arXiv:2603.15218v1 Announce Type: cross Abstract: Aggregating a consensus ranking from multiple input rankings is a fundamental problem with applications in recommendation systems, search engines, job recruitment, and elections. Despite decades of research in consensus ranking aggregation, minimizing the Kemeny distance remains computationally intractable. Specifically, determining an optimal aggregation of rankings with respect to the Kemeny […]
Hecate: A Modular Genomic Compressor
arXiv:2603.15390v1 Announce Type: cross Abstract: We present Hecate, a modular lossless genomic compression framework. It is designed around uncommon but practical source-coding choices. Unlike many single-method compressors, Hecate treats compression as a conditional coding problem over coupled FASTA/FASTQ streams (control, headers, nucleotides, case, quality, extras). It uses per-stream codecs under a shared indexed block container. […]
RSGen: Enhancing Layout-Driven Remote Sensing Image Generation with Diverse Edge Guidance
arXiv:2603.15484v1 Announce Type: cross Abstract: Diffusion models have significantly mitigated the impact of annotated data scarcity in remote sensing (RS). Although recent approaches have successfully harnessed these models to enable diverse and controllable Layout-to-Image (L2I) synthesis, they still suffer from limited fine-grained control and fail to strictly adhere to bounding box constraints. To address these […]
A Review of Deep Learning Methods for Photoplethysmography Data
arXiv:2401.12783v2 Announce Type: replace Abstract: Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices. In recent years, the integration of deep learning has substantially advanced PPG signal analysis and broadened its applications across both healthcare and non-healthcare […]
Agentic Exploration of Physics Models
arXiv:2509.24978v5 Announce Type: replace Abstract: The process of scientific discovery relies on an interplay of observations, analysis, and hypothesis generation. Machine learning is increasingly being adopted to address individual aspects of this process. However, it remains an open challenge to fully automate the heuristic, iterative loop required to discover the laws of an unknown system […]
Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection
arXiv:2603.12916v2 Announce Type: replace-cross Abstract: Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomous driving, for instance, a steering command might be internally consistent but decouple from the resulting lateral acceleration. Residual-based detectors can miss such anomalies when flexible sequence models still reconstruct signals plausibly despite […]
Empowering Future Cybersecurity Leaders: Advancing Students through FINDS Education for Digital Forensic Excellence
arXiv:2603.00222v2 Announce Type: replace-cross Abstract: The Forensics Investigations Network in Digital Sciences (FINDS) Research Center of Excellence (CoE), funded by the U.S. Army Research Laboratory, advances Digital Forensic Engineering Education (DFEE) through an integrated research education framework for AI enabled cybersecurity workforce development. FINDS combines high performance computing (HPC), secure software engineering, adversarial analytics, and […]
Jacobian Scopes: token-level causal attributions in LLMs
arXiv:2601.16407v2 Announce Type: replace-cross Abstract: Large language models (LLMs) make next-token predictions based on clues present in their context, such as semantic descriptions and in-context examples. Yet, elucidating which prior tokens most strongly influence a given prediction remains challenging due to the proliferation of layers and attention heads in modern architectures. We propose Jacobian Scopes, […]
Multi-hop Reasoning and Retrieval in Embedding Space: Leveraging Large Language Models with Knowledge
arXiv:2603.13266v1 Announce Type: new Abstract: As large language models (LLMs) continue to grow in size, their abilities to tackle complex tasks have significantly improved. However, issues such as hallucination and the lack of up-to-date knowledge largely remain unresolved. Knowledge graphs (KGs), which serve as symbolic representations of real-world knowledge, offer a reliable source for enhancing […]
Real-Time Monocular Scene Analysis for UAV in Outdoor Environments
arXiv:2603.13368v1 Announce Type: cross Abstract: In this thesis, we leverage monocular cameras on aerial robots to predict depth and semantic maps in low-altitude unstructured environments. We propose a joint deep-learning architecture, named Co-SemDepth, that can perform the two tasks accurately and rapidly, and validate its effectiveness on a variety of datasets. The training of neural […]
HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
arXiv:2603.14773v1 Announce Type: cross Abstract: Fine-tuning large models on edge devices is severely hindered by the memory-intensive backpropagation (BP) in standard frameworks like federated learning and split learning. While substituting BP with zeroth-order optimization can significantly reduce memory footprints, it typically suffers from prohibitively degraded convergence speed. To resolve this dilemma, we propose Hybrid-Order Split […]
MVHOI: Bridge Multi-view Condition to Complex Human-Object Interaction Video Reenactment via 3D Foundation Model
arXiv:2603.14686v1 Announce Type: cross Abstract: Human-Object Interaction (HOI) video reenactment with realistic motion remains a frontier in expressive digital human creation. Existing approaches primarily handle simple image-plane motion (e.g., in-plane translations), struggling with complex non-planar manipulations like out-of-plane reorientation. In this paper, we propose MVHOI, a two-stage HOI video reenactment framework that bridges multi-view reference […]