November 2022 arXiv papers — page 127

Jin-Bo Li, Long-Cheng Gui, Wei Sun, Jian Liang

We present the first calculation of the connected scalar matrix element and the momentum fraction of charm quark within the$\frac{3}{2}^{+}$ and $\frac{3}{2}^{-}$triply charmed baryons on lattice QCD. The results are based on overlap valence fermions on two ensembles of $N_f=2+1$ domain wall fermion configurations with two lattice spacings. The corresponding

Yixiao Yang

The control logic models built by Simulink or Ptolemy have been widely used in industry scenes. It is an urgent need to ensure the safety and security of the control logic models. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art model testing tools employ model checking techniques or search-based methods t

Tao Zhu, Wen Zhao, Anzhong Wang

The direct discovery of gravitational waves (GWs) from the coalescence of compact binary components by the LIGO/Virgo/KAGRA Collaboration provides an unprecedented opportunity for exploring the underlying theory of gravity that drives the coalescence process in the strong and highly dynamical field regime of gravity. In this paper, we consider the observatio

Ziqian Ning, Qicong Xie, Pengcheng Zhu, Zhichao Wang

Voice conversion for highly expressive speech is challenging. Current approaches struggle with the balancing between speaker similarity, intelligibility and expressiveness. To address this problem, we propose Expressive-VC, a novel end-to-end voice conversion framework that leverages advantages from both neural bottleneck feature (BNF) approach and informati

O. Contreras-Vergara, N. Sánchez-Salas, G. Valencia-Ortega, J. I. Jiménez-Aquino

This work obtains the efficiency at maximum power for a stochastic heat engine performing Carnot-like, Stirling-like and Ericsson-like cycles. For the mesoscopic engine a Brownian particle trapped by an optical tweezers is considered. The dynamics of this stochastic engine is described as an overdamped Langevin equation with a harmonic potential, whereas is

Yiannis Fam

In a 1987 letter, Serre proves that the systems of Hecke eigenvalues arising from mod $p$ modular forms (of fixed level $\Gamma(N)$ coprime to $p$, and any weight $k$) are the same as those arising from functions $\Omega(N) \to \bar{\mathbb F}_p$, where $\Omega(N)$ is some double quotient of $D^\times (\mathbb A_f)$ and $D$ is the unique quaternion algebra o

Marco Cecconello

The efficiency and energy resolution of the MAST neutron camera detectors, based on liquid scintillator of the EJ-301 type, is lower than cylindrical detector of similar size. An experimental investigation has identified light attenuation as the main cause of this deterioration. This finding is supported by Monte Carlo electron and photon transport calculati

Yingyi Ma, Zhe Liu, Xuedong Zhang

Neural network language model (NNLM) plays an essential role in automatic speech recognition (ASR) systems, especially in adaptation tasks when text-only data is available. In practice, an NNLM is typically trained on a combination of data sampled from multiple corpora. Thus, the data sampling strategy is important to the adaptation performance. Most existin

Ryutaro Tsuji, Yasumichi Aoki, Ken-Ichi Ishikawa, Yoshinobu Kuramashi

We present results for the axial charge and root-mean-square (RMS) radii of the nucleon obtained from 2+1 flavor lattice QCD at the physical point with a large spatial extent of about 10 fm. Our calculations are performed with the PACS10 gauge configurations generated by the PACS Collaboration with the six stout-smeared $O(a)$ improved Wilson-clover quark ac

Xiaowei Li, Xuqi Zhang, Zhiguo Wang, Xiaojing Shen

This paper considers the state estimation problem for nonlinear dynamic systems with unknown but bounded noises. Set membership filter (SMF) is a popular algorithm to solve this problem. In the set membership setting, we investigate the filter problem where the state estimation requires to be constrained by a linear or nonlinear equality. We propose a consen

Shuyi Ren, Kaiming Shen, Xin Li, Xin Chen

It remains an open problem to find the optimal configuration of phase shifts under the discrete constraint for intelligent reflecting surface (IRS) in polynomial time. The above problem is widely believed to be difficult because it is not linked to any known combinatorial problems that can be solved efficiently. The branch-and-bound algorithms and the approx

Ke Lei, Ali B. Syed, Xucheng Zhu, John M. Pauly

Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep-learning framework, trained by radiologists' supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are use

Sourav Chatterjee

This paper surveys some recent developments in measures of association related to a new coefficient of correlation introduced by the author. A straightforward extension of this coefficient to standard Borel spaces (which includes all Polish spaces), overlooked in the literature so far, is proposed at the end of the survey.

