April 2024 arXiv papers — page 53

Khai Nguyen, Nhat Ho

Sliced Wasserstein (SW) and Generalized Sliced Wasserstein (GSW) have been widely used in applications due to their computational and statistical scalability. However, the SW and the GSW are only defined between distributions supported on a homogeneous domain. This limitation prevents their usage in applications with heterogeneous joint distributions with ma

Siqi Feng, Rui Yao, Stephane Hess, Ricardo A. Daziano

Deep neural networks (DNNs) frequently present behaviorally irregular patterns, significantly limiting their practical potentials and theoretical validity in travel behavior modeling. This study proposes strong and weak behavioral regularities as novel metrics to evaluate the monotonicity of individual demand functions (known as the "law of demand"), and fur

Zhen Ye, Zeqian Ju, Haohe Liu, Xu Tan

Recent progress in large-scale zero-shot speech synthesis has been significantly advanced by language models and diffusion models. However, the generation process of both methods is slow and computationally intensive. Efficient speech synthesis using a lower computing budget to achieve quality on par with previous work remains a significant challenge. In thi

Brandon R. Ferrer, Juan Ruben Gomez-Solano

Transition paths are rare events occurring when a system, thanks to the effect of fluctuations, crosses successfully from one stable state to another by surmounting an energy barrier. Even though they are of great significance in many mesoscale processes, their direct determination is often challenging due to their short duration as compared to other relevan

Mahan Mj, Balarka Sen

We produce a large class of hyperbolic homology 3-spheres admitting arbitrarily many distinct tight contact structures. We also produce a sub-class admitting arbitrarily many distinct tight contact structures within the same homotopy class of oriented plane distributions. As a corollary, we give a recipe to construct hyperbolic L-spaces admitting arbitrarily

Victor Ukleev, Oleg I. Utesov, Chen Luo, Florin Radu

The cubic $\beta$-Mn-type alloy Co$_8$Zn$_8$Mn$_4$ is a chiral helimagnet that exhibits a peculiar temperature-dependent behavior in the spiral pitch, which decreases from 130 nm at room temperature to 70 nm below 20 K. Notably, this shortening is also accompanied by a structural transition of the metastable skyrmion texture, transforming from a hexagonal la

Jian Chen, Liping Tang. Xinmin Yang

Nonlinear conjugate gradient methods have recently garnered significant attention within the multiobjective optimization community. These methods aim to maintain consistency in conjugate parameters with their single-objective optimization counterparts. However, the preservation of the attractive conjugate property of search directions remains uncertain, even

Yuxiang Yang, Lu Wen, Yuanyuan Xu, Jiliu Zhou

Universal Multi-source Domain Adaptation (UniMDA) transfers knowledge from multiple labeled source domains to an unlabeled target domain under domain shifts (different data distribution) and class shifts (unknown target classes). Existing solutions focus on excavating image features to detect unknown samples, ignoring abundant information contained in textua

Tamanna Hossain, Sunipa Dev, Sameer Singh

Content Warning: This paper contains examples of misgendering and erasure that could be offensive and potentially triggering. Misgendering, the act of incorrectly addressing someone's gender, inflicts serious harm and is pervasive in everyday technologies, yet there is a notable lack of research to combat it. We are the first to address this lack of research

Sining Wei, Yong Wang

In this paper, we compute the lower-dimensional volume Vol(1,1) about transformation operators for 4- dimensional spin manifolds with boundary and we also get the Kastler-Kalau-Walze type theorem about transformation operators on 4-dimensional compact manifolds with boundary.

Yunming Zhang, Dengpan Ye, Caiyun Xie, Sipeng Shen

The wide deployment of Face Recognition (FR) systems poses privacy risks. One countermeasure is adversarial attack, deceiving unauthorized malicious FR, but it also disrupts regular identity verification of trusted authorizers, exacerbating the potential threat of identity impersonation. To address this, we propose the first double identity protection scheme

Qing Sima, Jianke Yu, Xiaoyang Wang, Wenjie Zhang

Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categories: algorithm-based models and Machine Learning-based (ML) models. Despite the long-standing focus on this topic within the database domain, current solutions face two major limitat

Han Zhao, Weihao Cui, Quan Chen, Shulai Zhang

Integrating GPUs into serverless computing platforms is crucial for improving efficiency. However, existing solutions for GPU-enabled serverless computing platforms face two significant problems due to coarse-grained GPU management: long setup time and low function throughput. To address these issues, we propose SAGE, a GPU serverless framework with fast set

