May 2023 arXiv papers — page 93

Forrest Fankhauser, J. Anthony Tyson, Jacob Askari

The apparent brightness of satellites is calculated as a function of satellite position as seen by a ground-based observer in darkness. Both direct illumination of the satellite by the Sun as well as indirect illumination due to reflection from the Earth are included. The reflecting properties of the satellite components and of the Earth must first be estima

Davi M. Febba, Kevin R. Talley, Kendal Johnson, Stephen Schaefer

Autonomous experimentation has emerged as an efficient approach to accelerate the pace of materials discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials and nanoparticles, examples of autonomous tools for physical vapor deposition are scarce yet important for th

Aningi Mokhalingam, Shakti S. Gupta, Roger A. Sauer

Understanding the interaction mechanics between graphene layers and co-axial carbon nanotubes (CNTs) is essential for modeling graphene and CNT-based nanoelectromechanical systems. This work proposes a new continuum contact model to study interlayer interactions between curved graphene sheets. The continuum model is calibrated and validated using molecular d

Joel Wagner, Simon Bauer, Sebastian Contreras, Luk Fleddermann

Classically, endemic infectious diseases are expected to display relatively stable, predictable infection dynamics. Accordingly, basic disease models such as the susceptible-infected-recovered-susceptible model display stable endemic states or recurrent seasonal waves. However, if the human population reacts to high infection numbers by mitigating the spread

Carter Lyons, Raghu G. Raj, Margaret Cheney

For solving linear inverse problems, particularly of the type that appears in tomographic imaging and compressive sensing, this paper develops two new approaches. The first approach is an iterative algorithm that minimizes a regularized least squares objective function where the regularization is based on a compound Gaussian prior distribution. The compound

Leonid Positselski

We construct examples of bounded below, noncontractible, acyclic complexes of finitely generated projective modules over some rings $S$, as well as bounded above, noncontractible, acyclic complexes of injective modules. The rings $S$ are certain rings of infinite matrices with entries in the rings of commutative polynomials or formal power series in infinite

R. R. Cuzinatto, C. A. M. de Melo, Juliano C. S. Neves

The co-varying physical couplings (CPC) framework states that physical parameters like the speed of light in vacuum $c$, the Newtonian constant $G$, and the cosmological constant $\Lambda$ could indeed vary with the spacetime coordinates $x^{\mu}$. Here, we assume a temporal variation, that is, $c(t),G(t)$ and $\Lambda(t)$. We show that the McVittie spacetim

Shohini Bhattacharya, Krzysztof Cichy, Martha Constantinou, Xiang Gao

For the first time, we present a lattice QCD determination of Mellin moments of unpolarized generalized parton distributions (GPDs) of the proton from an analysis of the quasi-GPD matrix elements within the short-distance factorization framework. We perform our calculation on an $N_f$=2+1+1 twisted mass fermions ensemble with a clover improvement at lattice

Yujie Lu, Xianjun Yang, Xiujun Li, Xin Eric Wang

Existing automatic evaluation on text-to-image synthesis can only provide an image-text matching score, without considering the object-level compositionality, which results in poor correlation with human judgments. In this work, we propose LLMScore, a new framework that offers evaluation scores with multi-granularity compositionality. LLMScore leverages the

Minsik Cho, Saurabh Adya, Devang Naik

DNN pruning is a popular way to reduce the size of a model, improve the inference latency, and minimize the power consumption on DNN accelerators. However, existing approaches might be too complex, expensive or ineffective to apply to a variety of vision/language tasks, DNN architectures and to honor structured pruning constraints. In this paper, we propose

Georg Oberdieck

We determine the Gromov-Witten invariants of the local Enriques surfaces for all genera and curve classes and prove the Klemm-Mari\~{n}o formula. In particular, we show that the generating series of genus $1$ invariants of the Enriques surface is the Fourier expansion of a certain power of Borcherds automorphic form on the moduli space of Enriques surfaces.

