July 2023 arXiv papers — page 33

Jessi Cisewski-Kehe, Brittany Terese Fasy, Dhanush Giriyan, Eli Quist

The weighted Euler characteristic transform (WECT) is a new tool for extracting shape information from data equipped with a weight function. Image data may benefit from the WECT where the intensity of the pixels are used to define the weight function. In this work, an empirical assessment of the WECT's ability to distinguish shapes on images with different p

Thiago R. P. Caramês

In this paper, we investigate the gravitational effects of a global monopole that couples nonminimally to gravity. Considering a coupling parameter of arbitrary strength, we have obtained an analytical solution for the field equations of the model in the asymptotic region outside the monopole's core, thus generalizing previous results. Within the weak coupli

Zhibo Tain, Xiaolin Zhang, Peng Zhang, Kun Zhan

Semi-supervised semantic segmentation (SSS) is an important task that utilizes both labeled and unlabeled data to reduce expenses on labeling training examples. However, the effectiveness of SSS algorithms is limited by the difficulty of fully exploiting the potential of unlabeled data. To address this, we propose a dual-level Siamese structure network (DSSN

Nikhil Ayyadevara, Nikhil Bansal, Milind Prabhu

We consider the Generalized Makespan Problem (GMP) on unrelated machines, where we are given $n$ jobs and $m$ machines and each job $j$ has arbitrary processing time $p_{ij}$ on machine $i$. Additionally, there is a general symmetric monotone norm $\psi_i$ for each machine $i$, that determines the load on machine $i$ as a function of the sizes of jobs assign

Lorcan. O. Conlon, Ping Koy Lam, Syed. M. Assad

This work compares the performance of single and two qubit probes for estimating several phase rotations simultaneously under the action of different noisy channels. We compute the quantum limits for this simultaneous estimation using collective and individual measurements by evaluating the Holevo and Nagaoka-Hayashi Cram\'er-Rao bounds respectively. Several

Xiaoqing Meng, Aijie Cheng, Zhengguang Liu

The nonlocal Allen-Cahn equation with nonlocal diffusion operator is a generalization of the classical Allen-Cahn equation. It satisfies the energy dissipation law and maximum bound principle (MBP), and is important for simulating a series of physical and biological phenomena involving long-distance interactions in space. In this paper, we construct first- a

Dingkang Yang, Shuai Huang, Zhi Xu, Zhenpeng Li

Driver distraction has become a significant cause of severe traffic accidents over the past decade. Despite the growing development of vision-driven driver monitoring systems, the lack of comprehensive perception datasets restricts road safety and traffic security. In this paper, we present an AssIstive Driving pErception dataset (AIDE) that considers contex

Mingming Cao, Jiao Chen, Zhengyang Li, Fanghui Liao

We develop a compact version of $T1$ theorem for singular integrals of Zygmund type on $\mathbb{R}^3$. More specifically, if a $(D_{\theta}, \delta_1, \delta_{2, 3})$-Calder\'{o}n-Zygmund operator $T$ associated with Zygmund dilations admits the compact full and partial kernel representations, and satisfies the weak compactness property and the cancellation

Xiaoqing Meng, Aijie Cheng, Zhengguang Liu

The nonlocal Cahn-Hilliard (NCH) equation with nonlocal diffusion operator is more suitable for the simulation of microstructure phase transition than the local Cahn-Hilliard (LCH) equation. In this paper, based on the exponential semi-implicit scalar auxiliary variable (ESI-SAV) method, the highly effcient and accurate schemes in time with unconditional ene

Jiangshan Wang, Yiming Yang, Zheng Peng

The mini-batch versions of StochAstic Recursive grAdient algoritHm and Semi-Stochastic Gradient Descent method, employed the random Barzilai-Borwein step sizes (shorted as MB-SARAH-RBB and mS2GD-RBB), have surged into prominence through timely step size sequence. Inspired by modern adaptors and variance reduction techniques, we propose two new variant rules

Kun Yang, Dingkang Yang, Jingyu Zhang, Mingcheng Li

Multi-agent collaborative perception as a potential application for vehicle-to-everything communication could significantly improve the perception performance of autonomous vehicles over single-agent perception. However, several challenges remain in achieving pragmatic information sharing in this emerging research. In this paper, we propose SCOPE, a novel co

Aamal Hussain, Francesco Belardinelli, Georgios Piliouras

The behaviour of multi-agent learning in competitive settings is often considered under the restrictive assumption of a zero-sum game. Only under this strict requirement is the behaviour of learning well understood; beyond this, learning dynamics can often display non-convergent behaviours which prevent fixed-point analysis. Nonetheless, many relevant compet

