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 reporting originating from Aversa,Italy,by NewsRx correspondents,research stated,"Classic task programming methods based on the specification of desired Cartesian frames can easily generate overconstra ined task specifications,reducing the motion capabilities of the involved robot (s) and increasing the total programming effort. This paper presents a general c onstraint-based programming framework for the specification of a task as minimum set of constraints and the automatic generation of motion optimization problems ." Our news editors obtained a quote from the research from the University of Campa nia,"The framework can handle constraints involving both robot joint and Cartes ian coordinates,as well as including explicit time dependency. The proposed for malism naturally scales to robotic applications with multiple robots,on which m ultiple frames might be of interest. Additionally,the paper proposes a theoreti cal comparison with already existing constraint-based programming methods."
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 from Lexington,Kentuc ky,by NewsRx journalists,research stated,"Soft tissue tumors (STTs) pose diag nostic and therapeutic challenges due to their rarity,complexity,and morpholog ical overlap. Accurate differentiation between benign and malignant STTs is impo rtant to set treatment directions,however,this task can be difficult." The news correspondents obtained a quote from the research from the University o f Kentucky College of Medicine,"The integration of machine learning and artific ial intelligence (AI) models can potentially be helpful in classifying these tum ors. The aim of this study was to investigate AI and machine learning tools in t he classification of STT into benign and malignant categories. This study consis ted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologis ts from different medical centers independently reviewed 100 WSIs,representing 100 different cases,with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPa th) and a cohort of 95 cases,several cell-specific parameters were found to be statistically significant,most notably cell count,nucleus/cell area ratio,nuc leus hematoxylin density mean,and cell max caliper. (3) Evaluation of machine l earning library (Scikit-learn) in differentiating benign and malignant STTs. A t otal of 195 STT cases (156 cases in the training group and 39 cases in the valid ation group) achieved approximately 70% sensitivity and specificit y,and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and ident ify parameters that can differentiate between benign and malignant STTs."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics is the subjec t of a report. According to news reporting originating from Auckland,New Zealan d,by NewsRx correspondents,research stated,"Videos on Robotic pancreaticoduod enectomy (RPD) may be watched by surgeons learning RPD. This study sought to app raise the educational quality of RPD videos on YouTube." Our news editors obtained a quote from the research from the University of Auckl and,"One-hundred videos showing RPD or ‘Robotic Whipple' were assessed using va lidated scales (LAP-VEGaS & Consensus Statement Score (CSS)). The association between the scores and the video characteristics (e.g. order of appe arance,provider type etc) was assessed. The minimum number of videos required t o cumulatively cover the entire LAP-VEGaS and CSS was also noted. The videos wer e of variable quality; median LAP-VEGaS = 0.67 (0.17-0.94),median CSS = 0.45 (0 .29-0.53). There was no association between the educational quality of the video s and their order of appearance,view counts,provider type,length or country o f origin. Videos lacked information such as patient consent (100%),potential pitfalls (97%) or surgeon credentials (84%) . The first 29 videos cumulatively met all the criteria of CSS and LAP-VEGaS sco res except for reporting consent. YouTube videos on RPD are of variable quality,without any recognised predictors of quality,and miss important safety informa tion. An impractical number of videos need to be watched to cumulatively fulfil educational criteria."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning - Artif icial Intelligence are presented in a new report. According to news reporting ou t of Orense,Spain,by NewsRx editors,research stated,"The evolution of anti-s pam filters has forced spammers to make greater efforts to bypass filters in ord er to distribute content over networks. The distribution of content encoded in i mages or the use of Leetspeak are concrete and clear examples of techniques curr ently used to bypass filters." Funders for this research include Department of Education,Universities and Rese arch of the Basque Country,Project Semantic Knowledge Integration for Content-B ased Spam Filtering from SMEIC,Sugar Research Australia,European Union (EU). Our news journalists obtained a quote from the research from the University of V igo,"Despite the importance of dealing with these problems,the number of studi es to solve them is quite small,and the reported performance is very limited. T his study reviews the work done so far (very rudimentary) for Leetspeak deobfusc ation and proposes a new technique based on using neural networks for decoding p urposes. In addition,we distribute an image database specifically created for t raining Leetspeak decoding models. We have also created and made available four different corpora to analyse the performance of Leetspeak decoding schemes. Usin g these corpora,we have experimentally evaluated our neural network approach fo r decoding Leetspeak."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating in Melbourne,Australia,by NewsRx journalists,research stated,"Gaming disorder [GD ] risk has been associated with the way gamers bond with thei r visual representation (i.e.,avatar) in the gameworld. More specifically,a g amer's relationship with their avatar has been shown to provide reliable mental health information about the user in their offline life,such as their current a nd prospective GD risk,if appropriately decoded." Financial supporters for this research include RMIT University,Early Career Res earcher Fund ECR 2020,Australian Research Council.