November 2022 arXiv papers — page 35

H. Garcilazo, A. Valcarce

We have recently studied hidden-charm pentaquarks, $c\bar c qqq$, using dynamical correlations between the heavy quarks arising from the Coulomb-like nature of the short-range interaction. A pattern was obtained that compares well with the experimental data. We extend our description to other flavor sectors which can be framed within the same type of structu

Evgeny A. Kochurin, Olga V. Zubareva, Nikolay M. Zubarev

The weakly nonlinear dynamics of the free surface of a dielectric liquid in an electric field directed tangentially to the unperturbed boundary is investigated numerically. Within the framework of the strong field model, when the effects of capillarity and gravity are not taken into account, it is shown that nonlinear surface waves have a tendency to break.

Rong Hu, Ling Chen, Shenghuan Miao, Xing Tang

In practice, Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain adaptation (UDA) becomes the natural solution to cross-user WHAR under annotation scarcity. Existing UDA models usually align samples across domains without differentiation, which ignores the difference

Hiroto Arima, Tatsuki Inui, Aichi Yamashita, Akira Miura

We investigated the temperature evolution of crystal structure of orthorhombic CoZr3, which is a superconductor with a transition temperature of 4.3 K, by synchrotron and laboratory (CuK{\alpha}) X-ray diffraction. Uniaxial negative thermal expansion along the c-axis, which is similar to that observed in tetragonal CoZr2, has been observed at a wide temperat

M. F. Bessmertnyi

The characteristic properties of Artin's denominators in Hilbert's 17th problem are obtained. It is proved that numerators of partial derivative of rational real function from the Nevanlinna class are SOS polynomials.

Tomohiro Asano, Stéphane Guillermou, Vincent Humilière, Yuichi Ike

We prove that for any element $L$ in the completion of the space of smooth compact exact Lagrangian submanifolds of a cotangent bundle equipped with the spectral distance, the $\gamma$-support of $L$ coincides with the reduced micro-support of its sheaf quantization. As an application, we give a characterization of the Vichery subdifferential in terms of $\g

Ryugo Morita, Zhiqiang Zhang, Man M. Ho, Jinjia Zhou

Recently, text-guided image manipulation has received increasing attention in the research field of multimedia processing and computer vision due to its high flexibility and controllability. Its goal is to semantically manipulate parts of an input reference image according to the text descriptions. However, most of the existing works have the following probl

Zijiang Yang, Zhongwei Qiu, Dongmei Fu

Modeling dynamics in the form of partial differential equations (PDEs) is an effectual way to understand real-world physics processes. For complex physics systems, analytical solutions are not available and numerical solutions are widely-used. However, traditional numerical algorithms are computationally expensive and challenging in handling multiphysics sys

Chen Shen, Zhixue He, Lei Shi, Zhen Wang

Costly punishment has been suggested as a key mechanism for stabilizing cooperation in one-shot games. However, recent studies have revealed that the effectiveness of costly punishment can be diminished by second-order free riders (i.e., cooperators who never punish defectors) and antisocial punishers (i.e., defectors who punish cooperators). In a two-stage

Zhuang Xiong, Yang Gao, Feng Liu, Hongfu Sun

Deep neural networks have demonstrated great potential in solving dipole inversion for Quantitative Susceptibility Mapping (QSM). However, the performances of most existing deep learning methods drastically degrade with mismatched sequence parameters such as acquisition orientation and spatial resolution. We propose an end-to-end AFfine Transformation Edited

Sankarshan Damle, Manisha Padala, Sujit Gujar

Civic Crowdfunding (CC) uses the ``power of the crowd'' to garner contributions towards public projects. As these projects are non-excludable, agents may prefer to ``free-ride,'' resulting in the project not being funded. For single project CC, researchers propose to provide refunds to incentivize agents to contribute, thereby guaranteeing the project's fund

Jiayin Sun, Hong Wang, Qiulei Dong

Triggered by the success of transformers in various visual tasks, the spatial self-attention mechanism has recently attracted more and more attention in the computer vision community. However, we empirically found that a typical vision transformer with the spatial self-attention mechanism could not learn accurate attention maps for distinguishing different c

Muyang Du, Chuan Liu, Jiaxing Qi, Junjie Lai

Incremental text-to-speech, also known as streaming TTS, has been increasingly applied to online speech applications that require ultra-low response latency to provide an optimal user experience. However, most of the existing speech synthesis pipelines deployed on GPU are still non-incremental, which uncovers limitations in high-concurrency scenarios, especi

