October 2024 arXiv papers — page 81

Sydney Anuyah Mary Akinyemi, Chika Yinka-Banjo

Financial models have increasingly become popular in recent times, and the focus of researchers has been to find the perfect model which fits all circumstances; however, this has not been thoroughly achieved, and as a result, many financial models have been created. Artificial Intelligence modelling has increasingly become more popular in the financial space

Yuchen Cao, Jianglai Dai, Zhongyan Wang, Yeyubei Zhang

The global increase in mental illness requires innovative detection methods for early intervention. Social media provides a valuable platform to identify mental illness through user-generated content. This systematic review examines machine learning (ML) models for detecting mental illness, with a particular focus on depression, using social media data. It h

Mustapha Nyenye Issah

We explore how dynamic entry deterrence operates through feedback strategies in markets experiencing stochastic demand fluctuations. The incumbent firm, aware of its own cost structure, can deter a potential competitor by strategically adjusting prices. The potential entrant faces a one-time, irreversible decision to enter the market, incurring a fixed cost,

Miguel Altamirano Cabrera, Muhammad Haris Khan, Ali Alabbas, Luis Moreno

This study explores the integration of auditory and tactile experiences in musical haptics, focusing on enhancing sensory dimensions of music through touch. Addressing the gap in translating auditory signals to meaningful tactile feedback, our research introduces a novel method involving a touch-sensitive recorder and a wearable haptic display that captures

Niclas Dern, John P. Cunningham, Geoff Pleiss

Classic ensembles generalize better than any single component model. In contrast, recent empirical studies find that modern ensembles of (overparameterized) neural networks may not provide any inherent generalization advantage over single but larger neural networks. This paper clarifies how modern overparameterized ensembles differ from their classic underpa

Pierre Auclair-Desrotour, Mohammad Farhat, Gwenaël Boué, Russell Deitrick

Thermal tides are atmospheric tides caused by variations in day-night insolation, similar to gravitational tides but with key differences. While both result in delayed mass redistribution, energy dissipation, and angular momentum exchanges between the planet and its host star, thermal tides can drive a planet's dynamics away from the rotational equilibrium s

Mihir Date, Francesco Petocchi, Yun Yen, Jonas A. Krieger

In a well-ordered crystalline solid, insulating behaviour can arise from two mechanisms: electrons can either scatter off a periodic potential, thus forming band gaps that can lead to a band insulator, or they localize due to strong interactions, resulting in a Mott insulator. For an even number of electrons per unit cell, either band- or Mott-insulators can

Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang

Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving interpretability and trustworthiness. However, current training recipes lack robust CoT reasoning data, relying on datasets dominated by short annotations with minimal rationales. In this work, we show that training VLM on short answers does not generalize well to reas

Ranjan Sapkota, Manoj Karkee

In this study, a robust method for 3D pose estimation of immature green apples (fruitlets) in commercial orchards was developed, utilizing the YOLO11(or YOLOv11) object detection and pose estimation algorithm alongside Vision Transformers (ViT) for depth estimation (Dense Prediction Transformer (DPT) and Depth Anything V2). For object detection and pose esti

Hao Gao, Jingyue Wang, Wenyang Fang, Jingwei Xu

Autonomous Driving Systems (ADS) require diverse and safety-critical traffic scenarios for effective training and testing, but the existing data generation methods struggle to provide flexibility and scalability. We propose LASER, a novel frame-work that leverage large language models (LLMs) to conduct traffic simulations based on natural language inputs. Th

Alex Clay, Ernesto Jiménez-Ruiz

LLMs are frequently used tools for conversational generation. Without additional information LLMs can generate lower quality responses due to lacking relevant content and hallucinations, as well as the perception of poor emotional capability, and an inability to maintain a consistent character. Knowledge graphs are commonly used forms of external knowledge a

Liam Pavlovic, David M. Rosen

Stein variational inference (SVI) is a sample-based approximate Bayesian inference technique that generates a sample set by jointly optimizing the samples' locations to minimize an information-theoretic measure of discrepancy with the target probability distribution. SVI thus provides a fast and significantly more sample-efficient approach to Bayesian infere

S. G. Baek, S. Ballinger, O. Grulke, C. Killer

The turbulence characteristics of the scrape-off-layer (SOL) plasma in the W7-X stellarator are investigated using a gas-puff-imaging (GPI) diagnostic, newly installed and operated during the OP 2.1 campaign. The SOL plasma on W7-X features a set of island divertors for heat and particle exhaust and provides a unique environment for studying SOL turbulence a

