July 2022 arXiv papers — page 107

An-An Lu, Yan Chen, Xiqi Gao

In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two dimensional (2D) beam based statistical channel model (BSCM) in massive MIMO systems.The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple receive pilot signals. A receive model shows the relation between the

Feiyu Chen, Xiangyu Jiang, Ying Chen, Ming Gong

We present the first theoretical prediction of the production rate of $1^{-+}$ light hybrid meson $\eta_1$ in $J/\psi$ radiative decays. In the $N_f=2$ lattice QCD formalism with the pion mass $m_\pi\approx 350$ MeV, the related electromagnetic multipole form factors are extracted from the three-point functions that involve necessarily quark annihilation dia

Yixiong Liang, Shuo Feng, Qing Liu, Hulin Kuang

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relatio

Owen Millwood, Jack Miskelly, Bohao Yang, Prosanta Gope

As the demand for highly secure and dependable lightweight systems increases in the modern world, Physically Unclonable Functions (PUFs) continue to promise a lightweight alternative to high-cost encryption techniques and secure key storage. While the security features promised by PUFs are highly attractive for secure system designers, they have been shown t

Calum Spring-Turner, Raj Thilak Rajan

Mega-constellations in Low Earth Orbit have the potential to revolutionise worldwide internet access. The concomitant potential of these mega-constellations to impact space sustainability, however, has prompted concern from space actors as well as provoking concern in the ground-based astronomy community. Increasing the knowledge of the orbital state of sate

Zhaohua Chen, Chang Wang, Qian Wang, Yuqi Pan

In today's online advertising markets, a crucial requirement for an advertiser is to control her total expenditure within a time horizon under some budget. Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in. This paper provides a

Franc Forstneric

We show that if $\Omega$ is an $m$-convex domain in $\mathbb R^n$ for some $2\le m<n$ whose boundary $b\Omega$ has a tubular neighbourhood of positive radius and is not $m$-flat near infinity, then $\Omega$ does not contain any immersed parabolic minimal submanifold of dimension $\ge m$. In particular, if $M$ is a properly embedded nonflat minimal hypersurfa

Alexei Vazquez, Iacopo Pozzana, Georgios Kalogridis, Christos Ellinas

Projects are characterised by activity networks with a critical path, a sequence of activities from start to end, that must be finished on time to complete the project on time. Watching over the critical path is the project manager's strategy to ensure timely project completion. This intense focus on a single path contrasts the broader complex structure of t

Abhijit Sen, Bikram Keshari Parida, Shailesh Dhasmana, Zurab K. Silagadze

The Eisenhart lift establishes a fascinating connection between non-relativistic and relativistic physics, providing a space-time geometric understanding of non-relativistic Newtonian mechanics. What is still little known, however, is the fact that there is a Hilbert space representation of classical mechanics (also called Koopman-von Neumann mechanics) that

Xuanzhao Gao, Zecheng Gan

We report spontaneous symmetry breaking (SSB) phenomena in symmetrically charged binary particle systems under planar nanoconfinement with negative dielectric constants.The SSB is triggered $solely$ via the dielectric confinement effect, without any external fields. The mechanism of SSB is found to be caused by the strong polarization field enhanced by nanoc

Prateek Varshney, Abhradeep Thakurta, Prateek Jain

We study the problem of differentially private linear regression where each data point is sampled from a fixed sub-Gaussian style distribution. We propose and analyze a one-pass mini-batch stochastic gradient descent method (DP-AMBSSGD) where points in each iteration are sampled without replacement. Noise is added for DP but the noise standard deviation is e

Run Jiang, Yonglin Li, Haijun Wu, Jun Zou

A nonlinear Helmholtz equation (NLH) with high wave number and Sommerfeld radiation condition is approximated by the perfectly matched layer (PML) technique and then discretized by the linear finite element method (FEM). Wave-number-explicit stability and regularity estimates and the exponential convergence are proved for the nonlinear truncated PML problem.

