Quantum topology is a rapidly developing field with the potential to revolutionize quantum computing and information processing. It leverages the unique properties of spacetime to create topologically protected quantum states, offering enhanced resilience against noise and decoherence. The accurate modeling of quantum...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the potential of utilizing biofeedback signals, specifically electroencephalogram (EEG) and electrocardiogram (ECG) data, as inputs to design adaptive and personalized machine learning algorithms. The core concept involves translating biofeedback signals into adjustable parameters within a machi...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
BACKGROUND
Traditional rehabilitation medicine, primarily dependent on qualitative clinical assessment and static therapeutic protocols, faces significant challenges in scalability, objectivity, and dynamic adaptability. The integration of Artificial Intelligence (AI) is catalyzing a paradigm shift from "experience-dri...
Hao Wu, Ya-Li Yang, Li Wang et al.· Journal of Evaluation In Cli...· 0 citations
We document Organismo Vivo, a multi-agent trading framework combining reinforcement learning agents with encoder-based regime detection, validated through walk-forward testing, preregistration, and adversarial auditing.
Augusto Toso· Zenodo (CERN European Organi...· 0 citations
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This paper explores a novel approach to program self-optimization, termed "Entropy-Driven Program Self-Optimization (EDPSO)." The core concept revolves around a program's ability to monitor and adapt its own execution based on the inherent information entropy within its processes. The system employs an "entropy-aware"...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Dynamic Topological Dependency Learning (TDTL), a novel approach to knowledge representation and reasoning that leverages reinforcement learning to dynamically construct and adapt an internal knowledge graph based on observed data and inference results. Unlike traditional methods that rely on pre-...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Automated theorem proving (ATP) aims to develop systems capable of mechanically proving mathematical theorems. Despite significant advancements, ATP systems often struggle with complex reasoning tasks, largely due to the inherent difficulty in representing and executing logical deduction rules. This work proposes a nov...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel dynamic probability field model designed to simulate complex system behavior and predict future states by dynamically adjusting probability field parameters based on real-time data and environmental changes. Leveraging the integration of machine learning and reinforcement learning, this mode...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces the Dynamic Neural Network Topology (DNTN), a novel neural network architecture designed to overcome the limitations of static, connection-based networks. The core claim of this work is that by dynamically adjusting the physical connection strengths and topology of neurons in real-time, adaptive l...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Automated theorem proving (ATP) aims to develop systems capable of mechanically proving mathematical theorems. Despite significant advancements, ATP systems often struggle with complex reasoning tasks, largely due to the inherent difficulty in representing and executing logical deduction rules. This work proposes a nov...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This survey traces the mathematical evolution of multi-period financial planning over the past several decades, focusing on selected key methods ranging from classical stochastic optimization to learning-enabled decision systems.
Yi-Rui Luo, John M. Mulvey· IMA Journal of Management Ma...· 0 citations
Abstract: Biological organisms are open systems operating under rigorous thermodynamic constraints, where homeostatic maintenance requires a constant expenditure of energy to suppress entropic decay. Conventional medical interventions are primarily reactive, acting only after homeostatic collapse exceeds critical thres...
Jeongwoo Han· Zenodo (CERN European Organi...· 0 citations
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.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
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.