Ewain Gwynne, Jinwoo Sung

A Liouville quantum gravity (LQG) surface is a natural random two-dimensional surface, initially formulated as a random measure space and later as a random metric space. We show that the LQG measure can be recovered as the Minkowski measure with respect to the LQG metric, answering a question of Gwynne and Miller (arXiv:1905.00383). As a consequence, we prov

Zhao Zhang, Suiyi Zhao, Xiaojie Jin, Mingliang Xu

Deep learning-based low-light image enhancement (LLIE) is a task of leveraging deep neural networks to enhance the image illumination while keeping the image content unchanged. From the perspective of training data, existing methods complete the LLIE task driven by one of the following three data types: paired data, unpaired data and zero-reference data. Eac

Yangjun Wu, Kebin Fang, Yao Zhao, Hao Zhang

To accomplish punctuation restoration, most existing methods focus on introducing extra information (e.g., part-of-speech) or addressing the class imbalance problem. Recently, large-scale transformer-based pre-trained language models (PLMS) have been utilized widely and obtained remarkable success. However, the PLMS are trained on the large dataset with mark

Chen Lin, Ye Liu, Siyu An, Di Yin

In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document. Previous studies focus more on employing statistical approaches or pre-trained language models (PLMs) to extract sentence embeddings, while ignoring the rich information inherent in the het

Kexue Fu, Jiazheng Luo, Xiaoyuan Luo, Shaolei Liu

3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registra

M. Reza Bradrania, Maurice Peat, Stephen Satchell

This paper considers liquidity as an explanation for the positive association between expected idiosyncratic volatility (IV) and expected stock returns. Liquidity costs may affect the stock returns, through bid-ask bounce and other microstructure-induced noise, which will affect the estimation of IV. We use a novel method (developed by Weaver, 1991) to elimi

Xi Hu, Lin Tang

We prove the existence of a critical Fujita exponent for a non-homogeneous semilinear heat equation which involves degenerate coefficients. More precisely, in order to give a rather complete theory, we focus on two types of weights $w(x)=|x_1|^a$ or $w(x)=|x|^b$ where $a, b>0$ in a suitable range. The coefficients under consideration admit either a singulari

Piotr Bargiela

We present recent advancements in the computation of three-loop four-particle helicity amplitudes in full-color massless QCD. In this contribution, we focus on the $gg \to \gamma\gamma$ process. We show how to obtain compact analytic formulae for the three-loop scattering amplitude. Our results can be expressed in terms of harmonic polylogarithms, which allo

Yuanlong Li, Gaopan Huang, Min Zhou, Chuan Fu

Learning an explainable classifier often results in low accuracy model or ends up with a huge rule set, while learning a deep model is usually more capable of handling noisy data at scale, but with the cost of hard to explain the result and weak at generalization. To mitigate this gap, we propose an end-to-end deep explainable learning approach that combines

Rui Xue

It is confirmed in this work that the graph isomorphism can be tested in polynomial time, which resolves a longstanding problem in the theory of computation. The contributions are in three phases as follows. 1. A description graph $\tilde{A}$ to a given graph $A$ is introduced so that labels to vertices and edges of $\tilde{A}$ indicate the identical or diff

Jidan Yang, Yu Zou, Wentao Tang, Jinxing Li

Topological spin and polar textures have fascinated people in different areas of physics and technologies. However, the observations are limited in magnetic and solid-state ferroelectric systems. Ferroelectric nematic is the first liquid-state ferroelectric that would carry many possibilities of spatially distributed polarization fields. Contrary to traditio

Thien An L. Nguyen

3D inference from monocular vision using neural networks is an important research area of computer vision. Applications of the research area are various with many proposed solutions and have shown remarkable performance. Although many efforts have been invested, there are still unanswered questions, some of which are fundamental. In this paper, I discuss a p

Dan Ling, Zhiping Mao

The diffusive-viscous wave equation (DVWE) is widely used in seismic exploration since it can explain frequency-dependent seismic reflections in a reservoir with hydrocarbons. Most of the existing numerical approximations for the DVWE are based on domain truncation with ad hoc boundary conditions. However, this would generate artificial reflections as well a

