July 2022 arXiv papers — page 42

Federico Balducci, Antonello Scardicchio, Carlo Vanoni

The melting of the corner of a crystal is a classical, real-world, non-equilibrium statistical mechanics problem which has shown several connections with other branches of physics and mathematics. For a perfect, classical crystal in two and three dimensions the solution is known: the crystal melts reaching a certain asymptotic shape, which keeps expanding ba

Iliya Esin, Ilya Esterlis, Eugene Demler, Gil Refael

Twisted bilayer graphene (TBG) exhibits extremely low Fermi velocities for electrons, with the speed of sound surpassing the Fermi velocity. This regime enables the use of TBG for amplifying vibrational waves of the lattice through stimulated emission, following the same principles of operation of free-electron lasers. Our work proposes a lasing mechanism re

Adarsh Sehgal, Muskan Sehgal, Hung Manh La, George Bebis

Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world. Deep learning (DL) methods have shown to be very effective for mass detection in mammograms. Additional improvements of current DL models will further improve the effectiveness of these methods. A critical issue in this context is how to pick th

Cheng-hsin Wuu, Ningyuan Zheng, Scott Ardisson, Rohan Bali

Photorealistic avatars of human faces have come a long way in recent years, yet research along this area is limited by a lack of publicly available, high-quality datasets covering both, dense multi-view camera captures, and rich facial expressions of the captured subjects. In this work, we present Multiface, a new multi-view, high-resolution human face datas

Rahul Dandekar, Kirone Mallick

The dynamics of an asymmetric tracer in the symmetric simple exclusion process (SEP) is mapped, in the continuous scaling limit, to the local current through the origin in the zero-range process (ZRP) with a biased bond. This allows us to study the hydrodynamics of the SEP with an asymmetric tracer with a step initial condition, leading to the average displa

Siegfried Van Hille

The symmetric function theorem states that a polynomial that is invariant under permutation of variables, is a polynomial in the elementary symmetric polynomials. We deduce this classical result, in the analytic setting, from the multivariate Fa\`a di Bruno formula. In two variables, this allows us to completely determine all coefficients that occur in the i

Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Mozer

Deep neural networks perform well on classification tasks where data streams are i.i.d. and labeled data is abundant. Challenges emerge with non-stationary training data streams such as continual learning. One powerful approach that has addressed this challenge involves pre-training of large encoders on volumes of readily available data, followed by task-spe

Joseph G. Wallwork, Jingyi Lu, Mingrui Zhang, Matthew D. Piggott

Given a partial differential equation (PDE), goal-oriented error estimation allows us to understand how errors in a diagnostic quantity of interest (QoI), or goal, occur and accumulate in a numerical approximation, for example using the finite element method. By decomposing the error estimates into contributions from individual elements, it is possible to fo

Prafull Sharma, Ayush Tewari, Yilun Du, Sergey Zakharov

We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene. Motivated by the bird's-eye-view (BEV) representation commonly used in vision and robotics, we propose conditional neural groundplans, ground-a

Zhen-Chen Guo, Xin Liang

Stochastic algebraic Riccati equations, also known as rational algebraic Riccati equations, arising in linear-quadratic optimal control for stochastic linear time-invariant systems, were considered to be not easy to solve. The-state-of-art numerical methods most rely on differentiability or continuity, such as Newton-type method, LMI method, or homotopy meth

Marco Cogoni, Giovanni Busonera, Francesco Versaci

We study the emergence of congestion patterns in urban networks by modeling vehicular interaction by means of a simple traffic rule and by using a set of measures inspired by the standard Betweenness Centrality (BC). We consider a topologically heterogeneous group of cities and simulate the network loading during the morning peak-hour by increasing the numbe

Aqeel Ahmed, Bohdan Grzadkowski, Anna Socha

A reheating phase in the early universe is an essential part of all inflationary models during which not only the Standard Model (SM) quanta are produced but it can also shed light on the production of dark matter. In this work, we explore a class of reheating models where the reheating is induced by a cubic interaction of the inflaton $\phi$ to the SM Higgs

N. J. Adams, C. J. Conselice, L. Ferreira, D. Austin

We present a reduction and analysis of the \textit{James Webb Space Telescope} (JWST) SMACS~0723 field using new post-launch calibrations to conduct a search for ultra-high-redshift galaxies ($z > 9$) present within the Epoch of Reionisation. We conduct this search by modelling photometric redshifts in several ways for all sources and by applying conservativ