Alan J. X. Guo, Cong Liang, Qing-Hu Hou

Storing information in DNA molecules is of great interest because of its advantages in longevity, high storage density, and low maintenance cost. A key step in the DNA storage pipeline is to efficiently cluster the retrieved DNA sequences according to their similarities. Levenshtein distance is the most suitable metric on the similarity between two DNA seque

Henrik Nordström, Lennart Söder, Robert Eriksson

With increasing penetration of variable renewable energy sources (vRES) and a higher rate of electrification in the society, there will be future challenges in maintaining a continuous balance between electricity supply and demand. To investigate the possibility of different technologies to provide balancing services, to dimension future balancing services a

Temma Hanyuda, Soichiro Mori, Sotaro Sugishita

We consider the target space entanglement in quantum mechanics of non-interacting fermions at finite temperature. Unlike pure states investigated in arXiv:2105.13726, the (R\'enyi) entanglement entropy for thermal states does not follow a simple bound because all states in the infinite-dimensional Hilbert space are involved. We investigate a general formula

Karsten Kruse

We study Saks spaces of functions with values in a normed space and the associated mixed topologies. We are interested in properties of such Saks spaces and mixed topologies which are relevant for applications in the theory of bi-continuous semigroups. In particular, we are interested if such Saks spaces are complete, semi-Montel, C-sequential or a (strong)

Sen Zhang, Jing Zhang, Dacheng Tao

Unsupervised monocular depth and ego-motion estimation has drawn extensive research attention in recent years. Although current methods have reached a high up-to-scale accuracy, they usually fail to learn the true scale metric due to the inherent scale ambiguity from training with monocular sequences. In this work, we tackle this problem and propose DynaDept

Liza Afeef, Abuu B. Kihero, Hüseyin Arslan

When using ultra-wideband (UWB) signaling on massive multiple-input multiple-output (mMIMO) systems, the electromagnetic wave at each array element incurs an extra propagation delay comparable to (or larger than) the symbol duration, producing a shift in beam direction known as beam squint. The beam squinting problem degrades the array gain and reduces the s

S. P. Glasby, Ferdinand Ihringer, Sam Mattheus

Given positive integers $e_1,e_2$, let $X_i$ denote the set of $e_i$-dimensional subspaces of a fixed finite vector space $V=(\mathbb{F}_q)^{e_1+e_2}$. Let $Y_i$ be a non-empty subset of $X_i$ and let $\alpha_i=|Y_i|/|X_i|$. We give a positive lower bound, depending only on $\alpha_1,\alpha_2,e_1,e_2,q$, for the proportion of pairs $(S_1,S_2)\in Y_1\times Y_

Beatriz Elizaga Navascués, Alejandro García-Quismondo, Guillermo A. Mena Marugán

We consider a general choice of integration constants in the resolution of the dynamical equations derived from a recently proposed effective model that describes black hole spacetimes in the context of loop quantum cosmology. The interest of our analysis is twofold. On the one hand, it allows for a study of the entire space of solutions of the model, which

Zhuo Li, Runqiu Xiao, Hangting Chen, Zhenduo Zhao

This paper describes the systems developed by the HCCL team for the NIST 2021 speaker recognition evaluation (NIST SRE21).We first explore various state-of-the-art speaker embedding extractors combined with a novel circle loss to obtain discriminative deep speaker embeddings. Considering that cross-channel and cross-linguistic speaker recognition are the key

Jeonghun Baek, Yusuke Matsui, Kiyoharu Aizawa

Recognizing irregular texts has been a challenging topic in text recognition. To encourage research on this topic, we provide a novel comic onomatopoeia dataset (COO), which consists of onomatopoeia texts in Japanese comics. COO has many arbitrary texts, such as extremely curved, partially shrunk texts, or arbitrarily placed texts. Furthermore, some texts ar

Muskan Garg, Chandni Saxena, Veena Krishnan, Ruchi Joshi

Research community has witnessed substantial growth in the detection of mental health issues and their associated reasons from analysis of social media. We introduce a new dataset for Causal Analysis of Mental health issues in Social media posts (CAMS). Our contributions for causal analysis are two-fold: causal interpretation and causal categorization. We in

Shi Hanyu, Wei Jiacheng, Wang Hao, Liu Fayao

LiDAR-based 3D scene perception is a fundamental and important task for autonomous driving. Most state-of-the-art methods on LiDAR-based 3D recognition tasks focus on single frame 3D point cloud data, and the temporal information is ignored in those methods. We argue that the temporal information across the frames provides crucial knowledge for 3D scene perc

NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the 200 language barri

Andrea Dotto, Matthew Emerton, Toby Gee

We show that the category of smooth representations of GL_2(Q_p) on p-power torsion modules localizes over a certain projective scheme, and give some applications.

