October 2024 arXiv papers — page 40

Deepak Vungarala, Sakila Alam, Arnob Ghosh, Shaahin Angizi

Large Language Models (LLMs) have shown great potential in automating code generation; however, their ability to generate accurate circuit-level SPICE code remains limited due to a lack of hardware-specific knowledge. In this paper, we analyze and identify the typical limitations of existing LLMs in SPICE code generation. To address these limitations, we pre

Björn Braun, Daniel McDuff, Tadas Baltrusaitis, Paul Streli

Recent work has shown that a person's sympathetic arousal can be estimated from facial videos alone using basic signal processing. This opens up new possibilities in the field of telehealth and stress management, providing a non-invasive method to measure stress only using a regular RGB camera. In this paper, we present SympCam, a new 3D convolutional archit

Alexander J. Silenko

Time-dependent terms in Hamiltonians and equations of motion are rather important for a quantum-mechanical description of particles with arbitrary spins in nonstationary fields. We use the Foldy-Wouthuysen representation which allows one to obtain the Schr\"{o}dinger picture of relativistic quantum mechanics. We determine exact nonexponential and exponential

Dongyu Lyu, Matthias Holzenkamp, Vivin Vinod, Yannick Marcel Holtkamp

Natural light-harvesting antenna complexes efficiently capture solar energy using chlorophyll, i.e., magnesium porphyrin pigments, embedded in a protein matrix. Inspired by this natural configuration, artificial clay-porphyrin antenna structures have been experimentally synthesized and have demonstrated remarkable excitation energy transfer properties. The s

Eduardo C. Garrido-Merchán, Maria Coronado-Vaca, Álvaro López-López, Carlos Martinez de Ibarreta

Traditional economic models often rely on fixed assumptions about market dynamics, limiting their ability to capture the complexities and stochastic nature of real-world scenarios. However, reality is more complex and includes noise, making traditional models assumptions not met in the market. In this paper, we explore the application of deep reinforcement l

Jonathan Hümmer, Dominik Riedelbauch, Dominik Henrich

Human-robot collaboration enables highly adaptive co-working. The variety of resulting workflows makes it difficult to measure metrics as, e.g. makespans or idle times for multiple systems and tasks in a comparable manner. This issue can be addressed with virtual commissioning, where arbitrary numbers of non-deterministic human-robot workflows in assembly ta

Dongyeong Ko, Xuan Yao

For a compact Riemannian $3$-manifold $(M^{3}, g)$ with mean convex boundary which is diffeomorphic to a weakly convex compact domain in $\mathbb{R}^{3}$, we prove that if scalar curvature is nonnegative and the scaled mean curvature comparison $H^{2}g \ge H_{0}^{2} g_{Eucl}$ holds, then $(M,g)$ is flat. Our result is a smooth analog of Gromov's dihedral rig

Gianfranco Bilardi, Lorenzo De Stefani

It is shown that $S(G) = O\left(m/\log_2 m + d\right)$ pebbles are sufficient to pebble any DAG $G=(V,E)$, with $m$ edges and maximum in-degree $d$. It was previously known that $S(G) = O\left(d n/\log n\right)$. The result builds on two novel ideas. The first is the notion of $B-budget\ decomposition$ of a DAG $G$, an efficiently computable partition of $G$

Ryan Liu, Jiayi Geng, Addison J. Wu, Ilia Sucholutsky

Chain-of-thought (CoT) prompting has become a widely used strategy for improving large language and multimodal model performance. However, it is still an open question under which settings CoT systematically reduces performance. In this paper, we seek to identify the characteristics of tasks where CoT reduces performance by drawing inspiration from cognitive

Chongxiao Liu

Single Image Super-Resolution (SISR) is a vital technique for improving the visual quality of low-resolution images. While recent deep learning models have made significant advancements in SISR, they often encounter computational challenges that hinder their deployment in resource-limited or time-sensitive environments. To overcome these issues, we present S

Zhuohao Jerry Zhang, John R. Thompson, Aditi Shah, Manish Agrawal

We introduce ChartA11y, an app developed to enable accessible 2-D visualizations on smartphones for blind users through a participatory and iterative design process involving 13 sessions with two blind partners. We also present a design journey for making accessible touch experiences that go beyond simple auditory feedback, incorporating multimodal interacti

Efim A. Brener, Eran Bouchbinder

The analogy between frictional cracks, propagating along interfaces in frictional contact, and ordinary cracks in bulk materials is important in various fields. We consider a stress-controlled frictional crack propagating at a velocity $c_{\rm r}$ along an interface separating two strips, each of height $H$, the frictional counterpart of the classical proble

