Spherical tanks are widely used pressure special equipment in the petrochemical, energy, and metallurgical industries, and their regular non-destructive testing is a critical link to ensuring safe operation. Path planning technology directly determines the coverage completeness, operational efficiency, motion safety, a...
Code accompanying A Spread-Aware Deep Reinforcement Learning Framework Using Directional Changes Sampling for High Frequency FX Trading (G. Rayment, T. Papastylianou, M. Kampouridis), revised for Array. Attached: per-trade logs behind the paper's results. Every completed trade of every evaluation run, with executed ent...
George Rayment· Zenodo (CERN European Organi...· 0 citations
ATTENTION ALLOCATION, SCARCITY & REINFORCEMENT AT THE LIMIT Selective Processing, Salience, Novelty, Attention Capture, Decay,Forgetting, and Future Accessibility Feng Cheng-en (33) x Starli When does selective attention measurably reshape the future accessibility of meaning? ATTENTION ALLOCATION, SCARCITY & REINFORCEM...
Various neurocognitive processes contribute to reinforcement learning (RL) and decision making, requiring careful task designs and computational modeling to disentangle them. People rely on capacity-limited working memory (WM) to rapidly and flexibly adapt behavior during learning, together with slower incremental RL p...
Krishn Bera, Alexander Fengler, Megan A. Boudewyn et al.· PLoS Computational Biology· 0 citations
Abstract Electricity demand from data centers is rising quickly with the deployment of artificial intelligence (AI), and cooling remains the largest non-IT load, particularly in hot-arid climates. This paper presents a performance analysis of leveraging AI to reduce the energy consumption of a 10 MW AI/cloud data cente...
Abstract Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone). Quantifying appropriate interventions to market price...
Panayiotis Danassis, Aris Filos-Ratsikas, Haipeng Chen et al.· Autonomous Agents and Multi-...· 0 citations
EEG, pupillometry, and behavioral data from 28 healthy adults performing an auditory fear conditioning paradigm with voice stimuli. Participants localized voices (left or right) across Pre-Conditioning, Conditioning, and Extinction phases, with conditioned voices paired with an aversive loud white noise using 50% parti...
Martina T. Cinca-Tomás, Judith Domínguez‐Borràs· UC San Diego· 0 citations
EEG recordings from a three-armed bandit reinforcement learning task in which alcohol-related and non-alcohol beverage cues were presented to 53 community participants, including hazardous drinkers and controls. The dataset supports investigation of electrophysiological markers of cue-specific exploration in hazardous...
Ethan Campbell, James F Cavanagh· UC San Diego· 0 citations
Magnetoencephalography (MEG) recordings from participants with major depressive disorder and non-depressed controls performing a probabilistic selection reinforcement learning task. The dataset was collected to examine the Reward Positivity and related ventromedial frontal cortex activity during feedback processing. It...
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.