1190310 results (page 92 of 47613)
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Minimizing Intellectual Property Risks via Self-Stabilizing Algorithms
In this paper, we examine the use of self-stabilizing algorithms, operating in a hierarchical manner, to determine intellectual property risks at a macro level. We are both interested in finding a solution that will support all defined intellectual property dimensions as well as suboptimal solutions in order to minimize risk.
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ZC-Swish: Stabilizing Deep BN-Free Networks for Edge and Micro-Batch Applications
Batch Normalization (BN) is a cornerstone of deep learning, yet it fundamentally breaks down in micro-batch regimes (e.g., 3D medical imaging) and non-IID Federated Learning. Removing BN from deep architectures, however, often leads to catastrophic training failures such as vanishing gradients and dying channels. We identify that standard activation functions, like Swish and ReLU, exacerbate this …
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Robust Nonlinear Trajectory Tracking Control for Autonomous Racing on Three-Dimensional Tracks
We propose a robust nonlinear model predictive control (MPC) scheme for trajectory-tracking control of autonomous vehicles at the limits of handling on non-planar road surfaces. We derive the dynamics from first principles and selectively omit terms with negligible dynamic influence to maintain real-time capability. The resulting MPC with a three-dimensional (3D) dynamic single-track model integra…
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Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics
Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data local and confidential. However, traditional federated models often assume homogeneity in the degradation processes across clients, an assumption that may not hold in many industrial settings. To overcome this, this pape…
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Cosmological constraints from the small scale clustering of Emission Line Galaxies
Spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid are mapping the spatial distribution of millions of galaxies, with Emission Line Galaxies (ELGs) serving as the dominant tracer in the redshift range $0.8<z<1.6$. Standard approaches for extracting cosmological information from galaxy clustering, however, typically discard highly constraining measurements from…
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Crash-free Deductive Verifiers
As deductive verifiers mature, their potential user base is growing from the initial core developers to other users. To convince external users of the suitability of verifiers, these tools must run reliably out of the box, give meaningful error messages and display correct results. Yet deductive verifiers are large and complex software systems and their own full verification is often out of reach.…
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'The Order in the Horse's Heart': A Case Study in LLM-Assisted Stylometry for the Discovery of Biblical Allusion in Modern Literary Fiction
We present a dual-track pipeline for detecting biblical allusions in literary fiction and apply it to the novels of Cormac McCarthy. A bottom-up embedding track uses inverse document frequency to identify rare vocabulary shared with the King James Bible, embeds occurrences in their local context for sense disambiguation, and passes candidate passage pairs through cascaded LLM review. A top-down re…
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Do Solar Energetic Electrons cross the Heliospheric Current Sheet? - A Statistical Study
Solar eruptive events such as flares and coronal mass ejection (CME)-driven shocks can release solar energetic particles (SEPs) into the heliosphere. The heliospheric current sheet (HCS) is a large-scale structure that separates regions of opposite magnetic polarity, and its influence on SEP propagation remains poorly understood. We classify SEE events into two groups: same-side events, where both…
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LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration under diverse real-world degradation conditions, including blur, low-light, haze, rain, and snow. It provided a unified benchmark to evaluate the robustness and generalization ability of restoration models across multipl…
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Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation
Reasoning language models can solve increasingly complex tasks, but struggle to produce the calibrated confidence estimates necessary for reliable deployment. Existing calibration methods usually depend on labels or repeated sampling at inference time, making them impractical in many settings. We introduce a method for unsupervised confidence calibration of reasoning LLMs when only a single genera…
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State Forecasting in an Estimation Framework with Surrogate Sensor Modeling
In recent years, computational power and data availability breakthroughs have revolutionized our ability to analyze complex physical systems through the inverse problem approach. Data-driven techniques like system identification and machine learning play an important role in this field, allowing us to gain insights into previously inaccessible phenomena. However, a major hurdle remains: How can me…
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What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search
Recent work has demonstrated the promise of orchestrating large language models (LLMs) within evolutionary and agentic optimization systems. However, the mechanisms driving these optimization gains remain poorly understood. In this work, we present a large-scale study of LLM-guided evolutionary search, collecting optimization trajectories for 15 LLMs across 8 tasks. Although zero-shot problem-solv…
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Malicious ML Model Detection by Learning Dynamic Behaviors
Pre-trained machine learning models (PTMs) are commonly provided via Model Hubs (e.g., Hugging Face) in standard formats like Pickles to facilitate accessibility and reuse. However, this ML supply chain setting is susceptible to malicious attacks that are capable of executing arbitrary code on trusted user environments, e.g., during model loading. To detect malicious PTMs, state-of-the-art detecto…
