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    New Machine Learning Research from Birla Institute of Technology and Science Des cribed (HAFedL: A Hessian-Aware Adaptive Privacy Preserving Horizontal Federated Learning Scheme for IoT Applications)

    41-41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 originating from Pilani, India, by NewsRx correspondents, research stated, “Federated Learning (FL) is a paradi gm in distributed machine learning, which has gained significant attention in th e recent years especially in the domain of Internet of Things.” Financial supporters for this research include Tih Foundation of Iot And Ioe.

    University of Maryland Reports Findings in Machine Learning (Combined Physics- a nd Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS -Hotspots)

    42-42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 out of Baltimore, Maryland, b y NewsRx editors, research stated, “Identifying druggable binding sites on prote ins is an important and challenging problem, particularly for cryptic, allosteri c binding sites that may not be obvious from X-ray, cryo-EM, or predicted struct ures. The Site-Identification by Ligand Competitive Saturation (SILCS) method ac counts for the flexibility of the target protein using all-atom molecular simula tions that include various small molecule solutes in aqueous solution.” Our news journalists obtained a quote from the research from the University of M aryland, “During the simulations, the combination of protein flexibility and com prehensive sampling of the water and solute spatial distributions can identify b uried binding pockets absent in experimentally determined structures. Previously , we reported a method for leveraging the information in the SILCS sampling to i dentify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the H otspots according to the likelihood they can accommodate drug-like molecules (e. g., molecular weight >200 Da). In the independent valida tion set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the to p 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model’s output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets.”

    Wenzhou University of Technology Reports Findings in Artificial Intelligence [Artificial intelligence (AI) -integrated educational applications and college st udents’ creativity and academic emotions: students and teachers’ perceptions and ...]

    43-43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 Artificial Intelligenc e is the subject of a report. According to news reporting originating in Zhejian g, People’s Republic of China, by NewsRx journalists, research stated, “Integrat ing Artificial Intelligence (AI) in educational applications is becoming increas ingly prevalent, bringing opportunities and challenges to the learning environme nt. While AI applications have the potential to enhance structured learning, the y may also significantly impact students’ creativity and academic emotions.” The news reporters obtained a quote from the research from the Wenzhou Universit y of Technology, “This study aims to explore the effects of AI-integrated educat ional applications on college students’ creativity and academic emotions from th e perspectives of both students and teachers. It also assessed undergraduate stu dents’ and faculty’s attitudes to AI-integrated applications. A mixed-method res earch design was used. In the first phase, a qualitative research approach was e mployed, utilizing theoretical sampling to select informants. Data were collecte d through in-depth interviews with undergraduate students and university lecture rs to gain comprehensive insights into their experiences and perceptions. A scal e was developed, validated, and administered to 120 students and faculty in the quantitative phase. Descriptive statistics was used to analyze the data. The stu dy revealed that AI applications often impose rigid frameworks that constrain cr eative thinking and innovation, leading to emotional disengagement due to AI int eractions’ repetitive and impersonal nature. Additionally, constant AI assessmen ts heightened performance anxiety, and technical frustrations disrupted the lear ning process. Conversely, AI applications stimulated creativity by introducing n ew ideas and problem-solving techniques, enhanced engagement through interactive elements, provided personalized feedback, and supported emotional well-being wi th gamified elements and constant availability. Quantitative data also verified that teachers and students have positive attitudes toward the benefits and chall enges of these applications. AI integration in educational applications has a du al-edged impact on college students’ creativity and academic emotions. While the re are notable benefits in stimulating creativity and enhancing engagement, sign ificant challenges such as creativity constraints, emotional disengagement, and performance anxiety must be addressed.”

    Bern University Hospital Reports Findings in Type 1 Diabetes (Detection of hypog lycaemia in type 1 diabetes through breath volatile organic compound profiling u sing gas chromatography-ion mobility spectrometry)

    44-45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 Nutritional and Metabo lic Diseases and Conditions - Type 1 Diabetes is the subject of a report. Accord ing to news originating from Bern, Switzerland, by NewsRx correspondents, resear ch stated, “To evaluate the relationship between breath volatile organic compoun ds (VOCs) and glycaemic states in individuals with type 1 diabetes (T1D), focusi ng on identifying specific VOCs as biomarkers for hypoglycaemia to offer a non-i nvasive diabetes-monitoring method. Ten individuals with T1D underwent induced h ypoglycaemia in a clinical setting.” Financial support for this research came from University of Bern.

