrained and ope rate intermittently due to the irregular energy availability in the environment. ” The news reporters obtained a quote from the research from the University of Tre nto, “Intermittent execution might lead to several side effects that might preve nt the correct execution of computational tasks. Even though recent studies prop osed methods to cope with these side effects and execute these tasks correctly, they overlooked the efficient intermittent execution of parallelizable data-inte nsive machine-learning tasks. In this article, we present PiMCo-a novel programm able CRAM-based in-memory coprocessor that exploits the Processing In-Memory (PI M) paradigm and facilitates the power-failure resilient execution of paralleliza ble computational loads. Contrary to existing PIM solutions for intermittent com puting, PiMCo promotes better programmability to accelerate a variety of paralle lizable tasks.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Computation - Neural C omputation is the subject of a report. According to news reporting out of Naples , Italy, by NewsRx editors, research stated, “We propose and analyze a continuou s-time firing-rate neural network, the positive firing-rate competitive network (PFCN), to tackle sparse reconstruction problems with nonnegativity constraints. These problems, which involve approximating a given input stimulus from a dicti onary using a set of sparse (active) neurons, play a key role in a wide range of domains, including, for example, neuroscience, signal processing, and machine l earning.” Our news journalists obtained a quote from the research, “First, by leveraging t he theory of proximal operators, we relate the equilibria of a family of continu ous-time firing-rate neural networks to the optimal solutions of sparse reconstr uction problems. Then we prove that the PFCN is a positive system and give rigor ous conditions for the convergence to the equilibrium. Specifically, we show tha t the convergence depends only on a property of the dictionary and is linear-exp onential in the sense that initially, the convergence rate is at worst linear an d then, after a transient, becomes exponential. We also prove a number of techni cal results to assess the contractivity properties of the neural dynamics of int erest. Our analysis leverages contraction theory to characterize the behavior of a family of firing-rate competitive networks for sparse reconstruction with and without nonnegativity constraints.”
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 Robotics is now availab le. According to news reporting from Quebec City, Canada, by NewsRx journalists, research stated, “This paper introduces a novel (6+2)-degree-offreedom (dof) k inematically redundant parallel robot. The proposed architecture is based on the 6-dof HEXA parallel robot.” Financial support for this research came from Natural Sciences and Engineering R esearch Council of Canada (NSERC).
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 from Stephenville , Texas, by NewsRx correspondents, research stated, “PurposeThe purpose of this research is to propose and empirically validate a theoretical framework to inves tigate the willingness of the elderly to disclose personal health information (P HI) to improve the operational efficiency of AI-integrated caregiver upon Privacy Calculus Theory (PCT) and the Technology Acceptance Model (TAM), 2 74 usable responses were collected through an online survey.FindingsEmpirical re sults reveal that trust, privacy concerns, and social isolation have a direct im pact on the willingness to disclose PHI.” Financial support for this research came from Toulouse Graduate School at the Un iversity of North Texas.
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 Machine Learn ing have been published. According to news reporting out of Beijing, People’s Re public of China, by NewsRx editors, research stated, “Leaf mass per area (LMA) s erves as a valuable metric within the field of agriculture, offering valuable in sights into various aspects of leaf structure, including photosynthetic capacity , carbon assimilation, water use efficiency, and overall crop productivity. Mach ine learning, in combination with spectral reflectance analysis, has proven to b e an highly advantageous approach for estimating LMA.” Funders for this research include National Key Technology R&D Progr am, Inner Mongolia Science and technol-ogy project.
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