cs papers | Gist.Science

Kernel Alignment-based Hybrid Heterogeneous Attribute Space Three-way Clustering for Disease Feature Recognition

This paper proposes a novel feature selection method that integrates kernel alignment to unify heterogeneous medical data within a Reproducing Kernel Hilbert Space and extends three-way clustering to explicitly model boundary uncertainty, thereby effectively reducing dimensionality while significantly improving disease prediction accuracy and stability on multimodal clinical datasets.

Cheng Qian, Tinggui Chen, Jianjun Yang2026-06-29

💻 computer science

Rule-to-Data Knowledge Transfer via Optimal Transport for Weakly Supervised Anomaly Detection on Transaction Graphs

This paper proposes a weakly supervised framework for transaction-graph anomaly detection that leverages optimal transport to align hierarchical rule semantics, derived from decision trees, with continuous transaction representations, thereby generating high-quality pseudo-labels and outperforming existing baselines on benchmark datasets.

Qiuyang Zhang, Keyang Chen, Mingxuan Jiang, Yuan Shui, Yandan Tan, Zhixin Li, Hongbin Zhu, Hongfeng Chai2026-06-29

💻 computer science

Public regulatory data infrastructure for lifecycle oversight of AI medical devices

This paper argues that current public regulatory records for AI medical devices are inadequate for lifecycle oversight due to a lack of structured, queryable data on software changes and AI-specific attributes, and proposes a new data specification to address these critical deficits across major global jurisdictions.

Yu Han, Aaron Ceross2026-06-29

💻 computer science

Multimodal Structural Anomaly Detection in Invoices Using Text–Logic Fusion

This paper proposes a multimodal unsupervised anomaly detection framework that fuses textual semantics from OCR and financial logical features to effectively identify structural irregularities and fraud in semi-structured invoices, outperforming single-modality baselines on real-world data.

Mridumay Saha, Payal Khanal, Gaurav Sarma2026-06-29

💻 computer science

Adaptive constrained reinforcement learning for energy-aware scheduling under changing load

This paper introduces CLASP, an adaptive constrained reinforcement learning scheduler that enforces explicit latency constraints via a PID-controlled Lagrangian price and price-conditioned policy, thereby maintaining stable energy efficiency and violation targets across varying workloads where traditional fixed-weight reward approaches fail.

Saher Elsayed, Khairi Azhar Aziz2026-06-29

💻 computer science

Enhancing Cybersecurity in Cloud Computing Environments through Zero Trust Architecture: A Multi-Layer Adaptive Framework with Simulation-Based Evaluation of Attack-Surface Reduction

This paper proposes and evaluates a multi-layer adaptive Zero Trust framework that utilizes a context-aware trust scoring mechanism and rigorous mathematical modeling to demonstrate a significant reduction in attack success rates and lateral movement within cloud environments compared to traditional perimeter-based security.

mohsin bashir hakak2026-06-29

💻 computer science

RadioAI: Physics-Grounded, Interpretable Deep Learning for Multi-Modal Medical Image Classification

RadioAI presents a reproducible, physics-grounded multi-modal deep learning system that integrates specialized architectures for chest CT, breast ultrasound, and brain MRI classification with an automatic modality-routing Gatekeeper and interpretable visualization tools, validated through controlled ablation studies demonstrating the empirical necessity of physics-informed design components.

Maitri Savaliya2026-06-29

💻 computer science

Quantum-Projected Semantic Retrieval: A Universal Low-Latency Backbone for Multi-Modal AI Systems

The paper introduces QuantumRAG, a universal, low-latency retrieval system that maps text embeddings to complex quantum states in Hilbert space to achieve BERT-level accuracy with 3.6× faster inference on standard CPU hardware, while remaining agnostic to data modalities like text, math, and video.

Amit Rana2026-06-29

💻 computer science

From Classroom to Cubicle: Academic Origins, Institutional Trajectories, and Research Impact of AI Scientists at MAANG Companies

This paper introduces the MAANG-AI-450 dataset to analyze the educational backgrounds and citation impacts of AI researchers at major tech companies, revealing that while PhD training is concentrated at elite institutions, citation success is highly skewed and often driven by current work environments rather than doctoral pedigree alone.

Kunal Dhanda2026-06-29

💻 computer science

Geometry-First Visual Intelligence: Deep Geometric Networks and Quantum Geometric Networks for Gesture Recognition

This paper introduces Deep Geometric and Quantum Geometric Networks (DGN/QGN), a neuro-symbolic architecture that achieves competitive gesture recognition by extracting explicit, interpretable differential-geometric features from motion and mapping them directly to quantum circuit parameters, demonstrating that current performance limitations stem from hardware constraints rather than algorithmic flaws.

Amit Rana2026-06-29

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