Qingyi Zhan, Jinqiao Duan, Xiaofan Li, Yuhong Li

In this work we construct a stochastic contact variational integrator and its discrete version via stochastic Herglotz variational principle for stochastic contact Hamiltonian systems. A general structure-preserving stochastic contact method is devised, and the stochastic contact variational integrators are established. The implementation of this approach is

Yan-Feng Hang, Hong-Jian He

We analyze the structure of scattering amplitudes of the Kaluza-Klein (KK) gravitons and of the KK gravitational Goldstone bosons in the compactified 5d General Relativity (GR). Using a general $R_{\xi}$ gauge-fixing, we study the geometric Higgs mechanism for the massive spin-2 KK gravitons. We newly propose and prove a Gravitational Equivalence Theorem (GR

Huan Liu, Li Gu, Zhixiang Chi, Yang Wang

Few-shot class-incremental learning (FSCIL) has been proposed aiming to enable a deep learning system to incrementally learn new classes with limited data. Recently, a pioneer claims that the commonly used replay-based method in class-incremental learning (CIL) is ineffective and thus not preferred for FSCIL. This has, if truth, a significant influence on th

Timo Sämann, Ahmed Mostafa Hammam, Andrei Bursuc, Christoph Stiller

Averaging predictions of a deep ensemble of networks is apopular and effective method to improve predictive performance andcalibration in various benchmarks and Kaggle competitions. However, theruntime and training cost of deep ensembles grow linearly with the size ofthe ensemble, making them unsuitable for many applications. Averagingensemble weights instea

Anna Broms, Mattias Sandberg, Anna-Karin Tornberg

Inexpensive numerical methods are key to enable simulations of systems of a large number of particles of different shapes in Stokes flow. Several approximate methods have been introduced for this purpose. We study the accuracy of the multiblob method for solving the Stokes mobility problem in free space, where the 3D geometry of a particle surface is discret

Weiguang Zhao, Yuyao Yan, Chaolong Yang, Jianan Ye

Instance segmentation on point clouds is crucially important for 3D scene understanding. Most SOTAs adopt distance clustering, which is typically effective but does not perform well in segmenting adjacent objects with the same semantic label (especially when they share neighboring points). Due to the uneven distribution of offset points, these existing metho

Kush Bhatia, Nikki Lijing Kuang, Yi-An Ma, Yixin Wang

Variational inference has recently emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) in large-scale Bayesian inference. The core idea is to trade statistical accuracy for computational efficiency. In this work, we study these statistical and computational trade-offs in variational inference via a case study in inferential mode

Emily J. Evans, Russell J. Hendel

In [Evans, Francis 2022; Hendel] the authors investigated resistance distance in triangular grid graphs and observed several types of asymptotic behavior. This paper extends their work by studying the initial, non-asymptotic, behavior found when equivalent circuit transformations are performed, reducing the rows in the triangular grid graph one row at a time

Pedro V. Silva, Benjamin Steinberg

The complex algebra of an inverse semigroup with finitely many idempotents in each $\mathcal D$-class is stably finite by a result of Munn. This can be proved fairly easily using $C^*$-algebras for inverse semigroups satisfying this condition that have a Hausdorff universal groupoid, or more generally for direct limits of inverse semigroups satisfying this c

Anja Janßen, Johan Segers

Motivated by examples from extreme value theory we introduce the general notion of a cluster process as a limiting point process of returns of a certain event in a time series. We explore general invariance properties of cluster processes which are implied by stationarity of the underlying time series under minimal assumptions. Of particular interest are the

Emily J. Evans, Russell J. Hendel

Except for Koshy who devotes seven pages to applications of Fibonacci Numbers to electric circuits, most books and the Fibonacci Quarterly have been relatively silent on applications of graphs and electric circuits to Fibonacci numbers. This paper continues a recent trend of papers studying the interplay of graphs, circuits, and Fibonacci numbers by presenti

Abdelrazek Dieb, Isabella Ianni, Alberto Saldaña

We consider positive solutions of a fractional Lane-Emden type problem in a bounded domain with Dirichlet conditions. We show that uniqueness and nondegeneracy hold for the asymptotically linear problem in general domains. Furthermore, we also prove that all the known uniqueness and nondegeneracy results in the local case extend to the nonlocal regime when t

