Search: “research”

arXiv:2604.18540v1 Announce Type: cross Abstract: Adversarial training of binary classifiers can be reformulated as regularized risk minimization involving a nonlocal total variation. Building on this

arXiv:2604.16483v1 Announce Type: new Abstract: Concept erasure in Text-To-Image (T2I) diffusion models is vital for safe content generation, but existing inference-time methods face significant limit

arXiv:2604.18080v1 Announce Type: cross Abstract: While attack graphs are useful for identifying major cybersecurity threats affecting a system, they do not provide operational support for determining

arXiv:2604.17319v1 Announce Type: cross Abstract: Grounded Multimodal Named Entity Recognition (GMNER) aims to jointly identify named entity mentions in text, predict their semantic types, and ground

arXiv:2604.17047v1 Announce Type: cross Abstract: We present E2E-WAVE, the first end-to-end learned waveform generation system for underwater video multicasting. Acoustic channels exhibit 20--46% bit

arXiv:2511.10834v3 Announce Type: replace Abstract: Low-latency delivery of satellite imagery is essential for time-critical applications such as disaster response, intelligence, and infrastructure mo

arXiv:2604.18367v1 Announce Type: new Abstract: Early action prediction seeks to anticipate an action before it fully unfolds, but limited visual evidence makes this task especially challenging. We in

arXiv:2602.03986v2 Announce Type: replace Abstract: Conformal prediction, a post-hoc, distribution-free, finite-sample method of uncertainty quantification that offers formal coverage guarantees under

arXiv:2604.16783v1 Announce Type: new Abstract: Vehicle trajectory prediction is central to highway perception, but deployment on roadside edge devices necessitates bounded, deterministic end-to-end l

arXiv:2604.18220v1 Announce Type: cross Abstract: Electroencephalography (EEG) signals have been promising for long-term braking intensity prediction but are prone to various artifacts that limit thei

arXiv:2510.12916v2 Announce Type: replace-cross Abstract: Systems of interacting continuous-time Markov chains are a powerful model class, but inference is typically intractable in high dimensional se

arXiv:2604.18106v1 Announce Type: new Abstract: Adapting large language models (LLMs) to low-resource languages (LRLs) is constrained by the scarcity of task data and computational resources. Although

arXiv:2604.17749v1 Announce Type: new Abstract: Understanding physical transformation processes is crucial for both human cognition and artificial intelligence systems, particularly from an egocentric

arXiv:2601.09825v2 Announce Type: replace Abstract: We establish a lower bound on the eluder dimension of generalised linear model classes, showing that standard eluder dimension-based analysis cannot

arXiv:2604.17211v1 Announce Type: new Abstract: We present EmbodiedHead, a speech-driven talking-head framework that equips LLMs with real-time visual avatars for conversation. A practical embodied av

arXiv:2602.07794v3 Announce Type: replace Abstract: Large language models (LLMs) exhibit emergent behaviors suggestive of human-like reasoning. While recent work has identified structured conceptual r

arXiv:2510.23969v2 Announce Type: replace-cross Abstract: We present a neuromuscular speech interface that translates electromyographic (EMG) signals recorded from orofacial muscles during speech arti

arXiv:2602.16161v3 Announce Type: replace-cross Abstract: Emotional expression underpins natural communication and effective human-computer interaction. We present Emotion Collider (EC-Net), a hyperbo

arXiv:2511.12554v2 Announce Type: replace Abstract: Visual Emotion Analysis (VEA) aims to bridge the affective gap between visual content and human emotional responses. Despite its promise, progress i