Kazuki Hasebe

We propose a scheme for the construction of deformed matrix geometries using Landau models. The Landau models are practically useful tools to extract matrix geometries. The level projection method however cannot be applied straightforwardly to the Landau models on deformed manifolds, as they do not generally exhibit degenerate energy levels. We overcome this

Andrei Lazanu, Peter Millington, Sergio Sevillano Muñoz

Diagrammatic approaches to perturbation theory transformed the practicability of calculations in particle physics. In the case of extended theories of gravity, however, obtaining the relevant diagrammatic rules is non-trivial: we must expand in metric perturbations and around (local) minima of the scalar field potentials, make multiple field redefinitions, a

Philip Bechtle, Sven Heinemeyer, Jenny List, Gudrid Moortgat-Pick

Some highlights of the physics case for running an $e^+e^-$ collider at 500 GeV and above are discussed with a particular emphasis on the experimental access to the Higgs potential via di-Higgs and (at sufficiently high energy) triple Higgs production. The information obtainable from Higgs pair production at about 500 GeV is compared with the prospects for t

Aidan Boyd, Patrick Tinsley, Kevin W. Bowyer, Adam Czajka

This work explores how human judgement about salient regions of an image can be introduced into deep convolutional neural network (DCNN) training. Traditionally, training of DCNNs is purely data-driven. This often results in learning features of the data that are only coincidentally correlated with class labels. Human saliency can guide network training usin

Chengchang Liu, Chaowen Guan, Jianhao He, John C. S. Lui

This paper considers the problem for finding the $(\delta,\epsilon)$-Goldstein stationary point of Lipschitz continuous objective, which is a rich function class to cover a great number of important applications. We construct a zeroth-order quantum estimator for the gradient of the smoothed surrogate. Based on such estimator, we propose a novel quantum algor

Mehmet Mars Seven

In this note, we introduce a novel performance rating system called Performance Rating Equilibrium (PRE). A PRE is a vector of ratings for each player, such that if these ratings were each player's initial rating at the start of a tournament, scoring the same points against the same opponents would leave each player's initial rating unchanged. In other words

Artur Rutkowski

We prove equivalence between nonnegative distributional solutions of the fractional heat equation and caloric functions, i.e., functions satisfying the mean value property with respect to the space-time isotropic $\alpha$-stable process. We also provide sufficient conditions for the boundary and exterior data under which the solutions are classical and we gi

Vincent Schroeder, Jakob Schwerter, Marjolein Fokkema, Philipp Doebler

Prediction Rule Ensembles (PREs) are robust and interpretable statistical learning techniques with potential for predictive analytics, yet their efficacy in the presence of missing data is untested. This study uses multiple imputation to fill in missing values, but uses a data stacking approach instead of a traditional model pooling approach to combine the r

Sanchit Ahuja, Varun Gumma, Sunayana Sitaram

Benchmark contamination refers to the presence of test datasets in Large Language Model (LLM) pre-training or post-training data. Contamination can lead to inflated scores on benchmarks, compromising evaluation results and making it difficult to determine the capabilities of models. In this work, we study the contamination of popular multilingual benchmarks

Raphaël Kou, Antony Lewis

One approach to reconciling local measurements of a high expansion rate with observations of acoustic oscillations in the CMB and galaxy clustering (the "Hubble tension") is to introduce additional contributions to the $\Lambda$CDM model that are relevant before recombination. While numerous possibilities exist, none are currently well-motivated or preferred

Yantao Liu, Zijun Yao, Rui Min, Yixin Cao

Reward models are critical in techniques like Reinforcement Learning from Human Feedback (RLHF) and Inference Scaling Laws, where they guide language model alignment and select optimal responses. Despite their importance, existing reward model benchmarks often evaluate models by asking them to distinguish between responses generated by models of varying powe

Javier Redolat, Daniel Arenas-Ortega, Ángela Barreda, Amadeu Griol

Amongst the available plasmonic nanostructures, nanoparticle-on-a-mirror (NPoM) cavities - consisting of metal nanoparticles separated from a metal mirror by a molecular-size monolayer - provide the ultimate light confinement in gaps even below 1 nm. A variation of the NPoM cavity is the nanoparticle-on-a-slit (NPoS) configuration, where the nanoparticle is

