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 Robotic s - Robotics and Automation. According to news originating from Ulm, Germany, by NewsRx correspondents, research stated, “Adverse weather conditions can severel y affect the performance of LiDAR sensors by introducing unwanted noise in the m easurements. Therefore, differentiating between noise and valid points is crucia l for the reliable use of these sensors.” Our news journalists obtained a quote from the research from Ulm University, “Cu rrent approaches for detecting adverse weather points require large amounts of l abeled data, which can be difficult and expensive to obtain. This letter propose s a label-efficient approach to segment LiDAR point clouds in adverse weather. W e develop a framework that uses few-shot semantic segmentation to learn to segme nt adverse weather points from only a few labeled examples. Then, we use a semi- supervised learning approach to generate pseudo-labels for unlabelled point clou ds, significantly increasing the amount of training data without requiring any a dditional labeling. We also integrate good weather data in our training pipeline , allowing for high performance in both good and adverse weather conditions. Res ults on real and synthetic datasets show that our method performs well in detect ing snow, fog, and spray.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Transplant Medicine - Kidney Transplants is the subject of a report. According to news originating fro m Rotterdam, Netherlands, by NewsRx correspondents, research stated, “Research o n solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learnin g (ML) to answer diagnostic, prognostic, and therapeutic questions for many year s. Nevertheless, despite the question of whether AI models add value to traditio nal modeling approaches, such as regression models, their ‘black box’ nature is one of the factors that have hindered the translation from research to clinical practice.” Our news journalists obtained a quote from the research from University Medical Center Rotterdam, “Several techniques that make such models understandable to hu mans were developed with the promise of increasing transparency in the support o f medical decision-making. These techniques should help AI to close the gap betw een theory and practice by yielding trust in the model by doctors and patients, allowing model auditing, and facilitating compliance with emergent AI regulation s. But is this also happening in the field of kidney transplantation? This revie w reports the use and explanation of ‘black box’ models to diagnose and predict kidney allograft rejection, delayed graft function, graft failure, and other rel ated outcomes after kidney transplantation. In particular, we emphasize the disc ussion on the need (or not) to explain ML models for biological discovery and cl inical implementation in kidney transplantation.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting originating in Johannesburg, South Afric a, by NewsRx journalists, research stated, “Chemical Looping Combustion (CLC) is a carbon capture technology that uses an oxygen carrier to transfer the oxidizi ng agent to the fuel for combustion. This study used different machine learning algorithms, Artificial neural network and Response surface methodology to estima te the surface region process performance and optimize the process condition for the CLC of different solid fuels waste paper, plastic waste, and sugarcane baga sse blends.” The news reporters obtained a quote from the research from the University of Joh annesburg, “Based on the combustion efficiency, CO2 yield and CO2 capture effici ency responses, A high performance correlation (R-2 > 0. 8) was obtained for all the combustion parameters analyzed. The perturbation plo t derived from the RSM analysis indicated that the most significant input parame ters include the steam to fixed carbon, blend ratio and the fuel reaction temper ature. The CLC process was optimized using RSM. For blends of SCB/WP, the best o perating conditions were found to be 800 degrees C, a solid flow rate of 197.7 k g/h, an oxygen carrier to fuel ratio of 1.1, a steam to fixed carbon ratio of 2. 16, and a blend ratio of 1. Similarly, for blends of SCB/PW, the optimal operati ng conditions were 800 degrees C, a solid flow rate of 199.4 kg/h, an oxygen car rier to fuel ratio of 1.3, steam to fixed carbon ratio of 2, and a blend ratio o f 0.3.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news originating from Macau, People’ s Republic of China, by NewsRx correspondents, research stated, “Model stealing attacks involve creating copies of machine learning models that have similar fun ctionalities to the original model without proper authorization. Such attacks ra ise significant concerns about the intellectual property of the machine learning models.” Financial support for this research came from Australian Research Council.
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 artificial in telligence have been published. According to news reporting from Zhengzhou, Peop le’s Republic of China, by NewsRx journalists, research stated, “Accurate land-s ea classification of ATL03 datasets is the prerequisite for signal photons ident ification and higherlevel data production. Current photon classification method s are mainly based on physical models and machine learning methods.” Funders for this research include National Natural Science Foundation of China; Ministry of Science And Technology of The People’s Republic of China; National K ey Research And Development Program of China.