Y. F. Yang, M. Y. Chen, F. P. Li, Y. P. Ruan

The orbital angular momentum (OAM) of photons provides a pivotal resource for carrying out high-dimensional classical and quantum information processing due to its unique discrete high-dimensional nature. The cyclic transformation of a set of orthogonal OAM modes is an essential building block for universal high-dimensional information processing. Its realiz

Mike Van Ness, Madeleine Udell

Survival analysis is widely used as a technique to model time-to-event data when some data is censored, particularly in healthcare for predicting future patient risk. In such settings, survival models must be both accurate and interpretable so that users (such as doctors) can trust the model and understand model predictions. While most literature focuses on

Zezheng Song, Jiaxin Yuan, Haizhao Yang

The fast simulation of dynamical systems is a key challenge in many scientific and engineering applications, such as weather forecasting, disease control, and drug discovery. With the recent success of deep learning, there is increasing interest in using neural networks to solve differential equations in a data-driven manner. However, existing methods are ei

Raehyuk Jung, Hyojun Go, Jaehyuk Yi, Jiho Jang

This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. Pegasus-1 is designed to address the unique challenges posed by video data, such as interpreting spatiotemporal information, to offer nuanced video content comprehension across various lengths. This tech

Thomas McAndrew, Maimuna S. Majumder, Andrew A. Lover, Srini Venkatramanan

In May 2022, mpox (formerly monkeypox) spread to non-endemic countries rapidly. Human judgment is a forecasting approach that has been sparsely evaluated during the beginning of an outbreak. We collected -- between May 19, 2022 and July 31, 2022 -- 1275 forecasts from 442 individuals of six questions about the mpox outbreak where ground truth data are now av

Palle E. T. Jorgensen, James Tian

Motivated by applications, we introduce a general and new framework for operator valued positive definite kernels. We further give applications both to operator theory and to stochastic processes. The first one yields several dilation constructions in operator theory, and the second to general classes of stochastic processes. For the latter, we apply our ope

Xian-Liang Wang, Shu-Cheng Yang, Wen-Biao Han

In the framework of general relativity (GR), gravitational waves (GWs) travel at the speed of light across all frequencies. However, massive gravity and weak equivalence principle (WEP) violation may lead to frequency-dependent variations in the propagation speed of GWs, which can be examined by comparing the theoretical and observed discrepancies in the arr

Maxim Raginsky

We revisit the work of Roger Brockett on controllability of the Liouville equation, with a particular focus on the following problem: Given a smooth controlled dynamical system of the form $\dot{x} = f(x,u)$ and a state-space diffeomorphism $\psi$, design a feedback control $u(t,x)$ to steer an arbitrary initial state $x_0$ to $\psi(x_0)$ in finite time. Thi

Yumou Wei, Paulo F. Carvalho, John Stamper

Gender and race inferred from an individual's name are a notable source of stereotypes and biases that subtly influence social interactions. Abundant evidence from human experiments has revealed the preferential treatment that one receives when one's name suggests a predominant gender or race. As large language models acquire more capabilities and begin to s

Tianyi Huang, Sen Lin, Jingyi Zou, Zexiao Wang

Recently tellurium (Te) has attracted resurgent interests due to its p-type characteristics and outstanding ambient environmental stability. Here we present a substrate engineering based physical vapor deposition method to synthesize high-quality Te nanoflakes and achieved a field-effect hole mobility of 1500 cm2/Vs, which is, to the best of our knowledge, t

Elijah Pelofske, Vincent Urias, Lorie M. Liebrock

The task of accurate and efficient language translation is an extremely important information processing task. Machine learning enabled and automated translation that is accurate and fast is often a large topic of interest in the machine learning and data science communities. In this study, we examine using local Generative Pretrained Transformer (GPT) model

Shuchi Chawla, Dimitris Christou, Trung Dang, Zhiyi Huang

We study multi-buyer multi-item sequential item pricing mechanisms for revenue maximization with the goal of approximating a natural fractional relaxation -- the ex ante optimal revenue. We assume that buyers' values are subadditive but make no assumptions on the value distributions. While the optimal revenue, and therefore also the ex ante benchmark, is ina

