May 2022 arXiv papers — page 70

Y. Y. Pan, Z. S. Li, C. M. Zhang, J. X. Zhong

NGC 300 ULX1 is a pulsating ultraluminous X-ray source (PULX) with the longest spin period of $P\simeq31.6\,\rm s$ and a high spin-up rate of $\dot P\simeq5.56\times10^{-7}\,\rm s\,s^{-1}$ that is ever seen in the confirmed PULXs. In this paper, the inferred magnetic field of NGC 300 ULX1 is $\sim3.0\times10^{14}\,\rm G$ using the recent observed parameters

Xinpeng Ding, Ziwei Liu, Xiaomeng Li

Self-supervised learning has witnessed great progress in vision and NLP; recently, it also attracted much attention to various medical imaging modalities such as X-ray, CT, and MRI. Existing methods mostly focus on building new pretext self-supervision tasks such as reconstruction, orientation, and masking identification according to the properties of medica

Amarjit Budhiraja, Adam Waterbury

Let $A$ be a transition probability kernel on a finite state space $\Delta^o =\{1, \ldots , d\}$ such that $A(x,y)>0$ for all $x,y \in \Delta^o$. Consider a reinforced chain given as a sequence $\{X_n, \; n \in \mathbb{N}_0\}$ of $\Delta^o$-valued random variables, defined recursively according to, $$L^n = \frac{1}{n}\sum_{i=0}^{n-1} \delta_{X_i}, \;\; P(X_{

Xing-Yu Xu, Zhong-Zhu Liang, Li Qin, Xue-Mei Su

In this paper, we propose a MIM (metallic metasurface-insulator-metal) stacked structure to realize perfect absorption in mid- and far- infrared bandwidth. A large number of metallic composite metallic units placed on a uniform layer of insulator Ge which is deposited on a uniform metallic Ti surface. Each of units consists of four right-angled triangular cu

Mrinal Mathur

Chip placement has been one of the most time consuming task in any semi conductor area, Due to this negligence, many projects are pushed and chips availability in real markets get delayed. An engineer placing macros on a chip also needs to place it optimally to reduce the three important factors like power, performance and time. Looking at these prior proble

Li-Na Ji, Yan Liang, Pu Shen, Zheng-Yuan Xue

Nonadiabatic holonomic quantum computation (NHQC) is implemented by fast evolution processes in a geometric way to withstand local noises. However, recent works of implementing NHQC are sensitive to the systematic noise and error. Here, we present a path-optimized NHQC (PONHQC) scheme based on the non-Abelian geometric phase, and find that a geometric gate c

James A. Latshaw, Dimitrie C. Popescu, John A. Snoap, Chad M. Spooner

Machine learning has become a powerful tool for solving problems in various engineering and science areas, including the area of communication systems. This paper presents the use of capsule networks for classification of digitally modulated signals using the I/Q signal components. The generalization ability of a trained capsule network to correctly classify

Hui Li, Ting Gao, Fengli Yan

In this paper, we first provide three general norm inequalities, which are used to give new uncertainty relations of any finite observables and quantum channels via metric-adjusted skew information. The results are applicable to its special cases as Wigner-Yanase-Dyson skew information. In quantifying the uncertainty of channels, we discuss two types of lowe

Chalapathi Charan Gajjela, Matthew Brun, Rupali Mankar, Sara Corvigno

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovarian cancer involves tissue staining followed by morphological pattern recognition. This process is time-consuming, subjective, and requires extensive expertise. This paper presents t

Zhengyu Yang, Kan Ren, Xufang Luo, Minghuan Liu

It is challenging for reinforcement learning (RL) algorithms to succeed in real-world applications like financial trading and logistic system due to the noisy observation and environment shifting between training and evaluation. Thus, it requires both high sample efficiency and generalization for resolving real-world tasks. However, directly applying typical

Xiao-Yun Wang, Fan-Cong Zeng, Xiang Liu

By the reaction of kaon interacting with a proton, we investigate the production of the newly observed $\eta _{1}(1855)$ predicted in the picture of the $K\bar{K}_1(1400)$ molecular state and hybrid state. The total and differential cross sections of the concrete $K^{-}p\to\eta _{1}(1855)\Lambda$ reaction are calculated. Taking the partial decay width of the