Zhongze Wang, Haitao Zhao, Lujian Yao, Jingchao Peng

In image dehazing task, haze density is a key feature and affects the performance of dehazing methods. However, some of the existing methods lack a comparative image to measure densities, and others create intermediate results but lack the exploitation of their density differences, which can facilitate perception of density. To address these deficiencies, we

Uma Girish, Makrand Sinha, Avishay Tal, Kewen Wu

The level-$k$ $\ell_1$-Fourier weight of a Boolean function refers to the sum of absolute values of its level-$k$ Fourier coefficients. Fourier growth refers to the growth of these weights as $k$ grows. It has been extensively studied for various computational models, and bounds on the Fourier growth, even for the first few levels, have proven useful in lear

Ruitao Chen, Guoyang Xie, Jiaqi Liu, Jinbao Wang

3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but most of them cannot meet the needs of IM. There are several disadvantages: i) difficult to deploy on production lines since their algorithms heavily rely on large pre-trained models; ii) hugely increa

Boris Ivanovic, Guanyu Song, Igor Gilitschenski, Marco Pavone

The field of trajectory forecasting has grown significantly in recent years, partially owing to the release of numerous large-scale, real-world human trajectory datasets for autonomous vehicles (AVs) and pedestrian motion tracking. While such datasets have been a boon for the community, they each use custom and unique data formats and APIs, making it cumbers

Yaxin Fan, Feng Jiang, Peifeng Li, Haizhou Li

Grammatical error correction aims to correct ungrammatical sentences automatically. Recently, some work has demonstrated the excellent capabilities of closed-source Large Language Models (LLMs, e.g., ChatGPT) in grammatical error correction. However, the potential of open-source LLMs remains unexplored. In this paper, we introduced GrammarGPT, an open-source

Aamal Hussain, Dan Leonte, Francesco Belardinelli, Georgios Piliouras

The behaviour of multi-agent learning in many player games has been shown to display complex dynamics outside of restrictive examples such as network zero-sum games. In addition, it has been shown that convergent behaviour is less likely to occur as the number of players increase. To make progress in resolving this problem, we study Q-Learning dynamics and d

Xinting Liao, Weiming Liu, Chaochao Chen, Pengyang Zhou

Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the class statistics shifting, (2) the insufficient hierarchical information utilization, and (3) the inconsistency in

Will Perkins, Yuzhou Wang

We consider the algorithmic problem of finding large \textit{balanced} independent sets in sparse random bipartite graphs, and more generally the problem of finding independent sets with specified proportions of vertices on each side of the bipartition. In a bipartite graph it is trivial to find an independent set of density at least half (take one of the pa

Raphael Bousso, Masamichi Miyaji

We use the gravitational path integral (GPI) to compute the fluctuations of the Hawking radiation entropy around the Page curve, in a two-dimensional model introduced by Penington \emph{et al}. Before the Page time, we find that $\delta S = e^{-S}/\sqrt{2}$, where $S$ is the black hole entropy. This result agrees with the Haar-averaged entropy fluctuations o

J. S. Zhang, H. H. Song, Z. L. Wang

Comparative Analysis on temporal and spatial behaviors of droplets produced in a converging co-flow has been investigated when interchanging of phases, NaAlg (non-Newtonian) and soybean oil (Newtonian). The Carreau model is promoted and gave rarely reported negative non-Newtonian index, $n<0$, by which phase diagrams of "butterfly distribution" on temporal $

Antoine Wehenkel, Laura Manduchi, Jens Behrmann, Luca Pegolotti

Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. While such tools are routinely used to simulate whole-body hemodynamics from physiological parameters, solving the corresponding inverse problem of mapping waveforms back to plausible physiological parameters rem

Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer

Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks. However, computational challenges arise due to joint inference over combinatorial space of Directed Acyclic Graphs (DAGs) and nonlinear functions. Despite recent progress towards efficien

Yongyi Guo, Ziping Xu, Susan Murphy

We consider the contextual bandit problem where at each time, the agent only has access to a noisy version of the context and the error variance (or an estimator of this variance). This setting is motivated by a wide range of applications where the true context for decision-making is unobserved, and only a prediction of the context by a potentially complex m