Junjie Zhang, Tianci Hu, Xiaoshui Huang, Yongshun Gong

Evaluating the performance of Multi-modal Large Language Models (MLLMs), integrating both point cloud and language, presents significant challenges. The lack of a comprehensive assessment hampers determining whether these models truly represent advancements, thereby impeding further progress in the field. Current evaluations heavily rely on classification an

A. Hammad, Mihoko M. Nojiri

Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally intensive and require substantial data for training. In this paper, we introduce a new jet classification network using an MLP mixer, where two subsequent MLP operations ser

Linxuan Xin, Zheng Zhang, Jinfu Wei, Wei Gao

Prior material creation methods had limitations in producing diverse results mainly because reconstruction-based methods relied on real-world measurements and generation-based methods were trained on relatively small material datasets. To address these challenges, we propose DreamPBR, a novel diffusion-based generative framework designed to create spatially-

Cemile Kurkoglu

Exceptional Belyi covering is a connected Belyi covering uniquely determined by its ramification scheme or the respective dessin d'enfant. We focus on rational exceptional Belyi coverings of compact Riemann surfaces of genus 0. Well known examples are cyclic, dihedral, and Chebyshev coverings. Using Maple, we identified all rational exceptional Belyi coverin

Yang Chen, Ruituo Wu, Yipeng Liu, Ce Zhu

Implicit neural representations (INR) suffer from worsening spectral bias, which results in overly smooth solutions to the inverse problem. To deal with this problem, we propose a universal framework for processing inverse problems called \textbf{High-Order Implicit Neural Representations (HOIN)}. By refining the traditional cascade structure to foster high-

Mingyuan Chen, Jiangshan Tang, Miao Cai, Franco Nori

High-dimensional quantum systems have been used to reveal interesting fundamental physics and to improve information capacity and noise resilience in quantum information processing. However, it remains a significant challenge to realize universal two-photon quantum gates in high dimensions with high success probability. Here, by considering an ion-cavity QED

Dave Benson, Srikanth B. Iyengar, Henning Krause, Julia Pevtsova

This work concerns the stable module category of a finite group over a field of characteristic dividing the group order. The minimal localising tensor ideals correspond to the non-maximal homogeneous prime ideals in the cohomology ring of the group. Given such a prime ideal, a number of characterisations of the dualisable objects in the corresponding tensor

Ming Nie, Xinyue Cai, Hang Xu, Li Zhang

Lane detection has evolved highly functional autonomous driving system to understand driving scenes even under complex environments. In this paper, we work towards developing a generalized computer vision system able to detect lanes without using any annotation. We make the following contributions: (i) We illustrate how to perform unsupervised 3D lane segmen

Hiromi Emoto, Yuni Iwamasa, Shin-ichi Minato

Matroids are often represented as oracles since there are no unified and compact representations for general matroids. This paper initiates the study of binary decision diagrams (BDDs) and zero-suppressed binary decision diagrams (ZDDs) as relatively compact data structures for representing matroids in a computer. This study particularly focuses on the sizes

Yu Nakayama

But if you treat it as a two-form, you get three nontrivial renormalization group fixed points! Which becomes the Heisenberg fixed point in three dimensions? Motivated by this question, we study the conformal bootstrap constraint in the $O(d)$ anti-symmetric matrix model in $d$ dimensions, varying $d$ as a continuous parameter. Besides the one that is natura

Chen Ling, Tanmoy Chowdhury, Jie Ji, Sirui Li

Source localization aims to locate information diffusion sources only given the diffusion observation, which has attracted extensive attention in the past few years. Existing methods are mostly tailored for single networks and may not be generalized to handle more complex networks like cross-networks. Cross-network is defined as two interconnected networks,

Siddharth Nijhawan, Takuya Yashima, Tamaki Kojima

Performing facial expression transfer under one-shot setting has been increasing in popularity among research community with a focus on precise control of expressions. Existing techniques showcase compelling results in perceiving expressions, but they lack robustness with extreme head poses. They also struggle to accurately reconstruct background details, th

Shunsuke Takano, Takuya Nakanishi, Kenta Nakagawa, Toru Asahi

For chiral liquid crystals that express topologically protected defects and thermally driven mechanical rotation, the size- and temperature-driven reversal of the rotational direction of their droplets was demonstrated even under a fixed temperature gradient. This unconventional reversal indicates the dependence of thermomechanical coupling on the molecular

