1028850 results (page 51 of 41154)
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ViBR: Automated Bug Replay from Video-based Reports using Vision-Language Models
Bug reports play a critical role in software maintenance by helping users convey encountered issues to developers. Recently, GUI screen capture videos have gained popularity as a bug reporting artifact due to their ease of use and ability to retain rich contextual information. However, automatically reproducing bugs from such recordings remains a significant challenge. Existing methods often rely …
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New Insights into Channel vs Subspace Codes for Large-Scale Beamspace MIMO Channel Sensing
This paper provides novel insights into channel and subspace codes in nonadaptive channel sensing with a single RF chain. Observing that this problem naturally maps to a noncoherent decoding problem, we show that the sensing performance of the maximum likelihood (ML) angle estimator, which does not require knowledge of the typically unknown channel coefficient, is governed by two key terms: the mi…
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A Multi-Plant Machine Learning Framework for Emission Prediction, Forecasting, and Control in Cement Manufacturing
Cement production is among the largest contributors to industrial air pollution, emitting ~3 Mt NOx/year. The industry-standard mitigation approach, selective non-catalytic reduction (SNCR), exhibits low NH3 utilization efficiency, resulting in operational inefficiencies and increased reagent costs. Here, we develop a data-driven framework for emission control using large-scale operational data fr…
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MMCORE: MultiModal COnnection with Representation Aligned Latent Embeddings
We present MMCORE, a unified framework designed for multimodal image generation and editing. MMCORE leverages a pre-trained Vision-Language Model (VLM) to predict semantic visual embeddings via learnable query tokens, which subsequently serve as conditioning signals for a diffusion model. This streamlined design effectively transfers the rich understanding and reasoning capabilities of VLMs into t…
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A Space-time Approach to Entropy-Stable Discontinuous Galerkin and Flux Reconstruction
We present a high-order space-time discretization equipped with fully-discrete entropy stability properties for general choices of volume and surface quadrature rules. The formulation uses flux reconstruction (FR) in the spatial dimension paired with a discontinuous Galerkin (DG) method in the temporal dimension. The result is a fully-implicit system using polynomial bases in space and time. An en…
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A Reproducibility Study of Metacognitive Retrieval-Augmented Generation
Recently, Retrieval Augmented Generation (RAG) has shifted focus to multi-retrieval approaches to tackle complex tasks such as multi-hop question answering. However, these systems struggle to decide when to stop searching once enough information has been gathered. To address this, \citet{zhou2024metacognitive} introduced Metacognitive Retrieval Augmented Generation (MetaRAG), a framework inspired …
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Radio detection of supernova remnant G310.7-5.4 with $γ$-ray counterpart: Abeona SNR
G310.7-5.4 is a supernova remnant (SNR) candidate identified as a faint shell in the second epoch Molonglo Galactic Plane Survey (MGPS-2), but this has not been followed up with multi-wavelength observations until now. It is an example of an SNR at high Galactic latitude showing spatially coinciding $γ$-ray emission. Here, we make the first detailed investigation of the radio emission from the G31…
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HI 21-cm absorption in low- and high-excitation radio-loud AGNs at $z<0.5$ from MALS
We present results from a search of cold neutral gas associated with radio-loud active galactic nuclei (AGNs) at $z < 0.5$ using HI 21-cm absorption measurements from the MeerKAT Absorption Line Survey (MALS). Cross-matching the MALS 1006 MHz and SDSS DR18 catalogs yields 1908 radio sources at $z < 0.5$. Of these, 613 are classified as AGNs using BPT diagnostics and radio luminosity criteria. We f…
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Learning When Not to Decide: A Framework for Overcoming Factual Presumptuousness in AI Adjudication
A well-known limitation of AI systems is presumptuousness: the tendency of AI systems to provide confident answers when information may be lacking. This challenge is particularly acute in legal applications, where a core task for attorneys, judges, and administrators is to determine whether evidence is sufficient to reach a conclusion. We study this problem in the important setting of unemployment…
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Output Feedback Backup Control Barrier Functions: Safety Guarantees Under Input Bounds and State Estimation Error
Guaranteeing the safety of controllers is vital for real-world applications, but is markedly difficult when the states are not perfectly known and when the control inputs are bounded. Backup control barrier functions (bCBFs) use predictions of the flow under a prescribed controller to achieve safety in the presence of bounded inputs and perfect state information. However, when only an estimate of …
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A Data-Free Membership Inference Attack on Federated Learning in Hardware Assurance
Federated Learning (FL) is an emerging solution to the data scarcity problem for training deep learning models in hardware assurance. While FL is designed to enhance privacy by not sharing raw data, it remains vulnerable to Membership Inference Attacks (MIAs) that can leak sensitive intellectual property (IP). Traditional MIAs are often impractical in this domain because they require access to aux…
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Efficient Arithmetic-and-Comparison Homomorphic Encryption with Space Switching
Fully homomorphic encryption (FHE) enables computation on encrypted data without decryption, making it central to privacy-preserving applications. However, no existing scheme efficiently supports both arithmetic and comparison operations in a unified framework. Prior approaches such as scheme switching and polynomial approximation face serious limitations: switching incurs prohibitive overhead for…
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SGAP-Gaze: Scene Grid Attention Based Point-of-Gaze Estimation Network for Driver Gaze