Atika Nashirah Hasyyati, Rina Indriani, Titi Kanti Lestari

Tourism data is one of the strategic data in Indonesia. In addition, tourism is one of the ten priority programs of national development planning in Indonesia. BPS-Statistics Indonesia has collected data related to tourism demand in Indonesia, but these data have different time period. Several data can be provided monthly, while the other data can be provide

Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand

We introduce new planning and reinforcement learning algorithms for discounted MDPs that utilize an approximate model of the environment to accelerate the convergence of the value function. Inspired by the splitting approach in numerical linear algebra, we introduce Operator Splitting Value Iteration (OS-VI) for both Policy Evaluation and Control problems. O

Christian Klein-Bösing

In the last two years various existing public outreach activities in ALICE have been adapted for online usage, this includes the well established particle physics masterclasses but also virtual visits to ALICE. Based on these foundations a six month online workshop was carried out in 2021 with the goal to design a LEGO model of the ALICE detector at the LHC

Ao Chen, Xiren Zhou, Yizhan Fan, Huanhuan Chen

Ground Penetrating Radar (GPR) has been widely used in pipeline detection and underground diagnosis. In practical applications, the characteristics of the GPR data of the detected area and the likely underground anomalous structures could be rarely acknowledged before fully analyzing the obtained GPR data, causing challenges to identify the underground struc

Yucong Liu, Simiao Jiao, Lek-Heng Lim

It is well-known that any matrix $A$ has an LU decomposition. Less well-known is the fact that it has a 'Toeplitz decomposition' $A = T_1 T_2 \cdots T_r$ where $T_i$'s are Toeplitz matrices. We will prove that any continuous function $f : \mathbb{R}^n \to \mathbb{R}^m$ has an approximation to arbitrary accuracy by a neural network that takes the form $L_1 \s

Karlheinz Gröchenig, Christine Pfeuffer, Joachim Toft

In the paper we extend the spectral invariance of pseudodifferential operators acting on (non-weighted) classical modulation spaces to allow the Lebesgue exponents to be smaller than one. These spaces occur naturally in approximation theory and data compression problems.

Xu Shi, Jintao Wang, Jian Song

True-time-delay (TTD) lines are recently applied inside Terahertz (THz) hybrid-precoding transceiver to acquire high beamforming gain against beam squint effect. However, beam tracking turns into a challenging puzzle where enormous potential beam directions bring about unacceptable overhead consumption. Frequency-scanning-based beam tracking is initially exp

Shi Pi, Misao Sasaki

We study the comoving curvature perturbation $\mathcal{R}$ in the single-field inflation models whose potential can be approximated by a piecewise quadratic potential $V(\varphi)$ by using the $\delta N$ formalism. We find a general formula for $\mathcal{R}(\delta\varphi, \delta\pi)$, consisting of a sum of logarithmic functions of the field perturbation $\d

Harsh Shah, Thomas Jacob, Amruta Parulekar, Anjali Amarapurkar

Normally eosinophils are present in the gastrointestinal (GI) tract of healthy individuals. When the eosinophils increase beyond their usual amount in the GI tract, a patient gets varied symptoms. Clinicians find it difficult to diagnose this condition called eosinophilia. Early diagnosis can help in treating patients. Histopathology is the gold standard in

Kebing Jin, Zhanhao Xiao, Hankui Hankz Zhuo, Hai Wan

There has been increasing attention on planning model learning in classical planning. Most existing approaches, however, focus on learning planning models from structured data in symbolic representations. It is often difficult to obtain such structured data in real-world scenarios. Although a number of approaches have been developed for learning planning mod

Subhabrata Paul, Kamal Santra

Let $G=(V, E)$ be a graph, where $V$ and $E$ are the vertex and edge sets, respectively. For two disjoint subsets $A$ and $B$ of $V$, we say $A$ \textit{dominates} $B$ if every vertex of $B$ is adjacent to at least one vertex of $A$ in $G$. A vertex partition $\pi = \{V_1, V_2, \ldots, V_k\}$ of $G$ is called a \emph{transitive $k$-partition} if $V_i$ domina

Weinan He, Canming Huang, Zhanhao Xiao, Yongmei Liu

Reasoning about actions and change (RAC) is essential to understand and interact with the ever-changing environment. Previous AI research has shown the importance of fundamental and indispensable knowledge of actions, i.e., preconditions and effects. However, traditional methods rely on logical formalization which hinders practical applications. With recent