Li Yu, Jie Xu, Fuqun Wang, Chui-Ping Yang

XOR oblivious transfer is a universal cryptographic primitive that can be related to linear polynomial evaluation. We firstly introduce some bipartite quantum protocols for XOR oblivious transfer, which are not secure if one party cheats, and some of them can be combined with a classical XOR homomorphic encryption scheme for evaluation of linear polynomials

Rodrigo Corso B. Santos, Carlos A. Hernaski, Pedro R. S. Gomes

We revisit the two dimensional non-Abelian Thirring model in order to investigate its fixed point structure and the corresponding renormalization group (RG) flow. For this purpose we discuss the bosonization of the model, and we present different, but of course equivalent, bosonic versions of the theory. The bosonic theories are illuminating in that they exh

Yunpeng Wang, Zelong Yuan, Jianchun Wang

An ensemble Kalman filter (EnKF)-based mixed model (EnKF-MM) is proposed for the subgrid-scale (SGS) closure in the large-eddy simulation (LES) of turbulence. The model coefficients are determined through the EnKF-based data assimilation technique. The direct numerical simulation (DNS) results are filtered to obtain the benchmark data for LES. Reconstructing

Shunyuan Mao, Ruobing Dong, Lu Lu, Kwang Moo Yi

We develop a tool, which we name Protoplanetary Disk Operator Network (PPDONet), that can predict the solution of disk-planet interactions in protoplanetary disks in real-time. We base our tool on Deep Operator Networks (DeepONets), a class of neural networks capable of learning non-linear operators to represent deterministic and stochastic differential equa

Megha Pandey, Mrinal Kanti Roychowdhury

In this work, we extend the classical framework of quantization for Borel probability measures defined on normed spaces $\mathbb{R}^k$ by introducing and analyzing the notions of the $n$th constrained quantization error, constrained quantization dimension, and constrained quantization coefficient. These concepts generalize the well-established $n$th quantiza

Bo Huang

This paper is devoted to the study of infinitesimal limit cycles that can bifurcate from zero-Hopf equilibria of differential systems based on the averaging method. We develop an efficient symbolic program using Maple for computing the averaged functions of any order for continuous differential systems in arbitrary dimension. The program allows us to systema

Fiona A. M. Porter, Anna M. M. Scaife

The volume of data from current and future observatories has motivated the increased development and application of automated machine learning methodologies for astronomy. However, less attention has been given to the production of standardised datasets for assessing the performance of different machine learning algorithms within astronomy and astrophysics.

Eugene R. Rush, Kaushik Jayaram, J. Sean Humbert

In motor neuroscience, artificial recurrent neural networks models often complement animal studies. However, most modeling efforts are limited to data-fitting, and the few that examine virtual embodied agents in a reinforcement learning context, do not draw direct comparisons to their biological counterparts. Our study addressing this gap, by uncovering stru

A. R. Soares, R. L. L. Vitória, C. F. S. Pereira

In the present paper, we study several aspects of gravitational lensing caused by a topologically charged Monopole/Wormhole, both in the weak field limit and in the strong field limit. We calculate the light deflection and then use it to determine the observables, with which one can investigate the existence of these objects through observational tools. We e

Chenhao Shuai, Chaohua Shi, Lu Gan, Hongqing Liu

Speech super-resolution (SSR) aims to recover a high resolution (HR) speech from its corresponding low resolution (LR) counterpart. Recent SSR methods focus more on the reconstruction of the magnitude spectrogram, ignoring the importance of phase reconstruction, thereby limiting the recovery quality. To address this issue, we propose mdctGAN, a novel SSR fra

R. Thiru Senthil

Block matrix structure is commonly arising is various physics and engineering applications. There are various advantages in preserving the blocks structure while computing the inversion of such partitioned matrices. In this context, using the blockwise matrix inversion technique, inversions of large matrices with different ways of memory handling are present

Aysegul Dundar, Jun Gao, Andrew Tao, Bryan Catanzaro

This paper presents a method to reconstruct high-quality textured 3D models from single images. Current methods rely on datasets with expensive annotations; multi-view images and their camera parameters. Our method relies on GAN generated multi-view image datasets which have a negligible annotation cost. However, they are not strictly multi-view consistent a

Lihui Qian, Xintong Han, Faqiang Wang, Hongyu Liu

We present XFormer, a novel human mesh and motion capture method that achieves real-time performance on consumer CPUs given only monocular images as input. The proposed network architecture contains two branches: a keypoint branch that estimates 3D human mesh vertices given 2D keypoints, and an image branch that makes predictions directly from the RGB image

Daniele De Gennaro, Antonia Diana, Andrea Kubin, Anna Kubin

We prove that, in the flat torus and in any dimension, the volume-preserving mean curvature flow and the surface diffusion flow, starting $C^{1,1}-$close to a strictly stable critical set of the perimeter $E$, exist for all times and converge to a translate of $E$ exponentially fast as time goes to infinity.

