July 2022 arXiv papers — page 82

Alan Frieze, Michael Krivelevich, Peleg Michaeli

We introduce a model of a controlled random graph process. In this model, the edges of the complete graph $K_n$ are ordered randomly and then revealed, one by one, to a player called Builder. He must decide, immediately and irrevocably, whether to purchase each observed edge. The observation time is bounded by parameter $t$, and the total budget of purchased

Han Zheng, Zimu Li, Sergii Strelchuk, Risi Kondor

We introduce a framework of the equivariant convolutional quantum algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU$(d)$ symmetries. It allows us to enhance a natural model of quantum computation -- permutational quantum computing (PQC) -- and define a more powerful model: PQC+. While PQC was shown to b

Mingzhe Guo, Bo Li, Tom Van Doorsselaere, Mijie Shi

We numerically follow the response of density-enhanced slabs to impulsive, localized, transverse velocity perturbations by working in the framework of ideal magnetohydrodynamics (MHD). Both linear and nonlinear regimes are addressed. Kink wave trains are seen to develop along the examined slabs, sharing the characteristics that more oscillatory patterns emer

Bobby Wilson, Xueying Yu

We use modified scattering theory to demonstrate that small-data solutions to the cubic nonlinear Schr\"odinger equation on rescaled waveguide manifolds, $\mathbb{R} \times \mathbb{T}^d$ for $d\geq 2$, demonstrate boundedness of Sobolev norms as well as weak instability.

Nirjal Sapkota, Janos Turi

We study parameter estimation for the two state model which describes the balance equation for carbon dioxide and oxygen in human respiratory system. These are nonlinear parameter dependent and because of the transport delay in the respiratory control system, they are modeled with delay differential equation. Numerically simulated noisy data are generated an

Rong Chen

I describe the first continuous space nuclear path integral quantum Monte Carlo method, and calculate the ground state properties of light nuclei including Deuteron, Triton, Helium-3 and Helium-4, using both local chiral interaction up to next-to-next-to-leading-order and the Argonne $v_6'$ interaction. Compared with diffusion based quantum Monte Carlo metho

Xuefei Yin, Yanming Zhu, Yi Xie, Jiankun Hu

Recent studies have demonstrated that smart grids are vulnerable to stealthy false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad data detection mechanisms. The SFDIA detection has become one of the focuses of smart grid research. Methods based on deep learning technology have shown promising accuracy in the detection of SFDIAs. How

Naif Alkhunaizi, Dmitry Kamzolov, Martin Takáč, Karthik Nandakumar

Collaboration among multiple data-owning entities (e.g., hospitals) can accelerate the training process and yield better machine learning models due to the availability and diversity of data. However, privacy concerns make it challenging to exchange data while preserving confidentiality. Federated Learning (FL) is a promising solution that enables collaborat

F. Michael Bartram, Yu-Chen Leng, Yongchao Wang, Liangyang Liu

The atomically thin MnBi2Te4 crystal is a novel magnetic topological insulator, exhibiting exotic quantum physics. Here we report a systematic investigation of ultrafast carrier dynamics and coherent interlayer phonons in few-layer MnBi2Te4 as a function of layer number using time-resolved pump-probe reflectivity spectroscopy. Pronounced coherent phonon osci

Samruddhi Deshmukh, Amartansh Dubey, Ross Murch

Inverse scattering problems, such as those in electromagnetic imaging using phaseless data (PD-ISPs), involve imaging objects using phaseless measurements of wave scattering. Such inverse problems can be highly non-linear and ill-posed under extremely strong scattering conditions such as when the objects have very high permittivity or are large in size. In t

Anita Mahinpei, Zona Kostic, Chris Tanner

Data visualization captions help readers understand the purpose of a visualization and are crucial for individuals with visual impairments. The prevalence of poor figure captions and the successful application of deep learning approaches to image captioning motivate the use of similar techniques for automated figure captioning. However, research in this fiel

Jeremy Brazas

It is a well-known fact that every path-connected Hausdorff space is arcwise connected. Typically, this result is viewed as a consequence of a sequence of fairly technical results from continuum theory. In this note, we exhibit a direct and simple proof of this statement, which makes explicit use of Zorn's Lemma. Additionally, by carefully breaking down the

