July 2023 arXiv papers — page 36

Suvashis Maity, H. V. Ragavendra, Shiv K. Sethi, L. Sriramkumar

[Abridged] In contemporary literature, the calculation of modifications to the inflationary scalar power spectrum due to the loops from the higher order interaction terms in the Hamiltonian have led to a discussion regarding the validity of perturbation theory. Recently, there have been efforts to examine the contributions to the scalar power spectrum due to

Ying Xiao, Hou-biao Li, Yu-pu Zhang

With the development of Big data technology, data analysis has become increasingly important. Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid selection and perform poorly on non-convex datasets. In this paper, we address these problems by proposing a data-driven Bregman divergence parameter optimization clusteri

Shuyi Liang, Chang-Xing Ma

Subjects in clinical studies that investigate paired body parts can carry a disease on either both sides (bilateral) or a single side (unilateral) of the organs. Data in such studies may consist of both bilateral and unilateral records. However, the correlation between the paired organs is often ignored, which may lead to biased interpretations. Recent liter

Valerio De Luca, Alex Kehagias, Antonio Riotto

We discuss the non-linear corrections entering in the calculation of the primordial black hole abundance from the non-linear radiation transfer function and the determination of the true physical horizon crossing. We show that the current standard techniques to calculate the abundance of primordial black holes suffer from uncertainties and argue that the pri

Roger Zhe Li, Julián Urbano, Alan Hanjalic

Mainstream bias, where some users receive poor recommendations because their preferences are uncommon or simply because they are less active, is an important aspect to consider regarding fairness in recommender systems. Existing methods to mitigate mainstream bias do not explicitly model the importance of these non-mainstream users or, when they do, it is in

Mourad Sarrouti

This thesis work falls within the framework of question answering (QA) in the biomedical domain where several specific challenges are addressed, such as specialized lexicons and terminologies, the types of treated questions, and the characteristics of targeted documents. We are particularly interested in studying and improving methods that aim at finding acc

Bea Zenteno-Quinteros, Pablo S Moya, Marian Lazar, Adolfo F Vinas

Temperature anisotropy and field-aligned skewness are commonly observed non-thermal features in electron velocity distributions in the solar wind. These characteristics can act as a source of free energy to destabilize different electromagnetic wave modes, which may alter the plasma state through wave-particle interactions. Previous theoretical studies have

S. Torres, P. Cruz, R. Murillo-Ojeda, F. M. Jiménez-Esteban

The characterization of white dwarf atmospheres is crucial for accurately deriving stellar parameters such as effective temperature, mass, and age. We aim to classify the population of white dwarfs up to 500 pc into hydrogen-rich or hydrogen-deficient atmospheres based on Gaia spectra and to derive an accurate spectral type-temperature distribution of white

Lu Ling, Yuntao Guo, Xiongfei Lai, Tianpei Tang

Electric bikes (e-bikes), including lightweight e-bikes with pedals and e-bikes in scooter form, are gaining popularity around the world because of their convenience and affordability. At the same time, e-bike-related accidents are also on the rise and many policymakers and practitioners are debating the feasibility of building e-bike lanes in their communit

Alireza Shafizadeh, Hossein Shahbeik, Mohammad Hossein Nadian, Vijai Kumar Gupta

Chemical and biomass processing systems release volatile matter compounds into the environment daily. Catalytic reforming can convert these compounds into valuable fuels, but developing stable and efficient catalysts is challenging. Machine learning can handle complex relationships in big data and optimize reaction conditions, making it an effective solution

Joshua S. North, Mark D. Risser, F. Jay Breidt

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents

Trevor M. Leslie, Changhui Tan

We show that the locations where finite- and infinite-time clustering occurs for the 1D Euler-alignment system can be determined using only the initial data. Our present work provides the first results on the structure of the finite-time singularity set and asymptotic clusters associated to a weak solution. In many cases, the eventual size of the cluster can

Arman Abgaryan, Utkarsh Sharma

Decentralised automated market makers (AMMs) have gained significant attention recently. We propose an adaptive and automated Dynamic Function Market Maker (DFMM) that addresses challenges in this space. Our DFMM protocol includes a data aggregator and an order routing mechanism. It synchronises price-sensitive market information, asserting the principle of