Anne Matthies, Nicolas Dannenfeld, Silvia Pappalardi, Achim Rosch

We systematically investigate whether classical hydrodynamic field theories can predict the long-time dynamics of many-particle quantum systems. We study both numerically and analytically the time evolution of a chain of spins (or qubits) subjected to stroboscopic dynamics. The time evolution is implemented by a sequence of local and nearest-neighbor gates t

Belle Collaboration, A. Boschetti, R. Mussa, U. Tamponi

In the bottomonium sector, the hindered magnetic dipole (M1) transitions between P-wave states $h_b(2P) \rightarrow \chi_{bJ}(1P) \gamma$, $J=0, \, 1, \, 2$, are expected to be severely suppressed according to the Relativized Quark Model, due to the spin flip of the $b$ quark. Nevertheless, a recent model following the coupled-channel approach predicts the c

Piotr Korcyl, Leszek Motyka, Tomasz Stebel

In the high-energy limit of DIS experiments the effective degrees of freedom of QCD are Wilson line operators. Their evolution in the rapidity variable is predicted by the set of Balitsky-JIMWLK evolution equations. We analyze a new class of two-point correlation functions of Wilson line operators where the Wilson lines are taken at different values of the r

Zhuoming Chen, Ranajoy Sadhukhan, Zihao Ye, Yang Zhou

Large language models (LLMs) with long context windows have gained significant attention. However, the KV cache, stored to avoid re-computation, becomes a bottleneck. Various dynamic sparse or TopK-based attention approximation methods have been proposed to leverage the common insight that attention is sparse. In this paper, we first show that TopK attention

James Chen, Sheehan Olver

The Stieltjes (or sometimes called the Cauchy) transform is a fundamental object associated with probability measures, corresponding to the generating function of the moments. In certain applications such as free probability it is essential to compute the inverses of the Stieltjes transform, which might be multivalued. This paper establishes conditions bound

Simon Deltadahl, Andreu Vall, Vijay Ivaturi, Niklas Korsbo

We present a novel framework for synthesizing patient data with complex covariates (e.g., eye scans) paired with longitudinal observations (e.g., visual acuity over time), addressing privacy concerns in healthcare research. Our approach introduces controlled association in latent spaces generating each data modality, enabling the creation of complex covariat

Bocheng Zhu, Liang Gao

We explore the effect of local stellar radiation on the formation and evolution of dwarf galaxies around Milky Way (MW) analogues. Using five simulations from the Auriga project, both with and without local stellar radiation, we find that local stellar radiation, as a pre-reionization source, is highly effective at photoionizing and heating the gas around th

Kevin Zhu, Connor Mattson, Shay Snyder, Ricardo Vega

Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless hours brainstorming robot configurations and policies to ``organically" create behaviors which do not need expensive sensors and perception.

Xinhui Liang, Zongpei Yue, Yu-Xin Chao, Zhen-Xing Hua

Quantum information scrambling, which describes the propagation and effective loss of localinformation, is crucial for understanding the dynamics of quantum many-body systems. We report the observation of anomalous information scrambling in an atomic tweezer array with dominant van der Waals interaction. We characterize information spreading by an out-of-tim

Josue N. Rivera, Jianqi Ruan, XiaoLin Xu, Shuting Yang

At the forefront of control techniques is Model Predictive Control (MPC). While MPCs are effective, their requisite to recompute an optimal control given a new state leads to sparse response to the system and may make their implementation infeasible in small systems with low computational resources. To address these limitations in stability control, this res

Helmut Ruhland

3 families of 4-dimensional lattices $L_k, M_k, M_k / 2 \subset \mathbb{R}^2$ are defined. Each lattice is defined by 2 quadratic extensions and has a \emph{finite} number of unit vectors, but the number of unit vectors in each of the 3 familes is \emph{unbounded}. $L_3$ is the Moser lattice.

Tian Li, Thomas E. Collett, Philip J. Marshall, Sydney Erickson

The time delay between multiple images of strongly lensed quasars has been used to infer the Hubble constant. The primary systematic uncertainty for time-delay cosmography is the mass-sheet transform (MST), which preserves the lensing observables while altering the inferred $H_0$. The TDCOSMO collaboration used velocity dispersion measurements of lensed quas

Levin Hornischer, Zoi Terzopoulou

Can neural networks be applied in voting theory, while satisfying the need for transparency in collective decisions? We propose axiomatic deep voting: a framework to build and evaluate neural networks that aggregate preferences, using the well-established axiomatic method of voting theory. Our findings are: (1) Neural networks, despite being highly accurate,