Rudraksh Kapil, Seyed Mojtaba Marvasti-Zadeh, Devin Goodsman, Nilanjan Ray

Bark beetle outbreaks can dramatically impact forest ecosystems and services around the world. For the development of effective forest policies and management plans, the early detection of infested trees is essential. Despite the visual symptoms of bark beetle infestation, this task remains challenging, considering overlapping tree crowns and non-homogeneity

Kate R. Schneider, Luc Christiaensen, Patrick Webb, William A. Masters

The cost and affordability of least-cost healthy diets by time and place are increasingly used as a proxy for access to nutrient-adequate diets. Recent work has focused on the nutrient requirements of individuals, although most food and anti-poverty programs target whole households. This raises the question of how the cost of a nutrient-adequate diet can be

Yoshiko Kanada-En'yo, Nobuo Hinohara

A microscopic $n\alpha$ cluster model was applied to $^{8}$Be, $^{12}$C, and $^{16}$O systems to investigate cluster motion in the ground state and radial excitation. In the microscopic calculation of $^{12}$C and $^{16}$O using the generator coordinate method with the coordinate $D$ of the $\alpha$-$\alpha$ distance, excited states were obtained as the larg

Eriq Augustine, Pegah Jandaghi, Alon Albalak, Connor Pryor

Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community. A recent pillar of research revolves around emotion recognition in conversation (ERC); a sub-field of emotion recognition that focuses on conversations or dialogues th

Samuel Stein, Nathan Wiebe, Yufei Ding, James Ang

The field of quantum computing has experienced a rapid expansion in recent years, with ongoing exploration of new technologies, a decrease in error rates, and a growth in the number of qubits available in quantum processors. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-

Wei Gao, Jie Chen, Cédric Richard, Wentao Shi

We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS). Like most kernel adaptive filters based on stochastic gradient descent, this algorithm uses a preset number of random Fourier features to save computation cost. However, as an extra flexibility, it can adapt the inherent kernel bandwidth in the random Fourier features in an onl

Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Narayanaswamy, Peer-Timo Bremer

We are interested in estimating the uncertainties of deep neural networks, which play an important role in many scientific and engineering problems. In this paper, we present a striking new finding that an ensemble of neural networks with the same weight initialization, trained on datasets that are shifted by a constant bias gives rise to slightly inconsiste

Siting Liu, Stanley Osher, Wuchen Li, Chi-Wang Shu

In this work, we propose a novel framework for the numerical solution of time-dependent conservation laws with implicit schemes via primal-dual hybrid gradient methods. We solve an initial value problem (IVP) for the partial differential equation (PDE) by casting it as a saddle point of a min-max problem and using iterative optimization methods to find the s

Ahmet A. Husainov

This paper continues the research of the author on the homology of cubical and semi-cubical sets with coefficients in systems of objects. The main result is the theorem that the homology of cubical sets with coefficients in contravariant systems in an Abelian category with exact coproducts is isomorphic to the left satellites of a colimit functor. This made

Sarosij Bose

Lipschitz Bound Estimation is an effective method of regularizing deep neural networks to make them robust against adversarial attacks. This is useful in a variety of applications ranging from reinforcement learning to autonomous systems. In this paper, we highlight the significant gap in obtaining a non-trivial Lipschitz bound certificate for Convolutional

Ying Yang, Ya Liu, Shi Shu

We discuss and analyze the virtual element method on general polygonal meshes for the time-dependent Poisson-Nernst-Planck equations, which are a nonlinear coupled system widely used in semiconductors and ion channels. The spatial discretization is based on the elliptic projection and the $L^2$ projection operator, and for the temporal discretization, the ba

Pulkit Garg, Timothy J. Rupert

Amorphous grain boundary complexions lack long-range crystalline order but are not featureless, as distinct gradients in structural short-range order have been reported through their thickness. In this work, we test the hypothesis that the distribution of short-range order is determined by the confining crystals using atomistic simulations of both Cu-Zr bicr

Erik D. Demaine, Robert A. Hearn, Dylan Hendrickson, Jayson Lynch

We prove PSPACE-completeness of several reversible, fully deterministic systems. At the core, we develop a framework for such proofs (building on a result of Tsukiji and Hagiwara and a framework for motion planning through gadgets), showing that any system that can implement three basic gadgets is PSPACE-complete. We then apply this framework to four differe