Pierre Fraigniaud, Patrick Lambein-Monette, Mikaël Rabie

We present a wait-free algorithm for proper coloring the n nodes of the asynchronous cycle $C_n$, where each crash-prone node starts with its (unique) identifier as input. The algorithm is independent of $n \geq 3$, and runs in $\mathrm{O}(\log^* n)$ rounds in $C_n$. This round-complexity is optimal thanks to a known matching lower bound, which applies even

Arturo Fernández-Pérez, Evelia R. García Barroso, Nancy Saravia-Molina

We generalize Mattei's result relative to the Brian\c{c}on-Skoda theorem for foliations to the family of foliations of the second type. We use this generalization to establish relationships between the Milnor and Tjurina numbers of foliations of second type, inspired by the results obtained by Liu for complex hypersurfaces and we determine a lower bound for

Sashank Tirumala, Thomas Weng, Daniel Seita, Oliver Kroemer

Robotic manipulation of cloth has applications ranging from fabrics manufacturing to handling blankets and laundry. Cloth manipulation is challenging for robots largely due to their high degrees of freedom, complex dynamics, and severe self-occlusions when in folded or crumpled configurations. Prior work on robotic manipulation of cloth relies primarily on v

Reza Gheissari, Alistair Sinclair

An important paradigm in the understanding of mixing times of Glauber dynamics for spin systems is the correspondence between spatial mixing properties of the models and bounds on the mixing time of the dynamics. This includes, in particular, the classical notions of weak and strong spatial mixing, which have been used to show the best known mixing time boun

Benjamin Steinberg

In this paper we study stable finiteness of ample groupoid algebras with applications to inverse semigroup algebras and Leavitt path algebras, recovering old results and proving some new ones. In addition, we develop a theory of (faithful) traces on ample groupoid algebras, mimicking the $C^*$-algebra theory but taking advantage of the fact that our function

O. Băzăvan, S. Saner, M. Minder, A. C. Hughes

A single bichromatic field near-resonant to a qubit transition is typically used for $\hat{\sigma}_x$ or $\hat{\sigma}_y$ M{\o}lmer-S{\o}rensen type interactions in trapped ion systems. Using this field configuration, it is also possible to synthesize a $\hat{\sigma}_z$ spin-dependent force by merely adjusting the beat-note frequency. Here, we expand on prev

Frederik vom Ende

In this article we explore the set of thermal operations from a mathematical and topological point of view. First we introduce the concept of Hamiltonians with resonant spectrum with respect to some reference Hamiltonian, followed by proving that when defining thermal operations it suffices to only consider bath Hamiltonians which satisfy this resonance prop

Gaetano Fiore, Monica De Angelis, Renato Fedele, Gabriele Guerriero

We determine conditions allowing to simplify the description of the impact of a short and arbitrarily intense laser pulse onto a cold plasma at rest. If both the initial plasma density and pulse profile have plane simmetry, then suitable matched upper bounds on the maximum and the relative variations of the initial density, as well as the intensity and durat

Ana Caraiani, Matthew Emerton, Toby Gee, David Savitt

We construct moduli stacks of two-dimensional mod p representations of the absolute Galois group of a p-adic local field, as well as their resolutions by moduli stacks of two-dimensional Breuil-Kisin modules with tame descent data. We study the local geometry of these moduli stacks by comparing them with local models of Shimura varieties at hyperspecial and

Yuxuan Hu, Zhaoyang Xia, Yanbo Zhao, Feng Xu

The generalization for different scenarios and dif-ferent users is an urgent problem for millimeter wave gesture recognition for indoor fiber-to-the-room (FTTR) scenario. In order to solve this problem and verify the feasibility of FTTR Q-band millimeter wave in gesture recognition, we build a real-time millimeter wave gesture recognition system. The moving

Kieran Calvert, Marcelo De Martino, Roy Oste

For a finite dimensional representation $V$ of a finite reflection group $W$, we consider the rational Cherednik algebra $\mathsf{H}_{t,c}(V,W)$ associated with $(V,W)$ at the parameters $t\neq 0$ and $c$. The Dunkl total angular momentum algebra $O_{t,c}(V,W)$ arises as the centraliser algebra of the Lie superalgebra $\mathfrak{osp}(1|2)$ containing a Dunkl

Shuang Wu, Wenjie Pei, Dianwen Mei, Fanglin Chen

Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy. However, it is not necessarily true si