R. Petti

We discuss a method to study free protons and neutrons using $\nu(\bar \nu)$-hydrogen (H) Charged Current (CC) inelastic interactions, together with various precision tests of the isospin (charge) symmetry using $\nu$ and $\bar \nu$ CC interactions on both H and nuclear targets. Probing free nucleons with (anti)neutrinos provides information about their part

Moisés Bravo-Gaete, Luis Guajardo, Julio Oliva

In this work, we consider the recently proposed well-defined theory that permits a healthy $D\to 4$ limit of the Einstein-Gauss-Bonnet combination, which requires the addition of a scalar degree of freedom. We continue the construction of exact, hairy black hole solutions in this theory in the presence of matter sources, by considering a nonlinear electrodyn

Raquel Aoki, Martin Ester

Many methods have been proposed to estimate treatment effects with observational data. Often, the choice of the method considers the application's characteristics, such as type of treatment and outcome, confounding effect, and the complexity of the data. These methods implicitly assume that the sample size is large enough to train such models, especially the

Emilia Barbisan, Jeff Huang, Kristen C. Dage, Daryl Haggard

Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87's well-studied GC system to implement supervised machine learning (ML) classification algorithms - specifically random fo

Ken-ichi Amano, Kentaro Tozawa, Maho Tomita, Hiroshi Nakano

Understanding the interaction between two bodies in a liquid metal is important for developing metals with high stiffness, strength, plasticity, and thermal stability. We conducted atomic force microscopy measurements in liquid Ga and performed a theoretical calculation in which the statistical mechanics of a simple liquid containing a quantum effect was use

Shufan Wang, Fangyuan Xu, Laure Thompson, Eunsol Choi

Exemplification is a process by which writers explain or clarify a concept by providing an example. While common in all forms of writing, exemplification is particularly useful in the task of long-form question answering (LFQA), where a complicated answer can be made more understandable through simple examples. In this paper, we provide the first computation

Masatake Sugita, Fumio Hirata

A new theoretical method, referred to as Generalized Langevin Mode Analysis (GLMA), is proposed to analyze the mode of structural fluctuations of a biomolecule in solution. The method combines the two theories in the statistical mechanics, or the Generalized Langevin theory and the RISM/3D-RISM theory, to calculate the second derivative, or the Hessian matri

Yasen Hou, Fabrizio Nichele, Hang Chi, Alessandro Lodesani

The macroscopic coherence in superconductors supports dissipationless supercurrents which could play a central role in emerging quantum technologies. Accomplishing unequal supercurrents in the forward and backward directions would enable unprecedented functionalities. This nonreciprocity of critical supercurrents is called superconducting (SC) diode effect.

Julio H. Toloza, Alfredo Uribe

We consider the perturbed Stark operator $H_q\varphi = -\varphi" + x\varphi + q(x)\varphi$, $\varphi(0)=0$, in $L^2(\mathbb{R}_+)$, where $q$ is a real-valued function that belongs to $\mathfrak{A}_r =\left\{ q\in\mathcal{A}_r\cap\text{AC}[0,\infty) : q'\in\mathcal{A}_r\right\}$, where $\mathcal{A}_r = L^2(\mathbb{R}_+,(1+x)^r dx)$ and $r>1$ is arbitrary but

Wei Xia

In this note, we discuss unpolarized, complex variation of Hodge structures for non-K\"ahler manifolds. In particular, given a holomorphic family of compact complex manifolds whose central fiber satisfies: the inclusions $F^{p}A^{p+q+1}(X)\hookrightarrow A^{p+q+1}(X), F^{p}A^{p+q}(X)\hookrightarrow A^{p+q}(X)$ are injective in cohomology, it is shown that th

Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng

Many language generation models are now available for a wide range of generation tasks, including machine translation and summarization. Combining such diverse models may lead to further progress, but ensembling generation models is challenging during inference: conventional ensembling methods (e.g., shallow fusion) require that the models share vocabulary/t

Yu Hu, Pan-Pan Wang, Yu-Jie Tan, Cheng-Gang Shao

Pulsar timing array (PTA) is expected to detect gravitational wave background (GWB) in the nanohertz band within the next decade. This provides an opportunity to test the gravity theory and cosmology. A typical data analysis method to detect GWB is cross-correlation analysis. The overlap reduction function (ORF) plays an important role in the correlation dat

