May 2023 arXiv papers — page 17

Kenneth H. Karlsen, John D. Towers

We establish quantitative compactness estimates for finite difference schemes used to solve nonlinear conservation laws. These equations involve a flux function $f(k(x,t),u)$, where the coefficient $k(x,t$ is $BV$-regular and may exhibit discontinuities along curves in the $(x,t)$ plane. Our approach, which is technically elementary, relies on a discrete int

Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran

Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to predict the ground-truth probabilities, which is indeed calibrated. However, typical machine learning models are trained to approximately minimize loss over restricted families of pred

Qi-Yi Wu, Chen Zhang, Ze-Zhong Li, Wen-Shan Hong

We reported the quasiparticle relaxation dynamics of an optimally doped triclinic iron-based superconductor (Ca$_{0.85}$La$_{0.15}$)$_{10}$(Pt$_3$As$_8$)(Fe$_2$As$_2$)$_5$ with bulk $T_c$ = 30 K using polarized ultrafast optical pump-probe spectroscopy. Our results reveal anisotropic transient reflectivity induced by nematic fluctuations develops below $T_{n

Can Wang, Tian-Cheng Yi, Jian Li, Rubem Mondaini

Using numerically exact diagonalization, we study the correlated Haldane-Hubbard model in the presence of dissipation. Such dissipation can be modeled at short times by the dynamics governed by an effective non-Hermitian Hamiltonian, of which we present a full characterization. If the dissipation corresponds to a two-body loss, the repulsive interaction of t

Yu Yang, Eric Gan, Gintare Karolina Dziugaite, Baharan Mirzasoleiman

Neural networks trained with (stochastic) gradient descent have an inductive bias towards learning simpler solutions. This makes them highly prone to learning spurious correlations in the training data, that may not hold at test time. In this work, we provide the first theoretical analysis of the effect of simplicity bias on learning spurious correlations. N

Yuxuan Wang, Jianghui Wang, Dongyan Zhao, Zilong Zheng

We introduce CDBERT, a new learning paradigm that enhances the semantics understanding ability of the Chinese PLMs with dictionary knowledge and structure of Chinese characters. We name the two core modules of CDBERT as Shuowen and Jiezi, where Shuowen refers to the process of retrieving the most appropriate meaning from Chinese dictionaries and Jiezi refers

Qi Li, Junfeng Liu, Ke Liu, Zi-Xiang Hu

We develop a numerical method for the time evolution of Gaussian wave packets on flat-band lattices in the presence of correlated disorder. To achieve this, we introduce a method to generate random on-site energies with prescribed correlations. We verify this method with a one-dimensional (1D) cross-stitch model, and find good agreement with analytical resul

Sungwon Kim, Junseok Lee, Namkyeong Lee, Wonjoong Kim

Although Graph Neural Networks (GNNs) have been successful in node classification tasks, their performance heavily relies on the availability of a sufficient number of labeled nodes per class. In real-world situations, not all classes have many labeled nodes and there may be instances where the model needs to classify new classes, making manual labeling diff

J. A. Montanez-Barrera, Pim van den Heuvel, Dennis Willsch, Kristel Michielsen

Combinatorial optimization problems are one of the target applications of current quantum technology, mainly because of their industrial relevance, the difficulty of solving large instances of them classically, and their equivalence to Ising Hamiltonians using the quadratic unconstrained binary optimization (QUBO) formulation. Many of these applications have

Yuxuan Wang, Zilong Zheng, Xueliang Zhao, Jinpeng Li

Video-grounded dialogue understanding is a challenging problem that requires machine to perceive, parse and reason over situated semantics extracted from weakly aligned video and dialogues. Most existing benchmarks treat both modalities the same as a frame-independent visual understanding task, while neglecting the intrinsic attributes in multimodal dialogue

Xinyu Luo, Christopher Musco, Cas Widdershoven

Finding the mode of a high dimensional probability distribution $D$ is a fundamental algorithmic problem in statistics and data analysis. There has been particular interest in efficient methods for solving the problem when $D$ is represented as a mixture model or kernel density estimate, although few algorithmic results with worst-case approximation and runt

