August 2022 arXiv papers — page 87

Lingqi Meng, Naoki Masuda

Network structure is a mechanism for promoting cooperation in social dilemma games. In the present study, we explore graph surgery, i.e., to slightly perturb the given network, towards a network that better fosters cooperation. To this end, we develop a perturbation theory to assess the change in the propensity of cooperation when we add or remove a single e

Cillian Cockrell, Kostya Trachenko

Universality aids consistent understanding of physical properties. This includes understanding the states of matter where a theory predicts how a property of a phase (solid, liquid, gas) changes with temperature or pressure. Here, we show that the matter above the critical point has a remarkable double universality not limited by pressure and temperature. Th

Xiaoyu Jiang, Raphael Blumenfeld, Takashi Matsushima

During quasi-static dynamics of granular systems, the stress and structure self-organise, but there is currently no quantitative measure or understanding of this phenomenon. Such an understanding is essential because local structural properties of the settled material are then correlated with the local stress, which calls into question existing linear theori

Chichuan Jin, Chris Done, Martin Ward, Francesca Panessa

RX J0134.2-4258 is one of the most super-Eddington narrow-line Seyfert 1 (NLS1) galaxies, on which we conducted a monitoring campaign from radio to X-rays. In this paper, we present a detailed analysis of its optical/UV spectra and broadband spectral energy distribution (SED). Our study shows that the preferred black hole mass of RX J0134.2-4258 is $M_{\rm B

Ashutosh Joshi, Akash Gayakwad, Manjuladevi V., Mahesh C. Varia

The development of optically active area consisting of organic molecules are essential for the devices like optical switches and waveguides, as it can be easily maneuvered by the application of suitable electromagnetic (EM) waves. In this article, we report the development of a photoactive surface by the deposition of a single layer of Langmuir-Blodgett (LB)

Ashutosh Holla B, Manohara Pai M. M, Ujjwal Verma, Radhika M. Pai

In recent years, the development of robust Intelligent transportation systems (ITS) is tackled across the globe to provide better traffic efficiency by reducing frequent traffic problems. As an application of ITS, vehicle re-identification has gained ample interest in the domain of computer vision and robotics. Convolutional neural network (CNN) based method

Revathy B. S., Victor Mukherjee, Uma Divakaran

Driving a quantum system across quantum critical points leads to non-adiabatic excitations in the system. This in turn may adversely affect the functioning of a quantum machine which uses a quantum critical substance as its working medium. Here we propose a bath-engineered quantum engine (BEQE), in which we use the Kibble--Zurek mechanism and critical scalin

Adrian Chun-Pong Chu

We prove that the Almgren-Pitts 6-width of the unit 3-ball is less than $2\pi$. We also prove that there exists a free boundary minimal surface in the unit 3-ball that has genus at most 1, index at most 5, area less than $2\pi$, and is not the equatorial disk or the critical catenoid.

Noushin Jafarpisheh, Laura Castaneda Martinez, Hayley Whitson, Ivan M. Rosado-Mendez

Pulse-echo Quantitative ultrasound (PEQUS), which estimates the quantitative properties of tissue microstructure, entails estimating the average attenuation and the backscatter coefficient (BSC). Growing recent research has focused on the regularized estimation of these parameters. Herein, we make two contributions to this field: First, we consider the physi

Boon Long Ng, Chang Hoong Chow, Christian Kurtsiefer

Resonance fluorescence from atomic systems consists of a single spectral peak that evolves into a Mollow triplet for a strong excitation field. Photons from different peaks of the triplet show distinct photon correlation that make the fluorescence a useful light source for quantum information purpose. We characterize the fluorescence of a single optically tr

G. Ramesh, Shanola S. Sequeira

Let $H_1$, $H_2$ be complex Hilbert spaces. A bounded linear operator $T : H_1 \to H_2$ is said to be norm attaining if there exists a unit vector $x \in H_1$ such that $\|Tx\| = \|T\|$. If $T|_{M} : M \to H_2$ is norm attaining for every closed subspace $M$ of $H_1$, then we say that $T$ is an absolutely norm attaining ($\mathcal{AN}$-operator). If the norm

Katrina Chen, Xiuqin Liang, Zheng Ma, Zhibin Zhang

Data imputation is an effective way to handle missing data, which is common in practical applications. In this study, we propose and test a novel data imputation process that achieve two important goals: (1) preserve the row-wise similarities among observations and column-wise contextual relationships among features in the feature matrix, and (2) tailor the