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SDSSJ110546.07+145202.4: The first long-duration radio changing-look NLS1 galaxy
SDSSJ110546.07+145202.4 stands out as a unique radio changing-look Narrow-line Seyfert 1 (NLS1) galaxy that has brightened dramatically and shows an exceptionally long duration of its "on" phase. We present the first high-frequency radio observations, the first simultaneous radio spectral energy distributions (SEDs), the first optical--UV--X-ray SEDs, and the first X-ray monitoring and spectroscop…
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DINO Eats CLIP: Adapting Beyond Knowns for Open-set 3D Object Retrieval
Vision foundation models have shown great promise for open-set 3D object retrieval (3DOR) through efficient adaptation to multi-view images. Leveraging semantically aligned latent space, previous work typically adapts the CLIP encoder to build view-based 3D descriptors. Despite CLIP's strong generalization ability, its lack of fine-grainedness prompted us to explore the potential of a more recent …
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EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment
Face Image Quality Assessment is crucial for reliable face recognition systems, yet existing Vision Transformer-based approaches rely exclusively on final-layer representations, ignoring quality-relevant information captured at intermediate network depths. This paper presents the first comprehensive investigation of how intermediate representations within ViTs contribute to face quality assessment…
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Counting Worlds Branching Time Semantics for post-hoc Bias Mitigation in generative AI
Generative AI systems are known to amplify biases present in their training data. While several inference-time mitigation strategies have been proposed, they remain largely empirical and lack formal guarantees. In this paper we introduce CTLF, a branching-time logic designed to reason about bias in series of generative AI outputs. CTLF adopts a counting worlds semantics where each world represents…
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Discerning Authorship in Online Health Communities: Experience, Trust, and Transparency Implications for Moderating AI
For online health communities, community trust is paramount. Yet, advances in Large Language Models (LLMs) generating advice may erode this trust, especially if users cannot identify whether LLMs have been used. We investigate the feasibility of community-based detection of health advice authorship and how self-moderation of LLMs could help enhance advice utilization. In an online experiment, we e…
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Direction-Dependent Path Loss Modeling in Olive Orchards for Precision Agriculture
Wireless links deployed in orchards often exhibit significant variability in the strength of the received signal that is not adequately captured by classical distance-based propagation models. In row-structured olive groves, signal attenuation differs markedly between along-row and cross-row propagation directions, leading to discrepancies when using omnidirectional propagation assumptions such as…
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seneca: A Personalized Conversational Planner
Knowledge work demands sustained self-regulation, prioritization, and reflection-yet existing planning tools only partially support these needs. Digital to-do list applications feature task persistence but lack goal representation. Paper-based planning frameworks offer effective planning strategies but cannot adapt to individual users. Conversational AI systems enable flexible reflection but lack …
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Seeing Your Mindless Face: How Viewing One's Live Self Interrupts Mindless Short-Form Video Scrolling
The widespread, addictive consumption of short-form videos, which allegedly causes "brain rot," has become an urgent public concern. This study proposes that self-related cues serve as an intrinsic, self-reflective strategy that enhances self-control over media overuse. We developed an app that de-immerses users by periodically displaying different self-related cues (live camera, selfie, name in t…
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Allow Me Into Your Dream: A Handshake-and-Pull Protocol for Sharing Mixed Realities in Spontaneous Encounters
Mixed reality systems support shared anchors and co-located interaction, yet they lack a socially legible protocol for entering another person's mixed reality in public settings. We frame this as a protocol problem: co-located MR sharing requires a staged sequence -- Discover, Consent, Confirm, Allow, Spatial Colocation, Sync Objects, Permission Management -- each demanding user understanding and …
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Secure Storage and Privacy-Preserving Scanpath Comparison via Garbled Circuits in Eye Tracking
With the growing use of eye tracking on VR and mobile platforms, gaze data is increasing. While scanpath comparison is important to gaze behavior analysis, existing methods lack privacy-preserving capabilities for real-world use. We present a garbled-circuit (GC)-based approach enabling secure storage and privacy-preserving scanpath comparison under the semi-honest model. It supports two configura…
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TESO: Online Tracking of Essential Matrix by Stochastic Optimization
Maintaining long-term accuracy of stereo camera calibration parameters is important for autonomous systems' perception. This work proposes Online Tracking of Essential Matrix by Stochastic Optimization (TESO). The core mechanisms of TESO are: 1) a robust loss function based on kernel correlation over tentative correspondences, 2) an adaptive online stochastic optimization on the essential manifold…
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Forward Dynamics of Variable Topology Mechanisms - The Case of Constraint Activation
Many mechanical systems exhibit changes in their kinematic topology altering the mobility. Ideal contact is the best known cause, but also stiction and controlled locking of parts of a mechanism lead to topology changes. The latter is becoming an important issue in human-machine interaction. Anticipating the dynamic behavior of variable topology mechanisms requires solving a non-smooth dynamic pro…