July 2023 arXiv papers — page 62

Tong Bill Xu, Saleh Kalantari

Over the last half century, the main application of Brain Computer Interfaces, BCIs has been controlling wheelchairs and neural prostheses or generating text or commands for people with restricted mobility. There has been very limited attention in the field to applications for computer aided design, despite the potential of BCIs to provide a new form of envi

Alexis Bossard, Nicolas James, Camille Aucouturier, Johan Fourdrinoy

Transcritical flows in free surface hydrodynamics emulate black hole horizons and their timereversed versions known as white fountains. Both analogue horizons have been shown to emit Hawking radiation, the amplification of waves via scattering at the horizon. Here we report on an experimental validation of the hydrodynamic laws that govern transcritical flow

M. H. M Rashid, Feras Bani-Ahmad

We provide a number of sharp inequalities involving the usual operator norms of Hilbert space operators and powers of the numerical radii. Based on the traditional convexity inequalities for nonnegative real numbers and some generalize earlier numerical radius inequalities, operator. Precisely, we prove that if $\A_i,\B_i,\X_i\in\bh$ ($i=1,2,\cdots,n$), $m\i

J. J. Zanazzi, Eugene Chiang

The orbits of some warm Jupiters are highly inclined (20$^\circ$-50$^\circ$) to those of their exterior companions. Comparable misalignments are inferred between the outer and inner portions of some transition discs. These large inclinations may originate from planet-planet and planet-disc secular resonances that sweep across interplanetary space as parent d

Shiraz Minwalla, Souparna Nath, Nikhil Tanwar, Vatsal

Most of the computational evidence for the Bose$\unicode{x2013}$Fermi duality of fundamental fields coupled to $U(N)$ Chern$\unicode{x2013}$Simons theories originates in large-$N$ calculations performed in the light-cone gauge. This gauge is ill-suited to computations in curved spacetimes, like the evaluation of the partition function on $\Sigma_g\times S^1$

Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao

Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge. However, it remains unclear how well LLMs are able to perceive their factual knowledge boundaries, particularly under retrieval augmentation settings. In this study, we present the first analysis on the factual knowledge boundaries of LLMs and ho

Charles C. Margossian, David M. Blei

In a probabilistic latent variable model, factorized (or mean-field) variational inference (F-VI) fits a separate parametric distribution for each latent variable. Amortized variational inference (A-VI) instead learns a common inference function, which maps each observation to its corresponding latent variable's approximate posterior. Typically, A-VI is used

Marcel Beetz, Abhirup Banerjee, Vicente Grau

Myocardial infarction (MI) is one of the most common causes of death in the world. Image-based biomarkers commonly used in the clinic, such as ejection fraction, fail to capture more complex patterns in the heart's 3D anatomy and thus limit diagnostic accuracy. In this work, we present the multi-objective point cloud autoencoder as a novel geometric deep lea

Ana Sofia Santos, Matheus Silva, Crystian Saraiva, José Soares

Brain metastases are a complication of primary cancer, representing the most common type of brain tumor in adults. The management of multiple brain metastases represents a clinical challenge worldwide in finding the optimal treatment for patients considering various individual aspects. Managing multiple metastases with stereotactic radiosurgery (SRS) is bein

Andre S. Sunahara, Arthur A. B. Pessa, Matjaz Perc, Haroldo V. Ribeiro

While extensive literature exists on the COVID-19 pandemic at regional and national levels, understanding its dynamics and consequences at the city level remains limited. This study investigates the pandemic in Maring\'a, a medium-sized city in Brazil's South Region, using data obtained by actively monitoring the disease from March 2020 to June 2022. Despite

István Juhász, Lajos Soukup, Zoltán Szentmiklóssy

Hart and Kunen, and independently in the recent preprint arXiv:2304.13113, R\'ios-Herrej\'on defined and studied the class $C({\omega}_1)$ of topological spaces $X$ having the property that for every neighborhood assignment $\{U(y) : y \in Y\}$ with $Y \in [X]^{\omega_1}$ there is $Z \in [Y]^{\omega_1}$ such that $$Z \subset \bigcap \{U(z) : z \in Z\}.$$ It

Selçuk Kutluay, Nuri Murat Yağmurlu, Ali Sercan Karakaş

In this article, non-linear Equal Width-Wave (EW) equation will be numerically solved . For this aim, the non-linear term in the equation is firstly linearized by Rubin-Graves type approach. After that, to reduce the equation into a solvable discretized linear algebraic equation system which is the essential part of this study, the Crank-Nicolson type approx

