November 2022 arXiv papers — page 16

Sunghwan Joo, Seokhyeon Jeong, Juyeon Heo, Adrian Weller

Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations. To address this, several methods for enhancing the local smoothness of the gradient while training have been proposed for attaining \textit{robust} feature attributions. However, the lack of considering the

Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang

Link prediction is a crucial problem in graph-structured data. Due to the recent success of graph neural networks (GNNs), a variety of GNN-based models were proposed to tackle the link prediction task. Specifically, GNNs leverage the message passing paradigm to obtain node representation, which relies on link connectivity. However, in a link prediction task,

Mohammed Mourabit, Nevin N. Weinberg

Delta Scuti ($\delta$ Sct) variables are intermediate mass stars that lie at the intersection of the main sequence and the instability strip on the Hertzsprung-Russel diagram. Various lines of evidence indicate that nonlinear mode interactions shape their oscillation spectra, including the particularly compelling detection of resonantly interacting mode trip

Peizhao Li, Ethan Xia, Hongfu Liu

Fairness is essential for machine learning systems deployed in high-stake applications. Among all fairness notions, individual fairness, deriving from a consensus that `similar individuals should be treated similarly,' is a vital notion to describe fair treatment for individual cases. Previous studies typically characterize individual fairness as a predictio

Yongjian Luo, Changan Wang, Chao Chen, Yuan Gao

Dielectric capacitors are widely used in pulsed power electronic devices due to their ultrahigh power densities and extremely fast charge/discharge speed. To achieve enhanced energy storage density, both maximum polarization (Pmax) and breakdown strength (Eb) need to be improved simultaneously. However, these two key parameters are inversely correlated. In t

Nihang Fu, Jeffrey Hu, Ying Feng, Gregory Morrison

Oxidation states are the charges of atoms after their ionic approximation of their bonds, which have been widely used in charge-neutrality verification, crystal structure determination, and reaction estimation. Currently only heuristic rules exist for guessing the oxidation states of a given compound with many exceptions. Recent work has developed machine le

Lukas Zhornyak, Zhengjie Xu, Haoran Tang, Jianbo Shi

We present HashEncoding, a novel autoencoding architecture that leverages a non-parametric multiscale coordinate hash function to facilitate a per-pixel decoder without convolutions. By leveraging the space-folding behaviour of hashing functions, HashEncoding allows for an inherently multiscale embedding space that remains much smaller than the original imag

Jie Fu, Zhili Chen, Xiao Han

Federated learning seeks to address the issue of isolated data islands by making clients disclose only their local training models. However, it was demonstrated that private information could still be inferred by analyzing local model parameters, such as deep neural network model weights. Recently, differential privacy has been applied to federated learning

Pratyaksh Prabhav Rao, Feng Qiao, Weide Zhang, Yiliang Xu

Accurate segmentation of power lines in aerial images is essential to ensure the flight safety of aerial vehicles. Acquiring high-quality ground truth annotations for training a deep learning model is a laborious process. Therefore, developing algorithms that can leverage knowledge from labelled synthetic data to unlabelled real images is highly demanded. Th

Anatoli S. Kheifets, Stephen Catsamas

Shape resonances in photoionization of atoms and molecules arise from a particular geometry of the ionic potential which traps the receding photoelectron in a quasi-bound state in a particular partial wave. This mechanism allows us to connect the photoionization cross section in the resonant region with the photoelectron scattering phase in this partial wave

Yanhong Li, Jack Xu, David C. Anastasiu

Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction. Diverse traditional and deep learning models have been applied to discover the nonlinear relationships and recognize the complex patt

Salman Alsubaihi, Mohammed Alkhrashi, Raied Aljadaany, Fahad Albalawi

The existence of label noise imposes significant challenges (e.g., poor generalization) on the training process of deep neural networks (DNN). As a remedy, this paper introduces a permutation layer learning approach termed PermLL to dynamically calibrate the training process of the DNN subject to instance-dependent and instance-independent label noise. The p

Siddharth Rout, Chao-An Lin

The feasibility of using reinforcement learning for airfoil shape optimization is explored. Deep Q-Network (DQN) is used over Markov's decision process to find the optimal shape by learning the best changes to the initial shape for achieving the required goal. The airfoil profile is generated using Bezier control points to reduce the number of control variab

