October 2023 arXiv papers — page 168

Daniel Huybrechts, Dominique Mattei

We generalise the notion of the Tate-Shafarevich group of an elliptic K3 surface with a section to the Tate-Shafarevich group of a K3 surface endowed with a linear system. The construction, which uses Grothendieck's special Brauer group, provides an efficient way to deal with moduli spaces of twisted sheaves supported on curves in a K3 surface.

Ajay Kumar, B. Schwarz, R. S. Dhaka

We unravel the effect of La substitution and hence antisite disorders on the exchange bias (EB) mechanism in Sr$_{2-x}$La$_x$CoNbO$_6$ ($x=$ 0, 0.2) double perovskite samples using the detailed analysis of the field cooled magnetization isotherms (M--H) and training effect. The field dependence of the freezing temperature deviates from both Gabay-Toulouse (G

Xinran Qi, Michael E. Belloy, Jiaqi Gu, Xiaoxia Liu

Genome-wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex diseases. Motivated by GWASs' limitations in identifying small effect loci to understand complex traits' polygenicity and fine-mapping putative causal variants from proxy ones, we propose a knockoff-based method which only requires s

Daniel Huybrechts, Dominique Mattei

We use twisted relative Picard varieties to split Brauer classes on projective varieties over algebraically closed fields by torsors for a fixed abelian scheme independent of the Brauer class. The construction is also used to prove that the index of an unramified Brauer class divides a fixed power of its period.

Lucie Bourguignon, Caroline Weis, Catherine R. Jutzeler, Michael Adamer

Machine learning and deep learning have been celebrating many successes in the application to biological problems, especially in the domain of protein folding. Another equally complex and important question has received relatively little attention by the machine learning community, namely the one of prediction of complex traits from genetics. Tackling this p

Boyu Zhang, Hongyang Yang, Tianyu Zhou, Ali Babar

Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their training datasets, which hampers their generalization capabilities and effectiveness in this field. Recently, Large Language Models (LLMs) pre-trained on extensive corpora have demo

Jianning Li, Dianzhen Cui, X. X. Yi

Quantum parameter estimation holds the promise of quantum technologies, in which physical parameters can be measured with much greater precision than what is achieved with classical technologies. However, how to obtain a best precision when the optimal measurement is not accessible is still an open problem. In this work, we present a theoretical framework to

Peiwen Tan

This study embarks on a comprehensive exploration of the decadal trends and future trajectories of twenty distinct infectious diseases in China from 1998 to 2021. A refined Hybrid Bayesian Structural Time Series (BSTS)-Markov Chain Monte Carlo (MCMC) model is employed, intertwining with Long Short-Term Memory (LSTM) networks to dissect intricate relationship

Alexander Poddubny, Janet Zhong, Shanhui Fan

We discuss a generalization of the non-Hermitian skin effect to finite-size photonic structures with neither gain nor loss in the bulk and purely real energy spectrum under periodic boundary conditions (PBC). We show that such systems can still have significant portions of eigenmodes concentrated at the edges and that this edge concentration can be linked to

Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Jihwan Jeong

Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format. Nevertheless, they often preclude direct interpretation. While downstream tasks make use of these compressed representations, meaningful interpretation usually requires visualization using dimens

Xueyan Wang, Lin Sun, Chen Ye, Zhen Huang

Electronic correlation and reconstruction are two important factors that play a critical role in shaping the magnetic and electronic properties of correlated low-dimensional systems. Here, we report a competition between the electronic correlation and structural reconstruction in La1-xSrxTiO3/SrTiO3 heterostructures by modulating material polarity and interf

Sandeep Kumar, Dinesha V. Hegde, Nandita Srivastava, Nikolai V. Pogorelov

Coronal Mass Ejections (CMEs) are subject to changes in their direction of propagation, tilt, and other properties. This is because CMEs interact with the ambient solar wind and other large-scale magnetic field structures. In this work, we report on the observations of the 2012 October 5 stealth CME using coronagraphic and heliospheric images. We find clear

James H. Adler, Anca S. Andrei, Timothy J. Atherton

Anisotropic fluids, such as nematic liquid crystals, can form non-spherical equilibrium shapes known as tactoids. Predicting the shape of these structures as a function of material parameters is challenging and paradigmatic of a broader class of problems that combine shape and order. Here, we consider a discrete shape optimization approach with finite elemen