Victor Churchill, Dongbin Xiu

Flow map learning (FML), in conjunction with deep neural networks (DNNs), has shown promises for data driven modeling of unknown dynamical systems. A remarkable feature of FML is that it is capable of producing accurate predictive models for partially observed systems, even when their exact mathematical models do not exist. In this paper, we present an overv

Bin Cheng, Zuhao Liu, Yunbo Peng, Yue Lin

Large-scale text-to-image models pre-trained on massive text-image pairs show excellent performance in image synthesis recently. However, image can provide more intuitive visual concepts than plain text. People may ask: how can we integrate the desired visual concept into an existing image, such as our portrait? Current methods are inadequate in meeting this

David Ardia, Clément Aymard, Tolga Cenesizoglu

We analyze Robinhood (RH) investors' trading reactions to intraday hourly and overnight price changes. Contrasting with recent studies focusing on daily behaviors, we find that RH users strongly favor big losers over big gainers. We also uncover that they react rapidly, typically within an hour, when acquiring stocks that exhibit extreme negative returns. Fu

Dong Huang, Qingwen Bu, Yichao Fu, Yuhao Qing

Deep Neural Networks~(DNNs) have been widely deployed in software to address various tasks~(e.g., autonomous driving, medical diagnosis). However, they could also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal the incorrect behaviors in DNN and repair them, DNN developers often collect rich unlabeled dat

Sara Fernandes, Ademar Aguiar, André Restivo

Complex software can be hard to read, adapt, and maintain. Refactoring it can create cleaner and self-explanatory code. Refactoring tools try to guide developers towards better code, with more quality. However, most of them take too long to provide feedback, support, and guidance on how developers should improve their software. To reduce this problem, we exp

Clément Berthiere, Gilles Parez

We investigate a separability criterion based on the computable cross-norm (CCNR), and a related quantity called the CCNR negativity. We introduce a reflected version of the CCNR negativity, and discuss its connection with other well-established entanglement-related quantities, namely the reflected entropy and the operator entanglement entropy. For free ferm

Ludovico Lami, Bartosz Regula

Computing the exact rate at which entanglement can be distilled from noisy quantum states is one of the longest-standing questions in quantum information. We give an exact solution for entanglement distillation under the set of dually non-entangling (DNE) operations -- a relaxation of the typically considered local operations and classical communication, com

Kaiyue Wen, Zhiyuan Li, Tengyu Ma

Despite extensive studies, the underlying reason as to why overparameterized neural networks can generalize remains elusive. Existing theory shows that common stochastic optimizers prefer flatter minimizers of the training loss, and thus a natural potential explanation is that flatness implies generalization. This work critically examines this explanation. T

Dmitriy F. Kuznetsov

The article is devoted to a new proof of the expansion for iterated Ito stochastic integrals with respect to the components of a multidimensional Wiener process. The above expansion is based on Hermite polynomials and generalized multiple Fourier series in arbitrary complete orthonormal systems of functions in a Hilbert space. In 2006, the author obtained a

Siddhant Arora, Hayato Futami, Yosuke Kashiwagi, Emiru Tsunoo

There has been an increased interest in the integration of pretrained speech recognition (ASR) and language models (LM) into the SLU framework. However, prior methods often struggle with a vocabulary mismatch between pretrained models, and LM cannot be directly utilized as they diverge from its NLU formulation. In this study, we propose a three-pass end-to-e

Amrutaa Vibho, Ali Al Bataineh

The prediction of spin magnitudes in binary black hole and neutron star mergers is crucial for understanding the astrophysical processes and gravitational wave (GW) signals emitted during these cataclysmic events. In this paper, we present a novel Neuro-Symbolic Architecture (NSA) that combines the power of neural networks and symbolic regression to accurate

Tobias Lenz, Anna Marie Schröter

We develop an analogue of the theory of $*$-modules in the world of simplicial sets, based on actions of a certain simplicial monoid $E\mathcal M$ originally appearing in the construction of global algebraic $K$-theory. As our main results, we show that strictly commutative monoids with respect to a certain box product on these simplicial $*$-modules yield m

Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh

We propose new techniques for reducing communication in private federated learning without the need for setting or tuning compression rates. Our on-the-fly methods automatically adjust the compression rate based on the error induced during training, while maintaining provable privacy guarantees through the use of secure aggregation and differential privacy.