Canhong Wen, Ruipeng Dong, Xueqin Wang, Weiyu Li

Sparse reduced rank regression is an essential statistical learning method. In the contemporary literature, estimation is typically formulated as a nonconvex optimization that often yields to a local optimum in numerical computation. Yet, their theoretical analysis is always centered on the global optimum, resulting in a discrepancy between the statistical g

Alex Treacher, Kevin Nguyen, Dylan Owens, Daniel Heitjan

This work demonstrates the ability to produce readily interpretable statistical metrics for model fit, fixed effects covariance coefficients, and prediction confidence. Importantly, this work compares 4 suitable and commonly applied epistemic UQ approaches, BNN, SWAG, MC dropout, and ensemble approaches in their ability to calculate these statistical metrics

Fangfang Dou, Zhonghua Liao, Xiaomin Zhu

In this paper, we prove a global Carleman estimate for the complex Ginzburg- Landau operator with a cubic nonlinear term in a bounded domain of Rn, n = 2, 3. As applications, we study state observation problems for the Ginzburg-Landau equation.

Charlie Ruan

Approximating Martingale Process (AMP) is proven to be effective for variance reduction in reinforcement learning (RL) in specific cases such as Multiclass Queueing Networks. However, in the already proven cases, the state space is relatively small and all possible state transitions can be iterated through. In this paper, we consider systems in which state s

Pablo S. Ocal, Kenta Ueyama, Padmini Veerapen

We review several techniques that twist an algebra's multiplicative structure. We first consider twists by an automorphism, also known as Zhang twists, and we relate them to 2-cocycle twists of certain bialgebras. We then outline the classification and properties of twisted tensor products, and we examine twisted Segre products. Our exposition emphasizes cla

Hiroaki Tanaka, Andrei V. Telegin, Yurii P. Sukhorukov, Vladimir A. Golyashov

We study the electronic structure of the ferromagnetic spinel $\mathrm{Hg}\mathrm{Cr}_2\mathrm{Se}_4$ by soft-x-ray angle-resolved photoemission spectroscopy (SX-ARPES) and first-principles calculations. While a theoretical study has predicted that this material is a magnetic Weyl semimetal, SX-ARPES measurements give direct evidence for a semiconducting sta

M. W. C. Dharma-wardana, Lucas J. Stanek, Michael S. Murillo

Accurate equations of state (EOS) and plasma transport properties are essential for numerical simulations of warm dense matter encountered in many high-energy-density situations. Molecular dynamics (MD) is a simulation method that generates EOS and transport data using an externally provided potential to dynamically evolve the particles without further refer

Teylama Miabey

We study the Spectral Analysis for a class of bounded linear operators T = D + F in a non Archimedean Hilbert space E, where D is a diagonal linear operator and where F is a finite rank linear operator. In this study of the Spectral Analysis, we use extensively the Theory of Fredholm Operators to deduce some of our main results.

Jing Yang, Xin-Yan Fan, Chao-Jun Feng, Xiang-Hua Zhai

Using various latest cosmological datasets including Type-Ia supernovae, cosmic microwave background radiation, baryon acoustic oscillations, and estimations of the Hubble parameter, we test some dark energy models with parameterized equations of state and try to distinguish or select observation-preferred models. We obtain the best fitting results of the si

Hyungjoon Soh, Dongyeob Kim, Juno Hwang, Junghyo Jo

Mirror descent is an elegant optimization technique that leverages a dual space of parametric models to perform gradient descent. While originally developed for convex optimization, it has increasingly been applied in the field of machine learning. In this study, we propose a novel approach for utilizing mirror descent to initialize the parameters of neural

Shota Fukushima, Yong-Gwan Ji, Hyeonbae Kang

We prove that the space of vector fields on the boundary of a bounded domain with the Lipschitz boundary in three dimensions is decomposed into three subspaces: elements of the first one extend to the inside the domain as divergence-free and rotation-free vector fields, the second one to the outside as divergence-free and rotation-free vector fields, and the

Matthew Howe, Boris Repasky, Timothy Payne

Semantic segmentation of aerial point cloud data can be utilised to differentiate which points belong to classes such as ground, buildings, or vegetation. Point clouds generated from aerial sensors mounted to drones or planes can utilise LIDAR sensors or cameras along with photogrammetry. Each method of data collection contains unique characteristics which c

Jacob Krantz, Stefan Lee, Jitendra Malik, Dhruv Batra

We consider the problem of embodied visual navigation given an image-goal (ImageNav) where an agent is initialized in an unfamiliar environment and tasked with navigating to a location 'described' by an image. Unlike related navigation tasks, ImageNav does not have a standardized task definition which makes comparison across methods difficult. Further, exist