Phalgun Nelaturu, Jason R. Hattrick-Simpers, Michael Moorehead, Vrishank Jambur

Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy design. Additive manufacturing via directed energy deposition is demonstrated as a high-throughput technique for synthes

Yinger Zhang, Hui Cai, Xeirui Song, Yicheng Chen

While enabling large language models to implement function calling (known as APIs) can greatly enhance the performance of Large Language Models (LLMs), function calling is still a challenging task due to the complicated relations between different APIs, especially in a context-learning setting without fine-tuning. This paper introduces ``Reverse Chain'', a c

Anand J Kulkarni, Ishaan R Kale, Apoorva Shastri, Aayush Khandekar

In this paper, a novel Snail Homing and Mating Search (SHMS) algorithm is proposed. It is inspired from the biological behaviour of the snails. Snails continuously travels to find food and a mate, leaving behind a trail of mucus that serves as a guide for their return. Snails tend to navigate by following the available trails on the ground and responding to

Claudio Pellegrini, Aliaksei Halavanau, Andrei Benediktovitch, Uwe Bergmann

In a recent paper we proposed to build an x-ray laser oscillator (XLO) in the 6-10 keV range providing intense, stable, transform-limited, x-ray pulses based on population inversion driven by an x-ray pulse train generated by an x-ray free-electron laser (XFEL) operated at a repetition rate of about 100 Hz. Here we present an analysis of recent experimental

Yifei Li, Donghua Yang, Jianzhong Li

Analysis of higher-order organizations, usually small connected subgraphs called motifs, is a fundamental task on complex networks. This paper studies a new problem of testing higher-order clusterability: given query access to an undirected graph, can we judge whether this graph can be partitioned into a few clusters of highly-connected motifs? This problem

George Henderson-Walshe, Michael Langton, Jeanette Claire McLeod, Phillip Lawrence Wilson

Polypolyhedra are edge-transitive compounds of polyhedra. In this paper we use group theory to determine the number of distinct polypolyhedra whose symmetry group is any given finite irreducible Coxeter group. We apply this result in order to enumerate the 3-dimensional polypolyhedra.

Rakesh Bal, Yijia Xiao, Wei Wang

Developing and discovering new drugs is a complex and resource-intensive endeavor that often involves substantial costs, time investment, and safety concerns. A key aspect of drug discovery involves identifying novel drug-target (DT) interactions. Existing computational methods for predicting DT interactions have primarily focused on binary classification ta

Euijin Jeon, Byeongguk Go, Yong Woon Kim

A random search of a partially absorbing target by a run-and-tumble particle in a confined one-dimensional space is investigated. We analytically obtain the mean searching time, which shows a non-monotonic behavior as a function of the self-propulsion speed of the active particle, indicating the existence of an optimal speed, when the absorption strength of

Adel Javanmard, Vahab Mirrokni

While personalized recommendations systems have become increasingly popular, ensuring user data protection remains a top concern in the development of these learning systems. A common approach to enhancing privacy involves training models using anonymous data rather than individual data. In this paper, we explore a natural technique called \emph{look-alike c

Hiroyuki Hirashita, Chian-Chou Chen

Coagulation in the dense interstellar medium (ISM) is an important process that determines the size of the largest grains. We use submillimetre galaxies (SMGs) as laboratories of grain coagulation, since some of them host the densest ISM on a galactic scale among various populations of galaxies known. We examine how large the grains can be in such dense envi

Hanyu Song

This paper presents a comprehensive survey of various established mathematical models pertaining to Somitogenesis, a biological process. The study begins by revisiting and replicating the findings from prominent research papers in this domain, subsequently offering a critical evaluation of the strengths and weaknesses inherent in each approach. By synthesizi

Jongeun Kim, MinChung Kim, Taehwan Kim

Slogans play a crucial role in building the brand's identity of the firm. A slogan is expected to reflect firm's vision and brand's value propositions in memorable and likeable ways. Automating the generation of slogans with such characteristics is challenging. Previous studies developted and tested slogan generation with syntactic control and summarization

Changchang Xi, Jin Zhang

The Nakayama permutations of two derived equivalent, self-injective Artin algebras are conjugate. A different but elementary approach is given to showing that the weak symmetry and self-injectivity of finite-dimensional algebras over an arbitrary field are preserved under derived equivalences.