Jose Damian Lopez Diaz

This article aims to describe and explain the theoretical foundations of concurrent and set concurrent algorithms, considering an asynchronous shared memory system where any number of processes can crash. Verification of concurrent algorithms is often described in terms of their progress condition, which guarantees that eventually something good will happen,

Tianhao Ren, Robert M. Konik

We study the time-dependent response of a Heisenberg spin ladder subjected to a time-dependent square form variation of its rung spin exchange coupling. To do so, we employ a field theoretic representation of the Heisenberg spin ladder consisting of a singlet and a triplet of Majorana fermions. Because this underlying description is free fermionic, we are ab

Zhenxin Liu, Zhe Wang

In this paper, we consider the convergence rate with respect to the Wasserstein distance in the invariance principle for sequential dynamical systems. We utilize and modify the techniques previously employed for stationary sequences to address our non-stationary case. Under certain assumptions, we can apply our result to a large class of dynamical systems, i

Chenyan Jia, Michelle S. Lam, Minh Chau Mai, Jeff Hancock

Can we design artificial intelligence (AI) systems that rank our social media feeds to consider democratic values such as mitigating partisan animosity as part of their objective functions? We introduce a method for translating established, vetted social scientific constructs into AI objective functions, which we term societal objective functions, and demons

Dana Lynn O. -L. Lavacot, Jessie Liu, Hannah Williams, Brandon E. Morgan

The importance of nonlocality of mean scalar transport in 2D Rayleigh-Taylor Instability (RTI) is investigated. The Macroscopic Forcing Method (MFM) is utilized to measure spatio-temporal moments of the eddy diffusivity kernel representing passive scalar transport in the ensemble averaged fields. Presented in this work are several studies assessing the impor

Jiajie Zhu, Yan Wang, Feng Zhu, Zhu Sun

The conventional single-target Cross-Domain Recommendation (CDR) aims to improve the recommendation performance on a sparser target domain by transferring the knowledge from a source domain that contains relatively richer information. By contrast, in recent years, dual-target CDR has been proposed to improve the recommendation performance on both domains sim

Chenyu Zhou, Hongzhou Chen, Shiman Wang, Xinyao Sun

Blockchain, pivotal in shaping the metaverse and Web3, often draws criticism for high energy consumption and carbon emission. The rise of sustainability-focused blockchains, especially when intersecting with innovative wireless technologies, revises this predicament. To understand blockchain's role in sustainability, we propose a three-layers structure encap

Tongya Zheng, Tianli Zhang, Qingzheng Guan, Wenjie Huang

Graph Neural Networks have emerged as an effective machine learning tool for multi-disciplinary tasks such as pharmaceutical molecule classification and chemical reaction prediction, because they can model non-euclidean relationships between different entities. Particle crushing, as a significant field of civil engineering, describes the breakage of granular

Chaohui Yu, Qiang Zhou, Jingliang Li, Zhe Zhang

Text-to-3D generation has recently garnered significant attention, fueled by 2D diffusion models trained on billions of image-text pairs. Existing methods primarily rely on score distillation to leverage the 2D diffusion priors to supervise the generation of 3D models, e.g., NeRF. However, score distillation is prone to suffer the view inconsistency problem,

Neelanjana Pal, Diego Manzanas Lopez, Taylor T Johnson

Data-driven, neural network (NN) based anomaly detection and predictive maintenance are emerging research areas. NN-based analytics of time-series data offer valuable insights into past behaviors and estimates of critical parameters like remaining useful life (RUL) of equipment and state-of-charge (SOC) of batteries. However, input time series data can be ex

Seokjin Oh, Su Ah Lee, Woohwan Jung

Despite the rapid growth in model architecture, the scarcity of large parallel corpora remains the main bottleneck in Neural Machine Translation. Data augmentation is a technique that enhances the performance of data-hungry models by generating synthetic data instead of collecting new ones. We explore prompt-based data augmentation approaches that leverage l

Ashkan Moradi, Vinay Chakravarthi Gogineni, Naveen K. D. Venkategowda, Stefan Werner

This paper proposes a Byzantine-resilient consensus-based distributed filter (BR-CDF) wherein network agents employ partial sharing of state parameters. We characterize the performance and convergence of the BR-CDF and study the impact of a coordinated data falsification attack. Our analysis shows that sharing merely a fraction of the states improves robustn

Salman Habib, David G. M. Mitchell

In this work, we propose reinforcement learning (RL) for sequential decoding of moderate length generalized low-density parity-check (GLDPC) codes. Here, sequential decoding refers to scheduling all the generalized constraint nodes (GCNs) and single parity-check nodes (SPCNs) of a GLDPC code serially in each iteration. A GLDPC decoding environment is modeled

Haibao Duan

Let $G$ be a Lie group with a maximal torus $T$. Combining Schubert calculus in the flag manifold $G/T$ with the Serre spectral sequence of the fibration $G\rightarrow G/T$, we construct the integral cohomology ring $H^{\ast}(G)$ uniformly for all compact and simply connected Lie groups $G$.