Gyojin Han, Jaehyun Choi, Hyeong Gwon Hong, Junmo Kim

Generally, regularization-based continual learning models limit access to the previous task data to imitate the real-world constraints related to memory and privacy. However, this introduces a problem in these models by not being able to track the performance on each task. In essence, current continual learning methods are susceptible to attacks on previous

Ni Li, Rong Ma

Let $[x]$ be the integral part of $x$, $n>1$ be a positive integer and $\chi_n$ denote the trivial Dirichlet character modulo $n$. In this paper, we use an identity established by Z. H. Sun to get congruences of $T_{m,k}(n)=\sum_{x=1}^{[n/m]}\frac{\chi_n(x)}{x^k}\left(\bmod n^{r+1}\right)$ for $r\in \{1,2\}$, any positive integer $m $ with $n \equiv \pm 1 \l

Yusuke Manita, Katsuki Aoki, Tomohiro Fujita, Shinji Mukohyama

Bigravity is one of the natural extensions of general relativity and contains an additional massive spin-2 field which can be a good candidate for dark matter. To discuss the production of spin-2 dark matter, we study fixed point solutions of the background equations for axisymmetric Bianchi type-I Universes in two bigravity theories without Boulware-Deser g

Sivaprasad Omanakuttan, Karthik Chinni, Philip Daniel Blocher, Pablo M. Poggi

Scrambling is a key concept in the analysis of nonequilibrium properties of quantum many-body systems. Most studies focus on its characterization via out-of-time-ordered correlation functions (OTOCs), particularly through the early-time decay of the OTOC. However, scrambling is a complex process which involves operator spreading and operator entanglement, an

Keshav Bimbraw, Christopher J. Nycz, Matt Schueler, Ziming Zhang

With the advancement in computing and robotics, it is necessary to develop fluent and intuitive methods for interacting with digital systems, augmented/virtual reality (AR/VR) interfaces, and physical robotic systems. Hand motion recognition is widely used to enable these interactions. Hand configuration classification and MCP joint angle detection is import

O. Gonzalez-Gaxiola, J. Ruiz de Chavez

In this work, the generalized scale-invariant analogue of the Korteweg-de Vries (gsiaKdV) equation is studied. For the first time, the tanh-coth methodology is used to find traveling wave solutions for this nonlinear equation. The considered generalized equation is a connection between the well-known KdV equation and the recently investigated SIdV equation.

Rashmi Gangadharaiah, Balakrishnan Narayanaswamy

It is expensive and difficult to obtain the large number of sentence-level intent and token-level slot label annotations required to train neural network (NN)-based Natural Language Understanding (NLU) components of task-oriented dialog systems, especially for the many real world tasks that have a large and growing number of intents and slot types. While zer

Matthieu Parizy, Norihiro Kakuko, Nozomu Togawa

In this paper, we propose a novel technique to accelerate Ising machines hyperparameter tuning. Firstly, we define Ising machine performance and explain the goal of hyperparameter tuning in regard to this performance definition. Secondly, we compare well-known hyperparameter tuning techniques, namely random sampling and Tree-structured Parzen Estimator (TPE)

Kyung-Min Jin, Byoung-Sung Lim, Gun-Hee Lee, Tae-Kyung Kang

Previous video-based human pose estimation methods have shown promising results by leveraging aggregated features of consecutive frames. However, most approaches compromise accuracy to mitigate jitter or do not sufficiently comprehend the temporal aspects of human motion. Furthermore, occlusion increases uncertainty between consecutive frames, which results

Rhiannon Udall, Derek Davis

Excess noise from scattered light poses a persistent challenge in the analysis of data from gravitational wave detectors such as LIGO. We integrate a physically motivated model for the behavior of these "glitches" into a standard Bayesian analysis pipeline used in gravitational wave science. This allows for the inference of the free parameters in this model,

Mohammadjavad Khosravi, Rushiv Arora, Saeede Enayati, Hossein Pishro-Nik

Utilizing autonomous drones or unmanned aerial vehicles (UAVs) has shown great advantages over preceding methods in support of urgent scenarios such as search and rescue (SAR) and wildfire detection. In these operations, search efficiency in terms of the amount of time spent to find the target is crucial since with the passing of time the survivability of th