Pritam Sarkar, Ali Etemad

We present XKD, a novel self-supervised framework to learn meaningful representations from unlabelled videos. XKD is trained with two pseudo objectives. First, masked data reconstruction is performed to learn modality-specific representations from audio and visual streams. Next, self-supervised cross-modal knowledge distillation is performed between the two

Jing Xu, Wentao Shi, Pan Gao, Zhengwei Wang

In recent works on semantic segmentation, there has been a significant focus on designing and integrating transformer-based encoders. However, less attention has been given to transformer-based decoders. We emphasize that the decoder stage is equally vital as the encoder in achieving superior segmentation performance. It disentangles and refines high-level c

Yuzhe Xiao, Linipun Phuttitarn, Trent Michael Graham, Chenghao Wan

Dielectric mirrors comprising thin-film multilayers are widely used in optical experiments because they can achieve substantially higher reflectance compared to metal mirrors. Here we investigate potential problems that can arise when dielectric mirrors are used at oblique incidence, in particular for focused beams. We found that light beams reflected from d

Bing Guan, Cailian Yang, Liu Zhang, Shanzhou Niu

The radiation dose in computed tomography (CT) examinations is harmful for patients but can be significantly reduced by intuitively decreasing the number of projection views. Reducing projection views usually leads to severe aliasing artifacts in reconstructed images. Previous deep learning (DL) techniques with sparse-view data require sparse-view/full-view

Shibsankar Das, Krishna Gopal Benerjee, Adrish Banerjee

In this paper, we present a novel design strategy of DNA codes with length $3n$ over the non-chain ring $R=\mathbb{Z}_4+u\mathbb{Z}_4+u^2\mathbb{Z}_4$ with $64$ elements and $u^3=1$, where $n$ denotes the length of a code over $R$. We first study and analyze a distance conserving map defined over the ring $R$ into the length-$3$ DNA sequences. Then, we deriv

Alessio Martini

We prove the $L^p$-boundedness for all $p \in (1,\infty)$ of the first-order Riesz transforms $X_j \mathcal{L}^{-1/2}$ associated with the Laplacian $\mathcal{L} = -\sum_{j=0}^n X_j^2$ on the $ax+b$-group $G = \mathbb{R}^n \rtimes \mathbb{R}$; here $X_0$ and $X_1,\dots,X_n$ are left-invariant vector fields on $G$ in the directions of the factors $\mathbb{R}$

Tulika Bhattacharyya, Khushboo Suman, Yogesh M. Joshi

While undergoing gelation transition, a material passes through a distinctive state called the critical gel state. In the neighborhood of this critical gel state, how viscosity, equilibrium modulus, and relaxation times evolve are correlated by scaling relations, and their universality has been validated for materials undergoing the sol to gel transition. In

Roberto García-Torres, Alitzel Adriana Macias-Infante, Santiago Enrique Conant-Pablos, José Carlos Ortiz-Bayliss

The Capacitated Vehicle Routing Problem is a well-known NP-hard problem that poses the challenge of finding the optimal route of a vehicle delivering products to multiple locations. Recently, new efforts have emerged to create constructive and perturbative heuristics to tackle this problem using Deep Learning. In this paper, we join these efforts to develop

Hohto Bekki

The conical zeta values are a generalization of the multiple zeta values which are defined by certain multiple sums over convex cones. In this paper, we present a relation between the values of the Dedekind zeta functions for totally real fields and the conical zeta values for certain algebraic cones. More precisely, we show that the values of the partial ze

Junguk Park, Sangseok Yun, Jeongseok Ha

This work studies secure communications for a cell-free massive multiple-input multiple-output (CF-mMIMO) network which is attacked by multiple passive eavesdroppers overhearing communications between access points (APs) and users in the network. It will be revealed that the distributed APs in CF-mMIMO allows not only legitimate users but also eavesdroppers

Kun Zhou, KenKun Liu, Wenbo Li, Xiaoguang Han

Previous studies show the necessity of global and local adjustment for image enhancement. However, existing convolutional neural networks (CNNs) and transformer-based models face great challenges in balancing the computational efficiency and effectiveness of global-local information usage. Especially, existing methods typically adopt the global-to-local fusi

Zhuoshu Wu, Libo Li

We study the valuation of an American put option with a random time horizon given by the last exit time of the underlying asset from a fixed level. Since this random time is not a stopping time, the problem falls outside the classical optimal stopping framework. Using enlargement of filtrations and the associated Az\'{e}ma supermartingale, we transform the p