Peter M. Miklavcic, Ethan Tokar, Esteban Wright, Paul Sanchez

The interactions of solid objects with granular media is countered by a resistance force that stems from frictional forces between the grains and the media's resistance to inertia imposed by the intruder. Earlier theories of granular intrusion have suggested an additive contribution of these two families of forces and had tremendous success in predicting res

Moslem Zamani, François Glineur

We study the convergence of the last iterate in subgradient methods applied to the minimization of a nonsmooth convex function with bounded subgradients. We first introduce a proof technique that generalizes the standard analysis of subgradient methods. It is based on tracking the distance between the current iterate and a different reference point at each i

Rongqing Li, Jiaqi Yu, Changsheng Li, Wenhan Luo

Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes ($e.g.$, the number of convolutional layers) of a target black-box neural network can be exposed through a sequence of queries. There is a crucial limitation: these works assume the dataset used for training the target model to

Ben Y. Israeli, Christopher Berg Smiet, Marien Simeni Simeni, Ahmed Diallo

Next generation EUV sources for photolithography use light produced by laser-produced plasmas (LPP) from ablated tin droplets. A major challenge for extending the lifetime of these devices is mitigating damage caused by deposition of tin debris on the sensitive collection mirror. Especially difficult to stop are high energy (up to 10 keV) highly charged tin

Thibaut Dumont

Asteroseismology provides a unique opportunity to probe the interiors of evolved stars and constrain their internal rotation. The correct reproduction of the core rotation evolution is key to understanding the internal processes involved in low-mass stars. We explore the efficiency required to reproduce the behaviour of the transport of angular momentum (AM)

Svetlana Pavlova

This paper aims to apply a new deep learning approach to the task of generating raw audio files. It is based on diffusion models, a recent type of deep generative model. This new type of method has recently shown outstanding results with image generation. A lot of focus has been given to those models by the computer vision community. On the other hand, reall

David A. Smith, Philippe Bruel, Colin J. Clark, Lucas Guillemot

We present 294 pulsars found in GeV data from the Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope. Another 33 millisecond pulsars (MSPs) discovered in deep radio searches of LAT sources will likely reveal pulsations once phase-connected rotation ephemerides are achieved. A further dozen optical and/or X-ray binary systems co-located with LA

Sotiris Lamprinidis

Large Language Models (LLMs) have lately been on the spotlight of researchers, businesses, and consumers alike. While the linguistic capabilities of such models have been studied extensively, there is growing interest in investigating them as cognitive subjects. In the present work I examine GPT-3 and ChatGPT capabilities on an limited-data inductive reasoni

Stephen Josè Hanson, Vivek Yadav, Catherine Hanson

Deep Learning (DL) , a variant of the neural network algorithms originally proposed in the 1980s, has made surprising progress in Artificial Intelligence (AI), ranging from language translation, protein folding, autonomous cars, and more recently human-like language models (CHATbots), all that seemed intractable until very recently. Despite the growing use o

Afsar Ahmed, Arnab Bhattacharya, Samik DuttaGupta, I. Das

Critical behaviour study in magnetism is important owing to its application for understanding the nature of underlying spin-spin interactions by determining the critical parameters in the vicinity of a phase transition. In this article, we report the novel manifestation of crossover behaviour between two universality classes governing spin interaction across

K. V. Kulikov, D. V. Anghel, M. Nashaat, M. Dolineanu

We investigate resonance phenomena in a system consisting of a nanomagnet coupled to a Josephson junction under external periodic drive. The coupling in the system leads to appearance of additional resonance peaks whose properties depend on the periodic signal and Josephson junction dynamics. In the linear regime, we derive an analytical description of the r

Paolo Climaco, Jochen Garcke

For regression tasks one often leverages large datasets for training predictive machine learning models. However, using large datasets may not be feasible due to computational limitations or high data labelling costs. Therefore, suitably selecting small training sets from large pools of unlabelled data points is essential to maximize model performance while

Matt MacDermott, Tom Everitt, Francesco Belardinelli

How should my own decisions affect my beliefs about the outcomes I expect to achieve? If taking a certain action makes me view myself as a certain type of person, it might affect how I think others view me, and how I view others who are similar to me. This can influence my expected utility calculations and change which action I perceive to be best. Whether a