Ayush Devkota, Manakamana Gautam, Uttam Dhakal, Suman Devkota

This systematic review examines the synergistic and individual influences of resistance exercise, dietary protein supplementation, and sleep/recovery on muscle protein synthesis (MPS). Electronic databases such as Scopus, Google Scholar, and Web of Science were extensively used. Studies were selected based on relevance to the criteria and were ensured to be

Divyanshu Aggarwal, Ashutosh Sathe, Sunayana Sitaram

Large Language Models (LLMs) demonstrate exceptional capabilities in a multitude of NLP tasks. However, the efficacy of such models to languages other than English is often limited. Prior works have shown that encoder-only models such as BERT or XLM-RoBERTa show impressive cross lingual transfer of their capabilities from English to other languages. In this

Ivan Demidov, Yuri Lyubarsky

Collisionless tearing instability with a power-law distribution function in a relativistic pair plasma with a guide field is studied. When the current sheet is supported by plasma pressure, the tearing mode is suppressed as the particle spectrum hardens. In the force-free limit, the instability growth rate becomes independent of the particle spectrum. We app

Han Huang, Yuqi Huo, Zijia Zhao, Haoyu Lu

Multimodal large language models (MLLMs) have made significant strides by integrating visual and textual modalities. A critical factor in training MLLMs is the quality of image-text pairs within multimodal pretraining datasets. However, $\textit {de facto}$ filter-based data quality enhancement paradigms often discard a substantial portion of high-quality im

Yuwei Wan, Tong Xie, Nan Wu, Wenjie Zhang

Exploring the predictive capabilities of language models in material science is an ongoing interest. This study investigates the application of language model embeddings to enhance material property prediction in materials science. By evaluating various contextual embedding methods and pre-trained models, including Bidirectional Encoder Representations from

Stefan Fritsch, Matthias Tschoepe, Vitor Fortes Rey, Lars Krupp

Medical procedures such as venipuncture and cannulation are essential for nurses and require precise skills. Learning this skill, in turn, is a challenge for educators due to the number of teachers per class and the complexity of the task. The study aims to help students with skill acquisition and alleviate the educator's workload by integrating generative A

Yufei Zhan, Hongyin Zhao, Yousong Zhu, Fan Yang

Large Multimodal Models (LMMs) have achieved significant breakthroughs in various vision-language and vision-centric tasks based on auto-regressive modeling. However, these models typically focus on either vision-centric tasks, such as visual grounding and region description, or vision-language tasks, like image caption and multi-scenario VQAs. None of the L

Yihong Tang, Ao Qu, Zhaokai Wang, Dingyi Zhuang

Vision language models (VLMs) perform well on many tasks but often fail at spatial reasoning, which is essential for navigation and interaction with physical environments. Many spatial reasoning tasks depend on fundamental two-dimensional (2D) skills, yet our evaluation shows that state-of-the-art VLMs give implausible or incorrect answers to composite spati

Alexander Bienstock, Ujjwal Kumar, Antigoni Polychroniadou

Federated Learning (FL) solutions with central Differential Privacy (DP) have seen large improvements in their utility in recent years arising from the matrix mechanism, while FL solutions with distributed (more private) DP have lagged behind. In this work, we introduce the distributed matrix mechanism to achieve the best-of-both-worlds; better privacy of di

Chanwoo Kim, Trinh T. Nguyen

We justify Prandtl equations and higher order Prandtl expansion from the hydrodynamic limit of the Boltzmann equations. Our fluid data is of the form $\text{shear flow}$, plus $\sqrt\kappa$ order term in analytic spaces in $x_\parallel \in\mathbb T^2$ and Sobolev in $x_3\in\mathbb{R}_+$. This work is the first to rigorously justify the Prandtl equations from

Suman Sapkota

Artificial Neural Networks of varying architectures are generally paired with affine transformation at the core. However, we find dot product neurons with global influence less interpretable as compared to local influence of euclidean distance (as used in Radial Basis Function Network). In this work, we explore the generalization of dot product neurons to $l

Blai Vidiella, Salva Duran-Nebreda, Sergi Valverde

Understanding the origins of complexity is a fundamental challenge with implications for biological and technological systems. Network theory emerges as a powerful tool to model complex systems. Networks are an intuitive framework to represent inter-dependencies among many system components, facilitating the study of both local and global properties. However

I. R. Khairulin, Y. V. Radeonychev

We propose a technique that makes it possible to transform the intensity of a quasi-monochromatic single-photon wave packet, emitted by a radioactive M\"ossbauer gamma-ray source, into a sequence of short bursts with an arbitrary number of bursts, including a single burst. In addition, the technique allows one to individually and independently control, on de

