May 2023 arXiv papers — page 145

Guillermo Currás-Lorenzo, Margarida Pereira, Go Kato, Marcos Curty

Imperfect bit-and-basis encoders compromise the security of quantum key distribution (QKD) systems via modulation flaws, side channels and inter-pulse correlations, which invalidate standard security proofs. Existing results addressing such imperfections suffer from critical limitations: they either consider only specific flaws, offer an unreasonably poor pe

V. Zh. Sakbaev, I. V. Volovich

Generalizations of the Jacobi and Weyl theorems on finite-dimensional linear flows to the case of linear flows on infinite-dimensional tori are presented. Conditions for periodicity, non-wandering, ergodicity and transitivity of trajectories of an infinite-dimensional linear flow are obtained. It is shown that for infinite-dimensional linear flows there is a

Kenichiro Ando, Satoshi Sekine, Mamoru Komachi

Wikipedia can be edited by anyone and thus contains various quality sentences. Therefore, Wikipedia includes some poor-quality edits, which are often marked up by other editors. While editors' reviews enhance the credibility of Wikipedia, it is hard to check all edited text. Assisting in this process is very important, but a large and comprehensive dataset f

Neslihan Bayramoglu, Martin Englund, Ida K. Haugen, Muneaki Ishijima

In this study, we propose a novel framework that utilizes deep learning (DL) and attention mechanisms to predict the radiographic progression of patellofemoral osteoarthritis (PFOA) over a period of seven years. This study included subjects (1832 subjects, 3276 knees) from the baseline of the MOST study. PF joint regions-of-interest were identified using an

Wei-Qi Peng, Yong Chen

In this work, we study the Cauchy problem of integrable nonlocal Lakshmanan-Porsezian-Daniel equation with rapid attenuation of initial data. The basis Riemann-Hilbert problem of integrable nonlocal Lakshmanan-Porsezian-Daniel equation is constructed from Lax pair. Using Deift-Zhou nonlinear steepest descent method, the explicit long-time asymptotic formula

Shijie Lin, Yingqiang Zhang, Dongyue Huang, Bin Zhou

Motion deblurring is a critical ill-posed problem that is important in many vision-based robotics applications. The recently proposed event-based double integral (EDI) provides a theoretical framework for solving the deblurring problem with the event camera and generating clear images at high frame-rate. However, the original EDI is mainly designed for offli

Hajime Ono, Yuji Sano, Naoto Yotsutani

We prove that the only Bott manifolds such that the Futaki invariant vanishes for any K\"ahler class are isomorphic to the products of the projective lines.

Adam Thompson

For each non-flat, unimodular Ricci soliton solvmanifold $(\mathsf{S}_0,g_0)$, we construct a one-parameter family of complete, expanding, gradient Ricci solitons that admit a cohomogeneity one isometric action by $\mathsf{S}_0$. The orbits of this action are hypersurfaces homothetic to $(\mathsf{S}_0,g_0)$. These metrics are asymptotic at one end to an Eins

Simon Roby

We consider the action of the d'Alembertian on the functions on the pseudo-Riemannian 3-dimensional Anti-de Sitter space. We determine the resonances of this operator. With each resonance one can associate a residue representation. We give an explicit description of these representations via Langlands parameters.

Anni Zou, Zhuosheng Zhang, Hai Zhao

Commonsense fact verification, as a challenging branch of commonsense question-answering (QA), aims to verify through facts whether a given commonsense claim is correct or not. Answering commonsense questions necessitates a combination of knowledge from various levels. However, existing studies primarily rest on grasping either unstructured evidence or poten

Bingyang Wu, Yinmin Zhong, Zili Zhang, Shengyu Liu

Large language models (LLMs) power a new generation of interactive AI applications exemplified by ChatGPT. The interactive nature of these applications demands low latency for LLM inference. Existing LLM serving systems use run-to-completion processing for inference jobs, which suffers from head-of-line blocking and long latency. We present FastServe, a dist

Yuehan Xu, Tao Wang, Huanxi Zhao, Peng Huang

The quantum network makes use of the quantum states to transmit data, which will revolutionize classical communication and allow for some breakthrough applications. The quantum key distribution (QKD) is one prominent application of quantum networks, and can protect the data transmission through quantum mechanics. In this work, we propose an expandable and co

