Tootfinder

Experimental investigation of pose informed reinforcement learning for skid-steered visual navigation
Ameya Salvi, Venkat Krovi
https://arxiv.org/abs/2506.21732

Experimental investigation of pose informed reinforcement learning for skid-steered visual navigation
Vision-based lane keeping is a topic of significant interest in the robotics and autonomous ground vehicles communities in various on-road and off-road applications. The skid-steered vehicle architecture has served as a useful vehicle platform for human controlled operations. However, systematic modeling, especially of the skid-slip wheel terrain interactions (primarily in off-road settings) has created bottlenecks for automation deployment. End-to-end learning based methods such as imitation l…

S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling
Suman Adhya, Debarshi Kumar Sanyal
https://arxiv.org/abs/2507.12451 https://

S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling
Modeling latent representations in a hyperspherical space has proven effective for capturing directional similarities in high-dimensional text data, benefiting topic modeling. Variational autoencoder-based neural topic models (VAE-NTMs) commonly adopt the von Mises-Fisher prior to encode hyperspherical structure. However, VAE-NTMs often suffer from posterior collapse, where the KL divergence term in the objective function highly diminishes, leading to ineffective latent representations. To miti…

A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV
Ziyi Chen, Yiyang Liu, Mattia Prosperi, Krishna Vaddiparti, Robert L Cook, Jiang Bian, Yi Guo, Yonghui Wu
https://arxiv.org/abs/2506.09279

A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV
Objective: To characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV (PLWHs) seeking care, using natural language processing methods applied to a large collection of electronic health record (EHR) clinical notes from a large integrated health system in the southeast United States. Methods: We identified 9,140 cohort of PLWHs from the UF Health IDR and performed topic modeling analysis using Latent Dirichlet Allocation (LDA) to uncover stigma dimen…

Cross-Subreddit Behavior as Open-Source Indicators of Coordinated Influence: A Case Study of r/Sino & r/China
Manon Pilaud, Ian McCulloh
https://arxiv.org/abs/2507.16857 htt…

Cross-Subreddit Behavior as Open-Source Indicators of Coordinated Influence: A Case Study of r/Sino & r/China
This study investigates potential indicators of coordinated influence activity among users participating in both r/Sino and r/China, two ideologically divergent Reddit communities focused on Chinese political discourse. Topic modeling and sentiment analysis are applied to all posts and comments authored by dual-subreddit users to construct a user-topic sentiment matrix. Individual sentiment patterns are compared to global topic baselines derived from the broader r/Sino and r/China populations. …

Metasurface-based Fluid Antennas: from Electromagnetics to Communications Model
Pablo Ram\'irez-Espinosa, Cleof\'as Segura-G\'omez, \'Angel Palomares-Caballero, F. Javier L\'opez-Mart\'inez, David Morales-Jim\'enez
https://arxiv.org/abs/2507.17982

Metasurface-based Fluid Antennas: from Electromagnetics to Communications Model
Fluid antenna systems (FASs) have become a popular topic in the wireless community as an effective yet simple means of exploiting spatial diversity. Due to the limitations of physically moving radiating elements, electronically reconfigurable antennas are emerging as practical implementations of FASs, since changing the radiation pattern is functionally equivalent to physically moving the device. However, electronically reconfigurable antennas pose a challenge in terms of analytical modeling, o…

Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
Mengze Hong, Chen Jason Zhang, Di Jiang
https://arxiv.org/abs/2507.08498 https://

Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
Latent Dirichlet Allocation (LDA) is a prominent generative probabilistic model used for uncovering abstract topics within document collections. In this paper, we explore the effectiveness of augmenting topic models with Large Language Models (LLMs) through integration into two key phases: Initialization and Post-Correction. Since the LDA is highly dependent on the quality of its initialization, we conduct extensive experiments on the LLM-guided topic clustering for initializing the Gibbs sampl…

'A Little Bubble of Friends': An Analysis of LGBTQ Pandemic Experiences Using Reddit Data
Dhruvee Birla, Nazia Akhtar
https://arxiv.org/abs/2507.15033

