查看更多>>摘要：Investigators publish new report on Machine Learning Artificial Intelligence. According to news reporting originating in Madrid, Spain, by NewsRx journalists, research stated, “Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets.” Funders for this research include Spanish Government, European Union (EU), Comunidad de Madrid under Convenio Plurianual, Universidad Politecnica de Madrid in the actuation line of Programa de Excelencia para el Profesorado Universitario. The news reporters obtained a quote from the research from the Polytechnic University of Madrid, “Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values.”
查看更多>>摘要：A new study on Machine Learning is now available. According to news originating from Zografos, Greece, by NewsRx correspondents, research stated, “We developed a cluster-detection method based on the code DBSCAN to identify star clusters in the central region of the Small Magellanic Cloud (SMC). Two approaches were used to determine the values of the free parameters of DBSCAN.” Financial support for this research came from European Space Agency (ESA) space mission Gaia. Our news journalists obtained a quote from the research from the National and Kapodistrian University of Athens, “They agree well with each other and can be used in the fields that are studied without any a priori knowledge of clustering, characteristic scales, or background density. We validated the success of the DBSCAN cluster-detection method on recent cluster catalogues after introducing a cluster-classification scheme based on three diagnostics that relie on colour-magnitude diagrams and growth curves. We used data from the Magellan Telescope at the Las Campanas Observatory in Chile and from Gaia Data Release 3. As a byproduct of the validation process, we revisited objects that were classified as clusters in recent compilations. We found that 40% fail all diagnostics and most probably are not clusters.” According to the news editors, the research concluded: “DBSCAN was very successful in recovering actual clusters with high precision and recall.”
查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Guizhou, People’s Republic of China, by NewsRx correspondents, research stated, “Machine learning models perform satisfactorily in landslide displacement prediction, but they are generally black-box models that are difficult to gain the trust of decision-makers. Therefore, a three-stage prediction framework based on the Hodrick-Prescott (HP) filter, double exponential smoothing (DES), natural gradient boosting (NGBoost), and Shapley additive explanations (SHAP) was proposed.” Funders for this research include Guizhou Provincial Science and Technology Projects, Guizhou University Research Initiation Fund. Our news editors obtained a quote from the research from Guizhou Normal University, “The framework quantifies the uncertainty in the predictions and provides fully transparent outputs. In the first stage, the HP filter decomposes cumulative displacements into trend and period displacements. The second stage uses DES and NGBoost to predict them separately. In the third stage, we compute the SHAP values of the features to analyze the impact of the features on the model output. It is applied to the Bazimen and Baishuihe landslides in the Three Gorges Reservoir area and compared with other literature. The results show that the framework can achieve high accuracy in both point and interval prediction, and its performance is similar to or even better than other models. And it is easier to operate and applicable to a wider range of people. Most importantly, the framework can interpret the model, allows users to verify the consistency of the model prediction basis with the landslide evolution mechanism, and reduces random errors by optimizing the data.”
查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting out of New Delhi, India, by NewsRx editors, research stated, “The faults in photovoltaic (PV) array lead to increased system losses and even fire hazards. The most frequent faults in PV strings are line-to-line (LL) and line-to-ground (LG) faults.” Our news journalists obtained a quote from the research from the Indian Institute of Technology, “Many efforts have been made to develop machine learning-based methods that are capable of detecting faults. However, these methods do not consider low mismatch faults, high impedance faults, active MPPT control, the effect of blocking diodes, step changes in irradiation levels and partial shading conditions in a single window. In this article, a novel and efficient modified binary genetic algorithm (MBGA) based on the weighted K-nearest neighbor method, which incorporates all the abovementioned constraints, has been proposed to identify and classify faults. In addition, it also gives information about the severity of faults. Unlike other machine learning (ML)-based methods, the developed technique considers features based on both frequency and time domain and employs MBGA to extract the optimal set of features, which further improves the accuracy of the algorithm and reduces the size of the dataset. The proposed method efficiently distinguishes faults from sudden shading conditions as both have similar characteristics and prevent false detection.” According to the news editors, the research concluded: “Moreover, it has been verified that the developed method detects faults with an accuracy of 97.3% and classifies LL and LG faults with a precision of 99.25%.”