Xiaochun Fang, Zhongli Wang

In this paper, we show that one of the conditions in the definition of weak tracial Rokhlin property for finite group actions on simple unital C*-algebras can be replaced by a seemingly weaker condition, or a seemingly stronger condition. As a corollary, this condition is redundant whenever the C*-algebra is not purely infinite. We also give a sufficient con

Jianyuan Sun, Xubo Liu, Xinhao Mei, Volkan Kılıç

Automated audio captioning (AAC) which generates textual descriptions of audio content. Existing AAC models achieve good results but only use the high-dimensional representation of the encoder. There is always insufficient information learning of high-dimensional methods owing to high-dimensional representations having a large amount of information. In this

Rui Yang, Lin Song, Yanwei Li, Sijie Zhao

This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly inaccessible data. To address these chal

Takemitsu Kato, Yasuhiro Utsumi, Ora Entin-Wohlman, Amnon Aharony

In connection to the chiral-induced spin-selectivity (CISS) effect, we theoretically analyze the electronic and spin states of edges of a finite $p$-orbital helical atomic chain with the intra-atomic spin-orbit interaction (SOI). This model can host the spin-filtering state in which two up spins propagate in one direction and two down spins propagate in the

Yuval Idan, M. N. Jayakody

The prime objective of this study is to seek a circuit diagram for a multi-inputs Toffoli gate including only single qubit gates and CNOTs. In this regard, we have developed two variational quantum algorithms that can be used to implement a multi-inputs Toffoli gate. The cost functions of these two VQAs are derived by using the Hilbert Schmidt inner product

Nguyen Dinh, Miguel A. Goberna, M. Volle

The first two authors of this paper asserted in Lemma 4 of "New Farkas-type constraint qualifications in convex infinite programming" (DOI: 10.1051/cocv:2007027) that a given reverse convex inequality is consequence of a given convex system satisfying the Farkas-Minkowski constraint qualification if and only if certain set depending on the data contains a pa

George Androulakis, Rabins Wosti

Consider a general quantum stochastic source that emits at discrete time steps quantum pure states which are chosen from a finite alphabet according to some probability distribution which may depend on the whole history. Also, fix two positive integers $m$ and $l$. We encode any tensor product of $ml$ many states emitted by the quantum stochastic source by b

Chenda Li, Yao Qian, Zhuo Chen, Naoyuki Kanda

State-of-the-art large-scale universal speech models (USMs) show a decent automatic speech recognition (ASR) performance across multiple domains and languages. However, it remains a challenge for these models to recognize overlapped speech, which is often seen in meeting conversations. We propose an approach to adapt USMs for multi-talker ASR. We first devel

Shital Saha, Suchandan Kayal

In this work, we propose two information generating functions: general weighted information and relative information generating functions, and study their properties. { It is shown that the general weighted information generating function (GWIGF) is shift-dependent and can be expressed in terms of the weighted Shannon entropy. The GWIGF of a transformed rand

Souravik Dutta, Yiyu Cai, Jianmin Zheng

Underactuated tower crane lifting requires time-energy optimal trajectories for the trolley/slew operations and reduction of the unactuated swings resulting from the trolley/jib motion. In scenarios involving non-negligible hook mass or long rig-cable, the hook-payload unit exhibits double-pendulum behaviour, making the problem highly challenging. This artic

Zhiheng Guo, Yuanzhang Xiao, Xiang Chen

In this paper, an unsupervised deep learning framework based on dual-path model-driven variational auto-encoders (VAE) is proposed for angle-of-arrivals (AoAs) and channel estimation in massive MIMO systems. Specifically designed for channel estimation, the proposed VAE differs from the original VAE in two aspects. First, the encoder is a dual-path neural ne

Wenshuo Chen, Xiang Zhou, Zhengdi Yu, Weixi Gu

Estimating human pose from video is a task that receives considerable attention due to its applicability in numerous 3D fields. The complexity of prior knowledge of human body movements poses a challenge to neural network models in the task of regressing keypoints. In this paper, we address this problem by incorporating motion prior in an adversarial way. Di

Shiyang Li, Yifan Gao, Haoming Jiang, Qingyu Yin

Answering complex questions often requires reasoning over knowledge graphs (KGs). State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which are then fed into KG encoder, e.g. graph neural networks (GNNs), to model their local structures and integrated into language models for question answering. However, this paradigm co

