Decoding neural activity into behaviorally-relevant variables such as speech or movement is an essential step in the development of brain-machine interfaces (BMIs)and can be used to clarify the role of distinct brain areas in relation to behavior. Two-photon (2p) calcium imaging provides access to thousands of neurons withsingle-cell resolution in genetically-defined populations and therefore...
Field-Programmable Gate Arrays (FPGAs) are increasingly becoming pivotal in the advancement of artificial intelligence (AI) and deep learning applications. Their unique architecture allows for customizable hardware acceleration, which is instrumental in handling the intensive computational demands of modern AI algorithms.
Transmission Electron Microscopy (TEM) provides exceptional...
In this work we show advancements in follow-up methods for detection of electromagnetic counterparts to gravitational wave signals. These multi-messenger observations are important targets for their ability to unlock science including measurement of the Hubble constant, which is a current major effort in cosmology. In this work we include a data-driven heuristic to select anomalous flares...
Accurate estimation of subglacial bed topography is crucial for understanding ice sheet dynamics and their responses to climate change. In this study, we employ machine learning models, enhanced with Spark parallelization, to predict subglacial bed elevation using surface attributes such as ice thickness, flow velocity, and surface elevation. Radar track data serves as ground truth for model...
In time-domain astronomy, rapid classification of astronomical transients is critical for determining candidates for follow-up observations. With the advent of the Vera Rubin Observatory’s Legacy Survey of Space and Time, the backlog of astronomical data will increase by terabytes a night. Machine learning models capable of processing and analyzing large quantities of data can advance the...
Binary black hole mergers can be located by collecting and analyzing the unique gravitational wave signals they produce. Deep learning computational models, specifically Aframe, are used to identify and filter gravitational wave signals more accurately and in less time than traditional matched filtering analyses. The current machine learning model that we use, Aframe, was originally developed...
The next phase of high energy particle physics research at CERN will
involve the High-Luminosity Large Hadron Collider (HL-LHC). In preparation for
this phase, the ATLAS Trigger and Data AcQuisition (TDAQ) system will undergo
upgrades to the online software tracking capabilities. Studies are underway to
assess a heterogeneous computing farm deploying GPUs and/or FPGAs, together
with the...
Pixel detectors are highly valuable for their precise measurement of charged particle trajectories. However, next-generation detectors will demand even smaller pixel sizes, resulting in extremely high data rates surpassing those at the HL-LHC. This necessitates a “smart” approach for processing incoming data, significantly reducing the data volume for a detector’s trigger system to select...
