863714 results (page 19 of 34549)
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Sensitivity of Dry Lava Planet Atmospheric Emission Spectra to Changes in Lava Compositions
The atmospheres of hot rocky exoplanets are among the first primary targets of the JWST. Interpreting their atmospheric spectra requires understanding the link between silicate lava compositions and overlying atmospheres. We investigate the sensitivity of simulated emission spectra of dry lava planets to variations in oxide abundances in silicate melt. Our goal is to determine which molten surface…
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Compositional security definitions for higher-order where declassification
To ensure programs do not leak private data, we often want to be able to provide formal guarantees ensuring such data is handled correctly. Often, we cannot keep such data secret entirely; instead programmers specify how private data may be declassified. While security definitions for declassification exist, they mostly do not handle higher-order programs. In fact, in the higher-order setting no c…
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Circadian Phase Locking of Epilepsy Seizures in Wearable Data: A Single-Patient Case Study
Epilepsy is a common, chronic neurological disorder characterized by recurrent seizures caused by sudden bursts of abnormal electrical activity in the brain. Seizures can often be unpredictable, leading to uncertainty and anxiety for people with epilepsy. To address this problem, the Epilepsy UK Priority Setting Partnership identified research into seizure forecasting technology as a priority. Sei…
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Exploring Concreteness Through a Figurative Lens
Static concreteness ratings are widely used in NLP, yet a word's concreteness can shift with context, especially in figurative language such as metaphor, where common concrete nouns can take abstract interpretations. While such shifts are evident from context, it remains unclear how LLMs understand concreteness internally. We conduct a layer-wise and geometric analysis of LLM hidden representation…
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An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal
Surprisal theory hypothesizes that the difficulty of human sentence processing increases linearly with surprisal, the negative log-probability of a word given its context. Computational psycholinguistics has tested this hypothesis using language models (LMs) as proxies for human prediction. While surprisal derived from recent neural LMs generally captures human processing difficulty on naturalisti…
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Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Large language models are increasingly expected to serve as general-purpose agents that interact with external, stateful tool environments. The Model Context Protocol (MCP) and broader agent skills offer a unified interface for connecting agents with scalable real-world services, but training robust agents remains limited by the lack of realistic environments and principled mechanisms for life-lon…
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Relative State Estimation using Event-Based Propeller Sensing
Autonomous swarms of multi-Unmanned Aerial Vehicle (UAV) system requires an accurate and fast relative state estimation. Although monocular frame-based camera methods perform well in ideal conditions, they are slow, suffer scale ambiguity, and often struggle in visually challenging conditions. The advent of event cameras addresses these challenging tasks by providing low latency, high dynamic rang…
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Dual formulations of geometric curvature flows and their discretizations
We propose new formulations of geometric curvature flows -- referred to as \emph{dual formulations} -- that are equivalent to the original formulations but provide a novel framework for constructing linearly implicit and energy-stable schemes for curvature-driven surface evolution, including mean curvature flow, surface diffusion, and solid-state dewetting on a substrate with a moving contact line…
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Spike-NVPT: Learning Robust Visual Prompts via Bio-Inspired Temporal Filtering and Discretization
Pre-trained vision models have found widespread application across diverse domains. Prompt tuning-based methods have emerged as a parameter-efficient paradigm for adapting pre-trained vision models. While effective on standard benchmarks, the continuous and dense nature of learned prompts can lead to sensitivity against input noise, as the high-capacity prompts tend to overfit task-irrelevant deta…
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Subcodes of Lambda-Gabidulin Codes for Compact-Ciphertext Cryptography
This paper investigates subcodes of lambda-Gabidulin codes, viewed as rank-metric analogues of generalized Reed--Solomon codes, and their applications to compact-ciphertext cryptosystems. We first analyze subspace and generalized subspace subcodes of lambda-Gabidulin codes and relate them to corresponding subcodes of classical Gabidulin codes through coordinate-wise scaling. This relation yields c…
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Two Potential Exoplanets around A-type Stars Selected from 18 Planetary Candidates
We screen and analyze exoplanet candidates around A-type stars (defined as Teff between 7500 and 10,000 K) observed by the Transiting Exoplanet Survey Satellite to evaluate their likelihood of being genuine exoplanets. Our analysis involves transit signal searches, light-curve detrending, estimation of nearby-source contamination, and calculation of false-positive probabilities (FPPs). Among the 1…
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RSMA-Aided Full-Duplex Networks Under Imperfect CSI and SIC: Performance Evaluation
This work investigates a full-duplex (FD)-enhanced Rate-Splitting Multiple Access (RSMA) system under practical constraints, including imperfect channel state information (CSI) and successive interference cancellation (SIC). We derive closed-form expressions for key performance metrics, such as outage probability and throughput, for both uplink and downlink users. The analysis considers co-channel…
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Characterizing the velocity anisotropy of the Milky Way's stellar halo