E. Kasai, P. Goldoni, S. Pita, D. A. Williams

Active galactic nuclei (AGNs) make up about 35 per cent of the more than 250 sources detected in very-high-energy (VHE) gamma rays to date with Imaging Atmospheric Cherenkov Telescopes. Apart from four nearby radio galaxies and two AGNs of unknown type, all known VHE AGNs are blazars. Knowledge of the cosmological redshift of gamma-ray blazars is key to enab

Sherry Ashour, Huaizhong Xu

Melt electrowriting (MEW) is an extrusion-based additive manufacturing technology to create a complex construct with micro-scale fidelity. The elevated nozzle-to-collector distance of MEW needs a high requirement to control the charged jet, which relies on the understanding of the corresponding jet kinematics at different printing conditions. This study focu

Zhuoqun Liu, Meiguang Jin, Ying Chen, Huaida Liu

Deep convolutional neural networks have achieved great progress in image denoising tasks. However, their complicated architectures and heavy computational cost hinder their deployments on mobile devices. Some recent efforts in designing lightweight denoising networks focus on reducing either FLOPs (floating-point operations) or the number of parameters. Howe

Pedro Faustini, Natasha Fernandes, Shakila Tonni, Annabelle McIver

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for applying privacy in the training of deep learning models. It applies isotropic Gaussian noise to gradients during training, which can perturb these gradients in any direction, damaging utility. Metric DP, however, can provide alternative mechanisms based on arbitrary metrics that

Talha Azfar, Jinlong Li, Hongkai Yu, Ruey Long Cheu

Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. This paper conducted an extensive literature

Sepehr Assadi, Vihan Shah

We present a streaming algorithm for the vertex connectivity problem in dynamic streams with a (nearly) optimal space bound: for any $n$-vertex graph $G$ and any integer $k \geq 1$, our algorithm with high probability outputs whether or not $G$ is $k$-vertex-connected in a single pass using $\widetilde{O}(k n)$ space. Our upper bound matches the known $\Omeg

Mo Yu, Qiujing Wang, Shunchi Zhang, Yisi Sang

When reading a story, humans can quickly understand new fictional characters with a few observations, mainly by drawing analogies to fictional and real people they already know. This reflects the few-shot and meta-learning essence of humans' inference of characters' mental states, i.e., theory-of-mind (ToM), which is largely ignored in existing research. We

Ivan Postolski, Victor Braberman, Diego Garbervetsky, Sebastian Uchitel

In this paper, we propose a novel approach that aims to offer an alternative to the prevalent paradigm to dynamic slicing construction. Dynamic slicing requires dynamic data and control dependencies that arise in an execution. During a single execution, memory reference information is recorded and then traversed to extract dependencies. Execute-once approach

Ranadeep Daw, Christopher K. Wikle

There has been a great deal of recent interest in the development of spatial prediction algorithms for very large datasets and/or prediction domains. These methods have primarily been developed in the spatial statistics community, but there has been growing interest in the machine learning community for such methods, primarily driven by the success of deep G

Seoktae Koh, Chong Oh Lee

We consider an accelerating black hole with a negative cosmological constant in four-dimensional spacetime. There are two configurations such as a black string (BS) phase when a mass parameter is zero and a black hole (BH) phase when a mass parameter is non-zero. We investigate their stability via the configuration entropy (CE). It is found that the BS is no

Aliakbar Alijani

In this paper, we study some properties of $*-$open and $*-$closed subsets of a space. The collection of all $*-$open subsets of a space $X$ form a topology on $X$ which is denoted by $^{*}O(X)$. We investigate the relations between topological properties of $X$ with the topology $^{*}O(X)$ and $X$. Also, we introduce the concept of a $*-$continuous map.