Zhuoshu Wu, Libo Li

In this article we provide a valuation formula for a defaultable perpetual Russian option in the Black-Scholes market where the default time is modelled as the last passage time of the running maximum of the stock price. In this setting, default occurs when the stock price fails to exceed its historical maximum, leading to a non-stopping time that depends on

Sangmin Woo, Sumin Lee, Yeonju Park, Muhammad Adi Nugroho

Standard multi-modal models assume the use of the same modalities in training and inference stages. However, in practice, the environment in which multi-modal models operate may not satisfy such assumption. As such, their performances degrade drastically if any modality is missing in the inference stage. We ask: how can we train a model that is robust to mis

Aryan Bhambu, Arabin Kumar Dey

In this paper we propose a novel procedure to construct a confidence interval for multivariate time series predictions using long short term memory network. The construction uses a few novel block bootstrap techniques. We also propose an innovative block length selection procedure for each of these schemes. Two novel benchmarks help us to compare the constru

Alejandro Montanez-Barrera, Dennis Willsch, Alberto Maldonado-Romo, Kristel Michielsen

Solving combinatorial optimization problems of the kind that can be codified by quadratic unconstrained binary optimization (QUBO) is a promising application of quantum computation. Some problems of this class suitable for practical applications such as the traveling salesman problem (TSP), the bin packing problem (BPP), or the knapsack problem (KP) have ine

Amir Akbary, Milad Fakhari

We apply the character sums method of Lenstra, Moree, and Stevenhagen, to explicitly compute the constants in the Titchmarsh divisor problem for Kummer fields and for division fields of Serre curves. We derive our results as special cases of a general result on the product expressions for the sums in the form $$\sum_{n=1}^{\infty}\frac{g(n)}{\#G(n)}$$ in whi

Kexin Shi, Yun Zhang, Bingyi Jing, Wenjia Wang

In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs). Effective and efficient negative sampling methods that suit these models, however, remain underdeveloped. One challenge is that existing hard negative samplers tend to suffer fr

KamLAND Collaboration, S. Abe, S. Asami, M. Eizuka

We report a measurement of the strange axial coupling constant $g_A^s$ using atmospheric neutrino data at KamLAND. This constant is a component of the axial form factor of the neutral-current quasielastic (NCQE) interaction. The value of $g_A^s$ significantly changes the ratio of proton and neutron NCQE cross sections. KamLAND is suitable for measuring NCQE

Hohto Bekki

In this paper we study the geodesic continued fraction in the case of the Shimura curve coming from the $(2,3,7)$-triangle group. We construct a certain continued fraction expansion of real numbers using the so-called coding of the geodesics on the Shimura curve, and prove the Lagrange type periodicity theorem for the expansion which captures the fundamental

Qihan Wang, Xinyue Kang, Pei-Luen Patrick Rau

Objective: We aimed to use adaptive psychophysics methods, which is a Bayesian Model, to measure users' time perception of various progress bar quantitatively. Background: Progress bar informs users about the status of ongoing processes. Progress bars frequently display nonuniform speed patterns, such as acceleration and deceleration. However, which progress

Keisuke Okumura, Sébastien Tixeuil

We study a novel graph path planning problem for multiple agents that may crash at runtime, and block part of the workspace. In our setting, agents can detect neighboring crashed agents, and change followed paths at runtime. The objective is then to prepare a set of paths and switching rules for each agent, ensuring that all correct agents reach their destin

Atharv Bhadange, Rohan Doshi, Tanmay Karmarkar, Snehal Shintre

It is observed that users have higher requirements for fairness, transparency, and privacy of transactions of energy exchanges that occur across platforms like Indian Energy Exchange (IEX) and Power Exchange India Limited (PXIL). As a decentralized distributed accounting system, blockchain is characterized by traceability, security, credibility, and non-tamp

Harrison Mitchell, Alexander Norcliffe, Pietro Liò

In the wake of the growing popularity of machine learning in particle physics, this work finds a new application of geometric deep learning on Feynman diagrams to make accurate and fast matrix element predictions with the potential to be used in analysis of quantum field theory. This research uses the graph attention layer which makes matrix element predicti

Zhen Li, Kun Ding, Guancong Ma

The spectrum of a non-Hermitian system generically forms a two-dimensional complex Riemannian manifold with distinct topology from the underlying parameter space. Spectral topology permits parametric loops to map the affiliated eigenvalue trajectories into knots. In this work, through analyzing exceptional points and their topology, we uncover the necessary

