查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on robotics are presented i n a new report. According to news originating from Tampere, Finland, by NewsRx e ditors, the research stated, “The Robot Operating System (ROS) has significantly gained popularity among robotic engineers and researchers over the past five ye ars, primarily due to its powerful infrastructure for node communication, which enables developers to build modular and large robotic applications. However, ROS presents a steep learning curve and lacks the intuitive usability of vendor-spe cific robotic Graphical User Interfaces (GUIs).” Funders for this research include European Union’s Horizon 2020 Research And Inn ovation Program Through The Project Entitled Robotic Technologies For The Manipu lation of Complex Deformable Linear Objects. Our news editors obtained a quote from the research from Tampere University: “Mo reover, its modular and distributed nature complicates the control and monitorin g of extensive systems, even for advanced users. To address these challenges, th is paper introduces a novel, highly adaptable and reconfigurable webbased GUI f or intuitively controlling, monitoring, and configuring complex ROS-based roboti c systems. The GUI leverages ROSBridge and roslibjs to ensure seamless communica tion with ROS systems via topics and services. Special attention has been paid t o enable the easy reconfiguration of the GUI and to minimize the modifications r equired to integrate it with new robotic systems. Thus, it has been designed as a versatile platform, which allows for the selective incorporation of modular fe atures to accommodate diverse robotic systems and applications. A predefined tem plate has been defined to ensure the compatibility of the developed features wit h the GUI platform. Additionally, an initial set of commonly used features in ro botic applications is presented.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in robotic s. According to news reporting out of Tokyo, Japan, by NewsRx editors, research stated, “People read human characteristics into the design of social robots, a v isual process with socio-cultural implications.” Our news journalists obtained a quote from the research from Tokyo Institute of Technology: “One factor may be nationality, a complex social characteristic that is linked to ethnicity, culture, and other factors of identity that can be embe dded in the visual design of robots. Guided by social identity theory (SIT), we explored the notion of “mukokuseki,” a visual design characteristic defined by t he absence of visual cues to national and ethnic identity in Japanese cultural e xports. In a two-phase categorization study (n=212), American (n=110) and Japane se (n=92) participants rated a random selection of nine robot stimuli from Ameri ca and Japan, plus multinational Pepper. We found evidence of made-in and two ki nds of mukokuseki effects.”
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 neural comput ation have been published. According to news reporting originating from the Univ ersity of Otago by NewsRx correspondents, research stated, “We discuss prototype formation in the Hopfield network. Typically, Hebbian learning with highly corr elated states leads to degraded memory performance.” Our news correspondents obtained a quote from the research from University of Ot ago: “We show that this type of learning can lead to prototype formation, where unlearned states emerge as representatives of large correlated subsets of states , alleviating capacity woes. This process has similarities to prototype learning in human cognition. We provide a substantial literature review of prototype lea rning in associative memories, covering contributions from psychology, statistic al physics, and computer science. We analyze prototype formation from a theoreti cal perspective and derive a stability condition for these states based on the n umber of examples of the prototype presented for learning, the noise in those ex amples, and the number of nonexample states presented. The stability condition i s used to construct a probability of stability for a prototype state as the fact ors of stability change. We also note similarities to traditional network analys is, allowing us to find a prototype capacity. We corroborate these expectations of prototype formation with experiments using a simple Hopfield network with sta ndard Hebbian learning.”