Pascal Höhn, Zhen-Peng Xu, Matthias Kleinmann

A set of quantum measurements exhibits quantum contextuality when any consistent value assignment to the measurement outcomes leads to a contradiction with quantum theory. In the original Kochen-Specker-type of argument the measurement projectors are assumed to be rays, that is, of unit rank. Only recently a contextuality scenario has been identified where s

Salma Salhi, Youssef Kora, Gisu Ham, Hadi Zadeh Haghighi

The underlying anatomical structure is fundamental to the study of brain networks, but the role of brainstem from a structural perspective is not very well understood. We conduct a computational and graph-theoretical study of the human structural connectome incorporating a variety of subcortical structures including the brainstem. Our computational scheme in

Kleber Stangherlin, Zhuanhao Wu, Hiren Patel, Manoj Sachdev

We propose a secure and lightweight key based challenge obfuscation for strong PUFs. Our architecture is designed to be resilient against learning attacks. Our obfuscation mechanism uses non-linear feedback shift registers (NLFSRs). Responses are directly provided to the user, without error correction or extra post-processing steps. We also discuss the cost

Xin Wang, Zhiqiang Wang, Catherine Kallin

The superconducting symmetry of Sr$_2$RuO$_4$ remains a puzzle. Time-reversal symmetry breaking $d_{x^2-y^2} + ig_{xy(x^2-y^2)}$ pairing has been proposed for reconciling multiple key experiments. However, its stability remains unclear. In this work, we theoretically study the superconducting instabilities in Sr$_2$RuO$_4$, including the effects of spin-orbi

Garrett B. King, Alessandro Baroni, Vincenzo Cirigliano, Stefano Gandolfi

We calculate the $\beta$ spectrum in the decay of $^6$He using Quantum Monte Carlo methods with nuclear interactions derived from chiral Effective Field Theory and consistent weak vector and axial currents. We work at second order in the multipole expansion, retaining terms suppressed by $\mathcal O(q^2/m_\pi^2)$, where $q$ denotes low-energy scales such as

Pierrick Martin, Luigi Tibaldo, Alexandre Marcowith, Soheila Abdollahi

The discovery of extended gamma-ray emission toward a number of middle-aged pulsars suggests the possibility of long-lived particle confinement beyond the classical pulsar wind nebula (PWN) stage. How this emerging source class can be extrapolated to a Galactic population remains unclear. We aim to evaluate how pulsar halos fit in existing TeV observations,

Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh

Geometric image transformations that arise in the real world, such as scaling and rotation, have been shown to easily deceive deep neural networks (DNNs). Hence, training DNNs to be certifiably robust to these perturbations is critical. However, no prior work has been able to incorporate the objective of deterministic certified robustness against geometric t

Samay Lakhani

A 20% rise in car crashes in 2021 compared to 2020 has been observed as a result of increased distraction and drowsiness. Drowsy and distracted driving are the cause of 45% of all car crashes. As a means to decrease drowsy and distracted driving, detection methods using computer vision can be designed to be low-cost, accurate, and minimally invasive. This wo

Shanli Ye, Guanghao Feng

Let $\mu$ be a positive Borel measure on the interval [0,1). For $\beta > 0$, The generalized Hankel matrix $\mathcal{H}_{\mu,\beta}= (\mu_{n,k,\beta})_{n,k\geq0}$ with entries $\mu_{n,k,\beta}= \int_{[0.1)}\frac{\Gamma(n+\beta)}{n!\Gamma(\beta)} t^{n+k}d\mu(t)$, induces formally the operator $$\mathcal{H}_{\mu,\beta}(f)(z)=\sum_{n=0}^\infty \left(\sum_{k=0}

Jiaxuan Xie, Yezi Liu, Yanning Shen

Graph Neural Networks (GNNs) have shown remarkable effectiveness in capturing abundant information in graph-structured data. However, the black-box nature of GNNs hinders users from understanding and trusting the models, thus leading to difficulties in their applications. While recent years witness the prosperity of the studies on explaining GNNs, most of th

Ji Guan, Wang Fang, Mingsheng Ying

Due to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verificatio

Mikhail Shcherbakov, Musard Balliu, Cristian-Alexandru Staicu

Prototype pollution is a dangerous vulnerability affecting prototype-based languages like JavaScript and the Node.js platform. It refers to the ability of an attacker to inject properties into an object's root prototype at runtime and subsequently trigger the execution of legitimate code gadgets that access these properties on the object's prototype, leading

