1273993 results (page 100 of 50960)
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On Cluster Randomized Trials with the Desirability of Outcome Ranking (DOOR) Endpoints
Cluster randomized trials are widely used when individual randomization is logistically infeasible or when correlations between observations cannot be ignored, especially in fields such as ophthalmology, infectious disease, vaccine research, and sociology. The desirability of outcome ranking (DOOR) framework evaluates patient-centric benefit-risk using an ordinal outcome and a Wilcoxon-Mann-Whitne…
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JSSFF: A Joint Structural-Semantic Fusion Framework for Remote Sensing Image Captioning
The encoder-decoder framework has become widely popular nowadays. In this model, the encoder extracts informative visual features from an input image, and the decoder employs a sequence-to-sequence formulation to generate the corresponding textual description from these features. The existing models focus more on the decision part. However, extracting meaningful information from the image can help…
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Numerical Analysis of a Variable-Order Time-Fractional Incompressible Magnetohydrodynamics System
We consider an incompressible magnetohydrodynamics (MHD) model in which the classical first-order time derivatives in the momentum and magnetic induction equations are replaced by variable-order Caputo time-fractional derivatives. This formulation allows the memory effect to vary during the evolution and represents a time-fractional generalization of the incompressible MHD system with nonstationar…
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DeepTaxon: An Interpretable Retrieval-Augmented Multimodal Framework for Unified Species Identification and Discovery
Identifying species in biology among tens of thousands of visually similar taxa while discovering unknown species in open-world environments remains a fundamental challenge in biodiversity research. Current methods treat identification and discovery as separate problems, with classification models assuming closed sets and discovery relying on threshold-based rejection. Here we present DeepTaxon, a…
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Vulnerability Identification by Harnessing Inter-connected Multi-Source Information
The utilization of third-party open-source libraries is widespread in modern software development. Due to the dependency relationships, vulnerabilities within open-source libraries pose significant security threats to downstream software. However, the library vulnerabilities are usually implicitly reported and patched, without explicit notification to dependent software, leaving the downstream sof…
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KubePACS: Kubernetes Cluster Using Performant, Highly Available, and Cost Efficient Spot Instances
Cloud users aim to minimize cost while maximizing performance by selecting the most suitable instance types for their workloads. To reduce expenses, spot instances have been widely adopted due to their steep discounts compared to on-demand pricing. However, their use introduces reliability risks due to potential interruptions, and existing research has primarily focused on mitigating this trade-of…
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From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills
LLM agents increasingly rely on reusable skills, capability packages that combine instructions, control flow, constraints, and tool calls. In most current agent systems, however, skills are still represented by text-heavy artifacts, including SKILL{.}md-style documents and structured records whose machine-usable evidence remains embedded largely in natural-language descriptions. This poses a chall…
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Breaking the Scalability Limit of Multi-Projector Calibration with Embedded Cameras
Conventional multi-projector calibration requires projecting and capturing structured light patterns for each projector sequentially, causing calibration time and effort to increase linearly with the number of projectors. This scalability bottleneck has long limited the deployment of large-scale projection mapping systems. We present a new calibration framework that breaks this limitation by embed…
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ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services
Recent image generation and editing models demonstrate robust adherence to instructions and high visual quality on academic benchmarks. However, their performance on paid, real-world design projects remains uncertain. We introduce \textbf{ServImage}, a benchmark that explicitly correlates model outputs with economic value in commercial design projects. ServImage consists of (i) \textbf{\textit{Ser…
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IPRU: Input-Perturbation-based Radio Frequency Fingerprinting Unlearning for LAWNs
Radio Frequency Fingerprinting (RFF) is a key technology for identity authentication in wireless networks. However, due to the rapid dynamics of Autonomous Aerial Vehicles (AAVs) in low-altitude wireless networks, RFF models require parameter updates to maintain authentication performance, posing a major challenge to existing schemes. Conventional retraining approaches for handling departed or com…
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QED: An Open-Source Multi-Agent System for Generating Mathematical Proofs on Open Problems
We explore a central question in AI for mathematics: can AI systems produce original, nontrivial proofs for open research problems? Despite strong benchmark performance, producing genuinely novel proofs remains an outstanding challenge for LLMs. Through systematic experiments with frontier LLMs on research-level proof tasks, we identify seven failure modes that prevent reliable proof generation, i…
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Poster: ClawdGo: Endogenous Security Awareness Training for Autonomous AI Agents
Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering, yet existing defences address only the platform perimeter, leaving the agent's own threat judgement entirely untrained. We present ClawdGo, a framework for endogenous security awareness training: we teach the agent to recognise and reason about threats …
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Betting for Sim-to-Real Performance Evaluation