Shiliang Zuo

I study the problem of learning a Lipschitz function with corrupted binary signals. The learner tries to learn a $L$-Lipschitz function $f: [0,1]^d \rightarrow [0, L]$ that the adversary chooses. There is a total of $T$ rounds. In each round $t$, the adversary selects a context vector $x_t$ in the input space, and the learner makes a guess to the true functi

Ziad Aldirany, Régis Cottereau, Marc Laforest, Serge Prudhomme

Deep operator networks (DeepONets) have demonstrated their capability of approximating nonlinear operators for initial- and boundary-value problems. One attractive feature of DeepONets is their versatility since they do not rely on prior knowledge about the solution structure of a problem and can thus be directly applied to a large class of problems. However

Ivan Lazarevich, Matteo Grimaldi, Ravish Kumar, Saptarshi Mitra

We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection models on 4 different datasets and 4 different embedded hardware platforms (x86 CPU, ARM CPU, Nvidia GPU, NPU). We collect accuracy and latency numbers for a variety of YOLO-based one-stage detectors at different model scales by performing a fair, controlled comparison of these d

Hyunjong Ok

We present FinTree, Financial Dataset Pretrain Transformer Encoder for Relation Extraction. Utilizing an encoder language model, we further pretrain FinTree on the financial dataset, adapting the model in financial domain tasks. FinTree stands out with its novel structure that predicts a masked token instead of the conventional [CLS] token, inspired by the P

Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai

This paper investigates methods for improving generative data augmentation for deep learning. Generative data augmentation leverages the synthetic samples produced by generative models as an additional dataset for classification with small dataset settings. A key challenge of generative data augmentation is that the synthetic data contain uninformative sampl

Bill Allombert, Daniel C. Mayer

It is proved that c = 689347 = 31*37*601 is the smallest conductor of a cyclic cubic number field K whose maximal unramified pro-3-extension E = F(3,infinity,K) possesses an automorphism group G = Gal(E/K) of order 6561 with coinciding relation and generator rank d2(G) = d1(G) = 3 and harmonically balanced transfer kernels kappa(G) in S(13).

Siyi Du, Nourhan Bayasi, Ghassan Hamarneh, Rafeef Garbi

Skin lesion segmentation (SLS) plays an important role in skin lesion analysis. Vision transformers (ViTs) are considered an auspicious solution for SLS, but they require more training data compared to convolutional neural networks (CNNs) due to their inherent parameter-heavy structure and lack of some inductive biases. To alleviate this issue, current appro

Jingang Jiang, Xiangyang Liu, Chenyou Fan

We study few-shot Natural Language Understanding (NLU) tasks with Large Language Models (LLMs) in federated learning (FL) scenarios. It is a challenging task due to limited labeled data and communication capacities in FL, especially with mobile devices. Recent studies show LLMs can be prompted to perform few-shot NLU tasks like sentiment analysis and arithme

Francesco Costa, Stephen F. King

In this work, we study the neutrino mixing sum rules arising from discrete symmetries, and the class of Littlest Seesaw (LS) neutrino models. These symmetry based approaches all offer predictions for the cosine of the leptonic CP phase $\cos \delta$ in terms of the mixing angles, $\theta_{13}$, $\theta_{12}$, $\theta_{23}$, while the LS models also predict t

Bram Renting, Phillip Wozny, Robert Loftin, Claudia Wieners

We present a critical analysis of the simulation framework RICE-N, an integrated assessment model (IAM) for evaluating the impacts of climate change on the economy. We identify key issues with RICE-N, including action masking and irrelevant actions, and suggest improvements such as utilizing tariff revenue and penalizing overproduction. We also critically en

Yu Qin, Duo Zhang, Yuren Pang

In this paper, we propose a dynamic grouping negotiation model for climate mitigation based on real-world business and political negotiation protocols. Within the AI4GCC competition framework, we develop a three-stage process: group formation and updates, intra-group negotiation, and inter-group negotiation. Our model promotes efficient and effective coopera

