July 2022 arXiv papers — page 139

Guillem Domènech, Roya Mohayaee, Subodh P. Patil, Subir Sarkar

In view of the growing tension between the dipole anisotropy of number counts of cosmologically distant sources and of the cosmic microwave background (CMB), we investigate the number count dipole induced by primordial perturbations with wavelength comparable to or exceeding the Hubble radius today. First, we find that neither adiabatic nor isocurvature supe

Sylvia Biscoveanu, Philippe Landry, Salvatore Vitale

Neutron star-black hole (NSBH) mergers detected in gravitational waves have the potential to shed light on supernova physics, the dense matter equation of state, and the astrophysical processes that power their potential electromagnetic counterparts. We use the population of four candidate NSBH events detected in gravitational waves so far with a false alarm

Wen Guo, Yuming Du, Xi Shen, Vincent Lepetit

This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences. State-of-the-art approaches provide good results, however, they rely on deep learning architectures of arbitrary complexity, such as Recurrent Neural Networks(RNN), Transformers or Graph Convolutional Networks(GCN), typ

Francesco Faccio, Aditya Ramesh, Vincent Herrmann, Jean Harb

Learning to evaluate and improve policies is a core problem of Reinforcement Learning (RL). Traditional RL algorithms learn a value function defined for a single policy. A recently explored competitive alternative is to learn a single value function for many policies. Here we combine the actor-critic architecture of Parameter-Based Value Functions and the po

Yannik Mahlau, Christian Nolde

In recent years, an abundance of feature attribution methods for explaining neural networks have been developed. Especially in the field of computer vision, many methods for generating saliency maps providing pixel attributions exist. However, their explanations often contradict each other and it is not clear which explanation to trust. A natural solution to

Ashish Mishra, Digjoy Paul, Pooja Singla

Let $G$ be a finite group and $p$ be a prime number dividing the order of $G$. An irreducible character $\chi$ of $G$ is called a quasi $p$-Steinberg character if $\chi(g)$ is nonzero for every $p$-regular element $g$ in $G$. In this paper, we classify quasi $p$-Steinberg characters of the complex reflection groups $G(r,q,n)$. In particular, we obtain this c

Takashi Suzuki

In SGA 2, Grothendieck conjectures that the \'etale fundamental group of the punctured spectrum of a complete noetherian local domain of dimension at least two with algebraically closed residue field is topologically finitely generated. In this paper, we prove a weaker statement, namely that the maximal pro-nilpotent quotient of the fundamental group is topo

Stanisław Pawlak, Filip Szatkowski, Michał Bortkiewicz, Jan Dubiński

We introduce a new method for internal replay that modulates the frequency of rehearsal based on the depth of the network. While replay strategies mitigate the effects of catastrophic forgetting in neural networks, recent works on generative replay show that performing the rehearsal only on the deeper layers of the network improves the performance in continu

Jan Dubiński, Kamil Deja, Sandro Wenzel, Przemysław Rokita

Generative Adversarial Networks (GANs) are powerful models able to synthesize data samples closely resembling the distribution of real data, yet the diversity of those generated samples is limited due to the so-called mode collapse phenomenon observed in GANs. Especially prone to mode collapse are conditional GANs, which tend to ignore the input noise vector

Paul Mangold, Aurélien Bellet, Joseph Salmon, Marc Tommasi

In this paper, we study differentially private empirical risk minimization (DP-ERM). It has been shown that the worst-case utility of DP-ERM reduces polynomially as the dimension increases. This is a major obstacle to privately learning large machine learning models. In high dimension, it is common for some model's parameters to carry more information than o

Paige Yeung, Quinn Perian, Peyton Robertson, Michael Fitzgerald

Based on the light an exoplanet blocks from its host star as it passes in front of it during a transit, the mid-transit time can be determined. Periodic variations in mid-transit times can indicate another planet's gravitational influence. We investigate 83 transits of TrES-1 b as observed from 6-inch telescopes in the MicroObservatory robotic telescope netw

Hao Tang, Wenxun Wu, Xian-Min Jin

The LIBOR Market Model (LMM) is a widely used model for pricing interest rate derivatives. While the Black-Scholes model is well-known for pricing stock derivatives such as stock options, a larger portion of derivatives are based on interest rates instead of stocks. Pricing interest rate derivatives used to be challenging, as their previous models employed e