Bichi Zhang, Holger Caesar, Raj Thilak Rajan

Multi-agent systems, e.g., automobiles and UAVs (Unmanned Ariel Vehicles), rely on the precision of onboard sensors to accurately perceive their environment, which in turn depends on the precision of onboard sensors and reliable in-field calibration. This paper introduces a novel targetless camera-LiDAR extrinsic calibration approach called Multi-FEAT (Multi

S. Meghna, N. Suresh, J. C. Usha

This study examines the impact of dividend policy on the performance of initial public offerings in India. The period of study is from the year 2011-2014. Monthly returns of the IPOs issued in the considered period and the Indian Stock Market Index (Nifty 50) were considered for the long-run performance study. The methodological tools used are long-run perfo

Debajyoti Choudhury, Suvam Maharana

We develop a new class of clockwork theories with an augmented structure of the near-neighbour interactions along a one-dimensional closed chain. Such a topology leads to new and attractive features in addition to generating light states with hierarchical couplings via the usual clockwork mechanism. For one, there emerges a $\mathbb{Z}_2$ symmetry under the

Ivan Ivanov, Stoyan K. Smoukov, Ehsan Nourafkan, Katharina Landfester

The origin of life is shrouded in mystery, with few surviving clues, obscured by evolutionary competition. Previous reviews have touched on the complementary approaches of top-down and bottom-up synthetic biology to augment our understanding of living systems. Here we point out the synergies between these fields, especially between bottom-up synthetic biolog

Haoyu Li, Tianyu Xiong, Han-Wei Shen

Particle tracing through numerical integration is a well-known approach to generating pathlines for visualization. However, for particle simulations, the computation of pathlines is expensive, since the interpolation method is complicated due to the lack of connectivity information. Previous studies utilize the k-d tree to reduce the time for neighborhood se

Jiayin Jin, Jiaxiang Ren, Yang Zhou, Lingjuan Lyu

The federated learning (FL) framework enables edge clients to collaboratively learn a shared inference model while keeping privacy of training data on clients. Recently, many heuristics efforts have been made to generalize centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc., to federated settings for improving convergence and accurac

Fatemeh Nosrat

Crowdfunding, which is the act of raising funds from a large number of people's contributions, is among the most popular research topics in economic theory. Due to the fact that crowdfunding platforms (CFPs) have facilitated the process of raising funds by offering several features, we should take their existence and survival in the marketplace into account.

Ningkun Zheng, Xin Qin, Di Wu, Gabe Murtaugh

This paper introduces and rationalizes a new model for bidding and clearing energy storage resources in wholesale energy markets. Charge and discharge bids in this model depend on the storage state-of-charge (SoC). In this setting, storage participants submit different bids for each SoC segment. The system operator monitors the storage SoC and updates their

Nicolas Folinsbee, Joel Friedman

By a {\em Riemann function} we mean a function $f\colon{\mathbb Z}^n\to{\mathbb Z}$ such that $f({\bf d})=f(d_1,\ldots,d_n)$ is equals $0$ for ${\rm deg}({\bf d})=d_1+\cdots+d_n$ sufficiently small, and equals $d_1+\cdots+d_n+C$ for a constant, $C$ -- the {\em offset of $f$} -- for ${\rm deg}({\bf d})$ sufficiently large. By adding $1$ to the Baker-Norine ra

Ambroise Baril, Miguel Couceiro, Victor Lagerkvist

We investigate the fine-grained and the parameterized complexity of several generalizations of binary constraint satisfaction problems (BINARY-CSPs), that subsume variants of graph colouring problems. Our starting point is the observation that several algorithmic approaches that resulted in complexity upper bounds for these problems, share a common structure

Ziran Min, Shashank Shekhar, Charif Mahmoudi, Valerio Formicola

This paper addresses the challenges of delivering fine-grained Quality of Service (QoS) and communication determinism over 5G wireless networks for real-time and autonomous needs of Industrial Internet of Things (IIoT) applications while effectively sharing network resources. Specifically, this work presents DANSM, a software-defined, dynamic and autonomous

Ery Arias-Castro, Phong Alain Chau

While classical scaling, just like principal component analysis, is parameter-free, other methods for embedding multivariate data require the selection of one or several tuning parameters. This tuning can be difficult due to the unsupervised nature of the situation. We propose a simple, almost obvious, approach to supervise the choice of tuning parameter(s):