Shikhar Murty, Pratyusha Sharma, Jacob Andreas, Christopher D. Manning

For humans, language production and comprehension is sensitive to the hierarchical structure of sentences. In natural language processing, past work has questioned how effectively neural sequence models like transformers capture this hierarchical structure when generalizing to structurally novel inputs. We show that transformer language models can learn to g

Dong-Won Jung, Kang Young Lee, Chaehyun Yu

We study constraints on the hidden sector model mediated by an additional SU(2) Higgs doublet from the phenomenology of Higgs bosons. The hidden sector is assumed to contain a hidden U(1) gauge symmetry and the hidden U(1) gauge boson gets the mass by the electroweak symmetry breaking to be a dark Z boson. The Higgs sector of the model is similar to that of

Jaeuk Byun, Youna Ji, Soo Whan Chung, Soyeon Choe

Enhancing speech quality is an indispensable yet difficult task as it is often complicated by a range of degradation factors. In addition to additive noise, reverberation, clipping, and speech attenuation can all adversely affect speech quality. Speech restoration aims to recover speech components from these distortions. This paper focuses on exploring the i

Shashank Hegde, Sumeet Batra, K. R. Zentner, Gaurav S. Sukhatme

Recent progress in Quality Diversity Reinforcement Learning (QD-RL) has enabled learning a collection of behaviorally diverse, high performing policies. However, these methods typically involve storing thousands of policies, which results in high space-complexity and poor scaling to additional behaviors. Condensing the archive into a single model while retai

Nathan K. Long, Robert Malaney, Kenneth J. Grant

Coherent measurement of quantum signals used for continuous-variable (CV) quantum key distribution (QKD) across satellite-to-ground channels requires compensation of phase wavefront distortions caused by atmospheric turbulence. One compensation technique involves multiplexing classical reference pulses (RPs) and the quantum signal, with direct phase measurem

Muskan Garg, Chandni Saxena, Debabrata Samanta, Bonnie J. Dorr

Social media is a potential source of information that infers latent mental states through Natural Language Processing (NLP). While narrating real-life experiences, social media users convey their feeling of loneliness or isolated lifestyle, impacting their mental well-being. Existing literature on psychological theories points to loneliness as the major con

Alexandre Anahory Simoes, Leonardo Colombo, Manuel de Leon, Modesto Salgado

We extend the Jacobi structure from $TQ\times \mathbb{R}$ and $T^{*}Q \times \mathbb{R}$ to $A\times \mathbb{R}$ and $A^{*}\times \mathbb{R}$, respectively, where $A$ is a Lie algebroid and $A^{*}$ carries the associated Poisson structure. We see that $A^*\times \mathbb{R}$ possesses a natural Jacobi structure from where we are able to model dissipative mech

Oladayo S. Ajani, Sri Srinivasa Raju M, Anand Paul, Rammohan Mallipeddi

The effectiveness of Constrained Multi-Objective Evolutionary Algorithms (CMOEAs) depends on their ability to reach the different feasible regions during evolution, by exploiting the information present in infeasible solutions, in addition to optimizing the several conflicting objectives. Over the years, researchers have proposed several CMOEAs to handle Con

Yanyan Wang, Qidi Li, Xiaohu Tang

This letter proposes a low-complexity signal detection method for the splitting receiver scheme, which achieves an excellent symbol error rate (SER) performance. Based on the three-dimensional (3D) received signal of the splitting receiver, we derive an equivalent two-dimensional (2D) signal model and develop a low-complexity signal detection method for the

Boran Han

Addressing imbalanced or long-tailed data is a major challenge in visual recognition tasks due to disparities between training and testing distributions and issues with data noise. We propose the Wrapped Cauchy Distributed Angular Softmax (WCDAS), a novel softmax function that incorporates data-wise Gaussian-based kernels into the angular correlation between

Jin Yuan, Yang Zhang, Yangzhou Du, Zhongchao Shi

In recent years, deep models have achieved remarkable success in various vision tasks. However, their performance heavily relies on large training datasets. In contrast, humans exhibit hybrid learning, seamlessly integrating structured knowledge for cross-domain recognition or relying on a smaller amount of data samples for few-shot learning. Motivated by th

Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang

In this paper, we consider non-convex multi-block bilevel optimization (MBBO) problems, which involve $m\gg 1$ lower level problems and have important applications in machine learning. Designing a stochastic gradient and controlling its variance is more intricate due to the hierarchical sampling of blocks and data and the unique challenge of estimating hyper

Yuechen Zhang, Jinbo Xing, Eric Lo, Jiaya Jia

Recent diffusion model advancements have enabled high-fidelity images to be generated using text prompts. However, a domain gap exists between generated images and real-world images, which poses a challenge in generating high-quality variations of real-world images. Our investigation uncovers that this domain gap originates from a latents' distribution gap i

Licong Lin, Tijana Zrnic

When predictions are performative, the choice of which predictor to deploy influences the distribution of future observations. The overarching goal in learning under performativity is to find a predictor that has low \emph{performative risk}, that is, good performance on its induced distribution. One family of solutions for optimizing the performative risk,

Muskan Garg, Amirmohammad Shahbandegan, Amrit Chadha, Vijay Mago

With a surge in identifying suicidal risk and its severity in social media posts, we argue that a more consequential and explainable research is required for optimal impact on clinical psychology practice and personalized mental healthcare. The success of computational intelligence techniques for inferring mental illness from social media resources, points t

Daegyu Kim, Chaehun Shin, Jooyoung Choi, Dahuin Jung

Generative steganography is the process of hiding secret messages in generated images instead of cover images. Existing studies on generative steganography use GAN or Flow models to obtain high hiding message capacity and anti-detection ability over cover images. However, they create relatively unrealistic stego images because of the inherent limitations of

John Bosco Mugeni, Steven Lynden, Toshiyuki Amagasa, Akiyoshi Matono

Entity Matching (EM) involves identifying different data representations referring to the same entity from multiple data sources and is typically formulated as a binary classification problem. It is a challenging problem in data integration due to the heterogeneity of data representations. State-of-the-art solutions have adopted NLP techniques based on pre-t

Yang Zhang, Lingbo Liu, Xinyu Xiong, Guanbin Li

Wind power is attracting increasing attention around the world due to its renewable, pollution-free, and other advantages. However, safely and stably integrating the high permeability intermittent power energy into electric power systems remains challenging. Accurate wind power forecasting (WPF) can effectively reduce power fluctuations in power system opera

Changyuan Wang, Ziwei Wang, Xiuwei Xu, Yansong Tang

In this paper, we propose an accurate data-free post-training quantization framework of diffusion models (ADP-DM) for efficient image generation. Conventional data-free quantization methods learn shared quantization functions for tensor discretization regardless of the generation timesteps, while the activation distribution differs significantly across vario

Andrew Weng, Everardo Olide, Iaroslav Kovalchuk, Jason B. Siegel

This work proposes a semi-empirical model for the SEI growth process during the early stages of lithium-ion battery formation cycling and aging. By combining a full-cell model which tracks half-cell equilibrium potentials, a zero-dimensional model of SEI growth kinetics, and a semi-empirical description of cell thickness expansion, the resulting model replic

Yi Tu, Ya Guo, Huan Chen, Jinyang Tang

Visually-rich Document Understanding (VrDU) has attracted much research attention over the past years. Pre-trained models on a large number of document images with transformer-based backbones have led to significant performance gains in this field. The major challenge is how to fusion the different modalities (text, layout, and image) of the documents in a u

Xingru Chen, Feng Fu

Evolutionary game theory provides a mathematical foundation for cross-disciplinary fertilization, especially for integrating ideas from artificial intelligence and game theory. Such integration offers a transparent and rigorous approach to complex decision-making problems in a variety of important contexts, ranging from evolutionary computation to machine be

Junfeng Hu, Yuxuan Liang, Zhencheng Fan, Hongyang Chen

We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data are sparsely deployed, resulting in a lack of fine-grained information due to high deployment and maintenance costs. Existing methods either use learning-based models like Neural Ne

Augustinos D. Saravanos, Yihui Li, Evangelos A. Theodorou

As the scale and complexity of multi-agent robotic systems are subject to a continuous increase, this paper considers a class of systems labeled as Very-Large-Scale Multi-Agent Systems (VLMAS) with dimensionality that can scale up to the order of millions of agents. In particular, we consider the problem of steering the state distributions of all agents of a