Theresa C. Anderson, Dominique Maldague, Lillian B. Pierce, Po-Lam Yung

We prove that a maximally modulated singular oscillatory integral operator along a hypersurface defined by $(y,Q(y))\subseteq \mathbb{R}^{n+1}$, for an arbitrary non-degenerate quadratic form $Q$, admits an a priori bound on $L^p$ for all $1<p<\infty$, for each $n \geq 2$. This operator takes the form of a polynomial Carleson operator of Radon-type, in which

Gabriel Poesia, Noah D. Goodman

General mathematical reasoning is computationally undecidable, but humans routinely solve new problems. Moreover, discoveries developed over centuries are taught to subsequent generations quickly. What structure enables this, and how might that inform automated mathematical reasoning? We posit that central to both puzzles is the structure of procedural abstr

Véronique Bazier-Matte, Aaron Chan, Kayla Wright

We initiate the investigation of representation theory of non-orientable surfaces. As a first step towards finding an additive categorification of Dupont and Palesi's quasi-cluster algebras associated marked non-orientable surfaces, we study a certain modification on the objects of the cluster category associated to the orientable double covers in the unpunc

Rin Gotou

We give a bracket polynomial expression for intermediate terms between discriminant and resultant for pair of binary forms. As an application of the bracket polynomial expression, we give an algebraic proof of the algebraic independence of intermediate terms, which was shown in the theory of dynamical systems.

Madeleine J. Zurowski

For nearly two decades the DAMA Collaboration has been observing a modulating signal compatible with that expected from a dark matter presence in our galaxy. However, interpretations of this with the standard assumptions for dark matter particles are strongly ruled out by a large number of other experiments. This tension can be relaxed somewhat by making mor

Kenneth L. Clarkson, Cristina Cornelio, Sanjeeb Dash, Joao Goncalves

This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms. We apply constrained first-order methods to optimize an appropriate selection criterion, using Hamiltonian Monte Carlo to sample from the prior.

Abdulrahman M. Alanazi, Singanallur Venkatakrishnan, Hector Santos-Villalobos, Gregery T. Buzzard

Collimated beam ultrasound systems are a technology for imaging inside multi-layered structures such as geothermal wells. These systems work by using a collimated narrow-band ultrasound transmitter that can penetrate through multiple layers of heterogeneous material. A series of measurements can then be made at multiple transmit frequencies. However, commonl

Amin Shojaeighadikolaei, Arman Ghasemi, Kailani Jones, Yousif Dafalla

This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential f

J. L. Figuiredo, H. Terças, J. T. Mendonça

Bose--Einstein condensation of a finite number of photons propagating inside a plasma-filled microcavity is investigated. The nonzero chemical potential is provided by the electrons, which induces a finite photon mass allowing condensation to occur. We derive an equation that models the evolution of the photon-mode occupancies, with Compton scattering taken

Elena Orlova, Haokun Liu, Raphael Rossellini, Benjamin A. Cash

Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This study explores an application of machine learning (ML) models as post-processing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the m

P. V. Lopes, Shyam Sundar, S. Salem-Sugui,, Wenshan Hong

We performed magnetization measurements in a single crystal of the anisotropic bilayer pnictide superconductor KCa$_2$Fe$_4$As$_4$F$_2$, with $T_c$ $\simeq$ 34 K, for $H$$\parallel$$c$-axis and $H$$\parallel$$ab$-planes. A second magnetization peak (SMP) was observed in the isothermal $M(H)$ curves measured below 16 K for $H$$\parallel$$ab$-planes. A peak in

Rodrigo F. da Silva, Daniel F. Luz, Célio V. T. Maciel, Emanuel P. Santos

We investigate the role of the Nd3+ content on the PA characteristics in NdxY1.00-xAl3(BO3)4 particles with x ranging from 0.05 to 1.00. It is known that the replacement of Y3 by Nd3+ into the YAl3(BO3)4 crystalline structure can introduce strong modifications of the lattice properties as well as in the photoluminescence characteristics, such as luminescence

Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg

A number of competing hypotheses have been proposed to explain why small-batch Stochastic Gradient Descent (SGD)leads to improved generalization over the full-batch regime, with recent work crediting the implicit regularization of various quantities throughout training. However, to date, empirical evidence assessing the explanatory power of these hypotheses

Kedan Li, Jeffrey Zhang, Shao-Yu Chang, David Forsyth

An outfit visualization method generates an image of a person wearing real garments from images of those garments. Current methods can produce images that look realistic and preserve garment identity, captured in details such as collar, cuffs, texture, hem, and sleeve length. However, no current method can both control how the garment is worn -- including tu