Boris Rubinstein, Jay Unruh, Julia Zeitlinger

We consider a model of mRNA production governed by the dynamics of a gene that exists in three possible states -- inactive, poised and active. The transitions between the adjacent states are controlled by stochastic processes characterized by corresponding on/off rates. mRNA is produced only when the gene is in active state and we also consider mRNA denatura

Thomas P Fay

The study of open system quantum dynamics has been transformed by the hierarchical equations of motion (HEOM) method, which gives the exact dynamics for a system coupled to a harmonic bath at arbitrary temperature and system-bath coupling strength. However in its standard form the method is only consistent with the weak-coupling quantum master equation at al

Emily Halina, Matthew Guzdial

To best assist human designers with different styles, Machine Learning (ML) systems need to be able to adapt to them. However, there has been relatively little prior work on how and when to best adapt an ML system to a co-designer. In this paper we present threshold designer adaptation: a novel method for adapting a creative ML model to an individual designe

Minghua Pan, Haozhen Situ, Shenggen Zheng

Coherence plays a very important role in Grover search algorithm (GSA). In this paper, we define the normalization coherence N(C), where C is a coherence measurement. In virtue of the constraint of large N and Shannon's maximum entropy principle, a surprising complementary relationship between the coherence and the success probability of GSA is obtained. Nam

Peiyu Zhang, Dajun Liu, Jiaqun Wei

n this article, firstly, we introduce the notion of star modules with respect to a balanced pair and obtain some properties. We mainly give the relationship between n-X star modules and n-X tilting modules [9], and a new characterization of n-X tilting modules.

Iosif Pinelis

Exact upper and lower bounds on the ratio $\mathsf{E}w(\mathbf{X}-\mathbf{v})/\mathsf{E}w(\mathbf{X})$ for a centered Gaussian random vector $\mathbf{X}$ in $\mathbb{R}^n$, as well as bounds on the rate of change of $\mathsf{E}w(\mathbf{X}-t\mathbf{v})$ in $t$, where $w\colon\mathbb{R}^n\to[0,\infty)$ is any even unimodal function and $\mathbf{v}$ is any vec

Edward W. Cliver, Carolus J. Schrijver, Kazunari Shibata, Ilya G. Usoskin

We trace the evolution of research on extreme solar and solar-terrestrial events from the 1859 Carrington event to the rapid development of the last twenty years. Our focus is on the largest observed/inferred/theoretical cases of sunspot groups, flares on the Sun and Sun-like stars, coronal mass ejections, solar proton events, and geomagnetic storms. The rev

Peiyu Zhang, Dajun Liu, Jiaqun Wei

In this paper, firstly, we mainly study the relationship of balanced pairs among three Abelian categories in a recollement. As an application of admissible balanced pairs, we introduce the notion of the relative tilting modules, and give a characterization of relative tilting modules, which similar to Bazzoni characterization of n-tilting modules [4]. Finall

Zhipeng Huang, Hadeel Soliman, Subhadeep Paul, Kevin S. Xu

Networks and temporal point processes serve as fundamental building blocks for modeling complex dynamic relational data in various domains. We propose the latent space Hawkes (LSH) model, a novel generative model for continuous-time networks of relational events, using a latent space representation for nodes. We model relational events between nodes using mu

Toshio Suzuki

Nuclear weak rates in stellar environments are obtained by shell-model calculations including Gamow-Teller (GT) and spin-dipole transitions, and applied to nuclear weak processes in stars. The important roles of accurate weak rates for the study of astrophysical processes are pointed out. The weak rates in $sd$-shell are used to study the evolution of ONeMg

Alberto Roper Pol

We propose that the recent observations reported by the different Pulsar Timing Array (PTA) collaborations (i.e.~IPTA, EPTA, PPTA, and NANOGrav) of a common process over several pulsars could correspond to a stochastic gravitational wave background (SGWB) produced by turbulent sources in the early universe, in particular due to the magnetohydrodynamic (MHD)

Peiyu Zhang, Qianqian Shu, Dajun Liu

In this paper, we study the relationship of Gorenstein projective objects among three Abelian categories in a recollement. As an application, we introduce the relation of $n$-Gorenstein tilting modules (and Gorenstein syzygy modules) in three Abelian categories. For a recollement of Abelian categories, we show that a resolving subcategory induce two resolvin

