836891 results (page 15 of 33476)
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Progressive Online Video Understanding with Evidence-Aligned Timing and Transparent Decisions
Visual agents operating in the wild must respond to queries precisely when sufficient evidence first appears in a video stream, a critical capability that is overlooked by conventional video LLMs evaluated in offline settings. The shift to an online, streaming paradigm introduces significant challenges: a lack of decision transparency, the difficulty of aligning response timing with visual evidenc…
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Geometric Trajectory Optimization for TRACON Arrivals: An NLP Approach with ATC Vectoring Maneuver Modeling
Terminal airspace congestion remains a major bottleneck in the global air traffic network. Although the Aircraft Sequencing and Scheduling Problem (ASSP) has been widely studied, many methods rely on simplified node-link abstractions that ignore the practical flight path, producing schedules that can be hard to execute under real airspace geometric constraints. This paper introduces a high-fidelit…
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On the Effect of Quadratic Regularization in Direct Data-Driven LQR
This paper proposes an explainability concept for direct data-driven linear quadratic regulation (LQR) with quadratic regularization. Our perspective follows the parametric effect of regularization, an analysis approach that translates regularization costs from auxiliary variables to system quantities, enabling intuitive interpretations. The framework further enables the elimination of auxiliary v…
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ESsEN: Training Compact Discriminative Vision-Language Transformers in a Low-Resource Setting
Vision-language modeling is rapidly increasing in popularity with an ever expanding list of available models. In most cases, these vision-language models have parameters in the tens of billions, which is necessary for some needs, but in many cases smaller models are necessary (e.g., on edge devices or independent robotic platforms). Unfortunately, there is little research in producing light-weight…
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Faster Linear-Space Data Structures for Path Frequency Queries
We present linear-space data structures for several frequency queries on trees, namely: path mode, path least frequent element, and path $α$-minority queries. We present the first linear-space data structures, requiring $O(n \sqrt{nw})$ preprocessing time, that can answer path mode and path least frequent element queries in $O(\sqrt{n/w})$ time. This improves upon the best previously known bound o…
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Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario
Empirical studies of trained models often report a transient regime in which signal is detectable in a finite gradient descent time window before overfitting dominates. We provide an analytically tractable random-matrix model that reproduces this phenomenon for gradient flow in a linear teacher--student setting. In this framework, learning occurs when an isolated eigenvalue separates from a noisy …
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From Awareness to Intent: Mitigating Silent Driving System Failures through Prospective Situation Awareness Enhancing Interfaces
Silent automation failures, where a system fails to detect a hazard without warning, pose a critical safety challenge for partially automated vehicles. While research has mostly focused on takeover requests, how to support a driver in silent failure remains underexplored. We conducted a multi-modal driving simulator study with 48 participants to investigate how different Prospective Situation Awar…
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AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning
Performance, power, and area (PPA) optimization is a fundamental task in RTL design, requiring a precise understanding of circuit functionality and the relationship between circuit structures and PPA metrics. Recent studies attempt to automate this process using LLMs, but neither feedback-based nor knowledge-based methods are efficient enough, as they either design without any prior knowledge or r…
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ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification
Zero-shot vision-language models (VLMs) have shown promise for chest radiograph classification, but their performance is often limited by confounding label co-occurrence, long-tail class imbalance, and transfer instability under domain shift. We propose ProtoCLIP, a refinement strategy for CLIP-style VLMs that improves zero-shot discrimination through targeted data curation and distilled anchor al…
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Conformal Robust Set Estimation
Conformal prediction provides finite-sample, distribution-free coverage under exchangeability, but standard constructions may lack robustness in the presence of outliers or heavy tails. We propose a robust conformal method based on a non-conformity score defined as the half-mass radius around a point, equivalently the distance to its $(\lfloor n/2\rfloor+1)$-nearest neighbour. We show that the r…
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Scalable Physics-Informed Neural Differential Equations and Data-Driven Algorithms for HVAC Systems
We present a scalable, data-driven simulation framework for large-scale heating, ventilation, and air conditioning (HVAC) systems that couples physics-informed neural ordinary differential equations (PINODEs) with differential-algebraic equation (DAE) solvers. At the component level, we learn heat-exchanger dynamics using an implicit PINODE formulation that predicts conserved quantities (refrigera…
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Quasi-Constant Modulus Design for Nonlinearity-Tolerant Geometric Shaped Four Dimensional Modulation Format
In this paper, the quasi-constant modulus (QCM) property is analyzed and leveraged in the design of nonlinearity-tolerant four-dimensional (4D) modulation formats. Accordingly, we propose a family of QCM-based quadrature amplitude modulation (QCM-QAM) constellations with high spectral efficiencies (SEs) of 9, 11, and 13 bit/4D-sym, respectively. The quasi-constant modulus design theoretically enha…
