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    Investigators from Huazhong University of Science and Technology Release New Dat a on Artificial Intelligence (Investigating the Causal Effects of Affiliation Di versity On the Disruption of Papers In Artificial Intelligence)

    39-39页
    查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Artificial Intelligen ce have been presented. According to news reporting from Wuhan, People's Republi c of China, by NewsRx journalists, research stated, "Growing multiple-affiliatio n collaboration in Artificial Intelligence (AI) can help solve complex integrate d problems, but will it trigger the disruption in AI? Scholars have discussed th e related topics in other fields. However, these studies did not specifically ta rget the field of AI and primarily relied on correlation methods, which may not lead to a causal conclusion." Financial supporters for this research include Ministry of Education, China, Nat ional Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities.
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    Reports from University of Toronto Mississauga Provide New Insights into Machine Learning (Mapping Canopy Cover for Municipal Forestry Monitoring: Using Free La ndsat Imagery and Machine Learning)

    40-41页
    查看更多>>摘要：2024 OCT 03 (NewsRx)-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 originating from Mississauga, Canada, by NewsRx correspondents, research stated, "Trees across the urbanrural contin uum are recognized for their ecological importance and ecosystem services. Munic ipalities often utilize spatial canopy cover data for monitoring this resource." Financial supporters for this research include Natural Sciences and Engineering Research Council of Canada (NSERC), Department of Geography, Geomatics and Envir onment, Centre for Urban Environments at the University of Toronto Mississauga, Natural Sciences and Engineering Research Council of Canada (NSERC). Our news journalists obtained a quote from the research from the University of T oronto Mississauga, "Monitoring frameworks typically rely on fine-scale maps der ived from very high spatial resolution sensors, which are high quality but expen sive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretat ion and/or digital hemispherical photographs, can be used to effectively map can opy cover at a scale appropriate for municipal monitoring. We compare linear mod els and random forest machine learning for predicting canopy cover across a land scape (general) and within specific land covers (specialized). We create 2018 ca nopy cover maps and track progress towards forestry objectives in a region of so uthern Ontario, Canada. Random forest models using all reference data perform be st for general use (R-2: 0.90, RMSE: 10.1 %), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate wit h Landsat at a moderate strength (R-2: 0.67, RMSE: 2.73 %), and in residential areas, capturing the totality of canopy cover variation (R-2: 0.85, RMSE: 5.66 %). Accuracy was assessed with standard cross-validation , which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R-2: 0.86, RMSE: 11.4 %), indicating that cross-validation was sl ightly overoptimistic. Caledon, a rural-dominant municipality within the study a rea, is the greenest (34 % canopy cover). The two cities (Brampton and Mississauga) have 15.9 % and 17.5 % canopy cove r. Residential canopy criteria indicate ‘Good' performance in Caledon, ‘Moderate ' in Mississauga, and ‘Low' in Brampton based on our 2018 assessment."
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    Amsterdam University Medical Center Reports Findings in Artificial Intelligence (Cardiovascular care with digital twin technology in the era of generative artif icial intelligence)

    41-42页
    查看更多>>摘要：2024 OCT 03 (NewsRx)-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 originating from Amste rdam, Netherlands, by NewsRx correspondents, research stated, "Digital twins, wh ich are in silico replications of an individual and its environment, have advanc ed clinical decisionmaking and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation." Financial supporters for this research include National Institutes of Health, Du tch Research Council, EU Horizon.
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    Studies from Ontario Tech University Reveal New Findings on Machine Learning (On the Effectiveness of Feature Selection Techniques in the Context of ML-Based Re gression Test Prioritization)

