Results 61 to 70 of about 57,740 (314)
Musicking Deep Reinforcement Learning
, 2022 In this paper, I relate an auto-reflexive analysis of my practice of designing and musicking deep reinforcement learning. Based on technical description of the Co-Explorer, a deep reinforcement learning agent designed to support sonic exploration through positive or negative human feedback, I discuss how deep reinforcement learning can be seen as a ...
openaire +2 more sources
FEBS Open Bio, EarlyView.This study presents a novel approach to teaching Python and bioinformatics using team‐based learning and cloud‐hosted notebooks. By integrating interactive coding into biomedical education, the method improves accessibility, student engagement, and confidence—especially for those without a computing background.
Nuno S. Osório, Leonardo D. Garma
wiley +1 more source
Bayesian Deep Reinforcement Learning via Deep Kernel Learning
International Journal of Computational Intelligence Systems, 2018 Reinforcement learning (RL) aims to resolve the sequential decision-making under uncertainty problem where an agent needs to interact with an unknown environment with the expectation of optimising the cumulative long-term reward. Many real-world problems
Junyu Xuan +3 more
doaj +1 more source
Diffusion‐based size determination of solute particles: a method adapted for postsynaptic proteins
FEBS Open Bio, EarlyView.We present a diffusion‐based approach for measuring the size of macromolecules and their complexes, and demonstrate its use on postsynaptic proteins. The method requires fluorescein‐labelled protein samples, a microfluidic device that maintains laminar flow for said samples, a microscope recording the emitted fluorescent signals, and an analytic ...
András László Szabó +7 more
wiley +1 more source
Sensors, 2023 Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the ...
Shokhikha Amalana Murdivien, Jumyung Um
doaj +1 more source
FEBS Open Bio, EarlyView.Mitochondria‐associated membranes (MAMs) are contact sites between the endoplasmic reticulum and mitochondria that regulate calcium signaling, lipid metabolism, autophagy, and stress responses. This review outlines their molecular organization, roles in cellular homeostasis, and how dysfunction drives neurodegeneration, metabolic disease, cancer, and ...
Viet Bui +3 more
wiley +1 more source
物联网学报, 2023 Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have ...
Cenhuishan LIAO +4 more
doaj +2 more sources
Applied Sciences, 2023 The optimization of caching mechanisms has long been a crucial research focus in cloud–edge collaborative environments. Effective caching strategies can substantially enhance user experience quality in these settings.
Xinyu Zhang +6 more
doaj +1 more source
Overview of molecular signatures of senescence and associated resources: pros and cons
FEBS Open Bio, EarlyView.Cells can enter a stress response state termed cellular senescence that is involved in various diseases and aging. Detecting these cells is challenging due to the lack of universal biomarkers. This review presents the current state of senescence identification, from biomarkers to molecular signatures, compares tools and approaches, and highlights ...
Orestis A. Ntintas +6 more
wiley +1 more source
Multiagent Deep Reinforcement Learning Algorithms in StarCraft II: A Review
IEEE AccessStarCraft II, as a real-time strategy game, features multiagent collaboration, complex decision-making processes, partially observable environments, and long-term credit assignment; thus, it is an ideal platform for exploring, validating, and optimizing ...
Yanyan Li, Yijun Wang, Yiwei Zhou
doaj +1 more source