Jin-Yu Liu, Guang-Quan Luo, Xiao-Qiong Wang, Andreas Hemmerich

Hexagonal optical lattices offer a tunable platform to study exotic orbital physics in solid state materials. Here, we present a versatile high-precision scheme to implement a hexagonal optical lattice potential, which is engineered by overlapping two independent triangular optical sublattices generated by laser beams with slightly different wavelengths arou

Zhen-Yuan Wu, Ryo Saito, Nobuyuki Sakai

The measurement of the inflationary stochastic gravitational-wave background (SGWB) is one of the main goals of future GW experiments. In direct GW experiments, an obstacle to achieving it is the isolation of the inflationary SGWB from the other types of SGWB. In this paper, as a distinguishable signature of the inflationary SGWB, we argue the detectability

Guowen Xu, Xingshuo Han, Anguo Zhang, Tianwei Zhang

The deep learning (DL) technology has been widely used for image classification in many scenarios, e.g., face recognition and suspect tracking. Such a highly commercialized application has given rise to intellectual property protection of its DL model. To combat that, the mainstream method is to embed a unique watermark into the target model during the train

Elena Losero, Somanath Jagannath, Maurizio Pezzoli, Valentin Goblot

Monitoring neuronal activity with simultaneously high spatial and temporal resolution in living cell cultures is crucial to advance understanding of the development and functioning of our brain, and to gain further insights in the origin of brain disorders. While it has been demonstrated that the quantum sensing capabilities of nitrogen-vacancy (NV) centers

Caleb Miller

The BABAR collaboration has demonstrated the first application of the new Tau Polarimetry technique. This polarimetry technique exploits the kinematic coupling of $\tau$ decay products to the spin states of the $\tau$ and initial state electron, to precisely determine the average beam polarization in an $e^+e^-$ collider. The Tau Polarimetry technique is exp

Gerassimos Barbatis, Konstantinos T. Gkikas, Achilles Tertikas

Let $\Omega$ be a bounded domain in $\mathbb{R}^N$ with $C^2$ boundary and let $K\subset\partial\Omega$ be either a $C^2$ submanifold of the boundary of codimension $k<N$ or a point. In this article we study various problems related to the Schr\"odinger operator $L_{\mu} =-\Delta - \mu d_K^{-2}$ where $d_K$ denotes the distance to $K$ and $\mu\leq k^2/4$. We

Takashi Goda, Kosuke Suzuki

We study randomized quasi-Monte Carlo integration by scrambled nets. The scrambled net quadrature has long gained its popularity because it is an unbiased estimator of the true integral, allows for a practical error estimation, achieves a high order decay of the variance for smooth functions, and works even for $L^p$-functions with any $p\geq 1$. The varianc

Kenta Tsukuura

Some models of combinatorial principles have been obtained by collapsing a huge cardinal in the case of the successors of regular cardinals. For example, saturated ideals, Chang's conjecture, polarized partition relations, and transfer principles for chromatic numbers of graphs. In this paper, we study these in the case of the successors of singular cardinal

Ulrich Langer, Richard Löscher, Olaf Steinbach, Huidong Yang

We consider standard tracking-type, distributed elliptic optimal control problems with $L^2$ regularization, and their finite element discretization. We are investigating the $L^2$ error between the finite element approximation $u_{\varrho h}$ of the state $u_\varrho$ and the desired state (target) $\bar{u}$ in terms of the regularization parameter $\varrho$

Kunran Xu, Huawei Zhang, Yishi Li, Yuhao Zhang

Tiny machine learning (TinyML), executing AI workloads on resource and power strictly restricted systems, is an important and challenging topic. This brief firstly presents an extremely tiny backbone to construct high efficiency CNN models for various visual tasks. Then, a specially designed neural co-processor (NCP) is interconnected with MCU to build an ul

S. Charpentier, N. Levenberg, F. Wielonsky

Let $\Gamma \subset \mathbb C$ be a curve of class $C(2,\alpha)$. For $z_{0}$ in the unbounded component of ${\mathbb C}\setminus \Gamma$, and for $n=1,2,...$, let $\nu_n$ be a probability measure with supp$(\nu_{n})\subset \Gamma$ which minimizes the Bergman function $B_{n}(\nu,z):=\sum_{k=0}^{n}|q_{k}^{\nu}(z)|^{2}$ at $z_{0}$ among all probability measure