Han Zhang, Vedant Das Swain, Leijie Wang, Nan Gao

Behavioral sensing technologies are rapidly evolving across a range of well-being applications. Despite its potential, concerns about the responsible use of such technology are escalating. In response, recent research within the sensing technology has started to address these issues. While promising, they primarily focus on broad demographic categories and o

Taewook Hwang, Hyein Seo, Sangkeun Jung

Recent deep learning models such as ChatGPT utilizing the back-propagation algorithm have exhibited remarkable performance. However, the disparity between the biological brain processes and the back-propagation algorithm has been noted. The Forward-Forward algorithm, which trains deep learning models solely through the forward pass, has emerged to address th

Yingjie Li, Ye Xu, Jingjing Li, Shuaibo Bian

The next generation of very long baseline interferometry (VLBI) is stepping into the era of microarcsecond ($\mu$as) astronomy, and pushing astronomy, especially astrometry, to new heights. VLBI with the Square Kilometre Array (SKA), SKA-VLBI, will increase current sensitivity by an order of magnitude, and reach astrometric precision routinely below 10 $\mu$

Ansong Ni, Miltiadis Allamanis, Arman Cohan, Yinlin Deng

A fundamental skill among human developers is the ability to understand and reason about program execution. As an example, a programmer can mentally simulate code execution in natural language to debug and repair code (aka. rubber duck debugging). However, large language models (LLMs) of code are typically trained on the surface textual form of programs, thu

Guangpeng Fan, Fei Yan, Xiangquan Zeng, Qingtao Xu

We have developed the world's first canopy height map of the distribution area of world-level giant trees. This mapping is crucial for discovering more individual and community world-level giant trees, and for analyzing and quantifying the effectiveness of biodiversity conservation measures in the Yarlung Tsangpo Grand Canyon (YTGC) National Nature Reserve.

Tom Zick, Mason Kortz, David Eaves, Finale Doshi-Velez

Public sector use of AI has been quietly on the rise for the past decade, but only recently have efforts to regulate it entered the cultural zeitgeist. While simple to articulate, promoting ethical and effective roll outs of AI systems in government is a notoriously elusive task. On the one hand there are hard-to-address pitfalls associated with AI-based too

Juanyong Wang, Lei Wang, Jiangxu Huang, Dinggen Li

Droplet impact behavior has attracted much attention recently due to its academic significance and diverse industrial applications. This study employs the lattice Boltzmann method to simulate the impact of a droplet on a hydrophobic plate featuring a square orifice. Unlike previous studies, the chemical property of the orifice considered in this work is not

Guerard Byrne, Christopher McGregor

Recent clinical xenotransplantation and human decedent studies demonstrate that clinical hyperacute rejection of genetically engineered porcine organs can be reliably avoided but that antibody mediated rejection continues to limit graft survival. We previously identified porcine glycans and proteins which are immunogenic after cardiac xenotransplantation in

Xiaoming Zhai

This chapter focuses on the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in science assessments. The paper begins with a discussion of the Framework for K-12 Science Education, which calls for a shift from conceptual learning to knowledge-in-use. This shift necessitates the development of new types of assessments that align w

Sandra Leticia Juárez Osorio, Mayra Alejandra Rivera Ruiz, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello

In this study, we apply 1D quantum convolution to address the task of time series forecasting. By encoding multiple points into the quantum circuit to predict subsequent data, each point becomes a feature, transforming the problem into a multidimensional one. Building on theoretical foundations from prior research, which demonstrated that Variational Quantum

Abhishek Aich, Yumin Suh, Samuel Schulter, Manmohan Chandraker

A powerful architecture for universal segmentation relies on transformers that encode multi-scale image features and decode object queries into mask predictions. With efficiency being a high priority for scaling such models, we observed that the state-of-the-art method Mask2Former uses 50% of its compute only on the transformer encoder. This is due to the re

Xiaoshuang Wang, Liwei Tan, Wenjun Ying, Enhao Wang

We present a Pressure-Oscillation-Free projection algorithm for large-density-ratio multiphase fluid-structure interaction simulations, implemented on a non-staggered Cartesian grid. The incompressible Navier-Stokes is decoupled with an improved five-step incremental pressure correction algorithm. Fluid-fluid interface is captured using the Cahn-Hilliard equ

