blog
Topological properties of curved spacetime Su-Schrieffer-Heeger model
The Su-Schrieffer-Heeger (SSH) model, a prime example of a one-dimensional topologically nontrivial insulator, has been extensively studied in flat space-time. In recent times, many studies have been conducted to understand the properties of the low-dimensional quantum matter in curved spacetime, which can...
Predictions for the Detectability of Milky Way Satellite Galaxies and Outer-Halo Star Clusters with the Vera C. Rubin Observatory
We predict the sensitivity of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) to faint, resolved Milky Way satellite galaxies and outer-halo star clusters. We characterize the expected sensitivity using simulated LSST data from the LSST Dark Energy...
A Theory of Spectral CSP Sparsification
We initiate the study of spectral sparsification for instances of Constraint Satisfaction Problems (CSPs). In particular, we introduce a notion of the \emph{spectral energy} of a fractional assignment for a Boolean CSP instance, and define a \emph{spectral sparsifier} as a weighted subset...
Injection locking of Rydberg dissipative time crystals
Non-equilibrium Rydberg gases exhibit exotic many-body phases stabilized by the interplay of coherent interactions and dissipation. Strong Rydberg interactions drive sustained limit cycle oscillations, whose robustness, long-range temporal order, and spontaneous time-translation symmetry breaking establish a dissipative time crystal (DTC). Collective self-entrainment...
One-Point Sampling for Distributed Bandit Convex Optimization with Time-Varying Constraints
This paper considers the distributed bandit convex optimization problem with time-varying constraints. In this problem, the global loss function is the average of all the local convex loss functions, which are unknown beforehand. Each agent iteratively makes its own decision subject to...
Hexcute: A Tile-based Programming Language with Automatic Layout and Task-Mapping Synthesis
Deep learning (DL) workloads mainly run on accelerators like GPUs. Recent DL quantization techniques demand a new matrix multiplication operator with mixed input data types, further complicating GPU optimization. Prior high-level compilers like Triton lack the expressiveness to implement key optimizations like...
Identifying Process Improvement Opportunities through Process Execution Benchmarking
Benchmarking functionalities in current commercial process mining tools allow organizations to contextualize their process performance through high-level performance indicators, such as completion rate or throughput time. However, they do not suggest any measures to close potential performance gaps. To address this limitation,...
Cohort Revenue & Retention Analysis: A Bayesian Approach
We present a Bayesian approach to model cohort-level retention rates and revenue over time. We use Bayesian additive regression trees (BART) to model the retention component which we couple with a linear model for the revenue component. This method is flexible enough...
ReGraph: A Tool for Binary Similarity Identification
Binary Code Similarity Detection (BCSD) is not only essential for security tasks such as vulnerability identification but also for code copying detection, yet it remains challenging due to binary stripping and diverse compilation environments. Existing methods tend to adopt increasingly complex neural...
Mass-Adaptive Admittance Control for Robotic Manipulators
Handling objects with unknown or changing masses is a common challenge in robotics, often leading to errors or instability if the control system cannot adapt in real-time. In this paper, we present a novel approach that enables a six-degrees-of-freedom robotic manipulator to...