Shanli Ye, Zhihui Zhou

Let $\mu$ be a positive Borel measure on the interval [0,1). For $\alpha>0$, the Hankel matrix $\mathcal{H}_{\mu,\alpha}=(\mu_{n,k,\alpha})_{n,k\geq 0}$ with entries $\mu_{n,k,\alpha}=\int_{[0,1)}\frac{\Gamma(n+\alpha)}{n!\Gamma(\alpha)}t^{n+k}d\mu(t)$ formally induces the operator $$\mathcal{H}_{\mu,\alpha}(f)(z)=\sum_{n=0}^{\infty}\left(\sum_{k=0}^{\infty}

Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai

Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain. However, few-shot algorithms are important in multiple domains; hence evaluation needs to reflect the broad applications. We propose a Multi-dOmain Few-Shot Object Detection (MoFSOD) benchmark consisting

Patrick Ofner, Joana Pereira, Reinmar Kobler, Andreas Schwarz

Bibian et al. show in their recent paper (Bibi\'an et al. 2021) that eye and head movements can affect the EEG-based classification in a reaching motor task. These movements can generate artefacts that can cause an overoptimistic estimation of the classification accuracy. They speculate that such artefacts jeopardise the interpretation of the results from se

Robert L. Grossman

A data commons is a cloud-based data platform with a governance structure that allows a community to manage, analyze and share its data. Data commons provide a research community with the ability to manage and analyze large datasets using the elastic scalability provided by cloud computing and to share data securely and compliantly, and, in this way, acceler

Bryan Zhu, Nicholas Lui, Jeremy Irvin, Jimmy Le

Reducing methane emissions is essential for mitigating global warming. To attribute methane emissions to their sources, a comprehensive dataset of methane source infrastructure is necessary. Recent advancements with deep learning on remotely sensed imagery have the potential to identify the locations and characteristics of methane sources, but there is a sub

Byoungwook Jang, Julia Nepper, Marc Chevrette, Jo Handelsman

Recent works in bandit problems adopted lasso convergence theory in the sequential decision-making setting. Even with fully observed contexts, there are technical challenges that hinder the application of existing lasso convergence theory: 1) proving the restricted eigenvalue condition under conditionally sub-Gaussian noise and 2) accounting for the dependen

Cory Frontin, David Darmofal

The use of numerical simulation for prediction of characteristics of chaotic dynamical systems inherently involves unpredictable processes. In this work, we develop a model for the expected error in the simulation of ergodic, chaotic ODE systems, which allows for discretization and statistical effects due to unpredictability. Using this model, we then genera

Robert A. Vandermeulen, René Saitenmacher

Recent work has shown that finite mixture models with $m$ components are identifiable, while making no assumptions on the mixture components, so long as one has access to groups of samples of size $2m-1$ which are known to come from the same mixture component. In this work we generalize that result and show that, if every subset of $k$ mixture components of

Chen Feng, Ioannis Patras

Self-supervised learning has recently achieved great success in representation learning without human annotations. The dominant method -- that is contrastive learning, is generally based on instance discrimination tasks, i.e., individual samples are treated as independent categories. However, presuming all the samples are different contradicts the natural gr

Matteo Bertolini, Francesco Mignosa, Jesse van Muiden

We generalize recent results regarding the phase space of the mass deformed $E_1$ fixed point to a full class of five-dimensional superconformal field theories, known as $X_{1,N}$. As in the $E_1$ case, a phase transition occurs as a supersymmetry preserving and a supersymmetry breaking mass deformations are appropriately tuned. The order of such phase trans

Huang Bojun

This paper discusses a new approach to the fundamental problem of learning optimal Q-functions. In this approach, optimal Q-functions are formulated as saddle points of a nonlinear Lagrangian function derived from the classic Bellman optimality equation. The paper shows that the Lagrangian enjoys strong duality, in spite of its nonlinearity, which paves the

Saranyamol V. S., Mohammed Ibrahim S

High temperature radiating Air is produced experimentally by focusing a shock wave with the help of a spherically converging test section attached to a shock tube. The converging section concentrates the shock to a point with minimum diffusion losses. A shift in radiation towards the UV region was observed with an increase in the strength of the focusing sho

Xi Chen, Jiameng Lyu, Yining Wang, Yuan Zhou

In addition to maximizing the total revenue, decision-makers in lots of industries would like to guarantee balanced consumption across different resources. For instance, in the retailing industry, ensuring a balanced consumption of resources from different suppliers enhances fairness and helps main a healthy channel relationship; in the cloud computing indus