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Active-region Modulation of Subsurface Meridional Flows and Magnetic Flux Transport on the Sun
Using time-distance helioseismology applied to 14-years of SDO/HMI observations spanning solar cycle 24 and rising phase of cycle 25, we present evidence that meridional flows in the lower half of the near-surface shear layer (NSSL), modulated by active-region magnetic fields, play a central role in the episodic global transport of magnetic flux. In particular, polar field buildup is tightly linke…
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Real-Time Algorithms for Model Predictive Control of Hybrid Dynamical Systems
Model predictive control (MPC) of hybrid dynamical systems is challenging because the associated optimization problem is nonsmooth and the resulting feedback law is discontinuous. This paper develops real-time MPC algorithms for nonlinear hybrid systems modeled as dynamical complementarity systems. The resulting optimal control problems are formulated as mathematical programs with complementarity …
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Care Trajectories Are Linked to Mental Health and Mortality in Cancer Patients
Treatment of cancer involves heterogeneous, complex care pathways. The relationship between these longitudinal trajectories, baseline mental health, and prognostic outcomes remains poorly understood. We introduce an interpretable time-analysis framework leveraging these temporal dynamics, analyzing care patterns spanning up to 37 years for >8,000 patients. Using Dynamic Time Warping (DTW) and Hier…
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Shrinkage through multiple identifiability
We propose an empirical Bayes framework for aggregating estimators obtained from several identification functionals associated to the same causal parameter. The central object is a posterior mean that pools a collection of asymptotically linear estimators of a scalar causal target. We establish consistency in two non-nested regimes: exact identifiability, in which every functional identifies the s…
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Revisiting Change VQA in Remote Sensing with Structured and Native Multimodal Qwen Models
Change visual question answering (Change VQA) addresses the problem of answering natural-language questions about semantic changes between bi-temporal remote sensing (RS) images. Although vision-language models (VLMs) have recently been studied for temporal RS image understanding, Change VQA remains underexplored in the context of modern multimodal models. In this letter, we revisit the CDVQA benc…
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Context-Aware Search and Retrieval Under Token Erasure
This paper introduces and analyzes a search and retrieval model for RAG-like systems under {token} erasures. We provide an information-theoretic analysis of remote document retrieval when query representations are only partially preserved. The query is represented using term-frequency-based features, and semantically adaptive redundancy is assigned according to feature importance. Retrieval is per…
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BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources
India's linguistic landscape, spanning 22 scheduled languages and hundreds of marginalized dialects, has driven rapid growth in NLP datasets, benchmarks, and pretrained models. However, no dedicated survey consolidates resources developed specifically for Indian languages. Existing reviews either focus on a few high-resource languages or subsume Indian languages within broader multilingual setting…
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Correcting radar meteor fluxes for observing biases
We report on an eight year survey of simultaneous optical and radar meteor detections with the goal of isolating the fraction of meteors missed by specular radars. A total of 10,503 Electron Multiplied Charge Couple Device (EMCCD) meteors with peak brightness above +7 were simultaneously detected by the Canadian Meteor Orbit Radar (CMOR) and used to estimate the fraction of radar echoes missed as …
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Spectral bandits for smooth graph functions
Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem, each item we can recommend is a node and its expected rating is similar to its…
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Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning
Large language models (LLMs) using chain-of-thought reasoning often waste substantial compute by producing long, incorrect responses. Abstention can mitigate this by withholding outputs unlikely to be correct. While most abstention methods decide to withhold outputs before or after generation, dynamic mid-generation abstention considers early termination of unpromising reasoning traces at each tok…
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MedProbeBench: Systematic Benchmarking at Deep Evidence Integration for Expert-level Medical Guideline
Recent advances in deep research systems enable large language models to retrieve, synthesize, and reason over large-scale external knowledge. In medicine, developing clinical guidelines critically depends on such deep evidence integration. However, existing benchmarks fail to evaluate this capability in realistic workflows requiring multi-step evidence integration and expert-level judgment. To ad…
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Classical and quantum evolution of inflationary fluctuations
We compare the correlation functions of inflationary perturbations computed either with quantum or classical dynamics. Even if they are enforced to agree at a specific time during inflation, classical and quantum correlations will differ at the end of inflation, provided that interactions are relevant. The difference between the results of the classical and quantum computations is exponentially se…
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Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms
Data-driven discovery of governing equations has advanced significantly in recent years; however, existing methods often struggle in multiscale systems where dynamically significant terms may have small coefficients. Therefore, we propose Balance-Guided SINDy (BG-SINDy) inspired by the principle of dominant balance, which reformulates $\ell_0$-constrained sparse regression as a term-level $\ell_{2…