Vahid Ziaei-Rad, Mostafa Mollaali, Thomas Nagel, Olaf Kolditz

We propose a decomposition of constitutive relations into crack-driving and persistent portions, specifically designed for materials with anisotropic/orthotropic behavior in the phase field approach to fracture to account for the tension-compression asymmetry. This decomposition follows a variational framework, satisfying the orthogonality condition for anis

Guoliang Cheng, Lele Liao, Kai Chen, Yuxiang Hu

The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic echo. However, the multiplicative transfer function (MTF) approximation makes it unsuitable for real-time applications especially in highly reverberant environments, and the natural g

Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama

Recent years have witnessed a great success of supervised deep learning, where predictive models were trained from a large amount of fully labeled data. However, in practice, labeling such big data can be very costly and may not even be possible for privacy reasons. Therefore, in this paper, we aim to learn an accurate classifier without any class labels. Mo

Noor Alakhawand, William Frier, Nathan F. Lepora

Mid-air haptics create a new mode of feedback to allow people to feel tactile sensations in the air. Ultrasonic arrays focus acoustic radiation pressure in space, to induce tactile sensation from the resulting skin deflection. In this work, we present a low-cost tactile robot to test mid-air haptics. By combining a desktop robot arm with a 3D-printed biomime

Pedro De la Torre Luque, Mario Nicola Mazziotta

The current great precision on cosmic-ray (CR) spectral data allows us to precisely test our simple models on propagation of charged particles in the Galaxy. However, our studies are severely limited by the uncertainties related to cross sections for CR interactions. Therefore we have developed a new set of cross sections derived from the FLUKA Monte Carlo c

Gopal Nath, Krishna K. Saha, Suojin Wang

In epidemiological cohort studies, the relative risk (also known as risk ratio) is a major measure of association to summarize the results of two treatments or exposures. Generally, it measures the relative change in disease risk as a result of treatment application. Standard approaches to estimating relative risk available in common software packages may pr

Sheng Yang, Samir Khuller, Sunav Choudhary, Subrata Mitra

We study the correlated stochastic knapsack problem of a submodular target function, with optional additional constraints. We utilize the multilinear extension of submodular function, and bundle it with an adaptation of the relaxed linear constraints from Ma [Mathematics of Operations Research, Volume 43(3), 2018] on correlated stochastic knapsack problem. T

Julian D'Costa, Engel Lefaucheux, Eike Neumann, Joël Ouaknine

We study the Escape Problem for discrete-time linear dynamical systems over compact semialgebraic sets. We establish a uniform upper bound on the number of iterations it takes for every orbit of a rational matrix to escape a compact semialgebraic set defined over rational data. Our bound is doubly exponential in the ambient dimension, singly exponential in t

H. G. Rinderknecht, G. Bruhaug, V. Muşat, G. Gregori

The physics basis for an electron-beam based Compton scattering (ECOS) x-ray source is investigated for single-shot experiments at major high energy density facilities such as the Omega Laser Facility, National Ignition Facility, and Z pulsed power facility. A source of monoenergetic ($\delta\epsilon/\epsilon < 5\%$) 10- to 50-keV x rays can be produced by s

Saeid Asgari Taghanaki, Ali Gholami, Fereshte Khani, Kristy Choi

Batch normalization (BN) is a ubiquitous technique for training deep neural networks that accelerates their convergence to reach higher accuracy. However, we demonstrate that BN comes with a fundamental drawback: it incentivizes the model to rely on low-variance features that are highly specific to the training (in-domain) data, hurting generalization perfor

Ariadna Sanchez, Alessio Falai, Ziyao Zhang, Orazio Angelini

An essential design decision for multilingual Neural Text-To-Speech (NTTS) systems is how to represent input linguistic features within the model. Looking at the wide variety of approaches in the literature, two main paradigms emerge, unified and separate representations. The former uses a shared set of phonetic tokens across languages, whereas the latter us

Nicola Rares Franco, Stefania Fresca, Andrea Manzoni, Paolo Zunino

Recently, deep Convolutional Neural Networks (CNNs) have proven to be successful when employed in areas such as reduced order modeling of parametrized PDEs. Despite their accuracy and efficiency, the approaches available in the literature still lack a rigorous justification on their mathematical foundations. Motivated by this fact, in this paper we derive ri

Jincen Jiang, Xuequan Lu, Lizhi Zhao, Richard Dazeley

Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks. Recently, self-supervised learning based upon masking local surface patches for 3D point cloud data has been under-explored. In this paper, we propose masked Autoencoders

Max Goering, Lukas Koch

We obtain improved fractional differentiability of solutions to the Banach-space valued Finsler $\gamma$-Laplacian defined on a $\sigma$-convex, $\tau$-smooth Banach space. The operators we consider are non-linear and very degenerately elliptic. Our results are new already in the $\mathbb R$-valued setting.

