查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news originating from Mexico City, Mexico, by NewsRx correspondents, research stated, “We present analytical and numer- ical models of the bright long GRB 210822A at z = 1.736. The intrinsic extreme brightness exhibited in the optical, which is very similar to other bright GRBs (e.g.” Funders for this research include Universidad Nacional Autonoma de Mexico, DGTIC UNAM on the supercomputer Miztli, Programa de Apoyo a Proyectos de Investigacion e Innovacion Tecnologica (PAPIIT),UK Swift Science Data Centre at the University of Leicester, Consejo Nacional de Ciencia y Tecnologia (CONACyT). Our news journalists obtained a quote from the research from the National Autonomous University of Mexico (UNAM), “GRBs 080319B, 130427A, 160625A 190114C, and 221009A), makes GRB 210822A an ideal case for studying the evolution of this particular kind of GRB. We use optical data from the RATIR instrument starting at T + 315.9 s, with publicly available optical data from other ground-based observatories, as well as Swift/UVOT, and X-ray data from the Swift/XRT instrument. The temporal profiles and spectral properties during the late stages align consistently with the conventional forward shock model, complemented by a reverse shock element that dominates optical emissions during the initial phases (T <300 s). Furthermore, we observe a break at T = 80 000 s that we interpreted as evidence of a jet break, which constrains the opening angle to be about theta(j) = (3-5) degrees. Finally, we apply a machine-learning technique to model the multiwavelength light curve of GRB 210822A using the AFTERGLOWPY LIBRARY.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Artificial Intelligence are presented in a new report. According to news reporting out of Southampton, United Kingdom, by NewsRx editors, the research stated, “Patient information leaflets (PILs) can supplement a clinical consultation and provide additional information for a patient to read in their own time. The objectives of this study were to determine whether artificical intelligence (AI) can produce PILs that include a similar degree of content to current British Association of Dermatologists (BAD) PILs using ChatGPT.” Our news journalists obtained a quote from the research from University Hospitals Southampton NHS Foundation Trust, “AI-generated PILs were found to include similar factual content to BAD PILs but ex- cluded information that was felt to be more pertinent to patient concerns such as curability and heritability. AI-generated PILs were produced at a reading age beyond that of a large number of UK adults. Caution is advised regarding medication-specific PILs. Patient information leaflets (PILs) can supplement a clinical consultation and provide additional information for a patient to read in their own time. A wide range of PILs are available for distribution by the British Association of Dermatologists (BAD) and undergo rigor- ous review ahead of publication. In the UK, 7.1 million adults are estimated to have the reading age of a 9-year-old child and 43% are unable to comprehend written health information.Objectives To determine whether artificial intelligence (AI) can produce PILs that include a similar degree of content to current BAD PILs.Methods Using the AI tool ChatGPT, 10 PILs were generated, and their contents compared with those of existing BAD PILs using an author-generated list of commonly included themes. Omissions were noted and a repeat series of PILs generated using targeted request phrasing. The readability of AI-generated PILs was also analysed.Results AI-generated PILs were found to include similar factual content to BAD PILs but excluded information that was felt to be more pertinent to patient concerns such as curability and heritability. Targeted request phrasing saw AI generate PILs including this content. The readability of AI-generated PILs was beyond that of a large number of UK adults.Conclusions Where a condition-specific PIL is not readily available, an AI-generated PIL can provide relevant information of lesser quality than existing BAD PILs, which may be inaccessible to some patients.”
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 subject of a new report. According to news originating from Bialystok, Poland, by NewsRx correspondents, research stated, “The trajectory or moving-target tracking feature is desirable, because it can be used in various applications where the usefulness of UAVs is already proven.” Financial supporters for this research include Department of Mechanical Engineering. The news reporters obtained a quote from the research from Bialystok University of Technology: “Track- ing moving targets can also be applied in scenarios of cooperation between mobile ground-based and flying robots, where mobile ground-based robots could play the role of mobile landing pads. This article presents a novel proposition of an approach to position-tracking problems utilizing artificial potential fields (APF) for quadcopter UAVs, which, in contrast to well-known APF-based path planning methods, is a dynamic problem and must be carried out online while keeping the tracking error as low as possible. Also, a new flight control is proposed, which uses roll, pitch, and yaw angle control based on the velocity vector. This method not only allows the UAV to track a point where the potential function reaches its minimum but also enables the alignment of the course and velocity to the direction and speed given by the velocity vector from the APF. Simulation results present the possibilities of applying the APF method to holonomic UAVs such as quadcopters and show that such UAVs controlled on the basis of an APF behave as non-holonomic UAVs during 90° turns.”