Zahid Hasan, Emon Dey, Sreenivasan Ramasamy Ramamurthy, Nirmalya Roy

In this demo paper, we design and prototype RhythmEdge, a low-cost, deep-learning-based contact-less system for regular HR monitoring applications. RhythmEdge benefits over existing approaches by facilitating contact-less nature, real-time/offline operation, inexpensive and available sensing components, and computing devices. Our RhythmEdge system is portabl

Jacob Ewaniuk, Jacques Carolan, Bhavin J. Shastri, Nir Rotenberg

Quantum photonic neural networks are variational photonic circuits that can be trained to implement high-fidelity quantum operations. However, work-to-date has assumed idealized components, including a perfect $\pi$ Kerr nonlinearity. Here, we investigate the limitations of realistic quantum photonic neural networks that suffer from fabrication imperfections

Yibin Zhang, Jinlong Sun, Guan Gui, Yun Lin

The potential advantages of intelligent wireless communications with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) are based on the availability of instantaneous channel state information (CSI) at the base station (BS). However, no existence of channel reciprocity leads to the difficult acquisition of accurate CSI at the BS in fr

Emon Dey, Jumman Hossain, Nirmalya Roy, Carl Busart

With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in real-world systems is tedious and may result in the wastage of valuable resources. Especially for heterogeneous multi-agent systems in battlefi

Mohammad Saidur Rahman, Scott E. Coull, Matthew Wright

Malicious software (malware) classification offers a unique challenge for continual learning (CL) regimes due to the volume of new samples received on a daily basis and the evolution of malware to exploit new vulnerabilities. On a typical day, antivirus vendors receive hundreds of thousands of unique pieces of software, both malicious and benign, and over th

Weixiong Liang, Yuehan Yang

Multiresponse data with complex group structures in both responses and predictors arises in many fields, yet, due to the difficulty in identifying complex group structures, only a few methods have been studied on this problem. We propose a novel algorithm called sequential stepwise screening procedure (SeSS) for feature selection in high-dimensional multires

Chenyan Wu

By using results on poles of $L$-functions and theta correspondence, we give a bound on $b$ for $(\chi,b)$-factors of the global Arthur parameter of a cuspidal automorphic representation $\pi$ of a classical group or a metaplectic group where $\chi$ is a conjugate self-dual automorphic character and $b$ is an integer which is the dimension of an irreducible

Hannah Bradley, Steven Louis, Cody Trevillian, Lily Quach

Spiking artificial neurons emulate the voltage spikes of biological neurons, and constitute the building blocks of a new class of energy efficient, neuromorphic computing systems. Antiferromagnetic materials can, in theory, be used to construct spiking artificial neurons. When configured as a neuron, the magnetizations in antiferromagnetic materials have an

Nashiba Parbin, Umananda Dev Goswami

We propose to test the viability of the recently introduced $f(\mathcal{R})$ gravity model in the galactic scales. For this purpose we consider test particles moving in stable circular orbits around the galactic center. We study the Palatini approach of $f(\mathcal{R})$ gravity via Weyl transformation, which is the frame transformation from the Jordan frame

Seonghoon Choi, Tzu-Ching Yen, Artur F. Izmaylov

Reducing the number of measurements required to estimate the expectation value of an observable is crucial for the variational quantum eigensolver to become competitive with state-of-the-art classical algorithms. To measure complicated observables such as a molecular electronic Hamiltonian, one of the common strategies is to partition the observable into lin

Atsushi Taruya, Shohei Saga

Ultralight bosonic dark matter called fuzzy dark matter (FDM) has attracted much attention as an alternative to the cold dark matter. An intriguing feature of the FDM model is the presence of a soliton core, a stable dense core formed at the center of halos. In this paper, we analytically study the dependence of the soliton core properties on the halo charac

Zhanpeng Yang, Yuanming Shi, Yong Zhou, Zixin Wang

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy prese

Dea Avega Editya

Indonesian Ministry of Finance had endorsed 10 Key Opinion Leaders to help promoting government retail bonds SBR011 during selling period of 25 May-16 June 2022. This study analyzed effectiveness of the endorsement by using several indicators; engagement rate, enthusiasm rate and sentiment analysis of feedbacks from KOL audiens. Data was gathered from social