Shih-Xian Yang, Pedro Figueroa-Romero, Min-Hsiu Hsieh

The presence of correlations in noisy quantum circuits will be an inevitable side effect as quantum devices continue to grow in size and depth. Randomized Benchmarking (RB) is arguably the simplest method to initially assess the overall performance of a quantum device, as well as to pinpoint the presence of temporal-correlations, so-called non-Markovianity;

J. E. Hirsch, F. Marsiglio

It has recently been reported that hydrogen-rich materials under high pressure trap magnetic flux, a tell-tale signature of superconductivity [arXiv:2206.14108v1]. Here we point out that under the protocol used in these experiments the measured results indicate that the materials don't trap magnetic flux. Instead, the measured results are either experimental

Tsukasa Ishibashi, Wataru Yuasa

Continuing to our previous work [IY21](arXiv:2101.00643) on the $\mathfrak{sl}_3$-case, we introduce a skein algebra $\mathscr{S}_{\mathfrak{sp}_4,\Sigma}^{q}$ consisting of $\mathfrak{sp}_4$-webs on a marked surface $\Sigma$ with certain "clasped" skein relations at special points, and investigate its cluster nature. We also introduce a natural $\mathbb{Z}_

M. H. Cheng, K. E. Khosla, C. N. Self, M. Lin

We present an initialisation method for variational quantum algorithms applicable to intermediate scale quantum computers. The method uses simulated annealing of the efficiently simulable Clifford parameter points as a pre-optimisation to find a low energy initial condition. We numerically demonstrate the effectiveness of the technique, and how it depends on

Aline Goeminne, Nicolas Markey, Ocan Sankur

Network congestion games are a convenient model for reasoning about routing problems in a network: agents have to move from a source to a target vertex while avoiding congestion, measured as a cost depending on the number of players using the same link. Network congestion games have been extensively studied over the last 40 years, while their extension with

Caroline Hillairet, Sarah Kaakai, Mohamed Mrad

This paper proposes and investigates an optimal pair investment/pension policy for a pay-as-you-go (PAYG) pension scheme. The social planner can invest in a buffer fund in order to guarantee a minimal pension amount. The model aims at taking into account complex dynamic phenomena such as the demographic risk and its evolution over time, the time and age depe

Mohammad Haidar, Vladimir I. Korobov, Laurent Hilico, Jean-Philippe Karr

We consider higher-order corrections to hyperfine coefficients related to the spin-orbit and spin-spin tensor interactions in hydrogen molecular ions. The $m\alpha^7 \ln(\alpha)$-order radiative correction is derived in the NRQED framework. We present complete numerical calculations, including as well the $m\alpha^6$-order relativistic correction, for the ca

M. Sedahmed, R. C. V. Coelho, N. A. M. Araújo, E. M. Wahba

We generalize to three dimensions (3D) a recently developed improved multi-component pseudopotential lattice Boltzmann method and analyze its applicability to simulate flows through realistic porous media. The model is validated and characterized via benchmarks, and we investigate its performance by simulating the displacement of immiscible fluids in 3D geom

Seojeong Lee, Siha Lee, Julius Owusu, Youngki Shin

We develop a Stata command $\texttt{csa2sls}$ that implements the complete subset averaging two-stage least squares (CSA2SLS) estimator in Lee and Shin (2021). The CSA2SLS estimator is an alternative to the two-stage least squares estimator that remedies the bias issue caused by many correlated instruments. We conduct Monte Carlo simulations and confirm that

M. Di Gioia, J. Gutowski

Supersymmetric warped product dS4 solutions in D=11 supergravity are classified. The Killing spinor is associated with two possible stabilizer groups, SU(3) and G_2. We show that there are no solutions to the Killing Spinor equations in the G_2 stabilizer case. For the SU(3) stablilzer case, all of the conditions imposed from supersymmetry on the 4-form flux