Yang Liu, Tianhuan Luo, Aman Rani, Hengrui Luo

This paper presents a computationally efficient framework for identifying resonance modes of 3D radio-frequency (RF) cavities with damping waveguide ports. The proposed framework relies on surface integral equation (IE) formulations to convert the task of resonance detection to the task of finding resonance frequencies at which the lowest few eigenvalues of

Rock Yuren Pang, Jack Cenatempo, Franklyn Graham, Bridgette Kuehn

Recommendation systems increasingly depend on massive human-labeled datasets; however, the human annotators hired to generate these labels increasingly come from homogeneous backgrounds. This poses an issue when downstream predictive models -- based on these labels -- are applied globally to a heterogeneous set of users. We study this disconnect with respect

Zhen-Ming Xu, Yu-Shan Wang, Bin Wu, Wen-Li Yang

Maxwell equal area law is an important and traditional analytical tool in thermodynamic phase transition, especially in the calculation of gas-liquid phase transition, which reconciles the theoretical calculation with the experimental results. Undoubtedly, its importance is also self-evident for the black hole thermodynamic system. In this study, we construc

Ziyu Du, Yantong Xie, Zhennan Zhou

The noisy leaky integrate-and-fire (NLIF) model describes the voltage configurations of neuron networks with an interacting many-particles system at a microscopic level. When simulating neuron networks of large sizes, computing a coarse-grained mean-field Fokker-Planck equation solving the voltage densities of the networks at a macroscopic level practically

Nan Jia, Ye Feng, Yujia Song, Jun Yang

The Galactic black hole candidate MAXI J0637-430 was first discovered by $\textit{MAXI/GSC}$ on 2019 November 02. We study the spectral properties of MAXI J0637-430 by using the archived $\textit{NuSTAR}$ data and $\textit{Swift}$/XRT data. After fitting the eight spectra by using a disk component and a powerlaw component model with absorption, we select the

Evan Kwiatkowski, Jiawen Zhu, Xiao Li, Herbert Pang

We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCT) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g. unmeasured confounders). The method represents a novel extension of the

Hideaki Hayashi

Generative models have advantageous characteristics for classification tasks such as the availability of unsupervised data and calibrated confidence, whereas discriminative models have advantages in terms of the simplicity of their model structures and learning algorithms and their ability to outperform their generative counterparts. In this paper, we propos

Ziqian Zhang, Lei Yuan, Lihe Li, Ke Xue

In cooperative multi-agent reinforcement learning (MARL), where an agent coordinates with teammate(s) for a shared goal, it may sustain non-stationary caused by the policy change of teammates. Prior works mainly concentrate on the policy change during the training phase or teammates altering cross episodes, ignoring the fact that teammates may suffer from po

Jialin Hong, Baohui Hou, Liying Sun, Xiaojing Zhang

Stochastic Klein--Gordon--Schr\"odinger (KGS) equations are important mathematical models and describe the interaction between scalar nucleons and neutral scalar mesons in the stochastic environment. In this paper, we propose novel structure-preserving schemes to numerically solve stochastic KGS equations with additive noise, which preserve averaged charge e

Lei Yuan, Zi-Qian Zhang, Ke Xue, Hao Yin

Cooperative multi-agent reinforcement learning (CMARL) has shown to be promising for many real-world applications. Previous works mainly focus on improving coordination ability via solving MARL-specific challenges (e.g., non-stationarity, credit assignment, scalability), but ignore the policy perturbation issue when testing in a different environment. This i

Shousuke Ohmori, Yoshihiro Yamazaki

Dynamical properties of tropically discretized and max-plus negative feedback models are investigated. Reviewing the previous study [S. Gibo and H. Ito, J. Theor. Biol. 378, 89 (2015)], the conditions under which the Neimark-Sacker bifurcation occurs are rederived with a different approach from their previous one. Furthermore, for limit cycles of the tropica

Xinyu Wang, Jiayi Li, Eesha Srivatsavaya, Sarah Rajtmajer

Since 2018, Twitter has steadily released into the public domain content discovered on the platform and believed to be associated with information operations originating from more than a dozen state-backed organizations. Leveraging this dataset, we explore inter-state coordination amongst state-backed information operations and find evidence of intentional,