Kinnary Patel, Bin Xu, Sergey Prosandeev, Romain Faye

A novel atomistic effective Hamiltonian scheme, incorporating an original and simple bilinear energetic coupling, is developed and used to investigate the temperature dependent physical properties of the prototype antiferroelectric PbZrO3 (PZO) system. This scheme reproduces very well the known experimental hallmarks of the complex Pbam orthorhombic phase at

Minqiang Xu, Yanting yuan, Waixiang Cao, Qingsong Zou

In this paper, we analyze two classes of spectral volume (SV) methods for one-dimensional hyperbolic equations with degenerate variable coefficients. The two classes of SV methods are constructed by letting a piecewise $k$-th order ($k\ge 1$ is an arbitrary integer) polynomial function satisfy the local conservation law in each {\it control volume} obtained

Zhichao Peng, Yanlai Chen, Yingda Cheng, Fengyan Li

Kinetic transport equations are notoriously difficult to simulate because of their complex multiscale behaviors and the need to numerically resolve a high dimensional probability density function. Past literature has focused on building reduced order models (ROM) by analytical methods. In recent years, there is a surge of interest in developing ROM using dat

Yijun Dong, Per-Gunnar Martinsson, Yuji Nakatsukasa

Randomized subspace approximation with "matrix sketching" is an effective approach for constructing approximate partial singular value decompositions (SVDs) of large matrices. The performance of such techniques has been extensively analyzed, and very precise estimates on the distribution of the residual errors have been derived. However, our understanding of

Jacob D. Beckmann, Kosta Popovic

Classification of cancer cellularity within tissue samples is currently a manual process performed by pathologists. This process of correctly determining cancer cellularity can be time intensive. Deep Learning (DL) techniques in particular have become increasingly more popular for this purpose, due to the accuracy and performance they exhibit, which can be c

Soh Kumabe, Yuichi Yoshida

Graph algorithms are widely used for decision making and knowledge discovery. To ensure their effectiveness, it is essential that their output remains stable even when subjected to small perturbations to the input because frequent output changes can result in costly decisions, reduced user trust, potential security concerns, and lack of replicability. In thi

Wannita Takerngsaksiri, Chakkrit Tantithamthavorn, Yuan-Fang Li

Code completion aims to help improve developers' productivity by suggesting the next code tokens from a given context. Various approaches have been proposed to incorporate abstract syntax tree (AST) information for model training, ensuring that code completion is aware of the syntax of the programming languages. However, existing syntax-aware code completion

Wenqi Shi, Xi Lin

Randomized experiments can provide unbiased estimates of sample average treatment effects. However, estimates of population treatment effects can be biased when the experimental sample and the target population differ. In this case, the population average treatment effect can be identified by combining experimental and observational data. A good experiment d

Rainer Buckdahn, Bowen He, Juan Li

Our work is devoted to the study of Pontryagin's stochastic maximum principle for a mean-field optimal control problem under Peng's $G$-expectation. The dynamics of the controlled state process is given by a stochastic differential equation driven by a $G$-Brownian motion, whose coefficients depend not only on the control, the controlled state process but al

Anique Tahir, Lu Cheng, Ruocheng Guo, Huan Liu

Machine learning algorithms typically assume that the training and test samples come from the same distributions, i.e., in-distribution. However, in open-world scenarios, streaming big data can be Out-Of-Distribution (OOD), rendering these algorithms ineffective. Prior solutions to the OOD challenge seek to identify invariant features across different traini

Shao-Ping Li, Xun-Jie Xu

In the early universe, Dirac neutrino magnetic moments due to their chirality-flipping nature could lead to thermal production of right-handed neutrinos, which would make a significant contribution to the effective neutrino number, $N_{\rm eff}$. We present in this paper a dedicated computation of the neutrino chirality-flipping rate in the thermal plasma. W

Chonghua Liao, Yanan Zheng, Zhilin Yang

Natural language prompts have been shown to facilitate cross-task generalization for large language models. However, with no or limited labeled examples, the cross-task performance is highly sensitive to the choice of prompts, while selecting a high-performing prompt is challenging given the scarcity of labels. To address the issue, we propose a Zero-Label P

Ikki Fukuda, Yota Irino

We consider the asymptotic behavior of solutions to the Cauchy problem for a dispersive-dissipative equation with a cubic nonlinearity. It is known that the leading term of the asymptotic profile for the solution to this problem is the Gaussian. Moreover, by analyzing the corresponding integral equation, the higher-order asymptotic expansion for the solution

Takahiro Onizuka, Shintaro Hashimoto, Shonosuke Sugasawa

Quantiles are useful characteristics of random variables that can provide substantial information on distributions compared with commonly used summary statistics such as means. In this paper, we propose a Bayesian quantile trend filtering method to estimate non-stationary trend of quantiles. We introduce general shrinkage priors to induce locally adaptive Ba