Robert Salzwedel, Lara Greten, Stefan Schmidt, Stephen Hughes

We present a self-consistent Maxwell-Bloch theory to analytically study the interaction between a nanostructure consisting of a metal nanoparticle and a monolayer of transition metal dichalcogenide. For the combined system, we identify an effective eigenvalue equation that governs the center-of-mass motion of the dressed excitons in a plasmon-induced potenti

Hiroyuki Kido

Statistical learning and logical reasoning are two major fields of AI expected to be unified for human-like machine intelligence. Most existing work considers how to combine existing logical and statistical systems. However, there is no theory of inference so far explaining how basic approaches to statistical learning and logical reasoning stem from a common

Thomas Nagler

Prior-data fitted networks (PFNs) were recently proposed as a new paradigm for machine learning. Instead of training the network to an observed training set, a fixed model is pre-trained offline on small, simulated training sets from a variety of tasks. The pre-trained model is then used to infer class probabilities in-context on fresh training sets with arb

Jinhui Ye, Wenxiang Jiao, Xing Wang, Zhaopeng Tu

End-to-end sign language translation (SLT) aims to convert sign language videos into spoken language texts directly without intermediate representations. It has been a challenging task due to the modality gap between sign videos and texts and the data scarcity of labeled data. Due to these challenges, the input and output distributions of end-to-end sign lan

Puyuan Peng, Brian Yan, Shinji Watanabe, David Harwath

We investigate the emergent abilities of the recently proposed web-scale speech model Whisper, by adapting it to unseen tasks with prompt engineering. We selected three tasks: audio-visual speech recognition (AVSR), code-switched speech recognition (CS-ASR), and speech translation (ST) on unseen language pairs. We design task-specific prompts, by either leve

Sicheng Yang, Zhiyong Wu, Minglei Li, Zhensong Zhang

Speech-driven gesture generation is highly challenging due to the random jitters of human motion. In addition, there is an inherent asynchronous relationship between human speech and gestures. To tackle these challenges, we introduce a novel quantization-based and phase-guided motion-matching framework. Specifically, we first present a gesture VQ-VAE module

Debjyoti Biswas, Gaurav M. Vaidya, Prabha Mandayam

Implementing quantum error correction (QEC) protocols is a challenging task in today's era of noisy intermediate-scale quantum devices. We present quantum circuits for a universal, noise-adapted recovery map, often referred to as the Petz map, which is known to achieve close-to-optimal fidelity for arbitrary codes and noise channels. While two of our circuit

Bin Deng, Kui Jia

Foundation models (e.g., CLIP or DINOv2) have shown their impressive learning and transfer capabilities in a wide range of visual tasks, by training on a large corpus of data and adapting to specific downstream tasks. It is, however, interesting that foundation models have not been fully explored for universal domain adaptation (UniDA), which is to learn mod

Chunyan Li, Antonina A. Arkhipova, Yaroslav V. Kartashov, Sergey A. Zhuravitskii

We study both, experimentally and theoretically, propagation of light in the fs-laser written rotating square waveguide arrays and present the first experimental evidence of light localization induced by the rotation of periodic structure in the direction of light propagation. Such linear light localization occurs either in the corners of truncated square ar

Jeffrey J. Langford, Richard S. Laugesen

The second eigenvalue of the Robin Laplacian is shown to be maximal for a spherical cap among simply connected Jordan domains on the 2-sphere, for substantial intervals of positive and negative Robin parameters and areas. Geodesic disks in the hyperbolic plane similarly maximize the eigenvalue on a natural interval of negative Robin parameters. These theorem

Javier E Santos, Zachary R. Fox, Nicholas Lubbers, Yen Ting Lin

Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, including many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state

Thomas Karam

Let $p$ be a prime, let $1 \le t < d < p$ be integers, and let $S$ be a non-empty subset of $\mathbb{F}_p$. We establish that if a polynomial $P:\mathbb{F}_p^n \to \mathbb{F}_p$ with degree $d$ is such that the image $P(S^n)$ does not contain the full image $A(\mathbb{F}_p)$ of any non-constant polynomial $A: \mathbb{F}_p \to \mathbb{F}_p$ with degree at mos