Justin M. Kennedy, Julian Heinovski, Daniel E. Quevedo, Falko Dressler

Cooperative adaptive cruise control presents an opportunity to improve road transportation through increase in road capacity and reduction in energy use and accidents. Clever design of control algorithms and communication systems is required to ensure that the vehicle platoon is stable and meets desired safety requirements. In this paper, we propose a centra

Marco Ripà

The starting point of our paper is Kashihara's open problem number $30$, concerning the sequence $A001292$ of the OEIS, asking how many terms are powers of integers. We confirm his last conjecture up to the $100128$-th term and provide a general theorem that rules out $4/9$ of the candidates. Moreover, we formulate a new, provocative, conjecture involving th

Jing Xia, Xichao Zhang, Oleg A. Tretiakov, Hung T. Diep

Manipulation of three-dimensional (3D) topological objects is of both fundamental interest and practical importance in many branches of physics. Here, we show by spin dynamics simulations that the bifurcation of a 3D skyrmion string in a layered frustrated system could be induced by the dampinglike spin-orbit torque. The bifurcation of a skyrmion string happ

Pengkun Liu, Ruoxin Xiong, Pingbo Tang

In bridge inspection, engineers should diagnose the observed bridge defects by identifying the factors underlying those defects. Traditionally, engineers search and organize structural condition-related information based on visual inspections. Even following the same qualitative inspection standards, experienced engineers tend to find the critical defects an

Liangke Gui, Yingshan Chang, Qiuyuan Huang, Subhojit Som

Vision-Language Transformers can be learned without low-level human labels (e.g. class labels, bounding boxes, etc). Existing work, whether explicitly utilizing bounding boxes or patches, assumes that the visual backbone must first be trained on ImageNet class prediction before being integrated into a multimodal linguistic pipeline. We show that this is not

Taylor A. Howell, Simon Le Cleac'h, Kevin Tracy, Zachary Manchester

We present a new solver for non-convex trajectory optimization problems that is specialized for robotics applications. CALIPSO, or the Conic Augmented Lagrangian Interior-Point SOlver, combines several strategies for constrained numerical optimization to natively handle second-order cones and complementarity constraints. It reliably solves challenging motion

Ed Bennett, Deog Ki Hong, Jong-Wan Lee, C. -J. David Lin

For Yang-Mills theories in four dimensions, we propose to rescale the ratio between topological susceptibility and string tension squared in a universal way, dependent only on group factors. We apply this suggestion to $SU(N_c)$ and $Sp(N_c)$ groups, and compare lattice measurements performed by several independent collaborations. We show that the two sequen

Augusto Martins, Kezheng Li, Guilherme S. Arruda, Donato Conteduca

The correction of multiple aberrations in an optical system requires different optical elements, which increases its cost and complexity. Metasurfaces hold great promise to providing new functionality for miniaturized and low-cost optical systems. A key advantage over their bulk counterparts is the metasurface's ability to respond to the polarization of ligh

Carlos Misael Madrid Padilla, Daren Wang, Zifeng Zhao, Yi Yu

We study the problem of change-point detection and localisation for functional data sequentially observed on a general d-dimensional space, where we allow the functional curves to be either sparsely or densely sampled. Data of this form naturally arise in a wide range of applications such as biology, neuroscience, climatology, and finance. To achieve such a

Wei-Di Chang, Juan Camilo Gamboa Higuera, Scott Fujimoto, David Meger

We present an algorithm for Inverse Reinforcement Learning (IRL) from expert state observations only. Our approach decouples reward modelling from policy learning, unlike state-of-the-art adversarial methods which require updating the reward model during policy search and are known to be unstable and difficult to optimize. Our method, IL-flOw, recovers the e

Mohammad Joshaghani, Amirabbas Davari, Faezeh Nejati Hatamian, Andreas Maier

Employing deep neural networks for Hyperspectral remote sensing (HSRS) image classification is a challenging task. HSRS images have high dimensionality and a large number of channels with substantial redundancy between channels. In addition, the training data for classifying HSRS images is limited and the amount of available training data is much smaller com

Hyounghun Kim, Aishwarya Padmakumar, Di Jin, Mohit Bansal

Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observations, and choosing appropriate actions to execute in the environment to produce desired changes. We experiment with augmenting a transformer model for this task with modules that

Anton Ratnarajah, Zhenyu Tang, Rohith Chandrashekar Aralikatti, Dinesh Manocha

We propose a mesh-based neural network (MESH2IR) to generate acoustic impulse responses (IRs) for indoor 3D scenes represented using a mesh. The IRs are used to create a high-quality sound experience in interactive applications and audio processing. Our method can handle input triangular meshes with arbitrary topologies (2K - 3M triangles). We present a nove