Junade Ali

Misinformation presents threats to societal mental well-being, public health initiatives, as well as satisfaction in democracy. Those who spread misinformation can leverage cognitive biases to make others more likely to believe and share their misinformation unquestioningly. For example, by sharing misinformation whilst claiming to be someone from a highly r

Arvind Pillai, Dimitris Spathis, Fahim Kawsar, Mohammad Malekzadeh

Photoplethysmography (PPG) is the leading non-invasive technique for monitoring biosignals and cardiovascular health, with widespread adoption in both clinical settings and consumer wearable devices. While machine learning models trained on PPG signals have shown promise, they tend to be task-specific and struggle with generalization. Current research is lim

Xin Mao, Anqi Dong, Ziqin He, Yidan Mei

A wide variety of data can be represented using third-order tensors, spanning applications in chemometrics, psychometrics, and image processing. However, traditional data-driven frameworks are not naturally equipped to process tensors without first unfolding or flattening the data, which can result in a loss of crucial higher-order structural information. In

Jyoti Narang, Nazif Can Tamer, Viviana De La Vega, Xavier Serra

Musical dynamics form a core part of expressive singing voice performances. However, automatic analysis of musical dynamics for singing voice has received limited attention partly due to the scarcity of suitable datasets and a lack of clear evaluation frameworks. To address this challenge, we propose a methodology for dataset curation. Employing the proposed

Shuchen Wu, Mirko Thalmann, Peter Dayan, Zeynep Akata

Humans excel at learning abstract patterns across different sequences, filtering out irrelevant details, and transferring these generalized concepts to new sequences. In contrast, many sequence learning models lack the ability to abstract, which leads to memory inefficiency and poor transfer. We introduce a non-parametric hierarchical variable learning model

Peiyu Li, Omar Bahri, Pouya Hosseinzadeh, Soukaïna Filali Boubrahimi

As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in AI-based systems, leading to the emergence of the Explainable Artificial Intelligence (XAI) field. Recently, a

Josh Alman, Hantao Yu

Algebraic matrix multiplication algorithms are designed by bounding the rank of matrix multiplication tensors, and then using a recursive method. However, designing algorithms in this way quickly leads to large constant factors: if one proves that the tensor for multiplying $n \times n$ matrices has rank $\leq t$, then the resulting recurrence shows that $M

Qi Zhang, Yifei Wang, Jingyi Cui, Xiang Pan

Deep learning models often suffer from a lack of interpretability due to polysemanticity, where individual neurons are activated by multiple unrelated semantics, resulting in unclear attributions of model behavior. Recent advances in monosemanticity, where neurons correspond to consistent and distinct semantics, have significantly improved interpretability b

Ranit Das, David Shih

A key step in any resonant anomaly detection search is accurate modeling of the background distribution in each signal region. Data-driven methods like CATHODE accomplish this by training separate generative models on the complement of each signal region, and interpolating them into their corresponding signal regions. Having to re-train the generative model

Michael Peter Hoffmann, Jan Fillies, Adrian Paschke

Recent advancements in Large Language Models (LLMs) like ChatGPT and GPT-4 have shown remarkable abilities in a wide range of tasks such as summarizing texts and assisting in coding. Scientific research has demonstrated that these models can also play text-adventure games. This study aims to explore whether LLMs can autonomously create text-based games based

Pavel Dvurechensky, Caroline Geiersbach, Michael Hintermüller, Aswin Kannan

We present a novel model of a coupled hydrogen and electricity market on the intraday time scale, where hydrogen gas is used as a storage device for the electric grid. Electricity is produced by renewable energy sources or by extracting hydrogen from a pipeline that is shared by non-cooperative agents. The resulting model is a generalized Nash equilibrium pr

Xuan He, Da Yin, Nanyun Peng

How can "weak teacher models" such as average human annotators or existing AI systems, effectively supervise LLMs to improve performance on hard reasoning tasks, especially those that challenge and requires expertise or daily practice from the teacher models? In this paper, we seek for empirical answers to this question by investigating various data-driven s

Javid Dadashkarimi, Valeria Pena Trujillo, Camilo Jaimes, Lilla Zöllei

Automated fetal brain extraction from full-uterus MRI is a challenging task due to variable head sizes, orientations, complex anatomy, and prevalent artifacts. While deep-learning (DL) models trained on synthetic images have been successful in adult brain extraction, adapting these networks for fetal MRI is difficult due to the sparsity of labeled data, lead