    42-43页
    查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting from Oshawa, C anada, by NewsRx journalists, research stated, "Regression testing is essential for maintaining software functionality in continuous integration (CI) systems, b ut it can become increasingly costly as software complexity grows. Machine learn ing-based Regression Test Prioritization (RTP) techniques have been developed to prioritize test cases based on their likelihood of failure, aiming to detect fa ilures early and optimize resource use." Funders for this research include Ibm Center For Advanced Studies; Natural Scien ces And Engineering Research Council of Canada.
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    Researchers from University of Sciences and Technology of Oran Describe Research in Machine Learning (LLR estimation using machine learning)

    43-43页
    查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting out of the University of Sciences and Technology of Oran by NewsRx editors, research stated, "Many decod ers of error-correcting codes use the Log-Likelihood Ratio (LLR) as an input, wh ich involves the probability density function (pdf) of the noise. In impulsive n oise, the pdf of the noise is not accessible in closed form and is only availabl e through very complex numerical computation." Our news correspondents obtained a quote from the research from University of Sc iences and Technology of Oran: "Therefore, the LLR calculation for Binary Phase Shift Keying (BPSK) is too complex. It becomes even more complex for high-order modulations. Moreover, the LLR computational complexity grows as the modulation order increases. The main contribution of our work lies in the LLR approximation for high-order modulations and its estimation using supervised machine learning , without requiring prior knowledge of the noise distribution model. To this end , we propose two approaches to approximate the LLR values using supervised machi ne learning, for high-order modulated symbols. The first approach can also be us ed for BPSK modulated symbols. The second approach aims to approximate the LLR f or high-order modulated symbols in a more simplified manner compared to the firs t approach. For both approaches, we estimate the parameters of the approximate L LR under known noise channel conditions using the linear regression algorithm. T o estimate these parameters without prior knowledge of the noise distribution mo del, we use a binary logistic regression algorithm. Our simulations focus on the second proposed approach to estimate the LLR with unknown noise distributions."
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    Findings in the Area of Machine Learning Reported from Chinese Academy of Scienc es (Thermokarst Landslides Susceptibility Evaluation Across the Permafrost Regio n of the Central Qinghai-tibet Plateau: Integrating a Machine Learning Model Wit h ...)

    44-45页
    查看更多>>摘要：2024 OCT 03 (NewsRx)-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 originating from Lanzhou, People's R epublic of China, by NewsRx correspondents, research stated, "Thermokarst landsl ides (TLs), which are made up of retrogressive thaw slumps (RTSs) and active-lay er detachments slides (ALDSs), are quickly increasing in the central Qinghai-Tib et Plateau (QTP) permafrost area. TLs induce many environmental problems and thr eaten the safety of infrastructure." Funders for this research include National Natural Science Foundation of China ( NSFC), Major Science and technology project of Gansu Province, Excellent Doctora l Pro-gramme of Gansu Province. Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "A landslide susceptibility map is crucial to prevent the negative imp acts of these landslides. However, traditional thermokarst landslides susceptibi lity (TLS) evaluations do not consider real-time surface deformation information . Therefore, we propose a novel method that integrates a conventional machine le arning model with ground surface deformation. Nine influencing factors, includin g slope, normalized difference vegetation index, elevation, precipitation, thawi ng degree days, soil content, active layer thickness, water content, and vegetat ion type were selected based on the qindex detector, and support vector machine was employed to obtain an initial model (IM). We obtained surface deformation in the study area using the enhanced Small Baseline Subset (SBAS) method. The accu racy of the InSAR results was validated through comparison with data from two fi eld monitoring cross-sections. Subsequently, we established an integrated model (ITM) by combining the initial model with surface deformation using the contribu tion matrix, and confirmed the fusion model's higher rationality and accuracy th rough comparison with the IM. Furthermore, we examined the impact of the quadtre e segmentation method on atmospheric correction and validated the TLS results ob tained using the ITM with high-resolution optical remote sensing imagery from GF 6."
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    New Data from Peking University Illuminate Findings in Robotics (A Multimodal Ap proach Based On Large Vision Model for Closerange Underwater Target Localizatio n)

