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    Study Findings on Robotics Reported by Researchers at Harbin Institute of Techno logy (An obstacle-avoidance inverse kinematics method for robotic manipulator in overhead multi-line environment)

    103-103页
    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 robotics have bee n published. According to news originating from Heilongjiang, People’s Republic of China, by NewsRx correspondents, research stated, “The inverse kinematics pro blem plays a crucial role in robotic manipulator planning, autonomous control, a nd object grasping. This problem can be solved in simple environments based on e xisting studies.” Funders for this research include National Natural Science Foundation of China; Hubei Electric Power Research Institute; Yunnan Power Grid Co. Ltd.. Our news correspondents obtained a quote from the research from Harbin Institute of Technology: “However, it is still challenging to quickly find a feasible inv erse kinematic solution when obstacle avoidance is required. In this paper, we p resent a nonconvex composite programming method to solve the inverse kinematics problem with overhead obstacle-avoidance requirements. Our method enables effici ent obstacle avoidance by directly calculating the minimum distance between the manipulator and the overhead environment. We construct end-effector error functi ons based on the Product of Exponentials model and explicitly provide their grad ient formula. We derive the minimum distance based on the geometry parametric eq uation and directly utilize it to construct the obstacle avoidance function. We propose an enhanced version of adaptive moment estimation based on short-time gr adient information to improve optimization performance.”
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    University of Bristol Researchers Detail New Studies and Findings in the Area of Machine Learning (Exacerbation predictive modelling using real-world data from the myCOPD app)

    104-104页
    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 from Bristol, United Kingdom, by NewsRx journalists, research stated, “Acute exacerbations of COPD ( AECOPD) are episodes of breathlessness, cough and sputum which are associated wi th the risk of hospitalisation, progressive lung function decline and death. The y are often missed or diagnosed late.” The news editors obtained a quote from the research from University of Bristol: “Accurate timely intervention can improve these poor outcomes. Digital tools can be used to capture symptoms and other clinical data in COPD. This study aims to apply machine learning to the largest available real-world digital dataset to d evelop AECOPD Prediction tools which could be used to support early intervention and improve clinical outcomes. To create and validate a machine learning predic tive model that forecasts exacerbations of COPD 1-8 days in advance. The model i s based on routine patient-entered data from myCOPD self-management app. Adaptat ions of the AdaBoost algorithm were employed as machine learning approaches. The dataset included 506 patients users between 2017 and 2021. 55,066 app records w ere available for stable COPD event labels and 1263 records of AECOPD event labe ls. The data used for training the model included COPD assessment test (CAT) sco res, symptom scores, smoking history, and previous exacerbation frequency. All e xacerbation records used in the model were confined to the 1-8 days preceding a self-reported exacerbation event. TheEasyEnsemble Classifier resulted in a Sensi tivity of 67.0 % and a Specificity of 65 % with a po sitive predictive value (PPV) of 5.0 % and a negative predictive v alue (NPV) of 98.9 %. An AdaBoost model with a cost-sensitive decis ion tree resulted in a a Sensitivity of 35.0 % and a Specificity o f 89.0 % with a PPV of 7.08 % and NPV of 98.3 % .”
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    Recent Studies from Zhejiang University Add New Data to Robotics (Closed Twisted Hydrogel Ribbons With Self-sustained Motions Under Static Light Irradiation)

    105-106页
    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 Ro botics. According to news originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Self-sustained motions are widespre ad in biological systems by harvesting energy from surrounding environments, whi ch inspire scientists to develop autonomous soft robots. However, most-existing soft robots require dynamic heterogeneous stimuli or complex fabrication with di fferent components.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Zhejiang University , “Recently, control of topological geometry has been promising to afford soft r obots with physical intelligence and thus life-like motions. Reported here are a series of closed twisted ribbon robots, which exhibit self-sustained flipping a nd rotation under constant light irradiation. Both M & ouml;bius s trip and Seifert ribbon robots are devised for the first time by using an identi cal hydrogel, which responds to light irradiation on either side. Experiment and simulation results indicate that the self-regulated motions of the hydrogel rob ots are related to fast and reversible response of muscle-like gel, self-shadowi ng effect, and topology-facilitated refresh of light-exposed regions. The motion speeds and directions of the hydrogel robots can be tuned over a wide range. Th ese closed twisted ribbon hydrogels are further applied to execute specific task s in aqueous environments, such as collecting plastic balls, climbing a vertical rod, and transporting objects. This work presents new design principle for auto nomous hydrogel robots by benefiting from material response and topology geometr y, which may be inspirative for the robotics community. A series of M & ouml;bius strip and Seifert ribbon robots are devised by using identical slender muscle-like anisotropic hydrogel. These closed twisted ribbon hydrogels exhibit continuous flipping and rotation under static light irradiation.”
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    Researchers from Beijing University of Chemical Technology Describe Findings in Machine Learning (Lattice Thermal Conductivity of Solid Lif Based On Machine Lea rning Force Fields and the Greenkubo Approach)

