Results 31 to 40 of about 144,645 (228)

The Block Point Process Model for Continuous-Time Event-Based Dynamic Networks

, 2019
We consider the problem of analyzing timestamped relational events between a set of entities, such as messages between users of an on-line social network.
Devabhaktuni, Vijay K., Haghdan, Maysam, Junuthula, Ruthwik R., Xu, Kevin S.   +3 more
core   +1 more source

Predictive intelligence to the edge through approximate collaborative context reasoning [PDF]

, 2017
We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event).
Anagnostopoulos, Christos, Kolomvatsos, Kostas   +1 more
core   +1 more source

Aggressive prostate cancer is associated with pericyte dysfunction

Molecular Oncology, EarlyView.
Tumor‐produced TGF‐β drives pericyte dysfunction in prostate cancer. This dysfunction is characterized by downregulation of some canonical pericyte markers (i.e., DES, CSPG4, and ACTA2) while maintaining the expression of others (i.e., PDGFRB, NOTCH3, and RGS5).
Anabel Martinez‐Romero, Ane Martinez‐Larrinaga, Joaquim Grego‐Bessa, Saioa Garcia‐Longarte, Hielke van Splunder, Ianire Astobiza, Amaia Ercilla, Laura Bozal‐Basterra, Isabel Mendizabal, Pilar Villacampa, Arkaitz Carracedo, Mariona Graupera   +11 more
wiley   +1 more source

Semantic segmentation and semi-transparent visualization of neuroblastoma based on ensemble learning

工程科学学报
Neuroblastoma is a cancer originating from immature nerve cells that mostly occurs in infants and young children. The morphology of neuroblastoma tumors is highly complex, exhibiting variations in location, shape, and size.
Jiao PAN, Shenyu JI, Liang LI, Satoshi TANAKA, Xiaokun WANG   +4 more
doaj   +1 more source

Predicting human preferences using the block structure of complex social networks [PDF]

, 2012
With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need.
Guimera, Roger, Llorente, Alejandro, Moro, Esteban, Sales-Pardo, Marta   +3 more
core   +3 more sources

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Hidden Markov Models and their Application for Predicting Failure Events

, 2020
We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates.
A Gelman, AR Cassandra, DM Blei, DM Blei, G Shani, GA Satten, GD Forney, KP Murphy, LJ Wei, LP Kaelbling, MD Hoffman, P Koprinkova-Hristova, WB Powell, Y Wu   +13 more
core   +1 more source

Residual Neural Processes

AAAI Conference on Artificial Intelligence, 2020
A Neural Process (NP) is a map from a set of observed input-output pairs to a predictive distribution over functions, which is designed to mimic other stochastic processes' inference mechanisms.
Byung-Jun Lee, Seunghoon Hong, Kyungmin Kim   +2 more
semanticscholar   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source