May 2022 arXiv papers — page 88

Yuxin Deng, Jiayi Ma

Deep-learning-based local feature extraction algorithms that combine detection and description have made significant progress in visible image matching. However, the end-to-end training of such frameworks is notoriously unstable due to the lack of strong supervision of detection and the inappropriate coupling between detection and description. The problem is

B. Rani, S. A. Mundo, R. Mushotzky, A. Y. Lien

We used 13 years of Swift/BAT observations to probe the nature and origin of hard X-ray (14-195 KeV) emission in Centaurus A. Since the beginning of the Swift operation in 2004, significant X-ray variability in the 14-195 KeV band is detected, with mild changes in the source spectrum. Spectral variations became more eminent after 2013, following a softer-whe

Jiahao Li, Alexis Samoylov, Jeeeun Kim, Xiang 'Anthony' Chen

One important vision of robotics is to provide physical assistance by manipulating different everyday objects, e.g., hand tools, kitchen utensils. However, many objects designed for dexterous hand-control are not easily manipulable by a single robotic arm with a generic parallel gripper. Complementary to existing research on developing grippers and control a

Toshiaki Fujimori, Syo Kamata, Tatsuhiro Misumi, Muneto Nitta

We discuss all-order transseries in one of the simplest quantum mechanical systems: a U(1) symmetric single-degree-of-freedom system with a first-order time derivative term. Following the procedure of the Lefschetz thimble method, we explicitly evaluate the path integral for the generating function of the Noether charge and derive its exact transseries expre

Yoshikazu Giga, Hirotoshi Kuroda, Michał Łasica

We define rigorously a solution to the fourth-order total variation flow equation in $\mathbb{R}^n$. If $n\geq3$, it can be understood as a gradient flow of the total variation energy in $D^{-1}$, the dual space of $D^1_0$, which is the completion of the space of compactly supported smooth functions in the Dirichlet norm. However, in the low dimensional case

CMS Collaboration

Results are presented from a search for CP violation in top quark pair production, using proton-proton collisions at a center-of-mass energy of 13 TeV. The data used for this analysis consist of final states with two charged leptons collected by the CMS experiment, and correspond to an integrated luminosity of 35.9 fb$^{-1}$. The search uses two observables,

Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou

Binary neural networks leverage $\mathrm{Sign}$ function to binarize weights and activations, which require gradient estimators to overcome its non-differentiability and will inevitably bring gradient errors during backpropagation. Although many hand-designed soft functions have been proposed as gradient estimators to better approximate gradients, their mech

Derchyi Wu

We prove the long-standing inverse scattering theory (IST) of perturbed Kadomtsev Petviashvili multi-line solitons. Our work is the first rigorous IST of a multi-dimensional integrable system when both continuous and discrete scattering data are present, and the support of continuous scattering data does not degenerate into contours in the complex plane. As

Ben Lund, Thang Pham, Vu Thi Huong Thu

Motivated by recent results on radial projections and applications to the celebrated Falconer distance problem, we study radial projections in the setting of finite fields. More precisely, we extend results due to Mattila and Orponen (2016), Orponen (2018), and Liu (2020) to finite spaces. In some cases, our results are stronger than the corresponding result

John Taylor, Wenyi Wang, Biswajit Bala, Tomasz Bednarz

We investigate the role of the optimizer in determining the quality of the model fit for neural networks with a small to medium number of parameters. We study the performance of Adam, an algorithm for first-order gradient-based optimization that uses adaptive momentum, the Levenberg and Marquardt (LM) algorithm a second order method, Broyden,Fletcher,Goldfar

Ming Fan, Wenying Wei, Wuxia Jin, Zijiang Yang

The fairness characteristic is a critical attribute of trusted AI systems. A plethora of research has proposed diverse methods for individual fairness testing. However, they are suffering from three major limitations, i.e., low efficiency, low effectiveness, and model-specificity. This work proposes ExpGA, an explanationguided fairness testing approach throu

Chenwei Lv, Qi Zhou

We show that quantum dynamics of any systems with $SU(1,1)$ symmetry give rise to emergent Anti-de Sitter spacetimes in 2+1 dimensions (AdS$_{2+1}$). Using the continuous circuit depth, a quantum evolution is mapped to a trajectory in AdS$_{2+1}$. Whereas the time measured in laboratories becomes either the proper time or the proper distance, quench dynamics

Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low

Measuring contributions is a classical problem in cooperative game theory where the Shapley value is the most well-known solution concept. In this paper, we establish the convergence property of the Shapley value in parametric Bayesian learning games where players perform a Bayesian inference using their combined data, and the posterior-prior KL divergence i

Weiyao Zhu, Ou Wu, Fengguang Su, Yingjun Deng

As learning difficulty is crucial for machine learning (e.g., difficulty-based weighting learning strategies), previous literature has proposed a number of learning difficulty measures. However, no comprehensive investigation for learning difficulty is available to date, resulting in that nearly all existing measures are heuristically defined without a rigor

Yuxin Dong, Tieliang Gong, Shujian Yu, Chen Li

The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions, thus has been widely adopted in numerous statistical learning and inference tasks. However, exactly calculating this new information quantity requires access to the eigenspectrum

Chiara Muscari Tomajoli, Luca Collini, Jitendra Bhandari, Abdul Khader Thalakkattu Moosa

Fabricating an integrated circuit is becoming unaffordable for many semiconductor design houses. Outsourcing the fabrication to a third-party foundry requires methods to protect the intellectual property of the hardware designs. Designers can rely on embedded reconfigurable devices to completely hide the real functionality of selected design portions unless

He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan

Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics. However, task performance is not the only req

Dong Li, Wei Chen

We report the observation of non-stationary Quasi-Periodic Pulsations (QPPs) in high-energy particles during the impulsive phase of an X4.8 flare on 2002 July 23 (SOL2002-07-23T00:35). The X4.8 flare was simultaneously measured by the Reuven Ramaty High Energy Solar Spectroscopic Imager, Nobeyama Radio Polarimeters, and Nobeyama Radioheliograph. The quasi-pe

Yang Liu, Xiaoqi Wang, Xi Wang, Zhen Wang

We assume that the state of a number of nodes in a network could be investigated if necessary, and study what configuration of those nodes could facilitate a better solution for the diffusion-source-localization (DSL) problem. In particular, we formulate a candidate set which contains the diffusion source for sure, and propose the method, Percolation-based E

Jay Hwan Lee, Yeonsoo Kim, Younghyun Ryu, Wasuwee Sodsong

In emerging scientific computing environments, matrix computations of increasing size and complexity are increasingly becoming prevalent. However, contemporary matrix language implementations are insufficient in their support for efficient utilization of cloud computing resources, particularly on the user side. We thus developed an extension of the Julia hig

Shengyu Hu, Zhiwei Guo, Haitao Jiang, Hong Chen

Recently, the gapless Dirac/Weyl nodal semimetals with linear dispersion and topologically protected modes degeneracy are rapidly growing frontiers of topological physics. Especially, type-I, type-II, and critical type-III nodal semimetals are discovered according to the tilt angles of the Dirac/Weyl cones. Here, by introducing hyperbolic metamaterials into

Yiming Huo

Recent years have seen unprecedentedly fast-growing prosperity in the commercial space industry. Several privately funded aerospace manufacturers, such as Space Exploration Technologies Corporation (SpaceX) and Blue Origin have innovated what we used to know about this capital-intense industry and gradually reshaped the future of human civilization. As priva

Takamitsu Ishiyama, Takashi Suemasu, Kaoru Toko

Heteroepitaxy of functional thin films on single-crystal substrates is one of the most general themes in electronic materials research. Here, we propose an algorithm based on image processing for the rapid simulation of heteroepitaxial relationships. The superposition and rotation of various lattice plane images of the film and substrate, which were automati

Dessislava H. Kochloukova

We prove some conditions for higher dimensional algebraic fibering of pro-$p$ group extensions and we establish corollaries about incoherence of pro-$p$ groups. In particular, if $G = K \rtimes \Gamma$ is a pro-$p$ group, $\Gamma$ a finitely generated free pro-$p$ group with $d(\Gamma) \geq 2$, $K$ a finitely presented pro-$p$ group with $N$ a normal pro-$p$

Dening Lu, Qian Xie, Mingqiang Wei, Kyle Gao

Transformers have been at the heart of the Natural Language Processing (NLP) and Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring the use of Transformers in point cloud processing. However, how do Transformers cope with the irregularity and unordered nature of point clouds? How suitable are Transformers for different

Mahmoud Gadalla

This report presents the implementation details of the chemical explosive mode analysis (CEMA), using analytical Jacobian formulation, into OpenFOAM.