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 subject of a new report. According to news reporting from Lubbock, Texas, by NewsRx journalists, research stated, “Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop’s genetic gain rate.” The news correspondents obtained a quote from the research from U.S. Department of Agriculture (USDA): “Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes.”
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 artificial intelligence have been published. According to news reporting from the Universitatea de Stat din Moldova by NewsRx journalists, research stated, “In this article, we address term creation in 3 terms from the domain of artificial intelligence, which are representative and revelatory for the study of reterminologization in the triad of the specialized domains of emotional intelligence, cognitive intelligence and artificial intelligence.” Our news correspondents obtained a quote from the research from Universitatea de Stat din Moldova: “The study of reterminologization in the announced triad traces conceptual interferences to state-of-the-art terms created in the domain of artificial intelligence, in particular, through metonymization. The domain of artificial intelligence is a dynamic and prolific one, with a terminological variety that allows the study of conceptual interferences inspired from the domain of human intelligence.” According to the news reporters, the research concluded: “The terms that are subject to analysis in the article have a high degree of complexity and conceptually encompass the triad of emotional, cognitive and artificial intelligence.” For more information on this research see: Metonymization Procedures in Artificial Intelligence (in English and Romanian Languages). Philologia, 2023,LXV(„PRO LIBRA” SRL):98-104. The publisher for Philologia is Pro Libra SRL.
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 neural computation. According to news reporting from Chiba, Japan, by NewsRx journalists, research stated, “Many cognitive functions are represented as cell assemblies.” Our news reporters obtained a quote from the research from University of Tokyo: “In the case of spatial navigation, the population activity of place cells in the hippocampus and grid cells in the entorhinal cortex represents self-location in the environment. The brain cannot directly observe self-location information in the environment. Instead, it relies on sensory information and memory to estimate self-location. There- fore, estimating low-dimensional dynamics, such as the movement trajectory of an animal exploring its environment, from only the high-dimensional neural activity is important in deciphering the information represented in the brain. Most previous studies have estimated the low-dimensional dynamics (i.e., latent variables) behind neural activity by unsupervised learning with Bayesian population decoding using artificial neural networks or gaussian processes. Recently, persistent cohomology has been used to estimate latent variables from the phase information (i.e., circular coordinates) of manifolds created by neural activity.” According to the news editors, the research concluded: “However, the advantages of persistent coho-mology over Bayesian population decoding are not well understood. We compared persistent cohomology and Bayesian population decoding in estimating the animal location from simulated and actual grid cell population activity. We found that persistent cohomology can estimate the animal location with fewer neurons than Bayesian population decoding and robustly estimate the animal location from actual noisy data.” For more information on this research see: Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity. Neural Computation, 2024,36(3). The publisher for Neural Computation is MIT Press.
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 artificial intelligence. According to news originating from Ottawa, Canada, by NewsRx correspondents, research stated, “Food demand is expected to rise significantly by 2050 due to the increase in population; additionally, receding water levels, climate change, and a decrease in the amount of available arable land will threaten food production. To address these challenges and increase food security, input cost reductions and yield optimization can be accomplished using yield precision maps created by machine learning models; however, without considering the spatial structure of the data, the precision map’s accuracy evaluation assessment risks being over-optimistic, which may encourage poor decision making that can lead to negative economic impacts (e.g., lowered crop yields).” Financial supporters for this research include Ontario Research Funds. Our news correspondents obtained a quote from the research from University of Ottawa: “In fact, most machine learning research involving spatial data, including the unmanned aerial vehicle (UAV) imagery- based yield prediction literature, ignore spatial structure and likely obtain over-optimistic results. The present work is a UAV imagery-based corn yield prediction study that analyzed the effects of image spatial and spectral resolution, image acquisition date, and model evaluation scheme on model performance. We used various spatial generalization evaluation methods, including spatial cross-validation (CV), to (a) identify over-optimistic models that overfit to the spatial structure found inside datasets and (b) estimate true model generalization performance. We compared and ranked the prediction power of 55 vegetation indices (VIs) and five spectral bands over a growing season. We gathered yield data and UAV-based multispectral (MS) and red-green-blue (RGB) imagery from a Canadian smart farm and trained random forest (RF) and linear regression (LR) models using 10-fold CV and spatial CV approaches. We found that imagery from the middle of the growing season produced the best results. RF and LR generally performed best with high and low spatial resolution data, respectively. MS imagery led to generally better performance than RGB imagery. Some of the best-performing VIs were simple ratio index(near-infrared and red-edge), normalized difference red-edge index, and normalized green index. We found that 10-fold CV coupled with spatial CV could be used to identify over-optimistic yield prediction models.”
