3 things Michelle Kim is into right now
Isegye Idol If you thought K-pop was weird, virtual idols—humans who perform as anime-style digital characters via motion capture—will blow your mind. My favorite is a girl group called Isegye Idol, c
Knowledge catalogue
Isegye Idol If you thought K-pop was weird, virtual idols—humans who perform as anime-style digital characters via motion capture—will blow your mind. My favorite is a girl group called Isegye Idol, c
arXiv:2604.19653v1 Announce Type: new Abstract: Human mobility data are used in numerous applications, ranging from public health to urban planning. Human mobility is inherently sensitive, as it can c
arXiv:2604.19715v1 Announce Type: new Abstract: Distribution networks with high penetration of Distributed Energy Resources (DERs) increasingly rely on communication networks to coordinate grid-intera
arXiv:2604.19465v1 Announce Type: cross Abstract: Understanding how complex systems respond to perturbations, such as whether they will remain stable or what their most sensitive patterns are, is a fu
arXiv:2604.18806v1 Announce Type: new Abstract: 3D-IC netlist partitioning is commonly optimized using proxy objectives, while final PPA is treated as a costly evaluation rather than an optimization s
arXiv:2507.09861v2 Announce Type: replace-cross Abstract: Visually Rich Document Understanding (VRDU) has become a pivotal area of research, driven by the need to automatically interpret documents tha
arXiv:2604.18889v1 Announce Type: new Abstract: We present AC-SINDy, a compositional extension of the Sparse Identification of Nonlinear Dynamics (SINDy) framework that replaces explicit feature libra
arXiv:2604.19374v1 Announce Type: new Abstract: Achieving truly fluid interaction with robots with speech interfaces remains a hard problem, and the experience of current Human-Robot Interaction (HRI)
arXiv:2510.14630v2 Announce Type: replace Abstract: We introduce Representation Tokenizer (RepTok), a generative modeling framework that represents an image using a single continuous latent token obta
arXiv:2604.19233v1 Announce Type: new Abstract: Deep learning-based object detectors have achieved remarkable success across numerous computer vision applications, yet they continue to struggle with s
arXiv:2601.07056v2 Announce Type: replace-cross Abstract: Medical hyperspectral imaging (MHSI) has shown strong potential for disease diagnosis by capturing spectral-spatial information of tissues. Wh
arXiv:2604.08404v2 Announce Type: replace Abstract: Out-of-distribution (OoD) generalization occurs when representation learning encounters a distribution shift. This occurs frequently in practice whe
arXiv:2604.19066v1 Announce Type: new Abstract: Physical activity (PA) is widely recognized as protective against mental distress, yet whether this benefit varies systematically across population subg
arXiv:2602.04983v2 Announce Type: replace-cross Abstract: Purpose: To investigate whether an AI-based method can detect subtle inter-fraction changes in MR-Linac images acquired during radiotherapy an
arXiv:2603.27889v2 Announce Type: replace Abstract: Framing theory posits that how information is presented shapes audience responses, but computational work has largely ignored audience reactions. Wh
arXiv:2604.19532v1 Announce Type: cross Abstract: Tokenizing music to fit the general framework of language models is a compelling challenge, especially considering the diverse symbolic structures in
arXiv:2508.21184v3 Announce Type: replace-cross Abstract: We propose a general-purpose approach for improving the ability of large language models (LLMs) to intelligently and adaptively gather informa
arXiv:2604.19196v1 Announce Type: new Abstract: Face Anti-Spoofing (FAS) remains challenging due to the requirement for robust domain generalization across unseen environments. While recent trends lev
arXiv:2604.18697v1 Announce Type: cross Abstract: Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to
arXiv:2601.03512v2 Announce Type: replace-cross Abstract: Code translation across multiple programming languages is essential yet challenging due to two vital obstacles: scarcity of parallel data pair
arXiv:2604.19052v1 Announce Type: new Abstract: Understanding a discourse requires tracking entities and the relations that hold between them. While Large Language Models (LLMs) perform well on relati
arXiv:2507.06321v2 Announce Type: replace Abstract: Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science,
arXiv:2602.12851v3 Announce Type: replace-cross Abstract: Deploying expressive learning models directly on programmable dataplanes promises line-rate, low-latency traffic analysis but remains hindered
arXiv:2604.18883v1 Announce Type: cross Abstract: Current AI-assisted programming tools are predominantly linear and chat-based, which deviates from the iterative and branching nature of programming i
arXiv:2604.19648v1 Announce Type: cross Abstract: SAM3 advances open-vocabulary semantic segmentation by introducing a prompt-driven mask generation paradigm. However, in multi-class open-vocabulary s
arXiv:2604.18725v1 Announce Type: new Abstract: The correlation between insect morphological traits and climate has been documented in physiological studies, but such studies remain limited by the tim
arXiv:2604.18920v1 Announce Type: cross Abstract: We test whether Speech Articulatory Coding (SPARC) features can linearly predict surface electromyography (sEMG) envelopes across aloud, mimed, and su
arXiv:2509.04072v2 Announce Type: replace-cross Abstract: Recent advances in text-to-speech (TTS) have been driven by large, multi-domain speech corpora, yet the expressive potential of audiobook data