Nozomi Magome, Naoki Morita, Shigeki Kaneko, Naoto Mitsume

This paper proposes a strategy to solve the problems of the conventional s-version of finite element method (SFEM) fundamentally. Because SFEM can reasonably model an analytical domain by superimposing meshes with different spatial resolutions, it has intrinsic advantages of local high accuracy, low computation time, and simple meshing procedure. However, it

YeongHyeon Park, Sungho Kang, Myung Jin Kim, Yeonho Lee

Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for training. This approach is based on the assumption that the trained UAD model will accurately reconstruct normal patterns but struggles with unseen anomalous patterns. To enhance the UAD

Mohammadreza Eskandari, Houssem-Eddine Gueziri, D. Louis Collins

One of the fundamental elements of both traditional and certain deep learning medical image registration algorithms is measuring the similarity/dissimilarity between two images. In this work, we propose an analytical solution for measuring similarity between two different medical image modalities based on the Hessian of their intensities. First, assuming a f

Daiki Hatanaka, Hiroaki Takeshita, Motoki Kataoka, Hajime Okamoto

Topological phononics enabling backscattering-immune transport is expected to improve the performance of electromechanical systems for classical and quantum information technologies. Nonetheless, most of the previous demonstrations utilized macroscale and low-frequency structures and thus offered little experimental insight into ultrahigh frequency phonon tr

Chenguang Zhao, Huan Yu

Various control strategies and field experiments have been designed for connected and automated vehicles (CAVs) to stabilize mixed traffic that contains both CAVs and Human-driven Vehicles (HVs). The effect of these stabilizing CAV control strategies on traffic safety is still under investigation. In an effort to prioritize safety over stability, a safety-cr

Shi Chen, Qin Li, Oliver Tse, Stephen J. Wright

The acceleration of gradient-based optimization methods is a subject of significant practical and theoretical importance, particularly within machine learning applications. While much attention has been directed towards optimizing within Euclidean space, the need to optimize over spaces of probability measures in machine learning motivates exploration of acc

M. J. Rendell, S. D. Liles, S. Bladwell, A. Srinivasan

Measurements of the Fermi surface are a fundamental technique for determining the electrical and magnetic properties of solids. In 2D systems, the area and diameter of the Fermi surface is typically measured using Shubnikov-de Haas oscillations and commensurability oscillations respectively. However, these techniques are unable to detect changes in the parit

Tao Li, Zhichao Wang, Xinfa Zhu, Jian Cong

Zero-shot speaker cloning aims to synthesize speech for any target speaker unseen during TTS system building, given only a single speech reference of the speaker at hand. Although more practical in real applications, the current zero-shot methods still produce speech with undesirable naturalness and speaker similarity. Moreover, endowing the target speaker w

Renan F. F. da Silva, Yulle G. F. Borges, Rafael C. S. Schouery

The Colored Bin Packing Problem (CBPP) is a generalization of the Bin Packing Problem (BPP). The CBPP consists of packing a set of items, each with a weight and a color, in bins of limited capacity, minimizing the number of used bins and satisfying the constraint that two items of the same color cannot be packed side by side in the same bin. In this article,

Zihan Chen, Howard H. Yang, Y. C. Tay, Kai Fong Ernest Chong

Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. The rapid advances in FMs serve as an important contextual backdrop for the vision of next-generation wireless networks, where federated learning (FL) is a key enabler of distributed network intelligence. C

Francisco Pipa

I propose a novel interpretation of quantum theory, which I will call Environmental Determinacy-based (EnDQT). In contrast to the well-known interpretations of quantum theory, EnDQT has the benefit of not adding non-local, superdeterministic, or retrocausal hidden variables. Also, it is not in tension with relativistic causality by providing a local causal e

A. M. Kamchatnov

We suggest the method of derivation of Hamilton equations which describe the motion of solitons along non-uniform and time dependent large-scale background in case of wave dynamics described by the completely integrable equations in the Ablowitz-Kaup-Newell-Segur scheme. The method is based on development of old Stokes' argumentation which allows one to cont

Philippe Rukimbira

Generalized $(\kappa ,\mu )$ structures occur in dimension 3 only. In this dimension 3, only K-contact structures can occur as generalized Eta-Einstein. On closed manifolds, Eta-Einstein, K-contact structures which are not D-homothetic to Einstein structures are almost regular. We also construct examples of compact, generalized Jacobi $(\kappa ,\mu )$-struct