Rugang Ma, Xiaowen Zhou

Using the Lyapunov criteria arguments, we find sufficient conditions on explosion/nonexplosion for continuous-state branching processes with competition in L\'evy random environment. In particular, we identify the necessary and sufficient conditions on explosion/nonexplosion when the competition function is a power function and the L\'evy measure of the asso

Daniel J. Slonim

For a shift-invariant weighted directed graph with vertex set $\mathbb{Z}$, we examine the minimal weight $\kappa_0$ exiting a finite, strongly connected set of vertices. Although $\kappa_0$ is defined as an infimum, it has been shown that the infimum is always attained by an actual set of vertices. We show that for each underlying directed graph (prior to a

Sicong He, Xinran Zhou, Dan Mordehai, Jaime Marian

Refractory multi-element alloys (RMEA) with body-centered cubic (bcc) structure have been the object of much research over the last decade due to their high potential as candidate materials for high-temperature applications. Most of these alloys display a remarkable strength at high temperatures, which cannot be explained by the standard model of bcc plastic

Grigoris Panotopoulos, Ángel Rincón, Ilídio Lopes

This study focuses on the X-ray emission of low-mass black hole binaries in massive Brans-Dicke gravity. First, we compute the accretion disk adopting the well-known Shakura-Sunyaev model for an optically thick, cool, and geometrically thin disk. Moreover, we assume that the gravitational field generated by the stellar-mass black hole is an analogue of the S

Dinil Mon Divakaran, Adam Oest

Phishing attacks trick victims into disclosing sensitive information. To counter rapidly evolving attacks, we must explore machine learning and deep learning models leveraging large-scale data. We discuss models built on different kinds of data, along with their advantages and disadvantages, and present multiple deployment options to detect phishing attacks.

Harideep Nair, Prabhu Vellaisamy, Santha Bhasuthkar, John Paul Shen

Temporal Neural Networks (TNNs), inspired from the mammalian neocortex, exhibit energy-efficient online sensory processing capabilities. Recent works have proposed a microarchitecture framework for implementing TNNs and demonstrated competitive performance on vision and time-series applications. Building on these previous works, this work proposes TNN7, a su

William Balderrama

We describe how power operations descend through homotopy limit spectral sequences. We apply this to describe how norms appear in the $C_2$-equivariant Adams spectral sequence, to compute norms on $\pi_0$ of the equivariant $KU$-local sphere, and to compute power operations for the $K(1)$-local sphere. An appendix contains material on equivariant Bousfield l

Stephen Whitelam, Viktor Selin, Ian Benlolo, Corneel Casert

We examine the zero-temperature Metropolis Monte Carlo algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical grounds and shown empirically by other authors, Metropolis Monte Carlo can train a neural net with an accuracy comparable to that of gradient descent, if not necessarily as quickly. T

Xiaohan Yang, Eduardo Peynetti, Vasco Meerman, Chris Tanner

Coreference resolution -- which is a crucial task for understanding discourse and language at large -- has yet to witness widespread benefits from large language models (LLMs). Moreover, coreference resolution systems largely rely on supervised labels, which are highly expensive and difficult to annotate, thus making it ripe for prompt engineering. In this p

Xiangjing Liu, Daniel Ebler, Oscar Dahlsten

We address a new setting where the second law is under question: thermalizations in a quantum superposition of causal orders, enacted by the so-called quantum switch. This superposition has been shown to be associated with an increase in the communication capacity of the channels, yielding an apparent violation of the data-processing inequality and a possibi

Xiang Zhang, Qi Lu, Yalei Zhu, Jing Zhao

Quantum interference occurs frequently in the interaction of laser radiation with materials, leading to a series of fascinating effects such as lasing without inversion, electromagnetically induced transparency, Fano resonance, etc. Such quantum interference effects are mostly enabled by single-photon resonance with transitions in the matter, regardless of h

Li Yan, Pengcheng Wei, Hong Xie, Jicheng Dai

Registration is a basic yet crucial task in point cloud processing. In correspondence-based point cloud registration, matching correspondences by point feature techniques may lead to an extremely high outlier ratio. Current methods still suffer from low efficiency, accuracy, and recall rate. We use a simple and intuitive method to describe the 6-DOF (degree

Nickos Papadatos

Let $(X_1,\ldots,X_n)$ be an exchangeable random vector with distribution function $F$, and denote by $Y_1\leq \cdots\leq Y_n$ the corresponding order statistics. We show that the conditional distribution of $(X_1,\ldots,X_n)$ given $(Y_1,\ldots,Y_n)$ does not depend on $F$.