Fabo Feng, R. Paul Butler, Steven S. Vogt, Bradford Holden

With its near-to-mid-infrared high contrast imaging capabilities, JWST is ushering us into a golden age of directly imaging Jupiter-like planets. As the two closest cold Jupiters, $\varepsilon$ Ind A b and $\varepsilon$ Eridani b have sufficiently wide orbits and adequate infrared emissions to be detected by JWST. To detect more Jupiter-like planets for dire

Malte Esders, Gimmy Alex Fernandez Ramirez, Michael Gastegger, Satya Swarup Samal

Idealized first-principles models of chemical plants can be inaccurate. An alternative is to fit a Machine Learning (ML) model directly to plant sensor data. We use a structured approach: Each unit within the plant gets represented by one ML model. After fitting the models to the data, the models are connected into a flowsheet-like directed graph. We find th

Lu Ling, Xinwu Qian, Satish V. Ukkusuri

While the growth of TNCs took a substantial part of ridership and asset value away from the traditional taxi industry, existing taxi market policy regulations and planning models remain to be reexamined, which requires reliable estimates of the sensitivity of labor supply and income levels in the taxi industry. This study aims to investigate the impact of TN

Jing Zhao, Li Sun, Qingli Li

End-to-end region-based object detectors like Sparse R-CNN usually have multiple cascade bounding box decoding stages, which refine the current predictions according to their previous results. Model parameters within each stage are independent, evolving a huge cost. In this paper, we find the general setting of decoding stages is actually redundant. By simpl

Raul Salles de Padua, Imran Qureshi, Mustafa U. Karakaplan

Financial analysis is an important tool for evaluating company performance. Practitioners work to answer financial questions to make profitable investment decisions, and use advanced quantitative analyses to do so. As a result, Financial Question Answering (QA) is a question answering task that requires deep reasoning about numbers. Furthermore, it is unknow

Marguerite Sauce, Antoine Chancel, Antoine Ly

The development of Machine Learning is experiencing growing interest from the general public, and in recent years there have been numerous press articles questioning its objectivity: racism, sexism, \dots Driven by the growing attention of regulators on the ethical use of data in insurance, the actuarial community must rethink pricing and risk selection prac

Sebastian Cespedes, Senarath de Alwis, Francesco Muia, Fernando Quevedo

We consider vacuum transitions by bubble nucleation among 4D vacua with different values and signs of the cosmological constant $\Lambda $, including both up and down tunnelings. Following the Hamiltonian formalism, we explicitly compute the decay rates for all possible combinations of initial and final values of $\Lambda $ and find that up-tunneling is allo

Aida Abiad, Antonina P. Khramova, Alberto Ravagnani

We consider the problem of deriving upper bounds on the parameters of sum-rank-metric codes, with focus on their dimension and block length. The sum-rank metric is a combination of the Hamming and the rank metric, and most of the available techniques to investigate it seem to be unable to fully capture its hybrid nature. In this paper, we introduce a new app

Aryan Tyagi, Paras Singh, Aryaman Rao, Gaurav Kumar

Enhancing aerodynamic efficiency is vital for optimizing aircraft performance and operational effectiveness. It enables greater speeds and reduced fuel consumption, leading to lower operating costs. Hence, the implementation of Gurney flaps represents a promising avenue for improving airfoil aerodynamics. The optimization of Gurney flaps holds considerable r

S. Mahesh Chandran, Karthik Rajeev, S. Shankaranarayanan

Understanding the emergence of classical behavior from a quantum theory is vital to establishing the quantum origin for the temperature fluctuations observed in the Cosmic Microwave Background (CMB). We show that a real-space approach can comprehensively address the quantum-to-classical transition problem in the leading order of curvature perturbations. To t

Cyril Juliani

Similarity analysis using neural networks has emerged as a powerful technique for understanding and categorizing complex patterns in various domains. By leveraging the latent representations learned by neural networks, data objects such as images can be compared effectively. This research explores the utilization of latent information generated by fully conv

David Medina, Navid Valizadeh, Esteban Samaniego, Alex X. Jerves

In this paper we tackle the problem of surfactant spreading on a thin liquid film in the framework of isogeometric analysis. We consider a mathematical model that describes this phenomenon as an initial boundary value problem (IBVP) that includes two coupled fourth order partial differential equations (PDEs), one for the film height and one for the surfactan

Shih-Hong Chen, Pin-Hsuan Chou, Yong-Fu Liu, Chien-An Han

In this paper, our objective is to improve the performance of the existing framework ShuttleNet in predicting badminton shot types and locations by leveraging past strokes. We participated in the CoachAI Badminton Challenge at IJCAI 2023 and achieved significantly better results compared to the baseline. Ultimately, our team achieved the first position in th