Pabitra Tripathy, Pritam Nanda, Amit Ghosh

We investigated the form and implications of the local first law of black hole thermodynamics in relation to an observer located at a finite distance from the black hole horizon. Our study is based on the quasilocal form of the first law for black hole thermodynamics, given by $\delta E=\frac{\bar{\kappa}}{8\pi}\delta A$, where $\delta E$ and $\delta A$ repr

Shaily Mishra, Manisha Padala, Sujit Gujar

Fair resource allocation is an important problem in many real-world scenarios, where resources such as goods and chores must be allocated among agents. In this survey, we delve into the intricacies of fair allocation, focusing specifically on the challenges associated with indivisible resources. We define fairness and efficiency within this context and thoro

Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai

Reconstructing accurate 3D scenes from images is a long-standing vision task. Due to the ill-posedness of the single-image reconstruction problem, most well-established methods are built upon multi-view geometry. State-of-the-art (SOTA) monocular metric depth estimation methods can only handle a single camera model and are unable to perform mixed-data traini

Alexandros Theloudis, Jorge Velilla, Pierre-André Chiappori, J. Ignacio Giménez-Nadal

The extent to which individuals commit to their partner for life has important implications. This paper develops a lifecycle collective model of the household, through which it characterizes behavior in three prominent alternative types of commitment: full, limited, and no commitment. We propose a test that distinguishes between all three types based on how

K. Krikova, T. M. D. Pereira, L. H. M. Rouppe van der Voort

Aims. We aim to understand how Hepsilon is formed in the quiet Sun. In particular, we consider the particular physical mechanism that sets its source function and extinction, how it is formed in different solar structures, and why it is sometimes observed in emission. Methods. We used a 3D radiative magnetohydrodynamic (MHD) simulation that accounts for non-

Theodorus Maria Nieuwenhuizen

In 2008 Nico van Kampen wrote in his letter {\it The scandal of quantum mechanics}: ``The scandal is that there are still many articles, discussions and textbooks, which advertise various interpretations and philosophical profundities." Not much has changed since then, while social media have given a platform for more of what Nico would term ``a scandal''. A

Anjali Raj, Shikhar Bharadwaj, Sriram Ganapathy, Min Ma

In the recent years, speech representation learning is constructed primarily as a self-supervised learning (SSL) task, using the raw audio signal alone, while ignoring the side-information that is often available for a given speech recording. In this paper, we propose MASR, a Multi-label Aware Speech Representation learning framework, which addresses the afo

Shiwei Ding, Lan Zhang, Miao Pan, Xiaoyong Yuan

Collaborative inference has been a promising solution to enable resource-constrained edge devices to perform inference using state-of-the-art deep neural networks (DNNs). In collaborative inference, the edge device first feeds the input to a partial DNN locally and then uploads the intermediate result to the cloud to complete the inference. However, recent r

Robert Beinert, Jonas Bresch, Gabriele Steidl

Manifold-valued signal- and image processing has received attention due to modern image acquisition techniques. Recently, a convex relaxation of the otherwise nonconvex Tikhonov-regularization for denoising circle-valued data has been proposed by Condat (2022). The circle constraints are here encoded in a series of low-dimensional, positive semi-definite mat

Giacomo Prando, Erik Piatti, Dario Daghero, Renato S. Gonnelli

The topotactic intercalation of transition-metal dichalcogenides with atomic or molecular ions acts as an efficient knob to tune the electronic ground state of the host compound. A representative material in this sense is 1$T$-TiSe$_{2}$, where the electric-field-controlled intercalations of lithium or hydrogen trigger superconductivity coexisting with the c

Xueshen Li, Zhenxing Dong, Hongshan Liu, Jennifer J. Kang-Mieler

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-base

Roman Vedernikov, Alexander Rogozin, Alexander Gasnikov

In this paper we study a generalization of distributed conditional gradient method to time-varying network architectures. We theoretically analyze convergence properties of the algorithm and provide numerical experiments. The time-varying network is modeled as a deterministic of a stochastic sequence of graphs.