Luo Qi Chan, Lynnette Hui Xian Ng

Singlish, or Colloquial Singapore English, is a language formed from oral and social communication within multicultural Singapore. In this work, we work on a fundamental Natural Language Processing (NLP) task: Parts-Of-Speech (POS) tagging of Singlish sentences. For our analysis, we build a parallel Singlish dataset containing direct English translations and

Tianyi Men, Pengfei Cao, Zhuoran Jin, Yubo Chen

With the development of large language models, they are widely used as agents in various fields. A key component of agents is memory, which stores vital information but is susceptible to jailbreak attacks. Existing research mainly focuses on single-agent attacks and shared memory attacks. However, real-world scenarios often involve independent memory. In thi

Anthony Bazhenov, Pahan Dewasurendra, Giri P. Krishnan, Jean Erik Delanois

Artificial neural networks (ANNs) show limited performance with scarce or imbalanced training data and face challenges with continuous learning, such as forgetting previously learned data after new tasks training. In contrast, the human brain can learn continuously and from just a few examples. This research explores the impact of 'sleep', an unsupervised ph

Ghazaleh Babanejaddehaki, Aijun An, Manos Papagelis

Infectious diseases occur when pathogens from other individuals or animals infect a person, resulting in harm to both individuals and society as a whole. The outbreak of such diseases can pose a significant threat to human health. However, early detection and tracking of these outbreaks have the potential to reduce the mortality impact. To address these thre

Xiang Yue, Yueqi Song, Akari Asai, Seungone Kim

Despite recent advances in multimodal large language models (MLLMs), their development has predominantly focused on English- and western-centric datasets and tasks, leaving most of the world's languages and diverse cultural contexts underrepresented. This paper introduces Pangea, a multilingual multimodal LLM trained on PangeaIns, a diverse 6M instruction da

Giannis Daras, Weili Nie, Karsten Kreis, Alex Dimakis

Using image models naively for solving inverse video problems often suffers from flickering, texture-sticking, and temporal inconsistency in generated videos. To tackle these problems, in this paper, we view frames as continuous functions in the 2D space, and videos as a sequence of continuous warping transformations between different frames. This perspectiv

Mostafa Hussien, Mahmoud Afifi, Kim Khoa Nguyen, Mohamed Cheriet

Recent advancements have scaled neural networks to unprecedented sizes, achieving remarkable performance across a wide range of tasks. However, deploying these large-scale models on resource-constrained devices poses significant challenges due to substantial storage and computational requirements. Neural network pruning has emerged as an effective technique

Robin Thériault, Francesco Tosello, Daniele Tantari

Restricted Boltzmann machines (RBM) are generative models capable to learn data with a rich underlying structure. We study the teacher-student setting where a student RBM learns structured data generated by a teacher RBM. The amount of structure in the data is controlled by adjusting the number of hidden units of the teacher and the correlations in the rows

Robert Beinert, Jonas Bresch

We introduce a novel relaxation strategy for denoising hyperbolic-valued data. The main challenge is here the non-convexity of the hyperbolic sheet. Instead of considering the denoising problem directly on the hyperbolic space, we exploit the Euclidean embedding and encode the hyperbolic sheet using a novel matrix representation. For denoising, we employ the

Azin Ghazimatin, Ekaterina Garmash, Gustavo Penha, Kristen Sheets

Listeners of long-form talk-audio content, such as podcast episodes, often find it challenging to understand the overall structure and locate relevant sections. A practical solution is to divide episodes into chapters--semantically coherent segments labeled with titles and timestamps. Since most episodes on our platform at Spotify currently lack creator-prov

Hoshang Sahib, Francesco Rosa, Aravind Raji, Giacomo Merzoni

Several reports about infinite-layer nickelate thin films suggest that the superconducting critical temperature versus chemical doping phase diagram has a dome-like shape, similar to cuprates. Here, we demonstrate a highly reproducible superconducting state in undoped PrNiO$_2$ thin films grown onto SrTiO$_3$. Scanning transmission electron microscopy measur

Xilin He, Jingyu Hu, Qinliang Lin, Cheng Luo

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information,

Wan Cong, David Kubizňák, Robert B. Mann, Manus R. Visser

We develop a novel holographic dictionary for the thermodynamics of black holes with Lifshitz and hyperscaling violating asymptotics, generalizing the dictionary for Anti-de Sitter black holes. Using our dictionary we show that the holographic Euler equation is dual to a generalized Smarr formula for these black holes, and we find a precise match between the