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in chemical engineeri ng. According to news originating from Ghent,Belgium,by NewsRx correspondents,research stated,"By combining machine learning with the design of experiments,thereby achieving so-called active machine learning,more efficient and cheaper research can be conducted." Financial supporters for this research include European Research Council; Fonds Wetenschappelijk Onderzoek; Horizon 2020; Horizon 2020 Framework Programme. Our news editors obtained a quote from the research from Ghent University: "Mach ine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering. While active machine learning algorithms are maturing,their applications are falling behind. In this article,three types of challenge s presented by active machine learning-namely,convincing the experimental resea rcher,the flexibility of data creation,and the robustness of active machine le arning algorithms-are identified,and ways to overcome them are discussed." According to the news editors,the research concluded: "A bright future lies ahe ad for active machine learning in chemical engineering,thanks to increasing aut omation and more efficient algorithms that can drive novel discoveries."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Palaiseau,France,by Ne wsRx journalists,research stated,"Protein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conf ormations,distinguished by a few elements within the catalytic domain." The news correspondents obtained a quote from the research from Ecole Polytechni que,"One is the activation loop,whose conserved DFG motif can occupy DFG-in,D FG-out,and some rarer conformations. Annotation and classification of the struc tural kinome are important,as different conformations can be targeted by differ ent inhibitors and activators. Valuable resources exist; however,large-scale ap plications will benefit from increased automation and interpretability of struct ural annotation. Interpretable machine learning models are described for this pu rpose,based on ensembles of decision trees. To train them,a set of catalytic d omain sequences and structures was collected,somewhat larger and more diverse t han existing resources. The structures were clustered based on the DFG conformat ion and manually annotated. They were then used as training input. Two main mode ls were constructed,which distinguished active/inactive and in/out/other DFG co nformations. They considered initially 1692 structural variables,spanning the w hole catalytic domain,then identified (‘learned') a small subset that sufficed for accurate classification. The first model correctly labeled all but 3 of 3289 structures as active or inactive,while the second assigned the correct DFG lab el to all but 17 of 8826 structures. The most potent classifying variables were all related to well-known structural elements in or near the activation loop and their ranking gives insights into the conformational preferences. The models we re used to automatically annotate 3850 kinase structures predicted recently with the Alphafold2 tool,showing that Alphafold2 reproduced the active/inactive but not the DFG-in proportions seen in the Protein Data Bank."
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 originating from Shenyang,Peopl e's Republic of China,by NewsRx correspondents,research stated,"The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our abili ty to extract quantitative information from digital histopathology images as a r apid growth in oncology data." Our news journalists obtained a quote from the research from the Shengjing Hospi tal of China Medical University,"Gynecological cancers are major diseases affec ting women's health worldwide. They are characterized by high mortality and poor prognosis,underscoring the critical importance of early detection,treatment,and identification of prognostic factors. This review highlights the various cli nical applications of AI in gynecological cancers using digitized histopathology slides. Particularly,deep learning models have shown promise in accurately dia gnosing,classifying histopathological subtypes,and predicting treatment respon se and prognosis. Furthermore,the integration with transcriptomics,proteomics,and other multiomics techniques can provide valuable insights into the molecul ar features of diseases. Despite the considerable potential of AI,substantial c hallenges remain."
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 originating from Guiyang,People's R epublic of China,by NewsRx correspondents,research stated,"For a tractor-trai ler wheeled mobile robot (TTWMR,for short) with an off-axle hitching,it is cha llenging for both tractor and trailer to track a desired path due to the insuffi cient steering capability of the trailer. As such,a passive steering angle is f irst introduced,which allows the trailer to follow the same path as the leading tractor." Financial supporters for this research include National Natural Science Foundati on of China (NSFC),Guizhou Provincial Science and Technology Projects,China. Our news editors obtained a quote from the research from Guizhou Normal Universi ty,"In this way,a given motion task can be reduced to the accurate path tracin g problem for the tractor. By considering such a passive steering angle,the mot ion speed relation between two vehicles of the TTWMR with an off-axle hitching i s then derived,based on which its kinematics and dynamics equations are establi shed. Thereafter,a proportion integration feedback controller is then developed by combining the curvature tracing method and the directed Lyapunov method,so that both vehicles can move along an identical path." According to the news editors,the research concluded: "Finally,simulation resu lts indicate the effectiveness and robustness of the resulting control strategy. "
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 out of Waterloo,Canada,by NewsRx e ditors,research stated,"Hybrid track/wheel-legged robots combine the advantage s of wheel-based and leg-based locomotion,granting adaptability across varied t errains through efficient transitions between rolling and walking modes. However ,automating these transitions remains a significant challenge." Our news journalists obtained a quote from the research from the University of W aterloo,"In this paper,we introduce a method designed for autonomous mode tran sition in a quadruped hybrid robot with a track/wheel-legged configuration,espe cially during step negotiation. Our approach hinges on a decision-making mechani sm that evaluates the energy efficiency of both locomotion modes using a propose d energy-based criterion. To guarantee a smooth negotiation of steps,we incorpo rate two climbing gaits designated for the assessment of energy usage in walking locomotion. Simulation results validate the method's effectiveness,showing suc cessful autonomous transitions across steps of diverse heights." According to the news editors,the research concluded: "Our suggested approach h as universal applicability and can be modified to suit other hybrid robots of si milar mechanical configuration,provided their locomotion energy performance is studied beforehand."
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