Search Results for author:
Found 13 papers, 8 papers with code
Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmark
This study provides an in-depth analysis of the distribution and types of noise in the widely used BIRD-Bench benchmark and the impact of noise on models.
Application of Time-Aware PC algorithm to compute Causal Functional Connectivity in Alzheimer's Disease from fMRI data
In contrast, Causal Functional Connectivity is more informative, providing a directed network representing causal relationships between brain regions.
Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC Algorithm
In this work, we propose to model and estimate the causal functional connectivity from neural time series using a novel approach that adapts directed probabilistic graphical modeling to the time series scenario.
Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study
Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality.
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
Enabling catalog simulations of transient and variable sources based on LSST cadence strategies
OpSim outputs contain a record of each simulated pointing of the survey along with a complete characterization of the pointing in terms of observing conditions, and some useful quantities derived from the characteristics of the pointing.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
RAPID: Early Classification of Explosive Transients using Deep Learning
We present RAPID (Real-time Automated Photometric IDentification), a novel time-series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve.
Machine Learning for the Zwicky Transient Facility
The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night.
Instrumentation and Methods for Astrophysics
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Data set
The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) is an open data challenge to classify simulated astronomical time-series data in preparation for observations from the Large Synoptic Survey Telescope (LSST), which will achieve first light in 2019 and commence its 10-year main survey in 2022.
Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics
Science-Driven Optimization of the LSST Observing Strategy
The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky and over ten years of repeated observation.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies Solar and Stellar Astrophysics
The Mira-Titan Universe: Precision Predictions for Dark Energy Surveys
The new sampling method allows us to build precision emulators from just 26 cosmological models and to increase the emulator accuracy by adding new sets of simulations in a prescribed way.
Cosmology and Nongalactic Astrophysics
LSST: from Science Drivers to Reference Design and Anticipated Data Products
About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18, 000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27. 5$.
