查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting from Leon, Spain, by NewsRx journalists, research stated, “Determining the maliciousness of a cybersecurity incident is essential to establish effective measures against it. To process large volumes of data in an automated way, machine learning techniques are commonly applied to the problem.” The news correspondents obtained a quote from the research from the University of Leon, “One of the main obstacles to apply machine learning effectively is that the data distribution is not stationary, so a model trained on old data tends to degrade as new data with a different distribution is processed. This change in the distribution of data over time is known as concept drift and affects the reports of new events, which may compromise model performance. To tackle this problem this paper evaluates the effectiveness of transfer learning techniques in reducing the impact of concept drift on the performance of models for assigning maliciousness to IPs. We compare this approach with the application of online-updated models, which are another common approach to adapt to concept drift in the data.”
查看更多>>摘要：New study results on artificial intelligence have been published. According to news reporting out of the Institute of State and Law by NewsRx editors, research stated, “The article examines various aspects of responsibility for irresponsible space activities related to the use of artificial intelligence.” The news journalists obtained a quote from the research from Institute of State and Law: “It is stated that the existing legal instruments for regulating AI do not allow us to fully make space law effective and compatible with this technology, as well as the risks that this technology can bring. An analysis of the Convention on International Liability for Damage Caused by Space Objects (1971) is carried out, an interim conclusion is made that its norms apply to spacecraft that used AI technology. It is found that the ‘problem of many hands’ inherent in space activity leads to difficulties in tracing the causal relationship between the action of a particular person in the chain of creation, maintenance, use of AI, etc. and the damage caused by an autonomous spacecraft.”
查看更多>>摘要：New research on Surgery - Surgical Technology is the subject of a report. According to news reporting originating in Massa, Italy, by NewsRx journalists, research stated, “Partial nephrectomy (PN) represents a procedure where the use of a robot has further enabled successful completion of this complex surgery. The results of this procedure using Versius Robotic Surgical System (VRSS) still need to be evaluated.” The news reporters obtained a quote from the research from the Department of Urology, “Our working group described the technique and reported the initial results of a series of PN using VRSS. We presented our setting, surgical technique and outcomes for PN, using VRSS. Between 2022 and 2023, 15 patients underwent PN performed by two surgeons in two different centers. Fifteen patients underwent PN. The median lesion size identified on preoperative imaging was 4 (IQR 2.3-5) cm. Median PADUA score was 8 (IQR 7-9). Two procedures were converted to radical nephrectomy for enhanced oncological disease control. Of the 13 nephrectomies that were completed as partial, 7 were performed clampless and 6 with warm ischemia clamping. Median clamping time was 10 (IQR 9-11) minutes. No procedure was converted to open. Median blood loss was 200 (IQR 100-250) mL. Median total operative time was 105 (IQR 100- 110) minutes. Median console time was 75 (IQR 66-80) minutes. Median set-up time was 13 (IQR 12-14) minutes. No intraoperative complications were reported. The median hospitalization time was 4 (IQR 3.5-4) days. None of the patients were transfused and none of the patients required readmission. In a pathology report, one patient had a positive surgical margin.”
查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news reporting from Dhahran, Saudi Arabia, by NewsRx journalists, research stated, “AC microgrids are becoming increasingly important for providing reliable and sustainable power to communities. However, the evolution of distribution systems into microgrids has changed the way they respond to faults and hence their protection requirements.” Funders for this research include Interdisciplinary Research Center in Sustainable Energy Systems (Ircses), King Fahd University of Petroleum And Minerals; Saudi Data And Ai Authority (Sdaia) And Kfupm Under Sdaia-kfupm Joint Research Center For Artificial Intelligence. Our news correspondents obtained a quote from the research from King Fahd University of Petroleum and Minerals: “Faults in microgrids could hinder operation stability and damage the system components. The types, locations, and resistances of faults, as well as microgrid operation modes, distributed generation penetration levels, load changes, and system topologies, all affect how the microgrid responds to faults. In order to offer quick restoration and to protect the microgrid components, fault detection and classification are therefore essential for microgrids. In this direction, unconventional methods such as artificial intelligence have been increasing in popularity over the last years. Pattern recognition is a methodology that machine learning as an approach to artificial intelligence is concerned with. The combination of protection with machine learning may be motivating in order to achieve the goal of intelligent operation in the smart grid. In this paper, fault detection, classification and location methods are reviewed for microgrid application. Different methods applied for both fault location and fault classification are being classified by the implemented technique. Such methods are explained and analyzed providing the main advantages and disadvantages of each category.”