Maryam Zahedian, Jietian Liu, Ricardo Vidrio, Shimon Kolkowitz

Optically active defects in diamond are widely used as bright single-photon sources for quantum sensing, computing, and communication. For many applications, it is useful to place the emitter close to the diamond surface, where the radiative properties of the emitter are strongly modified by its dielectric environment. It is well-known that the radiative pow

Junyan He, Diab Abueidda, Seid Koric, Iwona Jasiuk

A graph neural network (GCN) is employed in the deep energy method (DEM) model to solve the momentum balance equation in 3D for the deformation of linear elastic and hyperelastic materials due to its ability to handle irregular domains over the traditional DEM method based on a multilayer perceptron (MLP) network. Its accuracy and solution time are compared

Ali Olyanasab, Zahra Meskar, Mohsen Annabestani, Ali Mousavi Shaegh

The need for rapid fabrication of microfluidic devices has become increasingly critical as microfluidics become part of biomedical sensors. Using Warp and Weft Wiring (WWW) of copper wires, this paper presents a novel low-cost method for rapid, self-aligned, bonding-free, and modifiable fabrication of multi-electrodes microfluidic sensors. All the proposed f

Qi Xiong, Gui-Xian Tian, Shu-Yu Cui

A mixed graph $M_{G}$ is the graph obtained from an unoriented simple graph $G$ by giving directions to some edges of $G$, where $G$ is often called the underlying graph of $M_{G}$. In this paper, we introduce two classes of incidence matrices of the second kind of $M_{G}$, and discuss the determinants of these two matrices for rootless mixed trees and unicy

Cédric Dion, Antonio Lei, Anwesh Ray, Daniel Vallières

Let $\ell$ be a prime number. The Iwasawa theory of multigraphs is the systematic study of growth patterns in the number of spanning trees in abelian $\ell$-towers of multigraphs. In this context, growth patterns are realized by certain analogues of Iwasawa invariants, which depend on the prime $\ell$ and the abelian $\ell$-tower of multigraphs. We formulate

Ahmad Bdeir, Jonas K. Falkner, Lars Schmidt-Thieme

Machine Learning (ML) methods have become a useful tool for tackling vehicle routing problems, either in combination with popular heuristics or as standalone models. However, current methods suffer from poor generalization when tackling problems of different sizes or different distributions. As a result, ML in vehicle routing has witnessed an expansion phase

Pankaj K. Agarwal, Sariel Har-Peled

Let $P$ be a set of $n$ points in $\Re^2$. For a parameter $\varepsilon\in (0,1)$, a subset $C\subseteq P$ is an \emph{$\varepsilon$-kernel} of $P$ if the projection of the convex hull of $C$ approximates that of $P$ within $(1-\varepsilon)$-factor in every direction. The set $C$ is a \emph{weak $\varepsilon$-kernel} of $P$ if its directional width approxima

Marco Atzori, Pablo Torres, Alvaro Vidal, Soledad Le Clainche

High-fidelity large-eddy simulations of the flow around two rectangular obstacles are carried out at a Reynolds number of 10,000 based on the free-stream velocity and the obstacle height. The incoming flow is a developed turbulent boundary layer. Mean-velocity components, turbulence fluctuations, and the terms of the turbulent-kinetic-energy budget are analy

Václav Voráček, Matthias Hein

Randomized smoothing is sound when using infinite precision. However, we show that randomized smoothing is no longer sound for limited floating-point precision. We present a simple example where randomized smoothing certifies a radius of $1.26$ around a point, even though there is an adversarial example in the distance $0.8$ and extend this example further t

Václav Voráček, Matthias Hein

Nearest prototype classifiers (NPCs) assign to each input point the label of the nearest prototype with respect to a chosen distance metric. A direct advantage of NPCs is that the decisions are interpretable. Previous work could provide lower bounds on the minimal adversarial perturbation in the $\ell_p$-threat model when using the same $\ell_p$-distance for

Michał Fabisiak, Mikołaj Sierżęga

The question of triviality of solutions of the semilinear Ornstein-Uhlenbeck equation, \[ \Delta w-\frac{1}{2} \langle x,\nabla w\rangle-\frac{\lambda}{p-1}w+|w|^{p-1}w=0, \] is considered. It is shown, that if $p>1$ is Sobolev subcritical or critical and $\lambda\leq 1$, then all bounded entire solutions are constant. Moreover, in the critical case, the sam

Patrick Ingram

Given an endomorphism f of projective space, we exhibit explicit bounds on the difference between the naive height of a divisor and its canonical height relative to f.

