arXiv subjects

Sioan Zohar

Publications and source records attributed to Sioan Zohar.

4 recordsLinked to original sources

arXiv2026

LLM-based Vulnerability Discovery in Business Process Documentation

Just like software and hardware, business processes are susceptible to vulnerabilities that can lead to product quality issues, delays, and increased costs. Business process vulnerabilities can arise from a variety of sources, including conflicting requirements, ambiguous documentation, invalid measurement spec-ifications, omission of quality checks, or implementations that differ from speci-fications. MIRABELLE is a system that identifies and characterizes business logic (BL) vulnerabilities from available business process representations, in-cluding ISO 9000/9001 documentation, user guides, work instructions, and pro-cess execution logs. MIRABELLE leverages recent advances in AI/ML to pro-cess available business process documentation and generate attributed graph rep-resentations of the business logic that can be processed using both graph and for-mal logic approaches for identifying potential vulnerabilities. However, extract-ing the business logic (e.g., operation execution sequences, decisions, input/out-put resources) from mostly natural language artifacts is challenging due to the required domain expertise, inherent process complexity, and the sometimes very large volumes of information. This paper focuses on our experimentation with Large Language Models (LLMs) and their role within MIRABELLE. We report on the performance of several LLMs across vital stages of vulnerability detection, from grammatical and technical error-flagging in short phrasings, to complete process structure recovery and extraction.

cs.SE↗

arXiv2020

Using photoelectron spectroscopy to measure resonant inelastic X-ray scattering: A computational investigation

Resonant inelastic X-ray scattering (RIXS) has become an important scientific tool. Nonetheless, conventional high-resolution RIXS measurements (<100 meV), especially in the soft x-ray range, require large and low-throughput grating spectrometers that limits measurement accuracy and simplicity. Here, we computationally investigate the performance of a different method for measuring RIXS, Photoelectron Spectrometry for Analysis of X-rays (PAX). This method transforms the X-ray measurement problem of RIXS to an electron measurement problem, enabling use of compact, high-throughput electron spectrometers. In PAX, X-rays to be measured are incident on a converter material and the energy distribution of the resultant photoelectrons, the PAX spectrum, is measured with an electron spectrometer. The incident X-ray spectrum is then estimated through a deconvolution algorithm that leverages concepts from machine learning. We investigate a few example PAX cases. Using the 3d levels of Ag as a converter material, and with 10$^5$ detected electrons, we accurately estimate features with 100s of meV width in a model RIXS spectrum. Using a sharp Fermi edge to encode RIXS spectra, we accurately distinguish 100 meV FWHM peaks separated by 45 meV with 10$^7$ electrons detected that were photoemitted from within 0.4 eV of the Fermi level.

cond-mat.mtrl-sci↗

arXiv2019

Bi-cross validation for estimating spectral clustering hyper parameters

One challenge impeding the analysis of terabyte scale x-ray scattering data from the Linac Coherent Light Source LCLS, is determining the number of clusters required for the execution of traditional clustering algorithms. Here we demonstrate that previous work using bi-cross validation (BCV) to determine the number of singular vectors directly maps to the spectral clustering problem of estimating both the number of clusters and hyper parameter values. These results indicate that the process of estimating the number of clusters should not be divorced from the process of estimating other hyper parameters. Applying this method to LCLS x-ray scattering data enables the identification of dropped shots without manually setting boundaries on detector fluence and provides a path towards identifying rare and anomalous events.

stat.ML↗

arXiv2019

Orbital dynamics during an ultrafast insulator to metal transition

Phase transitions driven by ultrashort laser pulses have attracted interest both for understanding the fundamental physics of phase transitions and for potential new data storage or device applications. In many cases these transitions involve transient states that are different from those seen in equilibrium. To understand the microscopic properties of these states, it is useful to develop elementally selective probing techniques that operate in the time domain. Here we show fs-time-resolved measurements of V Ledge Resonant Inelastic X-Ray Scattering (RIXS) from the insulating phase of the Mott- Hubbard material V2O3 after ultrafast laser excitation. The probed orbital excitations within the d-shell of the V ion show a sub-ps time response, which evolve at later times to a state that appears electronically indistinguishable from the high-temperature metallic state. Our results demonstrate the potential for RIXS spectroscopy to study the ultrafast orbital dynamics in strongly correlated materials.

cond-mat.str-el↗