Sarmed A Rahman, Randy Lewis, Emanuele Mendicelli, Sarah Powell

Quantum computers offer the possibility to implement lattice gauge theory in Minkowski rather than Euclidean spacetime, thus allowing calculations of processes that evolve in real time. In this work, calculations within SU(2) pure gauge theory are able to show the motion of an excitation traveling across a spatial lattice in real time. This is accomplished b

Jack Roper

Recent advancements in large pre-trained transformer models (GPT2/3, T5) have found use in program synthesis to generate programs that satisfy a set of input/output examples. However, these models perform poorly on long-horizon and low-data tasks, and often don't seem to understand the semantics of the languages they generate. We investigate an approach that

Keren Censor-Hillel, Dean Leitersdorf, David Vulakh

The importance of classifying connections in large graphs has been the motivation for a rich line of work on distributed subgraph finding that has led to exciting recent breakthroughs. A crucial aspect that remained open was whether deterministic algorithms can be as efficient as their randomized counterparts, where the latter are known to be tight up to pol

Christopher Scarvelis, Justin Solomon

We introduce an optimal transport-based model for learning a metric tensor from cross-sectional samples of evolving probability measures on a common Riemannian manifold. We neurally parametrize the metric as a spatially-varying matrix field and efficiently optimize our model's objective using a simple alternating scheme. Using this learned metric, we can non

William Evans, David Kirkpatrick

We consider the problem of using location queries to monitor the congestion potential among a collection of entities moving, with bounded speed but otherwise unpredictably, in $d$-dimensional Euclidean space. Uncertainty in entity locations due to potential motion between queries gives rise to a space of possible entity configurations at each moment in time,

Gregory R. Chambers, Yevgeny Liokumovich, Alexander Nabutovsky, Regina Rotman

A geodesic flower is a finite collection of geodesic loops based at the same point $p$ that satisfy the following balancing condition: The sum of all unit tangent vectors to all geodesic arcs meeting at $p$ is equal to the zero vector. In particular, a geodesic flower is a stationary geodesic net. We prove that in every complete non-compact manifold with loc

Karthik Elamvazhuthi, Bahman Gharesifard, Andrea Bertozzi, Stanley Osher

We consider the controllability problem for the continuity equation, corresponding to neural ordinary differential equations (ODEs), which describes how a probability measure is pushedforward by the flow. We show that the controlled continuity equation has very strong controllability properties. Particularly, a given solution of the continuity equation corre

Yuantong Li, Xiaokai Wei, Zijian Wang, Shen Wang

People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair ranking methods provide a trade-offs between accuracy and fairness through adding a fairness-related regularization term in the loss function. However, there haven't been intuitive objec

Aftab Hussain

With the wide spread use of AI-driven systems in the edge (a.k.a edge intelligence systems), such as autonomous driving vehicles, wearable biotech devices, intelligent manufacturing, etc., such systems are becoming very critical for our day-to-day lives. A challenge in designing edge intelligence systems is that we have to deal with a large number of constra

Maximilian Aigner, Valérie Chavez-Demoulin

In this paper, we are interested in linear prediction of a particular kind of stochastic process, namely a marked temporal point process. The observations are event times recorded on the real line, with marks attached to each event. We show that in this case, linear prediction extends straightforwardly from the theory of prediction for stationary stochastic

Mauricio Islas-Gómez, Rafael Villarroel-Flores

To any simple graph \(G\), the clique graph operator \(K\) assigns the graph \(K(G)\) which is the intersection graph of the maximal complete subgraphs of \(G\). The iterated clique graphs are defined by \(K^{0}(G)=G\) and \(K^{n}(G)=K(K^{n-1}(G))\) for \(n\geq 1\). We associate topological concepts to graphs by means of the simplicial complex \(\mathrm{Cl}(

Devin W. Goodsman

Movements in imagery captivate the human eye and imagination. They are also of interest in variety of scientific disciplines that study spatiotemporal dynamics. Popular methods for quantifying movement in imagery include particle image velocimetry and digital image correlation. Both methods are widely applied in engineering and materials science, but less ap