Phillip Wozny, Bram Renting, Robert Loftin, Claudia Wieners

As our submission for track three of the AI for Global Climate Cooperation (AI4GCC) competition, we propose a negotiation protocol for use in the RICE-N climate-economic simulation. Our proposal seeks to address the challenges of carbon leakage through methods inspired by the Carbon Border Adjustment Mechanism (CBAM) and Climate Clubs (CC). We demonstrate th

STAR Collaboration, M. I. Abdulhamid, B. E. Aboona, J. Adam

Angular distributions of charged particles relative to jet axes are studied in $\sqrt{s_{\mathrm{NN}}}$ = 200 GeV Au+Au collisions as a function of the jet orientation with respect to the event plane. This differential study tests the expected path-length dependence of energy loss experienced by a hard-scattered parton as it traverses the hot and dense mediu

Mingao Yuan, Sharmin Hossain, Zuofeng Shang

Network data, characterized by interconnected nodes and edges, is pervasive in various domains and has gained significant popularity in recent years. In network data analysis, testing the presence of community structure in a network is one of the important research tasks. Existing tests are mainly developed for unweighted networks. In this paper, we study th

Simon Martin, Tarun Grover

Motivated by experiments on spin chains embedded in a metallic bath, as well as closed quantum systems described by long-range interacting Hamiltonians, we study a critical SU(N) spin chain perturbed by dissipation, or equivalently, after space-time rotation, long-range spatial interactions. The interplay of dissipation and the Wess-Zumino (Berry phase) term

Roghayeh Leila Barmaki, Kangsoo Kim, Zhang Guo, Qile Wang

Anatomy education is an indispensable part of medical training, but traditional methods face challenges like limited resources for dissection in large classes and difficulties understanding 2D anatomy in textbooks. Advanced technologies, such as 3D visualization and augmented reality (AR), are transforming anatomy learning. This paper presents two in-house s

Chang Han, Xinmeng Xu, Weiping Tu, Yuhong Yang

Acoustic echo cancellation (AEC) aims to remove interference signals while leaving near-end speech least distorted. As the indistinguishable patterns between near-end speech and interference signals, near-end speech can't be separated completely, causing speech distortion and interference signals residual. We observe that besides target positive information,

Julie Imig, Cathryn Price, Jon A. Holtzman, Alexander Stone-Martinez

We present new maps of the Milky Way disk showing the distribution of metallicity ([Fe/H]), $\alpha$-element abundances ([Mg/Fe]), and stellar age, using a sample of 66,496 red giant stars from the final data release (DR17) of the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey. We measure radial and vertical gradients, quantify the di

Duo Zhang, Yuren Pang, Yu Qin

The current framework for climate change negotiation models presents several limitations that warrant further research and development. In this track, we discuss mainly two key areas for improvement, focusing on the geographical impacts and utility framework. In the aspects of geographical impacts, We explore five critical aspects: (1) the shift from local t

Tessa Han, Suraj Srinivas, Himabindu Lakkaraju

Studying the robustness of machine learning models is important to ensure consistent model behaviour across real-world settings. To this end, adversarial robustness is a standard framework, which views robustness of predictions through a binary lens: either a worst-case adversarial misclassification exists in the local region around an input, or it does not.

Tianlu Yuan

Event signatures in IceCube are complex, modulated by both particle physics and properties of the ice and detector. Event reconstruction thus requires accurate modeling of ice properties and detector effects to fit for physics parameters, such as energy and direction. Here, we highlight how improvements in calibration can translate into substantially improvi

Kensen Shi, Joey Hong, Yinlin Deng, Pengcheng Yin

When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neural program synthesis methods have similar capabilities, we can measure whether they compositionally generalize, that is, whether a model that has been trained on the simpler subtas

Hao Wang

Human culture research has witnessed an opportunity of revolution thanks to the big data and social network revolution. Websites such as Douban.com, Goodreads.com, Pandora and IMDB become the new gold mine for cultural researchers. In 2021 and 2022, the author of this paper invented 2 data-free recommender systems for AI cold-start problem. The algorithms ca

Enda Hua, Kunjie Dai, Qing Wang, Huan Ye

As a prototypical half-metallic ferromagnet, La0.67Sr0.33MnO3 (LSMO) has been extensively studied due to its versatile physical properties and great potential in spintronic applications. However, the weak perpendicular magnetic anisotropy (PMA) limits the controllability and detection of magnetism in LSMO, thus hindering the realization of oxide-based spintr

