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 Erlange n, Germany, by NewsRx journalists, research stated, “Search engines often serve as a primary resource for patients to obtain drug information. However, the sear ch engine market is rapidly changing due to the introduction of artificial intel ligence (AI)-powered chatbots.” Financial support for this research came from Bundesministerium fur Bildung und Forschung.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting from Wuxi, People’s Repu blic of China, by NewsRx journalists, research stated, “Distantly supervised rel ation extraction (DSRE) aims to identify semantic relations in large-scale texts automatically labeled via knowledge base alignment. It has garnered significant attention due to its high efficiency, but existing methods are plagued by noise at both the word and sentence level and fail to address these issues adequately .” The news correspondents obtained a quote from the research from Jiangnan Univers ity: “The former level of noise arises from the large proportion of irrelevant w ords within sentences, while noise at the latter level is caused by inaccurate r elation labels for various sentences. We propose a novel multi-level noise reduc tion neural network (MLNRNN) to tackle both issues by mitigating the impact of m ultilevel noise. We first build an iterative keyword semantic aggregator (IKSA) to remove noisy words, and capture distinctive features of sentences by aggrega ting the information of keywords. Next, we implement multi-objective multi-insta nce learning (MOMIL) to reduce the impact of incorrect labels in sentences by id entifying the cluster of correctly labeled instances. Meanwhile, we leverage mis labeled sentences with cross-level contrastive learning (CCL) to further enhance the classification capability of the extractor. Comprehensive experimental resu lts on two DSRE benchmark datasets demonstrated that the MLNRNN outperformed sta te-of-the-art methods for distantly supervised relation extraction in almost all cases.”
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 reporting from Tianjin, People’s Republic of C hina, by NewsRx journalists, research stated, “In this paper, a novel Selective Compliance Assembly Robot Arm (SCARA) high-speed parallel manipulator that can r ealize three-translation and one-rotation motion is proposed, and an accurate dy namic modeling methodology is investigated. The mechanism is composed of four li mbs with a double parallelogram structure and a single moving platform.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of China (NSFC), Tianjin Research Innovation Project for Postgraduate Students.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Biomedical Engineering - Artificial Urinary Sphincter is the subject of a report. According to news re porting out of Rennes, France, by NewsRx editors, research stated, “Previous stu dies suggested better functional outcomes and longer device survival for female artificial urinary sphincter (AUS) implantation compared to male AUS implantatio n. We hypothesized that the adoption of robotic approaches for female implantati on might have influenced these comparisons.” Our news journalists obtained a quote from the research from University Hospital Rennes, “This study aimed to compare the outcomes of robotic female AUS and mal e AUS implantation for non-neurogenic stress urinary incontinence (SUI). We retr ospectively reviewed charts of male patients who had AUS implantation and female patients who underwent robotic AUS implantation for non-neurogenic SUI between 2010 and 2022 at a single center. Prior AUS implantations were exclusion criteri a. The primary endpoint was continence status at 3 months, categorized as comple te resolution of SUI (0 pad), improved SUI (1pad), or unchanged SUI (> 1pad). After excluding 79 patients, 171 were included: 70 women and 101 men. Ope rative time was shorter in males (126.9 vs. 165.5 min; p<0 .0001). Postoperative complication rates were similar (17.3% vs. 2 2.9%; p = 0.38). Continence status at 3 months and last follow-up f avored females. The ICIQ-SF decrease at 3 months was greater in females (-7.2 vs . -4.6; p<0.001). The 5-year estimated explantation-free s urvival was similar (78.6% vs. 73.7%; p = 0.94) as wa s the revision-free survival (67.4% vs. 61.7%; p = 0. 89). Multivariate analysis showed that female gender was associated with better continence at last follow-up (OR = 4.3; p = 0.03).”
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 Machine Learning. According to news originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “Mineralization temperatur e determination is fundamental to economic geology research, yet quantifying it across mineralization remains a challenge. Sphalerite is ubiquitous in various t ypes of mineral deposits and particularly abundant in Pb-Zn deposits, and its tr ace element composition is temperature-dependent, making it an ideal candidate f or geothermometry.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Hunan Science and Technology Innovation Program, Central Uni versities Fundamental Research Funds of the Central South University.