Piyali Saha, Maheswar G., D. K. Ojha, Tapas Baug

Bright-rimmed clouds (BRCs) are excellent laboratories to explore the radiation-driven implosion mode of star formation because they show evidence of triggered star formation. In our previous study, BRC 18 has been found to accelerate away from the direction of the ionizing Hii region because of the well known "Rocket Effect". Based on the assumption that bo

Wuhang Lin, Shasha Li, Chen Zhang, Bin Ji

Text summarization models are often trained to produce summaries that meet human quality requirements. However, the existing evaluation metrics for summary text are only rough proxies for summary quality, suffering from low correlation with human scoring and inhibition of summary diversity. To solve these problems, we propose SummScore, a comprehensive metri

Naoki Makishima, Satoshi Suzuki, Atsushi Ando, Ryo Masumura

In this paper, we investigate the semi-supervised joint training of text to speech (TTS) and automatic speech recognition (ASR), where a small amount of paired data and a large amount of unpaired text data are available. Conventional studies form a cycle called the TTS-ASR pipeline, where the multispeaker TTS model synthesizes speech from text with a referen

Jiafeng Liu, Haoyang Shi, Siyuan Zhang, Yin Yang

Quantization has proven effective in high-resolution and large-scale simulations, which benefit from bit-level memory saving. However, identifying a quantization scheme that meets the requirement of both precision and memory efficiency requires trial and error. In this paper, we propose a novel framework to allow users to obtain a quantization scheme by simp

Satoshi Osawa

The initial value problem for two-dimensional Zakharov-Kuznetsov equation on periodic boundary setting is shown to be locally well-posed in the cylinder for 9/10 < s < 1. We prove this theorem by using bilinear estimates thinking separetely the first variable and the second variable of space.

Mengxue Du, Shasha Li, Jie Yu, Jun Ma

Document retrieval enables users to find their required documents accurately and quickly. To satisfy the requirement of retrieval efficiency, prevalent deep neural methods adopt a representation-based matching paradigm, which saves online matching time by pre-storing document representations offline. However, the above paradigm consumes vast local storage sp

Jiacheng Wang, Yueming Jin, Liansheng Wang

Medical image segmentation under federated learning (FL) is a promising direction by allowing multiple clinical sites to collaboratively learn a global model without centralizing datasets. However, using a single model to adapt to various data distributions from different sites is extremely challenging. Personalized FL tackles this issue by only utilizing pa

Ilkka Helenius, Marina Walt, Werner Vogelsang

Nuclear parton distribution functions (nPDFs) can be determined in a global QCD analysis using a wide range of experimental data. In addition to older fixed-target deep inelastic scattering and Drell-Yan (DY) dilepton production data, several analyses from p+Pb collisions at the LHC provide further constraints and extend the kinematic reach of applicable dat

Joseph W. Kania, Kevin Bandura, Duncan R. Lorimer, Richard Prestage

Radio Frequency Interference (RFI) greatly reduces sensitivity of radio observations to astrophysical signals and creates false positive candidates in searches for radio transients. Real signals are missed while considerable computational and human resources are needed to remove RFI candidates. Effective RFI removal is vital to carry out successful searches

Paul Seibert, Alexander Raßloff, Karl Kalina, Marreddy Ambati

Microstructure characterization and reconstruction (MCR) is an important prerequisite for empowering and accelerating integrated computational materials engineering. Much progress has been made in MCR recently, however, in absence of a flexible software platform it is difficult to use ideas from other researchers and to develop them further. To address this

Lalita Kumari, Sukhdeep Singh, VVS Rathore, Anuj Sharma

Cursive handwritten text recognition is a challenging research problem in the domain of pattern recognition. The current state-of-the-art approaches include models based on convolutional recurrent neural networks and multi-dimensional long short-term memory recurrent neural networks techniques. These methods are highly computationally extensive as well model

Anaïs Fopma, Mingyang Cai, Stef van Buuren, Gerko Vink

Curve matching is a prediction technique that relies on predictive mean matching, which matches donors that are most similar to a target based on the predictive distance. Even though this approach leads to high prediction accuracy, the predictive distance may make matches look unconvincing, as the profiles of the matched donors can substantially differ from

Daichi Amagata, Takahiro Hara

Clustering multi-dimensional points is a fundamental task in many fields, and density-based clustering supports many applications as it can discover clusters of arbitrary shapes. This paper addresses the problem of Density-Peaks Clustering (DPC), a recently proposed density-based clustering framework. Although DPC already has many applications, its straightf