Paula Helm, Gábor Bella, Gertraud Koch, Fausto Giunchiglia

It is well known that AI-based language technology -- large language models, machine translation systems, multilingual dictionaries, and corpora -- is currently limited to 2 to 3 percent of the world's most widely spoken and/or financially and politically best supported languages. In response, recent research efforts have sought to extend the reach of AI tec

Tobias M. Peters, Roel W. Visser

In recent years the use of Artificial Intelligence (AI) has become increasingly prevalent in a growing number of fields. As AI systems are being adopted in more high-stakes areas such as medicine and finance, ensuring that they are trustworthy is of increasing importance. A concern that is prominently addressed by the development and application of explainab

Muhammad Ali Farooq, Waseem Shariff, Mehdi Sefidgar Dilmaghani, Wang Yao

Optical sensors have played a pivotal role in acquiring real world data for critical applications. This data, when integrated with advanced machine learning algorithms provides meaningful information thus enhancing human vision. This paper focuses on various optical technologies for design and development of state-of-the-art out-cabin forward vision systems

Suchetana Sadhukhan, Vivek Kumar Yadav

This study provides a comprehensive time series analysis of daily industry-specific, country-wise CO$_2$ emissions from January 2019 to February 2023. The research focuses on the Power, Industry, Ground Transport, Domestic Aviation, and International Aviation sectors in European countries (EU27 & UK, Italy, Germany, Spain) and India, utilizing near-real-time

Sina Chen, Ari Laor, Ehud Behar, Ranieri D. Baldi

The origin of the radio emission in radio-quiet quasars (RQQ) is not established yet. We present new VLBA observations at 1.6 and 4.9 GHz of ten RQQ (nine detected), which together with published earlier observations of eight RQQ (five detected), forms a representative sample of 18 RQQ drawn from the Palomar-Green sample of low z (< 0.5) AGN. The spectral sl

Xiaoyang Wang, Yahui Chai, Maria Demidik, Xu Feng

The variational quantum imaginary time evolution (VarQITE) algorithm is a near-term method to prepare the ground state and Gibbs state of Hamiltonians. Finding an appropriate parameterization of the quantum circuit is crucial to the success of VarQITE. This work provides guidance for constructing parameterized quantum circuits according to the locality and s

Andrea Torricelli, Elvira Zappale

We provide necessary and sufficient conditions on the density $W:\mathbb R^d\times\mathbb R ^d\to\mathbb R$ in order to ensure the sequential weak* lower semicontinuity of the functional $J: W^{1,\infty}(I;\mathbb R^d)\to \mathbb R$, defined as \begin{align*} J(u):=ess\,sup_{I\times I}W(u'(x), u'(y)), \end{align*} when $I$ is an open and bounded interval of

Vedanth N Kuchibhotla, Sujit Kumar Sahoo, Y. Sudhakar

Performing geometry-resolved simulations of flows over rough and porous walls is highly expensive due to their multiscale characteristics. Effective models that circumvent this difficulty are often used to investigate the interaction between the free-fluid and such complex walls. These models, by construction, employ an intrinsic averaging process and captur

Angelos Vourlidas, Amir Caspi, Yuan-Kuen Ko, J. Martin Laming

Our current theoretical and observational understanding suggests that critical properties of the solar wind and Coronal Mass Ejections (CMEs) are imparted within 10 Rs, particularly below 4 Rs. This seemingly narrow spatial region encompasses the transition of coronal plasma processes through the entire range of physical regimes from fluid to kinetic, and fr

Blagoy Rangelov, Hui Yang, Brice Williams, Oleg Kargaltsev

In the latest data release from the Fermi $\gamma$-ray Space Telescope (the 4th Fermi LAT 14 yr Catalog, or 4FGL), more than 50% of the Galactic sources are yet to be identified. We observed 13 unidentified Fermi LAT sources with the Chandra X-Ray Observatory to explore their nature. We report the results of the classification of X-ray sources in the fields

Vamika Longia, Anosh Joseph, Abhishek Samlodia

The critical behavior of the two-dimensional XY model has been explored in the literature using various methods. They include the high-temperature expansion (HTE) method, Monte Carlo (MC) approach, strong coupling expansion method, and tensor network (TN) methods. This model undergoes a Berezinskii-Kosterlitz-Thouless (BKT) type of phase transition. This mod