Jie Wang, Zhihao Jiang, Yash Vardhan Pant

As autonomous vehicles (AVs) become more common on public roads, their interaction with human-driven vehicles (HVs) in mixed traffic is inevitable. This requires new control strategies for AVs to handle the unpredictable nature of HVs. This study focused on safe control in mixed-vehicle platoons consisting of both AVs and HVs, particularly during longitudina

Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Yao-Zhong Zhang

Starting from a purely algebraic procedure based on the commutant of a subalgebra in the universal enveloping algebra of a given Lie algebra, the notion of algebraic Hamiltonians and the constants of the motion generating a polynomial symmetry algebra is proposed. The case of the special linear Lie algebra $\mathfrak{sl}(n)$ is discussed in detail, where an

Jackson Likens, Sanjay Prabhakar, Ratan Lal, Roderick Melnik

We consider the problem of analyzing spin-flip qubit gate operation in presence of Random Telegraph Noise (RTN). Our broad approach is the following. We calculate the spin-flip probability of qubit driven by composite pulses, (Constant pulse (C-pulse), Quantum Well pulse (QW-pulse) and Barrier Potential pulse (BP-pulse)) in the presence of RTN using Feynman

Maximiliano Ujevic, Henrique Gieg, Federico Schianchi, Swami Vivekanandji Chaurasia

Multi-messenger observations of binary neutron star mergers provide a unique opportunity to constrain the dense-matter equation of state. Although it is known from quantum chromodynamics that hadronic matter will undergo a phase transition to exotic forms of matter, e.g., quark matter, the onset density of such a phase transition cannot be computed from firs

Md Abdullah Al Alamin, Gias Uddin

As big data grows ubiquitous across many domains, more and more stakeholders seek to develop Machine Learning (ML) applications on their data. The success of an ML application usually depends on the close collaboration of ML experts and domain experts. However, the shortage of ML engineers remains a fundamental problem. Low-code Machine learning tools/platfo

Luc Binette, Yair Krongold, Sinhue A. R. Haro-Corzo, Andrew Humphrey

The temperature predicted by photoionization models for the Narrow Line Region of Seyfert 2 galaxies is lower than the value inferred from the observed [O III] {\lambda}4363A/{\lambda}5007A line ratio. We explore the possibility of considering a harder ionizing continuum than typically assumed. The spectral ionizing energy distribution, which can generate th

Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa

The extragradient method has gained popularity due to its robust convergence properties for differentiable games. Unlike single-objective optimization, game dynamics involve complex interactions reflected by the eigenvalues of the game vector field's Jacobian scattered across the complex plane. This complexity can cause the simple gradient method to diverge,

Rafael Martinez-Garcia-Peña, Mansoor Ali Teevno, Gilberto Ochoa-Ruiz, Sharib Ali

Domain shift is a well-known problem in the medical imaging community. In particular, for endoscopic image analysis where the data can have different modalities the performance of deep learning (DL) methods gets adversely affected. In other words, methods developed on one modality cannot be used for a different modality. However, in real clinical settings, e

Ziyao Wang, Jiandong Zhang, Jun Ma

One of the important topics in the research field of Chinese classical poetry is to analyze the poetic style. By examining the relevant works of previous dynasties, researchers judge a poetic style mostly by their subjective feelings, and refer to the previous evaluations that have become a certain conclusion. Although this judgment method is often effective

Daniel Davila, Dawei Du, Bryon Lewis, Christopher Funk

In this paper, we present the Multi-view Extended Videos with Identities (MEVID) dataset for large-scale, video person re-identification (ReID) in the wild. To our knowledge, MEVID represents the most-varied video person ReID dataset, spanning an extensive indoor and outdoor environment across nine unique dates in a 73-day window, various camera viewpoints,

Michael R. Metel

Motivated by neural network training in finite-precision arithmetic environments, this work studies the convergence of perturbed iterate SGD using adaptive step sizes in an environment with numerical error. Considering a general stochastic Lipschitz continuous loss function, an asymptotic convergence result to a Clarke stationary point is proven as well as t

