Results 41 to 50 of about 11,259 (184)

Predicting Single‐Cell Perturbation Responses Across Biological Contexts With a Deep Generative Model Integrating Optimal Transport

Advanced Science, EarlyView.
Single‐cell perturbation responses are predicted across held‐out biological contexts using scPILOT, a query‐conditioned two‐stage latent response‐transfer framework. A shared latent representation supports cell‐level response estimation by latent optimal transport, followed by Leiden‐localized query‐specific transfer and adaptive weighting.
Jialiang Wang, Ziqi Liu, Zhengqian Zhang, Yikun Cao, Junjun Ren, Peng Cheng, Jingjing Tian, Lingyun Xie, Xin Lu, Zhanwei Du, Yongzhuang Liu   +10 more
wiley   +1 more source

f-divergence Analysis of Generative Adversarial Network

Foundations of Computing and Decision Sciences
We aim to establish estimation bounds for various divergences, including total variation, Kullback-Leibler (KL) divergence, Hellinger divergence, and Pearson χ2 divergence, within the GAN estimator.
Hasan Mahmud, Sang Hailin
doaj   +1 more source

High‐Throughput Screening for Accelerated Oxide Glass Discovery

Advanced Science, EarlyView.
A high‐throughput screening platform is presented that allows for accelerating laboratory glass synthesis by a factor of 102, using multicomponent stereolithographic printing. Automatic sample evaluation is achieved via Raman spectroscopy and similarity analysis without any sample preparation.
Igor Seibel‐Geraschenko, Josef Slowik, Franziska Scheffler, Alexis Duval, N. M. Anoop Krishnan, Lothar Wondraczek   +5 more
wiley   +1 more source

Molecular Atlas of Key Food Odorants Reveals Mixture‐Level Organization and Enables Generative Aroma Design

Advanced Science, EarlyView.
KFO‐Atlas reveals how real‐world aromas are organized as structured mixtures rather than individual molecules. Building on these principles, KFO‐Gen, a generative AI framework, designs perceptually valid aroma formulations and reconstructs meat‐like aromas exclusively from plant‐derived odorants, providing a foundation for mixture‐level studies and AI ...
Jingzhi Zhang, Huadong Xing, Antonella Di Pizio, Qinfei Ke, Xingran Kou, Dachuan Zhang   +5 more
wiley   +1 more source

Bounds for Kullback-Leibler divergence

Electronic Journal of Differential Equations, 2016
Entropy, conditional entropy and mutual information for discrete-valued random variables play important roles in the information theory. The purpose of this paper is to present new bounds for relative entropy $D(p||q)$ of two probability distributions
Pantelimon G. Popescu, Sever S. Dragomir, Emil I. Slusanschi, Octavian N. Stanasila   +3 more
doaj  

Enhancing Specific Stiffness of Nano‐Architected Materials Through Generative AI and Active Learning

Advanced Science, EarlyView.
A structure‐centric generative framework searches voxelated design space directly, coupling a variational autoencoder with multi‐objective optimization and active learning to progressively expand the validated design domain. The discovered nano‐architectures, realized as pyrolytic carbon by two‐photon lithography, deliver 18–23% higher stiffness and 26–
Jinwook Yeo, Peter Serles, Donggeun Park, Julia R. Greer, Seunghwa Ryu   +4 more
wiley   +1 more source

Information theoretical approach to detecting quantum gravitational corrections

Journal of High Energy Physics
In this paper, we investigate the scales at which quantum gravitational corrections can be detected in a black hole using information theory. This is done by calculating the Kullback-Leibler divergence for the probability distributions obtained from the ...
Behnam Pourhassan, Xiaoping Shi, Salman Sajad Wani, Saif-Al-Khawari, Farideh Kazemian, İzzet Sakallı, Naveed Ahmad Shah, Mir Faizal   +7 more
doaj   +1 more source

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source

Rényi Entropy and Rényi Divergence in Product MV-Algebras

Entropy, 2018
This article deals with new concepts in a product MV-algebra, namely, with the concepts of Rényi entropy and Rényi divergence. We define the Rényi entropy of order q of a partition in a product MV-algebra and its conditional version ...
Dagmar Markechová, Beloslav Riečan
doaj   +1 more source

Uniqueness and Optimality of Dynamical Extensions of Divergences

PRX Quantum, 2021
We introduce an axiomatic approach for channel divergences and channel relative entropies that is based on three information-theoretic axioms of monotonicity under superchannels, i.e., generalized data processing inequality, additivity under tensor ...
Gilad Gour
doaj   +1 more source