Naaz Sibia, Angela Zavaleta Bernuy, Joseph Jay Williams, Michael Liut

Q&A forums are widely used in large classes to provide scalable support. In addition to offering students a space to ask questions, these forums aim to create a community and promote engagement. Prior literature suggests that the way students participate in Q&A forums varies and that most students do not actively post questions or engage in discussions. Stud

Alexey S. Koshelev, K. Sravan Kumar, Alexei A. Starobinsky

In this chapter we review the recent developments of realizing $R^2$-like inflation in the framework of a most general UV nonlocal extension of Einstein's general theory of relativity (GR). It is a well-motivated robust approach towards quantum gravity. In the past decades, nonlocal gravitational theories which are quadratic in curvature have been understood

S. Zeng, V. M. Rivilla, I. Jiménez-Serra, L. Colzi

Interstellar amides have attracted significant attentions as they are potential precursors for a wide variety of organics essential to life. However, our current understanding of their formation in space is heavily based on observations in star-forming regions and hence the chemical networks lack the constraints on their early origin. In this work, unbiased

Supeng Wang, Yuxi Li, Ming Xie, Mingmin Chi

Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes, previous effort mostly pays attention to learning representative feature descriptors of a single image, while the difference information is either modeled with simple difference

Teng Zhao, Shuangliang Zhao, Shenggao Zhou, Zhenli Xu

Cyclic voltammetry (CV) is a powerful technique for characterizing electrochemical properties of electrochemical devices. During charging-discharging cycles, thermal effect has profound impact on its performance, but existing theoretical models cannot clarify such intrinsic mechanism and often give poor prediction. Herein, we propose an interfacial model for

Jianfei Yang, Hanjie Qian, Yuecong Xu, Kai Wang

Unsupervised domain adaptation (UDA) involves adapting a model trained on a label-rich source domain to an unlabeled target domain. However, in real-world scenarios, the absence of target-domain labels makes it challenging to evaluate the performance of UDA models. Furthermore, prevailing UDA methods relying on adversarial training and self-training could le

Charuka D. Wickramasinghe

This paper introduces an approach to decoupling singularly perturbed boundary value problems for fourth-order ordinary differential equations that feature a small positive parameter $\epsilon$ multiplying the highest derivative. We specifically examine Lidstone boundary conditions and demonstrate how to break down fourth-order differential equations into a s

Junyi Wang, Ziao Li, Bangli Liu, Haibin Cai

Recently, the significant achievements have been made in skeleton-based human action recognition with the emergence of graph convolutional networks (GCNs). However, the state-of-the-art (SOTA) models used for this task focus on constructing more complex higher-order connections between joint nodes to describe skeleton information, which leads to complex infe

Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang

A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian Facet model is employed to fit the signal regions, which enhances the resistance

Yi Lu, Yadong Wang, Xingbo Jiang, Xiangzhi Bai

Infrared images captured under turbulent conditions are degraded by complex geometric distortions and blur. We address infrared deturbulence as an image restoration task, proposing DparNet, a parameter-assisted multi-frame network with a wide & deep architecture. DparNet learns a degradation prior (key parameter matrix) directly from degraded images without

Yaokun Li

The spatiotemporal variation in tropical air-sea interaction is investigated by applying a simple model that considers the fundamental dynamics in tropical oceans. The model decomposes sea surface temperature anomaly (SSTA) variation into a series of spatial modes that oscillates with their natural frequencies. The results suggest that the first mode associa

Fei Wang, Jun Cheng

Decoders play significant roles in recovering scene depths. However, the decoders used in previous works ignore the propagation of multilevel lossless fine-grained information, cannot adaptively capture local and global information in parallel, and cannot perform sufficient global statistical analyses on the final output disparities. In addition, the process

John Augustine, Dror Fried, Krishna V. Palem, Duc-Hung Pham

Inexact computing also referred to as approximate computing is a style of designing algorithms and computing systems wherein the accuracy of correctness of algorithms executing on them is deliberately traded for significant resource savings. Significant progress has been reported in this regard both in terms of hardware as well as software or custom algorith