Maria Arazo, Montserrat Guilleumas, Ricardo Mayol, Vicente Delgado

Persistent currents in quasi-one-dimensional Bose-Einstein condensates become chiral in the presence of current-density interactions. This phenomenon is explored in ultracold atoms loaded in a rotating ring geometry, where diverse current-carrying stationary states are analytically found to generalize previously known solutions to the mean-field equations of

Kevin Zelaya, Mohammad-Ali Miri

An integrated photonic circuit architecture to perform a modified-convolution operation based on the Discrete Fractional Fourier Transform (DFrFT) is introduced. This is accomplished by utilizing two nonuniformly-coupled waveguide lattices with equally-spaced eigenmode spectra and with different lengths that perform DFrDT operations of complementary orders s

Rogier van Dalen

The Transducer (e.g. RNN-Transducer or Conformer-Transducer) generates an output label sequence as it traverses the input sequence. It is straightforward to use in streaming mode, where it generates partial hypotheses before the complete input has been seen. This makes it popular in speech recognition. However, in streaming mode the Transducer has a mathemat

Zewei Xiong, Meng-Ru Wu, Sajad Abbar, Soumya Bhattacharyya

The fast flavor conversions (FFCs) of neutrinos generally exist in core-collapse supernovae and binary neutron-star merger remnants, and can significantly change the flavor composition and affect the dynamics and nucleosynthesis processes. Several analytical prescriptions were proposed recently to approximately explain or predict the asymptotic outcome of FF

Hebei Li, Yueyi Zhang, Zhiwei Xiong, Xiaoyan Sun

U-Net, known for its simple yet efficient architecture, is widely utilized for image processing tasks and is particularly suitable for deployment on neuromorphic chips. This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient S

Landon Hurley

Kemeny (1959) introduced a topologically complete metric space to study ordinal random variables, particularly in the context of Condorcet's paradox and the measurability of ties. Building on this, Emond & Mason (2002) reformulated Kemeny's framework into a rank correlation coefficient by embedding the metric space into a Hilbert structure. This transformati

Alexander Ek, Philip B. Stark, Peter J. Stuckey, Damjan Vukcevic

An election audit is risk-limiting if the audit limits (to a pre-specified threshold) the chance that an erroneous electoral outcome will be certified. Extant methods for auditing instant-runoff voting (IRV) elections are either not risk-limiting or require cast vote records (CVRs), the voting system's electronic record of the votes on each ballot. CVRs are

K. A. Garcia, A. Bader, H. Frerichs, G. J. Hartwell

Non-resonant divertors (NRDs) separate the confined plasma from the surrounding plasma facing components (PFCs). The resulting striking field line intersection pattern on these PFCs is insensitive to plasma equilibrium effects. However, a complex scrape-off layer (SOL), created by chaotic magnetic topology in the plasma edge, connects the core plasma to the

Bijan Saha

Within the scope of a Bianchi type-I (BI) cosmological model we study the interacting system of spinor and electromagnetic fields and its role in the evolution of the Universe. In some earlier studies it was found that in case of a pure spinor field the presence of nontrivial non-diagonal components of EMT leads to some severe restrictions both on the spacet

Armin Mostafavi, Tong Bill Xu, Saleh Kalantari

This paper presents a novel approach to assessing human lighting adjustment behavior and preference in diverse lighting conditions through the evaluation of emotional feedback and behavioral data using VR. Participants (n= 27) were exposed to different lighting (n=17) conditions with different levels of illuminance and correlated color temperature (CCT) with

Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos, Xun Jiao

The challenging deployment of compute-intensive applications from domains such as Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches. Approximate Computing appears as an emerging solution, allowing to tune the quality of results in the design of a system in order to imp

Jochen Blath, Dave Jacobi

We introduce and construct on/off super-Brownian motion (on/off SBM) as a measure-valued scaling limit of critical on/off branching Brownian motions. The distinguishing feature of this process is that its infinitesimal particles can switch individually into and out of a state of dormancy, in which they neither move nor reproduce. Related dormancy traits have

Masahiro Kato, Akari Ohda

Synthetic Control Methods (SCMs) have become a fundamental tool for comparative case studies. The core idea behind SCMs is to estimate treatment effects by predicting counterfactual outcomes for a treated unit using a weighted combination of observed outcomes from untreated units. The accuracy of these predictions is crucial for evaluating the treatment effe

Marco de Cesare, Giulia Gubitosi

We study the dynamics of the homogeneous and isotropic cosmological background in the recently proposed ``quantum phenomenological gravitational dynamics'', characterised by logarithmic corrections to the Bekenstein entropy. We show that the model admits a family of solutions that are self-accelerating both at early and late times: they approach de Sitter in

