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    Anhui Agricultural of University Details Findings in Machine Learning (Ensemble Machine Learning and Shapley Additive Explanations for the Ability of C-s-h Seed s To Accelerate Cement Hydration)

    46-47页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting originating from Hefei, People's Re public of China, by NewsRx correspondents, research stated, "Due to the complexi ty of the reaction mechanism of calcium-silicate-hydrate (C-S-H) seeds in cement , the influence pattern of various factors, especially Ca/Si, on the acceleratio n ability of C-S-H seeds is controversial. This study explored the use of ensemb le machine learning and Shapley additive explanations (SHAP) to identify the pot ential relations between various factors and the acceleration effect of C-S-H se eds." Financial supporters for this research include Anhui Provincial Quality Engineer ing Project, Anhui Provincial Quality Engineering Project.
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    Study Results from School of Computer Science Update Understanding of Machine Le arning (Dynamic Malware Classification and Api Categorisation of Windows Portabl e Executable Files Using Machine Learning)

    47-48页
    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 originating from Galway, Ireland, by New sRx correspondents, research stated, "The rise of malware attacks presents a sig nificant cyber-security challenge, with advanced techniques and offline command- and-control (C2) servers causing disruptions and financial losses. This paper pr oposes a methodology for dynamic malware analysis and classification using a mal ware Portable Executable (PE) file from the MalwareBazaar repository." Financial support for this research came from School of Computer Science, Univer sity of Galway, Ireland.
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    Reports from Lebanese American University Highlight Recent Research in Artificia l Intelligence (Advanced Fractional Mathematics, Fractional Calculus, Algorithms and Artificial Intelligence with Applications in Complex Chaotic Systems)

    48-50页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting from Lebanese American University by N ewsRx journalists, research stated, "Chaos, comprehended characteristically, is the mathematical property of a dynamical system which is a deterministic mathema tical model in which time can be either continuous or discrete as a variable. Th ese respective models are investigated as mathematical objects or can be employe d for describing a target system." The news editors obtained a quote from the research from Lebanese American Unive rsity: "As a long-term aperiodic and random-like behavior manifested by many non linear complex dynamic systems, chaos induces that the system itself is inherent ly unstable and disordered, which requires the revealing of representative and a ccessible paths towards affluence of complexity and experimental processes so th at novelty, diversity and robustness can be generated. Hence, complexity theory focuses on nondeterministic systems, whereas chaos theory rests on deterministi c systems. These entailments demonstrate that chaos and complexity theory provid e a synthesis of emerging wholes of individual components rather than the orient ation of analyzing systems in isolation. Therefore, mathematical modeling and sc ientific computing are among the chief tools to solve the challenges and problem s related to complex and chaotic systems through innovative ways ascribed to dat a science with a precisely tailored approach which can examine the data applied. The complexity definitions need to be weighed over different data offering a hi ghly extensive applicability spectrum with more practicality and convenience owi ng to the fact that the respective processes lie in the concrete mathematical fo undations, which all may as well indicate that the methods are required to be ex amined thoroughly regarding their mathematical foundation along with the related methods to be applied. Furthermore, making use of chaos theory can be considere d to be a way to better understand the internal machinations of neural networks, and the amalgamation of chaos theory as well as Artificial Intelligence (AI) ca n open up stimulating possibilities acting instrumental to tackle diverse challe nges, with AI algorithms providing improvements in the predictive capabilities v ia the introduction of adaptability, enabling chaos theory to respond to even sl ight changes in the input data, which results in a higher level of predictive ac curacy. Therefore, chaos-based algorithms are employed for the optimization of n eural network architectures and training processes. Fractional mathematics, with the application of fractional calculus techniques geared towards the problems' solutions, describes the existence characteristics of complex natural, applied s ciences, scientific, engineering related and medical systems more accurately to reflect the actual state properties co-evolving entities and patterns of the sys tems concerning nonlinear dynamic systems and modeling complexity evolution with fractional chaotic and complex systems. Complexity entails holistic understandi ng of various processes through multi-stage integrative models across spanning s cales for expounding complex systems while following actuality across evolutiona ry path. Moreover, Fractional Calculus (FC), related to the dynamics of complica ted real-world problems, ensures emerging processes adopting fractional dynamics rather than the ordinary integer-ordered ones, which means the related differen tial equations feature non integer valued derivatives. Given that slight perturb ation leads to a significantly divergent future concatenation of events, pinning down the state of different systems precisely can enable one to unveil uncertai nty to some extent. Predicting the future evolution of chaotic systems can scree n the direction towards distant horizons with extensive applications in order to understand the internal machinations of neural and chaotic complex systems."
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    Reports Outline Robotics Study Results from University of Science and Technology China (Sky-worker: a Heterogeneous Dual-arm Robot With Dynamic Authority Assign ment for Live-line Working)