Chih-Hong Cheng, Michael Luttenberger, Rongjie Yan

Deep neural networks (DNNs) are instrumental in realizing complex perception systems. As many of these applications are safety-critical by design, engineering rigor is required to ensure that the functional insufficiency of the DNN-based perception is not the source of harm. In addition to conventional static verification and testing techniques employed duri

Larissa Markwardt, Hsing Wen Lin, Bryan J. Holler, David W. Gerdes

In this work, we present 0.7-5.0 {\mu}m spectra of eight Neptunian Trojans (NTs) as observed by the JWST's NIRSpec instrument. The reddest NT, 2013 VX30, exhibits a unique spectrum with strong absorption features between 3 - 4 {\mu}m, while the bluest NT, 2006 RJ103, shows negligible water absorption. A principal component analysis comparing these spectra wi

Rashadul Kabir, Ryan G. Kim, Mahdi Nikdast

Recent trends see a move away from a fixed-resource server-centric datacenter model to a more adaptable "disaggregated" datacenter model. These disaggregated datacenters can then dynamically group resources to the specific requirements of an incoming workload, thereby improving efficiency. To properly utilize these disaggregated datacenters, workload allocat

Diego Saldaña Ulloa

This work combined different audio features to obtain a more robust fingerprint to be used in a music recommendation process. The combination of these methods resulted in a high-dimensional vector. To reduce the number of values, PCA was applied to the set of resulting fingerprints, selecting the number of principal components that corresponded to an explain

Jing Wu, Ruyi Guo, Daoxiong Wu, Xiuling Li

The development of two-dimensional (2D) room temperature magnets is of great significance to the practical application of spintronic devices. However, the number of synthesized intrinsic 2D magnets is limited and the performances of them are not satisfactory, e.g. typically with low Curie temperature and poor environmental stability. Magnetic modulation base

Enahoro Oriero, Faiq Khalid, Syed Rafay Hasan

The global semiconductor supply chain involves design and fabrication at various locations, which leads to multiple security vulnerabilities, e.g., Hardware Trojan (HT) insertion. Although most HTs target digital circuits, HTs can be inserted in analog circuits. Therefore, several techniques have been developed for HT insertions in analog circuits. Capacitan

Rafael Oliveira, Akash Kumar Sengupta

In this paper, we prove the following non-linear generalization of the classical Sylvester-Gallai theorem. Let $\mathbb{K}$ be an algebraically closed field of characteristic $0$, and $\mathcal{F}=\{F_1,\cdots,F_m\} \subset \mathbb{K}[x_1,\cdots,x_N]$ be a set of irreducible homogeneous polynomials of degree at most $d$ such that $F_i$ is not a scalar multip

Yan Zhao, Yuan Zong, Jincen Wang, Hailun Lian

In this paper, we propose a new unsupervised domain adaptation (DA) method called layer-adapted implicit distribution alignment networks (LIDAN) to address the challenge of cross-corpus speech emotion recognition (SER). LIDAN extends our previous ICASSP work, deep implicit distribution alignment networks (DIDAN), whose key contribution lies in the introducti

Abe Bohan Hou, Jingyu Zhang, Tianxing He, Yichen Wang

Existing watermarking algorithms are vulnerable to paraphrase attacks because of their token-level design. To address this issue, we propose SemStamp, a robust sentence-level semantic watermarking algorithm based on locality-sensitive hashing (LSH), which partitions the semantic space of sentences. The algorithm encodes and LSH-hashes a candidate sentence ge

C. -H. Chien, S. Goswami, C. -C. Wu, W. -S. Hiew

Scalable graph states are essential for measurement-based quantum computation and many entanglement-assisted applications in quantum technologies. Generation of these multipartite entangled states requires a controllable and efficient quantum device with delicate design of generation protocol. Here we propose to prepare high-fidelity and scalable graph state

Masaki Tsukamoto

We develop a variational principle for mean dimension with potential of $\mathbb{R}^d$-actions. We prove that mean dimension with potential is bounded from above by the supremum of the sum of rate distortion dimension and a potential term. A basic strategy of the proof is the same as the case of $\mathbb{Z}$-actions. However measure theoretic details are mor