Arpan Mukherjee, Ali Tajer

This paper investigates the best arm identification (BAI) problem in stochastic multi-armed bandits in the fixed confidence setting. The general class of the exponential family of bandits is considered. The existing algorithms for the exponential family of bandits face computational challenges. To mitigate these challenges, the BAI problem is viewed and anal

Moawwad El-Mikkawy, AbdelRahman Karawia

The principal minors of a tridiagonal matrix satisfy two-term and three-term recurrences [1, 2]. Based on these facts, the current article presents a new efficient and reliable hybrid numerical algorithm for evaluating general n-th order tridiagonal determinants in linear time. The hybrid numerical algorithm avoid all symbolic computations. The algorithm is

Quentin Ansel, Alexei D. Chepelianskii, Jose Lages

We explore the extent to which two quantum oscillators can exchange their quantum states efficiently through a three-level system which can be spin levels of colored centers in solids. High transition probabilities are obtained using Hamiltonian engineering and quantum control techniques. Starting from a weak coupling approximation, we derive conditions on t

Piero Bonatti, Gianluca Cima, Domenico Lembo, Lorenzo Marconi

Controlled Query Evaluation (CQE) has been recently studied in the context of Semantic Web ontologies. The goal of CQE is concealing some query answers so as to prevent external users from inferring confidential information. In general, there exist multiple, mutually incomparable ways of concealing answers, and previous CQE approaches choose in advance which

Baihe Huang, Shunhua Jiang, Zhao Song, Runzhou Tao

This paper studies a fundamental problem in convex optimization, which is to solve semidefinite programming (SDP) with high accuracy. This paper follows from the existing robust SDP-based interior point method analysis due to [Huang, Jiang, Song, Tao and Zhang, FOCS 2022]. While, the previous work only provides an efficient implementation in the classical se

Jack Clarke, Pascal Neveu, Kiran E. Khosla, Ewold Verhagen

Several optomechanics experiments are now entering the highly sought nonlinear regime where optomechanical interactions are large even for low light levels. Within this regime, new quantum phenomena and improved performance may be achieved, however, a corresponding theoretical formalism of cavity quantum optomechanics that captures the nonlinearities of both

Feiyang Pan, Tongzhe Zhang, Ling Luo, Jia He

Optimal execution is a sequential decision-making problem for cost-saving in algorithmic trading. Studies have found that reinforcement learning (RL) can help decide the order-splitting sizes. However, a problem remains unsolved: how to place limit orders at appropriate limit prices? The key challenge lies in the "continuous-discrete duality" of the action s

Richard Wiltshaw, Jacopo Maria De Ponti, Richard Vaughan Craster

We derive analytical solutions based on singular Green's functions, which enable efficient computations of scattering simulations or Floquet-Bloch dispersion relations for waves propagating through an elastic plate, whose surface is patterned by periodic arrays of elastic beams. Our methodology is versatile and allows us to solve a range of problems regardin

Michael Hoff, Isabel Stenger, José Ignacio Yáñez

We study Calabi-Yau manifolds which are complete intersections of hypersurfaces of multidegree $1$ in an $m$-fold product of $n$-dimensional projective spaces. Using the theory of Coxeter groups, we show that the birational automorphism group of such a Calabi-Yau manifold $X$ is infinite and a free product of copies of $\mathbb{Z}$ . Moreover, we give an exp

Kevin Galligan, Muriel Médard, Ken R. Duffy

Guessing Random Additive Noise Decoding (GRAND) is a family of universal decoding algorithms suitable for decoding any moderate redundancy code of any length. We establish that, through the use of list decoding, soft-input variants of GRAND can replace the Chase algorithm as the component decoder in the turbo decoding of product codes. In addition to being a

Zhengqi Li, Qianqian Wang, Noah Snavely, Angjoo Kanazawa

We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we s

Chen Liang, Ji-Rong Ren, Shi-Xian Sun, Yong-Qiang Wang

In this paper, we construct \textit{Dirac-boson stars} (DBSs) model composed of a scalar field and two Dirac fields. The scalar field and both Dirac fields are in the ground state. We consider the solution families of the DBSs for the synchronized frequency $\tilde{\omega}$ and the nonsynchronized frequency $\tilde{\omega}_D$ cases, respectively. We find sev