Laurent Vidal, Tommaso Nottoli, Filippo Lipparini, Eric Cancès

We explore Riemannian optimization methods for Restricted-Open-shell Hartree-Fock (ROHF) and Complete Active Space Self-Consistent Field (CASSCF) methods. After showing that ROHF and CASSCF can be reformulated as optimization problems on so-called flag manifolds, we review Riemannian optimization basics and their application to these specific problems. We co

Sergey Bezuglyi, Olena Karpel, Jan Kwiatkowski, Marcin Wata

Generalized Bratteli diagrams with a countable set of vertices in every level are models for aperiodic Borel automorphisms. This paper is devoted to the description of all ergodic probability tail invariant measures on the path spaces of generalized Bratteli diagrams. Such measures can be identified with inverse limits of infinite-dimensional simplices assoc

Achyut Paudel, Jostan Brown, Priyanka Upadhyaya, Atif Bilal Asad

Apple(\textit{Malus domestica} Borkh.) trees are deciduous, shedding leaves each year. This process is preceded by a gradual change in leaf color from green to yellow as chlorophyll is degraded prior to abscission. The initiation and rate of this color change are affected by many factors including leaf nitrogen (N) concentration. We predict that leaf color d

Sho Kuno, Hiroshi Kori

We tested the performance of reservoir computing (RC) in predicting the dynamics of a certain non-autonomous dynamical system. Specifically, we considered a van del Pol oscillator subjected to periodic external force with frequent phase shifts. The reservoir computer, which was trained and optimized with simulation data generated for a particular phase shift

Emmanuel Jerez

We study how the partial group (co)homology of a group $G$ with coefficient in a partial representation $M$ can be described using the usual group (co)homology. To address this, we introduce the concept of the \textit{universal globalization} $\Lambda(M)$ of a partial group representation $M$ of $G$. Our main result shows that the partial group homology $H^{

Zechen Hu, Daigo Shishika, Xuesu Xiao, Xuan Wang

In multi-robot systems, achieving coordinated missions remains a significant challenge due to the coupled nature of coordination behaviors and the lack of global information for individual robots. To mitigate these challenges, this paper introduces a novel approach, Bi-level Coordination Learning (Bi-CL), that leverages a bi-level optimization structure with

Hongjie Xu, Yunzhuang Shen, Yuan Sun, Xiaodong Li

Column generation (CG) is a powerful technique for solving optimization problems that involve a large number of variables or columns. This technique begins by solving a smaller problem with a subset of columns and gradually generates additional columns as needed. However, the generation of columns often requires solving difficult subproblems repeatedly, whic

Anna Wirth-Singh, Jinlin Xiang, Minho Choi, Johannes E. Fröch

Optical and hybrid convolutional neural networks (CNNs) recently have become of increasing interest to achieve low-latency, low-power image classification and computer vision tasks. However, implementing optical nonlinearity is challenging, and omitting the nonlinear layers in a standard CNN comes at a significant reduction in accuracy. In this work, we use

William He, Ryan O'Donnell

We study pseudorandomness properties of permutations on $\{0,1\}^n$ computed by random circuits made from reversible $3$-bit gates (permutations on $\{0,1\}^3$). Our main result is that a random circuit of depth $n \cdot \tilde{O}(k^2)$, with each layer consisting of $\approx n/3$ random gates in a fixed nearest-neighbor architecture, yields almost $k$-wise

Sooyung Byeon, Dawei Sun, Inseok Hwang

Human behavior modeling is important for the design and implementation of human-automation interactive control systems. In this context, human behavior refers to a human's control input to systems. We propose a novel method for human behavior modeling that uses human demonstrations for a given task to infer the unknown task objective and the variability. The

Zhen Yang, Fang Liu, Zhongxing Yu, Jacky Wai Keung

Code translation tools (transpilers) are developed for automatic source-to-source translation. Although learning-based transpilers have shown impressive enhancement against rule-based counterparts, owing to their task-specific pre-training on extensive monolingual corpora. Their current performance still remains unsatisfactory for practical deployment, and t

Yu-Xuan Li, Yichen Liu, Cheng-Cheng Liu

We propose to implement tunable higher-order topological states in a heterojunction consisting of a two-dimensional (2D) topological insulator and the recently discovered altermagnets, whose unique spin-polarization in both real and reciprocal space and null magnetization are in contrast to conventional ferromagnets and antiferromagnets. Based on symmetry an

