相关论文: Designing a Multi-petabyte Database for LSST
Latency is, unfortunately, a reality when working with large datasets. Guaranteeing imperceptible latency for interactivity is often prohibitively expensive: the application developer may be forced to migrate data processing engines or deal…
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes 40 open datasets, each having at least 20 classes with 40 examples per…
We present a unique method that allows the LSST to scan the sky for stellar variability on short timescales. The operational component of the strategy requires LSST to take star trail images. The image processing component uses deep…
The upcoming Legacy Survey of Space and Time (LSST) is expected to detect a few million transients per night, which will generate a live alert stream during the entire ten years of the survey. This stream will be distributed via community…
Traffic state data, such as speed, volume and travel time collected from ubiquitous traffic monitoring sensors require advanced network level analytics for forecasting and identifying significant traffic patterns. This paper leverages…
We present here the design, architecture, and first data release for the Solar System Notification Alert Processing System (SNAPS). SNAPS is a Solar System broker that ingests alert data from all-sky surveys. At present, we ingest data from…
A limitation of many compressive imaging architectures lies in the sequential nature of the sensing process, which leads to long sensing times. In this paper we present a novel architecture that uses fewer detectors than the number of…
With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged.…
Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical premises. Scaling video analytics to massive camera deployments,…
In today world, organizations like Google, Yahoo, Amazon, Facebook etc. are facing drastic increase in data. This leads to the problem of capturing, storing, managing and analyzing terabytes or petabytes of data, stored in multiple formats,…
The Palomar Transient Factory (PTF) is a synoptic sky survey in operation since 2009. PTF utilizes a 7.1 square degree camera on the Palomar 48-inch Schmidt telescope to survey the sky primarily at a single wavelength (R-band) at a rate of…
Understanding the preferences of transient types for host galaxies with certain characteristics is key to studies of transient physics and galaxy evolution, as well as to transient identification and classification in the LSST era. Here we…
Recent advances in object detection are mainly driven by deep learning with large-scale detection benchmarks. However, the fully-annotated training set is often limited for a target detection task, which may deteriorate the performance of…
Balancing temporal resolution and spatial detail under limited compute budget remains a key challenge for video-based multi-modal large language models (MLLMs). Existing methods typically compress video representations using predefined…
The Vera C. Rubin Observatory, through its Legacy Survey of Space and Time, will soon start producing 10 million alerts on transient astronomical objects per night. Due to logistics and bandwidth, alerts will not be dispatched directly to…
Traditional 3D content representations include dense point clouds that consume large amounts of data and hence network bandwidth, while newer representations such as neural radiance fields suffer from poor frame rates due to their…
Recent Vision Transformer (ViT)-based methods for Image Super-Resolution have demonstrated impressive performance. However, they suffer from significant complexity, resulting in high inference times and memory usage. Additionally, ViT…
In most process control systems nowadays, process measurements are periodically collected and archived in historians. Analytics applications process the data, and provide results offline or in a time period that is considerably slow in…
Long Short-Term Memory (LSTM) is a special class of recurrent neural network, which has shown remarkable successes in processing sequential data. The typical architecture of an LSTM involves a set of states and gates: the states retain…
The Large Synoptic Survey Telescope (LSST) will open a discovery frontier for faint and fast transients with its ability to detect variable flux components down to $\sim$24.5 mag in a $\sim$30 second exposure. Spectroscopic follow-up of…