Zehua Liu, Zenan Li, Xiaoming Yuan, Yuan Yao

We introduce a new analytic framework to analyze the convergence of the Randomized Stochastic Gradient Descent Ascent (RSGDA) algorithm for stochastic minimax optimization problems. Under the so-called NC-PL condition on one of the variables, our analysis improves the state-of-the-art convergence results in the current literature and hence broadens the appli

H. Yoshioka, M. Tsujimura

We propose a novel recursive utility for controlling stochastic processes under risk and uncertainty. Our formulation uses a robustified Orlicz risk that can evaluate risk and uncertainty simultaneously. We focus on a control problem of a photovoltaic power generation system that supplies excess electricity to a secondary purpose for generating green hydroge

Tianlu Yuan, Dmitry Chirkin

The initial evidence of astrophysical neutrinos by the IceCube Neutrino Observatory stemmed from the high-energy starting events (HESE) sample: a selection of the highest-energy neutrino interactions that occurred within the detector fiducial volume. Each event was reconstructed based on our best knowledge of the ice at the time, with the latest results publ

Egor Shelukhin, Jun Zhang

We prove that if the $m$-th homotopy group for $m \geq 2$ of a closed manifold has non-trivial invariants or coinvariants under the action of the fundamental group, then there exist infinitely many geometrically distinct closed geodesics for a $C^4$-generic Riemannian metric. If moreover there are infinitely many conjugacy classes in the fundamental group, t

Shivam Kamboj, Rembert A. Duine, Benedetta Flebus, Hilary M. Hurst

Spin torque oscillators (STOs) are dissipative magnetic systems that provide a natural platform for exploring non-Hermitian phenomena. We theoretically study a two-dimensional (2d) array of STOs and show that its dynamics can be mapped to a 2d, non-Hermitian Su-Schrieffer-Heeger (SSH) model. We calculate the energy spectrum and identify the one-dimensional (

André Carvalho

We prove that Brinkmann's problems are decidable for endomorphisms of $F_n\times F_m$: given $(x,y),(z,w)\in F_n\times F_m$ and $\Phi\in \text{End}(F_n\times F_m)$, it is decidable whether there is some $k\in \mathbb{N}$ such that $(x,y)\Phi^k=(z,w)$ (or $(x,y)\Phi^k\sim(z,w)$). We also prove decidability of a two-sided version of Brinkmann's conjugacy probl

Ruslan Skuratovskii

First time, we introduce Extended special linear group $ESL_2(F)$, which is generalization of matrix group $SL_2(F)$ over arbitrary field $F$. Extended special linear group $ESL_2(k)$, where $k$ is arbitrary perfect field, is storage of all square matrix roots from $ESL_2(k)$. The analytical formulas of roots of 2-nd, 3-rd, 4-th and $n$-th powers in $ SL_2(\

Ilaria Ruffa, Timothy A. Davis, Jacob S. Elford, Martin Bureau

We report the discovery of the ``mm fundamental plane of black-hole accretion'', which is a tight correlation between the nuclear 1 mm luminosity ($L_{\rm \nu, mm}$), the intrinsic $2$ -- $10$~keV X-ray luminosity ($L_{\rm X,2-10}$) and the supermassive black hole (SMBH) mass ($M_{\rm BH}$) with an intrinsic scatter ($\sigma_{\rm int}$) of $0.40$ dex. The pl

Matthew R. Carbone, Hyeong Jin Kim, Chandima Fernando, Shinjae Yoo

The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in vast sample spaces, although it requires framing experimental campaigns through the lens of maximizing some observable. This framing is insufficient for epistemic research goals tha

Andrey Itkin, Dmitry Muravey

Semi-analytical pricing of American options in a time-dependent Ornstein-Uhlenbeck model was presented in [Carr, Itkin, 2020]. It was shown that to obtain these prices one needs to solve (numerically) a nonlinear Volterra integral equation of the second kind to find the exercise boundary (which is a function of the time only). Once this is done, the option p

Takashi Matsubara, Takaharu Yaguchi

Physics-informed neural networks solve partial differential equations by training neural networks. Since this method approximates infinite-dimensional PDE solutions with finite collocation points, minimizing discretization errors by selecting suitable points is essential for accelerating the learning process. Inspired by number theoretic methods for numerica

Eric W. Bridgeford, Jaewon Chung, Brian Gilbert, Sambit Panda

Causal inference studies whether the presence of a variable influences an observed outcome. As measured by quantities such as the "average treatment effect," this paradigm is employed across numerous biological fields, from vaccine and drug development to policy interventions. Unfortunately, the majority of these methods are often limited to univariate outco