Yujin Jeong, Emily Fox, Ramesh Johari

Based on technological advances in sensing modalities, randomized trials with primary outcomes represented as high-dimensional vectors have become increasingly prevalent. For example, these outcomes could be week-long time-series data from wearable devices or high-dimensional neuroimaging data, such as from functional magnetic resonance imaging. This paper f

Chasen Milner, Hayden Jananthan, Jeremy Kepner, Vijay Gadepally

The Internet has become a critical domain for modern society that requires ongoing efforts for its improvement and protection. Network traffic matrices are a powerful tool for understanding and analyzing networks and are broadly taught in online graph theory educational resources. Network traffic matrix concepts are rarely available in online computer networ

Pengfei Li, Yejia Liu, Jianyi Yang, Shaolei Ren

The sharply increasing sizes of artificial intelligence (AI) models come with significant energy consumption and environmental footprints, which can disproportionately impact certain (often marginalized) regions and hence create environmental inequity concerns. Moreover, concerns with social inequity have also emerged, as AI computing resources may not be eq

Chao Chen, Chenghua Guo, Rui Xu, Jiujiu Chen

Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustworthiness are often limited by the inherent randomness in data generation and the lack of knowledge to accurately model real-world complexities. There has been increased interest in developing uncertainty quantific

Horng Sheng Chia, Zihan Zhou, Mikhail M. Ivanov

We present the first constraints on tidal heating for the binary systems detected in the LIGO-Virgo-KAGRA (LVK) gravitational wave data. Tidal heating, also known as tidal dissipation, characterizes the viscous nature of an astrophysical body and provides a channel for exchanging energy and angular momentum with the tidal environment. Using the worldline eff

Pedro Ramírez-Moreno

We study the symbolic $F$-splitness of families of binomial edge ideals. We also study the strong $F$-regularity of the symbolic blowup algebras of families of binomial edge ideals. We make use of Fedder-like criteria and combinatorial properties of the graphs associated to the binomial edge ideals in order to approach the aforementioned scenarios.

Thiago Bergamaschi, Chi-Fang Chen, Yunchao Liu

A quantum system coupled to a bath at some fixed, finite temperature converges to its Gibbs state. This thermalization process defines a natural, physically-motivated model of quantum computation. However, whether quantum computational advantage can be achieved within this realistic physical setup has remained open, due to the challenge of finding systems th

Ismar Volic, Zixu Wang

Building on previous work, this paper extends the modeling of political structures from simplicial complexes to hypergraphs. This allows the analysis of more complex political dynamics where agents who are willing to form coalitions contain subsets that would not necessarily form coalitions themselves. We extend topological constructions such as wedge, cone,

Larissa Salerno, Christoph Treude, Patanamon Thongtatunam

As the world of technology advances, so do the tools that software developers use to create new programs. In recent years, software development tools have become more popular, allowing developers to work more efficiently and produce higher-quality software. Still, installing such tools can be challenging for novice developers at the early stage of their care

N. Huang, Y. -H. Dai, D. Orban, M. A. Saunders

Augmented Lagrangian (AL) methods are a well known class of algorithms for solving constrained optimization problems. They have been extended to the solution of saddle-point systems of linear equations. We study an AL (SPAL) algorithm for unsymmetric saddle-point systems and derive convergence and semi-convergence properties, even when the system is singular

Matthew Colwell, Mahdi Abolghasemi

Prediction and optimisation are two widely used techniques that have found many applications in solving real-world problems. While prediction is concerned with estimating the unknown future values of a variable, optimisation is concerned with optimising the decision given all the available data. These methods are used together to solve problems for sequentia

Vandad Davoodnia, Saeed Ghorbani, Marc-André Carbonneau, Alexandre Messier

We introduce UPose3D, a novel approach for multi-view 3D human pose estimation, addressing challenges in accuracy and scalability. Our method advances existing pose estimation frameworks by improving robustness and flexibility without requiring direct 3D annotations. At the core of our method, a pose compiler module refines predictions from a 2D keypoints es

Ali Naseri Sadr

We classify all the lattices realized as the intersection form of a positive definite four manifold with boundary $S_n^3(K)$ for a knot $K$ in the three sphere and a positive integer $n$ greater than $4g_4(K)+3$. We then use this result to define a concordance invariant and generalize a theorem of Rasmussen on lens space surgeries.

