319 citations · 1.6k across the 35 of their papers we have counts for
36 papers
Emerging Flexible Designs for Geospatial Multimodal Foundation Models
Philipe Dias, Waqwoya Abebe, Abhishek Potnis +4
Foundation models are rapidly transforming Earth observation by enabling scalable pretraining across diverse unlabeled geospatial modalities. However, their architectural diversity…
FOCUS: Fused Observation of Channels for Unveiling Spectra
Xi Xiao, Aristeidis Tsaris, Anika Tabassum +4
Hyperspectral imaging (HSI) captures hundreds of narrow, contiguous wavelength bands, making it a powerful tool in biology, agriculture, and environmental monitoring. However, inte…
Distributed Cross-Channel Hierarchical Aggregation for Foundation Models
Aristeidis Tsaris, Isaac Lyngaas, John Lagregren +6
Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images fr…
ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling
Xiao Wang, Jong-Youl Choi, Takuya Kurihaya +15
Sparse observations and coarse-resolution climate models limit effective regional decision-making, underscoring the need for robust downscaling. However, existing AI methods strugg…
OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery
Philipe Dias, Aristeidis Tsaris, Jordan Bowman +4
While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to bil…
Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars
Wesley Brewer, Aditya Kashi, Sajal Dash +4
In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery. We propose that scaling up artificial intelligen…
Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier
Aristeidis Tsaris, Chengming Zhang, Xiao Wang +9
Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence length…
Pretraining Billion-scale Geospatial Foundational Models on Frontier
Aristeidis Tsaris, Philipe Ambrozio Dias, Abhishek Potnis +3
As AI workloads increase in scope, generalization capability becomes challenging for small task-specific models and their demand for large amounts of labeled training samples incre…
ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability
Xiao Wang, Siyan Liu, Aristeidis Tsaris +8
Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by le…
Ultra-Long Sequence Distributed Transformer
Xiao Wang, Isaac Lyngaas, Aristeidis Tsaris +7
Transformer models trained on long sequences often achieve higher accuracy than short sequences. Unfortunately, conventional transformers struggle with long sequence training due t…
Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction
Xiao Wang, Aristeidis Tsaris, Debangshu Mukherjee +5
Ptychography is a popular microscopic imaging modality for many scientific discoveries and sets the record for highest image resolution. Unfortunately, the high image resolution fo…
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Steven Farrell, Murali Emani, Jacob Balma +40
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing syste…
Measurement of the Double-Differential Muon-neutrino Charged-Current Inclusive Cross Section in the NOvA Near Detector
M. A. Acero, P. Adamson, L. Aliaga +203
We report cross-section measurements of the final-state muon kinematics for \numu charged-current interactions in the NOvA near detector using an accumulated 8.09 pr…
An Improved Measurement of Neutrino Oscillation Parameters by the NOvA Experiment
M. A. Acero, P. Adamson, L. Aliaga +202
We present new , , , and oscillation measurements by the NOvA exper…
Extended search for supernova-like neutrinos in NOvA coincident with LIGO/Virgo detections
M. A. Acero, P. Adamson, L. Aliaga +200
A search is performed for supernova-like neutrino interactions coincident with 76 gravitational wave events detected by the LIGO/Virgo Collaboration. For 40 of these events, full r…
Search for active-sterile antineutrino mixing using neutral-current interactions with the NOvA experiment
M. A. Acero, P. Adamson, L. Aliaga +196
This Letter reports results from the first long-baseline search for sterile antineutrinos mixing in an accelerator-based antineutrino-dominated beam. The rate of neutral-current in…
IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads
Aymen Al Saadi, Dario Alfe, Yadu Babuji +33
The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive…
Track Seeding and Labelling with Embedded-space Graph Neural Networks
Nicholas Choma, Daniel Murnane, Xiangyang Ju +16
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promisi…
The GlueX Beamline and Detector
S. Adhikari, C. S. Akondi, H. Al Ghoul +187
The GlueX experiment at Jefferson Lab has been designed to study photoproduction reactions with a 9-GeV linearly polarized photon beam. The energy and arrival time of beam photons…
Supernova neutrino detection in NOvA
NOvA Collaboration, M. A. Acero, P. Adamson +199
The NOvA long-baseline neutrino experiment uses a pair of large, segmented, liquid-scintillator calorimeters to study neutrino oscillations, using GeV-scale neutrinos from the Ferm…
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Xiangyang Ju, Steven Farrell, Paolo Calafiura +20
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and…
First measurement of neutrino oscillation parameters using neutrinos and antineutrinos by NOvA
M. A. Acero, P. Adamson, L. Aliaga +196
The NOvA experiment has made a -significant observation of appearance in a 2 GeV beam at a distance of 810 km. Using protons on tar…
Observation of seasonal variation of atmospheric multiple-muon events in the NOvA Near Detector
M. A. Acero, P. Adamson, L. Aliaga +188
Using two years of data from the NOvA Near Detector at Fermilab, we report a seasonal variation of cosmic ray induced multiple-muon event rates which has an opposite phase to the s…
FPGA-accelerated machine learning inference as a service for particle physics computing
Javier Duarte, Philip Harris, Scott Hauck +20
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large pote…