Ravshan Ashurov, Rajapboy Saparbayev

The Cauchy problem for the telegraph equation $(D_{t}^{\rho })^{2}u(t)+2\alpha D_{t}^{\rho }u(t)+Au(t)=f(t)$ ($0<t\leq T, \, 0<\rho<1$), with the Caputo derivative is considered. Here $A$ is a selfadjoint positive operator, acting in a Hilbert space $H$, $D_t$ is the Caputo fractional derivative. Existence and uniqueness theorems for the solution to the prob

Jing Yang, Yuanzhen Li, Yumeng Yang, Xinrong Xie

Flatbands play an important role in correlated quantum matter and have novel applications in photonic lattices. Synthetic magnetic fields and destructive interference in lattices are traditionally used to obtain flatbands. However, such methods can only obtain a few flatbands with most bands remaining dispersive. Here we realize all-band-flat photonic lattic

Linghu Fan

In this paper, we construct a crepant resolution for the quotient singularity $\mathbb{A}^4/A_4$ in characteristic 2, where $A_4$ is the alternating group of degree 4 with permutation action on $\mathbb{A}^4$. By computing the Euler number of the crepant resolution, we obtain a new counterexample to an analogous statement of McKay correspondence in positive

Nimish Jain, Aswani Kumar Cherukuri

Homomorphic encryption is a sophisticated encryption technique that allows computations on encrypted data to be done without the requirement for decryption. This trait makes homomorphic encryption appropriate for safe computation in sensitive data scenarios, such as cloud computing, medical data exchange, and financial transactions. The data is encrypted usi

Riya Barick, Indrajit Ghose, Amitabha Lahiri

Neutrino mixing is caused by the fact that neutrino flavors are not eigenstates of the free Hamiltonian. This causes oscillations among different neutrino flavors. When neutrinos pass through a medium, weak interactions produce different effective masses for neutrinos of different flavors, leading to a modification of the mixing parameters. In curved spaceti

Wenjie Liu, Baokai Liu, Shiqiang Du, Yuqing Shi

Dunhuang murals suffer from fading, breakage, surface brittleness and extensive peeling affected by prolonged environmental erosion. Image inpainting techniques are widely used in the field of digital mural inpainting. Generally speaking, for mural inpainting tasks with large area damage, it is challenging for any image inpainting method. In this paper, we d

Zhibin Tang, Tiantong He

With the advent of depth-to-image diffusion models, text-guided generation, editing, and transfer of realistic textures are no longer difficult. However, due to the limitations of pre-trained diffusion models, they can only create low-resolution, inconsistent textures. To address this issue, we present the High-definition Consistency Texture Model (HCTM), a

Chengkun Wei, Minghu Zhao, Zhikun Zhang, Min Chen

Differential privacy (DP), as a rigorous mathematical definition quantifying privacy leakage, has become a well-accepted standard for privacy protection. Combined with powerful machine learning techniques, differentially private machine learning (DPML) is increasingly important. As the most classic DPML algorithm, DP-SGD incurs a significant loss of utility,

Shiqi Chen, Jinwen Zhou, Menghao Li, Yueting Chen

In digital images, the performance of optical aberration is a multivariate degradation, where the spectral of the scene, the lens imperfections, and the field of view together contribute to the results. Besides eliminating it at the hardware level, the post-processing system, which utilizes various prior information, is significant for correction. However, d

Dong-Sheng Ding, Zhengyang Bai, Zong-Kai Liu, Bao-Sen Shi

It is challenging to probe ergodicity breaking trends of a quantum many-body system when dissipation inevitably damages quantum coherence originated from coherent coupling and dispersive two-body interactions. Rydberg atoms provide a test bed to detect emergent exotic many-body phases and non-ergodic dynamics where the strong Rydberg atom interaction compete

Xiyun Li, Ziyi Ni, Jingqing Ruan, Linghui Meng

Adaptive human-agent and agent-agent cooperation are becoming more and more critical in the research area of multi-agent reinforcement learning (MARL), where remarked progress has been made with the help of deep neural networks. However, many established algorithms can only perform well during the learning paradigm but exhibit poor generalization during coop

Eduardo Longa

We show there are no extremal metrics for the eigenvalues of the Neumann Laplacian on any compact manifold. Nonetheless, we construct examples of conformally extremal metrics for the eigenvalues of this operator in any annulus and characterise these special metrics in the general case of a compact manifold of dimension $n \geq 2$. As for the Dirichlet Laplac