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 Karlsru he, Germany, by NewsRx journalists, research stated, “Approaches aimed at regula ting artificial intelligence (AI) include a particular form of risk regulation, i.e. a risk-based approach. The most prominent example is the European Union’s A rtificial Intelligence Act (AI Act).” Funders for this research include Projekt DEAL, Federal Ministry of Education & Research (BMBF). The news reporters obtained a quote from the research from the Karlsruhe Institu te of Technology (KIT), “This article addresses the challenges for adequate risk regulation that arise primarily from the specific type of risks involved, i.e. risks to the protection of fundamental rights and fundamental societal values. T his is mainly due to the normative ambiguity of such rights and societal values when attempts are made to select, interpret, specify or operationalise them for the purposes of risk assessments and risk mitigation. This is exemplified by (1) human dignity, (2) informational self-determination, data protection and privac y, (3) anti-discrimination, fairness and justice, and (4) the common good. Norma tive ambiguities require normative choices, which are assigned to different acto rs under the regime of the AI Act. Particularly critical normative choices inclu de selecting normative concepts by which to operationalise and specify risks, ag gregating and quantifying risks (including the use of metrics), balancing value conflicts, setting levels of acceptable risks, and standardisation.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting out of Maribor, Slovenia, by NewsRx edit ors, research stated, “In solving the trust issues surrounding machine learning algorithms whose reasoning cannot be understood, advancements can be made toward the integration of machine learning algorithms into mHealth applications. The a im of this paper is to provide a transparency layer to black-box machine learnin g algorithms and empower mHealth applications to maximize their efficiency.” Our news journalists obtained a quote from the research, “Using a machine learni ng testing framework, we present the process of knowledge transfer between a whi te-box model and a black-box model and the evaluation process to validate the su ccess of the knowledge transfer. The presentation layer of the final output of t he base white-box model and the knowledge-infused white-box model shows clear di fferences in reasoning. The correlation between the base black-box model and the new knowledge-infused model is very high, indicating that the knowledge transfe r was successful. There is a clear need for transparency in digital health and h ealth in general.” According to the news editors, the research concluded: “Adding solutions to the toolbox of explainable artificial intelligence is one way to gradually decrease the obscurity of black-box models.” This research has been peer-reviewed.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Lung Cancer is the subject of a report. According to news reporting out of Jinan, People’s Republic of China, by NewsRx editors, research stated, “To develop and compare t hree different machine learning-based models of clinical information and integra ted radiomics features predicting the local recurrence of stage Ia lung adenocar cinoma after microwave ablation (MWA) for assisting clinical decision-making. Th e data of 360 patients with stage Ia lung adenocarcinoma receiving MWA were incl uded in the training (n = 208), internal test (n = 90), and external test (n = 6 4) sets based on the inclusion and exclusion criteria.” Our news journalists obtained a quote from the research from the Qilu Hospital o f Shandong University, “The predictors associated with local recurrence were ide ntified using univariate and multivariate analyses of clinical information. The integrated radiomics features were extracted from pre-MWA and post-MWA (scanned immediately after the ablation) CT images, and ten radiomics features were selec ted by the t-test and least absolute shrinkage and selection operator (LASSO). T he L2-logistic regression of machine learning was applied for the clinical model , CT radiomics model and combined model including clinical predictors and radiom ics features. Model performance was evaluated by the receiver operating characte ristic (ROC) and decision curve analysis (DCA). The ablative margin was an indep endent clinical predictor (p = 0.001, odds ratio [OR] = 0.46, 95%CI: 0.29, 0.73). The combined model showed the highest a rea under the curve (AUC) value among the three models (training: 0.86, 95% CI: 0.81, 0.91; internal test: 0.93, 95%CI: 0.87, 0.98; external te st: 0.89, 95%CI: 0.79, 0.96).”