Alonso Tapia, Carlos Saji, Alejandro Roldan, Alvaro S. Nunez

We reveal the role of the spin variables' zero-point fluctuations (ZPFs) on the stability of Bloch point (BP) singularities. As topological solitons, BPs are important in topological transitions in nanomagnets. BPs present a singularity at their core, where the long-length-scale approximation fails. We found that ZPFs bloom nearby this core, reducing the eff

Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng

Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of

Kejun Tang, Jiayu Zhai, Xiaoliang Wan, Chao Yang

Solving partial differential equations (PDEs) is a central task in scientific computing. Recently, neural network approximation of PDEs has received increasing attention due to its flexible meshless discretization and its potential for high-dimensional problems. One fundamental numerical difficulty is that random samples in the training set introduce statist

Devdhar Patel, Terrence Sejnowski, Hava Siegelmann

The current reinforcement learning framework focuses exclusively on performance, often at the expense of efficiency. In contrast, biological control achieves remarkable performance while also optimizing computational energy expenditure and decision frequency. We propose a Decision Bounded Markov Decision Process (DB-MDP), that constrains the number of decisi

Gregory Faletto, Jacob Bien

Training classifiers is difficult with severe class imbalance, but many rare events are the culmination of a sequence with much more common intermediate outcomes. For example, in online marketing a user first sees an ad, then may click on it, and finally may make a purchase; estimating the probability of purchases is difficult because of their rarity. We sho

Shokichi Takakura, Taiji Suzuki

Despite the great success of Transformer networks in various applications such as natural language processing and computer vision, their theoretical aspects are not well understood. In this paper, we study the approximation and estimation ability of Transformers as sequence-to-sequence functions with infinite dimensional inputs. Although inputs and outputs a

Jinming Zhuang, Zhuoping Yang, Peipei Zhou

As the increasing complexity of Neural Network(NN) models leads to high demands for computation, AMD introduces a heterogeneous programmable system-on-chip (SoC), i.e., Versal ACAP architectures featured with programmable logic (PL), CPUs, and dedicated AI engines (AIE) ASICs which has a theoretical throughput up to 6.4 TFLOPs for FP32, 25.6 TOPs for INT16 a

So Yamagata

We study a specific line arrangement obtained from a generic $2$-section of the braid arrangement, and compute the fundamental group of its complement via braid monodromy. We show that the resulting presentation of the fundamental group coincides, under the identification of generators, with the modified Artin presentation introduced by Margalit and McCammon

Yun-Ru Fan, Yue Luo, Zi-Chang Zhang, Yun-Bo Li

The coexistence of quantum and classical light in the same fiber link is extremely desired in developing quantum communication. It has been implemented for different quantum information tasks, such as classical light coexisting with polarization-entangled photons at telecom O-band, and with quantum signal based quantum key distribution (QKD). In this work, w

Changhao Liu, Songlin Zhou, Fan Yang, Shenheng Xu

This work presents a beam-steering reflectarray antenna that achieves arbitrary linear polarization (LP) reconfiguration. This antenna employs a dual-circular polarization (CP) reconfigurable reflectarray and an LP feed horn to generate an LP beam. The incident LP wave is decomposed into two CP components, whose reflection phases are independently adjusted.

Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su

The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs. However, when faced with real-world physical data, which are often highly non-uniformly distributed, it is challenging to use mesh-based techniques such as the FFT. To address this, we introduce the Non-Uniform Neural Operator (NUNO), a comprehensive fram

Siqi Feng, Tingting Liu, Wenya Chen, Feng Wu

The miniaturization of nonlinear light sources is central to the integrated photonic platform, driving a quest for high-efficiency frequency generation and mixing at the nanoscale. In this quest, the high-quality ($Q$) resonant dielectric nanostructures hold great promise, as they enhance nonlinear effects through the resonantly local electromagnetic fields

Yun Li, Dazhou Yu, Zhenke Liu, Minxing Zhang

In the era of big data, there has been a surge in the availability of data containing rich spatial and temporal information, offering valuable insights into dynamic systems and processes for applications such as weather forecasting, natural disaster management, intelligent transport systems, and precision agriculture. Graph neural networks (GNNs) have emerge