Aldo Garcia Guinto

Let $G\stackrel{\alpha}{\curvearrowright}(M,\tau)$ be a trace-preserving action of a finite group $G$ on a tracial von Neumann algebra. Suppose that $A \subset M$ is a finitely generated unital $*$-subalgebra which is globally invariant under $\alpha$. We give a formula relating the von Neumann dimension of the space of derivations on $A$ valued on its coars

Christopher Donnay

In legislative redistricting, most states draw their House and Senate maps separately. Ohio and Wisconsin require that their Senate districts be made with a 3:1 nesting rule, i.e., out of triplets of adjacent House districts. We seek to study the impact of this requirement on redistricting, specifically on the number of seats won by a particular political pa

Arnaud Ngopnang Ngompé

By a theorem of Christensen and Hovey, the category of non-negatively graded chain complexes has a model structure, called the h-model structure or Hurewicz model structure, where the weak equivalences are the chain homotopy equivalences. The Dold--Kan correspondence induces a model structure on the category of simplicial modules. In this paper, we give a de

Taha Emre, Marzieh Oghbaie, Arunava Chakravarty, Antoine Rivail

In the field of medical imaging, 3D deep learning models play a crucial role in building powerful predictive models of disease progression. However, the size of these models presents significant challenges, both in terms of computational resources and data requirements. Moreover, achieving high-quality pretraining of 3D models proves to be even more challeng

Laurence Boxer

We develop new tools for the construction of fixed point sets in digital topology. We define excludable points and show that these may be excluded from all freezing sets. We show that articulation points are excludable. We also present results concerning points that must belong to a freezing set and often are easily recognized. These include points of degree

Nadine H. Soliman, Andrea V. Macciò, Marvin Blank

We append two additional black hole (BH) accretion models, namely viscous disc and gravitational torque-driven accretion, into the Numerical Investigation of a Hundred Astrophysical Objects (NIHAO) project of galaxy simulations. We show that these accretion models, characterized by a weaker dependence on the BH mass compared to the commonly used Bondi-Hoyle

P. Armstrong, H. Qu, D. Brout, T. M. Davis

As the scale of cosmological surveys increases, so does the complexity in the analyses. This complexity can often make it difficult to derive the underlying principles, necessitating statistically rigorous testing to ensure the results of an analysis are consistent and reasonable. This is particularly important in multi-probe cosmological analyses like those

Dmitrii Krylov, Pooya Khajeh, Junhan Ouyang, Thomas Reeves

Automated design of analog and radio-frequency circuits using supervised or reinforcement learning from simulation data has recently been studied as an alternative to manual expert design. It is straightforward for a design agent to learn an inverse function from desired performance metrics to circuit parameters. However, it is more common for a user to have

M. Gatti, N. Jeffrey, L. Whiteway, V. Ajani

We demonstrate and measure the impact of source galaxy clustering on higher-order summary statistics of weak gravitational lensing data. By comparing simulated data with galaxies that either trace or do not trace the underlying density field, we show this effect can exceed measurement uncertainties for common higher-order statistics for certain analysis choi

Christopher West, Vecna, Raiyan Chowdhury

The 2021 Canadian census is notable for using a unique form of privacy, random rounding, which independently and probabilistically rounds discrete numerical attribute values. In this work, we explore how hierarchical summative correlation between discrete variables allows for both probabilistic and exact solutions to attribute values in the 2021 Canadian Cen

Dae Hyun Kim, Seulgi Choi, Juho Kim, Vidya Setlur

Recent work has shown that when both the chart and caption emphasize the same aspects of the data, readers tend to remember the doubly-emphasized features as takeaways; when there is a mismatch, readers rely on the chart to form takeaways and can miss information in the caption text. Through a survey of 280 chart-caption pairs in real-world sources (e.g., ne

Dominik Hintz, Sarah Peacock, Travis Barman, Birgit Fuhrmeister

Ahead of upcoming space missions intending to conduct observations of low-mass stars in the ultraviolet (UV) spectral region it becomes imperative to simultaneously conduct atmospheric modeling from the UV to the visible (VIS) and near-infrared (NIR). Investigations on extended spectral regions will help to improve the overall understanding of the diversity

Shashank Agnihotri, Kanchana Vaishnavi Gandikota, Julia Grabinski, Paramanand Chandramouli

