查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating in Aachen, Germany, by NewsRx journalists, research stated, “In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data.” Funders for this research include German Research Foundation (DFG), RWTH Junior Principal Investigator Fellowship under Germany's Excellence Strategy, Vienna Science and Technology Fund (WWTF), Bavarian state government, Hightech Agenda Bavaria. The news reporters obtained a quote from the research from RWTH Aachen University, “Here, we give a comprehensive overview of the algorithm's use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm's connection to (permutation- )equivariant neural architectures.” According to the news reporters, the research concluded: “Moreover, we give an overview of current applications and future directions to stimulate further research.”
查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting out of Lausanne, Switzerland, by NewsRx editors, research stated, “This article summarizes the recent advancements in the design, fabrication, and control of microrobotic devices for the diagnosis and treatment of brain disorders.” Financial support for this research came from EPFL Lausanne. Our news journalists obtained a quote from the research from the Swiss Federal Institute of Technology Lausanne (EPFL), “With a focus on diverse actuation methods, we discuss how advancements in materials science and microengineering can enable minimally invasive and safe access to brain tissue. From targeted drug delivery to complex interfacing with neural circuitry, these innovative technologies offer great clinical potential.” According to the news editors, the research concluded: “The article also underscores the importance of device mechanics for minimizing tissue damage and the growing role of advanced manufacturing techniques for maximizing functionality, offering an up-to-date multidisciplinary perspective on this rapidly evolving field.”
查看更多>>摘要：A new study on artificial intelligence is now available. According to news originating from Federal University by NewsRx correspondents, research stated, “The goal of the project is to develop a model to forecast the Foreign Exchange (FOREX) prices of United State Dollar to Nigerian Naira (USD/NGN), utilizing two machine learning algorithms, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).” The news reporters obtained a quote from the research from Federal University: “These were chosen for this study because they have been found to be effective in previous studies that have been examined. The principles of machine learning and its applications, as well as the many machine learning techniques and algorithms will be covered in this study. Additionally, various extraction methods that will be used in the study will be presented. Data from the Investing.com dataset would be retrieved for this study's purpose and divided into training and test sets. Using the two machine learning techniques previously mentioned, the model would be trained and tested. Then, to measure the model's performance in terms of accuracy and precision, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error would be utilized."
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Provo, Utah, by NewsRx correspondents, research stated, “Zr metallocenes have significant potential to be highly tunable polyethylene catalysts through modification of the aromatic ligand framework. Here we report the development of multiple machine learning models using a large library (>700 systems) of DFT-calculated zirconocene properties and barriers for ethylene polymerization.” Our news editors obtained a quote from the research from Brigham Young University, “We show that very accurate machine learning models are possible for HOMO-LUMO gaps of precatalysts but the performance significantly depends on the machine learning algorithm and type of featurization, such as fingerprints, Coulomb matrices, smooth overlap of atomic positions, or persistence images. Surprisingly, the description of the bonding hapticity, the number of direct connections between Zr and the ligand aromatic carbons, only has a moderate influence on the performance of most models. Despite robust models for HOMO-LUMO gaps, these types of machine learning models based on structure connectivity type features perform poorly in predicting ethylene migratory insertion barrier heights. Therefore, we developed several relatively robust and accurate machine learning models for barrier heights that are based on quantum-chemical descriptors (QCDs). The quantitative accuracy of these models depends on which potential energy surface structure QCDs were harvested from. This revealed a Hammett-type principle to naturally emerge showing that QCDs from the p-coordination complexes provide much better descriptions of the transition states than other potential-energy structures.”
查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting out of Guangzhou, People's Republic of China, by NewsRx editors, research stated, “Accurately estimating commuting flow is essential for optimizing urban planning and traffic design. The latest graph neural network (GNN) model with the encoder-decoder-predictor components has several limitations.” Funders for this research include National Natural Science Foundation of China (NSFC), Guangdong Basic and Applied Basic Research Foundation, Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai). Our news journalists obtained a quote from the research from Sun Yat-sen University, “First, it ignores the temporal dependency of node features for node embedding. Second, different estimation methods used in the decoder and predictor make it difficult to distinguish the contribution of node embedding or estimation method to flow estimation. Third, finer-grained socio-economic features of nodes are difficult to obtain due to low data availability. To address these problems, this study proposes a fusion model of temporal graph attention network and machine learning (TGAT-ML) to infer commuting flow from dynamic human activity intensity distribution. The model first constructs a commuting network with temporal human activity intensity as node features. A temporal graph attention network is then developed to capture the spatiotemporal dependency. The learned node embedding is generated by using a machine learning method in the decoder. Finally, based on learned node embedding and machine learning method used in the decoder, the commuting flow intensity is estimated. Results from an empirical study using the Baidu heat map data of Guangzhou city indicate that the proposed fusion model TGAT-ML outperforms all other baseline models. This study proves that the model performance can be significantly enhanced by determining the edge existence through commuting time-based approach, integrating temporal convolution with graph convolution, and unifying flow estimation method in both decoder and predictor.”