arXiv:2410.16431v4 Announce Type: replace Abstract: The semantic similarity between sample expressions measures the distance between their latent 'meaning'. These meanings are themselves typically rep
arXiv:2602.19991v2 Announce Type: replace Abstract: Speakers of under-represented languages face both a language barrier, as most online knowledge is in a few dominant languages, and a modality barrie
arXiv:2501.10258v2 Announce Type: replace-cross Abstract: We present a novel universal gradient method for solving convex optimization problems. Our algorithm, Dual Averaging with Distance Adaptation
arXiv:2604.18648v1 Announce Type: cross Abstract: Text-driven controllable dance generation remains under-explored, primarily due to the severe scarcity of high-quality datasets and the inherent diffi
arXiv:2604.19351v1 Announce Type: new Abstract: The quadratic computational complexity of the standard attention mechanism constitutes a fundamental bottleneck for large language models in long-contex
arXiv:2304.02296v2 Announce Type: replace Abstract: In our study, we conducted a comprehensive analysis of three widely used datasets in the domain of building footprint extraction using deep neural n
arXiv:2604.19176v1 Announce Type: cross Abstract: We study the deep image prior (DIP) framework applied to photoacoustic tomography (PAT) as an unsupervised reconstruction approach to mitigate limited
arXiv:2604.18745v1 Announce Type: new Abstract: Automated segmentation of structural defects from visual inspection imagery remains challenging due to the diversity of damage types, extreme class imba
arXiv:2604.19106v1 Announce Type: cross Abstract: Extreme-edge scientific applications use machine learning models to analyze sensor data and make real-time decisions. Their stringent latency and thro
arXiv:2604.19561v1 Announce Type: new Abstract: Large Language Models (LLMs) utilize large amounts of data for their training, some of which may come from copyrighted sources. Membership Inference Att
arXiv:2604.19062v1 Announce Type: new Abstract: Satellite constellation design requires optimizing orbital parameters across multiple satellites to maximize mission specific metrics. For many types of
arXiv:2604.18739v1 Announce Type: new Abstract: Masked diffusion large language models (dLLMs) are a promising alternative to autoregressive generation. While reinforcement learning (RL) methods have
arXiv:2602.19790v2 Announce Type: replace Abstract: Concept drift -- the change of the distribution over time -- poses significant challenges for learning systems and is of central interest for monito
arXiv:2603.04445v2 Announce Type: replace-cross Abstract: The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent mod
arXiv:2603.12592v2 Announce Type: replace-cross Abstract: Routing algorithms for public transport, particularly the widely used RAPTOR and its variants, often face performance bottlenecks during the t
arXiv:2604.18639v1 Announce Type: cross Abstract: Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entro
arXiv:2510.09477v2 Announce Type: replace-cross Abstract: Set-based transformer models for amortized probabilistic inference and meta-learning, such as neural processes, prior-fitted networks, and tab
arXiv:2604.19105v1 Announce Type: new Abstract: Faithfully modeling human behavior in dynamic environments is a foundational challenge for embodied intelligence. While conditional motion synthesis has
arXiv:2604.19564v1 Announce Type: cross Abstract: Egocentric assistants often rely on first-person view data to capture user behavior and context for personalized services. Since different users exhib
arXiv:2604.17458v2 Announce Type: replace Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi-hop reasoning. While recen
arXiv:2602.11631v2 Announce Type: replace-cross Abstract: Accurate and efficient wavefield modeling underpins seismic structure and source studies. Traditional methods comply with physical laws but ar
arXiv:2604.19699v1 Announce Type: new Abstract: The pursuit of truth is central to democratic deliberation and governance, yet political discourse reflects varying epistemic orientations, ranging from
arXiv:2504.01733v4 Announce Type: replace Abstract: This paper presents a class of epistemic logics that captures the dynamics of acquiring knowledge and descending into oblivion, while incorporating
arXiv:2411.16312v2 Announce Type: replace Abstract: Leveraging the overfitting property of deep neural networks (DNNs) is trending in video delivery systems to enhance video quality within bandwidth l
arXiv:2604.18916v1 Announce Type: new Abstract: In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be
arXiv:2512.20817v2 Announce Type: replace Abstract: Automated essay scoring (AES) has advanced significantly with neural language models, yet most systems remain opaque, offering little visibility int
arXiv:2604.19526v1 Announce Type: cross Abstract: Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious
arXiv:2604.19054v1 Announce Type: new Abstract: The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy wit
arXiv:2604.18786v1 Announce Type: cross Abstract: Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or ref
arXiv:2604.19702v1 Announce Type: new Abstract: Accurate reconstruction and tracking of dynamic human faces from image sequences is challenging because non-rigid deformations, expression changes, and
arXiv:2604.19091v1 Announce Type: cross Abstract: Estimating the number of components is a fundamental challenge in unsupervised learning, particularly when dealing with high-dimensional data with man
arXiv:2604.19259v1 Announce Type: new Abstract: Multi-class defect detection constitutes a critical yet challenging task in industrial quality inspection, where existing approaches typically suffer fr