Guangsheng Shi, Ruifeng Li, Chao Ma

Real-time and high-performance 3D object detection is of critical importance for autonomous driving. Recent top-performing 3D object detectors mainly rely on point-based or 3D voxel-based convolutions, which are both computationally inefficient for onboard deployment. In contrast, pillar-based methods use solely 2D convolutions, which consume less computatio

Léo R. Belzile, Christophe Dutang, Paul J. Northrop, Thomas Opitz

This review paper surveys recent development in software implementations for extreme value analyses since the publication of Stephenson and Gilleland (2006) and Gilleland et al. (2013), here with a focus on numerical challenges. We provide a comparative review by topic and highlight differences in existing routines, along with listing areas where software de

Michael P. Adams, Andreas Michels, Hamid Kachkachi

We consider a dilute ensemble of randomly-oriented noninteracting spherical nanomagnets and investigate its magnetization structure and ensuing neutron-scattering response by numerically solving the Landau-Lifshitz equation. Taking into account the isotropic exchange interaction, an external magnetic field, a uniaxial magnetic anisotropy for the particle cor

Michael P. Adams, Andreas Michels, Hamid Kachkachi

The magnetization profile and the related magnetic small-angle neutron scattering cross section of a single spherical nanoparticle with Neel surface anisotropy is analytically investigated. We employ a Hamiltonian that comprises the isotropic exchange interaction, an external magnetic field, a uniaxial magnetocrystalline anisotropy in the core of the particl

Cyprien Tamekue

We investigate the null controllability property of the parabolic equation associated with the Grushin operator defined by the canonical almost-Riemannian structure on the 2-dimensional sphere $\mathbb S^2$. This is the natural generalization of the Grushin operator $\mathcal G = \partial_x^2 + x^2\partial_y^2$ on $\mathbb R^2$ to this curved setting, and pr

Laurent Bako, Vincent Andrieu

This paper discusses an interval-valued state estimator for linear dynamic systems. In particular, we derive an expression of the tightest possible interval-valued estimator in the sense that it is the intersection of all interval-valued estimators. This estimator appears, in a general setting, to be an infinite dimensional dynamic system. Therefore, practic

Navid Anjum Aadit, Andrea Grimaldi, Giovanni Finocchio, Kerem Y. Camsari

The nearing end of Moore's Law has been driving the development of domain-specific hardware tailored to solve a special set of problems. Along these lines, probabilistic computing with inherently stochastic building blocks (p-bits) have shown significant promise, particularly in the context of hard optimization and statistical sampling problems. p-bits have

Sarnaduti Brahma, Hamid R. Ossareh, Mads R. Almassalkhi

The performance of frequency regulating units for automatic generation control (AGC) of power systems depends on their ability to track the AGC signal accurately. In addition, representative models and advanced analysis and analytics can yield forecasts of the AGC signal that aids in controller design. In this paper, time-series analyses are conducted on an

Fujun Hou

In social choice theory, Sen's value restriction and Pattanaik's not-strict value restriction are both attractive conditions for testing social preference transitivity and/or non-empty social choice set existence. This article introduces a novel mathematical representation tool, called possibility preference map (PPM), for weak orderings, and then reformulat

Sean I. Young, Yaël Balbastre, Adrian V. Dalca, William M. Wells

In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent results in several registration benchmarks. These approaches utilize a loss function that penalizes the intensity differences between the fixed and moving images, along with a suitable

Ruyi Liu, Zhen Wu, Detao Zhang

In this paper, we investigate two families of fully coupled linear Forward-Backward Stochastic Differential Equations (FBSDE). Within these families, one could get the same well-posedness of FBSDEs with totally different structures. The first family of FBSDEs are proved to be equivalent with respect to the Unified Approach. Thus one could get the well-posedn

Mehdi Biderang, Mohammad-Hossein Zare, Jesko Sirker

Superconductivity was recently observed in boron-doped ($\sqrt{3}\times\sqrt{3}$)Sn/Si(111). The material can be described by an extended Hubbard model on a triangular lattice. Here, we use the random-phase approximation to investigate the charge and spin fluctuations as well as the superconducting properties of the system with respect to filling and the rel

Jean Carlo Guella, Janin Jäger

We present sufficient condition for a family of positive definite kernels on a compact two-point homogeneous space to be strictly positive definite based on their representation as a series of spherical harmonics. The family analyzed is a generalization of the isotropic kernels and the case of a real sphere is analyzed in details.