Mingao Yuan

Topological indices play a significant role in mathematical chemistry. Given a graph $\mathcal{G}$ with vertex set $\mathcal{V}=\{1,2,\dots,n\}$ and edge set $\mathcal{E}$, let $d_i$ be the degree of node $i$. The degree-based topological index is defined as $\mathcal{I}_n=$ $\sum_{\{i,j\}\in \mathcal{E}}f(d_i,d_j)$, where $f(x,y)$ is a symmetric function. I

Lin Huang, Liyang Liao, Hongsong Qiu, Xianzhe Chen

N\'eel spin-orbit torque allows a charge current pulse to efficiently manipulate the N\'eel vector in antiferromagnets, which offers a unique opportunity for ultrahigh density information storage with high speed. However, the reciprocal process of N\'eel spin-orbit torque, the generation of ultrafast charge current in antiferromagnets has not been demonstrat

Md Kaykobad Reza, Ashley Prater-Bennette, M. Salman Asif

Multimodal learning seeks to utilize data from multiple sources to improve the overall performance of downstream tasks. It is desirable for redundancies in the data to make multimodal systems robust to missing or corrupted observations in some correlated modalities. However, we observe that the performance of several existing multimodal networks significantl

Zih-Jyun Lin, Yi-Ju Chen, Po-Chih Kuo, Likai Huang

Dementia diagnosis requires a series of different testing methods, which is complex and time-consuming. Early detection of dementia is crucial as it can prevent further deterioration of the condition. This paper utilizes a speech recognition model to construct a dementia assessment system tailored for Mandarin speakers during the picture description task. By

Zhenghai Xue, Qingpeng Cai, Bin Yang, Lantao Hu

The field of Reinforcement Learning (RL) has garnered increasing attention for its ability of optimizing user retention in recommender systems. A primary obstacle in this optimization process is the environment non-stationarity stemming from the continual and complex evolution of user behavior patterns over time, such as variations in interaction rates and r

Jeremy J. Redd, Antonio C. Cancio, Nathan Argaman, Kieron Burke

The non-relativistic large-$Z$ expansion of the exchange energy of neutral atoms provides an important input to modern non-empirical density functional approximations. Recent works report results of fitting the terms beyond the dominant term, given by the local density approximation (LDA), leading to an anomalous ZlnZ term that can not be predicted from naiv

Zhichen Zeng, Boxin Du, Si Zhang, Yinglong Xia

Finding node correspondence across networks, namely multi-network alignment, is an essential prerequisite for joint learning on multiple networks. Despite great success in aligning networks in pairs, the literature on multi-network alignment is sparse due to the exponentially growing solution space and lack of high-order discrepancy measures. To fill this ga

Weibin Liao, Xuhong Li, Qingzhong Wang, Yanwu Xu

While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on massive fine-annotated cell images [2] with bounding boxes, masks, and cell types for every cell in every image, to fine-tune the pre-trained model. To lower the cost of annotation,

Feng Pan, Hanfeng Gu, Lvlin Kuang, Bing Liu

Efficient simulation of quantum circuits has become indispensable with the rapid development of quantum hardware. The primary simulation methods are based on state vectors and tensor networks. As the number of qubits and quantum gates grows larger in current quantum devices, traditional state-vector based quantum circuit simulation methods prove inadequate d

Jingyu Liu, Huayi Tang, Yong Liu

Graph Contrastive Learning (GCL) aims to learn node representations by aligning positive pairs and separating negative ones. However, few of researchers have focused on the inner law behind specific augmentations used in graph-based learning. What kind of augmentation will help downstream performance, how does contrastive learning actually influence downstre

Yue Fu, Sami Foell, Xuhai Xu, Alexis Hiniker

In the rapidly evolving landscape of AI-mediated communication (AIMC), tools powered by Large Language Models (LLMs) are becoming integral to interpersonal communication. Employing a mixed-methods approach, we conducted a one-week diary and interview study to explore users' perceptions of these tools' ability to: 1) support interpersonal communication in the

Takashi Maekaku, Jiatong Shi, Xuankai Chang, Yuya Fujita

Recently, the usefulness of self-supervised representation learning (SSRL) methods has been confirmed in various downstream tasks. Many of these models, as exemplified by HuBERT and WavLM, use pseudo-labels generated from spectral features or the model's own representation features. From previous studies, it is known that the pseudo-labels contain semantic i

Jie Han, Xiaofan Yuan

Resolving a recent problem of Bell, Frieze, and Marbach, we establish both the threshold result of Frankston--Kahn--Narayanan--Park, and its strengthening by Spiro, in the rainbow setting. This has applications to the thresholds for rainbow structures in random graphs where each edge is given a uniformly random color from a set of given colors.