Ming Dai, Enhui Zheng, Jiahao Chen, Lei Qi

Image retrieval (IR) has emerged as a promising approach for self-localization in unmanned aerial vehicles (UAVs). However, IR-based methods face several challenges: 1) Pre- and post-processing incur significant computational and storage overhead; 2) The lack of interaction between dual-source features impairs precise spatial perception. In this paper, we pr

Weiwei Ding, Zhanghua Liang, Wenfeng Liu

This paper is concerned with pulsating waves for multi-dimensional reaction-diffusion equations in spatially periodic media. First, assuming the existence of pulsating waves connecting two linearly stable steady states, we study the continuity of wave speeds with respect to the direction of propagation. The continuity was proved in [15] under the extra condi

Venkata Siva Krishna, Praphul Kumar, Bharatkumar Sharma, Indranil Saha Dalal

Brownian dynamics simulations of fairly long, highly detailed polymer chains, at the resolution of a single Kuhn step, remains computationally prohibitive even on the modern processors. This is especially true when the beads on the chain experience hydrodynamic interactions (HI), which requires the usage of methods like Cholesky decomposition of large matric

Yuxun He, Huaqiao Wang

We consider the initial boundary value problem of Landau-Lifshitz-Bloch equation on three-dimensional ferromagnetic films, where the effective field contains the stray field controlled by Maxwell equation and the exchange field contains exchange constant. In this paper, we establish the existence of weak solutions of the equation by using the Faedo-Galerkin

Norman Matloff, Wenxi Zhang

A number of methods have been introduced for the fair ML issue, most of them complex and many of them very specific to the underlying ML moethodology. Here we introduce a new approach that is simple, easily explained, and potentially applicable to a number of standard ML algorithms. Explicitly Deweighted Features (EDF) reduces the impact of each feature amon

Dogyeong Kim, Sol Lee, Jiwon Park, Jinho Lee

Understanding the nature of molecular excitons in low-dimensional molecular solids is of paramount importance in fundamental photophysics and various applications such as energy harvesting, switching electronics and display devices. Despite this, the spatial evolution of molecular excitons and their transition dipoles have not been captured in the precision

Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins

We develop BenchPress, the first ML benchmark generator for compilers that is steerable within feature space representations of source code. BenchPress synthesizes compiling functions by adding new code in any part of an empty or existing sequence by jointly observing its left and right context, achieving excellent compilation rate. BenchPress steers benchma

Xinyue Hu, Lin Gu, Liangchen Liu, Ruijiang Li

Existing video domain adaption (DA) methods need to store all temporal combinations of video frames or pair the source and target videos, which are memory cost expensive and can't scale up to long videos. To address these limitations, we propose a memory-efficient graph-based video DA approach as follows. At first our method models each source or target vide

Jiping Zheng, Yuan Ma, Wei Ma, Yanhao Wang

Finding a happiness maximizing set (HMS) from a database, i.e., selecting a small subset of tuples that preserves the best score with respect to any nonnegative linear utility function, is an important problem in multi-criteria decision-making. When an HMS is extracted from a set of individuals to assist data-driven algorithmic decisions such as hiring and a

Jiajing Zheng, Jiaxi Wu, Alexander D'Amour, Alexander Franks

In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable t

Jia Cheng Hu, Roberto Cavicchioli, Alessandro Capotondi

We introduce a method called the Expansion mechanism that processes the input unconstrained by the number of elements in the sequence. By doing so, the model can learn more effectively compared to traditional attention-based approaches. To support this claim, we design a novel architecture ExpansionNet v2 that achieved strong results on the MS COCO 2014 Imag

Zhenyu Ni, Jing Wang, Liying Kang

The $kK_{r+1}$ is the union of $k$ disjoint copies of $(r+1)$-clique. Moon [Canad. J. Math. 20 (1968) 95--102] and Simonovits [Theory of Graphs (Proc. colloq., Tihany, 1996)] independently showed that if $n$ is sufficiently large, then $K_{k-1}\vee T_{n-k+1,r}$ is the unique extremal graph for $kK_{r+1}$. In this paper, we consider the graph which has the ma

Miklós Rásonyi, Hasanjan Sayit

Obtaining utility maximizing optimal portfolios in closed form is a challenging issue when the return vector follows a more general distribution than the normal one. In this note, we give closed form expressions, in markets based on finitely many assets, for optimal portfolios that maximize the expected exponential utility when the return vector follows norm