Jie Zhang, Wei Ma, Qiang Hu, Shangqing Liu

Existing methods for generating adversarial code examples face several challenges: limted availability of substitute variables, high verification costs for these substitutes, and the creation of adversarial samples with noticeable perturbations. To address these concerns, our proposed approach, RNNS, uses a search seed based on historical attacks to find pot

De Huang, Xiang Qin, Xiuyuan Wang, Dongyi Wei

We show that the $a$-parameterized family of the generalized Constantin-Lax-Majda model, also known as the Okamoto-Sakajo-Wunsch model, admits exact self-similar finite-time blowup solutions with interiorly smooth profiles for all $a\leq 1$. Depending on the value of $a$, these self-similar profiles are either smooth on the whole real line or compactly suppo

Yuyue Yan, Takahiro Kawaguchi, Yuichiro Yano, Yuko Hanado

In this article, we present a structured Kalman filter associated with the transformation matrix for observable Kalman canonical decomposition from conventional Kalman filter (CKF) in order to generate a more accurate time scale. The conventional Kalman filter is a special case of the proposed structured Kalman filter which yields the same predicted unobserv

Aaron Hong, Marco Oliva, Dominik Köppl, Hideo Bannai

FM-indexes are a crucial data structure in DNA alignment, for example, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-

Peng-Zhang He, Hai-Qing Zhang

In this paper we study the entanglement entropy in the CFT$_2$, whose gravity dual is AdS$_3$ spacetime with a Chern-Simons term. Using the generalized Rindler method, we obtain the Rindler transformation in the two-dimensional planar CFT and compute the entanglement entropy of the CFT with gravitational anomalies. The conditions under which the entanglement

Zefan Wang, Jun Zhao

This paper introduces the novel utility-oriented communications (UOC) concept and identifies its importance for 6G wireless technology. UOC encompasses existing communication paradigms and includes emerging human-centric and task-oriented communications concepts. The authors investigate semantic communications and semantic information transfer for vehicular

Yuxiao Cheng, Lianglong Li, Tingxiong Xiao, Zongren Li

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the

Zhi-Yuan Fan, Xuan Zuo, Hang Qian, Jie Li

A protocol for realizing discrete-variable quantum teleportation in an optomagnonic system is provided. Using optical pulses, an arbitrary photonic qubit state encoded in orthogonal polarizations is transferred onto the joint state of a pair of magnonic oscillators in two macroscopic yttrium-iron-garnet (YIG) spheres that are placed in an optical interferome

Yunshan Zhong, Mingbao Lin, Jingjing Xie, Yuxin Zhang

This paper introduces Distribution-Flexible Subset Quantization (DFSQ), a post-training quantization method for super-resolution networks. Our motivation for developing DFSQ is based on the distinctive activation distributions of current super-resolution models, which exhibit significant variance across samples and channels. To address this issue, DFSQ condu

Lingfeng He, Mengze Xu, Jie Ma

ROI extraction is an active but challenging task in remote sensing because of the complicated landform, the complex boundaries and the requirement of annotations. Weakly supervised learning (WSL) aims at learning a mapping from input image to pixel-wise prediction under image-wise labels, which can dramatically decrease the labor cost. However, due to the im

Shiqi Chen, Ting Lin, Huajun Feng, Zhihai Xu

Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task. Due to the limitation on volume and the demand for mass production, existing mobile terminals cannot rectify optical degradation. In this work, we systematically construct the perturbed lens system model to illustrate the relationship between the deviated sy

Hongjing Huang, Yingtao Li, Jie Sun, Xueying Zhu

Generalized linear models (GLMs) are a widely utilized family of machine learning models in real-world applications. As data size increases, it is essential to perform efficient distributed training for these models. However, existing systems for distributed training have a high cost for communication and often use large batch sizes to balance computation an

Matthew D. Feinstein, Euclides Almeida

Many critical applications in environmental and biological sensing, standoff detection, and astronomy rely on devices that operate in the mid-infrared range. Unfortunately, current mid-infrared devices are costly and have limited tunability. Nanostructured graphene has been proposed for active mid-infrared devices via excitation of tunable surface plasmons,

Xinyu Lin, Yingjie Zhou, Yipeng Liu, Ce Zhu

Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract increasing attention owing to their excellent detection efficiency. However, the existing methods are not robust enough due to the inadequate usage of image gradients for edge draw