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 artificial in telligence have been published. According to news reporting out of Naples, Italy , by NewsRx editors, research stated, “In the field of veterinary medicine, the detection of parasite eggs in the fecal samples of livestock animals represents one of the most challenging tasks, since their spread and diffusion may lead to severe clinical disease.” The news journalists obtained a quote from the research from University of Naple s Federico II: “Nowadays, the scanning procedure is typically performed by physi cians with professional microscopes and requires a significant amount of time, d omain knowledge, and resources. The Kubic FLOTAC Microscope (KFM) is a compact, low-cost, portable digital microscope that can autonomously analyze fecal specim ens for parasites and hosts in both field and laboratory settings. It has been s hown to acquire images that are comparable to those obtained with traditional op tical microscopes, and it can complete the scanning and imaging process in just a few minutes, freeing up the operator’s time for other tasks. To promote resear ch in this area, the first AI-KFM challenge was organized, which focused on the detection of gastrointestinal nematodes (GINs) in cattle using RGB images. The c hallenge aimed to provide a standardized experimental protocol with a large numb er of samples collected in a well-known environment and a set of scores for the approaches submitted by the competitors.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news originating from Urumqi, People’s Republic of China , by NewsRx correspondents, research stated, “Evapotranspiration (ET), net ecosy stem productivity (NEP), and ecosystem water use efficiency (EWUE) of forests ar e changing due to climate change. Traditional models using coarse-scale climate reanalysis data fail to capture local meteorological and hydrological conditions accurately.” Funders for this research include Tianshan Talent Cultivation, Key Projects of t he Natural Science Foundation of Xinjiang Autonomous Region, National Natural Sc ience Foundation of China (NSFC), Chinese Academy of Sciences, High-End Foreign Experts project of China.
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 Beijing, Peopl e’s Republic of China, by NewsRx editors, the research stated, “The integrated n avigation system based on the Global Navigation Satellite System (GNSS) in conju nction with the strapdown inertial navigation system (SINS) and the Doppler Velo city Logger (DVL) is essential for accurate and long-distance navigation in mari time environments.” Financial supporters for this research include Basic Science Center Program of T he National Natural Science Foundation of China; China Postdoctoral Science Foun dation. The news reporters obtained a quote from the research from Tsinghua University: “However, the error of the integrated navigation system gradually diverges due t o the inevitable velocity measurement error of DVL when GNSS outages occur. To e nsure the high navigational accuracy and stability of SINS, it is necessary to d ynamically adjust the damping state of SINS provided externally. In this paper, we have developed a novel method for damping state switching based on machine le arning with SINS. We construct a model of the change in reference velocity error and use sliding window technology to obtain the reference velocity error for mo del training. Before training, the digital compass loop is designed to process a nd highlight the change in reference velocity change errors. In order to reduce the impact of the damping switching, a variable damping system is used to transf orm the traditional one-time switching of the damping coefficient into a gradual switching, effectively reducing the impact of a sudden change in the damping co efficient on the system.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Neurodegenerative Dise ases and Conditions - Alzheimer Disease is the subject of a report. According to news reporting originating from Zhejiang, People’s Republic of China, by NewsRx correspondents, research stated, “Various machine learning (ML) models based on restingstate functional MRI (Rs-fMRI) have been developed to facilitate differ ential diagnosis of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) . However, the diagnostic accuracy of such models remains understudied.” Financial support for this research came from Zhejiang Traditional Chinese Medic ine Science and Technology Program. Our news editors obtained a quote from the research from Ningbo Medical Center L ihuili Hospital, “Therefore, we conducted this systematic review and meta-analys is to explore the diagnostic accuracy of Rs-fMRI-based radiomics in differentiat ing MCI from AD. PubMed, Embase, Cochrane, and Web of Science were searched from inception up to February 8, 2024, to identify relevant studies. Meta-analysis w as conducted using a bivariate mixed-effects model, and sub-group analyses were carried out by the types of ML tasks (binary classification and multi-class clas sification tasks). In total, 23 studies, comprising 5,554 participants were enro lled in the study. In the binary classification tasks (twenty studies), the diag nostic accuracy of the ML model for AD was 0.99 (95%CI: 0.34 1.00 ), with a sensitivity of 0.94 (95 %CI: 0.89 0.97) and a specificit y of 0.98 (95%CI: 0.95 1.00). In the multi-class classification t asks (six studies), the diagnostic accuracy of the ML model was 0.98 (95% CI: 0.98 0.99) for NC, 0.96 (95 %CI: 0.96 0.96) for early mild c ognitive impairment (EMCI), 0.97 (95%CI: 0.96 0.97) for late mild cognitive impairment (LMCI), and 0.95 (95%CI: 0.95 0.95) for AD. The Rs-fMRI-based ML model can be adapted to multi-class classification tasks.”
NETL
NSTL