Thomas Krendl Gilbert, Aaron J. Snoswell, Michael Dennis, Rowan McAllister

Autonomous Vehicles (AVs) will have a transformative impact on society. Beyond the local safety and efficiency of individual vehicles, these effects will also change how people interact with the entire transportation system. This will generate a diverse range of large and foreseeable effects on social outcomes, as well as how those outcomes are distributed.

Gagandeep Singh, Rakesh Nadig, Jisung Park, Rahul Bera

Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Recent research proposes various techniques that aim to accurately identify performance-critical data to place it in a "best-fit" storage device. Unfortunately, most of these techniques are rigid, which (1) limits their adapt

Xiaobing Feng, Akash Ashirbad Panda, Andreas Prohl

In this paper, a higher-order time-discretization scheme is proposed, where the iterates approximate the solution of the stochastic semilinear wave equation driven by multiplicative noise with general drift and diffusion. We employ a variational method for its error analysis and prove an improved convergence order of 3/2 for the approximates of the solution.

Mengwei Yuan, Gang Yang, Shijie Song, Luping Zhou

In this paper, we propose a pre-trained-combined neural network (PTCN) as a comprehensive solution to the inverse design of an integrated photonic circuit. By utilizing both the initially pre-trained inverse and forward model with a joint training process, our PTCN model shows remarkable tolerance to the quantity and quality of the training data. As a proof

Irina Đanković, Maria-Romina Ivan

For a given positive integer $k$ we say that a family of subsets of $[n]$ is $k$-antichain saturated if it does not contain $k$ pairwise incomparable sets, but whenever we add to it a new set, we do find $k$ such sets. The size of the smallest such family is denoted by $\text{sat}^*(n, \mathcal A_{k})$. Ferrara, Kay, Kramer, Martin, Reiniger, Smith and Sulli

César M. Silva

For linear nonautonomous differential equations we introduce a new family of spectrums defined with general nonuniform dichotomies: for a given growth rate $\mu$ in a large family of growth rates, we consider a notion of spectrum, named nonuniform $\mu$-dichotomy spectrum. This family of spectrums contain the nonuniform dichotomy spectrum as the very particu

Zhepei Wang, Cem Subakan, Xilin Jiang, Junkai Wu

In this paper, we work on a sound recognition system that continually incorporates new sound classes. Our main goal is to develop a framework where the model can be updated without relying on labeled data. For this purpose, we propose adopting representation learning, where an encoder is trained using unlabeled data. This learning framework enables the study

Yu-Rong Wu, Xiao-Fei Zhang, Chao-Fei Liu, Wu-Ming Liu

The superfluid properties of attractive Hubbard model in dice lattice are investigated. It is found that three superfluid order parameters increase as the interaction increases. When the filling factor falls into the flat band, due to the infinite large density of states, the resultant superfluid order parameters are proportional to interaction strength, whi

Hannah Spero, Donna Calhoun, Michael Schubert

Dam failures occur worldwide, often from factors including aging structures, extreme hydrologic loading, and design oversights related to the changing climate. Understanding and mitigating risk to downstream inhabited areas require developing and improving low-cost high-fidelity tools, such as numerical models, which allow emergency managers to predict the c

Charles F. Manski

Incomplete observability of data generates an identification problem. There is no panacea for missing data. What one can learn about a population parameter depends on the assumptions one finds credible to maintain. The credibility of assumptions varies with the empirical setting. No specific assumptions can provide a realistic general solution to the problem

Cheng Zhang, Hao Zhang, Jie Wang

We present a system called TP3 to perform a downstream task of transformers on generating question-answer pairs (QAPs) from a given article. TP3 first finetunes pretrained transformers on QAP datasets, then uses a preprocessing pipeline to select appropriate answers, feeds the relevant sentences and the answer to the finetuned transformer to generate candida