Sebastian Strönisch, Maximilian Sander, Andreas Knüpfer, Marcus Meyer

Mesh-based numerical solvers are an important part in many design tool chains. However, accurate simulations like computational fluid dynamics are time and resource consuming which is why surrogate models are employed to speed-up the solution process. Machine Learning based surrogate models on the other hand are fast in predicting approximate solutions but o

T. Peter Rakitzis

We present an alternative formulation of quantum mechanical angular momentum, based on spatial wavefunctions that depend on the Euler angles $\phi, \theta, \chi$, and have an additional internal projection $n$. The wavefunctions are Wigner D-functions, $D_{n m}^s (\phi, \theta, \chi)$, for which the body-fixed projection quantum number $n$ has the unusual va

O. Bertolami

Anthropogenic greenhouse gases have been changing significantly the climate and causing dire effects on the dynamics of the Earth System. We examine the conditions under which the well of a geostationary orbital lift can be used to dump greenhouse gases into space.

Ali Pourmand, Natalia Ivanova

Evolutionary calculations for stars in close binary systems are in high demand to obtain better constraints on gravitational wave source progenitors, understand transient events from stellar interactions, and more. Modern one-dimensional stellar codes make use of the Roche lobe radius $R_{\rm L}$ concept in order to treat stars in binary systems. If the stel

Behrouz Mirza, Parichehr Kangazian Kangazi, Fatemeh Sadeghi

We consider a class of three parameter static and axially symmetric metrics that reduce to the Janis-Newman-Winicour (JNW) and $ \gamma$-metrics in certain limits of the parameters. We obtain rotating form of the metrics that are asymptotically flat, stationary and axisymmetric. In certain values of the parameters, the solutions represent the rotating JNW me

Nadiia Derevianko, Gerlind Plonka

In this paper, we propose a method to approximate the Gaussian function on ${\mathbb R}$ by a short cosine sum. We generalise and extend the differential approximation method proposed in [4, 40] to approximate $\mathrm{e}^{-t^{2}/2\sigma}$ in the weighted space $L^{2}({\mathbb R}, \mathrm{e}^{-t^{2}/2\rho})$ where $\sigma, \, \rho >0$. We prove that the opti

Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du

A central issue lying at the heart of online reinforcement learning (RL) is data efficiency. While a number of recent works achieved asymptotically minimal regret in online RL, the optimality of these results is only guaranteed in a ``large-sample'' regime, imposing enormous burn-in cost in order for their algorithms to operate optimally. How to achieve mini

Paolo Vannucci

We consider in this paper the general properties of laminates designed to be isotropic in extension and in bending and with a coupling between the in- and out-of plane responses. In particular, we analyze the mathematical properties of the tensors describing the elastic and thermal behavior and the mechanical consequences of these properties. The differences

Ofir Gorodetsky, Lasse Grimmelt

Elliott and Halberstam proved that $\sum_{p<n} 2^{\omega(n-p)}$ is asymptotic to $\phi(n)$. In analogy to the Erd\H{o}s--Kac Theorem, Elliott conjectured that if one restricts the summation to primes $p$ such that $\omega(n-p)\le 2 \log \log n+\lambda(2\log \log n)^{1/2}$ then the sum will be asymptotic to $\phi(n)\int_{-\infty}^{\lambda} e^{-t^2/2}dt/\sqrt{

Charles J. C. Scott, George H. Booth

We derive a widely-applicable first principles approach for determining two-body, static effective interactions for low-energy Hamiltonians with quantitative accuracy. The algebraic construction rigorously conserves all instantaneous two-point correlation functions in a chosen model space at the level of the random phase approximation, improving upon the tra

Sarah McCarty

This paper analyzes representations of continuous piecewise linear functions with infinite width, finite cost shallow neural networks using the rectified linear unit (ReLU) as an activation function. Through its integral representation, a shallow neural network can be identified by the corresponding signed, finite measure on an appropriate parameter space. W

Alexis Ralli, Gabriel Greene-Diniz, David Muñoz Ramo, Nathan Fitzpatrick

The Quantum Singular Value Transformation (QSVT) is a technique that provides a unified framework for describing many of the quantum algorithms discovered to date. We implement a noise-free simulation of the technique to investigate how it can be used to perform matrix inversion, which is an important step in calculating the single-particle Green's function

