Knowledge catalogue
arXiv:2604.27247v1 Announce Type: new Abstract: Hedges and other linear woody features provide valuable ecosystem services, particularly within intensively managed agricultural landscapes. They are ke
arXiv:2504.18902v2 Announce Type: replace-cross Abstract: In the forthcoming era of 6G networks, characterized by unprecedented data rates, ultra-low latency, and ubiquitous connectivity, effective ma
arXiv:2604.27356v1 Announce Type: cross Abstract: Heterogeneous graphs are widely used to model multi-relational systems, but missing node attributes remain a major bottleneck for downstream learning.
arXiv:2507.13486v2 Announce Type: replace Abstract: Uncertainty quantification of the photogrammetry process is essential for providing per-point accuracy credentials of the point clouds. Unlike airbo
arXiv:2510.17196v3 Announce Type: replace-cross Abstract: Effectively processing long contexts is a critical challenge for language models. While standard Transformers are limited by quadratic complex
arXiv:2604.27491v1 Announce Type: new Abstract: Modeling 4D human-object interaction (HOI) is a compelling challenge in computer vision and an essential technology powering virtual and mixed-reality a
arXiv:2604.28021v1 Announce Type: cross Abstract: Cooking is a cultural expression of human creativity that transcends geography and time through the orchestration of ingredients and techniques, much
arXiv:2604.27126v1 Announce Type: new Abstract: This study presents an unsupervised machine learning workflow for electrofacies analysis in the offshore Keta Basin, Ghana, where core data are scarce.
arXiv:2411.15253v2 Announce Type: replace-cross Abstract: Osteoporosis, a prevalent condition among the aging population worldwide, is characterized by diminished bone mass and altered bone structure,
arXiv:2604.27017v1 Announce Type: cross Abstract: Deep learning models for 12-lead electrocardiogram (ECG) analysis achieve high diagnostic performance but lack the intuitive interpretability required
arXiv:2510.09696v2 Announce Type: replace-cross Abstract: The increasing scale of Deep Neural Networks (DNNs) increases the need for compression techniques such as pruning, quantization, and low-rank
arXiv:2604.27685v1 Announce Type: cross Abstract: While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computat
arXiv:2604.27259v1 Announce Type: new Abstract: Time-series classification (TSC) has advanced significantly with deep learning, yet most models rely solely on raw numerical inputs, overlooking alterna
We now know the Iran war price tag is more like $50 billion - hundreds of dollars per household - and counting. It's enough to cover all the health insurance premium credits that the Republicans got r
arXiv:2604.27398v1 Announce Type: new Abstract: For constructing text embeddings, mean pooling, which averages token embeddings, is the standard approach. This paper examines whether mean pooling actu
arXiv:2604.27743v1 Announce Type: cross Abstract: We develop a geometric and information-theoretic framework for encoder-decoder learning built on the Information Bottleneck (IB) principle. Recasting
arXiv:2604.27322v1 Announce Type: new Abstract: Recent advances in Diffusion Transformer (DiT)-based video generation technologies have shown impressive results for video object removal. However, thes
arXiv:2604.27606v1 Announce Type: cross Abstract: Learning informative representations from tabular data in remote sensing and environmental science is challenging due to heterogeneity, scarce labels,
arXiv:2604.26212v1 Announce Type: new Abstract: In this paper, we introduce GET-2D-1.0, a fast grasp planner for the GET asymmetrical gripper that operates from a single-view RGB-D image, using the Fe
arXiv:2601.18339v2 Announce Type: replace-cross Abstract: The Complete Vocal Technique (CVT) is a school of singing developed in the past decades by Cathrin Sadolin et al.. CVT groups the use of the v
arXiv:2602.23163v3 Announce Type: replace Abstract: Large language models are beginning to show steganographic capabilities. Such capabilities could allow misaligned models to evade oversight mechanis
arXiv:2603.11806v2 Announce Type: replace-cross Abstract: In this paper, we propose a novel mathematical framework for piecewise diffeomorphic image registration that involves discontinuous sliding mo
arXiv:2604.26351v1 Announce Type: new Abstract: Language models (LMs) behave more like humans when their cognitive resources are restricted, particularly in predicting sentence processing costs such a
arXiv:2510.08278v3 Announce Type: replace Abstract: Embodied Reference Understanding requires identifying a target object in a visual scene based on both language instructions and pointing cues. While
arXiv:2604.26230v1 Announce Type: new Abstract: I developed a new version of Latent Semantic Scaling (LSS) employing word2vec as a masked language model. Unlike original spatial models, it assigns pol
arXiv:2603.25980v2 Announce Type: replace-cross Abstract: Transition states, the first-order saddle points on the potential energy surfaces, govern the kinetics and mechanisms of chemical reactions an
arXiv:2604.25979v1 Announce Type: cross Abstract: We present a quantitative analysis of character-pair substitution ratios in the Voynich manuscript, testing whether Currier's A/B language distinction
arXiv:2604.07387v2 Announce Type: replace-cross Abstract: We present a design automation framework for analog circuit sizing that produces calibrated, topology-specific analytical equations from raw c
arXiv:2604.26348v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success in image generation, yet their training is predominantly driven by full-reference objectives that en
arXiv:2604.26492v1 Announce Type: cross Abstract: Visual data compression is shifting from human-centered reconstruction to machine-oriented representation coding. In this setting, an image is often m