Driver gaze estimation is essential for understanding the driver's situational awareness of surrounding traffic. Existing gaze estimation models use driver facial information to predict the Point-of-Gaze (PoG) or the 3D gaze direction vector. We propose a benchmark dataset, Urban Driving-Face Scene Gaze (UD-FSG), comprising synchronized driver-face and traffic-scene images. The scene images provid…
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Depression Risk Assessment in Social Media via Large Language Models
Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiagnosed and undertreated. The proliferation of social media platforms provides a rich source of naturalistic linguistic signals for the automated monitoring of psychological well-being. In this work, we propose a system based on Large Language Models (LLMs) for depression risk assessment …
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From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization
Post-Training Quantization (PTQ) is critical for the efficient deployment of Large Language Models (LLMs). While 4-bit quantization is widely regarded as an optimal trade-off, reducing the precision to 2-bit usually triggers a catastrophic ``performance cliff.'' It remains unclear whether the underlying mechanisms differ fundamentally. Consequently, we conduct a systematic mechanistic analysis, re…
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Quasi-Periodic Microstructures in Pulsar Emission: Automated Detection and Archival Survey
The study of quasi-period microstructures in pulsars offers valuable insights into the underlying emission mechanism. However, identifying these features through manual inspection of the intensity time series, often containing thousands to millions of pulses, is both laborious and time-consuming. To address this challenge, we have developed a Python-based software, Quasi-periodic MIcrostructure Se…
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Stable Mesh-Free Variational Radial Basis Function Approximation for Elliptic PDEs and Obstacle Problems
We present a comprehensive study of radial basis function (RBF) approximations for elliptic and obstacle-type boundary value problems under a variational formulation. Our focus is on practical accuracy, robustness and efficiency. To address ill-conditioning in dense systems, we apply truncated singular value decomposition (TSVD) and investigate its effect on stability and accuracy trade-offs. Nume…
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Ultra-High-Energy Tau Neutrinos as Probes of Lorentz Invariance
Neutrino telescopes have detected astrophysical neutrinos with energies up to ${O}(100)$ PeV. Several current and proposed experiments aim to observe neutrinos at even higher energies, with the goal of detecting cosmogenic neutrinos. This increase in neutrino energy makes tests of Lorentz invariance violation (LIV) particularly appealing, since the effects of higher-dimension LIV operators on neut…
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The fragmentation properties of massive star-forming regions in 30Dor-10 at 2000 au resolution
The fragmentation properties of parsec-scales clumps play a fundamental role in shaping the dense gas condensations known as cores, the immediate progenitor of stars. The distribution of core masses, the so-called core mass function, is the precursor of the stellar initial mass function, which governs the distribution of stellar masses and, consequently, the evolution of galaxies. The stellar init…
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Super Apriel: One Checkpoint, Many Speeds
We release Super Apriel, a 15B-parameter supernet in which every decoder layer provides four trained mixer choices -- Full Attention (FA), Sliding Window Attention (SWA), Kimi Delta Attention (KDA), and Gated DeltaNet (GDN). A placement selects one mixer per layer; placements can be switched between requests at serving time without reloading weights, enabling multiple speed presets from a single c…
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Self-Interaction and Galactic Magnetic Field Bounds on Millicharged Magnetic Monopole Dark Matter
A dark matter sector composed of magnetic monopoles of a dark U(1) symmetry having a small kinetic mixing with the Standard Model photon has a rich and interesting phenomenology. The model in itself is also of theoretical interest. Based on the temperature of the dark sector and scale of spontaneous symmetry breaking for this U(1), three phenomenologically distinct cases for this model of dark mat…
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Hamilton's Object Revisited: A challenging source redshift of a strong lensing configuration
Low-resolution spectrographs used to have difficulties to determine redshifts of galaxies at $z\approx1$ and $z\approx3$. Spectral emission and absorption lines of magnesium and iron redshifted to $z\approx1$ fall close to hydrogen, silicon, and oxygen lines at $z\approx3$. Here, we demonstrate that, even with modern, integrated field unit spectrographs, this task remains challenging. Hamilton's o…
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Excitability in quantum field theory
In quantum field theory, it is not always possible to excite one state out of another using only local operators. This paper establishes abstract algebraic criteria for (local) excitability in general quantum theories, and computes these criteria explicitly for zero-mean Gaussian states in (generalized) free field theories. We find that in this context, due to the special nature of Gaussian states…
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A population-based approach to understanding radio AGN feedback with LOFAR: The LoTSS Deep Fields
Feedback from radio AGN jets is regularly implemented into contemporary models of galaxy evolution to offset radiative cooling in the large-scale environments in which they typically reside. While previous studies suggest that the total kinetic power output from radio AGN is sufficient for this purpose, many have relied on jet-power estimation from radio luminosities using generalised scaling rela…
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Can radio occultations constrain Uranus or Neptune's internal rotation periods?
The shapes of fluid planets bear the signatures of rotational flattening and atmospheric flows. Precise knowledge of their shapes and wind profiles may therefore reveal their interior rotation rates. We re-examine this idea for the ice giants, where missions like the Uranus Orbiter and Probe could use radio occultations to measure atmospheric heights near 1 bar at multiple latitudes, complementing…