Caigao Jiang, Siqiao Xue, James Zhang, Lingyue Liu

Learning user sequence behaviour embedding is very sophisticated and challenging due to the complicated feature interactions over time and high dimensions of user features. Recent emerging foundation models, e.g., BERT and its variants, encourage a large body of researchers to investigate in this field. However, unlike natural language processing (NLP) tasks

Zvi Schreiber

It is well known that the R, the set of real numbers, is an abstract set, where almost all its elements cannot be described in any finite language. We investigate possible approaches to what might be called an epi-constructionist approach to mathematics. While most constructive mathematics is concerned with constructive proofs, the agenda here is that the ob

Yanqing Liu, Ruiqing Xue, Lei He, Xu Tan

Current text to speech (TTS) systems usually leverage a cascaded acoustic model and vocoder pipeline with mel-spectrograms as the intermediate representations, which suffer from two limitations: 1) the acoustic model and vocoder are separately trained instead of jointly optimized, which incurs cascaded errors; 2) the intermediate speech representations (e.g.

Jiayi Ouyang, Yuxuan Liao, Zhiyao Ma, Deyang Kong

The photonic Ising machine is a new paradigm of optical computing that takes advantage of the unique properties of light wave propagation, parallel processing, and low-loss transmission. Thus, the process of solving combinatorial optimization problems can be accelerated through photonic/optoelectronic devices, but implementing photonic Ising machines that ca

Shixu Meng

This paper investigates the inverse source problem with a single propagating mode at multiple frequencies in an acoustic waveguide. The goal is to provide both theoretical justifications and efficient algorithms for imaging extended sources using the sampling methods. In contrast to the existing far/near field operator based on the integral over the space va

Minoru Wakimoto

In this paper we study the branching functions of tensor products of N=3 superconformal modules.

Chanania Steinbock, Eytan Katzav, Arezki Boudaoud

We propose a mathematical model to describe the athermal fluctuations of thin sheets driven by the type of random driving that might be experienced prior to weak crumpling. The model is obtained by merging the F\"oppl-von K\'arm\'an equations from elasticity theory with techniques from out-of-equilibrium statistical physics to obtain a nonlinear strongly cou

Afi Maha, Sania Asif, Chouaibi Sami, Basdouri Imed

In the present paper, we define the new class of representation on $n$-Lie algebra that is called as generalized representation. We study the cohomology theory corresponding to generalized representations of $n$-Lie algebras and show its relation with the cohomology corresponding to the usual representations. Furthermore, we provide the computation for the l

Parveen, Jitender Kumar

The enhanced power graph $\mathcal{P}_E(G)$ of a finite group $G$ is the simple undirected graph whose vertex set is $G$ and two distinct vertices $x, y$ are adjacent if $x, y \in \langle z \rangle$ for some $z \in G$. In this article, we give an affirmative answer of the question posed by Cameron [6] which states that: Is it true that the complement of the

Yuhong Liu, Changyang She, Yi Zhong, Wibowo Hardjawana

In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks. To obtain time diversity within the channel coherence time, we first put forward a random repetition scheme that randomizes the interference power. Then, we optimize the number of reserved slots

Masahiro Hoshino, Ryuna Nagayama, Kohei Yoshimura, Jumpei F. Yamagishi

We derived a new speed limit in population dynamics, which is a fundamental limit on the evolutionary rate. By splitting the contributions of selection and mutation to the evolutionary rate, we obtained the new bound on the speed of arbitrary observables, named the selection bound, that can be tighter than the conventional Cram\'{e}r--Rao bound. Remarkably,

Tianwen Zhang, Xiaoling Zhang

How to fully utilize polarization to enhance synthetic aperture radar (SAR) ship classification remains an unresolved issue. Thus, we propose a dual-polarization information guided network (DPIG-Net) to solve it.

