Tootfinder

Prompt Engineer: Analyzing Skill Requirements in the AI Job Market
An Vu, Jonas Oppenlaender
https://arxiv.org/abs/2506.00058 https://

Prompt Engineer: Analyzing Skill Requirements in the AI Job Market
The rise of large language models (LLMs) has created a new job role: the Prompt Engineer. Despite growing interest in this position, we still do not fully understand what skills this new job role requires or how common these jobs are. We analyzed 20,662 job postings on LinkedIn, including 72 prompt engineer positions, to learn more about this emerging role. We found that prompt engineering is still rare (less than 0.5% of sampled job postings) but has a unique skill profile. Prompt engineers ne…

First Steps Towards Voice Anonymization for Code-Switching Speech
Sarina Meyer, Ekaterina Kolos, Ngoc Thang Vu
https://arxiv.org/abs/2507.01765 https://

First Steps Towards Voice Anonymization for Code-Switching Speech
The goal of voice anonymization is to modify an audio such that the true identity of its speaker is hidden. Research on this task is typically limited to the same English read speech datasets, thus the efficacy of current methods for other types of speech data remains unknown. In this paper, we present the first investigation of voice anonymization for the multilingual phenomenon of code-switching speech. We prepare two corpora for this task and propose adaptations to a multilingual anonymizati…

Enhancing Document Retrieval in COVID-19 Research: Leveraging Large Language Models for Hidden Relation Extraction
Hoang-An Trieu, Dinh-Truong Do, Chau Nguyen, Vu Tran, Minh Le Nguyen
https://arxiv.org/abs/2506.18311

Enhancing Document Retrieval in COVID-19 Research: Leveraging Large Language Models for Hidden Relation Extraction
In recent years, with the appearance of the COVID-19 pandemic, numerous publications relevant to this disease have been issued. Because of the massive volume of publications, an efficient retrieval system is necessary to provide researchers with useful information if an unexpected pandemic happens so suddenly, like COVID-19. In this work, we present a method to help the retrieval system, the Covrelex-SE system, to provide more high-quality search results. We exploited the power of the large lan…

Investigating Stochastic Methods for Prosody Modeling in Speech Synthesis
Paul Mayer, Florian Lux, Alejandro P\'erez-Gonz\'alez-de-Martos, Angelina Elizarova, Lindsey Vanderlyn, Dirk V\"ath, Ngoc Thang Vu
https://arxiv.org/abs/2507.00227

Investigating Stochastic Methods for Prosody Modeling in Speech Synthesis
While generative methods have progressed rapidly in recent years, generating expressive prosody for an utterance remains a challenging task in text-to-speech synthesis. This is particularly true for systems that model prosody explicitly through parameters such as pitch, energy, and duration, which is commonly done for the sake of interpretability and controllability. In this work, we investigate the effectiveness of stochastic methods for this task, including Normalizing Flows, Conditional Flow…

Ultrahigh Anomalous Nernst Thermopower and Thermal Hall Angle in YbMnBi2
Jiamin Wen, Kaustuv Manna, Dung Vu, Subhadeep Bej, Yu Pan, Claudia Felser, Brian Skinner, Joseph P. Heremans
https://arxiv.org/abs/2506.20721

Ultrahigh Anomalous Nernst Thermopower and Thermal Hall Angle in YbMnBi2
Thermoelectrics (TEs) are solid-state devices that can realize heat-electricity conversion. Transverse TEs require materials with a large Nernst effect, which typically requires a strong applied magnetic field. However, topological materials with magnetic order offer an alternative pathway for achieving large Nernst via the anomalous Hall effect and the accompanying anomalous Nernst effect (ANE) that arise from band topology. Here, we show that YbMnBi2 with a low Hall density and a chemical pot…

Distinguishing symmetric digraphs by proper arc-colourings of type I
Rafa{\l} Kalinowski, Monika Pil\'sniak, Magdalena Prorok
https://arxiv.org/abs/2506.13979

Distinguishing symmetric digraphs by proper arc-colourings of type I
A symmetric digraph $\overleftrightarrow{G}$ is obtained from a simple graph $G$ by replacing each edge $uv$ with a pair of opposite arcs $\vec{uv}$, $\overrightarrow{vu}$. An arc-colouring $c$ of a digraph $\overleftrightarrow{G}$ is distinguishing if the only automorphism of $\overleftrightarrow{G}$ preserving the colouring $c$ is the identity. Behzad introduced the proper arc-colouring of type I as an arc-colouring such that any two consecutive arcs $\overrightarrow{uv}$, $\overrightarrow{vw…

Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment
Long-Vu Hoang, Tuan Nguyen, Tran Huy Dat
https://arxiv.org/abs/2506.14148

Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment
This paper presents a novel non-invasive object classification approach using acoustic scattering, demonstrated through a case study on hair assessment. When an incident wave interacts with an object, it generates a scattered acoustic field encoding structural and material properties. By emitting acoustic stimuli and capturing the scattered signals from head-with-hair-sample objects, we classify hair type and moisture using AI-driven, deep-learning-based sound classification. We benchmark compr…

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
Vu Thi Huong, Ida Litzel, Thorsten Koch
https://arxiv.org/abs/2506.04045

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
Fuzzy clustering, which allows an article to belong to multiple clusters with soft membership degrees, plays a vital role in analyzing publication data. This problem can be formulated as a constrained optimization model, where the goal is to minimize the discrepancy between the similarity observed from data and the similarity derived from a predicted distribution. While this approach benefits from leveraging state-of-the-art optimization algorithms, tailoring them to work with real, massive dat…

Dynamics of rotationally invariant polynomial root sets under iterated differentiations
Andr\'e Galligo, Joseph Najnudel, Truong Vu
https://arxiv.org/abs/2506.06263

Dynamics of rotationally invariant polynomial root sets under iterated differentiations
We associate to an $N$-sample of a given rotationally invariant probability measure $μ_0$ with compact support in the complex plane, a polynomial $P_N$ with roots given by the sample. Then, for $t \in (0,1)$, we consider the empirical measure $μ_t^{N}$ associated to the root set of the $\lfloor t N\rfloor$-th derivative of $P_N$. A question posed by O'Rourke and Steinerberger \cite{o2021nonlocal}, reformulated as a conjecture by Hoskins and Kabluchko \cite{hoskins2021dynamics}, and recently…