Haoze Wu, Christopher Hahn, Florian Lonsing, Makai Mann

We present Self-Driven Strategy Learning ($\textit{sdsl}$), a lightweight online learning methodology for automated reasoning tasks that involve solving a set of related problems. $\textit{sdsl}$ does not require offline training, but instead automatically constructs a dataset while solving earlier problems. It fits a machine learning model to this data whic

Youwei Liang, Ruiyi Zhang, Li Zhang, Pengtao Xie

A ChatGPT-like system for drug compounds could be a game-changer in pharmaceutical research, accelerating drug discovery, enhancing our understanding of structure-activity relationships, guiding lead optimization, aiding drug repurposing, reducing the failure rate, and streamlining clinical trials. In this work, we make an initial attempt towards enabling Ch

Riddhipratim Basu, Timo Seppäläinen, Xiao Shen

Understanding the decay of correlations in time for (1+1)-dimensional polymer models in the KPZ universality class has been a challenging topic. Following numerical studies by physicists, concrete conjectures were formulated by Ferrari and Spohn (Ferrari-Spohn '16) in the context of planar exponential last passage percolation. These have mostly been resolved

V. S. Protsenko, A. A. Katanin

We study the effect of various configurations of vacancies on the magnetic properties of graphene nanoflake (GNF) with screened realistic long-range electron interaction [T. O. Wehling, et. al., Phys. Rev. Lett. 106, 236805 (2011)] within the functional renormalization group approach. In agreement with previous studies, the presence of vacancies in GNF yield

Chao Wang, Hengshu Zhu, Dazhong Shen, Wei wu

People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both intent and preference factors are usually entangled in the modeling process, which significantly limits the robustness and interpretability of recommendation performances. For exam

Stefan Tappe

We study a statistical model for infinite dimensional Gaussian random variables with unknown parameters. For this model we derive linear estimators for the mean and the variance of the Gaussian distribution. Furthermore, we construct confidence intervals and perform hypothesis testing. A linear regression problem in infinite dimensions and some perspectives

N. J Martin, M. Jalali Mehrabad, X. Chen, R. Dost

Chirality in integrated quantum photonics has emerged as a promising route towards achieving scalable quantum technologies with quantum nonlinearity effects. Topological photonic waveguides, which utilize helical optical modes, have been proposed as a novel approach to harnessing chiral light-matter interactions on-chip. However, uncertainties remain regardi

Zhaochun Ren, Na Huang, Yidan Wang, Pengjie Ren

Learning reinforcement learning (RL)-based recommenders from historical user-item interaction sequences is vital to generate high-reward recommendations and improve long-term cumulative benefits. However, existing RL recommendation methods encounter difficulties (i) to estimate the value functions for states which are not contained in the offline training da

Abhipsa Basu, R. Venkatesh Babu, Danish Pruthi

Recent progress in generative models has resulted in models that produce both realistic as well as relevant images for most textual inputs. These models are being used to generate millions of images everyday, and hold the potential to drastically impact areas such as generative art, digital marketing and data augmentation. Given their outsized impact, it is

Tolga Altinoluk, Guillaume Beuf, Jamal Jalilian-Marian

We derive the Leading Order DGLAP evolution of gluon distribution function in the target light cone gauge starting from its standard operator definition. The derivation is performed using the background field formalism also employed in the Color Glass Condensate effective theory of small $x$ QCD. We adopt Mandelstam-Leibbrandt prescription to regulate in an

Luke K. Davis, Karel Proesmans, Étienne Fodor

Active constituents burn fuel to sustain individual motion, giving rise to collective effects that are not seen in systems at thermal equilibrium, such as phase separation with purely repulsive interactions. There is a great potential in harnessing the striking phenomenology of active matter to build novel controllable and responsive materials that surpass p

Sahel Mohammad Iqbal, Danush Shekar, Subhankar Mishra

Contemporary face detection algorithms have to deal with many challenges such as variations in pose, illumination, and scale. A subclass of the face detection problem that has recently gained increasing attention is occluded face detection, or more specifically, the detection of masked faces. Three years on since the advent of the COVID-19 pandemic, there is

Robert M. Corless

A "blendstring" is a piecewise polynomial interpolant with high-degree two-point Hermite interpolational polynomials on each piece, analogous to a cubic spline. Blendstrings are smoother and can be more accurate than cubic splines, and can be used to represent smooth functions on a line segment or polygonal path in the complex plane. I sketch some properties