Satoru Hayami

We report our theoretical results on the emergence of rectangular- and square-shaped magnetic skyrmion crystals on a centrosymmetric square lattice with magnetic anisotropy. By performing the simulated annealing for a frustrated spin model with an easy-axis single-ion anisotropy on a square lattice, we find that a competition of the interactions in momentum

Mohammadsajad Abavisani, David Danks, Sergey Plis

Graphical structures estimated by causal learning algorithms from time series data can provide misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data. Existing algorithms provide limited resources to respond to this challenge, and so researchers must either use models that they kno

Yi-Cai Zhang, Yan-Yang Zhang

In this work, we investigate the Anderson localization problems of the generalized Aubry-Andr\'{e} model (Ganeshan-Pixley-Das Sarma's model) with an unbounded quasi-periodic potential where the parameter $|\alpha|\geq1$. The Lyapunov exponent $\gamma(E)$ and the mobility edges $E_c$ are exactly obtained for the unbounded quasi-periodic potential. With the Ly

Benjamin V. Lehmann, Ava Webber, Olivia G. Ross, Stefano Profumo

The vast datasets associated with extrasolar systems promise to offer sensitive probes of new physics in the near future. We consider the possibility that such systems may capture primordial black holes (PBHs) or other exotic compact objects, giving rise to unique observational signatures. We estimate the rate of captures by extrasolar systems, accounting fo

Muhammad Izzatullah, Tariq Alkhalifah

Full-waveform inversion is a cutting-edge methodology for recovering high-resolution subsurface models. However, one of the main conventional full-waveform optimization problems challenges is cycle-skipping, usually leading us to an inaccurate local minimum model. A highly investigated track to alleviate this challenge involves designing a more global measur

Andrei Popescu

I introduce renaming-enriched sets (rensets for short), which are algebraic structures axiomatizing fundamental properties of renaming (also known as variable-for-variable substitution) on syntax with bindings. Rensets compare favorably in some respects with the well-known foundation based on nominal sets. In particular, renaming is a more fundamental operat

Igor Sfiligoi, David Schultz, Frank Würthwein, Benedikt Riedel

NVIDIA has been making steady progress in increasing the compute performance of its GPUs, resulting in order of magnitude compute throughput improvements over the years. With several models of GPUs coexisting in many deployments, the traditional accounting method of treating all GPUs as being equal is not reflecting compute output anymore. Moreover, for appl

Francesco Caturano, Nicola d'Ambrosio, Gaetano Perrone, Luigi Previdente

A cyber range is a realistic simulation of an organization's network infrastructure, commonly used for cyber security training purposes. It provides a safe environment to assess competencies in both offensive and defensive techniques. An important step during the realization of a cyber range is the generation of vulnerable machines. This step is challenging

Canyu Chen, Kai Shu

Recent advances in large pre-trained language models (PLMs) lead to impressive gains in natural language understanding (NLU) tasks with task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on sufficient labeled training instances, which are usually hard to obtain. Prompt-based tuning on PLMs has shown to be powerful for various downst

Liang Liu, Peng Chen, Guangchun Luo, Zhao Kang

With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing multi-view methods operate in raw feature space and heavily depend on the quality of original feature representation. Moreover, they are often designed for feature data and ignore the rich topology structure information. Accordin

Peng Yang, Hai-Feng Wang, Zhi-Quan Luo, Thor Tepper-García

We investigate the kinematic-chemical distribution of Red Giant Branch (RGB) stars from the LAMOST survey crossed matched with Gaia DR2 proper motions, and present time tagging for the well-known ridge structures (diagonal distributions for $V_R$ in the $R$, $V_\phi$ plane) in the range of Galactocentric distance $R$ = 8 to 15 kpc. We detect six ridge struct

Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou, Nitish Shirish Keskar

Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work, we propose a simple generative approach

A. P. Snodin, T. Jitsuk, D. Ruffolo, W. H. Matthaeus

The transport of energetic charged particles (e.g., cosmic rays) in turbulent magnetic fields is usually characterized in terms of the diffusion parallel and perpendicular to a large-scale (or mean) magnetic field. The nonlinear guiding center theory (NLGC) has been a prominent perpendicular diffusion theory. A recent version of this theory, based on random

Danilo Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong

Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations, called entailment trees, were recently suggested as a way to explain and inspect a QA system's answer. In order to better generate such entailment trees, we propose an architectur