Giovanni Apruzzese, Rodion Vladimirov, Aliya Tastemirova, Pavel Laskov

Fifth Generation (5G) networks must support billions of heterogeneous devices while guaranteeing optimal Quality of Service (QoS). Such requirements are impossible to meet with human effort alone, and Machine Learning (ML) represents a core asset in 5G. ML, however, is known to be vulnerable to adversarial examples; moreover, as our paper will show, the 5G c

Antonios Mitsopoulos

The physical phenomena are described by physical quantities related by specific physical laws. In the context of a Physical Theory, the physical quantities and the physical laws are described, respectively, by suitable geometrical objects and relations between these objects. These relations are expressed with systems of (mainly second order) differential equ

Nicolás E. Díaz Ferreyra, Melina Vidoni, Maritta Heisel, Riccardo Scandariato

Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on users' engagement, namely their willingness to answer, comment or post technical questions. Still, many of these questions (including cybersecu

Tao He, Ming Liu, Yixin Cao, Tianwen Jiang

Knowledge Graph Completion (KGC) aims to reason over known facts and infer missing links but achieves weak performances on those sparse Knowledge Graphs (KGs). Recent works introduce text information as auxiliary features or apply graph densification to alleviate this challenge, but suffer from problems of ineffectively incorporating structure features and i

Ruina Sun, Yuexin Pang

With the development of computer technology, various models have emerged in artificial intelligence. The transformer model has been applied to the field of computer vision (CV) after its success in natural language processing (NLP). Radiologists continue to face multiple challenges in today's rapidly evolving medical field, such as increased workload and inc

Sergio Conti, Adriana Garroni, Roberta Marziani

We study variational models for dislocations in three dimensions in the line-tension scaling. We present a unified approach which allows to treat energies with subquadratic growth at infinity and other regularizations of the singularity near the dislocation lines. We show that the asymptotics via Gamma convergence is independent of the specific choice of the

Xiliang Fan, Ting Yu, Chenggui Yuan

In this paper, we study small-time asymptotic behaviors for a class of distribution dependent stochastic differential equations driven by fractional Brownian motions with Hurst parameter $H\in(1/2,1)$ and magnitude $\ep^H$. By building up a variational framework and two weak convergence criteria in the factional Brownian motion setting, we establish the larg

Illia Oleksiienko, Dat Thanh Tran, Alexandros Iosifidis

Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each input. In this paper, we propose a method for uncertainty estimation in neural networks which, instead of considering a distribution over weights, samples outputs of each layer from a

Takashi Ishizuka, Naoyuki Kamiyama

This paper deals with the complexity of the problem of computing a pure Nash equilibrium for discrete preference games and network coordination games beyond $O(\log n)$-treewidth and tree metric spaces. First, we estimate the number of iterations of the best response dynamics for a discrete preference game on a discrete metric space with at least three strat

A. J. Barclay, A. R. W. McKellar, N. Moazzen-Ahmadi

Widespread interest in weakly bound molecular clusters of medium size (5-50 molecules) is motivated by their complicated energy landscapes, which lead to hundreds or thousands of distinct isomers. But most studies are theoretical in nature, and there are no experimental results which provide definitive structural information on completion of the first solvat

Raz Halifa Levi, Yacov Kantor

We consider the quantum problem of a particle in either a spherical box or a finite spherical well confined by a circular cone with an apex angle $2\theta_0$ emanating from the center of the sphere, with $0<\theta_0<\pi$. This non-central potential can be solved by an extension of techniques used in spherically-symmetric problems. The angular parts of the ei

Okchul Jung, Dong Un Kang, Gwanghyun Kim, Se Young Chun

The world has suffered from COVID-19 (SARS-CoV-2) for the last two years, causing much damage and change in people's daily lives. Thus, automated detection of COVID-19 utilizing deep learning on chest computed tomography (CT) scans became promising, which helps correct diagnosis efficiently. Recently, transformer-based COVID-19 detection method on CT is prop

Lukas Körber, Alexander Hempel, Andreas Otto, Rodolfo Gallardo

In our recent work [AIP Adv. 11, 095006], we presented an efficient numerical method to compute dispersions and spatial mode profiles of spin waves propagating in waveguides with translationally invariant equilibrium magnetization. Using a finite-element method (FEM) allowed to model two-dimensional waveguide cross sections of arbitrary shape but only finite

Robert Baumgartner, Sicco Verwer

State machines are popular models to model and visualize discrete systems such as software systems, and to represent regular grammars. Most algorithms that passively learn state machines from data assume all the data to be available from the beginning and they load this data into memory. This makes it hard to apply them to continuously streaming data and res