Jinheng Wang, Hansong Zhou, Ting Song, Shaoguang Mao

Recent advances in 1-bit Large Language Models (LLMs), such as BitNet and BitNet b1.58, present a promising approach to enhancing the efficiency of LLMs in terms of speed and energy consumption. These developments also enable local LLM deployment across a broad range of devices. In this work, we introduce bitnet.cpp, a tailored software stack designed to unl

Chandravardhan Singh Raghaw, Parth Shirish Bhore, Mohammad Zia Ur Rehman, Nagendra Kumar

Pediatric pneumonia remains a significant global threat, posing a larger mortality risk than any other communicable disease. According to UNICEF, it is a leading cause of mortality in children under five and requires prompt diagnosis. Early diagnosis using chest radiographs is the prevalent standard, but limitations include low radiation levels in unprocesse

Mohamed Mahmoud Chems-Eddin, Badr Feryouch, Hakima Mouanis, Ali Tamoussit

Let $D\subseteq B$ be an extension of integral domains and $E$ a subset of the quotient field of $D$. We introduce the ring of \textit{$D$-valued $B$-rational functions on $E$}, denoted by $Int^R_B(E,D)$, which naturally extends the concepts of integer-valued polynomials, defined as $ Int^R_B(E,D) \:=\lbrace f \in B(X);\; f(E)\subseteq D\rbrace.$ The notion

Behkish Nassirzadeh, Albert Heinle, Stefanos Leonardos, Anwar Hasan

Due to the involvement of multiple intermediaries without trusted parties, lack of proper regulations, and a complicated supply chain, ad impression discrepancy affects online advertising. This issue causes up to $82 billion annual revenue loss for honest parties. The loss can be significantly reduced with a precise and trusted decentralized mechanism. This

Tianyu Yang, Shuangyang Li, Yi Song, Kangda Zhi

In this work, we consider the target detection problem in a multistatic integrated sensing and communication (ISAC) scenario characterized by the cell-free MIMO communication network deployment, where multiple radio units (RUs) in the network cooperate with each other for the sensing task. By exploiting the angle resolution from multiple arrays deployed in t

Eun-Kyoung Rosa Lee, Sathvik Nair, Naomi Feldman

We present a systematic evaluation of large language models' sensitivity to argument roles, i.e., who did what to whom, by replicating psycholinguistic studies on human argument role processing. In three experiments, we find that language models are able to distinguish verbs that appear in plausible and implausible contexts, where plausibility is determined

Fan Yang, Xingyue Huang

Line graph transformation has been widely studied in graph theory, where each node in a line graph corresponds to an edge in the original graph. This has inspired a series of graph neural networks (GNNs) applied to transformed line graphs, which have proven effective in various graph representation learning tasks. However, there is limited theoretical study

JaeWon Kim, Soobin Cho, Robert Wolfe, Jishnu Hari Nair

Through co-design interviews ($N=19$) and a design evaluation survey (N=136) with U.S. teens ages 13-18, we investigated teens' privacy management on social media. Our study revealed that 28% of teens with public accounts and 15% with private accounts experience "dysfunctional fear," that is, fear that diminishes their quality of life or paralyzes them from

Finn Schmidt, Polina Turishcheva, Suhas Shrinivasan, Fabian H. Sinz

The neural activity in the visual processing is influenced by both external stimuli and internal brain states. Ideally, a neural predictive model should account for both of them. Currently, there are no dynamic encoding models that explicitly model a latent state and the entire neuronal response distribution. We address this gap by proposing a probabilistic

Kang Zhao, Tao Yuan, Han Bao, Zhenfeng Su

To date, 2:4 sparsity has stood as the only sparse pattern that can be accelerated using sparse tensor cores on GPUs. In practice, 2:4 sparsity often possesses low actual speedups ($\leq 1.3$) and requires fixed sparse ratios, meaning that other ratios, such as 4:8, 8:16, or those exceeding 50% sparsity, do not incur any speedups on GPUs. Recent studies sugg

