1087714 results (page 62 of 43509)
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Does Self-Consistency Improve the Recall of Encyclopedic Knowledge?
While self-consistency is known to improve performance on symbolic reasoning, its effect on the recall of encyclopedic knowledge is unclear due to a lack of targeted evaluation grounds. To address this, we establish such a knowledge recall split for the popular MMLU benchmark by applying a data-driven heuristic from prior work. We validate this split by showing that the performance patterns on the…
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Can Continual Pre-training Bridge the Performance Gap between General-purpose and Specialized Language Models in the Medical Domain?
This paper narrows the performance gap between small, specialized models and significantly larger general-purpose models through domain adaptation via continual pre-training and merging. We address the scarcity of specialized non-English data by constructing a high-quality German medical corpus (FineMed-de) from FineWeb2. This corpus is used to continually pre-train and merge three well-known LLMs…
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Model-independent consistency tests of DESI DR2 BAO and SN Ia
Cosmic distances can be measured using two complementary probes: Type Ia supernovae (SN Ia), serving as standard candles, and baryon acoustic oscillations (BAO), serving as standard rulers. The luminosity distance derived from supernovae and the angular diameter distance obtained from BAO must be mutually consistent if these data are to be combined for cosmological inference. Hence, the existence …
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HarmoniDiff-RS: Training-Free Diffusion Harmonization for Satellite Image Composition
Satellite image composition plays a critical role in remote sensing applications such as data augmentation, disaste simulation, and urban planning. We propose HarmoniDiff-RS, a training-free diffusion-based framework for harmonizing composite satellite images under diverse domain conditions. Our method aligns the source and target domains through a Latent Mean Shift operation that transfers radiom…
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On the Practical Performance of Noise Modulation for Ultra-Low-Power IoT: Limitations, Capacity, and Energy Trade-offs
Ultra-low-power (ULP) Internet of Things (IoT) applications demand communication architectures with minimal energy consumption. Noise Modulation (NoiseMod) addresses this by encoding data through the statistical variance of a noise-like signal, eliminating the need for a coherent carrier. To bridge the gap between theoretical potential and practical deployment, this paper benchmarks NoiseMod again…
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Towards Formalising Stakeholder Context using SysML v2
This paper presents a framework to bridge the gap between subjective stakeholder context and formal system architecture. This is achieved using Soft Systems Methodology (SSM) and Systems Modelling Language version 2 (SysML v2). The methodology utilises the precision of Kernel Modelling Language (KerML) and the alignment of SysML v2 with ISO 42010 to define a reference architecture for the mapping …
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Blockage-Aware and Shadowing Aware RIS Assisted Joint Communication and Positioning for Urban Non Terrestrial Networks
Reconfigurable intelligent surfaces (RISs) have recently attracted interest for non-terrestrial networks (NTNs), especially for improving satellite communication performance. However, RIS-assisted urban NTN designs that jointly support reliable communication and user positioning under blockage, while maintaining low online complexity, remain limited. This paper proposes a blockage-aware and shadow…
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Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval
Composed Image Retrieval (CIR) has attracted significant attention due to its flexible multimodal query method, yet its development is severely constrained by the Noisy Triplet Correspondence (NTC) problem. Most existing robust learning methods rely on the "small loss hypothesis", but the unique semantic ambiguity in NTC, such as "partial matching", invalidates this assumption, leading to unreliab…
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Drift Correction of Scan Images by Snapshot Referencing
Reliable quantitative analysis in scanning (transmission) electron microscopy (S(T)EM) is often hindered by image drift during long-duration spectral mapping for elemental analysis or for various material functions. We here present snapshot-referencing (SSR) drift correction, a retrospective approach to eliminate spatial distortions based on the temporal nature of the scanning process; A continuou…
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Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties
Metal-organic frameworks (MOFs) are a major target of machine-learning-based property prediction, yet most models assume that a single framework representation maps to a single property value. This assumption becomes problematic for experimental MOFs, where samples reported as the same framework can exhibit different properties because of differences in crystallinity, phase purity, defects, and ot…
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Sharp recovery and landscape guarantees for the nonconvex matrix LASSO
Low-rank matrix recovery can be solved to statistical optimality by convex matrix optimization under the classical assumption of restricted isometry property (RIP). However, for large problems, the convex formulation is commonly replaced by a smooth rank-constrained factored nonconvex problem for which algorithmic theory typically only guarantees convergence to second-order critical points. In thi…
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PanDA: Unsupervised Domain Adaptation for Multimodal 3D Panoptic Segmentation in Autonomous Driving
This paper presents the first study on Unsupervised Domain Adaptation (UDA) for multimodal 3D panoptic segmentation (mm-3DPS), aiming to improve generalization under domain shifts commonly encountered in real-world autonomous driving. A straightforward solution is to employ a pseudo-labeling strategy, which is widely used in UDA to generate supervision for unlabeled target data, combined with an m…
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Random Reward Phase-Type Distributions with Applications in Latent Severity Modeling