Bo Han, Xiao Wen

In this paper, we study the centralizer of a separating continuous flow without fixed points. We show that if $M$ is a compact metric space and $\phi_t:M\to M$ is a separating flow without fixed points, then $\phi_t$ has a quasi-trivial centralizer, that is, if a continuous flow $\psi_t$ commutes with $\phi_t$, then there exists a continuous function $A: M\t

Rishov Sarkar, Hanxue Liang, Zhiwen Fan, Zhangyang Wang

Computer vision researchers are embracing two promising paradigms: Vision Transformers (ViTs) and Multi-task Learning (MTL), which both show great performance but are computation-intensive, given the quadratic complexity of self-attention in ViT and the need to activate an entire large MTL model for one task. M$^3$ViT is the latest multi-task ViT model that

Waseem Kamleh, Derek B. Leinweber, Adam Virgili

The first calculation of the response of the momentum space quark propagator to center vortices in the ground state fields of QCD is presented. Center vortices are identified on 2+1-flavour dynamical gauge fields with $m_\pi \simeq 156$ MeV to obtain the vortex-removed and vortex-only quark propagator. Dynamical mass generation is found to vanish upon vortex

Kyungmin Kim, Eungwang Seo, Chunglee Kim

The luminosity distance is a key observable of gravitational-wave (GW) observations. We demonstrate how one can correctly retrieve the luminosity distance of compact binary coalescences (CBCs) if the GW signal is strongly lensed. We perform a proof-of-concept parameter estimation for the luminosity distance supposing (i) strong lensing produces two lensed GW

Santiago Capriotti

The purpose of this article is to study the correspondence between $3d$-gravity and the Chern-Simons field theory from the perspective of geometric mechanics, specifically in the case where the structure group is the general affine group. To accomplish this, the paper discusses a variational problem of the Chern-Simons type on a principal fiber bundle with t

Zibo Liu, Parshin Shojaee, Chandan K Reddy

There is a recent surge in the development of spatio-temporal forecasting models in the transportation domain. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed in traffic networks. Current works primarily rely on road networks with graph structures and learn represent

Elliot Padgett, Megan E. Holtz, Anusorn Kongkanand, David A. Muller

Surface strain plays a key role in enhancing the activity of Pt-alloy nanoparticle oxygen reduction catalysts. However, the details of strain effects in real fuel cell catalysts are not well-understood, in part due to a lack of strain characterization techniques that are suitable for complex supported nanoparticle catalysts. This work investigates these effe

Wen-Xiang Chen, Yao-Guang Zheng

In this study, we conducted a comparison between the RVB method and the conventional method discussed in previous literature for calculating the Hawking temperature of Kerr-Newman black holes under f(R) gravity\cite{9,10,11}. Our research findings are in agreement with the results presented in the literature\cite{17}, with only a variation in the integration

Kangjun Liu, Ke Chen, Lihua Guo, Yaowei Wang

Mixup style data augmentation algorithms have been widely adopted in various tasks as implicit network regularization on representation learning to improve model generalization, which can be achieved by a linear interpolation of labeled samples in input or feature space as well as target space. Inspired by good robustness of alternative dropout strategies ag

Douglas W. Oard

Despite the plethora of born-digital content, vast troves of important content remain accessible only on physical media such as paper or microfilm. The traditional approach to indexing undigitized content is using manually created metadata that describes it at some level of aggregation (e.g., folder, box, or collection). Searchers led in this way to some sub

Mamiya Kawaguchi, Daiki Suenaga

We explore the topological susceptibility at finite quark chemical potential and zero temperature in two-color QCD (QC$_2$D) with two flavors. Through the Ward-Takahashi identities of QC$_2$D, we find that the topological susceptibility in the vacuum solely depends on three observables: the pion decay constant, the pion mass, and the $\eta$ mass in the low-e

Stanislav Minsker

The goal of this note is to present a modification of the popular median of means estimator that achieves sub-Gaussian deviation bounds with nearly optimal constants under minimal assumptions on the underlying distribution. We build on a recent work on the topic by the author, and prove that desired guarantees can be attained under weaker requirements.