查看更多>>摘要：Researchers detail new data in artificial intelligence. According to news reporting out of the Universidad San Francisco de Quito by NewsRx editors, research stated, “In this research we have developed experimental designs of the ultimatum game with supervised agents.” Our news editors obtained a quote from the research from Universidad San Francisco de Quito: “This agents have unbiased and biased thinking depending on the case. We used Reinforcement Learning and Bucket Brigade to program the artficial agentes. We used simulations and behavior comparison to answer the following questions: Does artificial intelligence reach a perfect subgame equilibrium in the ultimatum game experiment? How would Artificial Intelligence behave in the Ultimatum Game experiment if biased thinking is included in it? This exploratory analysis showed one important result: artificial inteligence by itself doesn´t reach a perfect subgame equilibrium. Whereas, the experimental designs with biased thinking agents quickly converge to an equilibrium.”
查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting from Chicago, Illinois, by NewsRx journalists, research stated, “Despite their heightening popularity and potential individual and societal implications, little scientific attention has been given to the news coverage of direct-to-consumer genetic testing (DTC-GT) kits (e.g. 23andMe, AncestryDNA, and MyHeritage). To accommodate, this study leverages the Analysis of Topic Model Networks to inductively develop a comprehensive overview of how major U.S. news media framed DTC-GT kits between 2009 and 2022, and then explicates the relationship of such coverage with kit sales between 2012 and 2019.” The news correspondents obtained a quote from the research from Northwestern University, “We find four frames through which the news media talk about the kits: Utility, Scientific, Political-Economic, and Commercial. Further, as kit sales increase over time, news media maintain a strong emphasis on the utility of kits in their coverage, but also increasingly promote kits through product reviews.”
查看更多>>摘要：A new study on Robotics - Robotics and Automation is now available. According to news reporting from Ultimo, Australia, by NewsRx journalists, research stated, “This letter introduces a novel method to estimate distance fields from noisy point clouds using Gaussian Process (GP) regression. Distance fields, or distance functions, gained popularity for applications like point cloud registration, odometry, SLAM, path planning, shape reconstruction, etc.” Financial support for this research came from Australian Research Council. The news correspondents obtained a quote from the research from the University of Technology Sydney, “A distance field provides a continuous representation of the scene defined as the shortest distance from any query point and the closest surface. The key concept of the proposed method is the transformation of a GPinferred latent scalar field into an accurate distance field by using a reverting function related to the kernel inverse. The latent field can be interpreted as a smooth occupancy map. This letter provides the theoretical derivation of the proposed method as well as a novel uncertainty proxy for the distance estimates. The improved performance compared with existing distance fields is demonstrated with simulated experiments. The level of accuracy of the proposed approach enables novel applications that rely on precise distance estimation: this work presents echolocation and mapping frameworks for ultrasonic-guided wave sensing in metallic structures. These methods leverage the proposed distance field with a physics-based measurement model accounting for the propagation of the ultrasonic waves in the material.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news originating from Sheffield, United Kingdom, by NewsRx correspondents, research stated, “Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for routinely measuring total kidney volume (TKV).” Our news journalists obtained a quote from the research from Sheffield Teaching Hospitals NHS Foundation Trust, “An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed. The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan. Our AI-based algorithm demonstrates performance comparable to manual segmentation.”
查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting from Gyeongsang National University by NewsRx journalists, research stated, “This study assessed the performance of single and modified algorithms based on machine learning and deep learning for wastewater treatment process. More specifically, this study adopted support vector machine (SVM), random forest (RF), and artificial neural network (ANN) for machine learning as well as long short-term memory (LSTM) for deep learning.” Financial supporters for this research include Rural Development Administration. Our news journalists obtained a quote from the research from Gyeongsang National University: “The performance of these (single) algorithms were compared with that of modified ones processed through hyperparameter tuning, ensemble learning (only for machine learning), and multi-layer stacking (i.e., two layers of LSTM units). The daily effluent of wastewater treatment process observed between 2017 and 2022 in the Cheong-Ju National Industrial Complex was used as input to all tested algorithms, which was evaluated with respect to mean squared error. For the model performance evaluation, discharge and biochemical oxygen demand are selected as dependent variables out of nine measured parameters. Results showed that the performance of any machine learning algorithms was superior to their competitor LSTM. This is mainly attributed to a small amount of input data provided to the LSTM algorithm and unstable effluent wastewater characteristics. Meanwhile, hyperparameter tuning improved the performance of all tested algorithms.”
查看更多>>摘要：A new study on Robotics is now available. According to news reporting out of Birmingham, United Kingdom, by NewsRx editors, research stated, “For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement.” Funders for this research include Honda Research Institute Europe GmbH, UK Research & Innovation (UKRI), EPSRC through the Robotics and Artificial Intelligence for Nuclear (RAIN) hub, Engineering & Physical Sciences Research Council (EPSRC). Our news journalists obtained a quote from the research from the University of Birmingham, “In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically.”
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