En 2021, les opposants à la vaccination craignaient 'une hécatombe' dans les années à venir. Nous sommes 5 ans plus tard et une immense étude sur les données de 29 millions de Français (23 millions va

arXiv:2601.05508v2 Announce Type: replace-cross Abstract: Hieroglyphs, as logographic writing systems, encode rich semantic and cultural information within their internal structural composition. Yet,

arXiv:2604.18066v1 Announce Type: cross Abstract: Anomaly-based Intrusion Detection Systems (IDSs) ensure protection against malicious attacks on networked systems. While deep learning-based IDSs achi

arXiv:2603.15299v2 Announce Type: replace Abstract: We propose a novel approach which exploits chaos to enhance classification accuracy. Specifically, the available data that need to be classified are

arXiv:2412.02904v2 Announce Type: replace Abstract: Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering cap

arXiv:2604.17233v1 Announce Type: new Abstract: Personalized image aesthetics assessment (PIAA) aims to predict an individual user's subjective rating of an image, which requires modeling user-specifi

arXiv:2510.00861v2 Announce Type: replace Abstract: While search-augmented large language models (LLMs) exhibit impressive capabilities, their reliability in complex multi-hop reasoning remains limite

arXiv:2505.15353v3 Announce Type: replace Abstract: Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterog

arXiv:2502.13464v2 Announce Type: replace Abstract: Commonsense plausibility estimation is critical for evaluating language models (LMs), yet existing generative approaches--reliant on likelihoods or

arXiv:2509.11206v4 Announce Type: replace-cross Abstract: Practitioners increasingly rely on Large Language Models (LLMs) to evaluate generative AI outputs through 'LLM-as-a-Judge' approaches. However

arXiv:2604.16575v1 Announce Type: new Abstract: Unsupervised anomaly detection is widely used to detect Distributed Denial-of-Service (DDoS) attacks in cloud-native 5G networks, yet most studies assum

arXiv:2508.17458v2 Announce Type: replace Abstract: Verbal multiword expressions (VMWEs) remain difficult for machine translation because their meanings are often not recoverable from their component

arXiv:2604.17087v1 Announce Type: new Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language understanding tasks, yet their inference efficie

arXiv:2604.18052v1 Announce Type: cross Abstract: Intrusion detection systems (IDSs) for 5G networks must handle complex, high-volume traffic. Although opaque 'black-box' models can achieve high accur

arXiv:2602.16090v2 Announce Type: replace-cross Abstract: The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and sl

arXiv:2604.18296v1 Announce Type: new Abstract: Static concreteness ratings are widely used in NLP, yet a word's concreteness can shift with context, especially in figurative language such as metaphor

arXiv:2502.13637v2 Announce Type: replace Abstract: Human affordance learning investigates contextually relevant novel pose prediction such that the estimated pose represents a valid human action with

arXiv:2604.16757v1 Announce Type: new Abstract: The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and

arXiv:2505.22226v2 Announce Type: replace Abstract: Recent theoretical advances reveal that the Hadamard product induces nonlinear representations and implicit high-dimensional mappings for the field

arXiv:2604.16865v1 Announce Type: cross Abstract: In this paper, we consider the problem of extraction of most informative features from time series that are regarded as observed values of stochastic

arXiv:2604.16522v1 Announce Type: new Abstract: This paper proposes a fast and online method for jointly performing 3D multi-object tracking and pose estimation using multiple monocular cameras. Our a

arXiv:2604.18491v1 Announce Type: new Abstract: Computational Fluid Dynamics (CFD) is central to race-car aerodynamic development, yet its cost -- tens of thousands of core-hours per high-fidelity eva

arXiv:2604.17149v1 Announce Type: cross Abstract: Accurate autoregressive prediction of 3D turbulent flows remains challenging for neural PDE solvers, as small errors in fine-scale structures can accu

arXiv:2603.19857v2 Announce Type: replace-cross Abstract: Recent Video-to-Audio (V2A) methods have achieved remarkable progress, enabling the synthesis of realistic, high-quality audio. However, they

arXiv:2505.11140v3 Announce Type: replace Abstract: We introduce fs1, a simple yet effective method that improves the factuality of reasoning traces by collecting them from large reasoning models and

arXiv:2604.17785v1 Announce Type: new Abstract: Unlearning in large language models (LLMs) has emerged as a promising safeguard against adversarial behaviors. When the forgetting loss is applied unifo

arXiv:2604.16376v1 Announce Type: new Abstract: This study investigates the applicability of authorship attribution based on stylistic features to support actor analysis in threat intelligence. As a f