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 Learning have been published. According to news reporting from Shenzhen, People’s Republic of China, by NewsRx journalists, research stated, “L12-strengthened Cobalt (Co)-based superalloys are promising high-temperature materials for aero-engine applications. To make first-generation Co-Al-W-based superalloys industrially viable, it’s crucial to enhance the mechanical properties and solvus temperature of the metastable L12 phase.” Funders for this research include National Natural Science Foundation of China (NSFC), Open research fund of Songshan Lake Materials Laboratory, National Key R & D Program of China, Key-Area Research and Development Program of GuangDong Province. The news correspondents obtained a quote from the research from the Harbin Institute of Technol- ogy, “Introducing additional transition metal ™ elements into the FCC matrix is a promising strategy. Although first-principles calculations are invaluable for materials design, their high computational cost and low-efficiency for the multi-component systems, particularly those doped with TM elements, limit their practical use. In this study, we combine machine learning with first-principles calculations to accelerate the predictions of atomic structure and mechanical property. Using datasets from first-principles calculations, our ML models predict the trend in element occupancy, doping position, and mechanical attributes of the L12 phase. The ML models, further refined with first principles data, efficiently predict properties for Nb-doped systems, outperforming traditional counterparts.” According to the news reporters, the research concluded: “This methodology expedites calculations and promises advancements in designing various advanced materials, including multiple-principal-element alloys.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Robotics are presented in a new report. According to news reporting out of Changzhou, People’s Republic of China, by NewsRx editors, research stated, “This paper proposed a learning algorithm for human-robot skill transfer based on dynamic movement primitive. Through the establishment of Transmission Control Protocol communication, the position trajectory and force charac- teristic of the human demonstrator while demonstrating the robot were recorded as the training data of the dynamic movement primitive model.” Our news journalists obtained a quote from the research from Hohai University, “The demonstrated skill/motion reproduction and generalization were successfully achieved through adjusting the parameters of the dynamic movement primitive model. The impedance control system investigated in this paper improved the compliance of the robot trajectory reproduction and skill generalization, and improved the robustness of robot control system in different environments. In addition, for the problem of inverse solution of redundant robots, this paper improved the dimension of constraint equations in the inverse solution of robots by training multiple dynamic movement primitive models, which guaranteed the constraints of inverse solutions of redundant robots.” According to the news editors, the research concluded: “The proposed algorithm had been validated on a simulated robot platform using MATLAB and a ROKAE xMate robot platform, respectively.” This research has been peer-reviewed.
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 Intelligence. According to news originating from Catania, Italy, by NewsRx correspondents, research stated, “The increasing use of Artificial Intelligence algorithms underscores the importance of large datasets. Recent trends highlight the need for high-quality training data, especially in scenarios where data may be outdated or insufficient.” Our news journalists obtained a quote from the research from the University of Catania, “This challenge is particularly evident in applications where sensors cannot be installed or data is limited, such as in the case of steel components widely used in various industries. To address this gap, modelbased approaches show promise by using advanced Digital Twin systems to generate synthetic data, closer to the real working scenarios, for training Artificial Intelligence algorithms. This paper introduces a novel Dynamic Reliability Digital Twin to model cumulative fatigue damage in steel components based on Wo center dot hler and Manson & Halford theory and on a Monte Carlo simulation, providing a dataset for training an AI predictor to estimate remaining useful life. The results demonstrate that machine learning algorithms yield favorable outcomes when the dataset is appropriately calibrated.” According to the news editors, the research concluded: “Therefore, a thorough understanding of the underlying physics is essential to avoid potential bias in the machine learning results.” This research has been peer-reviewed.
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