Haobo Wang, Shisong Yang, Gengyu Lyu, Weiwei Liu

In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demonstrate a good ability to estimate accurate confidence scores from candidate labels, have been prevalent to deal with PML problems. However, w

Jack S. Baker, Gilchan Park, Kwangmin Yu, Ara Ghukasyan

Supervised time-series classification garners widespread interest because of its applicability throughout a broad application domain including finance, astronomy, biosensors, and many others. In this work, we tackle this problem with hybrid quantum-classical machine learning, deducing pairwise temporal relationships between time-series instances using a time

Aozhu Chen, Ziyuan Wang, Chengbo Dong, Kaibin Tian

This paper introduces ChinaOpen, a dataset sourced from Bilibili, a popular Chinese video-sharing website, for open-world multimodal learning. While the state-of-the-art multimodal learning networks have shown impressive performance in automated video annotation and cross-modal video retrieval, their training and evaluation are primarily conducted on YouTube

Adam Martin, Michael Trott

We develop Standard Model Effective Field Theory (SMEFT) predictions of $\sigma(\mathcal{G} \,\mathcal{G}\rightarrow h)$, $\Gamma(h \rightarrow \mathcal{G} \,\mathcal{G})$, $\Gamma(h \rightarrow \mathcal{A} \mathcal{A})$ to incorporate full two loop Standard Model results at the amplitude level, in conjunction with dimension eight SMEFT corrections. We simul

Batmend Horoldagva, Kinkar Chandra Das

Let ${\mathcal G}_n$ be the set of class of graphs of order $n$. The first Zagreb index $M_1(G)$ is equal to the sum of squares of the degrees of the vertices, and the second Zagreb index $M_2(G)$ is equal to the sum of the products of the degrees of pairs of adjacent vertices of the underlying molecular graph $G$. The three set of graphs are as follows: \be

Jonathan Brundan, Weiqiang Wang, Ben Webster

We prove that the Grothendieck ring of the monoidal category of finitely generated graded projective modules for the nil-Brauer category is isomorphic to an integral form of the split iquantum group of rank one. Under this isomorphism, the indecomposable graded projective modules correspond to the icanonical basis. We also derive character formulae for irred

Rohil Prasad

An area-preserving homeomorphism isotopic to the identity is said to have rational rotation direction if its rotation vector is a real multiple of a rational class. We give a short proof that any area-preserving homeomorphism of a compact surface of genus at least two, which is isotopic to the identity and has rational rotation direction, is either the ident

Yulong Yang, Chenhao Lin, Qian Li, Zhengyu Zhao

Quantized neural networks (QNNs) have received increasing attention in resource-constrained scenarios due to their exceptional generalizability. However, their robustness against realistic black-box adversarial attacks has not been extensively studied. In this scenario, adversarial transferability is pursued across QNNs with different quantization bitwidths,

Chengxian Zhang, Jintao Tang, Ting Wang, Shasha Li

There is evidence that address matching plays a crucial role in many areas such as express delivery, online shopping and so on. Address has a hierarchical structure, in contrast to unstructured texts, which can contribute valuable information for address matching. Based on this idea, this paper proposes a novel method to leverage the hierarchical information

Qing Li, Huifang Feng, Kanle Shi, Yue Gao

We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e., unoriented normal estimation and normal orientat

Rong Guo, Yilv Guo, Yehui Zhang, Xiaoshu Gong

As a two-dimensional material with a hollow hexatomic ring structure, N\'eel-type anti-ferromagnetic (AFM) GdI3 can be used as a theoretical model to study the effect of electron doping. Based on first-principles calculations, we find that the Fermi surface nesting occurs when more than 1/3 electron per Gd is doped, resulting in the failure to obtain a stabl

Jiawei Mao, Shujian Guo, Yuanqi Chang, Xuesong Yin

Masked autoencoders (MAEs) have displayed significant potential in the classification and semantic segmentation of medical images in the last year. Due to the high similarity of human tissues, even slight changes in medical images may represent diseased tissues, necessitating fine-grained inspection to pinpoint diseased tissues. The random masking strategy o

Subhajit Dutta Chowdhury, Kaixin Yang, Pierluigi Nuzzo

Logic locking is a promising technique for protecting integrated circuit designs while outsourcing their fabrication. Recently, graph neural network (GNN)-based link prediction attacks have been developed which can successfully break all the multiplexer-based locking techniques that were expected to be learning-resilient. We present SimLL, a novel similarity