Kangjun Liu, Ke Chen, Kui Jia, Yaowei Wang

Deep representation learning is a subfield of machine learning that focuses on learning meaningful and useful representations of data through deep neural networks. However, existing methods for semantic classification typically employ pre-defined target codes such as the one-hot and the Hadamard codes, which can either fail or be less flexible to model inter

Jyothir S, Zuhaib Akhtar

Question answering (Q/A) can be formulated as a generative task (Mitra, 2017) where the task is to generate an answer given the question and the passage (knowledge, if available). Recent advances in QA task is focused a lot on language model advancements and less on other areas such as sampling(Krishna et al., 2021), (Nakano et al., 2021). Keywords play very

G. L. Villanueva, H. B. Hammel, S. N. Milam, V. Kofman

Enceladus is a prime target in the search for life in our solar system, having an active plume likely connected to a large liquid water subsurface ocean. Using the sensitive NIRSpec instrument onboard JWST, we searched for organic compounds and characterized the plume's composition and structure. The observations directly sample the fluorescence emissions of

Kai Murai, Fuminobu Takahashi, Wen Yin

In an axiverse with numerous axions, the cosmological moduli problem poses a significant challenge because the abundance of axions can easily exceed that of dark matter. The well-established stochastic axion scenario offers a simple solution, relying on relatively low-scale inflation. However, axions are typically subject to mixing due to mass and kinetic te

Pengzhi Li, QInxuan Huang, Yikang Ding, Zhiheng Li

Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining consistency between the subject and the background remains challenging. In this paper, we propose LayerDiffusion, a semant

Mehrnoosh Mirtaheri, Mohammad Rostami, Aram Galstyan

Temporal knowledge graph (TKG) completion models typically rely on having access to the entire graph during training. However, in real-world scenarios, TKG data is often received incrementally as events unfold, leading to a dynamic non-stationary data distribution over time. While one could incorporate fine-tuning to existing methods to allow them to adapt t

Kyrylo Ochkan, Raghav Chaturvedi, Viktor Könye, Louis Veyrat

Quantum devices characterized by non-Hermitian topology are predicted to show highly robust and potentially useful properties, but realizing them has remained a daunting experimental task. This is because non-Hermiticity is often associated with gain and loss, which would require precise tailoring to produce the signatures of nontrivial topology. Here, inste

Min-Seok Seo

The distance conjecture claims that as the modulus traverses along the trans-Planckian geodesic distance, the effective field theory becomes invalid by a descent of a tower of states from UV. Moreover, according to the recent emergence proposal, the kinetic term of the modulus is entirely generated by the wavefunction renormalization in which a tower of stat

Dening Lu, Jun Zhou, Kyle Yilin Gao, Dilong Li

Point cloud segmentation is one of the most important tasks in computer vision with widespread scientific, industrial, and commercial applications. The research thereof has resulted in many breakthroughs in 3D object and scene understanding. Previous methods typically utilized hierarchical architectures for feature representation. However, the commonly used

Yasufumi Hashimoto

After Voronin proved the universality theorem of the Riemann zeta function in the 1970s, universality theorems have been proposed for various zeta and L-functions. Drungilas-Garunkstis-Kacenas' work at 2013 on the universality theorem of the Selberg zeta function for the modular group is one of them and is probably the first universality theorem of the zeta

Yifei Liu, Rex Shen, Xiaotong Shen

This paper introduces a novel Perturbation-Assisted Inference (PAI) framework utilizing synthetic data generated by the Perturbation-Assisted Sample Synthesis (PASS) method. The framework focuses on uncertainty quantification in complex data scenarios, particularly involving unstructured data while utilizing deep learning models. On one hand, PASS employs a

Nazmul Karim, Umar Khalid, Mohsen Joneidi, Chen Chen

Text-to-Image (T2I) diffusion models have achieved remarkable success in synthesizing high-quality images conditioned on text prompts. Recent methods have tried to replicate the success by either training text-to-video (T2V) models on a very large number of text-video pairs or adapting T2I models on text-video pairs independently. Although the latter is comp

Tomoaki Nakaya

The main purpose of this paper is to determine all normalized extremal quasimodular forms of depth 1 whose Fourier coefficients are integers. By changing the local parameter at infinity from $q=e^{2\pi i \tau}$ to the reciprocal of the elliptic modular $j$-function, we prove that all normalized extremal quasimodular forms of depth 1 have a hypergeometric ser

Jiwei Guan, Lei Pan, Chen Wang, Shui Yu

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-base