Modeling the Milky Way stellar halo requires well-determined density and velocity anisotropy profiles. However, it has been challenging to gather a large sample of stars with six-dimensional data that extend beyond 40 kpc to map the outer halo. Our work investigates the velocity anisotropy in the Milky Way stellar halo with more than 10,000 blue horizontal-branch stars, combining Gaia astrometric …
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Dissipative Latent Residual Physics-Informed Neural Networks for Modeling and Identification of Electromechanical Systems
Accurate dynamical modeling is essential for simulation and control of embodied systems, yet first-principles models of electromechanical systems often fail to capture complex dissipative effects such as joint friction, stray losses, and structural damping. While residual-learning physics-informed neural networks (PINNs) can effectively augment imperfect first-principles models with data-driven co…
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Block-encodings as programming abstractions: The Eclipse Qrisp BlockEncoding Interface
Block-encoding is a foundational technique in modern quantum algorithms, enabling the implementation of non-unitary operations by embedding them into larger unitary matrices. While theoretically powerful and essential for advanced protocols like Quantum Singular Value Transformation (QSVT) and Quantum Signal Processing (QSP), the generation of compilable implementations of block-encodings poses a …
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LiquidTAD: An Efficient Method for Temporal Action Detection via Liquid Neural Dynamics
Temporal Action Detection (TAD) in untrimmed videos is currently dominated by Transformer-based architectures. While high-performing, their quadratic computational complexity and substantial parameter redundancy limit deployment in resource-constrained environments. In this paper, we propose LiquidTAD, a novel parameter-efficient framework that replaces cumbersome self-attention layers with parall…
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Rapid and Predictive Planet Population Synthesis Model (RAPPS) I. Upgraded model and resulting synthetic populations
Exoplanet surveys have revealed a wide diversity of planetary systems, requiring integrated models of planet formation to explain their origin. Planet population synthesis (PPS) modelling is a key tool for linking theory with the statistical properties of observed exoplanets. In the coming decade, the number of known exoplanets is expected to increase ten-fold, with a significant expansion in the …
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EmbodiedLGR: Integrating Lightweight Graph Representation and Retrieval for Semantic-Spatial Memory in Robotic Agents
As the world of agentic artificial intelligence applied to robotics evolves, the need for agents capable of building and retrieving memories and observations efficiently is increasing. Robots operating in complex environments must build memory structures to enable useful human-robot interactions by leveraging the mnemonic representation of the current operating context. People interacting with rob…
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Incremental learning for audio classification with Hebbian Deep Neural Networks
The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologically inspired learning process, to sound classification. We propose a kernel plasticity approach that selectively modulates network kernels during incremental learning, acting on selected kernels to learn new informatio…
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Impact of CSIR, SIC, and Hardware Impairments on the Ergodic Rate of Downlink RSMA
This work investigates the ergodic rate performance analysis of rate-splitting multiple access (RSMA) in a downlink communication system under practical impairments. Closed-form expressions are derived for key performance metrics such as ergodic rate, energy efficiency, sum-rate, and Jains fairness index, capturing the joint effects of imperfect channel state information at the receiver (CSIR), im…
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Scenario-Based Stochastic MPC for Energy Hubs with EV Fleets Under Persistent Grid Outages
Emissions reduction and resilience to outages motivate the adoption of renewable microgrids. Surprisingly, research integrating both probabilistic grid outages and electric vehicle (EV) charging requirements remains limited. This paper addresses this gap by developing a scenario-based stochastic model predictive controller (SMPC) for a microgrid energy hub comprising solar generation, battery stor…
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MARCO: Navigating the Unseen Space of Semantic Correspondence
Recent advances in semantic correspondence rely on dual-encoder architectures, combining DINOv2 with diffusion backbones. While accurate, these billion-parameter models generalize poorly beyond training keypoints, revealing a gap between benchmark performance and real-world usability, where queried points rarely match those seen during training. Building upon DINOv2, we introduce MARCO, a unified …
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Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation
Identifying anomalous instances in tabular data is essential for improving data reliability and maintaining system stability. Due to the scarcity of ground-truth anomaly labels, existing methods mainly rely on unsupervised anomaly detection models, or exploit a small number of labeled anomalies to facilitate detection via sample generation or contrastive learning. However, unsupervised methods lac…
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From Gaia to GaiaNIR: I. Probing dark matter halos in globular clusters
The proposed GaiaNIR mission would extend Gaia's astrometric capabilities into the near-infrared, improving astrometric precision and enabling observations in heavily dust-obscured regions. In this work, we investigate the impact of GaiaNIR on the detectability of dark matter halos in globular clusters by comparing its performance with that of Gaia. Expected observations from future Gaia data rele…
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Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling
Zeroth-Order optimization presents a promising memory-efficient paradigm for fine-tuning Large Language Models by relying solely on forward passes. However, its practical adoption is severely constrained by slow wall-clock convergence and high estimation variance. In this work, we dissect the runtime characteristics of ZO algorithms and identify a critical system bottleneck where the generation of…