    45-46页
    查看更多>>摘要：2024 OCT 03 (NewsRx)-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 Beijing, Peopl e's Republic of China, by NewsRx journalists, research stated, "Underwater targe t localization uses real-time sensory measurements to estimate the position of u nderwater objects of interest, providing critical feedback information for under water robots in tasks, such as obstacle avoidance, scientific exploration, and e nvironmental monitoring. While acoustic sensing is the most acknowledged and com monly used method in underwater robots and possibly the only effective approach for long-range underwater target localization, such a sensing modality generally suffers from low resolution, high cost, and high energy consumption, thus leadi ng to a mediocre performance when applied to close-range underwater target local ization."
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    Reports Outline Robotics and Automation Study Results from Georgia Institute of Technology (Affine Transformation-based Perfectly Undetectable False Data Inject ion Attacks On Remote Manipulator Kinematic Control With Attack Detector)

    46-46页
    查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics - Robotics and Automation. According to news reporting out of Atlanta, Ge orgia, by NewsRx editors, research stated, "This letter demonstrates the viabili ty of perfectly undetectable affine transformation attacks against robotic manip ulators where intelligent attackers can inject multiplicative and additive false data while remaining completely hidden from system users." Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from the Georgia Institu te of Technology, "The attacker can implement these communication line attacks b y satisfying three Conditions presented in this work. These claims are experimen tally validated on a FANUC 6 of freedom manipulator by comparing a nominal (non- attacked) trial and a detectable attack case against three perfectly undetectabl e trajectory attack Scenarios: scaling, reflection, and shearing."
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    Data on Machine Learning Reported by Gengmo Zhou and Colleagues (Bridging Machin e Learning and Thermodynamics for Accurate pK a Prediction)

    47-47页
    查看更多>>摘要：2024 OCT 03 (NewsRx)-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 Beijing, People's Republi c of China, by NewsRx journalists, research stated, "Integrating scientific prin ciples into machine learning models to enhance their predictive performance and generalizability is a central challenge in the development of AI for Science. He rein, we introduce Uni-p , a novel framework that successfully incorporates ther modynamic principles into machine learning modeling, achieving highprecision pr edictions of acid dissociation constants (p ), a crucial task in the rational de sign of drugs and catalysts, as well as a modeling challenge in computational ph ysical chemistry for small organic molecules." The news reporters obtained a quote from the research, "Uni-p utilizes a compreh ensive free energy model to represent molecular protonation equilibria accuratel y. It features a structure enumerator that reconstructs molecular configurations from p data, coupled with a neural network that functions as a free energy pred ictor, ensuring high-throughput, data-driven prediction while preserving thermod ynamic consistency."
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    Reports from University of Miskolc Describe Recent Advances in Robotics (Implici t Understanding: Decoding Swarm Behaviors in Robots through Deep Inverse Reinfor cement Learning)

    47-48页
    查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on robotics are presented i n a new report. According to news reporting originating from the University of M iskolc by NewsRx correspondents, research stated, "Using reinforcement learning to generate the collective behavior of swarm robots is a common approach." Our news journalists obtained a quote from the research from University of Misko lc: "Yet, formulating an appropriate reward function that aligns with specific o bjectives remains a significant challenge, particularly as the complexity of tas ks increases. In this paper, we develop a deep inverse reinforcement learning mo del to uncover the reward structures that guide autonomous robots in achieving t asks by demonstrations. Deep inverse reinforcement learning models are particula rly well-suited for complex and dynamic environments where predefined reward fun ctions may be difficult to specify. Our model can generate different collective behaviors according to the required objectives and effectively copes with contin uous state and action spaces, ensuring a nuanced recovery of reward structures. We tested the model using E-puck robots in the Webots simulator to solve two tas ks: searching for dispersed boxes and navigation to a predefined position. Recei ving rewards depends on demonstrations collected by an intelligent pre-trained s warm using reinforcement learning act as an expert."
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