Related papers: Event Indexing Systems for Efficient Selection and…
Event-based data are commonly encountered in edge computing environments where efficiency and low latency are critical. To interface with such data and leverage their rich temporal features, we propose a causal spatiotemporal convolutional…
Keypoint detection and tracking in traditional image frames are often compromised by image quality issues such as motion blur and extreme lighting conditions. Event cameras offer potential solutions to these challenges by virtue of their…
In nuclear physics experiments involving in-flight fragmentation of ions, usually a large number of different nuclei is produced and various detection systems are employed to identify the species event by event, e.g. by measuring their…
The algorithms used by the ATLAS Collaboration during Run 2 of the Large Hadron Collider to identify jets containing $b$-hadrons are presented. The performance of the algorithms is evaluated in the simulation and the efficiency with which…
Document-level Event Causality Identification (DECI) aims to identify causal relations between event pairs in a document. It poses a great challenge of across-sentence reasoning without clear causal indicators. In this paper, we propose a…
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton collisions, we train…
We consider a small extent sensor network for event detection, in which nodes take samples periodically and then contend over a {\em random access network} to transmit their measurement packets to the fusion center. We consider two…
This paper presents a novel hardware system for high-speed, event-sparse sampling-based electronic skin (e-skin)that integrates sensing and neuromorphic computing. The system is built around a 16x16 piezoresistive tactile array with front…
Event vision sensors (neuromorphic cameras) output sparse, asynchronous ON/OFF events triggered by log-intensity threshold crossings, enabling microsecond-scale sensing with high dynamic range and low data bandwidth. As a nonlinear system,…
Data scouting, introduced by CMS in 2011, is the use of specialized data streams based on reduced event content, enabling LHC experiments to record unprecedented numbers of proton-proton collision events that would otherwise be rejected by…
Modern astronomical surveys, such as the Zwicky Transient Facility (ZTF), are capable of detecting thousands of transient events per year, necessitating the use of automated and scalable data analysis techniques. Recent advances in machine…
Event cameras offer high temporal resolution and power efficiency, making them well-suited for edge AI applications. However, their high event rates present challenges for data transmission and processing. Subsampling methods provide a…
Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches yield sporadic successes, yet generally fall short of…
Distributed Search Engine Architecture (DSEA) hosts numerous independent topic-specific search engines and selects a subset of the databases to search within the architecture. The objective of this approach is to reduce the amount of space…
This paper introduces a novel asynchronous, event-driven algorithm for real-time detection of small event clusters in event camera data. Like other hierarchical agglomerative clustering algorithms, the algorithm detects the event clusters…
Rapid detection of spatial events that propagate across a sensor network is of wide interest in many modern applications. In particular, in communications, radar, IoT, environmental monitoring, and biosurveillance, we may observe…
High-speed vision sensing is essential for real-time perception in applications such as robotics, autonomous vehicles, and industrial automation. Traditional frame-based vision systems suffer from motion blur, high latency, and redundant…
Modern high-energy physics experiments collect data using dedicated complex multi-level trigger systems which perform an online selection of potentially interesting events. In general, this selection suffers from inefficiencies. A further…
Pixelated detectors in scanning transmission electron microscopy (STEM) generate large volumes of data, often tens to hundreds of GB per scan. However, to make current advancements scalable and enable widespread adoption, it is essential to…
Event-based cameras have recently drawn the attention of the Computer Vision community thanks to their advantages in terms of high temporal resolution, low power consumption and high dynamic range, compared to traditional frame-based…