Yi-Cai Zhang

In this work, we investigate the bound states in the continuum (BIC) of a one-dimensional spin-1 flat band system. It is found that, when the potential is sufficiently strong, there exists an effective attractive potential well surrounded by infinitely high self-sustained barriers. Consequently, there exist some BIC in the effective potential well. These bou

Emilio Said

We propose a theory of the market impact of metaorders based on a coarse-grained approach where the microscopic details of supply and demand is replaced by a single parameter $\rho \in [0,+\infty]$ shaping the supply-demand equilibrium and the market impact process during the execution of the metaorder. Our model provides an unified explanation of most of th

Ziyang Jiang, Tongshu Zheng, Yiling Liu, David Carlson

It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kernel in a Gaussian process (GP). Many deep learning applications could be enhanced by modeling such known properties. For example, convolutional

Bruce W. Jordan, Yevgeny Zaytman

We consider the structures formed by isogenies of abelian varieties with polarizations that are not necessarily principal, specifically with the $[\ell]$-polarizations we have previously defined. Our primary interest is in superspecial abelian varieties, where the isogenies are related to quaternionic hermitian forms. We first consider isogeny graphs. We sho

Tom Banks, Patrick Draper, Bingnan Zhang

We argue that two-dimensional dilaton gravity models can all be derived from an analog of Jacobson's covariant version of the first law of thermodynamics. We then specialize to the JT gravity model and couple it to massless fermions. This model is exactly soluble in quantum field theory, and we present a new derivation of that result. The field theory model

Jingfeng Yang, Haoming Jiang, Qingyu Yin, Danqing Zhang

Recent research showed promising results on combining pretrained language models (LMs) with canonical utterance for few-shot semantic parsing. The canonical utterance is often lengthy and complex due to the compositional structure of formal languages. Learning to generate such canonical utterance requires significant amount of data to reach high performance.

Jan Verschelde, Kylash Viswanathan

A polynomial homotopy is a family of polynomial systems, where the systems in the family depend on one parameter. If for one value of the parameter we know a regular solution, then what is the nearest value of the parameter for which the solution in the polynomial homotopy is singular? For this problem we apply the ratio theorem of Fabry. Richardson extrapol

Dmitriy Malovichko, Alex Rigby

This paper considers dynamic fracturing of the rockmass surrounding a tunnel statically loaded by compressional stress as a possible source of seismic events in underground mines. This begins with two-dimensional dynamic modelling of failure for six plausible scenarios. In each case, the seismic source derived from these models has significant negative isotr

Xinkai Zhou, Qiang Heng, Eric C. Chi, Hua Zhou

This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Bayesian imaging literature, ProxMCMC employs the Moreau-Yosida envelope for a smooth approximation of the total-variation regularization term, fixes variance and regu

V. Dolotin, A. Morozov

Machine Learning (ML) is applicable to scientific problems, i.e. to those which have a well defined answer, only if this answer can be brought to a peculiar form ${\cal G}: X\longrightarrow Z$ with ${\cal G}(\vec x)$ expressed as a combination of iterated Heaviside functions. At present it is far from obvious, if and when such representations exist, what are

Paulo A. Faria da Veiga, Michael O'Carroll

We consider the Yang-Mills (YM) QFT with group $U(N)$. We take a finite lattice regularization $\Lambda\subset a\mathbb Z^d$, $d = 2,3,4$, with $a\in (0,1]$ and $L$ (even) sites on a side. Each bond has a gauge variable $U\in U(N)$. The Wilson partition function is used and the action is a sum of gauge-invariant plaquette (minimal square) actions times $a^{d

Alireza Mohammadi, Mark W. Spong

Understanding the process of protein unfolding plays a crucial role in various applications such as design of folding-based protein engines. Using the well-established kinetostatic compliance (KCM)-based method for modeling of protein conformation dynamics and a recent nonlinear control theoretic approach to KCM-based protein folding, this paper formulates p

Lukas T. Hergt, Fruzsina J. Agocs, Will J. Handley, Michael P. Hobson

We investigate the effects of non-zero spatial curvature on cosmic inflation in the light of cosmic microwave background (CMB) anisotropy measurements from the Planck 2018 legacy release and from the 2015 observing season of BICEP2 and the Keck Array. Even a small percentage of non-zero curvature today would significantly limit the total number of e-folds of