Lu Zhenzhen, Yu Yongguang, Xu Weiyi, Ren Guojian

In recent years, discrete fractional epidemic models with reaction-diffusion have become increasingly popular in the literature, not only for its necessity of numerical simulation, but also for its defined physical processes. In this paper, by second order central difference scheme and L1 nonstandard finite difference scheme, a discrete counterpart of time-f

Si-Si Wang, Kangkang Li, Yi-Ming Dai, Hui-Hui Wang

We investigate the effects of disorder and shielding on quantum transports in a two dimensional system with all-to-all long range hopping. In the weak disorder, cooperative shielding manifests itself as perfect conducting channels identical to those of the short range model, as if the long range hopping does not exist. With increasing disorder, the average a

Xianjin Cheng, Zhenxin Liu, Lixin Zhang

In this paper, we study the existence of $n$-dimensional linear stochastic differential equations (SDEs) such that the sign of Lyapunov exponents is changed under an exponentially decaying perturbation. First, we show that the equation with all positive Lyapunov exponents will have $n-1$ linearly independent solutions with negative Lyapunov exponents under t

Yupei Wang, Nicolae C. Panoiu

Nonlinear topological photonics has been attracting increasing research interest, as it provides an exciting photonic platform that combines the advantages of active all-optical control offered by nonlinear optics with the unique features of topological photonic systems, such as topologically-protected defect-immune light propagation. In this paper, we demon

Parth Kumar, Charles A. Stafford

How to rigorously define thermodynamic quantities such as heat, work, and internal energy in open quantum systems driven far from equilibrium remains a significant open question in quantum thermodynamics. Heat is a quantity whose fundamental definition applies only to processes in systems infinitesimally perturbed from equilibrium, and as such, must be accou

Giancarlos Parady, Kay W. Axhausen

In this paper we review the academic transportation literature published between 2014 and 2018 to evaluate where the field stands regarding the use and misuse of statistical significance in empirical analysis, with a focus on discrete choice models. Our results show that 39% of studies explained model results exclusively based on the sign of the coefficient,

Dan Liu, Ran Wei, Lin Han, Chen Zhu

We study the ferroelectricity in a one-dimensional system composed of a double helix SnIP with absorbing water molecules. Our ab initio calculations reveal two factors that are critical to the electrical polarization. The first one is the orientation of polarized water molecules staying in the R2 region of SnIP. The second one is the displacement of I atom w

Müge Taşkın, R. Bedii Gümüş, Sinan Işık, M. ikbal Ulvi

Given a skew diagram $\gamma/\lambda$, we determine a set of lower and upper bounds that a partition $\mu$ must satisfy for Littlewood-Richards coefficients $c^{\gamma}_{\lambda,\mu}>0$. Our algorithm depends on the characterization of $c^{\gamma}_{\lambda,\mu}$ as the number of Littlewood-Richardson tableau of shape $\gamma/\lambda$ and content $\mu$ and us

Inés Corte, Marcelo Losada, Diego Tielas, Federico Holik

In this work, we show how to parameterize a density matrix that has an arbitrary symmetry, knowing the generators of the Lie algebra (if the symmetry group is a connected Lie group) or the generators of its underlying group (in case it is finite). This allows to pose MaxEnt and MaxLik estimation techniques as convex optimization problems with a substantial r

Hông Vân Lê

In this paper I discuss the relation between the concept of the Fisher metric and the concept of differentiability of a family of probability measures. I compare the concepts of smooth statistical manifolds, differentiable families of measures, $k$-integrable parameterized measure models, diffeological statistical models, differentiable measures, which arise

Mingyuan Fan, Cen Chen, Wenmeng Zhou, Yinggui Wang

The vulnerability of deep neural networks (DNNs) to black-box adversarial attacks is one of the most heated topics in trustworthy AI. In such attacks, the attackers operate without any insider knowledge of the model, making the cross-model transferability of adversarial examples critical. Despite the potential for adversarial examples to be effective across

Mingyuan Fan, Yang Liu, Cen Chen, Ximeng Liu

The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker-chosen ones. In this paper, we propose a novel backdoor defense method to mark and purify the infected neurons in the backdoored neural networks. Specifically, we first define a n

Soumya Basu, Abishek Sankararaman

Double Auction enables decentralized transfer of goods between multiple buyers and sellers, thus underpinning functioning of many online marketplaces. Buyers and sellers compete in these markets through bidding, but do not often know their own valuation a-priori. As the allocation and pricing happens through bids, the profitability of participants, hence sus