Shunta Koga, Masahiro N. Machida

We calculate the evolution of a star-forming cloud core using a three-dimensional resistive magnetohydrodynamics simulation, treating dust grains as Lagrangian particles, to investigate the dust motion in the early star formation stage. We prepare six different-sized set of dust particles in the range $a_{\rm d}=0.01$--$1000\,\mu$m, where $a_{\rm d}$ is the

Wei Chen, Weixiao Wan, Shiyue Wang, Peng Sun

To reduce multiuser interference and maximize the spectrum efficiency in orthogonal frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) estimated at the user equipment (UE) is required at the base station (BS). This paper presents a novel method for massive MIMO CSI feedback via a o

Daniel Bump, Slava Naprienko

We develop the theory of colored bosonic models (initiated by Borodin and Wheeler). We will show how a family of such models can be used to represent the values of Iwahori vectors in the "spherical model" of representations of $GL_r(F)$, where $F$ is a nonarchimedean local field. Among our results are a monochrome factorization, which is the realization of t

Chang Chen, Jiayao Zhang, Dan Roth, Ting Ye

Many recent studies have probed status bias in the peer-review process of academic journals and conferences. In this article, we investigated the association between author metadata and area chairs' final decisions (Accept/Reject) using our compiled database of 5,313 borderline submissions to the International Conference on Learning Representations (ICLR) fr

Arnold Overwijk, Chenyan Xiong, Xiao Liu, Cameron VandenBerg

ClueWeb22, the newest iteration of the ClueWeb line of datasets, provides 10 billion web pages affiliated with rich information. Its design was influenced by the need for a high quality, large scale web corpus to support a range of academic and industry research, for example, in information systems, retrieval-augmented AI systems, and model pretraining. Comp

Rachel Greenfeld, Terence Tao

The periodic tiling conjecture asserts that any finite subset of a lattice $\mathbb{Z}^d$ which tiles that lattice by translations, in fact tiles periodically. In this work we disprove this conjecture for sufficiently large $d$, which also implies a disproof of the corresponding conjecture for Euclidean spaces $\mathbb{R}^d$. In fact, we also obtain a counte

Shuyang Sun, Jie-Neng Chen, Ruifei He, Alan Yuille

Modern deep networks can be better generalized when trained with noisy samples and regularization techniques. Mixup and CutMix have been proven to be effective for data augmentation to help avoid overfitting. Previous Mixup-based methods linearly combine images and labels to generate additional training data. However, this is problematic if the object does n

Yuanning Cui, Yuxin Wang, Zequn Sun, Wenqiang Liu

Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and tr

Guang Yang, Yu Zhou, Wenhua Yang, Tao Yue

Pre-trained code generation models (PCGMs) have been widely applied in neural code generation which can generate executable code from functional descriptions in natural languages, possibly together with signatures. Despite substantial performance improvement of PCGMs, the role of method names in neural code generation has not been thoroughly investigated. In

Soheil Behnezhad, Mohammad Roghani, Aviad Rubinstein

Sublinear time algorithms for approximating maximum matching size have long been studied. Much of the progress over the last two decades on this problem has been on the algorithmic side. For instance, an algorithm of Behnezhad [FOCS'21] obtains a 1/2-approximation in $\tilde{O}(n)$ time for $n$-vertex graphs. A more recent algorithm by Behnezhad, Roghani, Ru

Souradeep Bhattacharya, Burhan Hyder, Manimaran Govindarasu

Industrial Control System (ICS) testbeds serve as a platform for evaluating and validating control system performances, cybersecurity tools and technologies. In order to build or enhance an ICS testbed, it is vital to have a deeper understanding of its design specifications and characteristic attributes. Satisfying this prerequisite involves examination and

Trevor Gale, Deepak Narayanan, Cliff Young, Matei Zaharia

We present MegaBlocks, a system for efficient Mixture-of-Experts (MoE) training on GPUs. Our system is motivated by the limitations of current frameworks, which restrict the dynamic routing in MoE layers to satisfy the constraints of existing software and hardware. These formulations force a tradeoff between model quality and hardware efficiency, as users mu