Jiyi Zhang, Han Fang, Hwee Kuan Lee, Ee-Chien Chang

Given a poorly documented neural network model, we take the perspective of a forensic investigator who wants to find out the model's data domain (e.g. whether on face images or traffic signs). Although existing methods such as membership inference and model inversion can be used to uncover some information about an unknown model, they still require knowledge

Tong Li, Qiang Zhou

Let $h(G)$ denote the largest $t$ such that $G$ contains $K_t$ as a minor and $\chi(G)$ be the chromatic number of $G$ respectively. In 1943, Hadwiger conjectured that $h(G) \geq \chi(G)$ for any graph $G$. In this paper, we prove that Hadwiger's conjecture holds for $H$-free graphs with independence number two, where $H$ is one of some specified graphs.

Shiqi Chen, Huajun Feng, Dexin Pan, Zhihai Xu

As the popularity of mobile photography continues to grow, considerable effort is being invested in the reconstruction of degraded images. Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing syst

Angus Gale, Dominic Scognamiglio, Ivan Zhigulin, Benjamin Whitefield

Negatively charged boron vacancies ($\small{V_B^-}$) in hexagonal boron nitride (hBN) have recently gained interest as spin defects for quantum information processing and quantum sensing by a layered material. However, the boron vacancy can exist in a number of charge states in the hBN lattice, but only the -1 state has spin-dependent photoluminescence and a

Ao Sun

In this paper, we present an implementation of JSON-diff framework JYCM, extending the existing framework by introducing the concept of "unordered" comparisons and allowing users to customize their comparison scenarios flexibly. Furthermore, we provide a diff-result renderer to visualize better and understand the differences between JSON objects. Our work en

Jia-Xiang Gao, Meng Liu, Wei-Min Wang

Laser-driven ion accelerators have the advantages of compact size, high density, and short bunch duration over conventional accelerators. Nevertheless, it is still challenging to generate ion beams with quasi-monoenergetic peak and low divergence in the experiments with the current ultra-high intensity laser and thin target technologies. Here we propose a sc

Congpei An, Jiashu Ran, Alvise Sommariva

We present an $\ell^2_2+\ell_1$-regularized discrete least squares approximation over general regions under assumptions of hyperinterpolation, named hybrid hyperinterpolation. Hybrid hyperinterpolation, using a soft thresholding operator and a filter function to shrink the Fourier coefficients approximated by a high-order quadrature rule of a given continuou

Xianzhi Li, Samuel Chan, Xiaodan Zhu, Yulong Pei

The most recent large language models(LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation. How effective are such models in the financial domain? Understanding this basic question would have a significant impact on many downs

Wang-Yu Tong, Yong Li, Shou-Dong Ye, An-Jing Wang

RNA-guided gene editing based on the CRISPR-Cas system is currently the most effective genome editing technique. Here, we report that the SviCas3 from the subtype I-B-Svi Cas system in Streptomyces virginiae IBL14 is an RNA-guided and DNA-guided DNA endonuclease suitable for the HDR-directed gene and/or base editing of eukaryotic cell genomes. The genome edi

Elkaïoum M. Moutuou, Obaï B. K. Ali, Habib Benali

Multilayer networks have permeated all the sciences as a powerful mathematical abstraction for interdependent heterogenous complex systems such as multimodal brain connectomes, transportation, ecological systems, and scientific collaboration. But describing such systems through a purely graph-theoretic formalism presupposes that the interactions that define

Theshani Nuradha, Mark M. Wilde

The fidelity-based smooth min-relative entropy is a distinguishability measure that has appeared in a variety of contexts in prior work on quantum information, including resource theories like thermodynamics and coherence. Here we provide a comprehensive study of this quantity. First we prove that it satisfies several basic properties, including the data-pro

Rahul Aralikatte, Ziling Cheng, Sumanth Doddapaneni, Jackie Chi Kit Cheung

We present V\=arta, a large-scale multilingual dataset for headline generation in Indic languages. This dataset includes 41.8 million news articles in 14 different Indic languages (and English), which come from a variety of high-quality sources. To the best of our knowledge, this is the largest collection of curated articles for Indic languages currently ava