A. Benchikha, B. Hamil, B. C. Lütfüoğlu

This paper presents an analytical treatment of the path integral formalism for time-dependent quantum systems within the framework of Wigner-Dunkl mechanics, emphasizing systems with varying masses and time-dependent potentials. By employing generalized canonical transformations, we reformulated the path integral to develop an explicit expression for the pro

Zachary Lee Andalman, Romain Teyssier, Avishai Dekel

Motivated by the early excess of bright galaxies seen by JWST, we run zoom-in cosmological simulations of a massive galaxy at Cosmic Dawn, in a halo of $10^{11} M_\odot$ at $z = 9$, using the hydro-gravitational code RAMSES at an effective resolution $\sim 10~{\rm pc}$. We investigate physical mechanisms that enhance the star-formation efficiencies (SFEs) at

Asier Alonso-Bardaji

We present a canonical model of spherical gravity with covariant corrections motivated by loop quantum gravity. The effective Hamiltonian defines univocally a family of geometries that generalizes the Lema\^itre-Tolman-Bondi spacetimes, and they can be matched to the vacuum of the theory across a timelike hypersurface comoving with the flow of matter. Such i

Adarsh Chandrashekar, Soumya Das, Goutam Paul

Benchmarking of noise that is induced during the implementation of quantum gates is the main concern for practical quantum computers. Several protocols have been proposed that empirically calculate various metrics that quantify the error rates of the quantum gates chosen from a preferred gate set. Unitarity randomized benchmarking (URB) protocol is a method

Ali TehraniJamsaz, Arijit Bhattacharjee, Le Chen, Nesreen K. Ahmed

Recent advancements in Large Language Models (LLMs) have renewed interest in automatic programming language translation. Encoder-decoder transformer models, in particular, have shown promise in translating between different programming languages. However, translating between a language and its high-performance computing (HPC) extensions remains underexplored

Zhengfu He, Wentao Shu, Xuyang Ge, Lingjie Chen

Sparse Autoencoders (SAEs) have emerged as a powerful unsupervised method for extracting sparse representations from language models, yet scalable training remains a significant challenge. We introduce a suite of 256 SAEs, trained on each layer and sublayer of the Llama-3.1-8B-Base model, with 32K and 128K features. Modifications to a state-of-the-art SAE va

Chandra Chekuri, Anand Louis

We consider algorithms and spectral bounds for sparsest cut and conductance in directed polymatrodal networks. This is motivated by recent work on submodular hypergraphs \cite{Yoshida19,LiM18,ChenOT23,Veldt23} and previous work on multicommodity flows and cuts in polymatrodial networks \cite{ChekuriKRV15}. We obtain three results. First, we obtain an $O(\sqr

Marco Castelli

In this paper, we focus on semiprime skew left braces provided by semidirect products. We show that if a semidirect product $B_1\rtimes B_2$ is semiprime and $B_1$ is Artinian, then $B_1$ must be semiprime. Moreover, we prove that the semidirect product of strongly semiprime skew left braces is strongly semiprime. Finally, following \cite[Question $1$]{smokt

Zhenyi Chen

We use Ng's LSFT algebra to upgrade Sabloff duality of Legendrian knots to a quasi-isomorphism of $A_\infty$ bimodules over the positive augmentation category $\mathcal{A}ug_+$. We also extend the Ekholm-Etnyre-Sabloff exact sequence to an exact sequence of $\mathcal{A}ug_+$-bimodules, using a quotient category $\mathcal{C}$ of short Reeb chords. In addition

Ji-Eun Byun, Hyeuk Ryu, Daniel Straub

Branch and bound algorithms have been developed for reliability analysis of coherent systems. They exhibit a set of advantages; in particular, they can find a computationally efficient representation of a system failure or survival event, which can be re-used when the input probability distributions change over time or when new data is available. However, ex

Ari Juels, Farinaz Koushanfar

We propose protected pipelines or props for short, a new approach for authenticated, privacy-preserving access to deep-web data for machine learning (ML). By permitting secure use of vast sources of deep-web data, props address the systemic bottleneck of limited high-quality training data in ML development. Props also enable privacy-preserving and trustworth

Farnaz Tahouni-Bonab, Matthias Hepting, Theodor Luibrand, Georg Cristiani

Strongly correlated insulators, such as Mott or charge-transfer insulators, exhibit a strong temperature dependence in their resistivity. Consequently, self-heating effects can lead to electrothermal instabilities in planar thin film devices of these materials. When the electrical bias current exceeds a device-specific threshold, the device can switch from a