Takuma Udagawa, Haruka Kiyohara, Yusuke Narita, Yuta Saito

Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, there is no single estimator that dominates the others, because the estimators' accuracy can vary greatly depending on a given OPE task such as the evaluation policy, number of action

Hidefumi Takahashi, Masaho Onose, Yasuhito Kobayashi, Takahiro Osaka

We systematically synthesized perovskite-type oxides Sr1-xCaxCoO3 containing unusually high valence Co4+ ions by a high pressure technique, and investigated the effect of systematic lattice change on the magnetic and electronic properties. As the Ca content x exceeds about 0.6, the structure changes from cubic to orthorhombic, which is supported by the first

Taoyong Cui, Jianze Li, Yuhan Dong, Li Liu

The orthogonality constraints, including the hard and soft ones, have been used to normalize the weight matrices of Deep Neural Network (DNN) models, especially the Convolutional Neural Network (CNN) and Vision Transformer (ViT), to reduce model parameter redundancy and improve training stability. However, the robustness to noisy data of these models with co

Yu Deng, Baoyuan Wang, Heung-Yeung Shum

A key challenge for novel view synthesis of monocular portrait images is 3D consistency under continuous pose variations. Most existing methods rely on 2D generative models which often leads to obvious 3D inconsistency artifacts. We present a 3D-consistent novel view synthesis approach for monocular portrait images based on a recent proposed 3D-aware GAN, na

Amir Jafari

In this article, we propose using deep learning and transformer architectures combined with classical machine learning algorithms to detect and identify text anomalies in texts. Deep learning model provides a very crucial context information about the textual data which all textual context are converted to a numerical representation. We used multiple machine

Amir Jafari

Unsupervised Machine Learning techniques have been applied to Natural Language Processing tasks and surpasses the benchmarks such as GLUE with great success. Building language models approach achieves good results in one language and it can be applied to multiple NLP task such as classification, summarization, generation and etc as an out of box model. Among

Dimitris Papamichail, Madeline Febinger, Shm Almeda, Georgios Papamichail

Protein variant libraries produced by site-directed mutagenesis are a useful tool utilized by protein engineers to explore variants with potentially improved properties, such as activity and stability. These libraries are commonly built by selecting residue positions and alternative beneficial mutations for each position. All possible combinations are then c

Steven A. Grosz, Anil K. Jain

The use of vision transformers (ViT) in computer vision is increasing due to limited inductive biases (e.g., locality, weight sharing, etc.) and increased scalability compared to other deep learning methods. This has led to some initial studies on the use of ViT for biometric recognition, including fingerprint recognition. In this work, we improve on these i

Xiangyu Xi, Jianwei Lv, Shuaipeng Liu, Wei Ye

Event detection (ED) identifies and classifies event triggers from unstructured texts, serving as a fundamental task for information extraction. Despite the remarkable progress achieved in the past several years, most research efforts focus on detecting events from formal texts (e.g., news articles, Wikipedia documents, financial announcements). Moreover, th

Aditya Thyagarajan, Elías Snorrason, Curtis Northcutt, Jonas Mueller

In multi-label classification, each example in a dataset may be annotated as belonging to one or more classes (or none of the classes). Example applications include image (or document) tagging where each possible tag either applies to a particular image (or document) or not. With many possible classes to consider, data annotators are likely to make errors wh

Peter A. Becker, Michael T. Wolff

We develop a new theoretical model describing the formation of the radiation spectrum in accretion-powered X-ray pulsars as a result of bulk and thermal Comptonization of photons in the accretion column. The new model extends the previous model developed by the authors in four ways: (1) we utilize a conical rather than cylindrical geometry; (2) the radiation

Jean Bénézech, Linus Seelinger, Peter Bastian, Richard Butler

In this paper, the first large-scale application of multiscale-spectral generalized finite element methods (MS-GFEM) to composite aero-structures is presented. The crucial novelty lies in the introduction of A-harmonicity in the local approximation spaces, which in contrast to [Babuska, Lipton, Multiscale Model. Simul. 9, 2011] is enforced more efficiently v

Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, Ves Stoyanov

Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts, but there has been limited understanding of exactly how these explanations function or why they are effective. This work aims to better understand the mechanisms by which explanations are used for in-context learning. We first study the impact of two