Masato Hirano, Ryosuke Sawata, Naoki Murata, Shusuke Takahashi

Although recent speech processing technologies have achieved significant improvements in objective metrics, there still remains a gap in human perceptual quality. This paper proposes Diffiner, a novel solution that utilizes the powerful generative capability of diffusion models' prior distributions to address this fundamental issue. Diffiner leverages the pr

Ling-Bing He, Jie Ji

In the present work, we investigate estimates of regularity for weak solutions to the non-cutoff Boltzmann equation with soft potentials. We restrict our focus to the so-called "typically rough and slowly decaying data", which is constructed to satisfy typical properties: low regularity and having exact polynomial decay in high velocity regimes. By exploring

Gang Bao, Wenmao Hua, Jun Lai, Jinrui Zhang

Accurate evaluation of nearly singular integrals plays an important role in many boundary integral equation based numerical methods. In this paper, we propose a variant of singularity swapping method to accurately evaluate the layer potentials for arbitrarily close targets. Our method is based on the global trapezoidal rule and trigonometric interpolation, r

Ivan Arraut

By using the Hamiltonian formulation, we demonstrate that the Merton-Garman equation emerges naturally from the Black-Scholes equation after imposing invariance (symmetry) under local (gauge) transformations over changes in the stock price. This is the case because imposing gauge symmetry implies the appearance of an additional field, which corresponds to th

Rui Wang, A-Li Luo, Shuo Zhang, Yuan-Sen Ting

Deriving stellar atmospheric parameters and chemical abundances from stellar spectra is crucial for understanding the evolution of the Milky Way. By performing a fitting with MARCS model atmospheric theoretical synthetic spectra combined with a domain-adaptation method, we estimate the fundamental stellar parameters (Teff, log g, [Fe/H], vmic, and vmac) and

Naoya Iwahara, Wataru Furukawa

In resonant inelastic x-ray scattering (RIXS) spectra of K$_2$IrCl$_6$, the peak for the $j=3/2$ multiplet states shows a splitting that resembles non-cubic crystal-field effect although the compound is cubic down to 0.3 K. Here we theoretically describe the RIXS spectra concomitantly treating the spin-orbit and vibronic interactions. We found that the dynam

Kerry He, Wesley P. Chan, Akansel Cosgun, Albin Joy

As robots have become increasingly common in human-rich environments, it is critical that they are able to exhibit social cues to be perceived as a cooperative and socially-conformant team member. We investigate the effect of robot gaze cues on people's subjective perceptions of a mobile robot as a socially present entity in three common hallway navigation s

Daeyeol Jeon

In this paper, we determine all tetraelliptic modular curves $X_1(N)$ over $\mathbb Q$, and find some tetraelliptic maps $\phi_N$ from $X_1(N)$ to elliptic curves for those tetraelliptic $X_1(N)$. Also we will construct $\phi_N$ explicitly as rational functions. Moreover, we will show that all $\phi_N$ we found are Galois and find elliptic curves with torsio

Sakitha Ariyarathne, Harsha Gangammanavar

We present new formulations of the stochastic electricity market clearing problem based on the principles of stochastic programming. Previous analyses have established that the canonical stochastic programming model effectively captures the relationship between the day-ahead and real-time dispatch and prices. The resulting quantities exhibit desirable guaran

Qing-Xian Lv, Hong-Zhi Liu, Yan-Xiong Du, Lin-Qing Chen

Non-Abelian gauge field (NAGF) plays a central role in understanding the geometrical and topological phenomena in physics. Here we experimentally induce a NAGF in the degenerate eigen subspace of a double-$\Lambda$ four-level atomic system. The non-Abelian nature of the gauge field is detected through the measurement of the non-commutativity of two successiv

Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song

Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users' historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items

Cheng-Jun Xia, Yong-Feng Huang, Hong-Bo Li, Lijing Shao

Seven ultra low-mass and small-radius white dwarfs (LSPM J0815+1633, LP 240-30, BD+20 5125B, LP 462-12, WD J1257+5428, 2MASS J13453297+4200437, and SDSS J085557.46+053524.5) have been recently identified with masses ranging from $\sim$0.02 $M_\odot$ to $\sim$0.08 $M_\odot$ and radii from $\sim$ 4270 km to 10670 km. The mass-radius measurements of these white

Zhu Junxi, Dai Fang, Zhao Fengqun, Guo Wenyan

Link prediction is one of the most important and challenging tasks in complex network analysis, which aims to predict the likelihood of the existence of missing links based on the known information in the network. As critical topological properties in the network, node degree and clustering coefficient are well-suited for describing the tightness of connecti