'A Little Bubble of Friends': An Analysis of LGBTQ+ Pandemic Experiences Using Reddit Data
Social media was one of the most popular forms of communication among young people with digital access during the pandemic. Consequently, crucial debates and discussions about the pandemic crisis have also developed on social media platforms, making them a great primary source to study the experiences of specific groups and communities during the pandemic. This study involved research using LDA topic modeling and sentiment analysis on data obtained from the social media platform Reddit to under…

Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse
Paulina DeVito, Akhil Vallala, Sean Mcmahon, Yaroslav Hinda, Benjamin Thaw, Hanqi Zhuang, Hari Kalva
https://arxiv.org/abs/2506.16412

Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse
Generative AI (GAI) technologies are quickly reshaping the educational landscape. As adoption accelerates, understanding how students and educators perceive these tools is essential. This study presents one of the most comprehensive analyses to date of stakeholder discourse dynamics on GAI in education using social media data. Our dataset includes 1,199 Reddit posts and 13,959 corresponding top-level comments. We apply sentiment analysis, topic modeling, and author classification. To support th…

Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers
Haoning Xue, Brian Nishimine, Martin Hilbert, Drew Cingel, Samantha Vigil, Jane Shawcroft, Arti Thakur, Zubair Shafiq, Jingwen Zhang
https://arxiv.org/abs/2507.12571

Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers
The prevalence of short form video platforms, combined with the ineffectiveness of age verification mechanisms, raises concerns about the potential harms facing children and teenagers in an algorithm-moderated online environment. We conducted multimodal feature analysis and thematic topic modeling of 4,492 short videos recommended to children and teenagers on Instagram Reels, TikTok, and YouTube Shorts, collected as a part of an algorithm auditing experiment. This feature-level and content-leve…

Understanding the Issue Types in Open Source Blockchain-based Software Projects with the Transformer-based BERTopic
Md Nahidul Islam Opu, Md Shahidul Islam, Sara Rouhani, Shaiful Chowdhury
https://arxiv.org/abs/2506.11451

Understanding the Issue Types in Open Source Blockchain-based Software Projects with the Transformer-based BERTopic
Blockchain-based software systems are increasingly deployed across diverse domains, yet a systematic understanding of their development challenges remains limited. This paper presents a large-scale empirical study of 497,742 issues mined from 1,209 open-source blockchain projects hosted on GitHub. Employing BERTopic, a transformer-based topic modeling technique, we identify 49 distinct issue topics and organize them hierarchically into 11 major subcategories. Our analysis reveals that both gene…

DTECT: Dynamic Topic Explorer & Context Tracker
Suman Adhya, Debarshi Kumar Sanyal
https://arxiv.org/abs/2507.07910 https://arxiv…

DTECT: Dynamic Topic Explorer & Context Tracker
The explosive growth of textual data over time presents a significant challenge in uncovering evolving themes and trends. Existing dynamic topic modeling techniques, while powerful, often exist in fragmented pipelines that lack robust support for interpretation and user-friendly exploration. We introduce DTECT (Dynamic Topic Explorer & Context Tracker), an end-to-end system that bridges the gap between raw textual data and meaningful temporal insights. DTECT provides a unified workflow that sup…

Distribution-free inference for LightGBM and GLM with Tweedie loss
Alokesh Manna, Aditya Vikram Sett, Dipak K. Dey, Yuwen Gu, Elizabeth D. Schifano, Jichao He
https://arxiv.org/abs/2507.06921

Distribution-free inference for LightGBM and GLM with Tweedie loss
Prediction uncertainty quantification is a key research topic in recent years scientific and business problems. In insurance industries (\cite{parodi2023pricing}), assessing the range of possible claim costs for individual drivers improves premium pricing accuracy. It also enables insurers to manage risk more effectively by accounting for uncertainty in accident likelihood and severity. In the presence of covariates, a variety of regression-type models are often used for modeling insurance clai…