Mingyu Xu, Zuo Quan Xu, Xun Yu Zhou

We define $g$-expectation of a distribution as the infimum of the $g$-expectations of all the terminal random variables sharing that distribution. We present two special cases for nonlinear $g$ where the $g$-expectation of distributions can be explicitly derived. As a related problem, we introduce the notion of law-invariant $g$-expectation and provide its s

N. Miranda, J. C. Freytag, J. Nordin, R. Biswas

With a rapidly rising number of transients detected in astronomy, classification methods based on machine learning are increasingly being employed. Their goals are typically to obtain a definitive classification of transients, and for good performance they usually require the presence of a large set of observations. However, well-designed, targeted models ca

Hyunseung Kang, Chan Park, Ralph Trane

The paper presents some models for the propensity score. Considerable attention is given to a recently popular, but relatively under-explored setting in causal inference where the no-interference assumption does not hold. We lay out some key challenges in propensity score modeling under interference and present a few promising models based on existing works

Prashant K. Gupta, Javier Andreu-Perez

Human beings have an inherent capability to use linguistic information (LI) seamlessly even though it is vague and imprecise. Computing with Words (CWW) was proposed to impart computing systems with this capability of human beings. The interest in the field of CWW is evident from a number of publications on various CWW methodologies. These methodologies use

Kenneth Christofferson, Fernando J. Yanez

This work explores three-player game training dynamics, under what conditions three-player games converge and the equilibria the converge on. In contrast to prior work, we examine a three-player game architecture in which all players explicitly interact with each other. Prior work analyzes games in which two of three agents interact with only one other playe

Colin G. Cess, Stacey D. Finley

Computational models are quantitative representations of systems. By analyzing and comparing the outputs of such models, it is possible to gain a better understanding of the system itself. Though as the complexity of model outputs increases, it becomes increasingly difficult to compare simulations to each other. While it is straightforward to only compare a

Masahito Hasegawa, Jean-Simon Pacaud Lemay

A traced monad is a monad on a traced symmetric monoidal category that lifts the traced symmetric monoidal structure to its Eilenberg-Moore category. A long-standing question has been to provide a characterization of traced monads without explicitly mentioning the Eilenberg-Moore category. On the other hand, a symmetric Hopf monad is a symmetric bimonad whos

Sudipto Banerjee, Xiang Chen, Ian Frankenburg, Daniel Zhou

We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the mechanistic system that can efficiently interpolate the output of the system from arbitrary inputs. The emulated learner can then be used to train the system from noisy data achieved by melding information from o

Xiangqi Zhu, Akanksha Singh, Barry Mather

Medium-voltage back-to-back (MVB2B) converters can connect two distribution systems and quantifiably transfer power between them. This function can enable the MVB2B converter to exchange distributed energy resource (DER)-generated power between two systems and bring significant value to enhancing distribution system DER adoption. Our previous work analyzed a

Sai Pavan Tadem

The original publication Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks served as the inspiration for this implementation project. Researchers developed a novel method for doing image-to-image translations using an unpaired dataset in the original study. Despite the fact that the pix2pix models findings are good, the matched

Maria Laricheva, Chiyu Zhang, Yan Liu, Guanyu Chen

Conversational data is essential in psychology because it can help researchers understand individuals cognitive processes, emotions, and behaviors. Utterance labelling is a common strategy for analyzing this type of data. The development of NLP algorithms allows researchers to automate this task. However, psychological conversational data present some challe

Nuozhou Wang, Shuzhong Zhang

A popular approach to minimize a finite-sum of convex functions is stochastic gradient descent (SGD) and its variants. Fundamental research questions associated with SGD include: (i) To find a lower bound on the number of times that the gradient oracle of each individual function must be assessed in order to find an $\epsilon$-minimizer of the overall object

Jisheng Zhang, Leslie Rogers

We present a thermal evolution model coupled with a Henyey solver to study the circumstances under which a rocky planet could potentially host a dynamo in its liquid iron core and/or magma ocean. We calculate the evolution of planet thermal profiles by solving the energy balance equations for both the mantle and the core. We use a modified mixing length theo

Isaac Bromley-Dulfano, Xiangqi Zhu, Barry Mather

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodo

Daniel Chui, Jason Hartline, James R. Wright

Modelling agent preferences has applications in a range of fields including economics and increasingly, artificial intelligence. These preferences are not always known and thus may need to be estimated from observed behavior, in which case a model is required to map agent preferences to behavior, also known as structural estimation. Traditional models are ba

