Results 191 to 200 of about 94,123 (313)
SiOx‐Based Probabilistic Bits Enabling Invertible Logic Gate for Cryptographic Applications
To enable lightweight hardware encryption and decryption, a Ti/SiOx/Ti threshold switching device is engineered to generate controllable stochastic oscillations. By tuning the input voltage, the device produces a programmable spike probability governed by intrinsic switching dynamics, enabling probabilistic bits that construct an invertible ...
Jihyun Kim, Hyeonsik Choi, Jiyong Woo
wiley +1 more source
Matrix-Product State Skeletons in Onsager-Integrable Quantum Chains. [PDF]
Camp I, Jones NG.
europepmc +1 more source
An Interior-Point algorithm for Nonlinear Minimax Problems [PDF]
We present a primal-dual interior-point method for constrained nonlinear, discrete minimax problems where the objective functions and constraints are not necessarily convex.
E. Obasanjo, G. Tzallas-Regas, B. Rustem
core
An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna +2 more
wiley +1 more source
PAC-ZNN for Robust Target Tracking in WSNs Against Complex Polynomial Noise. [PDF]
Zhan Z, Song Z, Huang S, Xie Q, Xiao X.
europepmc +1 more source
A unified, reusable modeling pipeline enables task‐driven design of soft robots across actuator families and task scenarios. High‐fidelity simulations are compressed into compact pseudo‐rigid‐body joint surrogates, while a design‐conditioned meta‐model generates new surrogates from geometry parameters without rerunning finite element method.
Yao Yao, David Howard, Perla Maiolino
wiley +1 more source
The Power of the Lorentz Quantum Computer. [PDF]
Zhang Q, Wu B.
europepmc +1 more source
Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning
A subjective risk‐driven inverse reinforcement learning framework is proposed to model driver decision‐making. It infers drivers' risk perception and risk tolerance from driving data. A learnable risk threshold is used to regulate decisions, enabling interpretable and human‐like driving behavior decisions.
Yang Liang +6 more
wiley +1 more source
Minimum Vertex Cut with Reachable Set (MVCRS) Problem for Suppressing Botnet Propagation in IoT Networks: Complexity and Algorithms. [PDF]
Yamaguchi S.
europepmc +1 more source
Single‐cell Spatial Transcriptomics Analysis and Denoising Engine is introduced as a unified deep learning framework that jointly performs denoising, clustering, and gene prioritization in spatial transcriptomics. By integrating linear and nonlinear representations within a dual‐channel architecture, it improves robustness and accuracy, uncovers ...
Yaxuan Cui +11 more
wiley +1 more source