This paper studies the problem of robot performance evaluation, focusing on how to obtain accurate and efficient estimates of real-world behavior under severe constraints on physical experimentation. Such estimates are essential for benchmarking algorithms, comparing design alternatives, validating controllers, and supporting certification or regulatory decision-making, yet real-world testing with…
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Neyman Jackknife: Design-Based Variance Estimation for Causal Inference under Interference
We propose a framework, the Neyman Jackknife, for conservative variance estimation in finite-population causal inference under interference. Our approach provides a general, flexible blueprint that enables conservative variance estimation whenever we are able to recompute our target estimator with some treatment assignments omitted. In classical settings, our approach recovers estimators closely r…
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Geometry-Aware Offline-to-Online Learning in Linear Contextual Bandits
We study offline-to-online learning in linear contextual bandits with biased offline regression data: the offline parameter need not match the online one, so history should not be treated as a single warm start. We model directional transfer with a shift certificate $(M_{\mathrm{shift}},ρ)$ and offline ridge estimation, yielding a geometry-aware confidence region for the online parameter rather th…
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QubitQuest: Learning Quantum Computing through Mini-Games
Quantum Computing (QC) is often challenging for beginners due to its abstract concepts and mathematical foundations. This paper explores the use of gamification to support the learning of introductory QC concepts. To investigate this, QubitQuest was developed as a set of three educational mini-games designed to teach key QC topics: the Bloch sphere, entanglement, and quantum circuits. The mini-gam…
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Query-Efficient Quantum Approximate Optimization via Graph-Conditioned Trust Regions
In low-depth implementations of the Quantum Approximate Optimization Algorithm (QAOA), the dominant cost is often the number of objective evaluations rather than circuit depth. We introduce a graph-conditioned trust-region method for reducing this query cost. A graph neural network predicts a Gaussian distribution N(mu, Sigma) over QAOA angles. The mean initializes a local optimizer, the covarianc…
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The Operation Control System for the Tianlai Experiment
The Tianlai 21cm intensity mapping experiment is located at the Hongliuxia Observing Station, which is a remote site with excellent electromagnetic environment. To facilitate the operation of the Tianlai experiment while reducing the required human power and travel cost, we have designed the system to be remotely controllable from the start. In this paper, we present the basic design of the operat…
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CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training
The rapid growth in the size of large language models has necessitated the partitioning of computational workloads across accelerators such as GPUs, TPUs, and NPUs. However, these parallelization strategies incur substantial data communication overhead significantly hindering computational efficiency. While communication-computation overlap presents a promising direction, existing data slicing bas…
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FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algorithms such as FedAvg suffer from slow convergence and high communication and memory costs in heterogeneous, resource-constrained environments. We introduce FedSLoP, a federated optimization algorithm that combines stochastic low-rank subspace proj…
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Primordial black hole production in scalar field inflation within $f(T)$ gravity
We investigate inflation in modified teleparallel gravity within a scalar-tensor framework. We focus on two viable extensions of the Teleparallel Equivalent of General Relativity: a power-law model and an exponential model, which introduce controlled deviations from standard teleparallel gravity through a correction parameter $α$. Inflation is driven by a string-inspired fiber inflation potential …
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Efficient Implementations of Extended Object PMBM Filters with Blocked Gibbs Sampling
This paper considers multiple extended object tracking based on Poisson multi-Bernoulli mixture (PMBM) filtering, which gives the closed-form Bayesian solution for standard multiple extended object models with Poisson birth. To efficiently address the challenging extended object data association problem in PMBM filtering, we develop implementations of the extended object PMBM filter using blocked …
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Safe Reconnection Time for Large-Scale Data Center Loads: An Analytical Framework for Transient Stability Assessment
The rapid growth of large, power-electronics-rich data center (DC) loads is creating new operational challenges for bulk power systems. A key risk arises when a DC uninterruptible power supply (UPS) disconnects the facility during voltage/frequency disturbances and then reconnects it while the bulk grid is still dynamically settling to a new equilibrium point. Poorly timed reconnection can amplify…
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Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels
Post-Training Quantization (PTQ) compresses large language models to low bit-widths using a small calibration set, and its quality depends strongly on which samples are chosen. We identify a failure mode in which calibration samples fail to activate outlier channels, hidden dimensions with unusually large activations, causing the quantizer to underestimate their dynamic range and producing per-cha…
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Performance Benchmarks for Line Spectral Estimation: Ordered Ziv-Zakai Characterization and Plug-In Amplitude Error Analysis
Line spectral estimation (LSE) involves estimating both spectral frequencies and their associated complex amplitudes. Existing Fisher-information-based benchmarks are local and therefore do not capture either the threshold behavior of frequency estimation or the propagation of frequency errors to subsequent amplitude reconstruction. This paper develops explicit performance benchmarks for LSE from …