Results 131 to 140 of about 411,514 (294)

Why Are Asset Returns Predictable? [PDF]

Starting from an information process governed by a geometric Brownian motion we show that asset returns are predictable if the elasticity of the pricing kernel is not constant.
Lüders, Erik
core  

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

ChicGrasp: Imitation‐Learning‐Based Customized Dual‐Jaw Gripper Control for Manipulation of Delicate, Irregular Bio‐Products

Advanced Robotics Research, EarlyView.
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar, Zhengtong Xu, Siavash Mahmoudi, Pouya Sohrabipour, Chaitanya Pallerla, Yu She, Wan Shou, Philip Glen Crandall, Dongyi Wang   +8 more
wiley   +1 more source

Posterior Consistency in Conditional Density Estimation by Covariate Dependent Mixtures [PDF]

This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways.
Norets, Andriy, Pelenis, Justinas
core  

Deep Learning Approach for Predicting Efficiency in Organic Photovoltaics from 2D Molecular Images of D/A Pairs

Advanced Theory and Simulations, EarlyView.
This study highlights the potential of deep learning, particularly Convolutional Neural Networks (CNNs), for predicting the photovoltaic performance of organic solar cells. By leveraging 2D images representing donor/acceptor molecular pairs, the model accurately estimates key performance indicators proving that this image‐based approach offers a fast ...
Khoukha Khoussa, Patrick Lévêque, Larbi Boubchir   +2 more
wiley   +1 more source

Multi‐Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects

Advanced Science, EarlyView.
The study proposes graph convolution network with sparse pooling to learn the hierarchical features of brain graph for MDD classification. Experiment is done on multi‐site fMRI samples (3335 subjects, the largest functional dataset of MDD to date) and transfer learning is applied, achieving an average accuracy of 70.14%.
Jianpo Su, Jian Qin, Hui Shen, Ling‐Li Zeng, Baojuan Li, Jin Liu, Xuemei Song, Li Liu, Weidan Pu, Tongjian Bai, Huaning Wang, Hong Yin, Kai Wang, Lingjiang Li, Dewen Hu   +14 more
wiley   +1 more source

Integrated Transcriptomics Reveals Evolutionary Trajectories and Cell Density‐Dependent Mechanisms in Aldosterone‐Producing Adenomas

Advanced Science, EarlyView.
Aldosterone‐producing adenomas (APAs) develop via two distinct paths: directly from adrenal zona glomerulosa (zG) cells, or stepwise from zG cells through aldosterone‐producing micronodules (APMs) before progressing to APAs. Advanced single‐cell and spatial analyses identified distinct cell states linked to oxidative stress and cell–cell interactions ...
Zhuolun Sun, Changying Jing, Martina Tetti, Siyuan Gong, Jia Wei, Yingxian Pang, Martin Reincke, Tracy Ann Williams   +7 more
wiley   +1 more source

The Estimation of Conditional Densities [PDF]

We discuss a number of issues in the smoothed nonparametric estimation of kernel conditional probability density functions for stationary processes. The kernel conditional density estimate is a ratio of joint and marginal density estimates.
Oliver Linton, Peter M Robinson, Xiaohong Chen   +2 more
core  

Estimation of nonlinear psychophysical kernels

Journal of Vision, 2004
Reverse correlation techniques have been extensively used in physiology (Marmarelis & Marmarelis 1978; Sakai, Naka, & Korenberg, 1988), allowing characterization of both linear and nonlinear aspects of neuronal processing (e.g., Emerson, Bergen, & Adelson, 1992; Emerson & Citron 1992).
openaire   +2 more sources

Ecologically‐Valid Emotion Signatures Enhance Mood Disorder Diagnostics

Advanced Science, EarlyView.
This study identifies ecologically‐valid Divergent Emotional Functional Networks (DEFN), derived from dynamic functional connectivity during naturalistic movie watching. The DEFN reliably enhances diagnostic accuracy for mood disorders, including major depressive and bipolar disorders, demonstrating strong reproducibility across demographic factors and
Shuyue Xu, Linling Li, Ting Luo, Gan Huang, Li Zhang, Benjamin Becker, Zhen Liang   +6 more
wiley   +1 more source