Emanuela Gussetti

We put forward a general framework for the study of a pathwise central limit theorem (CLT) and a moderate deviation principle (MDP) for stochastic partial differential equations perturbed with a small multiplicative linear noise by means of the theory of rough paths. The CLT can be interpreted as the convergence to a pathwise derivative of the It\^o-Lyons ma

Srivatsav Kunnawalkam Elayavalli, Koichi Oyakawa, Forte Shinko, Pieter Spaas

We identify natural conditions for a countable group acting on a countable tree which imply that the orbit equivalence relation of the induced action on the Gromov boundary is Borel hyperfinite. Examples of this condition include acylindrical actions. We also identify a natural weakening of the aforementioned conditions that implies measure hyperfinitenss of

Nan Fletcher-Lloyd, Alina-Irina Serban, Magdalena Kolanko, David Wingfield

Malnutrition and dehydration are strongly associated with increased cognitive and functional decline in people living with dementia (PLWD), as well as an increased rate of hospitalisations in comparison to their healthy counterparts. Extreme changes in eating and drinking behaviours can often lead to malnutrition and dehydration, accelerating the progression

Dake Zhou

The implications of perturbative QCD (pQCD) calculations on neutron stars are carefully examined. While pQCD calculations above baryon chemical potentials $\mu_B\simeq2.4$ GeV demonstrate the potential of ruling out a wide range of neutron star equations of state (EOSs), such constraints only affect the most massive neutron stars in the vicinity of the Tolma

Moreshwar Tayde, Zinnat Hassan, P. K. Sahoo

In this paper, we have systematically discussed the existence of the spherically symmetric wormhole solutions in the framework of $f(Q,\,T)$ gravity under two interesting non-commutative geometries such as Gaussian and Lorentzian distributions of the string theory. Also, to find the solutions, we consider two $f(Q,\,T)$ models such as linear $f(Q,\,T)=\alpha

Vasileios Leon, Muhammad Abdullah Hanif, Giorgos Armeniakos, Xun Jiao

The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performa

Julio Rebelo, Roland Roeder

We present several questions about the dynamics of the group of holomorphic automorphisms of the affine cubic surfaces $$S_{A,B,C,D} = \{(x,y,z) \in \mathbb{C}^3 \, : \, x^2 + y^2 + z^2 +xyz = Ax + By+Cz+D\},$$ where $A,B,C,$ and $D$ are complex parameters. This group action describes the monodromy of the famous Painlev\'e 6 Equation as well as the natural d

Tanmoy Mondal, Kornikar Sen, Chirag Srivastava, Ujjwal Sen

Entanglement is an advantageous but at the same time a costly resource utilized in various quantum tasks. For an efficient usage and deployment of entanglement, we envisage the scenario where a pair of spatially separated observers, Charu and Debu, want to share entanglement without interacting with each other. As a way out, their systems can separately and

Markus Reiß, Claudia Strauch, Lukas Trottner

We study a change point model based on a stochastic partial differential equation (SPDE) corresponding to the heat equation governed by the weighted Laplacian $\Delta_\vartheta = \nabla\vartheta\nabla$, where $\vartheta=\vartheta(x)$ is a space-dependent diffusivity. As a basic problem the domain $(0,1)$ is considered with a piecewise constant diffusivity wi

Yael Karshon, Eugene Lerman

This paper is part of a series of papers on differential geometry of $C^\infty$-ringed spaces. In this paper, we study vector fields and their flows on a class of singular spaces. Our class includes arbitrary subspaces of manifolds, as well as symplectic and contact quotients by actions of compact Lie groups. We show that derivations of the $C^\infty$-ring o

Moslem Mahdavifar, S. M. Hashemi Rafsanjani

We present an experimental investigation of two-photon interference using a continuous-wave laser. We demonstrate the violation of the CHSH inequality using the phase randomized weak coherent states from a continuous wave laser. Our implementation serves as an approach to reveal the quantum nature of a source that is considered to be a classical source.

