468 papers
This collection explores the fascinating intersection of computer science and emerging technologies, where algorithms meet real-world challenges. From artificial intelligence shaping our daily lives to quantum computing promising to revolutionize data processing, these studies reveal how modern tools are reshaping our understanding of the digital world. The research here often bridges abstract theory with practical applications, offering insights into how machines learn and solve complex problems.
Every new preprint in this category originates from arXiv, the leading repository for open science. At Gist.Science, we process each fresh submission to provide both accessible plain-language overviews and detailed technical summaries, ensuring that groundbreaking discoveries are understandable to everyone regardless of their background. This dual approach helps democratize knowledge, making cutting-edge findings available to students, professionals, and curious minds alike.
Below are the latest papers in this dynamic field, ready for you to explore and understand.
This paper argues that to overcome the limitations of static and fragmented scientific infrastructure, neuroscience should serve as a stress test for adopting a dynamic, AI-aligned ecosystem that fosters decentralized, self-learning collaboration between humans and machines to accelerate discovery and ensure reproducibility.
This paper introduces QR-SPPS, a quantum-native framework utilizing VQE, ADAPT-VQE, and DOS-QPE algorithms to model correlated supply chain cascade failures, rank crisis interventions, and quantify tail risks more accurately than classical methods.
This paper presents a CUDA-Q and SeQUeNCe co-simulation study demonstrating that an intelligence-guided, resource-penalized adaptive purification policy, which integrates real-time cyber-anomaly detection, significantly improves the delivery of high-fidelity entanglement in quantum networks under DDoS attacks by dynamically trading raw throughput for fidelity.
This paper introduces a unified quantum-classical hybrid framework featuring Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F) models that leverage efficient linear transformations and MIMO-based multi-horizon prediction to achieve robust, parameter-efficient time-series forecasting suitable for near-term NISQ hardware.
ThRIve is a noise-aware training methodology that leverages low-rank adaptation to store critical parameters on thermally stable hardware, enabling CNN inference on heterogeneous PIM architectures to maintain high accuracy across varying temperatures while achieving up to a 5.4x reduction in energy-delay product compared to SRAM-based systems.
The paper introduces COLIP-2, a multimodal embedding model that integrates olfaction, vision, and language to enable robots to probabilistically localize scents to objects, while highlighting the current limitations of open-source data and the need for new datasets to advance olfactory perception.
This study proposes and validates a two-stage image enhancement pipeline that combines HSV-based luminosity correction with CLAHE applied to the Value channel, demonstrating superior quantitative performance in contrast and structural fidelity over standard HE and AHE methods for retinal fundus images while maintaining clinical processing speeds.
This mixed-methods study demonstrates that a brief, scaffolded "AI-Lab" intervention across multiple undergraduate courses successfully increased students' comfort and openness toward using Generative AI for conceptual and debugging tasks while fostering more critical, iterative engagement strategies without increasing overall self-reported usage on graded assignments.
This paper proposes leveraging multimodal data, specifically logfile and verbal interactions from a VR escape room, to move beyond traditional retention tests and better assess the reasoning processes and metacognitive skills involved in immersive learning.
This paper proposes STAR, an astrocyte-inspired repair mechanism that enables supervised memristive AI hardware to recover from permanent stuck-at faults by augmenting Equilibrium Propagation with a retraining-based "repair nudge" that reconstructs pre-fault neural representations without requiring explicit fault localization or hardware redundancy.