Carl Qi, Xingyu Lin, David Held

Deformable object manipulation has many applications such as cooking and laundry folding in our daily lives. Manipulating elastoplastic objects such as dough is particularly challenging because dough lacks a compact state representation and requires contact-rich interactions. We consider the task of flattening a piece of dough into a specific shape from RGB-

Muqiao Yang, Ian Lane, Shinji Watanabe

Continual Learning, also known as Lifelong Learning, aims to continually learn from new data as it becomes available. While prior research on continual learning in automatic speech recognition has focused on the adaptation of models across multiple different speech recognition tasks, in this paper we propose an experimental setting for \textit{online continu

Guowen Xu, Xingshuo Han, Tianwei Zhang, Shengmin Xu

In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the server learns nothing. This problem is first formulated by Lehmkuhl \textit{et al.} with a solution (MUSE, Usenix Security'21), whose performance is then substantially improved by Chan

Sandeep Sharma, Alec F. White, Gregory Beylkin

In this article we present an algorithm to efficiently evaluate the exchange matrix in periodic systems when Gaussian basis set with pseudopotentials are used. The usual algorithm for evaluating exchange matrix scales cubically with the system size because one has to perform O(N2) fast Fourier transforms (FFT). Here we introduce an algorithm that retains the

Victor Sam Moses Babu K., Pratyush Chakraborty, Enrique Baeyens, Pramod P. Khargonekar

Incentive programs and ongoing reduction in costs are driving joint installation of solar PV panels and storage systems in residential households. There is a need for optimal investment decisions to reduce the electricity consumption costs of the households further. In this paper, we first develop analytical expression of storage investment decision and then

Hongjian Fang, Yi Zeng, Jianbo Tang, Yuwei Wang

How neural networks in the human brain represent commonsense knowledge, and complete related reasoning tasks is an important research topic in neuroscience, cognitive science, psychology, and artificial intelligence. Although the traditional artificial neural network using fixed-length vectors to represent symbols has gained good performance in some specific

Emiko Hiyama, Rimantas Lazauskas, Jaume Carbonell

We have investigated the possible existence of a $^7$H resonant state, considered as a five-body system consisting of a $^3$H core with four valence neutrons. To this aim, an effective n-$^3$H potential is constructed in order to reproduce the low energy elastic neutron scattering on $^3$H phase shifts and the $^5$H resonant ground state in terms of $^3$H-n-

Florian Sigger, Hendrik Lambers, Nisi Katharina, Julian Klein

Semiconducting two-dimensional materials and their heterostructures gained a lot of interest for applications as well as fundamental studies due to their rich optical properties. Assembly in van der Waals heterostacks can significantly alter the intrinsic optical properties as well as the wavelength-dependent absorption and emission efficiencies making a dir

Xiang Xu, Karl D. D. Willis, Joseph G. Lambourne, Chin-Yi Cheng

We present SkexGen, a novel autoregressive generative model for computer-aided design (CAD) construction sequences containing sketch-and-extrude modeling operations. Our model utilizes distinct Transformer architectures to encode topological, geometric, and extrusion variations of construction sequences into disentangled codebooks. Autoregressive Transformer

Firuz Kamalov, Amir F. Atiya, Dina Elreedy

Imbalanced data is a frequently encountered problem in machine learning. Despite a vast amount of literature on sampling techniques for imbalanced data, there is a limited number of studies that address the issue of the optimal sampling ratio. In this paper, we attempt to fill the gap in the literature by conducting a large scale study of the effects of samp

Yi Ma, Doris Tsao, Heung-Yeung Shum

Ten years into the revival of deep networks and artificial intelligence, we propose a theoretical framework that sheds light on understanding deep networks within a bigger picture of Intelligence in general. We introduce two fundamental principles, Parsimony and Self-consistency, that address two fundamental questions regarding Intelligence: what to learn an

Himanshu Gupta, Vladislav Taranchuk

Let $q = p^e$, where $p$ is a prime and $e$ is a positive integer. The family of graphs $D(k, q)$, defined for any positive integer $k$ and prime power $q$, were introduced by Lazebnik and Ustimenko in 1995. To this day, the connected components of the graphs $D(k, q)$, provide the best known general lower bound for the size of a graph of given order and giv

Karl Winsor

We show the existence of a dense orbit for real Rel flows on the area-1 locus of every connected component of every stratum of holomorphic 1-forms with at least 2 distinct zeros. For this purpose, we establish a general density criterion for ${\rm SL}(2,\mathbb{R})$-orbit closures, based on finding an orbit of a real Rel flow whose closure contains a horocyc

Soumyadip Acharyya, Rakesh Bharati, Atasi Deb Ray, Sudip Kumar Acharyya

For a measurable space ($X,\mathcal{A}$), let $\mathcal{M}(X,\mathcal{A})$ be the corresponding ring of all real valued measurable functions and let $\mu$ be a measure on ($X,\mathcal{A}$). In this paper, we generalize the so-called $m_{\mu}$ and $U_{\mu}$ topologies on $\mathcal{M}(X,\mathcal{A})$ via an ideal $I$ in the ring $\mathcal{M}(X,\mathcal{A})$. T