Colin Benjamin, Ritesh Das

We propose a set of thermoelectric experiments based on Aharonov-Bohm interferometry to probe Majorana bound states (MBS), which are generated in 2D topological insulators (TI) in the presence of superconducting and ferromagnetic correlations via the proximity effect. The existence and nature (coupled or uncoupled) of these MBS can be determined by studying

Ziyao Zhang, Alessio Falai, Ariadna Sanchez, Orazio Angelini

Training multilingual Neural Text-To-Speech (NTTS) models using only monolingual corpora has emerged as a popular way for building voice cloning based Polyglot NTTS systems. In order to train these models, it is essential to understand how the composition of the training corpora affects the quality of multilingual speech synthesis. In this context, it is com

Alexander Reeves, Laura Herold, Sunny Vagnozzi, Blake D. Sherwin

The early dark energy (EDE) solution to the Hubble tension comes at the cost of an increased clustering amplitude that has been argued to worsen the fit to galaxy clustering data. We explore whether freeing the total neutrino mass $M_{\nu}$, which can suppress small-scale structure growth, improves EDE's fit to galaxy clustering. Using Planck Cosmic Microwav

G. Guidi, A. Isella, L. Testi, C. J. Chandler

In this paper we analyze new observations from ALMA and VLA, at a high angular resolution corresponding to 5 - 8 au, of the protoplanetary disk around HD 163296 to determine the dust spatial distribution and grain properties. We fit the spectral energy distribution as a function of the radius at five wavelengths from 0.9 to 9\,mm, using a simple power law an

Oona Rainio

Inclusion properties are studied for balls of the triangular ratio metric, the hyperbolic metric, the $j^*$-metric, and the distance ratio metric defined in the unit ball domain. Several sharp results are proven and a conjecture about the relation between triangular ratio metric balls and hyperbolic balls is given. An algorithm is also built for drawing tria

Haissam Hanafi, Philip Menz, Allan McWilliam, Jörg Imbrock

Photonic lattices have emerged as a promising approach to localize light in space, for example, through topologically protected edge states and Aharonov-Bohm caging. They are of particular importance in the study of flat band systems via the associated nondiffracting compact localized states. However, such states typically appear static, thus not allowing ad

Antoine Venaille, Yohei Onuki, Nicolas Perez, Armand Leclerc

Inhomogeneous media commonly support a discrete number of wave modes that are trapped along interfaces defined by spatially varying parameters. When they are robust against continuous deformations of parameters, such waves are said to be of topological origin. It has been realized over the last decades that such waves of topological origin can be predicted b

Alessia De Biase, Nanna Maria Sijtsema, Lisanne van Dijk, Johannes A. Langendijk

Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC), simultaneous assessment of different image modalities is needed, and each image volume is explored slice-by-slice from different orientations. Moreover, the manual fixed boundary

Daniele Contessi, Alessio Recati, Matteo Rizzi

We investigate the number probability density function that characterizes sub-portions of a quantum many-body system with globally conserved number of particles. We put forward a linear fitting protocol capable of mapping out the ground-state phase diagram of the rich one-dimensional extended Bose-Hubbard model: The results are quantitatively comparable with

Arpita Nayek, Pinakinath Saha

Let $G=Spin(8n, \mathbb{C})(n\ge 1)$ and $T_{G}$ be a maximal torus of $G.$ Let $P^{\alpha_{4n}}(\supset T_{G})$ be the maximal parabolic subgroup of $G$ corresponding to the simple root $\alpha_{4n}.$ Let $X$ be a Schubert variety in $G/P^{\alpha_{4n}}$ admitting semi-stable point with respect to the $T$-linearized very ample line bundle $\mathcal{L}(2\omeg

Julia Westermayr, Joe Gilkes, Rhyan Barrett, Reinhard J. Maurer

The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measure or compute. While molecular structures, produced through generative deep learning, will satisfy those patterns, they often only possess specific target properties by chance and

S. A. Ghinescu, D. S. Delion

We propose a semi-microscopic model for the simultaneous emission of two protons. This model has the advantage of avoiding certain technical aspects of a fully microscopic 3-body framework, while also allowing the investigation of the influence of proton pairing on the total lifetime of the decaying nucleus. Thus, we use the standard singlet two-proton wave

Bin Chen, Peng-xiang Hao, Reiko Liu, Zhe-fei Yu

In this work, we continue our work on two dimensional Galilean conformal field theory (GCFT$_2$). Our previous work (arXiv:2011.11092) focused on the $\xi\neq 0$ sector, here we investigate the more subtle $\xi=0$ sector to complete the discussion. The case $\xi=0$ is degenerate since there emerge interesting null states in a general $\xi=0$ boost multiplet.