Beatrice Brienza, Anna Fino

Starting from the product of a $3$-torus and a compact K\"ahler (respectively, hyperK\"ahler) manifold we construct via mapping tori generalized K\"ahler manifolds of split (respectively, non-split) type. In this way we obtain new non-K\"ahler examples and we recover the known examples of generalized K\"ahler solvmanifolds. Moreover, we investigate the forma

Tong Ye, Lingfei Wu, Tengfei Ma, Xuhong Zhang

Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although neural language models achieve significant performance in this field, they are limited by their inability to access external knowledge. To address this limitation, an emerging trend is combining neural models with extern

Yifan Peng, Kwangyoun Kim, Felix Wu, Brian Yan

Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, including automatic speech recognition (ASR), speech translation (ST) and spoken language understanding (SLU). Recently, a new encoder called E-Branchformer has outperformed Conformer in

Heng-Jui Chang, Alexander H. Liu, James Glass

Self-supervised speech representation models have succeeded in various tasks, but improving them for content-related problems using unlabeled data is challenging. We propose speaker-invariant clustering (Spin), a novel self-supervised learning method that clusters speech representations and performs swapped prediction between the original and speaker-perturb

Sam O. M. Wright, Stevanus K. Nugroho, Matteo Brogi, Neale P. Gibson

Individual vibrational band spectroscopy presents an opportunity to examine exoplanet atmospheres in detail by distinguishing where the vibrational state populations of molecules differ from the current assumption of a Boltzmann distribution. Here, retrieving vibrational bands of OH in exoplanet atmospheres is explored using the hot Jupiter WASP-33b as an ex

Andrei Moroianu, Ángel J. Murcia, C. S. Shahbazi

We introduce a novel curvature flow, the Heterotic-Ricci flow, as the two-loop renormalization group flow of the Heterotic string common sector and study its three-dimensional compact solitons. The Heterotic-Ricci flow is a coupled curvature evolution flow, depending on a non-negative real parameter $\kappa$, for a complete Riemannian metric and a three-form

Daniel Gordon Gass, Robert William Walsh

Observations from the NASA Solar Dynamic Observatory Atmospheric Imaging Assembly were employed to investigate targeted physical properties of coronal active region structures across the entirety of Solar Cycle 24 (dates). This is the largest consistent study to date which analyses emergent trends in structural width, location, and occurrence rate by perform

Alex Barnett, Philip Greengard, Manas Rachh

The high efficiency of a recently proposed method for computing with Gaussian processes relies on expanding a (translationally invariant) covariance kernel into complex exponentials, with frequencies lying on a Cartesian equispaced grid. Here we provide rigorous error bounds for this approximation for two popular kernels -- Mat\'ern and squared exponential -

Federico Glaudo, Noah Kravitz

For a finite multiset $A$ of an abelian group $G$, let $\text{FS}(A)$ denote the multiset of the $2^{|A|}$ subset sums of $A$. It is natural to ask to what extent $A$ can be reconstructed from $\text{FS}(A)$. We fully solve this problem for $2$-torsion-free groups $G$ by giving characterizations, both algebraic and combinatorial, of the fibers of $\text{FS}$

Shay Snyder, Sumedh R. Risbud, Maryam Parsa

The ever-increasing demands of computationally expensive and high-dimensional problems require novel optimization methods to find near-optimal solutions in a reasonable amount of time. Bayesian Optimization (BO) stands as one of the best methodologies for learning the underlying relationships within multi-variate problems. This allows users to optimize time

Jihui Jin, Etienne Ollivier, Richard Touret, Matthew McKinley

Inverse problems describe the task of recovering an underlying signal of interest given observables. Typically, the observables are related via some non-linear forward model applied to the underlying unknown signal. Inverting the non-linear forward model can be computationally expensive, as it often involves computing and inverting a linearization at a serie

Quanjun Lang, Fei Lu

Regularization plays a pivotal role in ill-posed machine learning and inverse problems. However, the fundamental comparative analysis of various regularization norms remains open. We establish a small noise analysis framework to assess the effects of norms in Tikhonov and RKHS regularizations, in the context of ill-posed linear inverse problems with Gaussian