arXiv:2604.26326v1 Announce Type: cross Abstract: Reinforcement learning (RL) has unlocked complex reasoning abilities in large language models (LLMs). However, most RL algorithms suffer from performa
arXiv:2604.26571v1 Announce Type: new Abstract: Municipal solid waste incineration is increasingly central to urban waste management, yet its sustainability benefit depends on controlling carbon emiss
AI automates tasks, not jobs, and when a task gets cheaper, demand for the job grows. AI cannot automate jobs end-to-end because it lacks autonomy and cannot operate without supervision. There is stil
arXiv:2604.25931v1 Announce Type: new Abstract: We identify a previously unknown calibration property of large language models: providing one confirmed intermediate fact toward a multi-step reasoning
arXiv:2604.26237v1 Announce Type: new Abstract: This study applied the Apriori algorithm to analyze behavioral interaction patterns associated with learned helplessness (LH) in mathematics tutoring sy
arXiv:2604.26437v1 Announce Type: new Abstract: Purpose: Rapid and reliable diagnostic tools are crucial for managing respiratory diseases like COVID-19, where chest X-ray analysis coupled with artifi
arXiv:2603.13589v2 Announce Type: replace-cross Abstract: Estimating motion from spatiotemporal geoscientific data is a fundamental component of many environmental modeling and forecasting tasks. In t
arXiv:2604.26349v1 Announce Type: cross Abstract: We present a learning-augmented online algorithm for the preemptive FIFO buffer management problem, where packets arrive online to a finite-capacity b
arXiv:2604.26809v1 Announce Type: new Abstract: Federated Unlearning (FU) is an emerging paradigm in Federated Learning (FL) that enables participating clients to fully remove their contributions from
arXiv:2509.26184v5 Announce Type: replace-cross Abstract: Generation of citation-backed reports is a primary use case for retrieval-augmented generation (RAG) systems. While open-source evaluation too
arXiv:2604.26310v1 Announce Type: new Abstract: Fine-grained emotion classification, which identifies specific emotional states such as happiness, anger, sadness, and fear, remains a challenging task
arXiv:2604.26148v1 Announce Type: cross Abstract: AI agents operating on user interfaces must understand how interfaces communicate state and feedback to act reliably. As a core communicative modality
arXiv:2604.26910v1 Announce Type: new Abstract: This paper presents a planning pipeline framework for locomotion in rope-assisted robots climbing vertical surfaces. The proposed framework is formulate
arXiv:2604.26048v1 Announce Type: new Abstract: This paper presents a principled and scalable framework for systematically generating complex Question Answering (QA) data. In the core of this framewor
arXiv:2604.26209v1 Announce Type: cross Abstract: Some text generation tasks, such as Attribute Value Extraction (AVE), require decoding multiple independent sequences from the same document context.
arXiv:2604.02021v2 Announce Type: replace Abstract: Voxel-grid reinforcement learning is widely adopted for path planning in redundant manipulators due to its simplicity and reproducibility. However,
arXiv:2601.19216v2 Announce Type: replace-cross Abstract: The emerging applications of next-generation wireless networks demand high-fidelity environmental intelligence. 3D radio maps bridge physical
arXiv:2604.26169v1 Announce Type: new Abstract: Treatment allocation under budget constraints is a central challenge in digital advertising: advertisers must decide which users to show ads to while sp
arXiv:2604.26176v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) has significantly advanced Knowledge Graph Question Answerin
arXiv:2604.26241v1 Announce Type: new Abstract: Passive RFID tags offer a cost-effective and scalable solution for tracking numerous deployed assets. However, in forested environments, signal attenuat
arXiv:2601.13606v2 Announce Type: replace Abstract: Chart reasoning is a critical capability for Vision Language Models (VLMs). However, the development of open-source models is severely hindered by t
arXiv:2604.26301v1 Announce Type: new Abstract: Graph Contrastive Learning (GCL) has emerged as a prominent framework for unsupervised graph representation learning. However, relying on augmentation d
arXiv:2604.26664v1 Announce Type: cross Abstract: Traditional iterative reconstruction methods are accurate but computationally expensive, limiting their use in high-throughput and real-time ptychogra
arXiv:2604.25928v1 Announce Type: new Abstract: Professional domain knowledge underpins human civilization, serving as both the basis for industry entry and the core of complex decision-making and pro
arXiv:2405.13729v3 Announce Type: replace-cross Abstract: In this paper, we study an under-explored but important factor of diffusion generative models, i.e., the combinatorial complexity. Data sample
arXiv:2503.02332v3 Announce Type: replace-cross Abstract: Accurate segmentation of 3D vascular structures is essential for various medical imaging applications. The dispersed nature of vascular struct
arXiv:2604.26229v1 Announce Type: new Abstract: This study compares machine learning and deep learning approaches for cyberbullying detection in Indonesian-language Instagram comments. Using a balance
arXiv:2511.22958v2 Announce Type: replace Abstract: Deep learning has revolutionized solar image analysis, yet most approaches train task-specific encoders from scratch or rely on natural-image pretra
arXiv:2506.20876v4 Announce Type: replace Abstract: Technological progress has led to concrete advancements in tasks that were regarded as challenging, such as automatic fact-checking. Interest in ado
arXiv:2309.13722v3 Announce Type: replace-cross Abstract: Recently, several deep learning (DL) methods for approximating high-dimensional partial differential equations (PDEs) have been proposed. The