arXiv subjects

Ivan Lysenko

Publications and source records attributed to Ivan Lysenko.

3 recordsLinked to original sources

arXiv2026

Probing optical and acoustic phonons in heated nano-Si/epoxy composites

Understanding the thermal response of optical and acoustic phonons is crucial for designing functional polymer nanocomposites. We study silicon nanoparticle (Si NP)-epoxy composites using combined Raman and Brillouin spectroscopy under local(laser-induced) and global(stage-controlled) heating. Raman spectra reveal THz longitudinal optical(LO) phonon softening and spectral broadening under local heating, indicating nanoscale hot-spots and interfacial scattering. Brillouin data track GHz longitudinal acoustic(LA) phonons, showing temperature- and concentration-dependent evolution of elasticity and damping. Contrasting heating methods unravels Si loading thresholds for isolated thermal absorbers, thermal percolation, acoustic attenuation and elastic homogenization. Local heating induces greater phonon softening and damping than global heating, with this disparity amplified at higher loadings by thermal gradients and interfacial dissipation. Global heating correlates with viscoelastic relaxation, showing intensified acoustic attenuation near the glass transition. Raman thermometry coupled with finite-element opto-thermal modeling allows evaluation of thermal conductivity of the composites characterized by increase from 0.09 to 0.46 W/(mK) for 0.07 up to 2 wt% of Si NPs, respectively, outperforming SiC nanowires at 2 wt% [D. Shen et al, Sci. Rep. 7, 2606 (2017)] despite bulk conductivity of Si being more than 3 times smaller than that of SiC. However, effective heat conductivity of our nanocomposites remain far below bulk Si, confirming that interfacial thermal resistance, not filler conductivity, governs heat transport.

physics.app-ph↗

arXiv2025

ATGen: A Framework for Active Text Generation

Active learning (AL) has demonstrated remarkable potential in reducing the annotation effort required for training machine learning models. However, despite the surging popularity of natural language generation (NLG) tasks in recent years, the application of AL to NLG has been limited. In this paper, we introduce Active Text Generation (ATGen) - a comprehensive framework that bridges AL with text generation tasks, enabling the application of state-of-the-art AL strategies to NLG. Our framework simplifies AL-empowered annotation in NLG tasks using both human annotators and automatic annotation agents based on large language models (LLMs). The framework supports LLMs deployed as services, such as ChatGPT and Claude, or operated on-premises. Furthermore, ATGen provides a unified platform for smooth implementation and benchmarking of novel AL strategies tailored to NLG tasks. Finally, we present evaluation results for state-of-the-art AL strategies across diverse settings and multiple text generation tasks. We show that ATGen reduces both the effort of human annotators and costs associated with API calls to LLM-based annotation agents. The code of the framework is available on GitHub under the MIT license. The video presentation is available at http://atgen-video.nlpresearch.group

cs.CL↗

arXiv2023

Active Learning for Abstractive Text Summarization

Construction of human-curated annotated datasets for abstractive text summarization (ATS) is very time-consuming and expensive because creating each instance requires a human annotator to read a long document and compose a shorter summary that would preserve the key information relayed by the original document. Active Learning (AL) is a technique developed to reduce the amount of annotation required to achieve a certain level of machine learning model performance. In information extraction and text classification, AL can reduce the amount of labor up to multiple times. Despite its potential for aiding expensive annotation, as far as we know, there were no effective AL query strategies for ATS. This stems from the fact that many AL strategies rely on uncertainty estimation, while as we show in our work, uncertain instances are usually noisy, and selecting them can degrade the model performance compared to passive annotation. We address this problem by proposing the first effective query strategy for AL in ATS based on diversity principles. We show that given a certain annotation budget, using our strategy in AL annotation helps to improve the model performance in terms of ROUGE and consistency scores. Additionally, we analyze the effect of self-learning and show that it can further increase the performance of the model.

cs.CL↗