    106-106页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Machine Learning have be en published. According to news reporting out of Beijing, People’s Republic of C hina, by NewsRx editors, research stated, “Obtaining accurate lattice thermal co nductivity data of LiF under extreme conditions not only provides important refe rence for performance evaluation, prediction, and control of materials, but also helps to alleviate the significant challenges of precise experimental measureme nts. The high-temperature phonon properties and lattice thermal conductivity (LT C) of solid LiF were calculated by combining on-the-fly machine learning force f ields (MLFFs) with the Green-Kubo method.” Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of China (NSFC).
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    Purdue University Researchers Provide Details of New Studies and Findings in the Area of Machine Learning (Explaining vulnerabilities of heart rate biometric mo dels securing IoT wearables)

    107-107页
    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 ar tificial intelligence. According to news reporting originating from West Lafayet te, Indiana, by NewsRx correspondents, research stated, “In the field of health informatics, extensive research has been conducted to predict diseases and extra ct valuable insights from patient data.” Our news correspondents obtained a quote from the research from Purdue Universit y: “However, a significant gap exists in addressing privacy concerns associated with data collection. Therefore, there is an urgent need to develop a machine-le arning authentication model to secure the patients’ data seamlessly and continuo usly, as well as to find potential explanations when the model may fail. To addr ess this challenge, we propose a unique approach to secure patients’ data using novel eigenheart features calculated from coarse-grained heart rate data. Variou s statistical and visualization techniques are utilized to explain the potential vulnerabilities of the model. Though it is feasible to develop continuous user authentication models from readily available heart rate data with reasonable per formance, they are affected by factors such as age and Body Mass Index (BMI).”
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    Swiss Federal Institute of Technology Zurich (ETH) Reports Findings in Neural Co mputation (A Multimodal Fitting Approach to Construct Single-Neuron Models with Patch Clamp and High- Density Microelectrode Arrays)

    107-108页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Computation - Neural C omputation is the subject of a report. According to news reporting originating f rom Basel, Switzerland, by NewsRx correspondents, research stated, “In computati onal neuroscience, multicompartment models are among the most biophysically real istic representations of single neurons. Constructing such models usually involv es the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions.”Our news editors obtained a quote from the research from the Swiss Federal Insti tute of Technology Zurich (ETH), “The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold- standard approach to build multicompartment models, several studies have also ev idenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-c lamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patchclamp and HD-MEA data to construct multicompar tment models. We first validate our method on a ground-truth model with known pa rameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than usin g intracellular features alone. We also demonstrate our procedure using experime ntal data by constructing cell models from in vitro cell cultures.”
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    Studies from University of Macau Add New Findings in the Area of Robotics (Force -position Hybrid Control for Robot Assisted Thoracic-abdominal Puncture With Res piratory Movement)

    108-109页
    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 Robotics is now availab le. According to news reporting originating from Macau, People’s Republic of Chi na, by NewsRx correspondents, research stated, “Percutaneous puncture is a widel y used procedure in the diagnosis and therapy of cancer such as biopsy and ablat ion operations, while the organs in the thoracic and abdominal cavities are sign ificantly affected by patients’ respiratory movement. In this letter, a robotic puncture system with respiratory movement is firstly developed, which can simula te the different motions of body surface and internal organ during respiratory c ycle.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from the University of Macau , “Then a forceposition hybrid control framework is proposed, which includes ne edle insertion along the planned path at the end of inspiration and respiratory motion following according to interaction force sensing at the other phases. Sin ce it requires only one CT scan at the end of inspiration, which complies with c linical routine procedures. Furthurmore, to avoid tearing damage of tissue, the robotic needle takes a virtual RCM constraint at the needle insertion point when following the respiratory motion. And for better respiratory motion compensatio n, an admittance control optimization method is proposed based on the maximal ne edle-tissue interaction force, the respiratory following displacement percentage and the overshoot displacement/ force.”
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    Ulm University Reports Findings in Machine Learning (The predictive value of sup ervised machine learning models for insomnia symptoms through smartphone usage b ehavior)