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 from Mannheim, Germany , by NewsRx journalists, research stated, “Artificial intelligence and machine l earning are key technologies for purchasing organizations worldwide and their us age is still in a nascent stage. This systematic review offers an overview of th e state-of-the-art literature and practice, where 46 works meeting the inclusion criteria were interactively classified in 11 use case clusters.” The news correspondents obtained a quote from the research from the University o f Mannheim, “The work follows the content analysis approach where the material e valuation was empirically enriched with 20 interviews to assess the cluster’s bu siness value and ease of implementation through triangulation. This is the first systematic review in the area of operations and supply chain management utilizi ng the Computer Classification System as the de facto standard in computer scien ce for clarity in the terminology of these emerging technologies. In matching th e literature search with the interview results, a mismatch was found between the reviewed literature and the expert’s assessments. For instance, the cluster cos t analysis deserves higher research attention as well as supplier sustainability . Moreover, there seems to be a gap in the operational area, which many believe to be first considered due to data availability.”
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 Vancouver, Canada, by NewsRx correspondents, research stated, “People are increasingly turning to soc ial media and online forums like Reddit to cope with work-related concerns. Prev ious research suggests that how others respond can be an important determinant o f the sharer’s affective and well-being outcomes.” Financial support for this research came from Social Sciences and Humanities Res earch Council of Canada (SSHRC).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting from Lausanne, Switzerland, by NewsRx jo urnalists, research stated, “The present work proposes an extension to the appro ach of [Xi, C; et al. 6878] to calculate i on solvation free energies from first-principles (FP) molecular dynamics (MD) si mulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation.” The news correspondents obtained a quote from the research from the Swiss Federa l Institute of Technology Lausanne (EPFL), “Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus i mprove the convergence of statistical averages at a fraction of the original com putational cost, a machine-learned (ML) energy function is trained on FP energie s and forces and used in the MD simulations. The ML workflow and MD simulation t imes ( 200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV.”
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 artificial in telligence have been published. According to news originating from the Universit y of Rhode Island by NewsRx editors, the research stated, “Avian reoviruses cont inue to cause disease in turkeys with varied pathogenicity and tissue tropism. T urkey enteric reovirus has been identified as a causative agent of enteritis or inapparent infections in turkeys.” Our news editors obtained a quote from the research from University of Rhode Isl and: “The new emerging variants of turkey reovirus, tentatively named turkey art hritis reovirus (TARV) and turkey hepatitis reovirus (THRV), are linked to tenos ynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis r eoviruses are causing significant economic losses to the turkey industry. These infections can lead to poor weight gain, uneven growth, poor feed conversion, in creased morbidity and mortality and reduced marketability of commercial turkeys. To combat these issues, detecting and classifying the types of reoviruses in tu rkey populations is essential. This research aims to employ clustering methods, specifically K-means and Hierarchical clustering, to differentiate three types o f turkey reoviruses and identify novel emerging variants. Additionally, it focus es on classifying variants of turkey reoviruses by leveraging various machine le arning algorithms such as Support Vector Machines, Naive Bayes, Random Forest, D ecision Tree, and deep learning algorithms, including convolutional neural netwo rks (CNNs). The experiments use real turkey reovirus sequence data, allowing for robust analysis and evaluation of the proposed methods.”
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 Jalandhar, India, by New sRx journalists, research stated, “Robotics is intertwined with metrology, inclu ding aircraft component inspection, automotive processes, and part geometry opti mization. Optimized trajectory planning is essential for reliable robotic arm op eration and maintaining quality in inspections and geometric enhancements, as we ll as autonomous mobile robot navigation.” The news correspondents obtained a quote from the research from the Dr. B.R. Amb edkar National Institute of Technology, “Technically, a path planning is associa ted as an optimization problem that relies on various parameters such as length minimization problem, smooth trajectory planning, low time/space complexity, and computational load. While considering all these stated parameters, choosing an optimal path to reach the destination is the primary function of path planning t echniques. This research paper is focused on the implementation of adaptive bidi rectional A* (ABA*) algorithm along with new strategy of flexible controlling po ints technique (FCP) to reduce the trajectory error by generating smoother traje ctory. With the increased number of sharp turns, the wheel skidding error is gen erated that reduce the reliability of the path planning techniques by increasing the pose estimation error. By conducting multiple trials, the proposed techniqu e has been implemented, resulting in a 100% reduction in the numbe r of collisions. Furthermore, the application of the new FCP technique eliminate s all sharp turns, leading to a 38% decrease in time lag uncertain ty compared to conventional approaches.”
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