Xueyan Feng, Michael S. Dimitriyev, Edwin L. Thomas

A twin boundary (TB) is a common low energy planar defect in crystals including those with the atomic diamond structure (C, Si, Ge, etc.). We study twins in a self-assembled soft matter block copolymer (BCP) supramolecular crystal having the double diamond (DD) structure, consisting of 2 translationally shifted, interpenetrating diamond networks of the minor

Omobayode Fagbohungbe, Lijun Qian

The fast execution speed and energy efficiency of analog hardware has made them a strong contender for deployment of deep learning model at the edge. However, there are concerns about the presence of analog noise which causes changes to the weight of the models, leading to performance degradation of deep learning model, despite their inherent noise resistant

Zhaofeng Lin, Yanqi Qiu, Kai Wang

Let $U$ be a random unitary matrix drawn from the Hua-Pickrell distribution $\mu_{\mathrm{U}(n+m)}^{(\delta)}$ on the unitary group $\mathrm{U}(n+m)$. We show that the eigenvalues of the truncated unitary matrix $[U_{i,j}]_{1\leq i,j\leq n}$ form a determinantal point process $\mathscr{X}_n^{(m,\delta)}$ on the unit disc $\mathbb{D}$ for any $\delta\in\mathb

Joseph C. Straccia, John A. N. Farnsworth

The vortex dynamics resulting from the interaction of synthetic jets with turbulent boundary layers was investigated experimentally using stereoscopic particle image velocimetry (SPIV). Three aspect ratio 18 rectangular orifice geometries were tested including spanwise and streamwise-oriented orifices issuing normal to the wall and a spanwise-oriented orific

The Anh Han

The mechanisms of emergence and evolution of collective behaviours in dynamical Multi-Agent Systems (MAS) of multiple interacting agents, with diverse behavioral strategies in co-presence, have been undergoing mathematical study via Evolutionary Game Theory (EGT). Their systematic study also resorts to agent-based modelling and simulation (ABM) techniques, t

Vyacheslav I. Dokuchaev

We elucidate the physical origin of the dark spot in the image of supermassive black hole SgrA* presented very recently by the EHT collaboration. It is argued that this dark spot, which is noticeably smaller than the classical black hole shadow, is the northern hemisphere of the event horizon globe. The classical black hole shadow is unseen in the image of S

Peikai Li, Ipek Ilayda Onur, Scott Dodelson, Shreyas Chaudhari

Next-generation cosmic microwave background (CMB) surveys are expected to provide valuable information about the primordial universe by creating maps of the mass along the line of sight. Traditional tools for creating these lensing convergence maps include the quadratic estimator and the maximum likelihood based iterative estimator. Here, we apply a generati

D. Quang To, Zhengtianye Wang, Yongchen Liu, Weipeng Wu

We present a robust technique for computationally studying surface polariton modes in hybrid materials. We use a semi-classical model that allows us to understand the physics behind the interactions between collective excitations of the hybrid system and develop a scattering and transfer matrix method that imposes the proper boundary conditions to solve Maxw

M. Nakanotani, G. P. Zank, L. -L. Zhao

We investigate particle acceleration in an MHD-scale system of multiple current sheets by performing 2D and 3D MHD simulations combined with a test particle simulation. The system is unstable for the tearing-mode instability, and magnetic islands are produced by magnetic reconnection. Due to the interaction of magnetic islands, the system turns into a turbul

T. Lizée, B. Vollmer, J. Braine, P. Gratier

The ISM is a turbulent, multi-phase, and multi-scale medium following scaling relations. Analytical models of galactic gaseous disks need to take into account the multi-scale and multi-phase nature of the interstellar medium. They can be described as clumpy star-forming accretion disks in vertical hydrostatic equilibrium, with the mid-plane pressure balancin

E. E. Boos, V. E. Bunichev, S. S. Trykov

We present perspectives for searching for light dark matter production mediated by a leptophilic scalar {\phi} and a dark photon A' in in experiments at the Super c-tau Factory. Based on the analysis of the associative production of mediators and {\tau} -leptons at the energies of the future collider, the possibility of searching in the non-excluded region o