Xiang Yin, Nico Potyka, Francesca Toni

Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs). While there is a considerable body of research on qualitatively explaining the reasoning outcomes of AFs with debates/disputes/dialogues in the spirit of extension-based semantics, exp

Arindam Debnath, Lavanya Raman, Wenjie Li, Adam M. Krajewski

The rapid design of advanced materials is a topic of great scientific interest. The conventional, ``forward'' paradigm of materials design involves evaluating multiple candidates to determine the best candidate that matches the target properties. However, recent advances in the field of deep learning have given rise to the possibility of an ``inverse'' desig

M. Razzano, A. Fiori, P. M. Saz Parkinson, R. P. Mignani

Context. We have investigated the multiwavelength emission of PSR J2021+4026, the only isolated gamma-ray pulsar known to be variable, which in October 2011 underwent a simultaneous change in gamma-ray flux and spin-down rate, followed by a second mode change in February 2018. Multiwavelength monitoring is crucial to understand the physics behind these event

Sören Dittmer, Michael Roberts, Jacobus Preller, AIX COVNET

Survival analysis is an integral part of the statistical toolbox. However, while most domains of classical statistics have embraced deep learning, survival analysis only recently gained some minor attention from the deep learning community. This recent development is likely in part motivated by the COVID-19 pandemic. We aim to provide the tools needed to ful

Alejandro Contreras Reynoso, Thomas Gorin

We study general ``normally'' distributed random unitary transformations. These distributions can be defined in terms of a diffusive random walk in the respective group manifold, formally underpinned by the concept of infinite divisibility. On the one hand, a normal distribution induces a unital quantum channel. On the other hand, the diffusive random walk d

Evita Nestoridi, Dominik Schmid

We investigate the stationary distribution of asymmetric and weakly asymmetric simple exclusion processes with open boundaries. We project the stationary distribution onto a subinterval, whose size is allowed to grow with the length of the underlying segment. Depending on the boundary parameters of the exclusion process, we provide conditions such that the s

Rebecca Erbanni, Xiansong Xu, Tommaso Demarie, Dario Poletti

Digital quantum computers have the potential to study the dynamics of complex quantum systems. Nonequilibrium open quantum systems are, however, less straightforward to be implemented. Here we consider a collisional model representation of the nonequilibrium open dynamics for a boundary-driven XXZ spin chain, with a particular focus on its steady states. Mor

Dong Zhu, Ilya Charaev, Andreas Schilling

In a magnetic field, vortices significantly contribute to the dark counts of single-photon detectors made of superconducting wires, and they are also limiting the critical current of such devices. To address this issue, we prepared superconducting microwires with a pinhole grid from WSi thin films and report on corresponding critical-current and count-rate m

Qing-Hua Zhu, Zhi-Chao Zhao, Sai Wang, Xin Zhang

Pulsar timing array (PTA) data releases showed strong evidence for a stochastic gravitational-wave background in the nanohertz band. When the signal is interpreted by a scenario of scalar-induced gravitational waves (SIGWs), we encounter overproduction of primordial black holes (PBHs). We wonder if varying the equation of state (EoS) of the early Universe ca

Pedro Agostini

We investigate the path integral representation of the scalar propagator in a background gluon field, extending beyond the eikonal approximation by considering all gauge field components and incorporating its $x^-$ dependence. Utilizing the worldline formalism, we integrate the Schwinger proper time to express the scalar propagator in light-cone coordinates,

Te Ba, Guangming Hu, Yu Sun

This paper constructs hyperbolic polyhedral metrics via circle packings. We introduce the curvature of circles as a parameter to include all three types of constant curvature curves in the hyperbolic geometry. This provides a unified approach to producing polyhedral metrics for surfaces of broader topological types. The combinatorial total geodesic curvature

Xinran Liu, Yikun Bai, Huy Tran, Zhanqi Zhu

Optimal transport and its related problems, including optimal partial transport, have proven to be valuable tools in machine learning for computing meaningful distances between probability or positive measures. This success has led to a growing interest in defining transport-based distances that allow for comparing signed measures and, more generally, multi-

Kirill Andreev, Pavel Rybin, Alexey Frolov

Next-generation wireless communication systems impose much stricter requirements for transmission rate, latency, and reliability. The peak data rate of 6G networks should be no less than 1 Tb/s, which is comparable to existing long-haul optical transport networks. It is believed that using long error-correcting codes (ECC) with soft-decision decoding (SDD) i