Following their success in visual recognition tasks, Vision Transformers(ViTs) are being increasingly employed for image restoration. As a few recent works claim that ViTs for image classification also have better robustness properties, we investigate whether the improved adversarial robustness of ViTs extends to image restoration. We consider the recently p

Skyler Wu, Fred Lu, Edward Raff, James Holt

Convolutional layers have long served as the primary workhorse for image classification. Recently, an alternative to convolution was proposed using the Sharpened Cosine Similarity (SCS), which in theory may serve as a better feature detector. While multiple sources report promising results, there has not been to date a full-scale empirical analysis of neural

Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou

With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and tested in simplified synthetic environments, leading to a disconnect with real-world scenarios. In this paper, we build an environment for language-guided agents that is highly rea

Artemis Theano Theodoridis, Jamie Tayar

The recent NASA TESS mission has the potential to increase the available asteroseismic sample dramatically, but its precision and accuracy have yet to be confirmed. To date, NASA's Kepler mission has been considered the gold standard for asteroseismic samples, despite data only being available for a small portion of the sky. TESS's observations cover the who

Fan Li, Jialin Li, Junsheng Zheng, Yuanbiao Tong

Two-dimensional (2D) material heterostructures have attracted considerable attention owing to their interesting and novel physical properties, which expand the possibilities for future optoelectronic, photovoltaic, and nanoelectronic applications. A portable, fast, and deterministic transfer technique is highly needed for the fabrication of heterostructures.

Prabin Bhandari, Hannah Marie Brennan

Large Language Models (LLMs) have shown a tremendous capacity for generating literary text. However, their effectiveness in generating children's stories has yet to be thoroughly examined. In this study, we evaluate the trustworthiness of children's stories generated by LLMs using various measures, and we compare and contrast our results with both old and ne

Chamani Shiranthika, Zahra Hafezi Kafshgari, Parvaneh Saeedi, Ivan V. Bajić

Decentralized machine learning has broadened its scope recently with the invention of Federated Learning (FL), Split Learning (SL), and their hybrids like Split Federated Learning (SplitFed or SFL). The goal of SFL is to reduce the computational power required by each client in FL and parallelize SL while maintaining privacy. This paper investigates the robu

Vidhi Jain, Jayant Sravan Tamarapalli, Sahiti Yerramilli, Yonatan Bisk

Understanding multimodal perception for embodied AI is an open question because such inputs may contain highly complementary as well as redundant information for the task. A relevant direction for multimodal policies is understanding the global trends of each modality at the fusion layer. To this end, we disentangle the attributions for visual, language, and

Laura Doval, Ran Eilat

An analyst observes the frequency with which an agent takes actions, but not the frequency with which she takes actions conditional on a payoff relevant state. In this setting, we ask when the analyst can rationalize the agent's choices as the outcome of the agent learning something about the state before taking action. Our characterization marries the obedi

Mahyar Daneshpajooh, Niusha Moshrefi, Mahdi Darabi, Sina Hashemi

This paper introduces TeleBTC, a fully decentralized protocol designed to wrap Bitcoin (BTC) on programmable blockchains. The creation of a decentralized wrapped BTC presents challenges due to the non-programmable nature of Bitcoin, making it difficult to custody BTCs in a decentralized way. Existing solutions have addressed this challenge by introducing an

Lei Chen, Shuang-Shuang Li, Weiyao Zhao, Abdulhakim Bake

The Berry phase is an important concept in solids, correlated to the band topology, axion electrodynamics and potential applications of topological materials. Here, we investigate the magnetotransport and Berry phase of rare earth element Gd doped Bi2Se3 (Gd_Bi2Se3) topological insulator at low temperatures and high magnetic fields. Gd_Bi2Se3 single crystals

Narayan Banerjee, Valerio Faraoni, Robert Vanderwee, Andrea Giusti

The ``ugly duckling'' of the Segr\'e-Pleba\'nski-Hawking-Ellis classification of stress-energy tensors is believed to be either impossible or extremely difficult to realise in Einstein gravity. Effective stress-energy tensors in alternative gravity offer a wider range of possibilities. We report a class of type III realisations in ``first-generation'' scalar

S. Cervera, M. LoBue, E. Fontana, M. Eddrief

The effect of ion bombardment on MnAs single crystalline thin films is studied. The role of elastic collisions between ions and atoms of the material is singled-out as the main process responsible for modifying the properties of the material. Thermal hysteresis suppression, and the loss of sharpness of the magneto-structural phase transition are studied as a