Haibo Chen

In the present paper, a class of new simple modules over the $N=1$ Ramond algebra are constructed, which are induced from simple modules over some finite dimensional solvable Lie superalgebras. These new modules are simple restricted modules over the $N=1$ Ramond algebra. Combined with the result in \cite{L1}, a classification of simple weak $\psi$-twisted $

Xing Fan, Gerald Gabrielse, Peter W. Graham, Roni Harnik

We propose using trapped electrons as high-$Q$ resonators for detecting meV dark photon dark matter. When the rest energy of the dark photon matches the energy splitting of the two lowest cyclotron levels, the first excited state of the electron cyclotron will be resonantly excited. A proof-of-principle measurement, carried out with one electron, demonstrate

Sai Pavan Tadem

This is a review paper of traditional approaches for edge, corner, and boundary detection methods. There are many real-world applications of edge, corner, and boundary detection methods. For instance, in medical image analysis, edge detectors are used to extract the features from the given image. In modern innovations like autonomous vehicles, edge detection

Xiangqi Zhu, Partha Mishra, Barry Mather, Mingzhi Zhang

This paper presents a consolidated grid impact analysis design and corresponding mitigation strategies for heavy-duty electric vehicle (EV) charging stations. The charging load of heavy-duty charging station can reach several megawatts, which could induce adverse impacts on the distribution grid if not effectively mitigated. To analyze the impacts and provid

Allen Ibiapina, Ana Silva

A temporal graph ${\cal G}$ is a graph that changes with time. More specifically, it is a pair $(G, \lambda)$ where $G$ is a graph and $\lambda$ is a function on the edges of $G$ that describes when each edge $e\in E(G)$ is active. Given vertices $s,t\in V(G)$, a temporal $s,t$-path is a path in $G$ that traverses edges in non-decreasing time; and if $s,t$ a

Colin Jacobs, Karl Glazebrook, Antonello Calabrò, Tommaso Treu

We present a rest-frame optical morphological analysis of galaxies observed with the NIRCam imager on the James Webb Space Telescope (JWST) as part of the GLASS-JWST Early Release Science program. We select 388 sources at redshifts \(0.8 < z < 5.4\) and use the seven 0.9--5\micron\ NIRCam filters to generate rest-frame $gri$ composite color images, and condu

M. L. van Gelder, J. Jaspers, P. Nazari, A. Ahmadi

The deuteration of molecules forming in the ices such as methanol (CH$_3$OH) is sensitive to the physical conditions during their formation in dense cold clouds and can be probed through observations of deuterated methanol in hot cores. Observations with ALMA containing transitions of CH$_3$OH, CH$_2$DOH, CHD$_2$OH, $^{13}$CH$_3$OH, and CH$_3^{18}$OH are inv

Tim Mesikepp

We study two minimization questions: the nature of curves $\gamma \subset \mathbb{H}$ which minimize the Loewner energy among all curves from 0 to a fixed $z_0 \in \mathbb{H}$, and the nature of $\gamma$ which minimize the Loewner energy among all curves that weld a given pair $x<0 <y$. The former question was partially studied by Yilin Wang, who used SLE te

Francesco De Pellegrini, Vaibhav Kumar Gupta, Rachid El Azouzi, Serigne Gueye

The average coflow completion time (CCT) is the standard performance metric in coflow scheduling. However, standard CCT minimization may introduce unfairness between the data transfer phase of different computing jobs. Thus, while progress guarantees have been introduced in the literature to mitigate this fairness issue, the trade-off between fairness and ef

LHCb Collaboration

A measurement of charm mixing and $CP$-violating parameters is reported, using $\bar{B} \rightarrow D^0 ( \rightarrow K_S^0 \pi^+ \pi^-) \mu^- \bar{\nu}_\mu X$ decays reconstructed in proton-proton collisions collected by the LHCb experiment during the years 2016 to 2018, corresponding to an integrated luminosity of 5.4 fb$^{-1}$. The measured mixing and $CP

George M. Bergman

Let $R$ be a ring, and consider a left $R$-module given with two (generally infinite) direct sum decompositions, $A\oplus(\bigoplus_{i\in I} C_i)=M=B\oplus(\bigoplus_{j\in J} D_j),$ such that the submodules $A$ and $B$ and the $D_j$ are each finitely generated. We show that there then exist finite subsets $I_0\subseteq I,$ $J_0\subseteq J,$ and a direct summ