Mohammad Wali Ur Rahman, Murad Mehrab Abrar, Hunter Gibbons Copening, Salim Hariri

Large-scale transformer-based models like the Bidirectional Encoder Representations from Transformers (BERT) are widely used for Natural Language Processing (NLP) applications, wherein these models are initially pre-trained with a large corpus with millions of parameters and then fine-tuned for a downstream NLP task. One of the major limitations of these lar

Jing Li, Yifeng Xu, Shengfeng Zhu

In this paper, we discuss adaptive approximations of an elliptic eigenvalue optimization problem in a phase-field setting by a conforming finite element method. An adaptive algorithm is proposed and implemented in several two dimensional numerical examples for illustration of efficiency and accuracy. Theoretical findings consist in the vanishing limit of a s

Yanwu Lu, Howard Yang, Nikolaos Pappas, Giovanni Geraci

Non-terrestrial networks (NTN), particularly low Earth orbit (LEO) satellite networks, have emerged as a promising solution to overcome the limitations of traditional terrestrial networks in the context of next-generation (6G) wireless systems. In this paper, we focus on analyzing the timeliness of information delivery in NTN through the concept of Age of In

Paul M. Riechers

We address the fundamental limits of learning unknown parameters of any stochastic process from time-series data, and discover exact closed-form expressions for how optimal inference scales with observation length. Given a parametrized class of candidate models, the Fisher information of observed sequence probabilities lower-bounds the variance in model esti

Dong Lao, Yangchao Wu, Tian Yu Liu, Alex Wong

Vision Transformer (ViT) architectures represent images as collections of high-dimensional vectorized tokens, each corresponding to a rectangular non-overlapping patch. This representation trades spatial granularity for embedding dimensionality, and results in semantically rich but spatially coarsely quantized feature maps. In order to retrieve spatial detai

Mohammad Sababheh, Hamid Reza Moradi

In this paper, we begin by showing a new generalization of the celebrated Cauchy-Schwarz inequality for the inner product. Then, this generalization is used to present some bounds for the Euclidean operator radius and the Euclidean operator norm. These bounds will be used then to obtain some bounds for the numerical radius in a way that extends many well-kno

Junchi Yu, Ran He, Rex Ying

Large Language Models (LLMs) have achieved remarkable success in reasoning tasks with the development of prompting methods. However, existing prompting approaches cannot reuse insights of solving similar problems and suffer from accumulated errors in multi-step reasoning, since they prompt LLMs to reason \textit{from scratch}. To address these issues, we pro

Eunsong Kang, Da-woon Heo, Jiwon Lee, Heung-Il Suk

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature select

Yuke Li, Xinfa Zhu, Yi Lei, Hai Li

Zero-shot emotion transfer in cross-lingual speech synthesis aims to transfer emotion from an arbitrary speech reference in the source language to the synthetic speech in the target language. Building such a system faces challenges of unnatural foreign accents and difficulty in modeling the shared emotional expressions of different languages. Building on the

Manuel Beato Vásquez, Melvin Arias Polanco

A parametrization, given by the Euler angles, of Hermitian matrix generators of even and odd-degenerate Clifford algebras is constructed by means of the Kronecker product of a parametrized version of Pauli matrices and by the identification of all possible anticommutation sets for a given algebra. The internal parametrization of the matrix generators allows

Krutika Tawri, Suncica Canic

In this paper we study a nonlinear stochastic fluid-structure interaction problem with a multiplicative, white-in-time noise. The problem consists of the Navier-Stokes equations describing the flow of an incompressible, viscous fluid in a 2D cylinder interacting with an elastic lateral wall whose elastodynamics is described by a membrane/shell equation. The

Chee Han Tan, Robert Viator

We consider Steklov eigenvalues of nearly hyperspherical domains in $\mathbb{R}^{d + 1}$ with $d\ge 3$. In previous work, treating such domains as perturbations of the ball, we proved that the Steklov eigenvalues are analytic functions of the domain perturbation parameter. Here, we compute the first-order term of the asymptotic expansion and show that the fi