Qi Liu, Wei Wang, Wen Yang, Xiao Chen

We investigate the cyclotron resonant scattering features (CRSFs) of the accreting X-ray pulsar Cen X-3 and significantly detect the 29 keV cyclotron line features in the hard X-ray averaged spectroscopy studies based on the recent Insight-HXMT observations in 2022, when Cen X-3 has X-ray luminosity $L_{\rm X} > \sim 5 \times 10^{37}$ erg\ s$^{-1}$ in the ba

Yiquan Wang

The digital art market faces unprecedented challenges in authenticity verification and copyright protection. This study introduces an integrated framework to address these issues by combining neural style transfer, fractal analysis, and blockchain technology. We generate abstract artworks inspired by Jackson Pollock, using their inherent mathematical complex

Michał Wiszenko, Kacper Stefański, Piotr Malesa, Łukasz Pokorzyński

Symbolic music research plays a crucial role in music-related machine learning, but MIDI data can be complex for those without musical expertise. To address this issue, we present MidiTok Visualizer, a web application designed to facilitate the exploration and visualization of various MIDI tokenization methods from the MidiTok Python package. MidiTok Visuali

Ze-Ping Wang, Li-Hua Qin, Xue-Yi Chen

We first study $f$-biharmonicity of totally umbilical hypersurfaces in a generic Riemannian manifold and then prove that any totally umbilical proper $f$-biharmonic hypersurface in a nonpositively curved manifold has to be noncompact. We also explore $f$-biharmonicity of totally umbilical hyperplanes in a conformally flat space. Secondly, we construct $f$-bi

Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lazaro, Tommi Jaakkola

Efficiently processing structured point cloud data while preserving multiscale information is a key challenge across domains, from graphics to atomistic modeling. Using a curated dataset of simulated galaxy positions and properties, represented as point clouds, we benchmark the ability of graph neural networks to simultaneously capture local clustering envir

Paweł Skierś, Maksymilian Łazarski, Michał Kopeć, Mateusz Modrzejewski

Implementations of popular symbolic music generative models often differ significantly in terms of the libraries utilized and overall project structure. Therefore, directly comparing the methods or becoming acquainted with them may present challenges. To mitigate this issue we introduce Symbotunes, an open-source unified hub for symbolic music generative mod

Jian Zhou, Yulong Gao, Björn Olofsson, Erik Frisk

In this paper, we develop an uncertainty-aware decision-making and motion-planning method for an autonomous ego vehicle in forced merging scenarios, considering the motion uncertainty of surrounding vehicles. The method dynamically captures the uncertainty of surrounding vehicles by online estimation of their acceleration bounds, enabling a reactive but rapi

Guangliang Liu, Zimo Qi, Xitong Zhang, Lu Cheng

Although there has been growing interest in the self-correction capability of Large Language Models (LLMs), there are varying conclusions about its effectiveness. Prior research has largely concentrated on intrinsic self-correction, extrinsic self-correction, particularly the interplay between internal knowledge and external feedback, remains underexplored.

Do Kien Hoang, Vasily Krylov, Dmytro Matvieievskyi

Let $G$ be a complex reductive algebraic group. In arxiv:2108.03453 Ivan Losev, Lucas mason-Brown and the third-named author suggested a symplectic duality between nilpotent Slodowy slices in $\mathfrak{g}^\vee$ and affinizations of certain $G$-equivariant covers of special nilpotent orbits. In this paper, we study the various versions of Hikita conjecture f

Nathan Vani, Alejandro Ibarra, José Bico, Étienne Reyssat

We investigate the mechanics of two asymmetric ribbons bound at one end and pulled apart at the other ends. We characterize the elastic junction near the bonding and conceptualize it as a bending boundary layer. While the size of this junction decreases with the pulling force, we observe the surprising existence of the binding angle as a macroscopic signatur

Anton M. Zeitlin

We characterize a Courant algebroid with a Calabi-Yau structure as a homotopy BV algebra with certain properties. We explain how it fits into recent double copy constructions relating Yang-Mills homotopy algebras to the ones of Double Field Theory and Gravity.

A. A. Vasilev, A. I. Kochkova, A. Y. Polyakov, A. A. Romanov

Direct observation of capture cross-section is challenging due to the need of extremely short filling pulses in the two-gate Deep-Level Transient Spectroscopy (DLTS). Simple estimation of cross-section can be done from DLTS and Admittance Spectroscopy (AS) data, but it is not feasible to distinguish temperature dependence of pre-exponential and exponential p

Bo Miao, Mingtao Feng, Zijie Wu, Mohammed Bennamoun

We introduce Referring Human Pose and Mask Estimation (R-HPM) in the wild, where either a text or positional prompt specifies the person of interest in an image. This new task holds significant potential for human-centric applications such as assistive robotics and sports analysis. In contrast to previous works, R-HPM (i) ensures high-quality, identity-aware