Wataru Kawabe, Yuri Nakao, Akihisa Shitara, Yusuke Sugano

While AI technology is becoming increasingly prevalent in our daily lives, the comprehension of machine learning (ML) among non-experts remains limited. Interactive machine learning (IML) has the potential to serve as a tool for end users, but many existing IML systems are designed for users with a certain level of expertise. Consequently, it remains unclear

Rohan Dhesikan, Vignesh Rajmohan

The proliferation of video content demands efficient and flexible neural network based approaches for generating new video content. In this paper, we propose a novel approach that combines zero-shot text-to-video generation with ControlNet to improve the output of these models. Our method takes multiple sketched frames as input and generates video output tha

Thomas C. K. Ng, Maximiliano Isi, Kaze W. K. Wong, Will M. Farr

The propagation of gravitational waves can reveal fundamental features of the structure of spacetime. For instance, differences in the propagation of gravitational-wave polarizations would be a smoking gun for parity violations in the gravitational sector, as expected from birefringent theories like Chern-Simons gravity. Here we look for evidence of amplitud

Seah Kim, Hasan Genc, Vadim Vadimovich Nikiforov, Krste Asanović

Driven by the wide adoption of deep neural networks (DNNs) across different application domains, multi-tenancy execution, where multiple DNNs are deployed simultaneously on the same hardware, has been proposed to satisfy the latency requirements of different applications while improving the overall system utilization. However, multi-tenancy execution could l

Xinhai Liu, Zhizhong Han, Sanghuk Lee, Yan-Pei Cao

Learning and selecting important points on a point cloud is crucial for point cloud understanding in various applications. Most of early methods selected the important points on 3D shapes by analyzing the intrinsic geometric properties of every single shape, which fails to capture the importance of points that distinguishes a shape from objects of other clas

Shion Ishikawa, Yun Ching Liu, Young-Joo Chung, Yu Hirate

Estimating position bias is a well-known challenge in Learning to Rank (L2R). Click data in e-commerce applications, such as targeted advertisements and search engines, provides implicit but abundant feedback to improve personalized rankings. However, click data inherently includes various biases like position bias. Based on the position-based click model, R

Guoqing Yang, Chuang Zhu, Yu Zhang

Weakly supervised semantic segmentation (WSSS) based on image-level labels is challenging since it is hard to obtain complete semantic regions. To address this issue, we propose a self-training method that utilizes fused multi-scale class-aware attention maps. Our observation is that attention maps of different scales contain rich complementary information,

Thibault Laugel, Adulam Jeyasothy, Marie-Jeanne Lesot, Christophe Marsala

In the field of Explainable Artificial Intelligence (XAI), counterfactual examples explain to a user the predictions of a trained decision model by indicating the modifications to be made to the instance so as to change its associated prediction. These counterfactual examples are generally defined as solutions to an optimization problem whose cost function c

Xiaogang Xu, Ruixing Wang, Jiangbo Lu

This paper proposes a new framework for low-light image enhancement by simultaneously conducting the appearance as well as structure modeling. It employs the structural feature to guide the appearance enhancement, leading to sharp and realistic results. The structure modeling in our framework is implemented as the edge detection in low-light images. It is ac

Ping Wei, Ge Luo, Qi Song, Xinpeng Zhang

Generative steganography (GS) is a new data hiding manner, featuring direct generation of stego media from secret data. Existing GS methods are generally criticized for their poor performances. In this paper, we propose a novel flow based GS approach -- Generative Steganographic Flow (GSF), which provides direct generation of stego images without cover image

Braden Hoagland, Rick Durrett

Achlioptas processes are a class of dynamically grown random graphs where on each step several edges are chosen at random but only one is added. The sum rule, product rule, and bounded size rules have been extensively studied. Here we introduce a new collection of rules called two-choice rules. In these systems one first pick $m$ vertices at random from the

Hsiu-Wei Yang, Abhinav Agrawal

Accurate Named Entity Recognition (NER) is crucial for various information retrieval tasks in industry. However, despite significant progress in traditional NER methods, the extraction of Complex Named Entities remains a relatively unexplored area. In this paper, we propose a novel system that combines object detection for Document Layout Analysis (DLA) with