Shivam Aarya

Vehicle manufacturers are racing to create autonomous navigation and steering control algorithms for their vehicles. These software are made to handle various real-life scenarios such as obstacle avoidance and lane maneuvering. There is some ongoing research to incorporate pothole avoidance into these autonomous systems. However, there is very little researc

Ken Hinckley

This UIST Vision argues that "touch" input and interaction remains in its infancy when viewed in context of the seen but unnoticed vocabulary of natural human behaviors, activity, and environments that surround direct interaction with displays. Unlike status-quo touch interaction -- a shadowplay of fingers on a single screen -- I argue that our perspective o

Victor Akinwande, Yiding Jiang, Dylan Sam, J. Zico Kolter

Zero-shot learning in prompted vision-language models, the practice of crafting prompts to build classifiers without an explicit training process, has achieved impressive performance in many settings. This success presents a seemingly surprising observation: these methods suffer relatively little from overfitting, i.e., when a prompt is manually engineered t

Sara Fridovich-Keil, Fabrizio Valdivia, Gordon Wetzstein, Benjamin Recht

In computed tomography (CT), the forward model consists of a linear Radon transform followed by an exponential nonlinearity based on the attenuation of light according to the Beer-Lambert Law. Conventional reconstruction often involves inverting this nonlinearity and then solving a linear inverse problem. However, this nonlinear measurement preprocessing is

Jiming Ma, Baohua Xie

The Eisenstein-Picard modular surface $M$ is the quotient space of the complex hyperbolic plane by the modular group $\rm PU(2,1; \mathbb{Z}[\omega])$. We determine the global topology of $M$ as a 4-orbifold.

Enhua Xu, Yuma Shimomoto, Seiichiro L. Ten-no, Takashi Tsuchimochi

In this study, we utilize the many-body expansion (MBE) framework to decompose electronic structures into fragments by incrementing the virtual orbitals. Our work aims to accurately solve the ground and excited state energies of each fragment using the variational quantum eigensolver and deflation algorithms. Although our approach is primarily based on unita

Pablo Pueyo, Eduardo Montijano, Ana C. Murillo, Mac Schwager

This work presents CineTransfer, an algorithmic framework that drives a robot to record a video sequence that mimics the cinematographic style of an input video. We propose features that abstract the aesthetic style of the input video, so the robot can transfer this style to a scene with visual details that are significantly different from the input video. T

Jiali Zheng, Youngkyoon Jang, Athanasios Papaioannou, Christos Kampouris

This paper introduces the Imperial Light-Stage Head (ILSH) dataset, a novel light-stage-captured human head dataset designed to support view synthesis academic challenges for human heads. The ILSH dataset is intended to facilitate diverse approaches, such as scene-specific or generic neural rendering, multiple-view geometry, 3D vision, and computer graphics,

Deren Lei, Yaxi Li, Mengya Hu, Mingyu Wang

Large language models (LLMs) can generate fluent natural language texts when given relevant documents as background context. This ability has attracted considerable interest in developing industry applications of LLMs. However, LLMs are prone to generate hallucinations that are not supported by the provided sources. In this paper, we propose a hierarchical f

Martin Mootz, Liang Luo, Chuankun Huang, Jigang Wang

We present a comprehensive theory of light-controlled multi-band superconductivity and apply it to predict distinctive signatures of light-driven superconducting (SC) states in terahertz multi-dimensional coherent spectroscopy (THz-MDCS) experiments. We first derive gauge-invariant Maxwell-Bloch equations for multi-band BCS superconductors. For this, we go b

Damien Berriaud, Andrei Constantinescu, Roger Wattenhofer

A group of $n$ agents with numerical preferences for each other are to be assigned to the $n$ seats of a dining table. We study two natural topologies:~circular (cycle) tables and panel (path) tables. For a given seating arrangement, an agent's utility is the sum of their preference values towards their (at most two) direct neighbors. An arrangement is e

Hung M. Bui, Alexandra Florea, Micah B. Milinovich

We obtain conditional upper bounds for negative discrete moments of the derivative of the Riemann zeta-function averaged over a subfamily of zeros of the zeta function which is expected to have full density inside the set of all zeros. For $k\leq 1/2$, our bounds for the $2k$-th moments are expected to be almost optimal. Assuming a conjecture about the maxim