查看更多>>摘要：New study results on robotics have been published. According to news reporting out of Shenzhen, People’s Republic of China, by NewsRx editors, research stated, “Distance sensors are important for mobile robots to perceive surrounding environment.” Funders for this research include Southern University of Science And Technology (Sustech) Startup Fund; Sustech-dji Joint Laboratory Fund; Shenzhen Science And Technology Project. Our news journalists obtained a quote from the research from Southern University of Science and Technology (SUSTech): “Typical sensors like LiDARs and depth cameras have been widely used, yet each has its limitations, such as LiDARs’ relatively high cost, depth cameras’ limitation to indoor use, and their poor performance in detecting transparent objects directly. On the other hand, ultrasonic phased array that integrates multiple ultrasonic sensors not only enables 3D ranging and imaging, but also provides advantages of strong environmental adaptability, being cost-effective and being able to detect transparent objects. To explore the application of in-air ultrasonic phased arrays for mobile robots, we simulate a 40 kHz $5\times 5$ non-uniform sparse ultrasonic phased array. The simulator emulates the process of phased array transmission and reception, and utilizes algorithms such as beamforming and matched filtering to obtain depth information in three-dimensional space. Then, a multi-view indoor 3D reconstruction method fusing the ultrasonic phased array and a monocular camera is proposed, where two scanning strategies are developed to handle different scenarios. Finally, the method is validated in different Gazebo scenarios and compared with other baseline methods like LiDARs and depth cameras.”
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Brisbane, Australia, by NewsRx correspondents, research stated, “The indirect method for generating parametric images in positron emission tomography (PET) involves the acquisition and reconstruction of dynamic images and temporal modelling of tissue activity given a measured arterial input function. This approach is not robust, as noise in each dynamic image leads to a degradation in parameter estimation.” Financial support for this research came from Australian Research Council Training Centre for Innovation in Biomedical Imaging Technology. Our news journalists obtained a quote from the research from the University of Queensland, “Direct methods incorporate into the image reconstruction step both the kinetic and noise models, leading to improved parametric images. These methods require extensive computational time and large computing resources. Machine learning methods have demonstrated significant potential in overcoming these challenges. But they are limited by the requirement of a paired training dataset. A further challenge within the existing framework is the use of state-of-the-art arterial input function estimation via temporal arterial blood sampling, which is an invasive procedure, or an additional magnetic resonance imaging (MRI) scan for selecting a region where arterial blood signal can be measured from the PET image. We propose a novel machine learning approach for reconstructing high-quality parametric brain images from histoimages produced from time-of-flight PET data without requiring invasive arterial sampling, an MRI scan, or paired training data from standard field-of-view scanners. The proposed is tested on a simulated phantom and five oncological subjects undergoing an 18F-FDG-PET scan of the brain using Siemens Biograph Vision Quadra. Kinetic parameters set in the brain phantom correlated strongly with the estimated parameters (K, k and k, Pearson correlation coefficient of 0.91, 0.92 and 0.93) and a mean squared error of less than 0.0004. In addition, our method significantly outperforms (p <0.05, paired t-test) the conventional nonlinear least squares method in terms of contrast-to-noise ratio. At last, the proposed method was found to be 37% faster than the conventional method. We proposed a direct non-invasive DL-based reconstruction method and produced high-quality parametric maps of the brain. The use of histoimages holds promising potential for enhancing the estimation of parametric images, an area that has not been extensively explored thus far.”
查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting from Harbin, People’s Republic of China, by NewsRx journalists, research stated, “In order to ensure safe and comfortable human-robot navigation in close proximity, it is imperative for robots to possess the capability to understand human behavioral intention. With this objective in mind, this paper introduces a Human-Aware Navigation (HAN) algorithm.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Heilongjiang Province, State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT). The news correspondents obtained a quote from the research from the Harbin Institute of Technology, “The HAN system combines insights from studies on human detection, social behavioral model, and behavior prediction, all while incorporating social distance considerations. This information is integrated into a layer dedicated to human behavior intention cognition, achieved through the fusion of data from laser radar and Kinect sensors, employing Gaussian functions to account for individual private space and movement trend. To cater to the mapping requirements of the HAN system, we have reduced the computational complexity associated with traditional multilayer cost map by implementing a ‘first-come, first-served’ expansion method. Subsequently, we have enhanced the trajectory optimization equation by incorporating an improved dynamic triangle window method that integrates human behavior intention cognition, leading to the determination of an appropriate trajectory for the robot. Finally, experimental evaluations have been conducted to assess and validate the efficacy of the human behavior intention cognition and the HAN system.”