Rodrigo Andrade e Silva

Swimming in curved spacetimes is a phenomenon whereby free bodies in curved spacetimes are able to propel themselves by performing cyclic internal motions. When originally proposed, it was further suggested that, in the limit of fast internal cycles, the net motion would display a simple geometric-phase character, in which the displacement per cycle would no

Yushen Huang, Yifan Sun

We investigate a family of approximate multi-step proximal point methods, accelerated by implicit linear discretizations of gradient flow. The resulting methods are multi-step proximal point methods, with similar computational cost in each update as the proximal point method. We explore several optimization methods where applying an approximate multistep pro

Ying Zhang, Yuxuan Bao, Jinniu Hu, Hong Shen

To solve the Dirac equation with the finite difference method, one has to face up to the spurious-state problem due to the fermion doubling problem when using the conventional central difference formula to calculate the first-order derivative on the equal interval lattices. This problem is resolved by replacing the central difference formula with the asymmet

Ofer Busani, Timo Seppäläinen, Evan Sorensen

We show that the multi-type stationary distribution of the totally asymmetric simple exclusion process (TASEP) scales to a nontrivial limit around the Bernoulli measure of density $1/2$. This is obtained by showing that the TASEP speed process, introduced by Amir, Angel and Valk\'o, scales around the speed $v=0$ to the stationary horizon (SH), a function-val

Sunao Ouchi

A system of nonlinear differential equations $x^{1+\gamma}\frac{dY}{dx}= F_0(x)+A(x)Y+F(x,Y)$ is considered. We study more precisely the meaning of asymptotic expansion of transformations and solutions than preceding pioneering works, by using the theory of Borel summable functions in asymptotic analysis, and apply results to Painlev\'{e} equations.

Benjamín Hernández, Violeta Chang

Breast cancer is one of the most common cancer in women around the world. For diagnosis, pathologists evaluate biomarkers such as HER2 protein using immunohistochemistry over tissue extracted by a biopsy. Through microscopic inspection, this assessment estimates the intensity and integrity of the membrane cells' staining and scores the sample as 0, 1+, 2+, o

Ben Castor, Haohua Deng, Matt Kerr, Gregory Pearlstein

We introduce a simple calculus, extending a variant of the Steenbrink spectrum, for describing Hodge-theoretic invariants of (smoothings of) isolated singularities with (relative) automorphisms. After computing these "eigenspectra" in the quasi-homogeneous case, we give three applications to singularity bounding and monodromy of VHS.

Leonid Petrov

The ionospheric path delay impacts single-band very long baseline interferometry (VLBI) group delays, which limits their applicability for absolute astrometry. I consider two important cases: when observations are made simultaneously at two bands, but delays at only one band are available for a subset of observations and when observations are made at one ban

Y. N. Huang, Harald O. Jeschke, Igor I. Mazin

Kagome lattice materials are an important platform for highly frustrated magnetism as well as for a plethora of phenomena resulting from flat bands, Dirac cones and van Hove singularities in their electronic structures. We study the little known metallic magnet CrRhAs, which belongs to a vast family of materials that include $3d$, $4f$ and $5f$ magnetic elem

Yuchen Fan, Rong Yu, Tommaso Roscilde

The nature of the superfluid-to-Bose-glass (SF-BG) quantum phase transition, occurring in systems of interacting bosons immersed in a disordered environment, remains elusive. One fundamental open question is whether or not the transition obeys conventional scaling at quantum critical points (QCPs): this scaling would lock the value of the crossover exponent

Xu Chen, Zhiyong Feng, J. Andrew Zhang, Xin Yuan

In this paper, we propose a novel Kalman Filter (KF)-based uplink (UL) joint communication and sensing (JCAS) scheme, which can significantly reduce the range and location estimation errors due to the clock asynchronism between the base station (BS) and user equipment (UE). Clock asynchronism causes time-varying time offset (TO) and carrier frequency offset

Josh Alman

We use lookup tables to design faster algorithms for important algebraic problems over finite fields. These faster algorithms, which only use arithmetic operations and lookup table operations, may help to explain the difficulty of determining the complexities of these important problems. Our results over a constant-sized finite field are as follows. The Wals

Wei Huang, Zheng Zhang

We identify the average dose-response function (ADRF) for a continuously valued error-contaminated treatment by a weighted conditional expectation. We then estimate the weights nonparametrically by maximising a local generalised empirical likelihood subject to an expanding set of conditional moment equations incorporated into the deconvolution kernels. There