Andrea Braides, Antonin Chambolle

We give an interpretation of a class of discrete-to-continuum results for Ising systems using the theory of zonoids. We define the classes of rational zonotopes and zonoids, as those of the Wulff shapes of perimeters obtained as limits of finite-range homogeneous Ising systems and of general homogeneous Ising systems, respectively. Thanks to the characteriza

Sepehr Assadi, Janani Sundaresan

We continue the study of the communication complexity of gap cycle counting problems. These problems have been introduced by Verbin and Yu [SODA 2011] and have found numerous applications in proving streaming lower bounds. In the noisy gap cycle counting problem (NGC), there is a small integer $k \geq 1$ and an $n$-vertex graph consisted of vertex-disjoint u

Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng

Dense retrieval has shown promise in the first-stage retrieval process when trained on in-domain labeled datasets. However, previous studies have found that dense retrieval is hard to generalize to unseen domains due to its weak modeling of domain-invariant and interpretable feature (i.e., matching signal between two texts, which is the essence of informatio

Gianluca Carletti, Elio Giulianelli, Anna Sofia Lippolis, Giorgia Lodi

Recently, an increasing interest in the management of water and health resources has been recorded. This interest is fed by the global sustainability challenges posed to the humanity that have water scarcity and quality at their core. Thus, the availability of effective, meaningful and open data is crucial to address those issues in the broader context of th

Zewei Xiong, Gabriel Martínez-Pinedo, Oliver Just, Andre Sieverding

We present a new nucleosynthesis process that may take place on neutron-rich ejecta experiencing an intensive neutrino flux. The nucleosynthesis proceeds similarly to the standard $r$-process, a sequence of neutron-captures and beta-decays, however with charged-current neutrino absorption reactions on nuclei operating much faster than beta-decays. Once neutr

Xinheng Xie, Yue Wu, Hao Ni, Cuiyu He

Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neural network (CNN) blocks. NODE-ImgNet is intrinsically a PDE model, where the dynamic system is learned implic

Adam Heins, Angela P. Schoellig

We present the first controller for quasistatic robotic planar pushing with single-point contact using only force feedback. We consider a mobile robot equipped with a force-torque sensor to measure the force at the contact point with the pushed object (the "slider"). The parameters of the slider are not known to the controller, nor is feedback on the slider'

Arthur Perret, Yves Bérubé-Lauzière

Preparation of bosonic and general cavity quantum states usually relies on using open-loop control to reach a desired target state. In this work, a measurement-based feedback approach is used instead, exploiting the non-linearity of weak measurements alongside a coherent drive to prepare these states. The extension of previous work on Lyapunov-based control

Marwa El Halabi, George Orfanides, Tim Hoheisel

Minimizing the difference of two submodular (DS) functions is a problem that naturally occurs in various machine learning problems. Although it is well known that a DS problem can be equivalently formulated as the minimization of the difference of two convex (DC) functions, existing algorithms do not fully exploit this connection. A classical algorithm for D

Yan Liu, Bing Sun

The study of Kerr geodesics has a long history, particularly for those occurring within the equatorial plane, which is generally well-understood. However, upon comparison with the classification introduced by one of us https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.024075 , it becomes apparent that certain classes of geodesics, such as trapped or

Ryan Boldi, Aadam Lokhandwala, Edward Annatone, Yuval Schechter

Recommender systems influence almost every aspect of our digital lives. Unfortunately, in striving to give us what we want, they end up restricting our open-mindedness. Current recommender systems promote echo chambers, where people only see the information they want to see, and homophily, where users of similar background see similar content. We propose a n

Nikolai Terekhov, Maksim Zhukovskii

The weak saturation number $\mathrm{wsat}(n,F)$ is the minimum number of edges in a graph on $n$ vertices such that all the missing edges can be activated sequentially so that each new edge creates a copy of $F$. A usual approach to prove a lower bound for the weak saturation number is algebraic: if it is possible to embed edges of $K_n$ in a vector space in

Yifeng Chu, Maxim Raginsky

This paper presents a general methodology for deriving information-theoretic generalization bounds for learning algorithms. The main technical tool is a probabilistic decorrelation lemma based on a change of measure and a relaxation of Young's inequality in $L_{\psi_p}$ Orlicz spaces. Using the decorrelation lemma in combination with other techniques, such a

Ran Li, Chuanqing Pu, Junyi Tao, Canbing Li

The digitalization of energy sectors has expanded the coding responsibilities for power engineers and researchers. This research article explores the potential of leveraging Large Language Models (LLMs) to alleviate this burden. Here, we propose LLM-based frameworks for different programming tasks in power systems. For well-defined and routine tasks like the