    50-51页
    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 Ro botics. According to news reporting from Hefei, People's Republic of China, by N ewsRx journalists, research stated, "PurposeThe current difficulties of distribu tion network working robots are mainly in the performance and operation mode. On the one hand, high-altitude power operation tasks require high load-carrying ca pacity and dexterity of the robot; on the other hand, the fully autonomous mode is uncontrollable and the teleoperation mode has a high failure rate." Financial supporters for this research include National Key R&D Pro gram of China, State Grid Anhui Science and Technology Project.
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    Southeast University Reports Findings in Machine Learning (Automated machine lea rning-based model for the prediction of pedicle screw loosening after degenerati ve lumbar fusion surgery)

    51-52页
    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 from Jiangsu, Peo ple's Republic of China, by NewsRx correspondents, research stated, "The adequac y of screw anchorage is a critical factor in achieving successful spinal fusion. This study aimed to use machine learning algorithms to identify critical variab les and predict pedicle screw loosening after degenerative lumbar fusion surgery ." Our news editors obtained a quote from the research from Southeast University, " A total of 552 patients who underwent primary transpedicular lumbar fixation for lumbar degenerative disease were included. The LASSO method identified key feat ures associated with pedicle screw loosening. Patient clinical characteristics, intraoperative variables, and radiographic parameters were collected and used to construct eight machine learning models, including a training set (80% of participants) and a test set (20 % of participants). The XGBoost model exhibited the best performance, with an AUC of 0.884 (95% C I: 0.825-0.944) in the test set, along with the lowest Brier score. Ten crucial variables, including age, disease diagnosis: degenerative scoliosis, number of f used levels, fixation to S1, HU value, preoperative PT, preoperative PI-LL, post operative LL, postoperative PT, and postoperative PI-LL were selected. In the pr ospective cohort, the XGBoost model demonstrated substantial performance with an accuracy of 83.32%. This study identified crucial variables associ ated with pedicle screw loosening after degenerative lumbar fusion surgery and s uccessfully developed a machine learning model to predict pedicle screw loosenin g."
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    Researchers at Thomas Jefferson National Accelerator Facility Release New Study Findings on Machine Learning (A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia)

    52-52页
    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 originating from Newp ort News, Virginia, by NewsRx correspondents, research stated, "Low-lying coasta l cities, exemplified by Norfolk, Virginia, face the challenge of street floodin g caused by rainfall and tides, which strain transportation and sewer systems an d can lead to personal and property damage." Our news correspondents obtained a quote from the research from Thomas Jefferson National Accelerator Facility: "While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational comp lexity renders them unsuitable for real-time applications. Using data from Norfo lk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep lea rning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)." According to the news reporters, the research concluded: "The comparison of deep learning to the random forest algorithm is motivated by the desire to utilize a machine learning architecture that allows for the future inclusion of common un certainty quantification techniques and the effective integration of relevant, m ulti-modal features."
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    New Findings Reported from Polytechnic University of Valencia Describe Advances in Intelligent Systems (Team Formation Through an Assessor: Choosing Marl Agents In Pursuit-evasion Games)