Sourav Pal, Prajakta Sahasrabuddhe, Nitin Tomar

A tuple $\underline{T}=(T_1, \dotsc, T_k)$ of operators on a Hilbert space $\mathcal H$ is said to be \textit{$q$-commuting with} $\|q\|=1$ or simply $q$-\textit{commuting} if there is a family of scalars $q=\{q_{ij} \in \mathbb C : |q_{ij}|=1, \ q_{ij}=q_{ji}^{-1}, \ 1 \leq i < j \leq k \}$ such that $T_i T_j =q_{ij}T_j T_i$ for $1 \leq i < j \leq k$. Moreo

Bastian Castorene, Francisco J. Peña, Ariel Norambuena, Sergio E. Ulloa

This work investigates a system of three entangled qubits within the XXX model, subjected to an external magnetic field in the $z$-direction and incorporating an anisotropy term along the $y$-axis. We explore the thermodynamics of the system by calculating its magnetic susceptibility and analyzing how this quantity encodes information about entanglement. By

Runkang Guo, Bin Chen, Qi Zhang, Yong Zhao

Traditional rule-based physical models are limited by their reliance on singular physical formulas and parameters, making it difficult to effectively tackle the intricate tasks associated with crowd simulation. Recent research has introduced deep learning methods to tackle these issues, but most current approaches focus primarily on generating pedestrian tra

Panagiotis Kanellopoulos, Alexandros A. Voudouris

We consider a truthful facility location problem with agents that have private positions on the line of real numbers and known optional preferences over two obnoxious facilities that must be placed at locations chosen from a given set of candidate ones. Each agent wants to be as far away as possible from the facilities that affect her, and our goal is to des

Chun-Yi Kuan, Hung-yi Lee

Recent advancements in large audio-language models (LALMs) have shown impressive capabilities in understanding and reasoning about audio and speech information. However, these models still face challenges, including hallucinating non-existent sound events, misidentifying the order of sound events, and incorrectly attributing sound sources, which undermine th

Tommer D. Keidar, Shlomi Reuveni

First-passage processes are pervasive across numerous scientific fields, yet a general framework for understanding their response to external perturbations remains elusive. While the fluctuation-dissipation theorem offers a complete linear response theory for systems in steady-state, it fails to apply to transient first-passage processes. We address this cha

Rongxing Liu, Kumar Shridhar, Manish Prajapat, Patrick Xia

Tasks requiring deductive reasoning, especially those involving multiple steps, often demand adaptive strategies such as intermediate generation of rationales or programs, as no single approach is universally optimal. While Language Models (LMs) can enhance their outputs through iterative self-refinement and strategy adjustments, they frequently fail to appl

Kirill Kobialko, Dmitri Gal'tsov

We develop a perturbation theory for surfaces confining photons and massive particles in static spherically symmetric spacetimes in terms of two parameters: the mass-to-energy ratio and the deviation of metric functions from a given form, e.g., the Schwarzschild solution. Expansions of the gravitational shadow radius in terms of these parameters are construc

Wenbo Liao, Zhongtao Wu

In this paper, we introduce a notion of clock moves for spanning trees in plane graphs. This enables us to develop a spanning tree model of an Alexander polynomial for a plane graph and prove the unimodal property of its associate coefficient sequence. In particular, this confirms the trapezoidal conjecture for planar singular knots and gives new insights to

Søren Føns Nielsen, Darko Zibar, Mikkel N. Schmidt

Existing communication hardware is being exerted to its limits to accommodate for the ever increasing internet usage globally. This leads to non-linear distortion in the communication link that requires non-linear equalization techniques to operate the link at a reasonable bit error rate. This paper addresses the challenge of blind non-linear equalization us

Daniel C. H. Gomes, Gary M. Bernstein

We explore the potential of an array of O(100) small fixed telescopes, aligned along a meridian and automated to measure millions of occultations of Gaia stars by minor planets, to constrain gravitational signatures from a "Planet X" mass in the outer solar system. The accuracy of center-of-mass tracking for the occulters is limited by photon noise, uncertai

Tzu-Ching Lee, Han-Hsuan Lin

We give a near-optimal quantum algorithm for the longest common substring (LCS) problem between two run-length encoded (RLE) strings, with the assumption that the prefix-sums of the run-lengths are given. Our algorithm costs $\tilde{\mathcal{O}}(n^{2/3}/d^{1/6-o(1)}\cdot\mathrm{polylog}(\tilde{n}))$ time, while the query lower bound for the problem is $\tild

Polina Turishcheva, Laura Hansel, Martin Ritzert, Marissa A. Weis

Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines. Especially in biology, clustering is often used to gain insights into the structure of such datasets, for instance to understand the organization of different cell types. However, clustering is known to scale poorly to high dimen