Joshua Akin, Yunlei Zhao, A. K. M. Naziul Haque, Kejie Fang

Nonlinear optical materials are essential for the development of both nonlinear and quantum optics and have advanced recently from bulk crystals to integrated material platforms. In this Perspective, we provide an overview of the emerging InGaP $\chi^{(2)}$ nonlinear integrated photonics platform and its experimental achievements. With its exceptional $\chi^

Pankaj Mehta

The molecular biology revolution of the last seventy five years has transformed our view of living systems. Scientific explanations of biological phenomena are now synonymous with the identification of the genes, proteins, and signaling molecules involved. The hegemony of the molecular paradigm has only become more pronounced as new technologies allow us to

Mehdi Gholami, Soheil Khajavi, Mohammad Neshat, Simon Tewes

In this paper, a novel approach for wireless localization is proposed and experimentally validated that leverages space-time coded reconfigurable intelligent surfaces (RIS). It is demonstrated that applying proper single-bit codes to each RIS element, enables accurate determination of the direction of arrival (AOA) at the receiver. Moreover, we introduce dif

Max Blans, Thomas Blom

We prove a generalization of the Arone-Ching chain rule for Goodwillie derivatives by showing that for any pair of reduced finitary functors $F \colon \mathcal{D} \to \mathcal{E}$ and $G \colon \mathcal{C} \to \mathcal{D}$ between differentiable $\infty$-categories, there is an equivalence $\partial_*(FG) \simeq \partial_*F \circ_{\partial_*{\mathrm{id}_{\ma

Mehdi Gholami, Soheil Khajavi, Mohammad Neshat, Simon Tewes

This paper presents experimental realization of a reconfigurable intelligent surface (RIS) using space-time coding metasurfaces to enable concurrent beam steering and data modulation. The proposed approach harnesses the capabilities of metasurfaces, allowing precise temporal control over individual unit cells of the RIS. We show that by employing proper bina

Zongyi Li, Shujie Hu, Shujie Liu, Long Zhou

Text-to-video models have recently undergone rapid and substantial advancements. Nevertheless, due to limitations in data and computational resources, achieving efficient generation of long videos with rich motion dynamics remains a significant challenge. To generate high-quality, dynamic, and temporally consistent long videos, this paper presents ARLON, a n

Michel-Andrès Breton

We present PySCo, a fast and user-friendly Python library designed to run cosmological $N$-body simulations across various cosmological models, such as $\Lambda$CDM and $w_0w_a$CDM, and alternative theories of gravity, including $f(R)$, MOND and time-dependent gravitational constant parameterisations. PySCo employs Particle-Mesh solvers, using multigrid or F

Piotr Achinger, Alex Youcis

For a complete discrete valuation field $K$, we show that one may always glue a separated formal algebraic space $\mathfrak{X}$ over $\mathcal{O}_K$ to a separated algebraic space $U$ over $K$ along an open immersion of rigid spaces $j\colon \mathfrak{X}^{\rm rig}\to U^{\rm an}$, producing a separated algebraic space $X$ over $\mathcal{O}_K$. This process gi

Marc Blancafort, Roger Ferrer, Guillaume Houzeaux, Marta Garcia-Gasulla

A current trend in HPC systems is the utilization of architectures with SIMD or vector extensions to exploit data parallelism. There are several ways to take advantage of such modern vector architectures, each with a different impact on the code and its portability. For example, the use of intrinsics, guided vectorization via pragmas, or compiler autovectori

Alkida Balliu, Pierre Fraigniaud, Dennis Olivetti, Mikaël Rabie

We study the awake complexity of graph problems that belong to the class O-LOCAL, which includes a subset of problems solvable by sequential greedy algorithms, such as $(\Delta+1)$-coloring and maximal independent set. It is known from previous work that, in $n$-node graphs of maximum degree $\Delta$, any problem in the class O-LOCAL can be solved by a deter

Noga Alon, Maria Axenovich, John Goldwasser

Let $d \geq 1$ and $s \leq 2^d$ be nonnegative integers. For a subset $A$ of vertices of the hypercube $Q_n$ and $n\geq d$, let $\lambda(n,d,s,A)$ denote the fraction of subcubes $Q_d$ of $Q_n$ that contain exactly $s$ vertices of $A$. Let $\lambda(n,d,s)$ denote the maximum possible value of $\lambda(n,d,s,A)$ as $A$ ranges over all subsets of vertices of $