    Nanjing University of Information Science and Technology (NUIST) Reports Finding s in Photoelectrochemicals (Language Model- Assisted Machine Learning, Photoelect rochemical, and First- Principles Investigation of Compatible Solvents for a ...)

    45-46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 Biotechnology - Photoe lectrochemicals is the subject of a report. According to news reporting from Nan jing, People’s Republic of China, by NewsRx journalists, research stated, “Machi ne learning and data-driven methods have attracted a significant amount of atten tion for the acceleration of the design of molecules and materials. In this stud y, a material design protocol based on multimode modeling that combines literatu re modeling, numerical data collection, textual descriptor design, genetic model ing, experimental validation, first-principles calculation, and theoretical effi ciency calculation is proposed, with a case study on designing compatible comple x solvent molecules for a halide perovskite film, which is notorious for optoele ctronic deactivation under hostile conditions, especially in water.”

    Department of Vascular and Endovascular Surgery Reports Findings in Artificial I ntelligence (Digital twin and artificial intelligence technologies for predictiv e planning of endovascular procedures)

    46-46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 Artificial Intelligenc e is the subject of a report. According to news reporting originating in Marseil les, France, by NewsRx journalists, research stated, “Current planning of aortic and peripheral endovascular procedures is based largely on manual measurements performed from the 3-dimensional reconstruction of preoperative computed tomogra phy scans. Assessment of device behavior inside patient anatomy is often difficu lt, and available tools, such as 3-dimensional-printed models, have several limi tations.” The news reporters obtained a quote from the research from the Department of Vas cular and Endovascular Surgery, “Digital twin (DT) technology has been used succ essfully in automotive and aerospace industries and applied recently to endovasc ular aortic aneurysm repair. Artificial intelligence allows the treatment of lar ge amounts of data, and its use in medicine is increasing rapidly. The aim of th is review was to present the current status of DTs combined with artificial inte lligence for planning endovascular procedures. Patient-specific DTs of the aorta are generated from preoperative computed tomography and integrate aorta mechani cal properties using finite element analysis. The same methodology is used to ge nerate 3-dimensional models of aortic stent-grafts and simulate their deployment . Post processing of DT models is then performed to generate multiple parameters related to stent-graft oversizing and apposition. Machine learning algorithms a llow parameters to be computed into a synthetic index to predict Type 1A endolea k risk. Other planning and sizing applications include custom-made fenestrated a nd branched stent-grafts for complex aneurysms. DT technology is also being inve stigated for planning peripheral endovascular procedures, such as carotid artery stenting. DT provides detailed information on endovascular device behavior.”

    University of New Mexico Health Sciences Center Reports Findings in Machine Lear ning (Machine learning elucidates electrophysiological properties predictive of multi- and single-firing human and mouse dorsal root ganglia neurons)

    47-48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 Albuquerque, New Mexico, by NewsRx correspondents, research stated, “Human and mouse dorsal root ganglia (hDRG and mDRG) neurons are important tools in understanding the mo lecular and electrophysiological mechanisms that underlie nociception and drive pain behaviors. One of the simplest differences in firing phenotypes is that neu rons are single-firing (exhibit only one action potential) or multi-firing (exhi bit 2 or more action potentials).” Financial support for this research came from HHS | National Institutes of Healt h.

    University of Haifa Researcher Updates Knowledge of Machine Learning (Automatic Era Identification in Classical Arabic Poetry)