Yao Li, Yaping Yuan

This paper studies the sensitivity analysis of mass-action systems against their diffusion approximations, particularly the dependence on population sizes. As a continuous time Markov chain, a mass-action system can be described by a equation driven by finite many Poisson processes, which has a diffusion approximation that can be pathwisely matched. The magn

Trung Chau, Selvi Kara

We construct cellular resolutions for monomial ideals via discrete Morse theory. In particular, we develop an algorithm to create homogeneous acyclic matchings and we call the cellular resolutions induced from these matchings Barile-Macchia resolutions. These resolutions are minimal for edge ideals of weighted oriented forests and (most) cycles. As a result,

Billy Jin, Nathan Klein, David P. Williamson

A long-standing conjecture for the traveling salesman problem (TSP) states that the integrality gap of the standard linear programming relaxation of the TSP is at most 4/3. Despite significant efforts, the conjecture remains open. We consider the half-integral case, in which the LP has solution values in $\{0, 1/2, 1\}$. Such instances have been conjectured

Karl-Dieter Crisman

There has been much recent work on multiwinner voting systems. However, sometimes a committee is highly structured, and if we want to vote for such a committee, our voting method should be more structured as well. We consider committees consisting of representatives for disjoint parts of a collective; for instance, n departments of an organization might each

Marlon Sproesser Mathias, Marcello Augusto Faraco de Medeiros

Global instability analysis of flows is often performed via time-stepping methods, based on the Arnoldi algorithm. When setting up these methods, several computational parameters must be chosen, which affect intrinsic errors of the procedure, such as the truncation errors, the discretization error of the flow solver, the error associated with the nonlinear t

Kein Yukiyoshi, Naoki Ishikawa

A binary constant weight code is a type of error-correcting code with a wide range of applications. The problem of finding a binary constant weight code has long been studied as a combinatorial optimization problem in coding theory. In this paper, we propose a quantum search algorithm for binary constant weight codes. Specifically, the search problem is newl

N. Agafonova, A. Alexandrov, A. Anokhina, S. Aoki

This paper describes a new $\nu_e$ identification method specifically designed to improve the low-energy ($< 30\,\mathrm{GeV}$) $\nu_e$ identification efficiency attained by enlarging the emulsion film scanning volume with the next generation emulsion readout system. A relative increase of 25-70% in the $\nu_e$ low-energy region is expected, leading to impro

Haichuan Yang, Zhaojun Yang, Li Wan, Biqiao Zhang

This paper proposes a hardware-efficient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting. It is optimized specifically for low-power processor units like microcontrollers. ML operators exhibit heterogeneous efficiency profiles on power-efficient hardware. Given the exact theoretical computation cost, int8 operators are more compu

Meiby Ortiz-Bouza, Selin Aviyente

Many real-world systems can be represented as graphs where the different entities in the system are presented by nodes and their interactions by edges. An important task in studying large datasets with graphical structure is graph clustering. While there has been a lot of work on graph clustering using the connectivity between the nodes, many real-world netw

Benjamin A. Burrington, A. W. Peet

We consider correlation functions in symmetric product ($S_N$) orbifold CFTs at large $N$ with arbitrary seed CFT. Specifically, we consider correlators of descendant operators constructed using both the full Virasoro generators $L_{m}$ and fractional Virasoro generators $\ell_{m/n_i}$. Using covering space techniques, we show that correlators of descendants

Mingwu, Gao, Samer Haidar

Software has been developed for knowledge discovery, prediction and management for over 30 years. However, there are still unresolved pain points when using existing project development and artifact management methodologies. Historically, there has been a lack of applicable methodologies. Further, methodologies that have been applied, such as Agile, have sev

T. Mageshwaran, Sudip Bhattacharyya

Tidal disruption events (TDEs) show a correlation between the UV to X-ray spectral index and the Eddington ratio, with non-thermal X-ray emission at the low Eddington ratio. We consider the corona surrounding the accretion disc as a non-thermal X-ray source. We construct a time-dependent and non-relativistic advective accretion disc-corona model for TDEs. Th

Sander Thuijsman, Michel Reniers

In this paper a framework for engineering supervisory controllers for product lines with dynamic feature configuration is proposed. The variability in valid configurations is described by a feature model. Behavior of system components is achieved using (extended) finite automata and both behavioral and dynamic configuration constraints are expressed by means