Sungguen Ryu, H. -S. Sim

Scattering or tunneling of an electron at a potential barrier is a fundamental quantum effect. Electron-electron interactions often affect the scattering, and understanding of the interaction effect is crucial in detection of various phenomena of electron transport and their application to electron quantum optics. We theoretically study the partition and col

Rui Wang, Chongwei Liu, Xudong Mou, Kai Gao

The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based methods, which reveal only a certain aspect of the whole normality, are incapable of tasks involved with a large number of anomalies. Specifically, Contrastive Learning (CL) methods dis

Yi Zhang, Yuhao Gu, Hongming Weng, Kun Jiang

We investigate strong electron-electron correlation effects on 2-dimensional van der Waals materials Nb$_3$X$_8$ (X=Cl, Br, I). We find that the monolayers Nb$_3$X$_8$ are ideal systems close to the strong correlation limit. They can be described by a half-filled single band Hubbard model in which the ratio between the Hubbard, U, and the bandwidth, W, U/W $

Xing Hu, Sam Toueg

The implementation of registers from (potentially) weaker registers is a classical problem in the theory of distributed computing. Since Lamport's pioneering work [13], this problem has been extensively studied in the context of asynchronous processes with crash failures. In this paper, we investigate this problem in the context of Byzantine process failures

Xin Huang, Yangyu Guo, Yunhui Wu, Satoru Masubuchi

In recent times, the unique collective transport physics of phonon hydrodynamics motivates theoreticians and experimentalists to explore it in micro- and nanoscale and at elevated temperatures. Graphitic materials have been predicted to facilitate hydrodynamic heat transport with their intrinsically strong normal scattering. However, owing to the experimenta

Daniel Williams, John Veitch, Maria Luisa Chiofalo, Patricia Schmidt

Since the first detection in 2015 of gravitational waves from compact binary coalescence, improvements to the Advanced LIGO and Advanced Virgo detectors have expanded our view into the universe for these signals. Searches of the of the latest observing run (O3) have increased the number of detected signals to 90, at a rate of approximately 1 per week. Future

Siyuan Hui, Benrong Mu, Jun Tao

In this paper, we extend Joule-Thomson expansion to the low-dimensional regime in rainbow gravity by considering the rainbow rotating BTZ metric in the (2+1)-dimensional spacetime. After the metric of the black hole is obtained, we get the Joule-Thomson expansion of the black hole, including the Joule-Thomson coefficient, inversion curves, and isenthalpic cu

Alexandros Kontogiannis, Matthew P. Juniper

We formulate a physics-informed compressed sensing (PICS) method for the reconstruction of velocity fields from noisy and sparse phase-contrast magnetic resonance signals. The method solves an inverse Navier-Stokes boundary value problem, which permits us to jointly reconstruct and segment the velocity field, and at the same time infer hidden quantities such

Mads T. Frandsen, Martin Rosenlyst

We study constraints on Composite Higgs models with fermion partial compositeness from electroweak precision measurements, including the 2022 $W$-boson mass result from the CDF collaboration. We focus on models where the Composite Higgs sector arises from underlying four-dimensional strongly interacting gauge theories with fermions, and where the SM fermions

Cheng Zhang, Wen-Biao Han, Shu-Cheng Yang

Gravitational waves from precessing binary black holes exhibit new features that are absent in non-precessionary systems. All current waveform models take into account only the modulation of the signal due to precession. In this letter, we find that this effect has its own signature, by gravitational emission of a short and transient signal, or burst. The fr

Xincheng Yao, Ruoqi Li, Jing Zhang, Jun Sun

Most anomaly detection (AD) models are learned using only normal samples in an unsupervised way, which may result in ambiguous decision boundary and insufficient discriminability. In fact, a few anomaly samples are often available in real-world applications, the valuable knowledge of known anomalies should also be effectively exploited. However, utilizing a