    109-110页
    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 originating from Ulm, Germany, by NewsR x correspondents, research stated, “Digital phenotyping can be an innovative and unobtrusive way to improve the detection of insomnia. This study explores the c orrelations between smartphone usage features (SUF) and insomnia symptoms and th eir predictive value for detecting insomnia symptoms.” Our news journalists obtained a quote from the research from Ulm University, “In an observational study of a German convenience sample, the Insomnia Severity In dex (ISI) and smartphone usage data (e.g., time the screen was active, longest t ime the screen was inactive in the night) for the previous 7 days were obtained. SUF (e.g., min, mean) were calculated from the smartphone usage data. Correlati on analyses between the ISI and SUF were conducted. For the specification of the machine learning models (ML), 80 % of the data was allocated to t raining, 20 % to testing, and five-fold cross-validation was used. Six algorithms (support vector machine, XGBoost, Random Forest, k-Nearest-Neigh bor, Naive Bayes, and Logistic Regressions) were specified to predict ISI scores 15. 752 participants (51.1 % female, mean ISI = 10.23, mean age = 41.92) were included in the analyses. Small correlations between some of the SU F and insomnia symptoms were found. In the ML models, sensitivity was low, rangi ng from 0.05 to 0.27 in the testing subsample. Random Forest and Naive Bayes wer e the best-performing algorithms. Yet, their AUCs (0.57, 0.58 respectively) in t he testing subsample indicated a low discrimination capacity. Given the small ma gnitude of the correlations and low discrimination capacity of the ML models, SU Fs, as measured in this study, do not appear to be sufficient for detecting inso mnia symptoms.”
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    Patent Application Titled 'Robotic System Transfer Unit Cell And Method Of Opera tion Thereof' Published Online (USPTO 20240157565)

    110-113页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting originatin g from Washington, D.C., by NewsRx journalists, a patent application by the inve ntors CHEN, Zhenjia (Shanghai, CN); COATS, Brandon (Sandy Springs, GA, US); GAO, Lingping (Guangzhou, CN); HUANG, Guohao (Guangzhou, CN); LEI, Lei (Shanghai, CN ); XU, Yi (Guangzhou, CN), filed on November 15, 2023, was made available online on May 16, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: “With their ever-increasing performance and lowering cost, man y robots (e.g., machines configured to automatically/autonomously execute physic al actions) are now extensively used in various different fields. Robots, for ex ample, can be used to execute various tasks (e.g., manipulate or transfer an obj ect through space) in manufacturing and/or assembly, packing and/or packaging, t ransport and/or shipping, etc. In executing the tasks, the robots can replicate human actions, thereby replacing or reducing human involvements that are otherwi se required to perform dangerous or repetitive tasks.
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    Patent Application Titled 'Selectively Generating Expanded Responses That Guide Continuance Of A Human-To-Computer Dialog' Published Online (USPTO 20240161743)

    113-118页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting originatin g from Washington, D.C., by NewsRx journalists, a patent application by the inve ntors Berant, Jonathan (Tel Aviv, IL); Borovoy, Richard (Boston, MA, US); Callah an, Andrew (Somerville, MA, US); Cohen, Deborah (Tel Aviv, IL); Fink, Michael (T el Aviv, IL); Kogan, David (Natick, MA, US); Matias, Yossi (Tel Aviv, IL); Ofek, Eran (Rdhovot, IL); Revach, Asaf (Nordia Center District, IL); Richardson, Andr ew (Cambridge, MA, US); Salant, Shimon Or (Rechovot Center District, IL); Szpekt or, Idan (Kfar Saba, IL); Vuskovic, Vladimir (Zolikerberg, CH), filed on January 8, 2024, was made available online on May 16, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: “Humans may engage in human-to-computer dialogs with interacti ve software applications referred to herein as “automated assistants” (also refe rred to as “chatbots,” “interactive personal assistants,” “intelligent personal assistants,” “conversational agents,” etc.). For example, humans (which when the y interact with automated assistants may be referred to as “users”) may provide commands and/or requests using spoken natural language input (i.e. utterances) w hich may in some cases be converted into text and then processed, and/or by prov iding textual (e.g., typed) natural language input. An automated assistant respo nds to a request by, for example, controlling a peripheral device referenced in the request, providing responsive user interface output (e.g., audible and/or gr aphical) that is responsive to the request, etc.
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