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2,609 papers
This package contains the extended test results for my Bachelor thesis MineAgent: Deep Reinforcement Learning-based Testing for Minecraft at the University of Trento, supervised by Fondazione Bruno Kessler (FBK) researchers.
An augmented friction model that incorporates the Stribeck effect to capture the low-velocity nonlinearities characteristic of high-gear-ratio actuators and indicates that explicitly modeling low-velocity friction is beneficial for high-fidelity sim-to-real transfer in high-reduction systems.
This study proposes a source-study-aware validation framework to estimate the shear capacity of stirrup-free steel-fiber-reinforced concrete corbels and assess the reliability and generalizability of machine-learning models in structural engineering. A database of 108 specimens, compiled from seven independent studies,...
This study aims to identify the factors causing students' learning difficulties in History and to describe the teacher's role in helping tenth-grade students overcome these learning difficulties at MA Negeri Insan Cendekia Palu. This research employed a descriptive qualitative approach. The participants consisted of th...
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Procrastination is widespread, costly, and persistent despite strong intentions to change. We show how procrastination can arise in stationary environments, where there are no deadlines, changing incentives, or new information to justify delay. With hyperbolic discounting, stochastic delay, i.e., acting with a fixed pr...
Regression testing in continuous integration (CI) helps preserve delivered software value by exposing regressions early, but limited feedback windows make test order consequential. This study evaluates a leakage-safe dynamic test case prioritization framework that reconstructs job–build–commit provenance, combines stri...
Overview This preprint presents the Autonomous Transduction Machine (ATM), a computational system that translates the Primitive Architecture of Spiral Geometry into an executable autonomous kernel capable of acting, constructing, learning, and self-correcting without fine human supervision. The architecture reproduces,...
In Chapter 2 , we explored the pivotal shift from rigid automation to the era of intelligent machines, introducing AI and Machine Learning, particularly Reinforcement Learning, as the core technologies enabling robots to learn. We learned that humanoids could begin to “see” and “learn to walk” through these advancement...
Urban rail transit systems face a pronounced contradiction between escalating traction energy consumption and passenger service quality under the carbon peaking and carbon neutrality goals. This study proposes a hybrid optimization framework offline combining deep reinforcement learning (DRL) and Non-dominated Sorting...
This release corresponds to the code used for the manuscript: "Reinforcement learning discovers new mechanisms of reentry in excitable media" The repository contains code for cellular automaton simulations, reinforcement-learning environments and training scripts, policy evaluation, simulations in the geometries studie...
This package contains the extended test results for my Bachelor thesis MineAgent: Deep Reinforcement Learning-based Testing for Minecraft at the University of Trento, supervised by Fondazione Bruno Kessler (FBK) researchers.
Code associated with the manuscript “Reinforcement learning discovers new mechanisms of reentry in excitable media.” This archive corresponds to the current manuscript submission and includes the cellular automaton simulations, reinforcement-learning environments and training scripts, policy evaluation, simulations in...
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
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.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
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