Blog
Effect of Coriolis Force on the Shear Viscosity of Rotating Nuclear Medium
Following the recent observation of non-zero spin polarization and spin alignment of a few hadrons, the rotational aspect of quark-gluon plasma formed in heavy ion collisions has attracted considerable interest. The present work explores the effect of the Coriolis force, arising due...
$π_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
In order for robots to be useful, they must perform practically relevant tasks in the real world, outside of the lab. While vision-language-action (VLA) models have demonstrated impressive results for end-to-end robot control, it remains an open question how far such models...
How to make CLEAN variants faster? Using clustered components informed by the autocorrelation function
Deconvolution, imaging and calibration of data from radio interferometers is a challenging computational (inverse) problem. The upcoming generation of radio telescopes poses significant challenges to existing, and well proven data reduction pipelines due to the large data sizes expected from these experiments,...
On the convergence of a perturbed one dimensional Mann’s process
We consider the perturbed Mann’s iterative process \begin{equation} x_{n+1}=(1-\theta_n)x_n+\theta_n f(x_n)+r_n, \end{equation} where $f:[0,1]\rightarrow[0,1]$ is a continuous function, $\{\theta_n\}\in [0,1]$ is a given sequence, and $\{r_n\}$ is the error term. We establish that if the sequence $\{\theta_n\}$ converges relatively slowly to $0$ and...
A Mysterious Connection Between Tolerant Junta Testing and Agnostically Learning Conjunctions
The main conceptual contribution of this paper is identifying a previously unnoticed connection between two central problems in computational learning theory and property testing: agnostically learning conjunctions and tolerantly testing juntas. Inspired by this connection, the main technical contribution is a pair...
High-performance training and inference for deep equivariant interatomic potentials
Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in atomistic modeling tasks like molecular dynamics and high-throughput screening. The size of datasets and demands of downstream workflows are growing rapidly, making...
A Markov Chain Monte Carlo Method for Efficient Finite-Length LDPC Code Design
Low-density parity-check (LDPC) codes are among the most prominent error-correction schemes. They find application to fortify various modern storage, communication, and computing systems. Protograph-based (PB) LDPC codes offer many degrees of freedom in the code design and enable fast encoding and decoding....
DATETIME: A new benchmark to measure LLM translation and reasoning capabilities
This paper introduces DATETIME, a new high-quality benchmark designed to evaluate the translation and reasoning abilities of a Large Language Model (LLM) on datetimes. A datetime is simply a date and a time, for example ’11th.february.2023 ,1:12:31′. Datetimes are an interesting domain...
Guiding VLM Agents with Process Rewards at Inference Time for GUI Navigation
Recent advancements in visual language models (VLMs) have notably enhanced their capabilities in handling complex Graphical User Interface (GUI) interaction tasks. Despite these improvements, current frameworks often struggle to generate correct actions in challenging GUI environments. State-of-the-art commercial VLMs are black-boxes, and...
From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning
Recent text-to-image diffusion models achieve impressive visual quality through extensive scaling of training data and model parameters, yet they often struggle with complex scenes and fine-grained details. Inspired by the self-reflection capabilities emergent in large language models, we propose ReflectionFlow, an inference-time...