Results 121 to 130 of about 23,797 (211)

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

Advanced Robotics Research, EarlyView.
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek, Herman van der Kooij, Douwe Dresscher, Mark Vlutters   +3 more
wiley   +1 more source

Mutual Adaptation and Influence: Review of Latent Dynamics Models in Human–Robot Interaction

Advanced Robotics Research, EarlyView.
Robots are becoming better teammates by learning hidden patterns in how people act. This review covers how state‐of‐the‐art approaches leverage these patterns to help robots not only react but also anticipate and guide cooperation. Here, we synthesize a unifying framework for these approaches, classify and review existing works, and highlight key ...
Mason O. Smith, Sunny Amatya, Seyed Yousef Soltanian, Jonathan Bush, Wenlong Zhang   +4 more
wiley   +1 more source

Dual Role of Tamoxifen in Enhancing STING and CEACAM1 Expression to Prime a Favorable Tumor Microenvironment for Anti‐TIM3 Immunotherapy

Advanced Science, EarlyView.
Anti‐PD‐1/PD‐L1 blockade has revolutionized cancer immunotherapy, but is ineffective against endocrine‐treated (i.e., Tamoxifen), relapsed ER+ breast cancer (BC) patients. This study provides insight into the sub‐optimal response of ER+BCs to anti‐PD‐1/PD‐L1 blockade – highlighting the induction of STING and the CEACAM1/TIM3 axis after chronic ...
Marvin Angelo E Aberin, Saurabh Singh, Soumya Chatterjee, Guang‐Zhi Sui, Yi‐Fu Wang, Ya‐Ting Lu, Yu‐Ling Lee, Kun‐Yuan Lin, Joy Khag, Ta‐Yu Liu, Shao‐Han Chang, Wai‐Mui Cheung, Hsiao‐Chin Hong, Ren‐Jun Hsu, Chen‐Yang Shen, Chia‐Wei Li, Weng‐Lang Yang, Yao‐Ming Chang, Shih‐Yu Chen, Chandan Guha, Shu‐Ping Wang   +20 more
wiley   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

A Geometric Interpretation of Stochastic Gradient Descent Using Diffusion Metrics. [PDF]

Entropy (Basel), 2020
Fioresi R, Chaudhari P, Soatto S.
europepmc   +1 more source

Generation of Probabilistic Bits by Exploiting Orthogonal Spin Currents in Magnetic Trilayers

Advanced Science, EarlyView.
Fe/Ti/CoFeB trilayers generate orthogonal spin currents that drive stochastic spin–orbit‐torque switching for probabilistic‐bit operation. The switching probability is continuously controlled by the in‐plane magnetic field and drive current, enabling tunable random bit generation.
Donghyeon Han, Chaehyeon Shin, Seok‐Jong Kim, Yunho Jang, Daekyu Koh, Taehwan Kim, Jaeheon Jung, Geon‐Woo Baek, Minseok Kang, Eunseok Kim, Geun‐Hee Lee, Jeongchun Ryu, Makoto Kohda, Junsaku Nitta, Kab‐Jin Kim, Jongsun Park, Kyung‐Jin Lee, Byong‐Guk Park   +17 more
wiley   +1 more source

Diverse Landscape of Tunable Magnetic, Topological, and Ferroelectric States in 2D Ti3Se3Te2

Advanced Science, EarlyView.
Ti3Se3Te2 emerges as a multifunctional 2D van der Waals platform. The monolayer is a dynamically stable ferromagnetic quantum anomalous Hall insulator. In bilayers, two stacking configurations yield distinct phases: AA‐stacking hosts an altermagnetic quantum spin Hall insulator, while AA′‐stacking exhibits three‐state in‐plane ferroelectricity ...
Jiangtao Yu, Jingbo Bai, Yali Yang, Shifeng Qian, Xiaotian Wang, Zhuhong Liu   +5 more
wiley   +1 more source

Tighter privacy auditing of differentially private stochastic gradient descent in the hidden state threat model. [PDF]

Sci Rep
Bhuekar A.
europepmc   +1 more source

SSGD: SPARSITY-PROMOTING STOCHASTIC GRADIENT DESCENT ALGORITHM FOR UNBIASED DNN PRUNING. [PDF]

Proc IEEE Int Conf Acoust Speech Signal Process, 2020
Lee CH, Fedorov I, Rao BD, Garudadri H.
europepmc   +1 more source

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 more
wiley   +1 more source