    52-53页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning - In telligent Systems have been presented. According to news reporting originating i n Valencia, Spain, by NewsRx journalists, research stated, "Team formation in mu lti-agent systems usually assumes the capabilities of each team member are known , and the best formation can be derived from that information. As AI agents beco me more sophisticated, this characterisation is becoming more elusive and less p redictive about the performance of a team in cooperative or competitive situatio ns." 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 the Polytechnic Unive rsity of Valencia, "In this paper, we introduce a general and flexible way of an ticipating the outcome of a game for any lineups (the agents, sociality regimes and any other hyperparameters for the team). To this purpose, we simply train an assessor using an appropriate team representation and standard machine learning techniques."
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    La Trobe University Reports Findings in Machine Learning (A Machine Learning-Dri ven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Ima ging)

    53-54页
    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 Bundoora, Aust ralia, by NewsRx journalists, research stated, "Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies." The news reporters obtained a quote from the research from La Trobe University, "MALDI-2 (laserinduced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies. Mass spectra acquired usi ng MALDI-2 constitute a combination of ions generated by both MALDI and MALDI-2 processes. Until now, no studies have focused on a detailed comparison between t he ion images (as opposed to the generated values) produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI) experiments. Herein, we investigated the io n images produced by both MALDI and MALDI-2 on the same tissue section using cor relation analysis (to explore similarities in ion images for ions common to both MALDI and MALDI-2) and a deep learning approach. For the latter, we used an ana lytical workflow based on the Xception convolutional neural network, which was o riginally trained for human-like natural image classification but which we adapt ed to elucidate similarities and differences in ion images obtained using the tw o MSI techniques. Correlation analysis demonstrated that common ions yielded sim ilar spatial distributions with low-correlation species explained by either poor signal intensity in MALDI or the generation of additional unresolved signals us ing MALDI-2. Using the Xception-based method, we identified many regions in the t-SNE space of spatially similar ion images containing MALDI and MALDI-2-related signals. More notably, the method revealed distinct regions containing only MAL DI-2 ion images with unique spatial distributions that were not observed using M ALDI."
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    Research from Saga University Provide New Insights into Androids (Reinforcement Learning of Bipedal Walking Using a Simple Reference Motion)

    54-55页
    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 androids is now availab le. According to news reporting from Saga, Japan, by NewsRx journalists, researc h stated, "In this paper, a novel reinforcement learning method that enables a h umanoid robot to learn bipedal walking using a simple reference motion is propos ed." Funders for this research include Japan Society For The Promotion of Science. The news correspondents obtained a quote from the research from Saga University: "Reinforcement learning has recently emerged as a useful method for robots to l earn bipedal walking, but, in many studies, a reference motion is necessary for successful learning, and it is laborious or costly to prepare a reference motion . To overcome this problem, our proposed method uses a simple reference motion c onsisting of three sine waves and automatically sets the waveform parameters usi ng Bayesian optimization. Thus, the reference motion can easily be prepared with minimal human involvement. Moreover, we introduce two means to facilitate reinf orcement learning: (1) we combine reinforcement learning with inverse kinematics (IK), and (2) we use the reference motion as a bias for the action determined v ia reinforcement learning, rather than as an imitation target."
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    Xidian University Researcher Yields New Study Findings on Robotics (Continuum Ro bots and Magnetic Soft Robots: From Models to Interdisciplinary Challenges for M edical Applications)

    55-56页
    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 originating from Xidian University by NewsRx correspondents, research stated, "This article explores the challenges of contin uum and magnetic soft robotics for medical applications, extending from model de velopment to an interdisciplinary perspective." Financial supporters for this research include China Scholarship Council. The news reporters obtained a quote from the research from Xidian University: "F irst, we established a unified model framework based on algebra and geometry. Th e research progress and challenges in principle models, data-driven, and hybrid modeling were then analyzed in depth. Simultaneously, a numerical analysis frame work for the principle model was constructed. Furthermore, we expanded the model framework to encompass interdisciplinary research and conducted a comprehensive analysis, including an in-depth case study."
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