Budhi Arta Surya

This paper revisits the work of Rauch et al. (1965) and develops a novel method for recursive maximum likelihood particle filtering for general state-space models. The new method is based on statistical analysis of incomplete observations of the systems. Score function and conditional observed information of the incomplete observations/data are introduced an

Marek Gagolewski

Minimalist Data Wrangling with Python is envisaged as a student's first introduction to data science, providing a high-level overview as well as discussing key concepts in detail. We explore methods for cleaning data gathered from different sources, transforming, selecting, and extracting features, performing exploratory data analysis and dimensionality redu

Eduardo Martínez, Javier Montaño-Domínguez, Héctor Novales-Sánchez, Mónica Salinas

Current experimental data guarantees the presence of physics beyond the Standard Model in the neutrino sector. The responsible physical description might show itself through virtual effects on low-energy observables. In particular, massive neutrinos are able to produce contributions to the triple gauge coupling $WW\gamma$. The present paper deals with the ca

Hezam Albaqami, Ghulam Mubashar Hassan, Amitava Datta

Seizure type identification is essential for the treatment and management of epileptic patients. However, it is a difficult process known to be time consuming and labor intensive. Automated diagnosis systems, with the advancement of machine learning algorithms, have the potential to accelerate the classification process, alert patients, and support physician

Valerie King, Alex Thomo, Quinton Yong

The problem of finding the degeneracy of a graph is a subproblem of the k-core decomposition problem. In this paper, we present a (1 + epsilon)-approximate solution to the degeneracy problem which runs in O(n log n) time, sublinear in the input size for dense graphs, by sampling a small number of neighbors adjacent to high degree nodes. Our algorithm can als

Harley Katz, Shenghua Liu, Taysun Kimm, Martin P. Rey

We introduce the PRISM interstellar medium (ISM) model for thermochemistry and its implementation in the RAMSES-RTZ code. The model includes a non-equilibrium primordial, metal, and molecular chemistry network for 115 species coupled to on-the-fly multifrequency radiation transport. PRISM accurately accounts for the dominant ISM cooling and heating processes

Yang Liu, Shen Yan, Laura Leal-Taixé, James Hays

Modern neural networks are over-parameterized and thus rely on strong regularization such as data augmentation and weight decay to reduce overfitting and improve generalization. The dominant form of data augmentation applies invariant transforms, where the learning target of a sample is invariant to the transform applied to that sample. We draw inspiration f

Cameron R. Wolfe, Anastasios Kyrillidis

The ability to dynamically adapt neural networks to newly-available data without performance deterioration would revolutionize deep learning applications. Streaming learning (i.e., learning from one data example at a time) has the potential to enable such real-time adaptation, but current approaches i) freeze a majority of network parameters during streaming

Evgeny Stupachenko

When a neural network (NN) is used to decode a polar code, its training complexity scales exponentially as the code block size (or to be precise, as a number of message bits) increases. Therefore, existing solutions that use a neural network for polar decoders are stuck with short block sizes like 16 or 32. Despite the fact that the NN training is very compl

Paulo H. L. Martins, Ronald Dickman, Robert M. Ziff

We consider two-species random sequential adsorption (RSA) in which species A and B adsorb randomly on a lattice with the restriction that opposite species cannot occupy nearest-neighbor sites. When the probability $x_A$ of choosing an A particle for an adsorption trial reaches a critical value $0.626441(1)$, the A species percolates and/or the blocked sites

Samantha Allen, Kenan Ince, Seungwon Kim, Benjamin Matthias Ruppik

The untwisting number of a knot K is the minimum number of null-homologous twists required to convert K to the unknot. Such a twist can be viewed as a generalization of a crossing change, since a classical crossing change can be effected by a null-homologous twist on 2 strands. While the unknotting number gives an upper bound on the smooth 4-genus, the untwi

Zhu Danhao, Shen Si, Huang Shujian, Yin Chang

Recently, neural network based methods have shown their power in learning more expressive features on the task of knowledge graph embedding (KGE). However, the performance of deep methods often falls behind the shallow ones on simple graphs. One possible reason is that deep models are difficult to train, while shallow models might suffice for accurately repr