Zhi Jiang

We apply Angehrn-Siu-Helmke's method to estimate basepoint freeness thresholds of higher dimensional polarized abelian varieties. We showed that a conjecture of Caucci holds for very general polarized abelian varieties in the moduli spaces $\mathcal A_{g, l}$ with only finitely many possible exceptions of polarization types $l$ in each dimension $g$. We impr

Hisashi Ogawa, Tatsuya Kemmochi, Tetsushi Takano

We report on an interference-filter-stabilized external cavity diode laser using a gallium-nitride-based violet laser diode. Surface-activated-bonded glass substrates were employed as cat's eye output couplers in order to suppress power degradation due to optical damage. From the results of a long-term frequency-stabilization test, mode-hop-free operation fo

Yashael Faith Arthanto, David Ojika, Joo-Young Kim

By providing highly efficient one-sided communication with globally shared memory space, Partitioned Global Address Space (PGAS) has become one of the most promising parallel computing models in high-performance computing (HPC). Meanwhile, FPGA is getting attention as an alternative compute platform for HPC systems with the benefit of custom computing and de

Dooseop Choi, KyoungWook Min

Variational autoencoder (VAE) has widely been utilized for modeling data distributions because it is theoretically elegant, easy to train, and has nice manifold representations. However, when applied to image reconstruction and synthesis tasks, VAE shows the limitation that the generated sample tends to be blurry. We observe that a similar problem, in which

Junjie He, Zhihang Xu, Qifeng Liao

In this paper, we present a deep neural network based adaptive learning (DNN-AL) approach for switched systems. Currently, deep neural network based methods are actively developed for learning governing equations in unknown dynamic systems, but their efficiency can degenerate for switching systems, where structural changes exist at discrete time instants. In

Heng Guo, Hiroaki Santo, Boxin Shi, Yasuyuki Matsushita

This paper presents a near-light photometric stereo method that faithfully preserves sharp depth edges in the 3D reconstruction. Unlike previous methods that rely on finite differentiation for approximating depth partial derivatives and surface normals, we introduce an analytically differentiable neural surface in near-light photometric stereo for avoiding d

Clément Foucart, Matija Vidmar

We introduce a class of one-dimensional positive Markov processes generalizing continuous-state branching processes (CBs), by taking into account a phenomenon of random collisions. Besides branching, characterized by a general mechanism $\Psi$, at a constant rate in time two particles are sampled uniformly in the population, collide and leave a mass of parti

Guowen Xu, Xingshuo Han, Shengmin Xu, Tianwei Zhang

In this paper, we address the problem of privacy-preserving federated neural network training with $N$ users. We present Hercules, an efficient and high-precision training framework that can tolerate collusion of up to $N-1$ users. Hercules follows the POSEIDON framework proposed by Sav et al. (NDSS'21), but makes a qualitative leap in performance with the f

Prashant Shukla

The superconducting circuits involving Josephson junction offer macroscopic quantum two-level system (qubit) which are coupled to cavity resonators and are operated via microwave signals. In this work, we study the dynamics of superconducting qubits coupled to a cavity with including dissipation in a subkelvin temperature domain. In the first step, a classic

G. Gitgeatpong, Y. Zhao, J. A. Fernandez-Baca, T. Hong

Magnetic properties of the antiferromagnet Zn$_{0.15}$Cu$_{1.85}$V$_2$O$_7$ (ZnCVO) have been thoroughly investigated on powder and single-crystal samples. The crystal structure determination using powder x-ray and neutron diffraction confirms that ZnCVO with Zn = 0.15 is isostructural with $\beta$-Cu$_{2}$V$_2$O$_7$ ($\beta$-CVO) with small deviation in the

Zenghui Bao, Zhiling Wang, Yukai Wu, Yan Li

Generalized cat states represent arbitrary superpositions of coherent states, which are of great importance in various quantum information processing protocols. Here we demonstrate a versatile approach to creating generalized itinerant cat states in the microwave domain, by reflecting coherent state photons from a microwave cavity containing a superconductin

Zixin Wang, Yadan Luo, Peng-Fei Zhang, Sen Wang

A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain. Nevertheless, prior works strictly assume that each source domain shares the identical group of classes with the target domain, which could hardly be guaranteed as the target label space is not obser