William H. Kinney, Suvashis Maity, L. Sriramkumar

The Borde-Guth-Vilenkin (BGV) theorem states that any spacetime with net positive expansion must be geodesically incomplete. We derive a new version of the theorem using the fluid flow formalism of General Relativity. The theorem is purely kinematic, depending on the local expansion properties of geodesics, and makes no assumptions about energy conditions. W

Zhiwei Xue, Yuhang Li, Yash Patel, Jeffrey Regier

Telescopes capture images with a particular point spread function (PSF). Inferring what an image would have looked like with a much sharper PSF, a problem known as PSF deconvolution, is ill-posed because PSF convolution is not an invertible transformation. Deep generative models are appealing for PSF deconvolution because they can infer a posterior distribut

Christian Düll, Piotr Gwiazda, Anna Marciniak-Czochra, Jakub Skrzeczkowski

This paper presents a mathematical framework for modeling the dynamics of heterogeneous populations. Models describing local and non-local growth and transport processes, dependent on dynamically changing population structures, appear in a variety of applications such as crowd dynamics, tissue regeneration, cancer development, and coagulation-fragmentation p

Greeshma Gopinath, Yong Li, Sankar Davuluri

This article proposes a new method to entangle two spatially separated output laser fields from an optomechanical cavity with a membrane in the middle. The radiation pressure force coupling is used to modify the correlations between the input and the output field quadratures. Then the laser fields at the optomechanical cavity output are entangled using the q

Shaowu Peng, Pengcheng Zhao, Yongyu Ye, Junying Chen

Endoscopic surgery is currently an important treatment method in the field of spinal surgery and avoiding damage to the spinal nerves through video guidance is a key challenge. This paper presents the first real-time segmentation method for spinal nerves in endoscopic surgery, which provides crucial navigational information for surgeons. A finely annotated s

Tao Ni, Francesco Pesavento, Mirco Zaccariotto, Ugo Galvanetto

In this paper, a novel hybrid FEM and Peridynamic modeling approach proposed in Ni et al. (2020) is used to predict the dynamic solution of hydro-mechanical coupled problems. A modified staggered solution algorithm is adopted to solve the coupled system. A one-dimensional dynamic consolidation problem is solved first to validate the hybrid modeling approach,

Xi Fang, Daeseung Kim, Xuanang Xu, Tianshu Kuang

In CMF surgery, the planning of bony movement to achieve a desired facial outcome is a challenging task. Current bone driven approaches focus on normalizing the bone with the expectation that the facial appearance will be corrected accordingly. However, due to the complex non-linear relationship between bony structure and facial soft-tissue, such bone-driven

Xiangchen Yin, Zhenda Yu, Zetao Fei, Wenjun Lv

Current object detection models have achieved good results on many benchmark datasets, detecting objects in dark conditions remains a large challenge. To address this issue, we propose a pyramid enhanced network (PENet) and joint it with YOLOv3 to build a dark object detection framework named PE-YOLO. Firstly, PENet decomposes the image into four components

S. O. Sperling, T. Guo, R. H. J. Peerlings, V. G. Kouznetsova

Elastomeric mechanical metamaterials exhibit unconventional behaviour, emerging from their microstructures often deforming in a highly nonlinear and unstable manner. Such microstructural pattern transformations lead to non-local behaviour and induce abrupt changes in the effective properties, beneficial for engineering applications. To avoid expensive simula

Malo Duportal, Luca M. Berger, Stefan A. Maier, Andreas Tittl

Surface-enhanced spectroscopy techniques are the method-of-choice to characterize adsorbed intermediates occurring during electrochemical reactions, which are crucial in realizing a green sustainable future. Characterizing species with low coverages or short lifetimes have so far been limited by low signal enhancement. Recently, metasurface-driven surface-en

Ivan Boikov, Daniel Brunner, Alfredo De Rossi

We consider theoretically a network of directly coupled optical microcavities to implement a space-multiplexed optical neural network in an integrated nanophotonic circuit. Nonlinear photonic network integrations based on direct coupling ensures a highly dense integration, reducing the chip footprint by several orders of magnitude compared to other implement

Landon Hurley

An inner-product Hilbert space formulation is defined over a domain of all permutations with ties upon the extended real line. We demonstrate this work to resolve the common first and second order biases found in the pervasive Kendall and Spearman non-parametric correlation estimators, while presenting as unbiased minimum variance (Gauss-Markov) estimators.