Shigeo S. Kimura, Shinsuke Takasao, Kengo Tomida

We investigate gamma-ray emission in the impulsive phase of solar flares and the detectability of non-thermal signatures from protostellar flares. Energetic solar flares emit high-energy gamma rays of GeV energies, but their production mechanism and emission site are still unknown. Young stellar objects, including protostars, also exhibit luminous X-ray flar

Dana Černá, Kateřina Fiňková

The paper focuses on pricing European-style options on several underlying assets under the Black-Scholes model represented by a nonstationary partial differential equation. The proposed method combines the Galerkin method with $L^2$-orthogonal sparse grid spline wavelets and the Crank-Nicolson scheme with Rannacher time-stepping. To this end, we construct an

Fei Xue, Hongzhe Li

The Genotype-Tissue Expression (GTEx) project collects samples from multiple human tissues to study the relationship between genetic variation or single nucleotide polymorphisms (SNPs) and gene expression in each tissue. However, most existing eQTL analyses only focus on single tissue information. In this paper, we develop a multi-tissue eQTL analysis that i

Thomas Ludwig Kaiser, Gregoire Varillon, Wolfgang Polifke, Feichi Zhang

This study performs a linear analysis of a turbulent reacting methane-air jet flame, with the goal of predicting the response of the reacting flow to upstream acoustic actuation. Accounting for heat release fluctuations is a vital component when investigating thermoacoustic instabilities and flame noise in a linearized framework. Unlike previous studies this

Yuxing Qiu, Feng Gao, Minchen Li, Govind Thattai

Recent breakthroughs in Vision-Language (V&L) joint research have achieved remarkable results in various text-driven tasks. High-quality Text-to-video (T2V), a task that has been long considered mission-impossible, was proven feasible with reasonably good results in latest works. However, the resulting videos often have undesired artifacts largely because th

Zheng Ren, Hong Li, He Zhao, Shrinkhala Sharma

Understanding the interplay of structural and electronic symmetry breaking in Fe-based high temperature superconductors remains of high interest. In this work we grow strain-patterned multilayer FeSe thin films in a range of thicknesses using molecular beam epitaxy. We study the formation of electronic nematic domains and spatially-varying strain using scann

Shiqi Zhou, Erbil Gügercinoğlu, Jianping Yuan, Mingyu Ge

$\sim 6\%$ of all known pulsars have been observed to exhibit sudden spin-up events, known as glitches. For more than fifty years, these phenomena have played an important role in helping to understand pulsar (astro)physics. Based on the review of pulsar glitches search method, the progress made in observations in recent years is summarized, including the ac

Masahiro O. Takahashi, Masahiko G. Yamada, Masafumi Udagawa, Takeshi Mizushima

In the Kitaev chiral spin liquid, Ising anyons are realized as $Z_2$ fluxes binding Majorana zero modes, which, however, are thermal excitations with finite decay rates. On the other hand, a lattice vacancy traps a $Z_2$ flux even in the ground state, resulting in the stable realization of a Majorana zero mode in a vacancy. We demonstrate that spin-spin corr

Tianyin Wang, Jianwei Wang, Ziqian Zeng

The superior performance of supervised relation extraction (RE) methods heavily relies on a large amount of gold standard data. Recent zero-shot relation extraction methods converted the RE task to other NLP tasks and used off-the-shelf models of these NLP tasks to directly perform inference on the test data without using a large amount of RE annotation data

Ryan Hildebrant, Quoc-Tung Le, Duy-Hoang Ta, Hoa T. Vu

We revisit the problem of finding small $\epsilon$-separation keys introduced by Motwani and Xu (2008). In this problem, the input is $m$-dimensional tuples $x_1,x_2,\ldots,x_n $. The goal is to find a small subset of coordinates that separates at least $(1-\epsilon){n \choose 2}$ pairs of tuples. They provided a fast algorithm that runs on $\Theta(m/\epsilo

Genggeng Huang, Yating Niu

In this paper, we classify the solution of the following mixed-order conformally invariant system with coupled nonlinearity in $ \mathbb{R}^4$: \begin{equation}\left\{ \begin{aligned} & -\Delta u(x) = u^{p_1}(x) e^{q_1v(x)}, \quad x\in \mathbb{R}^4,\\ & (-\Delta)^2 v(x) = u^{p_2}(x) e^{q_2v(x)}, \quad x\in \mathbb{R}^4, \end{aligned} \right. \end{equation} w