Ning Xie, Fa Peng Huang

The axion or axion-like particle motivated from a natural solution of strong CP problem or string theory is a promising dark matter candidate. We study the new observational effects of ultralight axion-like particles by the space-borne gravitational wave detector and the radio telescope. Taking the neutron star-black hole binary as an example, we demonstrate

Mao-Jun Yan, Fang-Zheng Peng, Mario Sánchez Sánchez, Manuel Pavon Valderrama

The LHCb collaboration has detected a new hidden-charm pentaquark with the quantum numbers of a $\Lambda$ baryon: the $P_{\psi s}^{\Lambda}(4338)$. This pentaquark will be interpreted as a $\bar{D}_s \Lambda_c$-$\bar{D} \Xi_c$ resonance within a contact-range theory. Here we briefly comment on the relation of the new $P_{\psi s}^{\Lambda}(4338)$ with the $P^

Andrew M. Thomas

We consider the stochastic behavior of a class of local $U$-statistics of Poisson processes$-$which include subgraph and simplex counts as special cases, and amounts to quantifying clustering behavior$-$for point clouds lying in diverging halfspaces. We provide limit theorems for distributions with light and heavy tails. In particular, we prove finite-dimens

Marvin Syed, Natalia G. Berloff

Many physical systems with the dynamical evolution that at its steady state gives a solution to optimization problems were proposed and realized as promising alternatives to conventional computing. Systems of oscillators such as coherent Ising and XY machines based on lasers, optical parametric oscillators, memristors, polariton and photon condensates are pa

Elena Celledoni, Helge Glöckner, Jørgen Riseth, Alexander Schmeding

One of the fundamental problems in shape analysis is to align curves or surfaces before computing geodesic distances between their shapes. Finding the optimal reparametrization realizing this alignment is a computationally demanding task, typically done by solving an optimization problem on the diffeomorphism group. In this paper, we propose an algorithm for

Ramu Kumar Yadav

Using Monte Carlo simulations, we study the dynamic transitions in the unzipping of an adsorbed homogeneous polymer on a surface (or wall). We consider three different types of surfaces. One end of the polymer is always kept anchored, and other end monomer is subjected to a periodic force with frequency $\omega$ and amplitude $g_0$. We observe that the force

Arif Dönmez, Markus Reineke

We study moduli spaces of (semi-)stable representations of one-point extensions of quivers by rigid representations. This class of moduli spaces unifies Grassmannians of subrepresentations of rigid representations and moduli spaces of representations of generalized Kronecker quivers. With homological methods, we find numerical criteria for non-emptiness and

Luis F. Alday, Shai M. Chester, Himanshu Raj

We study graviton scattering on $AdS_4\times S^7$, which is dual to the stress tensor multiplet four-point function in the maximally supersymmetric 3d $U(N)_1\times U(N)_{-1}$ ABJM theory. We compute 1-loop corrections to this holographic correlator coming from Witten diagrams with supergravity $R$ and higher derivative $R^4$ vertices, up to contact term amb

Dennis Lim, Wenjie Wang, Yichong Zhang

We consider a linear combination of jackknife Anderson-Rubin (AR), jackknife Lagrangian multiplier (LM), and orthogonalized jackknife LM tests for inference in IV regressions with many weak instruments and heteroskedasticity. Following I.Andrews (2016), we choose the weights in the linear combination based on a decision-theoretic rule that is adaptive to the

Ayoub Raji, Alexander Liniger, Andrea Giove, Alessandro Toschi

This paper presents a multi-layer motion planning and control architecture for autonomous racing, capable of avoiding static obstacles, performing active overtakes, and reaching velocities above 75 $m/s$. The used offline global trajectory generation and the online model predictive controller are highly based on optimization and dynamic models of the vehicle

N. Leethochawalit, M. Trenti, P. Santini, L. Yang

We present the first James Webb Space Telescope/NIRCam-led determination of $7<z<9$ galaxy properties based on broadband imaging from 0.8 to 5~$\mathrm{\mu m}$ as part of the GLASS-JWST Early Release Science program. This is the deepest dataset acquired at these wavelengths to date, with an angular resolution $\lesssim0.14$ arcsec. We robustly identify 13 ga

El Jabri Chaymae, Frappier Marc, Ecarot Thibaud, Tardif Pierre-Martin

Anomaly-based intrusion detection systems are essential defenses against cybersecurity threats because they can identify anomalies in current activities. However, these systems have difficulties providing entity processing independence through a programming language. In addition, a degradation of the detection process is caused by the complexity of schedulin