Yerlan Myrzakulov, Kairat Myrzakulov, Sudhaker Upadhyay, Dharm Veer Singh

In this paper, we present an exact regular black hole solution in Einstein-Gauss-Bonnet coupled with nonlinear matter fields. It is a generalization of a regular Einstein-Gauss-Bonnet black hole in $5D$ $AdS$ spacetime. The causal structure of the obtained solution identifies with Boulware-Deser black hole solution, except for the curvature singularity at th

Nouhaila Innan, Mohamed Bennai

The quantum perceptron, the variational circuit, and the Grover algorithm have been proposed as promising components for quantum machine learning. This paper presents a new quantum perceptron that combines the quantum variational circuit and the Grover algorithm. However, this does not guarantee that this quantum variational perceptron with Grover's algorith

Soumyadeep Hore, Jalal Ghadermazi, Diwas Paudel, Ankit Shah

Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms, coupled with the availability of faster computing infrastructure, have enhanced the security posture of cybersecurity operations centers (defenders) through the development of ML-aided network intrusion detection systems (NIDS). Concurrently, the abilities of adversarie

Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou

Named entity recognition in real-world applications suffers from the diversity of entity types, the emergence of new entity types, and the lack of high-quality annotations. To address the above problems, this paper proposes an in-context learning-based NER approach, which can effectively inject in-context NER ability into PLMs and recognize entities of novel

Yue Meng, Wei Li, Ruben Juanes

We study fluid-induced deformation and fracture of cohesive granular media, and apply photoporomechanics to uncover the underpinning grain-scale mechanics. We fabricate photoelastic spherical particles of diameter d=2mm, and make a monolayer granular pack with tunable intergranular cohesion in a circular Hele-Shaw cell that is initially filled with viscous s

Sébastien Dechamps, Frédéric Meunier

In a simple model of assigning workers to tasks, every solution that minimizes the load difference between the most loaded worker and the least loaded one actually minimizes the maximal load and maximizes the minimal load. This can be seen as a consequence of standard results of optimization over polymatroids. We show that similar phenomena still occur in cl

Piotr Faliszewski, Jarosław Flis, Dominik Peters, Grzegorz Pierczyński

We provide a library of participatory budgeting data (Pabulib) and open source tools (Pabutools and Pabustats) for analysing this data. We analyse how the results of participatory budgeting elections would change if a different selection rule was applied. We provide evidence that the outcomes of the Method of Equal Shares would be considerably fairer than th

Samuel Mensah, Kai Sun, Nikolaos Aletras

State-of-the-art target-oriented opinion word extraction (TOWE) models typically use BERT-based text encoders that operate on the word level, along with graph convolutional networks (GCNs) that incorporate syntactic information extracted from syntax trees. These methods achieve limited gains with GCNs and have difficulty using BERT wordpieces. Meanwhile, BER

Ana Cláudia Akemi Matsuki de Faria, Felype de Castro Bastos, José Victor Nogueira Alves da Silva, Vitor Lopes Fabris

Visual Question Answering (VQA) is an emerging area of interest for researches, being a recent problem in natural language processing and image prediction. In this area, an algorithm needs to answer questions about certain images. As of the writing of this survey, 25 recent studies were analyzed. Besides, 6 datasets were analyzed and provided their link to d

Qinghua Liu, Gellért Weisz, András György, Chi Jin

While policy optimization algorithms have played an important role in recent empirical success of Reinforcement Learning (RL), the existing theoretical understanding of policy optimization remains rather limited -- they are either restricted to tabular MDPs or suffer from highly suboptimal sample complexity, especial in online RL where exploration is necessa

Shoukang Hu, Kaichen Zhou, Kaiyu Li, Longhui Yu

Neural Radiance Fields (NeRF) has demonstrated remarkable 3D reconstruction capabilities with dense view images. However, its performance significantly deteriorates under sparse view settings. We observe that learning the 3D consistency of pixels among different views is crucial for improving reconstruction quality in such cases. In this paper, we propose Co

Carol S. Lee, Morgan Ramsey, Catherine M. Hicks

Background: Previous research highlights that common misconceptions about developer productivity lead to harmful and inaccurate evaluations of software work, pointing to the need for organizations to differentiate between measures of production, productivity, and performance as an important step that helps to suggest improvements to how we measure the succes