    48-49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 ar tificial intelligence. According to news originating from Haifa, Israel, by News Rx correspondents, research stated, “The authenticity of classical Arabic poetry has long been challenged by claims that some part of the pre-Islamic poetic her itage should not be attributed to this era. According to these assertions, some of this legacy was produced after the advent of Islam and ascribed, for differen t reasons, to pre-Islamic poets.” The news reporters obtained a quote from the research from University of Haifa: “As pre-Islamic poets were illiterate, medieval Arabic literature devotees relie d on Bedouin oral transmission when writing down and collecting the poems about two centuries later. This process left the identity of the real poets who compos ed these poems and the period in which they worked unresolved. In this work, we seek to answer the questions of how and to what extent we can identify the perio d in which classical Arabic poetry was composed, where we exploit modern-day aut omatic text processing techniques for this aim. We consider a dataset of Arabic poetry collected from the diwans (‘collections of poems’) of thirteen Arabic poe ts that corresponds to two main eras: the pre- Abbasid era (covering the period between the 6th and the 8th centuries CE) and the Abbasid era (starting in the y ear 750 CE). Some poems in each diwan are considered ‘original’; i.e., poems tha t are attributed to a certain poet with high confidence. The diwans also include , however, an additional section of poems that are attributed to a poet with res ervations, meaning that these poems might have been composed by another poet and /or in another period. We trained a set of machine learning algorithms (classifi ers) in order to explore the potential of machine learning techniques to automat ically identify the period in which a poem had been written. In the training pha se, we represent each poem using various types of features (characteristics) des igned to capture lexical, topical, and stylistic aspects of this poetry. By trai ning and assessing automatic models of period prediction using the ‘original’ po etry, we obtained highly encouraging results, measuring between 0.73-0.90 in ter ms of F1 for the various periods. Moreover, we observe that the stylistic featur es, which pertain to elements that characterize Arabic poetry, as well as the ot her feature types, are all indicative of the period in which the poem had been w ritten.”

    Universidade Federal de Sao Paulo Reports Findings in Artificial Intelligence (A rtificial Intelligence, the Production of Scientific Texts, and the Implications for Sleep Science: Exploring Emerging Paradigms and Perspectives)

    49-49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 Artificial Intelligenc e is the subject of a report. According to news reporting originating from Sao P aulo, Brazil, by NewsRx correspondents, research stated, “The emergence of artif icial intelligence (AI) has revolutionized many fields, including natural langua ge processing, and marks a potential paradigm shift in the way we evaluate knowl edge. One significant innovation in this area is ChatGPT, a large language model based on the GPT-3.5 architecture created by OpenAI, with one of its main aims being to aid in general text writing, including scientific texts.” Our news editors obtained a quote from the research from Universidade Federal de Sao Paulo, “Here, we highlight the challenges and opportunities related to usin g generative AI and discuss both the benefits of its use, such as saving time by streamlining the writing process and reducing the amount of time spent on munda ne tasks, and the potential drawbacks, including concerns regarding the accuracy and reliability of the information generated and its ethical use. In respect of both education and the writing of scientific texts, clear rules and objectives and institutional principles must be established for the use of AI. We also cons ider the positive and negative effects of the use of AI technologies on interper sonal interactions and behavior, and, as sleep scientists, its potential impacts on sleep.”

    Reports on Robotics Findings from Xi’an University of Science and Technology Pro vide New Insights (Visual SLAM keyframe selection method with multiple constrain ts in underground coal mines)

    50-50页
    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 robotics. According to news reporting from Xi’an, People’s Republic of China, by NewsRx journalists, research stated, “The significant demand of coal mine intel ligence has put forward some higher requirements for the intelligent perception of underground mobile robots in coal mines, and the Visual Simultaneous Localiza tion and Mapping (VSLAM) is a key technology for the intelligent perception of c oal mine robots. However, due to unstructured environmental features, weak textu res, uneven illumination, and small space in underground coal mines, the existin g methods that rely on heuristic thresholds for keyframe selection cannot meet t he localization and mapping requirements of visual SLAM in underground coal mine s.” The news correspondents obtained a quote from the research from Xi’an University of Science and Technology: “Therefore, a visual SLAM keyframe selection method with multiple constraints in underground coal mines was proposed, which achieves a real-time and robust pose estimation of mobile robot in coal mines and provid es data for digital twin in coal mines. Firstly, the proposed method was constra ined according to geometric structure, adaptive thresholding was used instead of static heuristic thresholding for keyframe selection to achieve the effectivene ss and robustness of keyframe selection. Secondly, the distribution of effective feature points was homogenized by the balance of gravity principle to further e nsure the stability of keyframe selection and the denseness and accuracy of crea ted map points. Finally, the steering place was further constrained by using the heading angle threshold to reduce the impact of viewpoint abrupt change on the visual SLAM accuracy. In order to verify the effectiveness of the proposed metho d, an experimental analysis was conducted in indoor scenes and underground coal mines respectively using an independently designed mobile robot data acquisition platform. Then, the qualitative and quantitative evaluations were made from Abs olute Trajectory Error (ATE) and Root Mean Square Error (RMSE).”