Yuchen Dang, Zheyuan Hu, Miles Cranmer, Michael Eickenberg

Turbulence is notoriously difficult to model due to its multi-scale nature and sensitivity to small perturbations. Classical solvers of turbulence simulation generally operate on finer grids and are computationally inefficient. In this paper, we propose the Turbulence Neural Transformer (TNT), which is a learned simulator based on the transformer architectur

3 Collaboration

The cross section of the process $e^+e^- \to K_S^0 K^{\pm}\pi^{\mp}\pi^+\pi^-$ has been measured for the first time using a data sample of 185.4 pb$^{-1}$ collected with the CMD-3 detector at the VEPP-2000 $e^+e^-$ collider. With the $K_S^0\to\pi^+\pi^-$ decay detection, 373$\pm$20 and 514$\pm$28 signal events have been selected with six and five reconstruct

Tianyu Wang, Xiaowei Hu, Pheng-Ann Heng, Chi-Wing Fu

This paper formulates a new problem, instance shadow detection, which aims to detect shadow instance and the associated object instance that cast each shadow in the input image. To approach this task, we first compile a new dataset with the masks for shadow instances, object instances, and shadow-object associations. We then design an evaluation metric for q

Qi Zhang, Bing Li, Lingzhou Xue

We introduce a new approach to nonlinear sufficient dimension reduction in cases where both the predictor and the response are distributional data, modeled as members of a metric space. Our key step is to build universal kernels (cc-universal) on the metric spaces, which results in reproducing kernel Hilbert spaces for the predictor and response that are ric

Lechao Xiao, Jeffrey Pennington

Although learning in high dimensions is commonly believed to suffer from the curse of dimensionality, modern machine learning methods often exhibit an astonishing power to tackle a wide range of challenging real-world learning problems without using abundant amounts of data. How exactly these methods break this curse remains a fundamental open question in th

Yongsheng Song

We consider a sequence of i.i.d. random variables $\{\xi_k\}$under a sublinear expectation $\mathbb{E}=\sup_{P\in\Theta}E_P$. We first give a new proof to the fact that, under each $P\in\Theta$, any cluster point of the empirical averages $\bar{\xi}_n=(\xi_1+\cdots+\xi_n)/n$ lies in $[\underline{\mu}, \bar{\mu}]$ with $\underline{\mu}=-\mathbb{E}[-\xi_1], \b

Jihao Liu, Yujie Luo

The second largest accumulation point of the set of minimal log discrepancies of threefolds is $\frac{5}{6}$. In particular, the minimal log discrepancies of $\frac{5}{6}$-lc threefolds satisfy the ACC.

Shota Kikuchi, Tatsuo Kobayashi, Morimitsu Tanimoto, Hikaru Uchida

We study systematically derivation of the specific texture zeros, that is the nearest neighbor interaction (NNI) form of the quark mass matrices at the fixed point $\tau=\omega$ in modular flavor symmetric models. We present models that the NNI forms of the quark mass matrices are simply realized at the fixed point $\tau=\omega$ in the $A_4$ modular flavor s

Abram H. Clark, H. John Nasrin, Stephanie E. Taylor, Emily E. Brodsky

We computationally study the frictional properties of sheared granular media subjected to harmonic vibration applied at the boundary. Such vibrations are thought to play an important role in weakening flows, yet the independent effects of amplitude, frequency, and pressure on the process have remained unclear. Based on a dimensional analysis and DEM simulati

Anthony Vento, Qingyu Zhao, Robert Paul, Kilian M. Pohl

Translating machine learning algorithms into clinical applications requires addressing challenges related to interpretability, such as accounting for the effect of confounding variables (or metadata). Confounding variables affect the relationship between input training data and target outputs. When we train a model on such data, confounding variables will bi

Zihao Ye, Ruihang Lai, Junru Shao, Tianqi Chen

Sparse tensors are rapidly becoming critical components of modern deep learning workloads. However, developing high-performance sparse operators can be difficult and tedious, and existing vendor libraries cannot satisfy the escalating demands from new operators. Sparse tensor compilers simplify the development of operators, but efficient sparse compilation f

Kyung Soo Rim

In this paper, we introduce a method of converting implicit equations to the usual forms of functions locally without differentiability. For a system of implicit equations which are equipped with continuous functions, if there are unique analytic implicit functions, that satisfies the system in some rectangle, then each analytic function is represented as a