Charles C. Tam, Jaewon Choi, Xiang Ding, Stefano Agrestini

Charge density waves (CDW) have been reported in NdNiO$_2$ and LaNiO$_2$ thin films grown on SrTiO$_3$ substrates using Ni-$L_3$ resonant x-ray scattering in Refs. [1-3]. In their comment [arXiv:2306.15086] on these reports, Pelliciari et al. found no evidence for a CDW in a NdNiO$_2$ film by performing fixed-momentum energy-dependent measurements. Instead,

L. Zurek, S. K. Bogner, R. J. Furnstahl, R. Navarro Pérez

Nuclear energy density functionals successfully reproduce properties of nuclei across almost the entire nuclear chart. However, nearly all available functionals are phenomenological in nature and lack a rigorous connection to systematically improvable nuclear forces. This issue might be solved with an energy density functional obtained from first principles.

Chen Chen, Bongshin Lee, Yunhai Wang, Yunjeong Chang

To facilitate the reuse of existing charts, previous research has examined how to obtain a semantic understanding of a chart by deconstructing its visual representation into reusable components, such as encodings. However, existing deconstruction approaches primarily focus on chart styles, handling only basic layouts. In this paper, we investigate how to dec

Niranjan Sitapure, Joseph S Kwon

Most existing digital twins rely on data-driven black-box models, predominantly using deep neural recurrent, and convolutional neural networks (DNNs, RNNs, and CNNs) to capture the dynamics of chemical systems. However, these models have not seen the light of day, given the hesitance of directly deploying a black-box tool in practice due to safety and operat

Enqiang Zhu, Yu Zhang, Shengzhi Wang, Darren Strash

Given a graph, the minimum dominating set (MinDS) problem is to identify a smallest set $D$ of vertices such that every vertex not in $D$ is adjacent to at least one vertex in $D$. The MinDS problem is a classic $\mathcal{NP}$-hard problem and has been extensively studied because of its many disparate applications in network analysis. To solve this problem e

Katelyn Morrison, Philipp Spitzer, Violet Turri, Michelle Feng

Explainability techniques are rapidly being developed to improve human-AI decision-making across various cooperative work settings. Consequently, previous research has evaluated how decision-makers collaborate with imperfect AI by investigating appropriate reliance and task performance with the aim of designing more human-centered computer-supported collabor

Jayanta Mandi, James Kotary, Senne Berden, Maxime Mulamba

Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning (ML) and constrained optimization to enhance decision quality by training ML models in an end-to-end system. This approach shows significant potential to revolutionize combinatorial decision-making in real-world applications that operate under uncertainty, where estimati

Giuseppina di Blasio, Filomena Feo, Gabriella Zecca

We prove the existence and uniqueness of weak solutions to a class of anisotropic elliptic equations with coefficients of convection term belonging to some suitable Marcinkiewicz spaces. Some useful a priori estimates and regularity results are also derived.

Richard Matthews

The method of realizability was first developed by Kleene and is seen as a way to extract computational content from mathematical proofs. Traditionally, these models only satisfy intuitionistic logic, however this method was extended by Krivine to produce models which satisfy full classical logic and even Zermelo Fraenkel set theory with choice. The purpose

Amar Bharti, Misha Ivanov, Gopal Dixit

Circularly polarized light fails to generate currents in inversion-symmetric Weyl semimetals with degenerate Weyl nodes. While each node generates current with the direction depending on its chirality, the two currents in the two degenerate nodes of opposite chirality cancel each other. By extension, it is also generally expected that the currents generated

Nora Weickgenannt

We derive equations of motion for dissipative spin hydrodynamics from kinetic theory up to first order in a gradient expansion. Choosing a specific form of the matching conditions, relating the change in the spin potential to the spin diffusion and spin energy, we then show that the equations of motion, linearized around homogeneous global equilibrium, are c

Linyao Chen, Aosong Feng, Boming Yang, Zihui Li

Recently, diffusion models have excelled in image generation tasks and have also been applied to neural language processing (NLP) for controllable text generation. However, the application of diffusion models in a cross-lingual setting is less unexplored. Additionally, while pretraining with diffusion models has been studied within a single language, the pot

Abdolnaser Bahlekeh, Fahimeh Sadat Fotouhi, Armin Nateghi, Shokrollah Salarian

Let $(S, \n)$ be a commutative noetherian local ring and $\omega\in\n$ be non-zerodivisor. This paper deals with the behavior of the category $\mon(\omega, \cp)$ consisting of all monomorphisms between finitely generated projective $S$-modules with cokernels annihilated by $\omega$. We introduce a homotopy category $\HT\mon(\omega, \cp)$, which is shown to b