Yihang Luo, R. S. MacKay, Nick Chater

In this paper, we test predictions of a new theory of macroeconomics, called "thermal macroeconomics." The theory aims to apply the mathematical structure of classical thermodynamics, including analogues of temperature and entropy, to predict aspects of the aggregate behaviour of populations of economic agents without analyzing their detailed interactions. W

Dong Hae Mangalindan, Ericka Rovira, Vaibhav Srivastava

Our goal is to model and experimentally assess trust evolution to predict future beliefs and behaviors of human-robot teams in dynamic environments. Research suggests that maintaining trust among team members in a human-robot team is vital for successful team performance. Research suggests that trust is a multi-dimensional and latent entity that relates to p

Alexander Bondarenko, Adrian Viehweger

The retrieval-augmented generation (RAG) approach is used to reduce the confabulation of large language models (LLMs) for question answering by retrieving and providing additional context coming from external knowledge sources (e.g., by adding the context to the prompt). However, injecting incorrect information can mislead the LLM to generate an incorrect an

Advik Raj Basani, Siddharth Chaitra Vivek, Advaith Krishna, Arnab K. Paul

Distributed Machine Learning (DML) on resource-constrained edge devices holds immense potential for real-world applications. However, achieving fast convergence in DML in these heterogeneous environments remains a significant challenge. Traditional frameworks like Bulk Synchronous Parallel and Asynchronous Stochastic Parallel rely on frequent, small updates

Ghazal Khalighinejad, Sharon Scott, Ollie Liu, Kelly L. Anderson

Multimodal information extraction (MIE) is crucial for scientific literature, where valuable data is often spread across text, figures, and tables. In materials science, extracting structured information from research articles can accelerate the discovery of new materials. However, the multimodal nature and complex interconnections of scientific content pres

D. Amadori, F. A. Chiarello, C. Christoforou

We consider a hydrodynamic model of flocking-type with all-to-all interaction kernel in a periodic domain in one-space dimension with linear pressure term. The main result is the global existence of periodic entropy weak solutions, for periodic initial data having finite total variation and initial density bounded away from zero.

Do Trong Hoang, Thanh Vu

We associate a {\it skew tableau ideal} to each filling of a skew Ferrers diagram with positive integers. We classify all unmixed and sequentially Cohen-Macaulay skew tableau ideals. Consequently, we classify all Cohen-Macaulay, Buchsbaum, and generalized Cohen-Macaulay skew tableau ideals.

Suadath V, Muhammad Sajeer P

Nanopore sequencing, a next-generation sequencing technology, holds the potential to revolutionize multiple facets of life sciences, forensics, and healthcare. While previous research has focused on its technical intricacies and biomedical applications, this paper offers a unique perspective by scrutinizing the societal dimensions (ethical, legal, and social

Ananya Malik, Kartik Sharma, Shaily Bhatt, Lynnette Hui Xian Ng

Large Language Models (LLMs) offer a lucrative promise for scalable content moderation, including hate speech detection. However, they are also known to be brittle and biased against marginalised communities and dialects. This requires their applications to high-stakes tasks like hate speech detection to be critically scrutinized. In this work, we investigat

Huabin Ge, Yunpeng Meng, Chuwen Wang, Yuxuan Yang

For a twist knot $\mathcal{K}_{p'}$, let $M$ be the closed $3$-manifold obtained by doing $(p, q)$ Dehn-filling along $\mathcal{K}_{p'}$. In this article, we prove that Chen-Yang's volume conjecture holds for sufficiently large $|p| + |q|$ and $|p'|$ for $M$. In the proof, we construct a new ideal triangulation of the Whitehead link complement which is diffe

Pengfei Wu, Jiahao Liu, Zhuocheng Gong, Qifan Wang

Recent advancements in Large Language Models (LLMs) have shown remarkable performance across a wide range of tasks. Despite this, the auto-regressive nature of LLM decoding, which generates only a single token per forward propagation, fails to fully exploit the parallel computational power of GPUs, leading to considerable latency. To address this, we introdu

Kaiyan Zhao, Yiming Wang, Yuyang Chen, Yan Li

Experience replay is widely used to improve learning efficiency in reinforcement learning by leveraging past experiences. However, existing experience replay methods, whether based on uniform or prioritized sampling, often suffer from low efficiency, particularly in real-world scenarios with high-dimensional state spaces. To address this limitation, we propo

Luc Dessart

High-cadence high-resolution spectroscopic observations of infant Type II supernovae (SNe) represent an exquisite probe of the atmospheres and winds of exploding red-supergiant (RSG) stars. Using radiation hydrodynamics and radiative transfer, we study the gas and radiation properties during and after the phase of shock breakout, considering RSG progenitors

