Results 161 to 170 of about 160,958 (306)

Estimating Probabilities of Default With Support Vector Machines [PDF]

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default.
Rouslan Moro, Wolfgang Härdle, Dorothea Schäfer   +2 more
core  

Brain dynamics of attentional, default-mode and limbic networks are disrupted at rest in post-COVID-19 syndrome. [PDF]

Brain Behav Immun Health
Cahart MS, O' Daly O, Cai Z, Mariani N, Borsini A, Mondelli V, Eiff B, Rota S, Nicholson T, Rida L, Hampshire A, Dipasquale O, Fernandes L, Turkheimer F, Williams SCR, Martins D.   +15 more
europepmc   +1 more source

People Counting and Positioning Using Low‐Resolution Infrared Images for FeFET‐Based In‐Memory Computing

Advanced Electronic Materials, EarlyView.
In this work, low‐resolution infrared imaging is combined with a 28 nm FeFET IMC architecture to enable compact, energy‐efficient edge inference. MLC FeFET devices are experimentally characterized, and controlled multi‐level current accumulation is validated at crossbar array level.
Alptekin Vardar, Li Zhang, Alexander Rehmer, Marc Lupp, Subhashree Baskaran, Osama Abdelaal, Franz Müller, Nandakishor Yadav, Bing Li, Thomas Kämpfe   +9 more
wiley   +1 more source

Estimating Default Probabilities of Emerging Market Sovereigns: A New Look at a Not-So-New Literature [PDF]

The January 2001 proposal for a New Basel Capital Accord has renewed the interest in obtaining default probabilities for various types of borrowers. This paper uses a panel logit model to estimate default probabilities of 78 emerging market countries ...
Marcel Peter
core  

Network-Level Convergence of Adiposity-Related Gray Matter Alterations: A Coordinate-Based Connectome Mapping Meta-Analysis With Imaging Transcriptomics. [PDF]

Hum Brain Mapp
Li Y, Lin M, Zhang G, Zheng H, Zheng W.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Proceso de ASC - PREDICTING COLOMBIAN SOVEREIGN DEFAULT PROBABILITY USING MACHINE LEARNING

El propósito de esta investigación es utilizar una muestra para predecir la probabilidad de default del gobierno colombiano, mediante técnicas de machine learning que buscan crear algoritmos de predicción.
Galeano, Juan, Mena, Luis, Cortés, Lina M, Mosquera, Stephania   +3 more
core  

CLARISA: Connexin-43 Lateralization Automated ROI-Based Image Signal Analyzer. [PDF]

Int J Mol Sci
Gattari D, Sancho-Zamora J, Chan D, Prado NJ, Diez ER, Soria ML, Rossi M.   +6 more
europepmc   +1 more source

Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State Conductors

Advanced Energy Materials, EarlyView.
Large‐scale machine learning‐based molecular dynamics simulations are used to investigate isovalent amorphous oxyhalides, revealing a remarkable chemically independent ionic conductivity. A rigorous analysis of alkali residence times across different metal–anion environments identifies divalent anions as key diffusion bottlenecks.
Luca Binci, KyuJung Jun, Bowen Deng, Gerbrand Ceder   +3 more
wiley   +1 more source

CVaR and Credit Risk Measurement [PDF]

The link between credit risk and the current financial crisis accentuates the importance of measuring and predicting extreme credit risk. Conditional Value at Risk (CVaR) has become an increasingly popular method for measuring extreme market risk.
David E Allen, Robert Powell
core