查看更多>>摘要：New research on Robotics is the subject of a report. According to news originating from Singapore, Singapore, by NewsRx correspondents, research stated, “Miniature locomotion robots with the ability to navigate confined environments show great promise for a wide range of tasks, including search and rescue operations. Soft miniature locomotion robots, as a burgeoning field, have attracted significant research interest due to their exceptional terrain adaptability and safety features.” Our news journalists obtained a quote from the research from the National University of Singapore, “Here, a fully-soft centimeter-scale miniature crawling robot directly powered by fluid kinetic energy generated by an electrohydraulic actuator is introduced. Through optimization of the operating voltage and design parameters, the average crawling velocity of the robot is dramatically enhanced, reaching 16 mm s. The optimized robot weighs 6.3 g and measures 5 cm in length, 5 cm in width, and 6 mm in height. By combining two robots in parallel, the robot can achieve a turning rate of 3° s. Additionally, by reconfiguring the distribution of electrodes in the electrohydraulic actuator, the robot can achieve 2 degrees-of-freedom translational motion, improving its maneuverability in narrow spaces. Finally, the use of a soft water-proof skin is demonstrated for underwater locomotion and actuation.”
查看更多>>摘要：A new study on artificial intelligence is now available. According to news originating from Washington, District of Columbia, by NewsRx correspondents, research stated, “We consider the looming threat of bad actors using artificial intelligence (AI)/Generative Pretrained Transformer to generate harms across social media globally.” Funders for this research include Air Force Office of Scientific Research; John Templeton Foundation. Our news editors obtained a quote from the research from George Washington University: “Guided by our detailed mapping of the online multiplatform battlefield, we offer answers to the key questions of what bad-actor-AI activity will likely dominate, where, when-and what might be done to control it at scale. Applying a dynamical Red Queen analysis from prior studies of cyber and automated algorithm attacks, predicts an escalation to daily bad-actor-AI activity by mid-2024-just ahead of United States and other global elections.” According to the news editors, the research concluded: “We then use an exactly solvable mathematical model of the observed bad-actor community clustering dynamics, to build a Policy Matrix which quantifies the outcomes and trade-offs between two potentially desirable outcomes: containment of future bad-actorAI activity vs. its complete removal. We also give explicit plug-and-play formulae for associated risk measures.”
查看更多>>摘要：New research on Robotics and Machine Learning is the subject of a report. According to news originating from Freiburg, Germany, by NewsRx correspondents, research stated, “Various MRI-based techniques were tested for the differentiation of neurodegenerative Parkinson syndromes (NPS); the value of these techniques in direct comparison and combination is uncertain. We thus compared the diagnostic performance of macrostructural, single compartmental, and multicompartmental MRI in the differentiation of NPS.” Financial support for this research came from Universitatsklinikum Freiburg. Our news journalists obtained a quote from the research from the University of Freiburg Medical Center, “We retrospectively included patients with NPS, including 136 Parkinson’s disease (PD), 41 multiple system atrophy (MSA) and 32 progressive supranuclear palsy (PSP) and 27 healthy controls (HC). Macrostructural tissue probability values (TPV) were obtained by CAT12. The microstructure was assessed using a mesoscopic approach by diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and diffusion microstructure imaging (DMI). After an atlas-based read-out, a linear support vector machine (SVM) was trained on a training set (n = 196) and validated in an independent test cohort (n = 40). The diagnostic performance of the SVM was compared for different inputs individually and in combination. Regarding the inputs separately, we observed the best diagnostic performance for DMI. Overall, the combination of DMI and TPV performed best and correctly classified 88% of the patients. The corresponding area under the receiver operating characteristic curve was 0.87 for HC, 0.97 for PD, 1.0 for MSA, and 0.99 for PSP.”
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