Michael Timothy Bennett

Are biological self-organising systems more ``intelligent'' than artificial intelligence (AI)? If so, why? I address this question using a mathematical framework that defines intelligence in terms of adaptability. Systems are modelled as stacks of abstraction layers (\emph{Stack Theory}) and compared by how effectively they delegate agentic control down thei

Muhammad Adnan, Amar Phanishayee, Janardhan Kulkarni, Prashant J. Nair

In this paper, we present a novel technique to search for hardware architectures of accelerators optimized for end-to-end training of deep neural networks (DNNs). Our approach addresses both single-device and distributed pipeline and tensor model parallel scenarios, latter being addressed for the first time. The search optimized accelerators for training rel

Chaohao Yang, Chris Ding

Distributed word representation (a.k.a. word embedding) is a key focus in natural language processing (NLP). As a highly successful word embedding model, Word2Vec offers an efficient method for learning distributed word representations on large datasets. However, Word2Vec lacks consideration for distances between center and context words. We propose two nove

J. E. Abrão, E. Gomes da Silva, G. Rodrigues-Junior, J. B. S. Mendes

In this work, we explore the intriguing spin-momentum locking phenomenon within the Rashba states of antimony (Sb) films. By combining spin pumping with the flow of an external charge current, we reveal the topological properties of surface states in Sb films. Taking advantage of the well-defined spin polarization of both spin-momentum-locked charge currents

Yuichi Masubuchi

A few experiments have reported that the time development of shear stress under fast start-up shear deformations exhibits double peaks before reaching the steady state for bimodal blends of entangled linear polymers in specific conditions. To analyze the molecular origin of this phenomenon, multi-chain slip-link simulations based on the primitive chain netwo

Charles Bickham, Kia Kazemi-Nia, Luca Luceri, Kristina Lerman

Social media platforms actively moderate content glorifying harmful behaviors like eating disorders, which include anorexia and bulimia. However, users have adapted to evade moderation by using coded hashtags. Our study investigates the prevalence of moderation evaders on the popular social media platform TikTok and contrasts their use and emotional valence

James Coe, Leonardo Tolomeo

We consider the 1-dimensional cubic Szegő equation with data distributed according to the Gaussian measure with inverse covariance operator $(1-\partial_x^2)^\frac s2$, where $s>\frac12$. We show that, for $s>1$, this measure is quasi-invariant under the flow of the equation, while for $s<1$, $s\neq \frac34$, the transported measure and the initial Gaussian

Lin Lawrence Guo, Jason Fries, Ethan Steinberg, Scott Lanyon Fleming

Foundation models hold promise for transforming AI in healthcare by providing modular components that are easily adaptable to downstream healthcare tasks, making AI development more scalable and cost-effective. Structured EHR foundation models, trained on coded medical records from millions of patients, demonstrated benefits including increased performance w

Dimitris Koukoulopoulos, James Maynard, Daodao Yang

We prove a quantitative version of the Duffin-Schaeffer conjecture with an almost sharp error term. Precisely, let $\psi:\mathbb{N}\to[0,1/2]$ be a function such that the series $\sum_{q=1}^\infty \varphi(q)\psi(q)/q$ diverges. In addition, given $\alpha\in\mathbb{R}$ and $Q\geqslant1$, let $N(\alpha;Q)$ be the number of coprime pairs $(a,q)\in\mathbb{Z}\tim

Bertrand J. Guillou, Daniel C. Isaksen

We compute the 2-primary $C_2$-equivariant stable homotopy groups $\pi^{C_2}_{s,c}$ for stems between 0 and 25 (i.e., $0 \leq s \leq 25$) and for coweights between -1 and 7 (i.e., $-1 \leq c \leq 7)$. Our results, combined with periodicity isomorphisms and sufficiently extensive $\mathbb{R}$-motivic computations, would determine all of the $C_2$-equivariant

Deokkeun An, Timothy C. Beers, Anirudh Chiti

Using a grid of empirically calibrated synthetic spectra developed in our previous study, we construct an all-sky 3D extinction map from the large collection of low-resolution XP spectra in Gaia DR3. Along each line of sight, with an area ranging from $0.2$ to $13.4$ deg$^2$, we determine both the reddening and metallicity of main-sequence stars and model th

Alican Mertan, Nick Cheney

Finding controllers that perform well across multiple morphologies is an important milestone for large-scale robotics, in line with recent advances via foundation models in other areas of machine learning. However, the challenges of learning a single controller to control multiple morphologies make the `one robot one task' paradigm dominant in the field. To