DS@GT at LongEval: Evaluating Temporal Performance in Web Search Systems and Topics with Two-Stage Retrieval
Anthony Miyaguchi, Imran Afrulbasha, Aleksandar Pramov
https://arxiv.org/abs/2507.08360

DS@GT at LongEval: Evaluating Temporal Performance in Web Search Systems and Topics with Two-Stage Retrieval
Information Retrieval (IR) models are often trained on static datasets, making them vulnerable to performance degradation as web content evolves. The DS@GT competition team participated in the Longitudinal Evaluation of Model Performance (LongEval) lab at CLEF 2025, which evaluates IR systems across temporally distributed web snapshots. Our analysis of the Qwant web dataset includes exploratory data analysis with topic modeling over time. The two-phase retrieval system employs sparse keyword se…

High heating rate effects in sintering: A phase-field study of La-doped alumina
Marco Seiz, Tomohiro Takaki
https://arxiv.org/abs/2506.07353 https://

High heating rate effects in sintering: A phase-field study of La-doped alumina
In recent years high heating rate sintering methods have become a hot topic for reasons of energy efficiency and microstructural optimization. This paper aims to shed light on the microstructural evolution these methods induce by means of phase-field modeling and simulations. A particle-based temperature model suited for the sintering process is developed and coupled with a multiphysics phase-field solver. It is shown that the coupled model exhibits many characteristics typical of high heating …

Agentic Retrieval of Topics and Insights from Earnings Calls
Anant Gupta, Rajarshi Bhowmik, Geoffrey Gunow
https://arxiv.org/abs/2507.07906 https://arxiv.org/pdf/2507.07906 https://arxiv.org/html/2507.07906
arXiv:2507.07906v1 Announce Type: new
Abstract: Tracking the strategic focus of companies through topics in their earnings calls is a key task in financial analysis. However, as industries evolve, traditional topic modeling techniques struggle to dynamically capture emerging topics and their relationships. In this work, we propose an LLM-agent driven approach to discover and retrieve emerging topics from quarterly earnings calls. We propose an LLM-agent to extract topics from documents, structure them into a hierarchical ontology, and establish relationships between new and existing topics through a topic ontology. We demonstrate the use of extracted topics to infer company-level insights and emerging trends over time. We evaluate our approach by measuring ontology coherence, topic evolution accuracy, and its ability to surface emerging financial trends.
toXiv_bot_toot

Function-on-function Differential Regression
Tongyu Li, Fang Yao
https://arxiv.org/abs/2506.02363 https://arxiv.org/pdf/2506.02363

Function-on-function Differential Regression
Function-on-function regression has been a topic of substantial interest due to its broad applicability, where the relation between functional predictor and response is concerned. In this article, we propose a new framework for modeling the regression mapping that extends beyond integral type, motivated by the prevalence of physical phenomena governed by differential relations, which is referred to as function-on-function differential regression. However, a key challenge lies in representing th…

A mean field game model with non-local spatial interactions and resources accumulation
Daria Ghilli, Fausto Gozzi, Cristiano Ricci, Giovanni Zanco
https://arxiv.org/abs/2506.01200

A mean field game model with non-local spatial interactions and resources accumulation
We study a family of Mean Field Games arising in modeling the behavior of strategic economic agents which move across space maximizing their utility from consumption and have the possibility to accumulate resources for production (such as human capital). The resulting mean field game PDE system is not covered in the actual literature on the topic as it displays weaker assumptions on the regularity of the data (in particular Lipschitz continuity and boundedness of the objective are lost), state …

This https://arxiv.org/abs/2505.19669 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Zero-Shot Streaming Text to Speech Synthesis with Transducer and Auto-Regressive Modeling
Zero-shot streaming text-to-speech is an important research topic in human-computer interaction. Existing methods primarily use a lookahead mechanism, relying on future text to achieve natural streaming speech synthesis, which introduces high processing latency. To address this issue, we propose SMLLE, a streaming framework for generating high-quality speech frame-by-frame. SMLLE employs a Transducer to convert text into semantic tokens in real time while simultaneously obtaining duration align…