查看更多>>摘要：New research on robotics is the subject of a new report. According to news originating from Modena, Italy, by NewsRx editors, the research stated, “This paper addresses the crucial challenge of maintaining the directed graph topology in multi-robot systems, particularly when operating under limited field-of-view constraints and with a lack of communication among robots.” Funders for this research include Technology Innovation Institute. The news reporters obtained a quote from the research from University of Modena and Reggio Emilia: “Traditional methods for multi-robot coordination rely heavily on inter-robot communication, which may not always be feasible, particularly in constrained or hostile environments. Our work presents a novel distributed control algorithm that leverages Control Barrier Functions (CBFs) to maintain the graph topology of a multi-robot system based solely on local, onboard sensor data. This approach is particularly beneficial in situations where external communication channels are disrupted or unavailable. The key contributions of this research are threefold: First, we design a novel control algorithm that efficiently maintains the graph topology in multi-robot systems using CBFs, which operate on neighbor detection data. Second, we perform an experimental evaluation of the algorithm, demonstrating its efficacy in controlling the flight of a team of drones using only local robot data.”
查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating in Lawrence, Kansas, by NewsRx journalists, research stated, “Hydrogen exchange-mass spectrometry (HX-MS) is a valuable analytical technique that can provide insight into protein interactions and structure. The deuterium labeling necessary to gain this insight is affected by many physical and chemical factors, making it challenging to achieve high reproducibility.” The news reporters obtained a quote from the research from the University of Kansas, “Poor precision during dispensing, transfer, and mixing of solutions during the experiment contributes substantially to the overall variability. While the use of a robotic liquid handler can potentially improve precision, its operation must be optimized. We observed poor precision in data collected using a robotic liquid handler to perform HX-MS. In this work, we describe how we were able to improve that system's precision considerably based on tracking performance using caffeine, caffeine-, and caffeine- as tracers for the sample, label, and quench to report on each operation of the liquid handling workflow. The insights gained about liquid handler performance and the three-tracer approach can aid in optimizing HX-MS workflow operations, whether performed manually or when using a liquid handling system.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Shenzhen, People's Republic of China, by NewsRx correspondents, research stated, “High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis.” Financial supporters for this research include Basic and Applied Basic Research Foundation of Guang- dong Province, Science and Technology Foundation of Shenzhen City, Chinese Academy of Sciences, National Key Research and Development Program of China, National Natural Science Foundation of China.
查看更多>>摘要：Data detailed on robotics have been presented. According to news reporting out of Innopolis, Russia, by NewsRx editors, research stated, “The control of deformable linear objects (DLOs) such as cables presents a significant challenge for robotic systems due to their unpredictable behavior during manipulation.” Our news journalists obtained a quote from the research from Innopolis University: “This paper introduces a novel approach for cable shape control using dual robotic arms on a two-dimensional plane. A discrete point model is utilized for the cable, and a path generation algorithm is developed to define intermediate cable shapes, facilitating the transformation of the cable into the desired profile through a formulated optimization problem. The problem aims to minimize the discrepancy between the cable configuration and the targeted shape to ensure an accurate and stable deformation process. Moreover, a cable dynamic model is developed in which the manipulation approach is validated using this model.”
查看更多>>摘要：Researchers detail new data in Robotics. According to news originating from Xi'an, People's Republic of China, by NewsRx correspondents, research stated, “An underwater hexapod robot, driven by six C-shaped legs and eight thrusters, has the potential to traverse diverse terrains with unknown deformable properties, which can lead to unknown leg-terrain interaction forces. However, it is hard to use exteroceptive sensors such as cameras and sonars to recognize these properties.” Funders for this research include National Natural Science Foundation of China (NSFC), Doctorate Foundation of Northwestern Polytechnical University. Our news journalists obtained a quote from the research from Northwestern Polytechnic University, “Here we propose a method to perceive the interaction forces and feed them into a controller for determining thrust inputs. The key idea lies in using supervised learning to obtain the properties from reliable proprioceptive sensory data. First, we propose a new expression called zero moment point (ZMP) bias that can indirectly represent the leg-terrain interaction force, removing the effects caused by gravity, buoyancy, and thrust. Second, we gather a walking cycle's discrete ZMP biases and then parameterize them as polynomials. Third, we use several previous walking cycles' parameterized biases to predict the current walking cycle's biases to generate the needed pitch and roll moments. Finally, we propose a terrain-adaptive locomotion controller for the robot, which incorporates these moments into a base control module and uses thrust to compensate for the interaction force for smooth walking.”
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