Qi Sun, Kun Huang, Xiaocui Yang, Pengfei Hong

Document-level relation extraction (DocRE) aims to infer complex semantic relations among entities in a document. Distant supervision (DS) is able to generate massive auto-labeled data, which can improve DocRE performance. Recent works leverage pseudo labels generated by the pre-denoising model to reduce noise in DS data. However, unreliable pseudo labels br

O. P. Dovhopiatyi, E. A. Sevost'yanov

The article is devoted to the study of mappings that distort the modulus of families of paths according to the Poletsky inequality type. At the boundary points of the domain, we have obtained an estimate of the distance distortion for such mappings provided that their characteristic has finite integral averages over the balls, and the image domain of mapping

Nadav Frenkel, Einav Scharf, Gur Lubin, Adar Levi

Coupled colloidal quantum dot molecules are an emerging class of nanomaterials, introducing new degrees of freedom for designing quantum dot-based technologies. The properties of multiply excited states in these materials are crucial to their performance as quantum light emitters but cannot be fully resolved by existing spectroscopic techniques. Here we stud

Armand Brumer, Kenneth Kramer

We say that an abelian variety $A_{/\mathbf Q}$ of dimension $g$ is {\em prosaic} if it is semistable, with good reduction at 2 and its points of order $2$ generate a $2$-extension of ${\mathbf Q}$. For $p \equiv 1 \bmod{8}$, let $M_u$ be the maximal 2-primary unramified abelian extension of $K = {\mathbf Q}(\sqrt{-p})$ and let $h_2 =[M_u:K]$. We construct a

Aleksei B. Aleksandrov, Evgueni Doubtsov

Let $I$ be an inner function in $\mathcal{D} = B_{n_1}\times B_{n_2}\cdots \times B_{n_k}$, where $B_n$ denotes the open unit ball of $\mathbb{C}^n$, $n\ge 1$. We construct dominant sets for the space $H^2 \ominus I H^2$, where $H^2 = H^2(\mathcal{D})$ denotes the standard Hardy space.

Xinyu Zhao, Sa Huang, Wei Pang, You Zhou

We study the problem of registration for medical CT images from a novel perspective -- the sensitivity to degree of deformations in CT images. Although some learning-based methods have shown success in terms of average accuracy, their ability to handle regions with local large deformation (LLD) may significantly decrease compared to dealing with regions with

Harshil Shah, Arthur Wilcke, Marius Cobzarenco, Cristi Cobzarenco

Natural language understanding includes the tasks of intent detection (identifying a user's objectives) and slot filling (extracting the entities relevant to those objectives). Prior slot filling methods assume that each intent type cannot occur more than once within a message, however this is often not a valid assumption for real-world settings. In this wor

Thomas Heap, Gavin Leech, Laurence Aitchison

Reweighted wake-sleep (RWS) is a machine learning method for performing Bayesian inference in a very general class of models. RWS draws $K$ samples from an underlying approximate posterior, then uses importance weighting to provide a better estimate of the true posterior. RWS then updates its approximate posterior towards the importance-weighted estimate of

Qishen Han, Grant Schoenebeck, Biaoshuai Tao, Lirong Xia

We study the voting game where agents' preferences are endogenously decided by the information they receive, and they can collaborate in a group. We show that strategic voting behaviors have a positive impact on leading to the ``correct'' decision, outperforming the common non-strategic behavior of informative voting and sincere voting. Our results give meri

Hao-Hsuan Chen, Ching-Ming Lee, Ching-Ray Chang

Using Legendre transformation, a standard theoretical approach extensively used in classical mechanics as well as thermal dynamics, two-dimensional non-linear auto-oscillators including spin torque nano-oscillators (STNOs) can be equivalently expressed either in phase space or in configuration space where all of them can be modeled by terminal velocity motio

Siyuan Hui, Benrong Mu

In this paper, we investigate the effective potential and echoes from the dyonic black hole with quintessence. For a dyonic black hole, the quasi-topological electromagnetism provides proper matter energy-momentum tensor to curve the spacetime, and quintessence strengthens this force. We find that when the effect of quintessence becomes stronger, the black h

Munib Mesinovic, Xin Ci Wong, Giri Shan Rajahram, Barbara Wanjiru Citarella

By September, 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in C