Simona Boyadzhiyska, Thomas Lesgourgues

A graph $G$ is $q$-Ramsey for a $q$-tuple of graphs $(H_1,\ldots,H_q)$ if for every $q$-coloring of the edges of $G$ there exists a monochromatic copy of $H_i$ in color $i$ for some $i\in[q]$. Over the last few decades, researchers have investigated a number of questions related to this notion, aiming to understand the properties of graphs that are $q$-Ramse

Yu He, Petar Veličković, Pietro Liò, Andreea Deac

Neural algorithmic reasoning studies the problem of learning algorithms with neural networks, especially with graph architectures. A recent proposal, XLVIN, reaps the benefits of using a graph neural network that simulates the value iteration algorithm in deep reinforcement learning agents. It allows model-free planning without access to privileged informati

Matthew Feickert, Lukas Heinrich, Giordon Stark

The HistFactory p.d.f. template is per-se independent of its implementation in ROOT and it is useful to be able to run statistical analysis outside of the ROOT, RooFit, RooStats framework. pyhf is a pure-Python implementation of that statistical model for multi-bin histogram-based analysis and its interval estimation is based on the asymptotic formulas of "A

Sushil Thapa

Deep learning has been the subject of growing interest in recent years. Specifically, a specific type called Multimodal learning has shown great promise for solving a wide range of problems in domains such as language, vision, audio, etc. One promising research direction to improve this further has been learning rich and robust low-dimensional data represent

Lang Yin, Ruta Mehta

We study the problem of allocating a set of indivisible chores to three agents, among whom two have additive cost functions, in a fair manner. Two fairness notions under consideration are envy-freeness up to any chore (EFX) and a relaxed notion, namely envy-freeness up to transferring any chore (tEFX). In contrast to the case of goods, the case of chores rem

Shiang Tang

In this paper, we prove new instances of the inverse Galois problem over global function fields for finite groups of Lie type. This is done by constructing compatible systems of $\ell$-adic Galois representations valued in a semisimple group $G$ using Galois theoretic and automorphic methods, and then proving that the Galois images are maximal for a set of p

Nick Collins

The application of music information retrieval techniques in popular music studies has great promise. In the present work, a corpus of Radiohead songs across their career from 1992 to 2017 are subjected to automated audio analysis. We examine findings from a number of granularities and perspectives, including within song and between song examination of both

Bing Liu, Harrisen Scells, Wen Hua, Guido Zuccon

Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled data only. In this work, we argue that different entities within one KG should have compatible counterparts in the other KG due to the potential dependencies among the entities. M

Iulia Cristian, Marina A. Ferreira, Eugenia Franco, Juan J. L. Velázquez

In this paper we study a class of coagulation equations including a source term that injects in the system clusters of size of order one. The coagulation kernel is homogeneous, of homogeneity $γ< 1$, such that $K(x,y)$ is approximately $x^{γ+ λ} y^{-λ}$, when $x$ is larger than $y$. We restrict the analysis to the case $γ+ 2 λ\geq 1 $. In this range of expon

Kakei Yamamoto, Yusuke Sakemi, Kazuyuki Aihara

We propose a novel backpropagation algorithm for training spiking neural networks (SNNs) that encodes information in the relative multiple spike timing of individual neurons without single-spike restrictions. The proposed algorithm inherits the advantages of conventional timing-based methods in that it computes accurate gradients with respect to spike timing

Satoru Fukasawa, Kei Miura, Takeshi Takahashi

In Part I, the present authors introduced the notion of a quasi-Galois point, for investigating the automorphism groups of plane curves. In this second part, the number of quasi-Galois points for smooth plane curves is described. In particular, sextic or quartic curves with many quasi-Galois points are characterized.

Anne-Marie Aubert, Yujie Xu

We study the endomorphism algebras attached to Bernstein components of reductive $p$-adic groups and construct a local Langlands correspondence with the appropriate set of enhanced $L$-parameters, using certain &#34;desiderata&#34; properties for the LLC for supercuspidal representations of proper Levi subgroups. We give several applications of our LLC to va

Saachi Mutreja, Jonathan Shafer

Goldwasser et al. (2021) recently proposed the setting of PAC verification, where a hypothesis (machine learning model) that purportedly satisfies the agnostic PAC learning objective is verified using an interactive proof. In this paper we develop this notion further in a number of ways. First, we prove a lower bound of $\Omega\left(\sqrt{d}/\varepsilon^2\ri

Eunok Bae, Soojoon Lee

Quantum approximate optimization algorithms are hybrid quantum-classical variational algorithms designed to approximately solve combinatorial optimization problems such as the MAX-CUT problem. In spite of its potential for near-term quantum applications, it has been known that quantum approximate optimization algorithms have limitations for certain instances