Markus B. Fröb, Daniela Cadamuro

We consider the simplest non-trivial local composite operators in the massless Sine-Gordon model, which are $\partial_\mu \phi \, \partial_\nu \phi$ and the stress tensor $T_{\mu\nu}$. We show that even in the finite regime $\beta^2 < 4 \pi$ of the theory, these operators need additional renormalisation (beyond the free-field normal-ordering) at each order i

Claude Carlet, Ulises Pastor-Díaz, José María Tornero

In this paper, we focus on the links between Boolean function theory and quantum computing. In particular, we study the notion of what we call fully-balanced functions and analyse the Fourier--Hadamard and Walsh supports of those functions having such property. We study the Walsh and Fourier supports of other relevant classes of functions, using what we call

R. Mckinven, B. M. Gaensler, D. Michilli, K. Masui

Fast radio burst (FRB) source 20180916B exhibits a 16.33-day periodicity in its burst activity. It is as of yet unclear what proposed mechanism produces the activity, but polarization information is a key diagnostic. Here, we report on the polarization properties of 44 bursts from FRB 20180916B detected between 2018 December and 2021 December by CHIME/FRB, t

Laura Okpara, Colin Werner, Adam Murray, Daniela Damian

Building a shared understanding of non-functional requirements (NFRs) is a known but understudied challenge in requirements engineering, especially in organizations that adopt continuous software engineering (CSE) practices. During the peak of the COVID-19 pandemic, many CSE organizations complied with working remotely due to the imposed health restrictions;

Devanshu Agrawal, James Ostrowski

When trying to fit a deep neural network (DNN) to a $G$-invariant target function with $G$ a group, it only makes sense to constrain the DNN to be $G$-invariant as well. However, there can be many different ways to do this, thus raising the problem of ``$G$-invariant neural architecture design'': What is the optimal $G$-invariant architecture for a given pro

Gabriele Naselli, Ali G. Moghaddam, Solange Di Napoli, Veronica Vildosola

We analyze the electronic structure of topological surface states in the family of magnetic topological insulators MnBi$_{2n}$Te$_{3n+1}$. We show that, at natural-cleavage surfaces, the Dirac cone warping changes its symmetry from hexagonal to trigonal at the magnetic ordering temperature. In particular, an energy splitting develops between the surface stat

Anqi Mu, Zhiyuan Sun, Andrew J. Millis

We compute the frequency dependent conductivity of the two dimensional square lattice Hubbard model at zero temperature as a function of density to second order in the interaction strength, and compare the results to the predictions of single-site dynamical mean field theory computed at the same order. We find that despite the neglect of vertex corrections,

James M. Callahan, John Sous, Timothy C. Berkelbach

We introduce coupled-cluster (CC) theory for the numerical study of the normal state of two-component, dilute Fermi gases with attractive, short-range interactions at zero temperature. We focus on CC theory with double excitations (CCD) and discuss its close relationship with -- and improvement upon -- the t-matrix approximation, i.e., the resummation of lad

Ionas Erb

Here we show an application of our recently proposed information-geometric approach to compositional data analysis (CoDA). This application regards relative count data, which are, e.g., obtained from sequencing experiments. First we review in some detail a variety of necessary concepts ranging from basic count distributions and their information-geometric de

Charles King, Warren R. Brown, Margaret J. Geller, Scott J. Kenyon

We measure the anisotropy of the Milky Way stellar halo traced by a dense sample of 18<r<21 mag F-type main sequence turnoff stars using Gaia eDR3 proper motions and new radial velocity measurements published here.

Jing Li, Yong Niu, Hao Wu, Bo Ai

Millimeter-wave (mmWave) communication technology offers a potential and promising solution to support 5G and B5G wireless networks in dynamic scenarios and applications. However, mobility introduces many challenges as well as opportunities to mmWave applications. To address these problems, we conduct a survey of the opportunities and technologies to support

Ruifeng Gu

Improper lane-changing behaviors may result in breakdown of traffic flow and the occurrence of various types of collisions. This study investigates lane-changing behaviors of multiple vehicles and the stimulative effect on following drivers in a consecutive lane-changing scenario. The microscopic trajectory data from the dataset are used for driving behavior

Krzysztof J. Szajowski

The decision-maker (DM) sequentially evaluates up to N of different, rankable options. DM must select exactly the best one at the moment of its appearance. In the process of searching, DM finds out with each applicant whether she is the best applicant among those assessed so far (we call him a candidate). DM cannot return to rejected candidates. We discuss t