Jan Manschot, Swapnamay Mondal

The entropy of supersymmetric black holes in string theory compactifications can be related to that of a D- or M-brane system, which in many cases can be further reduced to a two-dimensional conformal field theory (CFT). For black holes in M-theory, this relation involves a decoupling limit where the black hole mass diverges. We suggest that moving away from

Luigi Rodino, Patrik Wahlberg

We study an anisotropic version of the Shubin calculus of pseudodifferential operators on $\mathbf R^d$. Anisotropic symbols and Gabor wave front sets are defined in terms of decay or growth along curves in phase space of power type parametrized by one positive parameter that distinguishes space and frequency variables. We show that this gives subcalculi of

T. Chang, T. Cohen, I. Holzman, G. Catelani

In this work, we study a series of tunable flux qubits inductively coupled to a coplanar waveguide resonator fabricated on a sapphire substrate. Each qubit includes an asymmetric superconducting quantum interference device which is controlled by the application of an external magnetic field and acts as a tunable Josephson junction. The tunability of the qubi

Itai Boneh, Robert Krauthgamer

In vertex-cut sparsification, given a graph $G=(V,E)$ with a terminal set $T\subseteq V$, we wish to construct a graph $G'=(V',E')$ with $T\subseteq V'$, such that for every two sets of terminals $A,B\subseteq T$, the size of a minimum $(A,B)$-vertex-cut in $G'$ is the same as in $G$. In the most basic setting, $G$ is unweighted and undirected, and we wish t

Carlos Quezada-Calonge, Antonio Dobado, Juan José Sanz-Cillero

We study the one-loop corrections to Vector Boson Scattering (in particular $W^+W^-$ elastic scattering) within the framework of effective theories. Re-scattering via intermediate electroweak would-be-Goldstone bosons dominate at high energies, as the corresponding loop diagrams with these intermediate bosons scale like $\mathcal{O}(s^2/v^4)$ in the chiral e

Jade Maï Cock, Mirko Marras, Christian Giang, Tanja Käser

Interactive simulations allow students to discover the underlying principles of a scientific phenomenon through their own exploration. Unfortunately, students often struggle to learn effectively in these environments. Classifying students' interaction data in the simulations based on their expected performance has the potential to enable adaptive guidance an

Giuliano Cornacchia, Matteo Böhm, Giovanni Mauro, Mirco Nanni

Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., carbon dioxide emissions and population exposure to pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algo

Ernesto Araya, Eglantine Karlé, Hemant Tyagi

In many applications, such as sport tournaments or recommendation systems, we have at our disposal data consisting of pairwise comparisons between a set of $n$ items (or players). The objective is to use this data to infer the latent strength of each item and/or their ranking. Existing results for this problem predominantly focus on the setting consisting of

Magdalena Proszewska, Grzegorz Beringer, Daniel Sáez-Trigueros, Thomas Merritt

In this paper, we propose GlowVC: a multilingual multi-speaker flow-based model for language-independent text-free voice conversion. We build on Glow-TTS, which provides an architecture that enables use of linguistic features during training without the necessity of using them for VC inference. We consider two versions of our model: GlowVC-conditional and Gl

Negin Ghamsarian, Mario Taschwer, Raphael Sznitman, Klaus Schoeffmann

Semantic segmentation in cataract surgery has a wide range of applications contributing to surgical outcome enhancement and clinical risk reduction. However, the varying issues in segmenting the different relevant structures in these surgeries make the designation of a unique network quite challenging. This paper proposes a semantic segmentation network, ter

Jun Cen, Peng Yun, Shiwei Zhang, Junhao Cai

Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e.g., autonomous driving, since it is closed-set and static. The closed-set assumption makes the network only able to output labels of trained classes, even for objects never seen before, while a static network cannot update its knowledge base according to what

Maximilian Brunner, Weixuan Zhang, Ahmad Roumie, Marco Tognon

The growing field of aerial manipulation often relies on fully actuated or omnidirectional micro aerial vehicles (OMAVs) which can apply arbitrary forces and torques while in contact with the environment. Control methods are usually based on model-free approaches, separating a high-level wrench controller from an actuator allocation. If necessary, disturbanc

Yinya Huang, Lemao Liu, Kun Xu, Meng Fang

Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence). However, such structures are unexplored as current QA systems focus on entity-base

John Wallace, Asaf Pe'er

In this paper, we expand upon previous work that argued for the possibility of a sub-equipartition magnetic field in the accretion flow of a black hole binary system. Using X-ray observations of the three well-known sources A0620-00, XTE J1118+480 and V404 Cyg during the quiescent state, we compare the theoretically expected spectral shape with the observed

Nathan Nichols

It is an open conjecture that the Enots Wolley sequence is surjective onto the set of positive integers with a binary weight of at least 2. In this paper, this property is proved for an analog of the Enots Wolley sequence which operates on the binary representation of a number rather than the prime factorization.