This paper proposes an extension to discrete Phase-Type distributions (DPH) by introducing random rewards. These allow for modeling a system in which a visit to a certain state does not emit a deterministic reward. Instead, the rewards follow either a Bernoulli or a geometric distribution. Utilizing this increased flexibility, we further sketch a possible use case for these random rewards by intro…
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Towards Energy Impact on AI-Powered 6G IoT Networks: Centralized vs. Decentralized
The emergence of sixth-generation (6G) technologies has introduced new challenges and opportunities for machine learning (ML) applications in Internet of Things (IoT) networks, particularly concerning energy efficiency. As model training and data transmission contribute significantly to energy consumption, optimizing these processes has become critical for sustainable system design. This study fir…
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Achieving Interaction Fluidity in a Wizard-of-Oz Robotic System: A Prototype for Fluid Error-Correction
Achieving truly fluid interaction with robots with speech interfaces remains a hard problem, and the experience of current Human-Robot Interaction (HRI) remains laboured and frustrating. Some of the barriers to fluid interaction stem from a lack of a suitable development platform for HRI for improving interaction, even in robotic Wizard-of-Oz (WoZ) modes of operation used for data collection and p…
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Systematic Detection of Energy Regression and Corresponding Code Patterns in Java Projects
Green software engineering is emerging as a crucial response to information technology's rising energy impact, especially in continuous development. However, there remain challenges in devising automated methods for identifying energy regressions across commits and their associated code change patterns. In particular, little effort has been put into automatically detecting regressions at the commi…
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TACENR: Task-Agnostic Contrastive Explanations for Node Representations
Graph representation learning has achieved notable success in encoding graph-structured data into latent vector spaces, enabling a wide range of downstream tasks. However, these node representations remain opaque and difficult to interpret. Existing explainability methods primarily focus on supervised settings or on explaining individual representation dimensions, leaving a critical gap in explain…
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Suffix Random Access via Function Inversion: A Key for Asymmetric Streaming String Algorithms
Many string processing problems can be phrased in the streaming setting, where the input arrives symbol by symbol and we have sublinear working space. The area of streaming algorithms for string processing has flourished since the seminal work of Porat and Porat [FOCS 2009]. Unfortunately, problems with efficient solutions in the classical setting often do not admit efficient solutions in the st…
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IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging
Peak picking is a fundamental preprocessing step in Mass Spectrometry Imaging (MSI), where each sample is represented by hundreds to thousands of ion images. Existing approaches require careful dataset-specific hyperparameter tuning, and often fail to generalize across acquisition protocols. We introduce IonMorphNet, a spatial-structure-aware representation model for ion images that enables fully …
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Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving
Predicting driver intention from neurophysiological signals offers a promising pathway for enhancing proactive safety in advanced driver assistance systems, yet remains challenging in real-world driving due to EEG signal non-stationarity and the complexity of cognitive-motor preparation. This study proposes and evaluates an EEG-based driver intention prediction framework using a synchronised multi…
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Exploring the central region of SNR 0540-69.3 with JWST I: 3D morphology
The young supernova remnant SNR 0540-69.3 in the Large Magellanic Cloud offers a detailed view of an energetic pulsar-wind nebula interacting with the surrounding ejecta. We present infrared observations of the central region of SNR 0540-69.3 obtained with the JWST NIRSpec and MRS integral field units. From the observations we reconstruct the 3D morphology of the strongest emission lines in the in…
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HOTDISK. Finding Massive Protostellar Disks with Water and Refractory Molecular Species
We present high-angular-resolution ($\sim0.05^{\prime\prime}$) ALMA Band~6 observations from the HOTDISK project (Hot-Origin Tracer survey of DISKs of massive protostars) aimed at investigating the "hot-disk" chemical pattern traced by vibrationally excited water, NaCl, SiS, and SiO in the innermost regions around massive protostars. Ten targets were selected based on strong CH$_3$CN emission exhi…
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Detection of T-shirt Presentation Attacks in Face Recognition Systems
Face recognition systems are often used for biometric authentication. Nevertheless, it is known that without any protective measures, face recognition systems are vulnerable to presentation attacks. To tackle this security problem, methods for detecting presentation attacks have been developed and shown good detection performance on several benchmark datasets. However, generalising presentation at…
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CROWDio: A Practical Mobile Crowd Computing Framework with Developer-Oriented Design, Adaptive Scheduling, and Fault Resilience
Mobile Crowd Computing (MCdC) leverages the idle computational capacity of consumer smartphones to enable distributed task processing at scale; however, widespread real-world adoption remains constrained by the absence of developer-oriented frameworks capable of transparently managing device heterogeneity, fault tolerance, and connectivity volatility. This paper introduces CROWDio, a centralized M…
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Physical Analysis of Bennu Samples Reveals Regolith Production by Collisional Disruption on Near-Earth Asteroids
Owing to the extremely low gravity of small near-Earth asteroids (NEAs), it has been assumed that impact-generated rock fragments escape into space and thus do not contribute to the accumulation of regolith. However, centimeter-sized stones returned from the small NEA Bennu by NASA's OSIRIS-REx mission exhibit impact craters up to a few millimeters wide, implying that impact fragments and impact-p…