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We report the measurement of the inclusive cross sections for $e^+e^-$$\rightarrow$nOCH (where nOCH denotes non-open charm hadrons) with improved precision at center-of-mass (c.m.) energies from 3.645 to 3.871 GeV. We observe three resonances: $\mathcal R(3760)$, $\mathcal R(3780)$, and $\mathcal R(3810)$ with significances of $8.1\sigma$, $13.7\sigma$, and

Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc Van Gool

Autonomous driving requires accurate local scene understanding information. To this end, autonomous agents deploy object detection and online BEV lane graph extraction methods as a part of their perception stack. In this work, we propose an architecture and loss formulation to improve the accuracy of local lane graph estimates by using 3D object detection ou

Marius de Leeuw, Charlotte Kristjansen, Georgios Linardopoulos, Matthias Volk

We compute type-B Weyl anomaly coefficients for the domain wall version of N = 4 SYM that is holographically dual to the D3-D5 probe-brane system with flux. Our starting point is the explicit expression for the improved energy momentum tensor of N = 4 SYM. We determine the two-point function of this operator in the presence of the domain wall and extract the

Omar Otoniel Flores-Cortez, Bruno Gonzales Crespin

Freight transport of goods and raw materials is a central part of the supply chain in the commercial exchange in Latin America. Control and monitoring of this activity are vital for an efficient economic flow and, more importantly, without losing money. Most of the problems that generate financial losses occur in cargo freight by land. Losses due to changes

Gabriele Coiana, Johannes Lischner, Paul Tangney

We present a series of detailed images of the distribution of kinetic energy among frequencies and wavevectors in the bulk of an MgO crystal as it is heated slowly until it melts. These spectra, which are Fourier transforms of mass-weighted velocity-velocity correlation functions calculated from accurate molecular dynamics (MD) simulations, provide a valuabl

Hyungmin Kim, Sungho Suh, Daehwan Kim, Daun Jeong

Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learning scenario remains a challenging problem due to the lack of prior knowledge about the number and nature of new categories. Existing methods for novel category discovery are limite

ATLAS Collaboration

A search for a new heavy scalar particle $X$ decaying into a Standard Model (SM) Higgs boson and a new singlet scalar particle $S$ is presented. The search uses a proton-proton ($pp$) collision data sample with an integrated luminosity of 140 fb$^{-1}$ recorded at a centre-of-mass energy of $\sqrt{s} = 13$ TeV with the ATLAS detector at the Large Hadron Coll

Haoran Hu

We investigate the application of Parisi-Wu stochastic quantization to the construction of random fields within the sublinear expectation framework. Using the semigroup approach and the infinite dimensional $G$-Ornstein Uhlenbeck process, we derive the unique mild solution to the robust Langevin dynamics of bosonic free field -- a parabolic linear stochastic

Madhur Tulsiani, June Wu

The ellipsoid fitting conjecture of Saunderson, Chandrasekaran, Parrilo and Willsky considers the maximum number $n$ random Gaussian points in $\mathbb{R}^d$, such that with high probability, there exists an origin-symmetric ellipsoid passing through all the points. They conjectured a threshold of $n = (1-o_d(1)) \cdot d^2/4$, while until recently, known low

M. Bousder, A. Riadsolh, A. El Fatimy, M. El Belkacemi

The purpose of this work is to investigate the formation and evaporation of the primordial black holes in the inflationary scenarios. Thermodynamic parameters such as mass, temperature and entropy are expressed in terms of NANOGrav frequency. By numerical calculations we show that the constraint on the mass range $10^{-5}kg-10^{50}kg$ is well confirmed. We d

Kui Lin

We investigated the dynamics of a binary mixture confined within van der Waals (vdW) walls using molecular dynamics simulations. We discovered a novel phenomenon named perpendicular separations of two phases (PSTP). In the initial stage, central water molecules diffused, subsequently condensing symmetrically within the confinement's mid-plane. In the later s

Florian Theuss, Avi Shragai, Gael Grissonnanche, Ian M Hayes

The microscopic mechanism of Cooper pairing in a superconductor leaves its fingerprint on the symmetry of the order parameter. UTe$_2$ has been inferred to have a multi-component order parameter that entails exotic effects like time reversal symmetry breaking. However, recent experimental observations in newer-generation samples have raised questions about t

K. M. Amarilo, M. B. Ferreira Filho, A. A. Araújo Filho, J. A. A. S. Reis

This paper focuses on how the production and polarization of gravitational waves are affected by spontaneous Lorentz symmetry breaking, which is driven by a self-interacting vector field. Specifically, we examine the impact of a smooth quadratic potential and a non-minimal coupling, discussing the constraints and causality features of the linearized Einstein