Longyan Li, Chao Ning, Guangsheng Pan, Leiqi Zhang

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subse

Ramakant Kumar

The integration of LoRaWAN (Long Range Wide Area Network) technology with both active and passive sensors presents a transformative opportunity for the development of smart home systems. This paper explores how active sensors, such as motion detectors and ultrasonic sensors, and passive sensors, including temperature and humidity sensors, work together to en

Yiyang Sun, Tong Wang, Cynthia Rudin

Sparsity is a central aspect of interpretability in machine learning. Typically, sparsity is measured in terms of the size of a model globally, such as the number of variables it uses. However, this notion of sparsity is not particularly relevant for decision-making; someone subjected to a decision does not care about variables that do not contribute to the

Libo Qin, Qiguang Chen, Hao Fei, Zhi Chen

Recently, rapid advancements in Multi-Modal In-Context Learning (MM-ICL) have achieved notable success, which is capable of achieving superior performance across various tasks without requiring additional parameter tuning. However, the underlying rules for the effectiveness of MM-ICL remain under-explored. To fill this gap, this work aims to investigate the

Seyedmojtaba Seyedraoufi, Graeme M. Day, Kristian Berland

Organic molecular ferroelectrics, including organic proton-transfer ferroelectrics and antiferroelectrics, are potentially attractive in organic electronics and have significant chemical tunability. Among these, acid-base proton transfer (PT) salts stand out due to their low coercive fields and the possibility to tune their properties with different acid-bas

Ala Eddine Bahrouni, Anouar Bahrouni, Patrick Winkert

In this paper, we establish continuous and compact embeddings for a new class of Musielak-Orlicz Sobolev spaces in unbounded domains driven by a double phase operator with variable exponents that depend on the unknown solution and its gradient. Using these embeddings and an abstract critical point theorem, we prove the existence and multiplicity of weak solu

Sven Weerdenburg, Roland Horsten, Wim Coene

We demonstrate a simple, lossless method for monitoring photon flux in short-wavelength metrology systems, with a particular focus on applications using high harmonic generation (HHG) light sources. In HHG-based metrology, where photon scarcity often limits precision and efficiency, the ability to monitor flux without sacrificing photons is critical. This de

K R Prajwal, Bowen Shi, Matthew Lee, Apoorv Vyas

We introduce MusicFlow, a cascaded text-to-music generation model based on flow matching. Based on self-supervised representations to bridge between text descriptions and music audios, we construct two flow matching networks to model the conditional distribution of semantic and acoustic features. Additionally, we leverage masked prediction as the training ob

Hans Gersbach, Kremena Valkanova

This paper introduces the Voting with Random Proposers (VRP) procedure to address the challenges of agenda manipulation in voting. In each round of VRP, a randomly selected proposer suggests an alternative that is voted on against the previous round's winner. In a framework with single-peaked preferences, we show that the VRP procedure guarantees that the Co

Sicheng Liu, Bo Yang, Xin Li, Xu Yang

Defense hardening can effectively enhance the resilience of distribution networks against extreme weather disasters. Currently, most existing hardening strategies focus on reducing load shedding. However, for electricity-hydrogen distribution networks (EHDNs), the leakage risk of hydrogen should be controlled to avoid severe incidents such as explosions. To

Phillip Y. Lee, Taehoon Yoon, Minhyuk Sung

We introduce GrounDiT, a novel training-free spatial grounding technique for text-to-image generation using Diffusion Transformers (DiT). Spatial grounding with bounding boxes has gained attention for its simplicity and versatility, allowing for enhanced user control in image generation. However, prior training-free approaches often rely on updating the nois

Xiaobo Hou, Xueting Tian, Yiwei Zhang

In this paper, we study the topological entropy and the Hausdorff dimension of a shrinking target set. We give lower and upper bounds of topological entropy and Hausdorff dimension for dynamical systems with exponential specification property and Lipschitz continuity for maps and homeomorphisms. It generally applies to uniformly hyperbolic systems, expanding

Lucas C. F. Ferreira, Pham T. Xuan

We consider the generalized Boussinesq (GBq) equation on the real hyperbolic space $\mathbb{H}^{n}$ ($n\geq2$) in a rough framework based on Lorentz spaces. First, we establish dispersive estimates for the GBq-prototype group, which is associated with a core term of the linear part of the GBq equation, through a manifold-intrinsic Fourier analysis and estima

Greg Bodwin, Tuong Le

A reachability preserver is a basic kind of graph sparsifier, which preserves the reachability relation of an $n$-node directed input graph $G$ among a set of given demand pairs $P$ of size $|P|=p$. We give constructions of sparse reachability preservers in the online setting, where $G$ is given on input, the demand pairs $(s, t) \in P$ arrive one at a time,