L. M. Abreu, A. L. M. Britto, F. S. Navarra, H. P. L. Vieira

In a previous work we computed the thermally-averaged cross sections for the production and absorption of the $\chi_{c1}(4274)$ state in the hot hadron gas formed in heavy ion collisions. In the present work we estimate the final yield of this exotic state in these collisions. We use the coalescence model to fix the initial multiplicities. The state is is tr

Qinian Jin

In this paper, we investigate the growth error bound condition. By using the proximal point algorithm, we first provide a more accessible and elementary proof of the fact that Kurdyka-{\L}ojasiewicz conditions imply growth error bound conditions for convex functions which has been established before via a subgradient flow. We then extend the result for nonco

Ho-Joon Lee, Prashant S. Emani, Mark B. Gerstein

The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with vary

Keaton Hamm, Andrzej Korzeniowski

We expound on some known lower bounds of the quadratic Wasserstein distance between random vectors in $\mathbb{R}^n$ with an emphasis on affine transformations that have been used in manifold learning of data in Wasserstein space. In particular, we give concrete lower bounds for rotated copies of random vectors in $\mathbb{R}^2$ by computing the Bures metric

Andrés N. Salcedo, Hao-Yi Wu, Eduardo Rozo, David H. Weinberg

The recent Dark Energy Survey Year 1 (DES-Y1) analysis of galaxy cluster abundances and weak lensing produced $\Omega_{\rm m}$ and $\sigma_8$ constraints in 5.6$\sigma$ tension with Planck. It is suggested in that work that this tension is driven by unmodelled systematics in optical cluster selection. We present a novel simulation-based forward modeling fram

Scott Ellis Perkins, Peter McGill, William Dawson, Natasha S. Abrams

From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible v

Marc Grau Davis, Joaquin Chung, Dirk Englund, Rajkumar Kettimuthu

Distributed quantum computing is motivated by the difficulty in building large-scale, individual quantum computers. To solve that problem, a large quantum circuit is partitioned and distributed to small quantum computers for execution. Partitions running on different quantum computers share quantum information using entangled Bell pairs. However, entanglemen

Kaiqun Fu, Yangxiao Bai, Weiwei Zhang, Deepthi Kolady

The COVID-19 pandemic has accentuated socioeconomic disparities across various racial and ethnic groups in the United States. While previous studies have utilized traditional survey methods like the Household Pulse Survey (HPS) to elucidate these disparities, this paper explores the role of social media platforms in both highlighting and addressing these cha

Fabio Ferreira, Ivo Rapant, Jörg K. H. Franke, Frank Hutter

Self-Supervised Learning (SSL) methods typically rely on random image augmentations, or views, to make models invariant to different transformations. We hypothesize that the efficacy of pretraining pipelines based on conventional random view sampling can be enhanced by explicitly selecting views that benefit the learning progress. A simple yet effective appr

Daniele Artico, Lorenzo Magnea

Integration-by-parts (IBP) identities and differential equations are the primary modern tools for the evaluation of high-order Feynman integrals. They are commonly derived and implemented in the momentum-space representation. We provide a different viewpoint on these important tools by working in Feynman-parameter space, and using its projective geometry. Ou

Tejes Srivastava, Jiatong Shi, William Chen, Shinji Watanabe

Self-Supervised Learning (SSL) models have demonstrated exceptional performance in various speech tasks, particularly in low-resource and multilingual domains. Recent works show that fusing diverse SSL models could achieve superior performance compared to using one SSL model. However, fusing models increases the overall parameter size, leading to higher comp

Elvis Nunez, Yanzi Jin, Mohammad Rastegari, Sachin Mehta

Over the past several years, the synchronization between audio and visual signals has been leveraged to learn richer audio-visual representations. Aided by the large availability of unlabeled videos, many unsupervised training frameworks have demonstrated impressive results in various downstream audio and video tasks. Recently, Masked Audio-Video Learners (M

A. M. Martínez-Argüello, K. B. Hidalgo-Castro, J. A. Méndez-Bermúdez

Within the scattering matrix approach to electronic transport, the scattering and transport properties of tight-binding random graphs are analyzed. In particular, we compute the scattering matrix elements, the transmission, the channel-to-channel transmission distributions (including the total transmission distribution), the shot noise power, and the elastic