Blog
Deep Neural Network Emulation of the Quantum-Classical Transition via Learned Wigner Function Dynamics
The emergence of classical behavior from quantum mechanics as Planck’s constant $\hbar$ approaches zero remains a fundamental challenge in physics [1-3]. This paper introduces a novel approach employing deep neural networks to directly learn the dynamical mapping from initial quantum state parameters...
MPAD: A New Dimension-Reduction Method for Preserving Nearest Neighbors in High-Dimensional Vector Search
High-dimensional vector embeddings are widely used in retrieval systems, yet dimensionality reduction (DR) is seldom applied due to its tendency to distort nearest-neighbor (NN) structure critical for search. Existing DR techniques such as PCA and UMAP optimize global or manifold-preserving criteria, rather...
Review of the experimental and theoretical landscape of electron transport in noble liquids
We present a review of the current experimental and theoretical understanding of electron transport in noble liquids. Special attention is given to recent measurements that coincide with the development of time projection chambers (TPCs) using liquid xenon and argon as detector media....
Transitive Array: An Efficient GEMM Accelerator with Result Reuse
Deep Neural Networks (DNNs) and Large Language Models (LLMs) have revolutionized artificial intelligence, yet their deployment faces significant memory and computational challenges, especially in resource-constrained environments. Quantization techniques have mitigated some of these issues by reducing data precision, primarily focusing on General...
Real-time Bayesian inference at extreme scale: A digital twin for tsunami early warning applied to the Cascadia subduction zone
We present a Bayesian inversion-based digital twin that employs acoustic pressure data from seafloor sensors, along with 3D coupled acoustic-gravity wave equations, to infer earthquake-induced spatiotemporal seafloor motion in real time and forecast tsunami propagation toward coastlines for early warning with quantified...
An efficient offline sensor placement method for flow estimation
We present an efficient method to optimize sensor placement for flow estimation using sensors with time-delay embedding in advection-dominated flows. Our solution allows identifying promising candidates for sensor positions using solely preliminary flow field measurements with non-time-resolved Particle Image Velocimetry (PIV), without...
Supermassive Binaries in Ultralight Dark Matter Solitons
Ultralight (or fuzzy) dark matter (ULDM) is an alternative to cold dark matter. A key feature of ULDM is the presence of solitonic cores at the centers of collapsed halos. These would potentially increase the drag experienced by supermassive black hole (SMBH)...
Property-Preserving Hashing for $\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks
Perceptual hashing is used to detect whether an input image is similar to a reference image with a variety of security applications. Recently, they have been shown to succumb to adversarial input attacks which make small imperceptible changes to the input image...
Exact steady states and fragmentation-induced relaxation in the no-passing asymmetric simple exclusion process
We introduce a multi-species generalization of the asymmetric simple exclusion process (ASEP) with a “no-passing” constraint, forbidding overtaking, on a one-dimensional open chain. This no-passing rule fragments the Hilbert space into an exponential number of disjoint sectors labeled by the particle sequence,...
Hamiltonian quantization of complex Chern-Simons theory at level-$k$
This paper develops a framework for the Hamiltonian quantization of complex Chern-Simons theory with gauge group $\mathrm{SL}(2,\mathbb{C})$ at an even level $k\in\mathbb{Z}_+$. Our approach follows the procedure of combinatorial quantization to construct the operator algebras of quantum holonomies on 2-surfaces and develop...