Aatif Kaisar Khan, Salman Sajad Wani, Aasiya Shaikh, Yas Yamin

In this article, we have investigated the consequences of the next to the leading order correction to the effective field theory of nanostructures. This has been done by analyzing the effects of deformed Heisenberg algebra on nanowires and nanotubes. We first deform the Schrodinger equation with cylindrical topology. Then specific solutions to the deformed S

Henry Glunz

Benford's Law describes the prevalence of small numbers as the leading digits of numbers in many sets of integers. We prove a variant of Benford's law for many positive-density subsets of the primes. This follows from a more general result over number fields.

Viviana G. R. Lobo, Thais C. O. Fonseca, Mariane B. Alves

This paper focuses on modelling surrender time for policyholders in the context of life insurance. In this setup, a large lapse rate at the first months of a contract is often observed, with a decrease in this rate after some months. The modelling of the time to cancellation must account for this specific behaviour. Another stylised fact is that policies whi

Nan Wu, Sophia N. Yaliraki, Mauricio Barahona

Allostery commonly refers to the mechanism that regulates protein activity through the binding of a molecule at a different, usually distal, site from the orthosteric site. The omnipresence of allosteric regulation in nature and its potential for drug design and screening render the study of allostery invaluable. Nevertheless, challenges remain as few comput

Sahidul Islam, Shanglin Zhou, Ran Ran, Yufang Jin

IoT devices are increasingly being implemented with neural network models to enable smart applications. Energy harvesting (EH) technology that harvests energy from ambient environment is a promising alternative to batteries for powering those devices due to the low maintenance cost and wide availability of the energy sources. However, the power provided by t

Haolin Li, Anne Juel, Finn Box, Draga Pihler-Puzovic

We study experimentally the propagation of an air finger through the Y-bifurcation of an elastic, liquid-filled Hele-Shaw channel, as a benchtop model of airway reopening. With channel compliance provided by an elastic upper boundary, we can impose collapsed channel configurations into which we inject air with constant volume-flux. We typically observe stead

Shaul Barkan

Let $\mathbb{E}_d$ denote the little discs operad for $1 \le d \le \infty$ and let $\mathcal{C}$ be an $\infty$-category all of whose mapping spaces are $n$-truncated. We prove that when considering $\mathbb{E}_d$-monoids in $\mathcal{C}$, all coherence diagrams of arity $>n+3$ are redundant. More generally, for an $\infty$-operad $\mathcal{O}$ we bound the

Amélie Ferran, Nathanaël Machicoane, Alberto Aliseda, Martín Obligado

We propose an experimental study on the gravitational settling velocity of dense, sub-Kolmogorov inertial particles under different background turbulent flows. We report Phase Doppler Particle Analyzer measurements in a low-speed wind tunnel uniformly seeded with micrometer scale water droplets. Turbulence is generated with three different grids (two consist

Zhe Leng, Mark A. Minor

Jackknifing refers to the serious situation where a vehicle-trailer system enters a jackknife state and the vehicle and trailer eventually collide if trailer operation is not corrected. This paper considers low speed trailer maneuvering typical of trailer backing where jackknife state limits can vary due to sideslip caused by physical interaction between the

Satoya Nakano, Yoichi Tamura, Akio Taniguchi, Sachiko Okumura

We report the results of the performance characterization of a prototype wavefront sensor for millimetric adaptive optics (MAO) installed on the Nobeyama 45 m radio telescope. MAO is a key component to realize a future large-aperture submillimeter telescope, such as Large Submillimeter Telescope (LST) or Atacama Large Aperture Submillimeter Telescope (AtLAST

Michele Botti, Daniele A. Di Pietro, Marwa Salah

In this work we address the reduction of face degrees of freedom (DOFs) for discrete elasticity complexes. Specifically, using serendipity techniques, we develop a reduced version of a recently introduced two-dimensional complex arising from traces of the three-dimensional elasticity complex. The keystone of the reduction process is a new estimate of symmetr