Enrico Le Donne, Gabriel Pallier, Xiangdong Xie

We consider Lie groups that are either Heintze groups or Sol-type groups, which generalize the three-dimensional Lie group SOL. We prove that all left-invariant Riemannian metrics on each such a Lie group are roughly similar via the identity. This allows us to reformulate in a common framework former results by Le Donne-Xie, Eskin-Fisher-Whyte, Carrasco Piag

Louigi Addario-Berry, Jordan Barrett

We consider invasion percolation on the randomly-weighted complete graph $K_n$, started from some number $k(n)$ of distinct source vertices. The outcome of the process is a forest consisting of $k(n)$ trees, each containing exactly one source. Let $M_n$ be the size of the largest tree in this forest. Logan, Molloy and Pralat (arXiv:1806.10975) proved that if

Ronen Eldan, Dan Mikulincer, Prasad Raghavendra

We consider a variant of the classical notion of noise on the Boolean hypercube which gives rise to a new approach to inequalities regarding noise stability. We use this approach to give a new proof of the Majority is Stablest theorem by Mossel, O'Donnell, and Oleszkiewicz, improving the dependence of the bound on the maximal influence of the function from l

Robert A. Marsden, Felix Wiewel, Mario Döbler, Yang Yang

In recent years, there has been tremendous progress in the field of semantic segmentation. However, one remaining challenging problem is that segmentation models do not generalize to unseen domains. To overcome this problem, one either has to label lots of data covering the whole variety of domains, which is often infeasible in practice, or apply unsupervise

Nate Veldt

We study the approximability of an existing framework for clustering edge-colored hypergraphs, which is closely related to chromatic correlation clustering and is motivated by machine learning and data mining applications where the goal is to cluster a set of objects based on multiway interactions of different categories or types. We present improved approxi

Iwan Setiawan, Ryan Sugihakim, Bobby Eka Gunara

In this paper we consider the central force problem in the special theory of relativity. We derive the special relativistic version of the Binet equation describing the orbit of a body. Then, the motion of a planet in a solar-like system where the gravitational potential is modified by adding a term that is proportional to the inverse of the square of the ra

Steven R. Groen

Curves in positive characteristic have a Cartier operator acting on their space of regular differentials. The $a$-number of a curve is defined to be the dimension of the kernel of the Cartier operator. In \cite{BoCaASc}, Booher and Cais used a sheaf-theoretic approach to give bounds on the $a$-numbers of Artin-Schreier covers. In this paper, that approach is

Ivan Dimitrov, Runxuan Zhang

In this paper, we study the existence and classification problems of left-symmetric superalgebras on special linear Lie superalgebras ${\mathfrak{sl}}(m|n)$ with $m\neq n$. The main three results of this paper are: (i) a complete classification of the left-symmetric superalgebras on ${\mathfrak{sl}}(2|1)$, (ii) ${\mathfrak{sl}}(m|1)$ does not admit left-symm

Zifeng Ding, Zongyue Li, Ruoxia Qi, Jingpei Wu

Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information from temporal knowledge bases. The only existing TKGQA dataset, i.e., CronQuestions, consists of temporal questions based on the facts from a fixed time period, where a temporal kno

Marcelo A. Colominas, Hau-Tieng Wu

Nonsinusoidal oscillatory signals are everywhere. In practice, the nonsinusoidal oscillatory pattern, modeled as a 1-periodic wave-shape function (WSF), might vary from cycle to cycle. When there are finite different WSFs, $s_1,\ldots,s_K$, so that the WSF jumps from one to another suddenly, the different WSFs and jumps encode useful information. We present

Shubham Jadhav, Ved Gund, Benyamin Davaji, Debdeep Jena

This paper demonstrates a hafnium zirconium oxide (HZO)-based ferroelectric NEMS unimorph as the fundamental building block for very low-energy capacitive readout in-memory computing. The reported device consists of a 250 $\mu$m $\times$ 30 $\mu$m unimorph cantilever with 20 nm thick ferroelectric HZO on 1 $\mu$m $SiO_2$.Partial ferroelectric switching in HZ