A. Arul Anne Elden, M. Ponmurugan

The effect of opposite sign of interactions in a single-walled Ising nanotube is investigated using the Wang-Landau algorithm. The thermodynamic observables are calculated from the estimated density of states (DOS) with and without the presence of an external magnetic field. Irrespective of the applied magnetic field, a symmetric trend of DOS is observed for

Abodh Poudyal, Anamika Dubey

This paper presents sensitivity analyses of resilience-based active distribution system planning solutions with respect to different parameters. The distribution system planning problem is formulated as a two-stage risk-averse stochastic optimization model with conditional value-at-risk (CVaR) as the risk measure. The probabilistic scenarios are obtained usi

Xupeng Miao, Yujie Wang, Youhe Jiang, Chunan Shi

Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) models. However, how to train these models over multiple GPUs efficiently is still challenging due to a large number of parallelism choices. Existing DL systems either rely on manual e

Jian Li, Zhihong Jeff Xia, Fumi Yoshida, Nikolaos Georgakarakos

Context. More than 10000 Jupiter Trojans have been detected so far. They are moving around the L4 and L5 triangular Lagrangian points of the Sun-Jupiter system and their distributions can provide important clues to the early evolution of the Solar System. Aims. The number asymmetry of the L4 and L5 Jupiter Trojans is a longstanding problem. We aim to test a

Yuan Yao

Given a contact three manifold $Y$ with a nondegenerate contact form $\lambda$, and an almost complex structure $J$ compatible with $\lambda$, its embedded contact homology $ECH(Y,\lambda)$ is defined (arXiv:1303.5789) and only depends on the contact structure. In this paper we explain how to compute ECH for Morse-Bott contact forms whose Reeb orbits appear

Haoran Bai, Di Kang, Haoxian Zhang, Jinshan Pan

We present a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions, which are desired characteristics for rendering realistic 3D face models under different lighting conditions. The dataset is derived from a large-scale face image dataset namely FF

Yuxin Jiang, Linhan Zhang, Wei Wang

Due to the absence of explicit connectives, implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis. The critical step for IDRR is to learn high-quality discourse relation representations between two arguments. Recent methods tend to integrate the whole hierarchical information of senses into discourse relation represe

Min Jun Jo, Junha Kim

We give a vorticity-dynamical proof of $C^1\cap H^2$-illposedness of the 2D Euler equations. Our construction shows that the unique Yudovich solution escapes both $C^1$ and $H^2$ instantaneously.

Junichiro Yoshida, Nakahiro Yoshida

The purpose of this article is to develop a general parametric estimation theory that allows the derivation of the limit distribution of estimators in non-regular models where the true parameter value may lie on the boundary of the parameter space or where even identifiability fails. For that, we propose a more general local approximation of the parameter sp

Zhaoxuan Zhang, Lu Huang, Yang Liu, Si-Yu Li

Time-ordered data (TOD) from ground-based CMB experiments are generally filtered before map-making to remove or reduce the contamination from the ground and the atmospheric emissions. However, when the observation region contains strong point sources, the filtering process will result in considerable leakage around the point sources in a measured CMB map, an

Yang Yang, Yao Wang, Zhibo Jiang, Zhiwei Chen

The study of the physical and chemical properties of gas infall motion in the molecular clumps helps us understand the initial stages of star formation. We used the FTS wide-sideband mode of the IRAM 30-m telescope to observe nine infall sources with significant double peaked blue line profile. The observation frequency range are 83.7 - 91.5 GHz and 99.4 - 1

Xuan Shi, Erica Cooper, Xin Wang, Junichi Yamagishi

With the similarity between music and speech synthesis from symbolic input and the rapid development of text-to-speech (TTS) techniques, it is worthwhile to explore ways to improve the MIDI-to-audio performance by borrowing from TTS techniques. In this study, we analyze the shortcomings of a TTS-based MIDI-to-audio system and improve it in terms of feature c

Chang hoon Song, Geonho Hwang, Jun ho Lee, Myungjoo Kang

A recurrent neural network (RNN) is a widely used deep-learning network for dealing with sequential data. Imitating a dynamical system, an infinite-width RNN can approximate any open dynamical system in a compact domain. In general, deep networks with bounded widths are more effective than wide networks in practice; however, the universal approximation theor

Kushaagra Gupta

Investing in children and youth is a critical step towards inclusive, equitable, and sustainable development for current and future generations. Several international agendas for accomplishing common global goals emphasize the need for active youth participation and engagement for sustainable development. The 2030 Agenda for Sustainable Development emphasize