Andrei Ioan Dogaru, Ruben Campos Delgado

We show that any 2D scalar field theory compactified on a cylinder and with a Fourier expandable potential $V$ is equivalent, in the small coupling limit, to a 1D theory involving a massless particle in a potential $V$ and an infinite tower of free massive Kaluza-Klein (KK) modes. Moving slightly away from the deep IR region has the effect of switching on in

Lek-Heng Lim, Bradley J. Nelson

We explain equivariant neural networks, a notion underlying breakthroughs in machine learning from deep convolutional neural networks for computer vision to AlphaFold 2 for protein structure prediction, without assuming knowledge of equivariance or neural networks. The basic mathematical ideas are simple but are often obscured by engineering complications th

Xu Guo, Runze Li, Zhe Zhang, Changliang Zou

This paper aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. With the aid of new reformulation, we propose a new test statistic and show that its asymptotic distribution is $\chi^2$ distribution whose degree of freedom does n

Aurélien Desoeuvres, Peter Szmolyan, Ovidiu Radulescu

We discuss a method to describe the qualitative dynamics of chemical reaction networks in terms of symbolic dynamics. The method, that can be applied to mass-action reaction networks with separated timescales, uses solutions of the partial tropical equilibration problem as proxies for symbolic states. The partial tropical equilibration solutions are found al

Simone Ciani, Umberto Guarnotta, Vincenzo Vespri

In this brief note we show that under a volume non-preserving scaling it is possible to recover the basics for a regularity theory regarding local weak solutions to a parabolic fully anisotropic equation. We characterize self-similar solutions regarding this particular scaling and we show that semi-continuity for solutions to this equation is a consequence o

Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen

Cone Beam CT plays an important role in many medical fields nowadays, but the potential of this imaging modality is hampered by lower image quality compared to the conventional CT. A lot of recent research has been directed towards reconstruction methods relying on deep learning. However, practical application of deep learning to CBCT reconstruction is compl

Fernando Febres Cordero, Manfred Kraus, Guanda Lin, Michael S. Ruf

We compute the conservative two-body Hamiltonian of a compact binary system with a spinning black hole through $\mathcal{O}(G^3)$ to all orders in velocity, including linear and quadratic spin terms. To obtain our results we calculate the classical limit of the two-loop amplitude for the scattering of a massive scalar particle with a massive spin-1 particle

Conor Rosato, John Harris, Jasmina Panovska-Griffiths, Simon Maskell

State-space models have been widely used to model the dynamics of communicable diseases in populations of interest by fitting to time-series data. Particle filters have enabled these models to incorporate stochasticity and so can better reflect the true nature of population behaviours. Relevant parameters such as the spread of the disease, $R_t$, and recover

Yongxin Wu, Hui Xia

The pinning-depinning phase transitions of interfaces for two classes of discrete elastic-string models are investigated numerically. In the (1+1)-dimensions, we revisit these two elastic-string models with slight modification to growth rule, and compare the estimated values with the previous numerical and experimental results. For the (2+1)-dimensional case

Yihang Zeng, Zhengchao Xia, Roei Dery, Kenji Watanabe

Strongly correlated bosons in a lattice are a platform to realize rich bosonic states of matter and quantum phase transitions. While strongly correlated bosons in a lattice have been studied in cold-atom experiments, their realization in a solid-state system has remained challenging. Here we trap interlayer excitons--bosons composed of bound electron-hole pa

Stefan Sandner

The light neutrino masses are at present most stringently constraint via cosmological probes. In particular the Planck collaboration reports $ \sum m_\nu \leq 0.12\,\mathrm{eV}$ at $95\%$ CL within the standard cosmological model. This is more than one order of magnitude stronger than the one arising from laboratory searches. The cosmological bound taken at

Ramya Ramakrishnan, Hashan Buddhika Narangodage, Mauro Schilman, Kilian Q. Weinberger

Current approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic. In this work, we focus on constrained long-term dialogue generation, which involves more fine-grained control and requires a given set of control words to appear in generated responses. This setting requires a model

Antti Käenmäki, Petteri Nissinen

We compare the dimension of a non-invertible self-affine set to the dimension of the respective invertible self-affine set. In particular, for generic planar self-affine sets, we show that the dimensions coincide when they are large and differ when they are small. Our study relies on thermodynamical formalism where, for dominated and irreducible matrices, we