Results 91 to 100 of about 438,874 (275)
Learning in Markov Random Fields with Contrastive Free Energies [PDF]
Learning Markov random field (MRF) models is notoriously hard due to the presence of a global normalization factor. In this paper we present a new framework for learning MRF models based on the contrastive free energy (CF) objective function.
Sutton, Charles, Welling, Max
core +1 more source
Depletion of the RNA‐Editing Enzyme ADAR1 Invigorates the Antitumor Immunity of NK Cells
ADAR1 is upregulated in NK cells from melanoma patients, impairing their function. Its loss enhances NK cell tumor infiltration and cytotoxicity in vitro and in vivo. Mechanistically, ADAR1 deficiency destabilizes CD38 mRNA to reduce its expression, thereby increasing NK cell mobility and killing, which nominates it as a therapeutic target for NK cell ...
Shuhan Chen +11 more
wiley +1 more source
On the continuity of the vector valued and set valued conditional expectations
In this paper we study the dependence of the vector valued conditional expectation (for both single valued and set valued random variables), on the σ–field and random variable that determine it. So we prove that it is continuous for the L1(X) convergence
Nikolaos S. Papageorgiou
doaj +1 more source
Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification
We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for predicting entity types and ...
Adel, Heike, Schütze, Hinrich
core +1 more source
Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain
A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio +3 more
wiley +1 more source
Road detection based on the fusion of Lidar and image data
In this article, we propose a road detection method based on the fusion of Lidar and image data under the framework of conditional random field. Firstly, Lidar point clouds are projected into the monocular images by cross calibration to get the sparse ...
Xiaofeng Han +3 more
doaj +1 more source
Factorizing Probabilistic Graphical Models Using Co-occurrence Rate [PDF]
Factorization is of fundamental importance in the area of Probabilistic Graphical Models (PGMs). In this paper, we theoretically develop a novel mathematical concept, \textbf{C}o-occurrence \textbf{R}ate (CR), for factorizing PGMs.
Zhu, Zhemin
core +2 more sources
Grancalcin (GCA), a myeloid‐derived protein, is enriched in gingival tissues of periodontitis patients and mouse models. Through interactions with CD44 and activation of MYH9, GCA promotes NF‐κB signaling and exacerbates periodontal inflammation and bone loss.
Min Zhou +6 more
wiley +1 more source
Model of circNrip1 (cNrip1) upregulation driving neuropathic pain mechanisms. After peripheral nerve injury, increased FUS triggers the formation and upregulation of cNrip1 in injured DRG neurons. Upregulated cNrip1 recruits SYNCRIP to the 3′‐UTR of Tlr2 mRNA by binding to both, thereby promoting SYNCRIP‐triggered Tlr2 mRNA stability and increasing ...
Xiaozhou Feng +14 more
wiley +1 more source
Direct Numerical Simulations of the Kraichnan Model: Scaling Exponents and Fusion Rules
We present results from direct numerical simulations of the Kraichnan model for passive scalar advection by a rapidly-varying random scaling velocity field for intermediate values of the velocity scaling exponent.
Fairhall, Adrienne L. +3 more
core +1 more source

