Results 91 to 100 of about 38,563 (254)

A study on the use of Lévy processes in quantitative risk management

, 2012
Diese Arbeit beschäftigt sich mit der Berechnung des Value-at-Risks und Expected Shortfalls von Lévy-Prozessen. Hauptziel ist es dabei die Verwendung von Sprungprozessen zu motivieren und eine neue Berechnungsmethode mit Hilfe einer Fourier-Formel ...
Mitterhuber, Jürgen
core  

stMixer for Scalable Mosaic Integration and Label Transfer in Spatial Histology and Multi‐Omics

Advanced Science, EarlyView.
stMixer is an unsupervised framework for scalable integration and label transfer across spatial histology and multi‐slide multi‐omics data with incomplete modality overlap. It combines self‐looped cross‐attention, multimodal metric learning, and graph‐guided cluster voting to align heterogeneous sections, correct batch effects, and propagate ...
Qixing Yang, Yan Wang, Luonan Chen, Chunman Zuo   +3 more
wiley   +1 more source

Unconventional Hysteretic Charge Filling in Moiré‐Reconstructed Helical Trilayer Graphene

Advanced Science, EarlyView.
In helical trilayer graphene, sequential twisting reconstructs the moiré landscape into periodic domains separated by aperiodic boundaries. Longitudinal transport reveals sweep‐direction‐dependent hysteresis, while the Hall response traces this behavior to hysteretic charge filling at the aperiodic boundaries.
Hangyeol Park, Junhyeok Oh, Rasoul Ghadimi, Chiranjit Mondal, Yungi Jeong, Won Beom Choi, Kenji Watanabe, Takashi Taniguchi, Bohm‐Jung Yang, Joonho Jang   +9 more
wiley   +1 more source

De Novo Design of Membrane‐Targeting Antimicrobial Peptides Against Gram‐Negative Bacteria Using a Generative Artificial Intelligence Framework

Advanced Science, EarlyView.
Antimicrobial resistance caused by Gram‐negative bacteria remains difficult to overcome due to the protective outer membrane. To address this challenge, a multi‐condition constrained generative AI framework, GenMTAMP is proposed for de novo membrane‐targeting antimicrobial peptide design by integrating physicochemical and spatial structure descriptors.
Jingxiao Yu, Da‐Wen Sun, QingYi Wei, Yibing Zhang, Jidong Tang, Hongbin Pu   +5 more
wiley   +1 more source

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

Thin‐Film Transistor Based Active Taxel for Multimode Tactile Perception and Fused Processing

Advanced Science, EarlyView.
Skin serves as the largest‐area organ for human and embodied intelligent robots, providing tactile interaction. An active multimode fused (AMF) artificial skin is developed using standard TFT processes, featuring 2T‐1C taxels with optical and electrostatic capacitive receptors for cross‐modal sensing.
Sihao Wu, Zheng Zhou, Aoran Xu, Lutong Wang, Tingchen Yi, Chuanlin Sun, Haotong Zhu, Jiaqi Li, Junchen Dong, Li Zhou, Lifeng Liu, Yimao Cai, Dedong Han, Xing Zhang   +13 more
wiley   +1 more source

Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates

Advanced Science, EarlyView.
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao, Yufei Liu, Ruixi Huang, Zhongxue Gan, Siyang Leng   +4 more
wiley   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Autonomous High‐Throughput Characterization of Liquid‐Liquid Phase Behavior

Advanced Science, EarlyView.
This study introduces an automated dual modality platform, combining asymmetric capacitance deviation and multiangle turbidimetry, for high‐throughput characterization of liquid‐liquid phase behavior across chemically diverse fluid systems. The platform enables miscibility classification, resolution of phase separation kinetics and emulsion stability ...
Tarek Eid, Maryam Ebrahimiazar, Mohammad Zargartalebi, David Sinton   +3 more
wiley   +1 more source