Yannick Feld, Alexander K. Hartmann

We numerically study disease dynamics that lead to the disease switching from one host species to another, resulting in diseases gaining the ability to infect, e.g., humans. Unlike previous studies that focused on branching processes starting with the first infected humans, we begin by considering a disease pathogen that initially cannot infect humans. We mo

Jose A. Troyano

The usual FIFA World Cup-TM group stage format is eight groups of four teams for a total of 32 teams. The top two teams from each group advance to the next round, resulting in a 16-team knockout stage. The next edition of the World Cup will have 48 teams. Although FIFA initially proposed a configuration of 16 three-team groups, it has ultimately settled on 1

Vanderson Lima, Ana Menezes

Given a compact surface with boundary, we introduce a family of functionals on the space of its Riemannian metrics, defined via eigenvalues of a Steklov-type problem. We prove that each such functional is uniformly bounded from above, and we characterize maximizing metrics as induced by free boundary minimal immersions in some geodesic ball of a round sphere

Hiroshi Iritani

We prove a decomposition theorem of the quantum cohomology D-module of the blowup of a smooth projective variety X along a smooth subvariety Z. The main tools we use are shift operators and Fourier analysis for equivariant quantum cohomology.

Nikita Medvedev

A nonperturbative dynamical coupling approach based on tight-binding molecular dynamics is used to evaluate the electron-ion (electron-phonon) coupling parameter in irradiated semiconductors as a function of the electronic temperature up to ~25,000 K. The method accounts for arbitrary electronic distribution function via the Boltzmann equation, enabling a co

Dong Liu, Yi Yang, Zhaoyi Xu, Zheng-Wen Long

In this paper, the structure of a dark matter halo can be well described by the mass model of M87 and the Einasto profile for the cold dark matter model, i.e., $\rho_{\text{eina}} (r)=\rho_\text{e} \exp ( -2 \alpha ^{-1} ((r/r_\text{e})^\alpha -1 ) )$ (Wang et al. in Nature 585:39-42, 2020). Under these conditions, we construct a solution of a static spheric

Bharath Muppasani, Vishal Pallagani, Biplav Srivastava, Forest Agostinelli

Rubik's Cube (RC) is a well-known and computationally challenging puzzle that has motivated AI researchers to explore efficient alternative representations and problem-solving methods. The ideal situation for planning here is that a problem be solved optimally and efficiently represented in a standard notation using a general-purpose solver and heuristics. T

Habib Ammari, Silvio Barandun, Ping Liu

The aim of this paper is fourfold: (i) to obtain explicit formulas for the eigenpairs of perturbed tridiagonal block Toeplitz matrices; (ii) to make use of such formulas in order to provide a mathematical justification of the non-Hermitian skin effect in dimer systems by proving the condensation of the system's bulk eigenmodes at one of the edges of the syst

James Michael Wilson

We prove a general result implying the $L^2$ stability of Haar decompositions of $L^2({\bf R}^d)$ functions when the Haar functions are distorted by arbitrary, independent, affine changes of variable that are close to the identity. We apply our method to get fully $d$-dimensional generalizations of results of Aimar, Bernardis, Gorosito, Govil, and Zalik, on

Bharath Muppasani, Vishal Pallagani, Biplav Srivastava, Raghava Mutharaju

Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we consider the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state. We hypoth

Oualid Zari, Javier Parra-Arnau, Ayşe Ünsal, Melek Önen

In this paper, we present a stealthy and effective attack that exposes privacy vulnerabilities in Graph Neural Networks (GNNs) by inferring private links within graph-structured data. Focusing on the inductive setting where new nodes join the graph and an API is used to query predictions, we investigate the potential leakage of private edge information. We a

M. -N. Célérier

In a recent series of papers, new exact analytical solutions to field equations of General Relativity representing interior spacetimes sourced by stationary rigidly rotating cylinders of fluids with various equations of state have been displayed. This work is currently extended to the case of differentially rotating irrotational fluids. The results are prese

Haoyang Cao, Haotian Gu, Xin Guo, Mathieu Rosenbaum

In this work, we explore the possibility of utilizing transfer learning techniques to address the financial portfolio optimization problem. We introduce a novel concept called "transfer risk", within the optimization framework of transfer learning. A series of numerical experiments are conducted from three categories: cross-continent transfer, cross-sector t