S. M. Souliou, T. Lacmann, R. Heid, C. Meingast

We use diffuse and inelastic x-ray scattering to study the formation of an incommensurate charge-density-wave (I-CDW) in BaNi$_2$As$_2$, a candidate system for charge-driven electronic nematicity. Intense diffuse scattering is observed around the modulation vector of the I-CDW, $Q_{I-CDW}$. It is already visible at room temperature and collapses into superst

Ata Atay, Christian Trudeau

This paper studies queueing problems with an endogenous number of machines with and without an initial queue, the novelty being that coalitions not only choose how to queue, but also on how many machines. For a given problem, agents can (de)activate as many machines as they want, at a cost. After minimizing the total cost (processing costs and machine costs)

Ivica Dimitrovski, Ivan Kitanovski, Dragi Kocev, Nikola Simidjievski

We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-

Quinn MacPherson

We derive the Frank elastic constants for nematic solutions of semiflexible polymers. We plot these results as a function of the coarse-grained Maier-Saupe quadrupole aligning strength and polymer stiffness ranging from rigid to highly flexible. The derivation uses the random phase approximation and combines the exact results for the statistics of a worm-lik

Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen

The rise of deep neural networks offers new opportunities in optimizing recommender systems. However, optimizing recommender systems using deep neural networks requires delicate architecture fabrication. We propose NASRec, a paradigm that trains a single supernet and efficiently produces abundant models/sub-architectures by weight sharing. To overcome the da

Jozef Bobok, Jernej Činč, Piotr Oprocha, Serge Troubetzkoy

In this paper we provide a detailed topological and measure-theoretic study of Lebesgue measure-preserving circle maps that are rotated with inner and outer rotations which are independent of each other. In particular, we analyze the stability of the locally eventually onto and measure-theoretic mixing properties.

Haoteng Tang, Guixiang Ma, Lei Guo, Xiyao Fu

Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain n

Yakov Eliashberg, Dishant Pancholi

Following the overall strategy of the paper ``Convex hypersurfaces in contact topology" by Ko Honda and Yang Huang on contact convexity in high dimensions, we present a simplified proof of their main result.

Andrea M. Salazar, Eli Tziperman

Stratocumulus clouds cover about a fifth of Earths surface, and due to their albedo and low-latitude location, they have a strong effect on Earths radiation budget. Previous studies using Large Eddy Simulations have shown that multiple equilibria (both cloud-covered and cloud-free states) exist as a function of fixed-SST, with relevance to equatorward advect

Bhaskarjit Sarmah, Nayana Nair, Dhagash Mehta, Stefano Pasquali

Understanding non-linear relationships among financial instruments has various applications in investment processes ranging from risk management, portfolio construction and trading strategies. Here, we focus on interconnectedness among stocks based on their correlation matrix which we represent as a network with the nodes representing individual stocks and t

Makoto Natsuume, Takashi Okamura

The holographic superconductor is the holographic dual of superconductivity, but there is no Meissner effect in the standard holographic superconductor. This is because the boundary Maxwell field is added as an external source and is not dynamical. We show the Meissner effect analytically by imposing the semiclassical Maxwell equation on the AdS boundary. Un

Diego Gutierrez-Oribio, Ioannis Stefanou, Franck Plestan

Passivity property gives a sense of energy balance. The classical definitions and theorems of passivity in dynamical systems require time invariance and locally Lipschitz functions. However, these conditions are not met in many systems. A characteristic example is nonautonomous and discontinuous systems due to presence of Coulomb friction. This paper present

Michael Zhang, Christopher Ré

While large pretrained foundation models (FMs) have shown remarkable zero-shot classification robustness to dataset-level distribution shifts, their robustness to subpopulation or group shifts is relatively underexplored. We study this problem, and find that FMs such as CLIP may not be robust to various group shifts. Across 9 robustness benchmarks, zero-shot

Peter Albers, Jungsoo Kang

This article is concerned with the Rabinowitz Floer homology of negative line bundles. We construct a refined version of Rabinowitz Floer homology and study its properties. In particular, we build a Gysin-type long exact sequence for this new invariant and discuss an application to the orderability problem for prequantization spaces. We also construct a shor

Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg

In two-party machine learning prediction services, the client's goal is to query a remote server's trained machine learning model to perform neural network inference in some application domain. However, sensitive information can be obtained during this process by either the client or the server, leading to potential collection, unauthorized secondary use, an