Yuntong Huang, Shuyang Dai

Dislocation dynamic is a typically gradient flow problem, and most of work solves it just as ODE, which means that the interacting energy of dislocations is ignored. We take the interaction energy into account and use it to introduce new methods to speed up the simulation. The non-singular stress field theory is used to make sure that the interacting energy

David Crnčević, Felipe Hernández, Kevin Rizk, Khunpob Sereesuchart

In this article we investigate different forms of multiplicative independence between the sequences $n$ and $\lfloor n \alpha \rfloor$ for irrational $\alpha$. Our main theorem shows that for a large class of arithmetic functions $a, b \colon \mathbb{N} \to \mathbb{C}$ the sequences $(a(n))_{n \in \mathbb{N}}$ and $(b ( \lfloor \alpha n \rfloor))_{n \in \mat

Han Yan, Kevin Slagle, Andriy H. Nevidomskyy

Unlike ordinary topological quantum phases, fracton orders are intimately dependent on the underlying lattice geometry. In this work, we study a generalization of the X-cube model, dubbed the Y-cube model, on lattices embedded in $H_2\times S^1$ space, i.e., a stack of hyperbolic planes. The name `Y-cube' comes from the Y-shape of the analog of the X-cube's

Lia C. Corazza, Oswaldo D. Miranda, Carlos A. Wuensche

We propose a semi-analytic model that is developed to understand the cosmological evolution of the mean metallicity in the Universe. In particular, we study the contributions of Population III (Pop III) and Population II (Pop II) stars to the production of $\mathrm{Fe,~Si,~Zn, ~Ni,~P, ~Mg, ~Al, ~S, ~C, ~N}$, and $\mathrm{~O}$. We aim to quantify the roles of

Daria Holdenried-Chernoff, David A. King, Bruce A. Buffett

Variations in the geomagnetic field occur on a vast range of time scales, from milliseconds to millions of years. The advent of satellite measurements has allowed for detailed studies of the short timescale geomagnetic field behaviour, but understanding the long timescale evolution remains challenging due to the sparsity of the paleomagnetic record. This pap

Emily K. Roberts, Michael R. Elliott, Jeremy M. G. Taylor

A common practice in clinical trials is to evaluate a treatment effect on an intermediate endpoint when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate endpoints in a causally-valid way when the trial outcomes are time-to-event. Using counterfactual outcomes, those that would be observed if the c

Behnam Mafakheri, Iman Shames, Jonathan H. Manton

The success of deep learning over the past decade mainly relies on gradient-based optimisation and backpropagation. This paper focuses on analysing the performance of first-order gradient-based optimisation algorithms, gradient descent and proximal gradient, with time-varying non-convex cost function under (proximal) Polyak-{\L}ojasiewicz condition. Specific

Elie Aljalbout, Maximilian Karl, Patrick van der Smagt

Multi-robot manipulation tasks involve various control entities that can be separated into dynamically independent parts. A typical example of such real-world tasks is dual-arm manipulation. Learning to naively solve such tasks with reinforcement learning is often unfeasible due to the sample complexity and exploration requirements growing with the dimension

Jessica Maghakian, Paul Mineiro, Kishan Panaganti, Mark Rucker

In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions. Due to the scarcity of explicit user feedback, modern recommender systems typically optimize for the same fixed combination of implicit feedback signals across all users. However, this approach disregards a gr

Zahi Hazan

In this note, presented as a ``community service", followed by the PhD research of the author, we draw the relation between Casselman's theorem regarding the asymptotic behavior of matrix coefficients of reductive algebraic groups over $p$-adic fields and its expression as a finite sum of finite functions. In addition, we write the expansion explicitly for g

Kaïs Ammari, Farhat Shel, Zhuangyi Liu

In this paper we try to complete the stability analysis for an abstract system of coupled hyperbolic and parabolic equations $$ \left\{ \begin{array}{lll} \ds u_{tt} + Au - A^\alpha w = 0, \\ w_t + A^\alpha u_t + A^\beta w = 0,\\ u(0) = u_0, u_t(0) = u_1, w(0) = w_0, \end{array} \right. $$ where $A$ is a self-adjoint, positive definite operator on a complex

