96 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Northwestern UniversityUS17 papers
- University of TorontoCA2 papers
- AMA Computer UniversityPH1 paper
- Anna Needs Neuroblastoma AnswersUS1 paper
- Argonne National LaboratoryUS1 paper
- Austin CollegeUS1 paper
- Brookhaven National LaboratoryUS1 paper
- Budker Institute of Nuclear PhysicsRU1 paper
- California Northstate UniversityUS1 paper
- California Urological AssociationUS1 paper
- Canada Research ChairsCA1 paper
- Carnegie Mellon UniversityUS1 paper
21 papers
SELDON: Supernova Explosions Learned by Deep ODE Networks
Jiezhong Wu, Jack O'Brien, Jennifer Li +6
The discovery rate of optical transients will explode to 10 million public alerts per night once the Vera C. Rubin Observatory's Legacy Survey of Space and Time comes online, overw…
Uncertainty-Aware Digital Twins: Robust Model Predictive Control using Time-Series Deep Quantile Learning
Yi-Ping Chen, Ying-Kuan Tsai, Vispi Karkaria +1
Digital Twins, virtual replicas of physical systems that enable real-time monitoring, model updates, predictions, and decision-making, present novel avenues for proactive control s…
Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks
Yi-Ping Chen, Vispi Karkaria, Ying-Kuan Tsai +5
Digital Twin -- a virtual replica of a physical system enabling real-time monitoring, model updating, prediction, and decision-making -- combined with recent advances in machine le…
Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning
Keqin Li, Jin Wang, Xubo Wu +7
With the rapid growth of global e-commerce, the demand for automation in the logistics industry is increasing. This study focuses on automated picking systems in warehouses, utiliz…
Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning
Jie Chen, Pengfei Ou, Yuxin Chang +4
High-performance catalysts are crucial for sustainable energy conversion and human health. However, the discovery of catalysts faces challenges due to the absence of efficient appr…
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation
Vandan Gorade, Sparsh Mittal, Debesh Jha +1
Knowledge distillation (KD) has demonstrated remarkable success across various domains, but its application to medical imaging tasks, such as kidney and liver tumor segmentation, h…
Probabilistic Prediction for Binary Treatment Choice: with focus on personalized medicine
Charles F. Manski
This paper extends my research applying statistical decision theory to treatment choice with sample data, using maximum regret to evaluate the performance of treatment rules. The s…
Jurassic Mark: Inattentional Blindness for a Datasaurus Reveals that Visualizations are Explored, not Seen
Tal Boger, Steven B. Most, Steven L. Franconeri
Graphs effectively communicate data because they capitalize on the visual system's ability to rapidly extract patterns. Yet, this pattern extraction does not occur in a single glan…
Strange Hadron Spectroscopy with Secondary KL Beam in Hall D
KLF Collaboration, Moskov Amaryan, Mikhail Bashkanov +150
We propose to create a secondary beam of neutral kaons in Hall D at Jefferson Lab to be used with the GlueX experimental setup for strange hadron spectroscopy. The superior CEBAF e…
Estimating Shortest Path Length Distributions via Random Walk Sampling
Minhui Zheng, Bruce D. Spencer
In a network, the shortest paths between nodes are of great importance as they allow the fastest and strongest interaction between nodes. However measuring the shortest paths betwe…
An End-to-end Argument in Mechanism Design (Prior-independent Auctions for Budgeted Agents)
Yiding Feng, Jason D. Hartline
This paper considers prior-independent mechanism design, namely identifying a single mechanism that has near optimal performance on every prior distribution. We show that mechanism…
Superlinearly Convergent Asynchronous Distributed Network Newton Method
Fatemeh Mansoori, Ermin Wei
The problem of minimizing a sum of local convex objective functions over a networked system captures many important applications and has received much attention in the distributed…
Electroosmosis in a finite cylindrical pore: simple models of end effects
John D Sherwood, Mao Mao, Sandip Ghosal
A theoretical model of electroosmosis through a circular pore of radius that traverses a membrane of thickness is investigated. Both the cylindrical surface of the pore and…
The Formation and Imprinting of Network Effects Among the Business Elite
Brian Uzzi, Yang Yang, Kevin Gaughan
The business elite constitutes a small but strikingly influential subset of the population, oftentimes affecting important societal outcomes such as the consolidation of political…
Lattice Discrete Particle Model (LDPM) for pressure-dependent inelasticity in granular rocks
Shiva Esna Ashari, Giuseppe Buscarnera, Gianluca Cusatis
This paper deals with the formulation, calibration, and validation of a Lattice Discrete Particle Model (LDPM) for the simulation of the pressure-dependent inelastic response of gr…
-Strong Simulation for Multidimensional Stochastic Differential Equations via Rough Path Analysis
Jose Blanchet, Xinyun Chen, Jing Dong
Consider a multidimensional diffusion process . Let be a \textit{deterministic}, user defined, tolerance error parameter. Un…
Correlations between user voting data, budget, and box office for films in the Internet Movie Database
Max Wasserman, Satyam Mukherjee, Konner Scott +3
The Internet Movie Database (IMDb) is one of the most-visited websites in the world and the premier source for information on films. Like Wikipedia, much of IMDb's information is u…
Instrumental Variables Estimation of a Generalized Correlated Random Coefficients Model
Matthew A. Masten, Alexander Torgovitsky
We study identification and estimation of the average treatment effect in a correlated random coefficients model that allows for first stage heterogeneity and binary instruments. T…
Local Gaussian process approximation for large computer experiments
Robert B. Gramacy, Daniel W. Apley
We provide a new approach to approximate emulation of large computer experiments. By focusing expressly on desirable properties of the predictive equations, we derive a family of l…
Quantitative nanostructure characterization using atomic pair distribution functions obtained from laboratory electron microscopes
Milinda Abeykoon, Christos D. Malliakas, Pavol Juhas +3
Quantitatively reliable atomic pair distribution functions (PDFs) have been obtained from nanomaterials in a straightforward way from a standard laboratory transmission electron mi…
Model Selection Consistency for Cointegrating Regressions
Eduardo F. Mendes
We study the asymptotic properties of the adaptive Lasso in cointegration regressions in the case where all covariates are weakly exogenous. We assume the number of candidate I(1)…