Andreu F. Gallen, J. Roberto Romero-Arias, Rafael A. Barrio, Aurora Hernandez-Machado

The process of fission and vesicle formation depends on the geometry of the membrane that will split. For instance, a flat surface finds it difficult to form vesicles because of the lack of curved regions where to start the process. Here we show that vesicle formation can be promoted by temperature, by using a membrane phase field model with Gaussian curvatu

J. V. Bennett

Upgrades at the Belle II experiment and the SuperKEKB asymmetric-energy electron-positron collider enable precise measurements of particle decays. Even with early data, Belle II has made several world-leading measurements of particle lifetimes, which are useful as tests of effective models used in searches for physics beyond the standard model. Especially fo

Samuel C. Hoffman, Payel Das, Karthikeyan Shanmugam, Kahini Wadhawan

Training generative models that capture rich semantics of the data and interpreting the latent representations encoded by such models are very important problems in un-/self-supervised learning. In this work, we provide a simple algorithm that relies on perturbation experiments on latent codes of a pre-trained generative autoencoder to uncover an attribute g

Yuankun Xu, Dong Huang, Chang-Dong Wang, Jian-Huang Lai

Deep clustering has shown its promising capability in joint representation learning and clustering via deep neural networks. Despite the significant progress, the existing deep clustering works mostly utilize some distribution-based clustering loss, lacking the ability to unify representation learning and multi-scale structure learning. To address this, this

T. Jonckheere, J. Rech, B. Grémaud, T. Martin

The fractional quantum Hall effect (FQHE) is known to host anyons, quasiparticles whose statistics is intermediate between bosonic and fermionic. We show here that Hong-Ou-Mandel (HOM) interferences between excitations created by narrow voltage pulses on the edge states of a FQHE system at low temperature show a direct signature of anyonic statistics. The wi

Devin Sullivan, David Wilner, Luca Matra, Mark C. Wyatt

Millimeter emission from debris disks around stars of different ages provides constraints on the collisional evolution of planetesimals. We present ALMA 1.3 millimeter observations of a sample of 76 Solar-type stars in the ~115 Myr old Pleiades star cluster. These ALMA observations complement previous infrared observations of this sample by providing sensiti

Jacqueline Antwi-Danso, Casey Papovich, Joel Leja, Danilo Marchesini

We present a new rest-frame color-color selection method using "synthetic $u_s-g_s$ and $g_s-i_s$'', $(ugi)_s$ colors to identify star-forming and quiescent galaxies. Our method is similar to the widely-used $U-V$ versus $V-J$ ($UVJ$) diagram. However, $UVJ$ suffers known systematics. Spectroscopic campaigns have shown that $UVJ$-selected quiescent samples a

Guantao Chen, Yanli Hao, Guoning Yu

A linear forest is a union of vertex-disjoint paths, and the linear arboricity of a graph $G$, denoted by $\operatorname{la}(G)$, is the minimum number of linear forests needed to partition the edge set of $G$. Clearly, $\operatorname{la}(G) \ge \lceil\Delta(G)/2\rceil$ for a graph $G$ with maximum degree $\Delta(G)$. On the other hand, the Linear Arboricity

Davood Hajinezhad, Afshin Oroojlooy, Mohammadreza Nazari, Xin Hunt

In this work, we propose a Machine Learning model that generates an adjustable 3D dose distribution for external beam radiation therapy for head-and-neck cancer treatments. In contrast to existing Machine Learning methods that provide a single model, we create pairs of models for each organ-at-risk, namely lower-extreme and upper-extreme models. These model

Gabriel Nicolosi, Terry Friesz, Christopher Griffin

In this paper we study the problem of approximating the general solution to an optimal control problem whose dynamics arise from a $2\times 2$ skew-symmetric evolutionary game with arbitrary initial condition. Our approach uses a Fourier approximation method and generalizes prior work in the use of orthogonal function approximation for optimal control. At th

Peiling Yi, Arkaitz Zubiaga

Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modeling and understanding the two defining characteristics of cyberbullying: repetitive behavior

Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob N. Foerster

The standard problem setting in cooperative multi-agent settings is self-play (SP), where the goal is to train a team of agents that works well together. However, optimal SP policies commonly contain arbitrary conventions ("handshakes") and are not compatible with other, independently trained agents or humans. This latter desiderata was recently formalized b