Pei Zhang, Baosong Yang, Haoran Wei, Dayiheng Liu

Neural machine translation (NMT) is often criticized for failures that happen without awareness. The lack of competency awareness makes NMT untrustworthy. This is in sharp contrast to human translators who give feedback or conduct further investigations whenever they are in doubt about predictions. To fill this gap, we propose a novel competency-aware NMT by

Alexander Bertoloni Meli, Masao Oi

This article is on the parametrization of the local Langlands correspondence over local fields for non-quasi-split groups according to the philosophy of Vogan. We show that a parametrization indexed by the basic part of the Kottwitz set (which is an extension of the set of pure inner twists) implies a parametrization indexed by the full Kottwitz set. On the

Koji Hashimoto, Daichi Takeda, Koichiro Tanaka, Shingo Yonezawa

We propose a way to discover, in tabletop experiments, spacetime-emergent materials, that is, materials holographically dual to higher-dimensional quantum gravity systems under the AdS/CFT correspondence. The emergence of the holographic spacetime is verified by a mathematical imaging transform of the response function on the material. We consider theories o

Jing Guo, Maria Lopez-Fernandez

We consider the application of the generalized Convolution Quadrature (gCQ) to approximate the solution of an important class of sectorial problems. The gCQ is a generalization of Lubich's Convolution Quadrature (CQ) that allows for variable steps. The available stability and convergence theory for the gCQ requires non realistic regularity assumptions on the

Tatsuki Nakajima, Tadakatsu Sakai, Ryo Yokokura

We investigate $2n$-dimensional axion electrodynamics for the purpose of exploring a higher-group structure underlying it. This is manifested as a Green-Schwarz transformation of the background gauge fields that couple minimally to the conserved currents. The $n=3$ case is studied most intensively. We derive the identities of correlation functions among the

Mao V. Ngo, Tram Truong-Huu, Dima Rabadi, Jia Yi Loo

In malware detection, dynamic analysis extracts the runtime behavior of malware samples in a controlled environment and static analysis extracts features using reverse engineering tools. While the former faces the challenges of anti-virtualization and evasive behavior of malware samples, the latter faces the challenges of code obfuscation. To tackle these dr

Yiqun Chen, Qiang Chen, Qinghao Hu, Jian Cheng

Fully convolutional detectors discard the one-to-many assignment and adopt a one-to-one assigning strategy to achieve end-to-end detection but suffer from the slow convergence issue. In this paper, we revisit these two assignment methods and find that bringing one-to-many assignment back to end-to-end fully convolutional detectors helps with model convergenc

Shubham Gupta, Jeet Kanjani, Mengtian Li, Francesco Ferroni

We focus on the task of far-field 3D detection (Far3Det) of objects beyond a certain distance from an observer, e.g., $>$50m. Far3Det is particularly important for autonomous vehicles (AVs) operating at highway speeds, which require detections of far-field obstacles to ensure sufficient braking distances. However, contemporary AV benchmarks such as nuScenes

Zihao Li, Chunhua Wu, Shenglin Wu, Wenbo Wan

In recent years, some researchers focused on using a single image to obtain a large number of samples through multi-scale features. This study intends to a brand-new idea that requires only ten or even fewer samples to construct the low-rank structural-Hankel matrices-assisted score-based generative model (SHGM) for color image inpainting task. During the pr

Shubham Gupta, Rahul Kunigal Ravishankar, Madhoolika Gangaraju, Poojasree Dwarkanath

Image inpainting is the process of regenerating lost parts of the image. Supervised algorithm-based methods have shown excellent results but have two significant drawbacks. They do not perform well when tested with unseen data. They fail to capture the global context of the image, resulting in a visually unappealing result. We propose a novel self-supervised

Koji Yamazaki

A condensed set is a sheaf on the site of Stone spaces and continuous maps. We prove that condensed sets are equivalent to sheaves on the site of compact Hausdorff spaces and continuous maps. As an application, we show that there exists a model structure on the category of condensed sets.

Kenan Jiang, Xuehai He, Ruize Xu, Xin Eric Wang

Contrastive Language-Image Pretraining (CLIP) has demonstrated great zero-shot performance for matching images and text. However, it is still challenging to adapt vision-lanaguage pretrained models like CLIP to compositional image and text matching -- a more challenging image and text matching task requiring the model understanding of compositional word conc