Results 51 to 60 of about 6,879 (215)

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

New Improvements of the Jensen–Mercer Inequality for Strongly Convex Functions with Applications

Axioms
In this paper, we use the generalized version of convex functions, known as strongly convex functions, to derive improvements to the Jensen–Mercer inequality. We achieve these improvements through the newly discovered characterizations of strongly convex
Muhammad Adil Khan, Slavica Ivelić Bradanović, Haitham Abbas Mahmoud   +2 more
doaj   +1 more source

ncertain cross pseudo supervision for semisupervised semantic segmentation

四川大学学报. 自然科学版, 2023
Predictions based on deep learning algorithms are often blindly assumed to be accurate,and this disadvantage is more pronounced in semi-supervised learning.
LIU Meng-Jie, PU Yi-Fei, ZHANG Wei-Hua
doaj  

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

PCoA plots using the Jensen-Shannon divergence for ARK SEK.

, 2015
PCoA plots using the Jensen-Shannon divergence for ARK SEK.
Saikat Chatterjee (817514), Alan W. Walker (50247), Jukka Corander (105139), Damon Shahrivar (817515), Louise J. Fraser (817516), Mikko Vehkaperä (817517), David Koslicki (535161), Suzanna C. Francis (230018), Yueheng Lan (736247)   +8 more
core   +1 more source

Information Geometric Approach on Most Informative Boolean Function Conjecture

Entropy, 2018
Let X n be a memoryless uniform Bernoulli source and Y n be the output of it through a binary symmetric channel. Courtade and Kumar conjectured that the Boolean function f : { 0 , 1 } n → { 0 , 1 } that maximizes the ...
Albert No
doaj   +1 more source

Advanced information criterion for environmental data quality assurance [PDF]

Advances in Science and Research, 2012
A new method for testing time series of environmental data for internal inconsistencies is presented. The method divides the dataset into several disjunct blocks.
A. Düsterhus, A. Hense
doaj   +1 more source

Correlations, Information Backflow, and Objectivity in a Class of Pure Dephasing Models

Entropy, 2022
We critically examine the role that correlations established between a system and fragments of its environment play in characterising the ensuing dynamics.
Nina Megier, Andrea Smirne, Steve Campbell, Bassano Vacchini   +3 more
doaj   +1 more source

Extrinsic Jensen–Shannon Divergence: Applications to Variable-Length Coding [PDF]

IEEE Transactions on Information Theory, 2015
17 pages (two-column), 4 figures, to appear in IEEE Transactions on Information ...
Mohammad Naghshvar, Tara Javidi, Michèle A. Wigger   +2 more
openaire   +2 more sources

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