Trisha Mittal, Puneet Mathur, Rohan Chandra, Apurva Bhatt

We present a computational approach for estimating emotion contagion on social media networks. Built on a foundation of psychology literature, our approach estimates the degree to which the perceivers' emotional states (positive or negative) start to match those of the expressors, based on the latter's content. We use a combination of deep learning and socia

Deng Wang

We use the latest Type Ia supernovae sample Pantheon+ to explore new physics on cosmic scales. Specifically, in light of this new sample, we constrain the interacting dark energy and Hu-Sawicki $f(R)$ gravity models and employ the model-independent Gaussian processes to investigate whether there is an evidence of dark energy evolution. We find that Pantheon+

Nicole St-Louis, Kenneth Gayley, D. John Hillier, Richard Ignace

The winds of massive stars are important for their direct impact on the interstellar medium, and for their influence on the final state of a star prior to it exploding as a supernova. However, the dynamics of these winds is understood primarily via their illumination from a single central source. The Doppler shift seen in resonance lines is a useful tool for

James Marien, Sam Leroux, Bart Dhoedt, Cedric De Boom

Over 60,000 songs are released on Spotify every day, and the competition for the listener's attention is immense. In that regard, the importance of captivating and inviting cover art cannot be underestimated, because it is deeply entangled with a song's character and the artist's identity, and remains one of the most important gateways to lead people to disc

Richard Sarmento, Evan Scannapieco

The recently launched James Webb Space Telescope promises unparalleled advances in our understanding of the first stars and galaxies, but realizing this potential requires cosmological simulations that capture the key physical processes that affected these objects. Here we show that radiative transfer and subgrid turbulent mixing are two such processes. By c

Marina O. Lisnichenko, Stanislav I. Protasov

One of the crucial tasks in computer science is the processing time reduction of various data types, i.e., images, which is important for different fields -- from medicine and logistics to virtual shopping. Compared to classical computers, quantum computers are capable of parallel data processing, which reduces the data processing time. This quality of quant

Kwok Sun Tang, Matthew Turk

Chemical kinetics plays an important role in governing the thermal evolution in reactive flows problems. The possible interactions between chemical species increase drastically with the number of species considered in the system. Various ways have been proposed before to simplify chemical networks with an aim to reduce the computational complexity of the che

Raghuvansh R. Saxena, Noah Singer, Madhu Sudan, Santhoshini Velusamy

We initiate a study of the streaming complexity of constraint satisfaction problems (CSPs) when the constraints arrive in a random order. We show that there exists a CSP, namely $\textsf{Max-DICUT}$, for which random ordering makes a provable difference. Whereas a $4/9 \approx 0.445$ approximation of $\textsf{DICUT}$ requires $\Omega(\sqrt{n})$ space with ad

S. I. Godunov, E. K. Karkaryan, V. A. Novikov, A. N. Rozanov

Analytical formulas for the cross section of the reaction $pp \to p + \ell^+ \ell^- + X$ are presented. Fiducial cross sections are compared with those measured recently by the ATLAS collaboration.

Peter J. Cameron, Veronica Phan

A graph is weakly perfect if its clique number and chromatic number are equal. We show that the enhanced power graph of a finite group $G$ is weakly perfect: its clique number and chromatic number are equal to the maximum order of an element of $G$. The proof requires a combinatorial lemma. We give some remarks about related graphs.

Antonio Rojas-León

We provide an effective version of Katz' criterion for finiteness of the monodromy group of a lisse, pure of weight zero, $\ell$-adic sheaf on a normal variety over a finite field, depending on the numerical complexity of the sheaf

Jie Chen, Daniel R. Chavas

The impacts of a tropical cyclone after landfall depend not only on storm intensity but also on the size and structure of the wind field. Hence, a simple predictive model for the wind field after landfall has significant potential value. This work tests existing theory for wind structure and size over the ocean against idealized axisymmetric landfall experim

Bryan Kinzer, Rohini Bala Chandran

Nanoparticle reinforced alloys offer the potential of high strength, high temperature alloys. While promising, during rapid solidification processes, alloys suffer from nanoparticle clustering, which can discount any strength benefit. An open-source phase-field model is developed using PRISMS-PF to explore the impact of nanoparticles and clustering on alloy