Jorge J. Betancor, Estefanía Dalmasso, Pablo Quijano, Roberto Scotto

In this paper we introduce the atomic Hardy space $\mathcal{H}^1((0,\infty),\gamma_\alpha)$ associated with the non-doubling probability measure $d\gamma_\alpha(x)=\frac{2x^{2\alpha+1}}{\Gamma(\alpha+1)}e^{-x^2}dx$ on $(0,\infty)$, for ${\alpha>-\frac12}$. We obtain characterizations of $\mathcal{H}^1((0,\infty),\gamma_\alpha)$ by using two local maximal fun

Sanjar M. Abrarov, Rehan Siddiqui, Rajinder K. Jagpal, Brendan M. Quine

In this work we develop a new algorithm for the efficient computation of the Voigt/complex error function. In particular, in this approach we propose a two-domain scheme where the number of the interpolation grid-points is dependent on the input parameter $y$. The error analysis we performed shows that the MATLAB implementation meets the requirements for rad

Oscar Moll, Manuel Favela, Samuel Madden, Vijay Gadepally

As image datasets become ubiquitous, the problem of ad-hoc searches over image data is increasingly important. Many high-level data tasks in machine learning, such as constructing datasets for training and testing object detectors, imply finding ad-hoc objects or scenes within large image datasets as a key sub-problem. New foundational visual-semantic embedd

Edison Mucllari, Vasily Zadorozhnyy, Cole Pospisil, Duc Nguyen

In recent years, using orthogonal matrices has been shown to be a promising approach in improving Recurrent Neural Networks (RNNs) with training, stability, and convergence, particularly, to control gradients. While Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) architectures address the vanishing gradient problem by using a variety of gates an

Casey McGrath, Daniel J. D'Orazio, Jolien Creighton

Pulsar timing arrays (PTAs) are searching for gravitational waves from supermassive black hole binaries (SMBHBs). Here we show how future PTAs could use a detection of gravitational waves from individually resolved SMBHB sources to produce a purely gravitational wave-based measurement of the Hubble constant. This is achieved by measuring two separate distanc

Amir Yazdani, Roya Sabbagh Novin, Andrew Merryweather, Tucker Hermans

3D posture estimation is important in analyzing and improving ergonomics in physical human-robot interaction and reducing the risk of musculoskeletal disorders. Vision-based posture estimation approaches are prone to sensor and model errors, as well as occlusion, while posture estimation solely from the interacting robot's trajectory suffers from ambiguous s

V. León, B. Scárdua

One of the various versions of the classical Lyapunov-Poincar\'e center theorem states that a nondegenerate real analytic center type planar vector field singularity admits an analytic first integral. In a more proof of this result, R. Moussu establishes important connection between this result and the theory of singularities of holomorphic foliations (\cite

Vincent Dumont, Xiangyang Ju, Juliane Mueller

The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating events in High Energy Physics (HEP), including simulating detector responses and physics events. However, training GANs is notoriously hard and optimizing their hyperparameters even

Junhao Liu, Qizhou Zhang, Keping Qiu

Linearly polarized emission from dust grains and molecular spectroscopy is an effective probe of the magnetic field topology in the interstellar medium and molecular clouds. The longstanding Davis-Chandrasekhar-Fermi (DCF) method and the recently developed Histogram of Relative Orientations (HRO) analysis and the polarization-intensity gradient (KTH) method

Tibor Krisztin, Hans-Otto Walther

We show that for a system $$ x'(t)=g(x(t-d_1(Lx_t)),\dots,x(t-d_k(Lx_t))) $$ of $n$ differential equations with $k$ discrete state-dependent delays the solution manifold, on which solution operators are differentiable, is nearly as simple as a graph over a closed subspace in $C^1([-r,0],\mathbb{R}^n)$. The map $L$ is continuous and linear from $C([-r,0],\mat

Islam Boussaada, Guilherme Mazanti, Silviu-Iulian Niculescu, Ayrton Hammoumou

This paper presents the software entitled "Partial Pole Placement via Delay Action", or "P3$\delta$" for short. P3$\delta$ is a Python software with a friendly user interface for the design of parametric stabilizing feedback laws with time-delays for dynamical systems. After recalling the theoretical foundation of the so-called "Partial Pole Placement" metho

J. L. Domínguez-Juárez, M. A. Quiroz-Juárez, J. L. Aragón, R. Quintero-Bermúdez

In the past, laser propagation in a fluid with heat transfer has been modeled using simplistic conduction and convection conditions yielding inaccurate predictions. Here we present a detailed numerical study describing the thermal profile of the fluid and its interaction with the laser. Furthermore, we evaluate the diffraction field in the far field produced