Ming Liu, Ran Liu, Ye Zhu, Hua Wang

ChatGPT has changed the AI community and an active research line is the performance evaluation of ChatGPT. A key challenge for the evaluation is that ChatGPT is still closed-source and traditional benchmark datasets may have been used by ChatGPT as the training data. In this paper, (i) we survey recent studies which uncover the real performance levels of Cha

M. Homma, K. Nomura

We investigate parameter dependence of the calculated $\beta$-decay properties, as well as low-lying states for the neutron-rich Zr isotopes within the neutron-proton interacting boson model (IBM-2) and interacting boson-fermion-fermion model (IBFFM-2). It is shown that the calculated $\log_{10}ft$ values for the transitions of the $0^+_1$ ground states of t

Seongmin Lee, Benjamin Hoover, Hendrik Strobelt, Zijie J. Wang

Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex internal structures and operations often pose challenges for non-experts to grasp. We introduce Diffusion Explainer, the first interactive visualization tool designed to elucidate how Stable Diffusion transforms text prompts

Alex Rodrigo dos S. Sousa, Mauricio Zevallos

This work proposes a Bayesian rule based on the mixture of a point mass function at zero and the logistic distribution to perform wavelet shrinkage in nonparametric regression models with stationary errors (with short or long-memory behavior). The proposal is assessed through Monte Carlo experiments and illustrated with real data. Simulation studies indicate

Vytautas Paškūnas, Julian Quast

We study deformation theory of mod $p$ Galois representations of $p$-adic fields with values in generalised reductive group schemes, such as $L$-groups and $C$-groups. We show that the corresponding deformation rings are complete intersections of expected dimension. We determine their irreducible components in many cases and show that they and their special

Hristu Culetu

The geodesics in various spherical Rindler frames are investigated. A display of some kinematical quantities of the spacetime is given. The constant acceleration from the metric acts as the surface gravity of the horizon $r = 0$. The radial geodesics are computed both for the Balasubramanian et al. form of the spherical Rindler space and for the non-diagonal

Jesse Thibodeau, Hadi Nekoei, Afaf Taïk, Janarthanan Rajendran

The use of dynamic pricing by profit-maximizing firms gives rise to demand fairness concerns, measured by discrepancies in consumer groups' demand responses to a given pricing strategy. Notably, dynamic pricing may result in buyer distributions unreflective of those of the underlying population, which can be problematic in markets where fair representation i

Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton

The reproducibility and transparency of large language models are crucial for advancing open research, ensuring the trustworthiness of results, and enabling investigations into data and model biases, as well as potential risks. To this end, we release OpenELM, a state-of-the-art open language model. OpenELM uses a layer-wise scaling strategy to efficiently a

Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim

Large language models (LLMs) excel in most NLP tasks but also require expensive cloud servers for deployment due to their size, while smaller models that can be deployed on lower cost (e.g., edge) devices, tend to lag behind in terms of response quality. Therefore in this work we propose a hybrid inference approach which combines their respective strengths t

Maryam Babaie, Ayaz Akram, Wendy Elsasser, Brent Haukness

As SRAM-based caches are hitting a scaling wall, manufacturers are integrating DRAM-based caches into system designs to continue increasing cache sizes. While DRAM caches can improve the performance of memory systems, existing DRAM cache designs suffer from high miss penalties, wasted data movement, and interference between misses and demand requests. In thi

Lin Ai, Yu-Wen Chen, Yuwen Yu, Seoyoung Kweon

In recent years, radicalization is being increasingly attempted on video-sharing platforms. Previous studies have been proposed to identify online radicalization using generic social context analysis, without taking into account comprehensive viewer traits and how those can affect viewers' perception of radicalizing content. To address the challenge, we exam

Vytautas Paškūnas, Julian Quast

We study deformation theory of mod $p$ Galois representations of $p$-adic fields with values in generalised tori, such as $L$-groups of (possibly non-split) tori. We show that the corresponding deformation rings are formally smooth over a group algebra of a finite abelian $p$-group. We compute their dimension and the set of irreducible components.

Víctor Nopal-Coello

Let $\mathbb{C}_K$ be a complete and algebraic closed non-archimedean field with residual characteristic $2$. In this paper we prove that there exist $a,b\in\mathbb{C}_K$ such that the rational function $R(z)=\frac{z^2-z}{bz-\frac{1}{a}}$ has wandering components in its Fatou set.