Peter Holderrieth, Yilun Xu, Tommi Jaakkola

Classical Hamiltonian mechanics has been widely used in machine learning in the form of Hamiltonian Monte Carlo for applications with predetermined force fields. In this work, we explore the potential of deliberately designing force fields for Hamiltonian ODEs, introducing Hamiltonian velocity predictors (HVPs) as a tool for score matching and generative mod

Yassine Abbahaddou, Johannes F. Lutzeyer, Michalis Vazirgiannis

In light of the recent success of Graph Neural Networks (GNNs) and their ability to perform inference on complex data structures, many studies apply GNNs to the task of text classification. In most previous methods, a heterogeneous graph, containing both word and document nodes, is constructed using the entire corpus and a GNN is used to classify document no

Ananya Bhattacharjee, Jina Suh, Mahsa Ershadi, Shamsi T. Iqbal

Communication traits in text-based human-AI conversations play pivotal roles in shaping user experiences and perceptions of systems. With the advancement of large language models (LLMs), it is now feasible to analyze these traits at a more granular level. In this study, we explore the preferences of information workers regarding chatbot communication traits

Zachary Norfolk

We introduce a third-order differential condition, analogous to nonzero torsion of a curve, which guarantees a submanifold of Euclidean space is totally skew in a small neighborhood. This condition is used to construct improved totally skew embeddings of $\mathbb{R}^n$, and to solve the totally skew embedding problem for $\mathbb{R}^n$ with $n$ a power of 2.

Zhicheng Zhao, Juanjuan Gu, Chenglong Li, Chun Wang

Optics-guided Thermal UAV image Super-Resolution (OTUAV-SR) has attracted significant research interest due to its potential applications in security inspection, agricultural measurement, and object detection. Existing methods often employ single guidance model to generate the guidance features from optical images to assist thermal UAV images super-resolutio

Lucas C. F. Ferreira, Rafael P. da Silva

In this paper, we address the 3D incompressible Hall-magnetohydrodynamic system (Hall-MHD). Our objective is to provide local and global well-posedness results for initial velocity $u_{0}$, magnetic field $B_{0}$ and the current $J_{0}:=\nabla\times B_{0}$ in a new critical framework, namely critical Besov-Morrey spaces. These spaces combine typical characte

Mikko Partio, Leila Hieta, Anniina Kokkonen

Cloud cover plays a critical role in weather prediction and impacts several sectors, including agriculture, solar power generation, and aviation. Despite advancements in numerical weather prediction (NWP) models, forecasting total cloud cover remains challenging due to the small-scale nature of cloud formation processes. In this study, we introduce CloudCast

Nathan Cristello, Fan Zou, William N. Brandt, Zhibo Yu

Dust-obscured galaxies (DOGs) containing central supermassive black holes (SMBHs) that are rapidly accreting (i.e., having high Eddington ratios, $\lambda_\mathrm{Edd}$) may represent a key phase closest to the peak of both the black-hole and galaxy growth in the coevolution framework for SMBHs and galaxies. In this work, we present a 68 ks XMM-Newton observ

Reham Marzouk, Sondos Krouna, Nizar Habash

We introduce the new concept of an Arabic Derivational Chain Bank CHAINBANK to leverage the relationship between form and meaning in modeling Arabic derivational morphology. We constructed a knowledge graph network of abstract patterns and their derivational relations and aligned it with the lemmas of the CAMELMORPH morphological analyzer database. This proc

Ziyuan Wang, Lei Zhang, Jianhua Tu, Liming Xiong

Let $G$ be a simple graph. A dissociation set of $G$ is defined as a set of vertices that induces a subgraph in which every vertex has a degree of at most 1. A dissociation set is maximal if it is not contained as a proper subset in any other dissociation set. We introduce the notation $\Phi(G)$ to represent the number of maximal dissociation sets in $G$. Th

S. Vineeth, Noble P. Abraham

Charged dust is present in almost all astrophysical and laboratory plasma environments. They alter the plasma charge density and also give rise to various modes of electrostatic waves and oscillations. In this paper we study the properties of Dust Acoustic Rogue Waves (DARW) in a cometary environment with positively and negatively charged dust components, ka

Bruce E. Sagan, Alexander N. Wilson

Let u be a word over the positive integers. Motivated in part by a question from representation theory, we study the centralizer set of u which is C(u) = {w | uw is Knuth-equivalent to wu}. In particular, we give various necessary conditions for w to be in C(u). We also characterize C(u) when u has few letters, when it has a single repeated entry, or when it