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 Machine Learn ing - Computational Intelligence have been published. According to news reportin g originating in Chongqing, People’s Republic of China, by NewsRx journalists, r esearch stated, “As the increasing heterogeneous data, mining valuable informati on from various views is in demand. Currently, deep matrix factorization (DMF) r eceives extensive attention because of its ability to discover latent hierarchic al semantics of the data.” Funders for this research include National Natural Science Foundation of China ( NSFC), Natural Science Foundation of Chongqing, Science and Technology Research Program of Chongqing Municipal Education Commission, Open Fund of Key Laboratory of Cyber-Physical Fusion Intelligent Computing (South Central Minzu University) , State Ethnic Affairs Commission.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning - Comp utational Intelligence is now available. According to news reporting originating from Xi’an, People’s Republic of China, by NewsRx correspondents, research stat ed, “Multi-layer networks effectively describe and model complex systems in natu re and society, with each layer corresponding to a different type of interaction relationship. Community detection in multi-layer networks aims to identify modu les with strong connectivity in all layers, thus revealing interactions in compl ex systems.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Shaanxi Natural Science Funds for Distinguished Young Schola r Program, Key Research and Development Program of Shaanxi.
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 Robotics have been published. According to news originating from Cambridge, Massachusetts, by NewsRx correspondents, research stated, “Modern tools for classagnostic image segmentation (e.g., SegmentAnything) and open-set semantic understanding (e.g., CLIP) provide unprecedented opportunities for robot perception and mapping. Whil e traditional closed-set metric-semantic maps were restricted to tens or hundred s of semantic classes, we can now build maps with a plethora of objects and coun tless semantic variations.” Financial supporters for this research include National Science Foundation (NSF) , Swiss National Science Foundation (SNSF), MIT Lincoln Laboratory’s Autonomy al Fresco program, ARL DCIST program, ONR RAPID program.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting from Seoul National Universi ty College of Medicine by NewsRx journalists, research stated, “The American Soc iety of Anesthesiologist’s Physical Status (ASA-PS) classification system assess es comorbidities before sedation and analgesia, but inconsistencies among raters have hindered its objective use. This study aimed to develop natural language p rocessing (NLP) models to classify ASA-PS using pre-anesthesia evaluation summar ies, comparing their performance to human physicians.” Financial supporters for this research include National Research Foundation of K orea (Nrf) Grant Funded By The Korean Government; Seoul National University Hosp ital; New Faculty Startup Fund From Seoul National University.
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 out of Istanbul, Turke y, by NewsRx editors, research stated, “To quantitatively assess the performance of ChatGPTv4, an Artificial Intelligence Language Model, in adhering to clinica l guidelines for Diminished Ovarian Reserve (DOR) over two months, evaluating th e model’s consistency in providing guideline-based responses. A longitudinal stu dy design was employed to evaluate ChatGPTv4’s response accuracy and completenes s using a structured questionnaire at baseline and at a two-month follow-up.” Our news journalists obtained a quote from the research from the Vocational Scho ol of Health Services, “ChatGPTv4 was tasked with interpreting DOR questionnaire s based on standardized clinical guidelines. The study did not involve human par ticipants; the questionnaire was exclusively administered to the ChatGPT model t o generate responses about DOR. A guideline-based questionnaire with 176 open-en ded, 166 multiple-choice, and 153 true/false questions were deployed to rigorous ly assess ChatGPTv4’s ability to provide accurate medical advice aligned with cu rrent DOR clinical guidelines. AI-generated responses were rated on a 6-point Li kert scale for accuracy and a 3-point scale for completeness. The two-phase desi gn assessed the stability and consistency of AI-generated answers over two month s. ChatGPTv4 achieved near-perfect scores across all question types, with true/f alse questions consistently answered with 100% accuracy. In multip le-choice queries, accuracy improved from 98.2 to 100% at the two- month follow-up. Open-ended question responses exhibited significant positive en hancements, with accuracy scores increasing from an average of 5.38 ± 0.71 to 5. 74 ± 0.51 (max: 6.0) and completeness scores from 2.57 ± 0.52 to 2.85 ± 0.36 (ma x: 3.0). It underscored the improvements as significant (p <0.001), with positive correlations between initial and follow-up accuracy (r = 0.597) and completeness (r = 0.381) scores. The study was limited by the relianc e on a controlled, albeit simulated, setting that may not perfectly mirror real- world clinical interactions. ChatGPTv4 demonstrated exceptional and improving ac curacy and completeness in handling DOR-related guideline queries over the studi ed period.”
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