Qiang Fu, Jia-Qi Dai, Xin-Yu Huang, Yun-Yun Dai

Advanced exfoliation techniques are crucial for exploring the intrinsic properties and applications of 2D materials. Though the recently discovered Au-enhanced exfoliation technique provides an effective strategy for preparation of large-scale 2D crystals, the high cost of gold hinders this method from being widely adopted in industrial applications. In addi

Liling Gong, Ye Guo, Hongbin Sun

The optimal operation problem of electric vehicle aggregator (EVA) is considered. An EVA can participate in energy and regulation markets with its current and upcoming EVs, thus reducing its total cost of purchasing energy to fulfill EVs' charging requirements. A model predictive control (MPC) based optimization is developed to consider the future arrival of

Hoai Nguyen Huynh

As more and more people continue to live in highly urbanised areas across the globe, reliable accessibility to amenities and services plays a vital role in sustainable development. One of the challenges in addressing this issue is the consistent and equal provision of public services, including transport for residents across the urban system. In this study,

Benoit Laslier, Fabio Toninelli

The broad motivation of this work is a rigorous understanding of reversible, local Markov dynamics of interfaces, and in particular their speed of convergence to equilibrium, measured via the mixing time $T_{mix}$. In the $(d+1)$-dimensional setting, $d\ge2$, this is to a large extent mathematically unexplored territory, especially for discrete interfaces. O

Christian Löwens, Inaam Ashraf, Alexander Gembus, Genesis Cuizon

This work presents solutions to the Traveling Salesperson Problem with precedence constraints (TSPPC) using Deep Reinforcement Learning (DRL) by adapting recent approaches that work well for regular TSPs. Common to these approaches is the use of graph models based on multi-head attention (MHA) layers. One idea for solving the pickup and delivery problem (PDP

Qi Bao, DunKun Yang

We define two common $q$-orthogonal polynomials: homogeneous $q$-Laguerre polynomials and homogeneous little $q$-Jacobi polynomials. They can be viewed separately as solutions to two $q$-partial differential equations. Then, we proved that if an analytic function satisfies a certain system of $q$-partial differential equations, if and only if it can be expan

Kirill Koshelev, Zarina Sadrieva, Alexey Shcherbakov, Yuri Kivshar

Bound states in the continuum provide a remarkable example of how a simple problem solved about a century ago in quantum mechanics can drive the research on a whole spectrum of resonant phenomena in wave physics. Due to their huge radiative lifetime, bound states in the continuum have found multiple applications in various areas of physics devoted to wave pr

Manki Kim

We study the D3-brane moduli dependence of the non-perturbative superpotential generated by the D7-brane gaugino condensation in a type IIB compactification on an orientifold of the elliptic Calabi-Yau threefold $\Bbb{P}_{[1,1,1,6,9]}[18].$ Building on the work of Ganor, in the weak coupling limit, we compute the D3-brane dependent one-loop pfaffian in the f

Adam Gaier, James Stoddart, Lorenzo Villaggi, Peter J Bentley

Real-world design problems are a messy combination of constraints, objectives, and features. Exploring these problem spaces can be defined as a Multi-Criteria Exploration (MCX) problem, whose goals are to produce a set of diverse solutions with high performance across many objectives, while avoiding low performance across any objectives. Quality-Diversity al

A. Tichai, S. Knecht, A. T. Kruppa, Ö. Legeza

We propose a novel many-body framework combining the density matrix renormalization group (DMRG) with the valence-space (VS) formulation of the in-medium similarity renormalization group. This hybrid scheme admits for favorable computational scaling in large-space calculations compared to direct diagonalization. The capacity of the VS-DMRG approach is highli

Abel Díaz Berenguer, Tanmoy Mukherjee, Matias Bossa, Nikos Deligiannis

Successful data representation is a fundamental factor in machine learning based medical imaging analysis. Deep Learning (DL) has taken an essential role in robust representation learning. However, the inability of deep models to generalize to unseen data can quickly overfit intricate patterns. Thereby, we can conveniently implement strategies to aid deep mo