Thoa Thieu, Roderick Melnik

Neuronal oscillations are closely related to the symptoms of Parkinson's disease (PD). In this study, we explore how random fluctuations (or "stochastic inputs") affect these oscillations in brain states, which reflect the collective activity of interconnected neurons. These random inputs are modeled in the context of the subthalamic nucleus (STN), a brain r

Matthieu Haeberle, Puck van Gerwen, Ruben Laplaza, Ksenia R. Briling

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning applied to chemistry, we demonstrate the relevance of an ILP formulation to select molecular training sets for predictions of size-extensive properties. We

Lele Zheng, Yang Cao, Renhe Jiang, Kenjiro Taura

Recent works show that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primarily on image data, these methods do not directly transfer to other domains, such as spatiotemporal data. To understand privacy risks in spatiotemporal federated learning, we first

Aristide Grange

SQL adventure builder (SQLab) is an open-source framework for creating SQL games that are embedded within the very database they query. Students' answers are evaluated using query fingerprinting, a novel technique that allows for better feedback than traditional SQL online judge systems. Fingerprints act as tokens that are used to unlock messages encrypted i

Xinyi Zhou, Xing Li, Yingzhao Lian, Yiwen Wang

We introduce SeaDAG, a semi-autoregressive diffusion model for conditional generation of Directed Acyclic Graphs (DAGs). Considering their inherent layer-wise structure, we simulate layer-wise autoregressive generation by designing different denoising speed for different layers. Unlike conventional autoregressive generation that lacks a global graph structur

Qingyi Zhou, S. Ali Hassani Gangaraj, Ming Zhou, Zongfu Yu

We propose a numerical algorithm that integrates quantum two-level systems (TLSs) into the finite-difference time-domain (FDTD) framework for simulating quantum emitters in arbitrary 3D photonic environments. Conventional methods struggle with these systems due to their semi-classical nature and spurious self-interactions that arise when a TLS is driven by i

R. Alfaro, C. Alvarez, J. C. Arteaga-Velázquez, D. Avila Rojas

Microquasars are laboratories for the study of jets of relativistic particles produced by accretion onto a spinning black hole. Microquasars are near enough to allow detailed imaging of spatial features across the multiwavelength spectrum. The recent extension of the spatial morphology of a microquasar, SS 433, to TeV gamma rays \cite{abeysekara2018very} loc

Grégoire Francisco, Sabrina Guastavino, Teresa Barata, João Fernandes

Solar flare forecasting mainly relies on photospheric magnetograms and associated physical features to predict forthcoming flares. However, it is believed that flare initiation mechanisms often originate in the chromosphere and the lower corona. In this study, we employ deep learning as a purely data-driven approach to compare the predictive capabilities of

Aidan Boyd, Mohamed Trabelsi, Huseyin Uzunalioglu, Dan Kushnir

Understanding specifically where a model focuses on within an image is critical for human interpretability of the decision-making process. Deep learning-based solutions are prone to learning coincidental correlations in training datasets, causing over-fitting and reducing the explainability. Recent advances have shown that guiding models to human-defined reg

Tomer Shenar

Binary interactions are commonplace among massive stars, giving rise observed phenomena such as X-ray binaries, stripped stars & supernovae, and gravitational-wave sources. The multiplicity properties of massive stars thus represent a fundamental observable to calibrate, test, and benchmark models of single-star and binary evolution. In these proceedings, I

Hung-Pin Chang

Brown constructed a series of threefold flips given by the GIT quotient of a hypersurface in $\mathbb{C}^5$. In this article, we classify threefold flips and flops which are the GIT quotients of complete intersections in $\mathbb{C}^6$. We also show that there are no more new examples as GIT quotients of complete intersections in $\mathbb{C}^n$ with $n\geq7$

Sylvia Klosin

This paper identifies an important bias - termed dynamic bias - in fixed effects panel estimators that arises when dynamic feedback is ignored in the estimating equation. Dynamic feedback occurs if past outcomes impact current outcomes, a feature of many settings ranging from economic growth to agricultural and labor markets. When estimating equations omit p

Dibyendu Mondal, Chayan Patra, Dipanjali Halder, Rahul Maitra

Determination of molecular energetics and properties is one of the core challenges in the near-term quantum computing. To this end, hybrid quantum-classical algorithms are preferred for Noisy Intermediate Scale Quantum (NISQ) architectures. The Projective Quantum Eigensolver (PQE) is one such algorithms that optimizes the parameters of the chemistry-inspired