arXiv Machine Learning

Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source

arXiv:2605. 31259v2 Announce Type: replace Abstract: Unscheduled trips of high-power pulsed converters are a leading source of downtime at large accelerator facilities.

arXiv AI

Jun 3

Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate

arXiv:2606. 02670v1 Announce Type: cross Abstract: Many recent multivariate time series anomaly detection (MT-SAD) models incorporate cross-channel modeling, under the implicit assumption that the structure of anomalies may be spread across multiple channels.

By Marc Pinet (LIG), Julien Cumin (LIG), Samuel Berlemont (LIG), Dominique Vaufreydaz (LIG)

benchmarks

arXiv Machine Learning

Jun 30

Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data

arXiv:2606. 29339v1 Announce Type: cross Abstract: Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring.

By Isao Kurosawa

llms benchmarks

Hugging Face Trending Papers

Jun 28

Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data

Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring. In each setting a detector must stay reliable both when sensors fail and when the signal is buried in noise.

llms benchmarks

arXiv AI

Jun 4

An Empirical Audit of Input Encoders for Multi-Channel Signal Transformers

arXiv:2606. 04752v1 Announce Type: cross Abstract: Transformers consuming multi-channel scalar signals must embed $C$ simultaneous values into one $d_{\text{model}}$-dimensional vector per time step.

By Ossi Lehtinen

llms benchmarks

arXiv AI

Jun 24

Lightweight Transformer Models for On-Device Fault Detection: A Benchmark Study on Resource-Constrained Deployment

arXiv:2606. 24173v1 Announce Type: cross Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardware demands careful tradeoffs between accuracy, latency, and model size.

By Disha Patel

llms efficiency benchmarks

arXiv Machine Learning

Jun 19

PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection

arXiv:2606. 20055v1 Announce Type: new Abstract: Time-series anomaly detection has significant practical value for industrial and medical monitoring, as well as other critical domains.

By Youji Zhu, Hongbing Wang, Wenchao Liu, Xiaodong Liu, Xiangguang Xiong

llms rag benchmarks

arXiv AI

Jun 19

Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software

arXiv:2606. 20502v1 Announce Type: cross Abstract: Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved.

By Arastoo Zibaeirad, Marco Vieira

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 31

Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks

arXiv:2607. 27990v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) communicate through sparse binary spike events rather than dense activations, enabling energy-efficient inference on neuromorphic hardware and motivating their use in always-on, battery-powered edge systems.

By Spyridon Raptis, Haralampos-G. Stratigopoulos

safety

arXiv Machine Learning

Jul 15

Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis

arXiv:2607. 12868v1 Announce Type: cross Abstract: Deep learning systems often fail due to subtle implementation faults that alter training behavior.

By Sigma Jahan

benchmarks

arXiv AI

Jun 24

End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing

arXiv:2606. 24075v1 Announce Type: cross Abstract: Although deep learning-based methods can achieve high accuracy in automatic modulation recognition (AMR) tasks, their high computational cost makes it difficult to strike a balance between accuracy and power consumption, thereby limiting their application on resource-constrained platforms.

By Xiaohu Li, Chongxiao Qu, Caiyong Lin, Chenxiao Dou, Wei Hua

llms benchmarks

arXiv Machine Learning

Jun 10

Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017

arXiv:2606. 11098v1 Announce Type: cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017.

By Zach Moczkodan (Royal Military College of Canada, Kingston, Canada), Hany Ragab (Royal Military College of Canada, Kingston, Canada)

llms benchmarks

arXiv Machine Learning

Jul 2

Computer vision-based neural networks for radioisotope identification in urban environments

arXiv:2607. 00270v1 Announce Type: cross Abstract: Algorithm development for radioisotope identification in mobile urban search scenarios face significant challenges from non-uniform backgrounds, momentary source encounters, and severe class imbalance between rare threat signatures and background measurements.

By Masen Bachleda, Peter Lalor

llms computer-vision benchmarks

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