Mahsa Yousefi, Angeles Martinez

While first-order methods are popular for solving optimization problems that arise in large-scale deep learning problems, they come with some acute deficiencies. To diminish such shortcomings, there has been recent interest in applying second-order methods such as quasi-Newton based methods which construct Hessians approximations using only gradient informat

Won Eui Hong

We uncover the gradient structure to investigate the convergence of solutions in nonlocal nonlinear dynamical systems. Mainly but not exclusively, we use the Lojasiewicz inequality to prove convergence results in various spaces with continuous, or discrete temporal domain, and finite, or infinite dimensional spatial domain. To be more specific, we analyze Lo

Paul Marrero, Eduardo Acuña

In this paper we propose a conjecture about integer solutions to any equations, based on Primal algebra specifically this conjecture is a corollary of the Acu\~na Theorem in that article. Also some problems are proposed which, if the conjecture is correct, could be solved.

Kyungchan Lee, Na Hyun Jo, Lin-Lin Wang, R. A. Ribeiro

We use high-resolution angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT) calculations to study the electronic and magnetic properties of La2Ni7, an itinerant magnetic system with a series of three magnetic transition temperatures upon cooling, which end in a weak itinerant antiferromagnetic (wAFM) ground state. Our APRES d

Benjamin Lévêque, Moritz Mühlenthaler, Thomas Suzan

For a fixed graph H, the H-Recoloring problem asks whether for two given homomorphisms from a graph G to H, we can transform one into the other by changing the image of a single vertex of G in each step and maintaining a homomorphism from G to H throughout. We extend an algorithm of Wrochna for H-Recoloring where H is a square-free loopless undirected graph

Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani

As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity in order to avoid perpetuating existing societal harms. Many datasets for measuring bias currently exist, but they are restricted in their coverage of demographic axes and are commonly used with preset bias tests that presu

Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko

Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is a framework for computing with distributed representations by exploiting properties of random high-dimensional vector spaces. The commitment of the scientific community to aggregate and disseminate research in this particularly multidisciplinary area has been fundamental f

Peter Thalmeier, Alireza Akbari

We derive the dynamical magnetic response functions in the Fulde-Ferrell (FF) state of a superconductor with inversion symmetry. The pair momentum 2q is obtained by minimization of the condensation energy and the resulting quasiparticle states and spectral functions exhibit the segmentation into paired and unpaired regions due to the finite q. The dynamical

Chengming Bai, Li Guo, Yunhe Sheng

The broadly applied notions of Lie bialgebras, Manin triples, classical $r$-matrices and $\mathcal{O}$-operators of Lie algebras owe their importance to the close relationship among them. Yet these notions and their correspondences are mostly understood as classes of objects and maps among the classes. To gain categorical insight, this paper introduces, for

Melissa Lopez, Vincent Boudart, Stefano Schmidt, Sarah Caudill

In the field of gravitational-wave (GW) interferometers, the most severe limitation to the detection of transient signals from astrophysical sources comes from transient noise artefacts, known as glitches, that happens at a rate around $1$ per minute. Because glitches reduce the amount of scientific data available, there is a need for better modelling and in

Guglielmo Mazzola, Giuseppe Carleo

Recently, Huggins et. al. [Nature, 603, 416-420 (2022)] devised a general projective Quantum Monte Carlo method suitable for implementation on quantum computers. This hybrid approach, however, relies on a subroutine -the computation of the local energy estimator on the quantum computer -that is intrinsically affected by an exponential scaling of the computat

Yekaterina Sadovaya, Dmitri Moltchanov, Wei Mao, Oner Orhan

The recently proposed NR-ready integrated access and backhaul (IAB) architecture promises to bring a cost-efficient deployment solution for both coverage extension and capacity boosting in future 5G/5G+ systems. While its impact on the coverage extension was thoughtfully addressed in the literature, the effect of advanced functionalities such as multi-hop, m

Giuseppe De Giacomo, Dror Fried, Fabio Patrizi, Shufang Zhu

Devising a strategy to make a system mimicking behaviors from another system is a problem that naturally arises in many areas of Computer Science. In this work, we interpret this problem in the context of intelligent agents, from the perspective of LTLf, a formalism commonly used in AI for expressing finite-trace properties. Our model consists of two separat