Florent G. Mertens, Jérôme Bobin, Isabella P. Carucci

Direct detection of the Cosmic Dawn and Epoch of Reionization via the redshifted 21-cm line of neutral Hydrogen will have unprecedented implications for studying structure formation in the early Universe. This exciting goal is challenged by the difficulty of extracting the faint 21-cm signal buried beneath bright astrophysical foregrounds and contaminated by

Andrea Della Vecchia, Kibidi Neocosmos, Daniel B. Larremore, Cristopher Moore

We present a physics-inspired method for inferring dynamic rankings in directed temporal networks - networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction. The inferred ranking of each node is real-valued and varies in time as each new edge, encoding an outcome like a win or loss, raises or lowers the n

Matteo Della Rossa, Raphaël M. Jungers

We propose a novel framework for the Lyapunov analysis of an important class of hybrid systems, inspired by the theory of symbolic dynamics and earlier results on the restricted class of switched systems. This new framework allows us to leverage language theory tools in order to provide a universal characterization of Lyapunov stability for this class of sys

Song Yu

We prove the integrality and finiteness of open BPS invariants of toric Calabi-Yau 3-folds relative to Aganagic-Vafa outer branes, defined from open Gromov-Witten invariants by the Labastida-Mari\~no-Ooguri-Vafa formula. Specializing to disk invariants, we extend the open/closed correspondence of Gromov-Witten invariants to BPS invariants and prove the integ

Chuanchuan Wang, Ahmad Sufril Azlan Mohamed

Group activity recognition is a hot topic in computer vision. Recognizing activities through group relationships plays a vital role in group activity recognition. It holds practical implications in various scenarios, such as video analysis, surveillance, automatic driving, and understanding social activities. The model's key capabilities encompass efficientl

Binglu Chen, Guillaume Bal

This paper develops a scattering theory for the asymmetric transport observed at interfaces separating two-dimensional topological insulators. Starting from the spectral decomposition of an unperturbed interface Hamiltonian, we present a limiting absorption principle and construct a generalized eigenfunction expansion for perturbed systems. We then relate a

Jiawei Liu, Changkun Ye, Shan Wang, Ruikai Cui

For safety-related applications, it is crucial to produce trustworthy deep neural networks whose prediction is associated with confidence that can represent the likelihood of correctness for subsequent decision-making. Existing dense binary classification models are prone to being over-confident. To improve model calibration, we propose Adaptive Stochastic L

Louis Serrano, Leon Migus, Yuan Yin, Jocelyn Ahmed Mazari

For numerical design, the development of efficient and accurate surrogate models is paramount. They allow us to approximate complex physical phenomena, thereby reducing the computational burden of direct numerical simulations. We propose INFINITY, a deep learning model that utilizes implicit neural representations (INRs) to address this challenge. Our framew

Bo Miao, Mohammed Bennamoun, Yongsheng Gao, Ajmal Mian

Current referring video object segmentation (R-VOS) techniques extract conditional kernels from encoded (low-resolution) vision-language features to segment the decoded high-resolution features. We discovered that this causes significant feature drift, which the segmentation kernels struggle to perceive during the forward computation. This negatively affects

David Le Bolloc'h, E. Bellec, N. Kirova, V. L. R. Jacques

In this article, we review different studies based on advanced x-ray diffraction techniques - especially coherent x-ray diffraction - that allowed us to reveal the behaviour of such symmetry-breaking systems as Charge Density Wave (CDW) and Spin density Wave (SDW), through their local phase. After a brief introduction on the added value of using coherent x-r

Guanyi Wang, Mengqi Lou, Ashwin Pananjady

We study a principal component analysis problem under the spiked Wishart model in which the structure in the signal is captured by a class of union-of-subspace models. This general class includes vanilla sparse PCA as well as its variants with graph sparsity. With the goal of studying these problems under a unified statistical and computational lens, we esta

David W. Tam, Nicola Colonna, Neeraj Kumar, Cinthia Piamonteze

The microscopic mechanism of heavy band formation, relevant for unconventional superconductivity in CeCoIn$_5$ and other Ce-based heavy fermion materials, depends strongly on the efficiency with which $f$ electrons are delocalized from the rare earth sites and participate in a Kondo lattice. Replacing Ce$^{3+}$ ($4f^1$, $J=5/2$) with Sm$^{3+}$ ($4f^5$, $J=5/