Yu Jiang, John Eisenmann, William Graves, Vijayaraghavan Sridhar

This paper, for the first time, presents a terrain-based localization approach using sensor data from an active suspension system. The contribution is four-fold. First, it is shown that a location dependent road height profile can be created from sensor data of the active suspension system. Second, an algorithm is developed to extract a pitch profile from th

M. Rooks, S. Abbaszadeh, J. Asaadi, M. Febbraro

Detection of the vacuum ultraviolet (VUV) scintillation light produced by liquid noble elements is a central challenge in order to fully exploit the available timing, topological, and calorimetric information in detectors leveraging these media. In this paper, we characterize a novel, windowless amorphous selenium based photodetector with direct sensitivity

Orin Levy, Yishay Mansour

We present regret minimization algorithms for stochastic contextual MDPs under minimum reachability assumption, using an access to an offline least square regression oracle. We analyze three different settings: where the dynamics is known, where the dynamics is unknown but independent of the context and the most challenging setting where the dynamics is unkn

Tu Nguyen, Amitabh Basu, Támas Budavári

Building on previous Bayesian approaches, we introduce a novel formulation of probabilistic cross-identification, where detections are directly associated to (hypothesized) astronomical objects in a globally optimal way. We show that this new method scales better for processing multiple catalogs than enumerating all possible candidates, especially in the lim

Yangtao Du, Qingqing Wang, Yujie Xiong

Facial Expression Recognition (FER) suffers from data uncertainties caused by ambiguous facial images and annotators' subjectiveness, resulting in excursive semantic and feature covariate shifting problem. Existing works usually correct mislabeled data by estimating noise distribution, or guide network training with knowledge learned from clean data, neglect

Paul Brunzema, Alexander von Rohr, Sebastian Trimpe

Changing conditions or environments can cause system dynamics to vary over time. To ensure optimal control performance, controllers should adapt to these changes. When the underlying cause and time of change is unknown, we need to rely on online data for this adaptation. In this paper, we will use time-varying Bayesian optimization (TVBO) to tune controllers

Josnei Novacoski, Enric Nart

In this paper we present a characterization for the defect of a simple algebraic extensions of valued fields. This characterization generalizes the known result for the henselian case, namely that the defect is the product of the relative degrees of limit augmentations. The main tool used here is the graded algebra associated to a valuation on a polynomial r

Christian M. M. Frey, Matthias Schubert

Semantic Web or Knowledge Graphs (KG) emerged to one of the most important information source for intelligent systems requiring access to structured knowledge. One of the major challenges is the extraction and processing of unambiguous information from textual data. Following the human perception, overlapping semantic linkages between two named entities beco

Yangjun Mao, Long Chen, Zhihong Jiang, Dong Zhang

Distinctive Image Captioning (DIC) -- generating distinctive captions that describe the unique details of a target image -- has received considerable attention over the last few years. A recent DIC work proposes to generate distinctive captions by comparing the target image with a set of semantic-similar reference images, i.e., reference-based DIC (Ref-DIC).

Ognjen Kundacina, Miodrag Forcan, Mirsad Cosovic, Darijo Raca

Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-dr

V. A. Traag

Citations in science are being studied from several perspectives, among which approaches such as scientometrics and science of science. In this chapter I briefly review some of the literature on citations, citation distributions and models of citations. These citations feature prominently in another part of the literature which is dealing with research evalu

Victor Roca i Lucio

The main goal of this thesis is to develop the integration theory of curved homotopy Lie algebras. In the first chapter, we develop the operadic calculus needed: we encode non-necessarily conilpotent coalgebras with operads and introduce their dual notion of an algebra over a cooperad, which we call "absolute algebras". The first sections of this chapter are

Roel Burgwal, Ewold Verhagen

The nonlinear component of the optomechanical interaction between light and mechanical vibration promises many exciting classical and quantum mechanical applications, but is generally weak. Here we demonstrate enhancement of nonlinear optomechanical measurement of mechanical motion by using pairs of coupled optical and mechanical modes in a photonic crystal

Josephine Nauschütz, Hedwig Knötig, Robert Weih, Julian Scheuermann

We present GaSb-based interband cascade lasers emitting at a center wavelength of 6.12 $\mu$m at 20$^\circ$C in continuous-wave operation up to a maximum operating temperature of 40$^\circ$C. Pulsed measurements based on broad area devices show improved performance by applying the recently published approach of adjusting the GaInSb layer thickness in the act