Silvia Paparini, Epifanio G. Virga

Chromonic liquid crystals are lyotropic materials which are attracting growing interest for their adapatbility to living systems. To describe their elastic properties, the classical Oseen-Frank theory requires anomalously small twist constants and (comparatively) large saddle-splay constants, so large as to violate one of Ericksen's inequalities, which guara

Peter Allen, Julia Böttcher

We prove asymptotically optimal bounds on the number of edges a graph $G$ must have in order that any $r$-colouring of $E(G)$ has a colour class which contains every $D$-degenerate graph on $n$ vertices with bounded maximum degree. We also improve the upper bounds on the number of edges $G$ must have in order that any $r$-colouring of $E(G)$ has a colour cla

Ziyu Huang, Thomas Michael Keller, Shane Kissinger, Wen Plotnick

The prime graph $\Gamma(G)$ of a finite group $G$ (also known as the Gruenberg-Kegel graph) has as its vertices the prime divisors of $|G|$, and $p\text-q$ is an edge in $\Gamma(G)$ if and only if $G$ has an element of order $pq$. Since their inception in the 1970s these graphs have been studied extensively; however, completely classifying the possible prime

Mostapha Alsaidi, Ali Saleem Altaher, Muhammad Tanveer Jan, Ahmed Altaher

In this paper, deep-learning-based approaches namely fine-tuning of pretrained convolutional neural networks (VGG16 and VGG19), and end-to-end training of a developed CNN model, have been used in order to classify X-Ray images into four different classes that include COVID-19, normal, opacity and pneumonia cases. A dataset containing more than 20,000 X-ray s

Tianfan Fu, Wenhao Gao, Connor W. Coley, Jimeng Sun

Structure-based drug design (SBDD) aims to discover drug candidates by finding molecules (ligands) that bind tightly to a disease-related protein (targets), which is the primary approach to computer-aided drug discovery. Recently, applying deep generative models for three-dimensional (3D) molecular design conditioned on protein pockets to solve SBDD has attr

Rebecca L. Davies, Emma Ryan-Weber, Valentina D'Odorico, Sarah E. I. Bosman

Intervening metal absorption lines in the spectra of z > 6 quasars are fundamental probes of the ionization state and chemical composition of circumgalactic and intergalactic gas near the end of the reionization epoch. Large absorber samples are required to robustly measure typical absorber properties and to refine models of the synthesis, transport, and ion

Valeriy V. Dvoeglazov

We compare various formalisms for neutral particles. It is found that they contain unexplained contradictions. Next, we investigate the spin-1/2 and spin-1 cases in different bases. Next, we look for relations with the Majorana-like field operator. We show explicitly incompatibility of the Majorana anzatzen with the Dirac-like field operators in both the ori

Ioan Baldea

Temperature ($T$) dependent conductance $G = G(T)$ data measured in molecular junctions are routinely taken as evidence for a two-step hopping mechanism. The present paper emphasizes that this is not necessarily the case. A curve of $\ln G$ versus $1/T$ decreasing almost linearly (Arrhenius-like regime) and eventually switching to a nearly horizontal plateau

Damien P. Huet, Anthony Wachs

We present an open-source adaptive front-tracking solver for biological capsules in viscous flows. The membrane elastic and bending forces are solved on a Lagrangian triangulation using a linear Finite Element Method and a paraboloid fitting method. The fluid flow is solved on an octree adaptive grid using the open-source platform Basilisk. The Lagrangian an

Jochem De Schutter, Jakob Harzer, Moritz Diehl

This paper proposes and simulates vertical airborne wind energy (AWE) farms based on multi-aircraft systems with high power density (PD) per ground area. These farms consist of many independently ground located systems that are flying at the same inclination angle, but with different tether lengths, such that all aircraft fly in a large planar elliptical are

Kelvin Dsouza, Daryoosh Vashaee

The effects of electromagnetic fields (EMF) have been widely debated concerning their role in chemical reactions. Reactions usually took hours or days to complete, and have been shown to happen a thousand times faster using EMF radiations. This work develops a formalism and a computer program to evaluate and quantify the EMF effects in chemical reactions. Th

Sahil D. Patel, Kamyar Parto, Michael Choquer, Sammy Umezawa

Surface acoustic waves (SAWs) are a versatile tool for coherently interfacing with a variety of solid-state quantum systems spanning microwave to optical frequencies, including superconducting qubits, spins, and quantum emitters. Here, we demonstrate SAW cavity optomechanics with quantum emitters in 2D materials, specifically monolayer WSe$_2$, on a planar l