Results 131 to 140 of about 12,964 (262)

Comparative Analysis of Model‐Agnostic Explanation Methods in Materials Science

Advanced Intelligent Systems, EarlyView.
To address the critical lack of explainable artificial intelligence (XAI) benchmarks in materials science, we present a quantitative and qualitative analysis of six XAI methods applied to molecular fingerprints. Our results reveal significant discrepancies in feature importance rankings, demonstrating that the chosen explanation approach introduces ...
Anna Przybyłowska, Joanna Neubauer, Monika Sztuder, Witold Taisner, Agnieszka Hryniewicka, Julia Kolodziejczyk, Marta Eliza Plonska‐Brzezinska, Dariusz Brzezinski   +7 more
wiley   +1 more source

A Scalable and Resource‐Efficient Pipelined p‐Computer for Probabilistic Ising Machines

Advanced Intelligent Systems, EarlyView.
(a) Block diagram of the portfolio optimization problem: given M assets, the goal is to determine the optimal weights w that maximize the expected return (based on the mean historical assets return u), while minimizing the risk, quantified by the assets covariance matrix S.
Deborah Volpe, Eleonora Raimondo, Andrea Grimaldi, Pedram Khalili Amiri, Stefano Chiappini, Anna Giordano, Mario Carpentieri, Hyunsoo Yang, Massimo Chiappini, Giovanni Finocchio   +9 more
wiley   +1 more source

Exploiting Edge Semantics in Job Shop Scheduling Problem With Heterogeneous Graph Transformers

Advanced Intelligent Systems, EarlyView.
A heterogeneous graph transformer (HGT) is introduced for reinforcement learning‐based job shop scheduling by explicitly distinguishing precedence and machine‐contention relations through edge‐type‐specific attention. The proposed framework learns richer scheduling representations, improves decision quality over homogeneous graph models, and highlights
Bulent Soykan, Fatih Kasimoglu
wiley   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Study on ice phase change prediction model for transmission lines based on multiphysics coupling. [PDF]

Sci Rep
Pei Y, Wang Q, Wang L, Zuo S, Zhang H, Jing L.   +5 more
europepmc   +1 more source

A Multidimensional Perspective on Network Conditions in Telesurgery

Advanced Intelligent Systems, EarlyView.
A multidimensional framework systematically evaluates how combined network impairments influence telesurgical performance. By jointly analyzing latency, jitter, bandwidth, and packet loss, the proposed methodology identifies relevant network condition parameters and their interactions, supporting the design of reliable and safe telesurgical systems ...
Florian Heemeyer, Marco Cina, Ege Cem Kirci, Laurent Vanbever, Quentin Boehler   +4 more
wiley   +1 more source

Locally Recoverable Codes with availability from a family of fibered surfaces. [PDF]

Des Codes Cryptogr
Salgado C, Vicino L.
europepmc   +1 more source

Machine Learning‐Enabled Field Prediction and Inverse Design of Functionally Graded Materials Inspired by Tendon–Bone Enthesis

Advanced Intelligent Systems, EarlyView.
A mechanics‐informed machine learning framework enables mechanics field prediction and inverse design of tendon–bone enthesis‐inspired functionally graded materials that reduce stress concentration and failure risk. By coupling multiscale finite element modeling, convolutional field prediction, and kernel‐based inverse design, the study uncovers ...
Zhangke Yang, Zhaoxu Meng
wiley   +1 more source

Accelerated Multisecret Sharing Scheme Using Fast Matrix Spectral Factorization. [PDF]

Entropy (Basel)
Çalkavur S, Solé P, Ephremidze L.
europepmc   +1 more source

Choice experiments on land managers' participation in environmental programs: A systematic review and meta‐analysis of estimate validity

American Journal of Agricultural Economics, EarlyView.
Abstract Discrete choice experiments are increasingly being used to estimate land managers' willingness to accept participation in incentive‐based environmental programs. This is a specific application of discrete choice experiments: the estimation of willingness to accept for a private good (program participation) where respondents have to make trade ...
Anastasio J. Villanueva, Angel Perni, Jesus Barreiro‐Hurle   +2 more
wiley   +1 more source