July 2022 arXiv papers — page 143

Layan El Hajj, Seongmin Jeon, Henrik Shahgholian

In this paper we prove radial symmetry for solutions to a free boundary problem with a singular right hand side, in both elliptic and parabolic regime. More exactly, in the unit ball $B_1$ we consider a solution to the fully nonlinear elliptic problem $$ \begin{cases} F(D^2u)=f(u)&\text{in }B_1 \cap \{u >0 \},\\ u=M&\text{on }\partial B_1,\\ 0\le u<M&\text{i

Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi

Adversarial training (AT) defends deep neural networks against adversarial attacks. One challenge that limits its practical application is the performance degradation on clean samples. A major bottleneck identified by previous works is the widely used batch normalization (BN), which struggles to model the different statistics of clean and adversarial trainin

Dinh Dũng, Van Kien Nguyen

We investigate the numerical approximation of integrals over $\mathbb{R}^d$ equipped with the standard Gaussian measure $\gamma$ for integrands belonging to the Gaussian-weighted Sobolev spaces $W^\alpha_p(\mathbb{R}^d, \gamma)$ of mixed smoothness $\alpha \in \mathbb{N}$ for $1 < p < \infty$. We prove the asymptotic order of the convergence of optimal quadr

Hideki Yanaoka

This study analysed a three-dimensional Taylor decaying vortex under an applied magnetic field as a benchmark test problem to verify the calculation method of an electromagnetic fluid flow and investigated the validity of the decaying magnetic field model. First, we observed the flow structure of a three-dimensional Taylor decaying vortex without an applied

Xi-Wen Guan, Peng He

Over the past two decades quantum engineering has made significant advances in our ability to create genuine quantum many-body systems using ultracold atoms. In particular, some prototypical exactly solvable Yang-Baxter systems have been successfully realized allowing us to confront elegant and sophisticated exact solutions of these systems with their experi

Bin Chen, Yi Lei, Gabriele Liga, Zhiwei Liang

Shaping modulation formats in multi-dimensional (MD) space is an effective approach to harvest spectral efficiency gains in both the additive white Gaussian noise (AWGN) channel and the optical fiber channel. In the first part of this paper, existing MD geometrically-shaped modulations for fiber optical communications are reviewed. It is shown that large gai

Di Zhang, Qiang Niu, Youzhou Zhou

Volatility clustering is a common phenomenon in financial time series. Typically, linear models can be used to describe the temporal autocorrelation of the (logarithmic) variance of returns. Considering the difficulty in estimating this model, we construct a Dynamic Bayesian Network, which utilizes the conjugate prior relation of normal-gamma and gamma-gamma

E. Rubín de Celis, C. Simeone

Traversability in relation with tides in thin-shell wormholes is revisited to investigate the possibility of improving recently noted restrictive conditions for a safe travel across a wormhole throat. We consider wormholes mathematically constructed starting from background geometries which are solutions of scalar-tensor theories as dilaton gravity and Brans

Laszlo B. Kish

The information theoretically secure Kirchhoff-law-Johnson-noise (KLJN) key exchange scheme, similarly to quantum key distribution (QKD), is also potentially vulnerable against clock attacks, where Eve takes over the control of clock synchronization in the channel. This short note aims to introduce a time synchronization protocol scheme for Alice and Bob, wh

Keshav Kasichainula, Hadi Mansourifar, Weidong Shi

Recent successful adversarial attacks on face recognition show that, despite the remarkable progress of face recognition models, they are still far behind the human intelligence for perception and recognition. It reveals the vulnerability of deep convolutional neural networks (CNNs) as state-of-the-art building block for face recognition models against adver

Valentina Breschi, Andrea Sassella, Simone Formentin

In this paper, we propose a data-driven approach to derive explicit predictive control laws, without requiring any intermediate identification step. The keystone of the presented strategy is the exploitation of available priors on the control law, coming from model-based analysis. Specifically, by leveraging on the knowledge that the optimal predictive contr

Gennady Y. Chitov, Karun Gadge, P. N. Timonin

We study the exactly solvable 1D model: the dimerized $XY$ chain with uniform and staggered transverse fields, equivalent upon fermionization to the noninteracting dimerized Kitaev-Majorana chain with modulation. The model has three known gapped phases with local and nonlocal (string) orders, along with the gapless incommensurate (IC) phase in the $U(1)$ lim

Alexandros Dimitrios Keros, Kartic Subr

Many computational algorithms applied to geometry operate on discrete representations of shape. It is sometimes necessary to first simplify, or coarsen, representations found in modern datasets for practicable or expedited processing. The utility of a coarsening algorithm depends on both, the choice of representation as well as the specific processing algori

Julio Hurtado, Alain Raymond-Saez, Vladimir Araujo, Vincenzo Lomonaco

Catastrophic forgetting, the phenomenon of forgetting previously learned tasks when learning a new one, is a major hurdle in developing continual learning algorithms. A popular method to alleviate forgetting is to use a memory buffer, which stores a subset of previously learned task examples for use during training on new tasks. The de facto method of fillin

Meghal Gupta, Rachel Yun Zhang

Given a noiseless protocol $\pi_0$ computing a function $f(x, y)$ of Alice and Bob's private inputs $x, y$, the goal of interactive coding is to construct an error-resilient protocol $\pi$ computing $f$ such that even if some fraction of the communication is adversarially corrupted, both parties still learn $f(x, y)$. Ideally, the resulting scheme $\pi$ shou

David Eisenbud, Bernd Ulrich

Schemes defined by residual intersections have been extensively studied in the case when they are Cohen-Macaulay, but this is a very restrictive condition. In this paper we make the first study of a class of natural examples far from satisfying this condition, the rank 1 loci of generic $2\times n$ matrices. Here we compute their depths and many other proper

Charles Godfrey

We prove invariance results for the cohomology groups of ideal sheaves of simple normal crossing divisors under (a restricted class of) birational morphisms of pairs in arbitrary characteristic, assuming a conjecture regarding the existence of normal Cohen-Macaulayfications. As an application, we extend some foundational results in the theory of rational pai

Erickson Tjoa

In this work we first collect and generalize several existing non-perturbative models for the interaction between a single two-level qubit detector and a relativistic quantum scalar field in arbitrary curved spacetimes, where the time evolution is given by simple-generated unitaries, i.e., those generated by Schmidt rank-1 interaction Hamiltonians. We then e

Stanisław Szufa, Piotr Faliszewski, Łukasz Janeczko, Martin Lackner

We study the multifaceted question of how to sample approval elections in a meaningful way. Our analysis aims to discern the properties of various statistical cultures (both established and new ones). Based on the map-of-elections framework by Szufa et al. [2020], we graphically represent statistical cultures; and, by that, provide an intuitive understanding

J. Adriazola

Tikhonov regularization is a common technique used when solving poorly behaved optimization problems. Often, and with good reason, this technique is applied by practitioners in an ad hoc fashion. In this note, we systematically illustrate the role of Tikhonov regularizations in two simple, yet instructive examples. In one example, we use regular perturbation

Dimitrios Kollias

This paper describes the fourth Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with European Conference on Computer Vision (ECCV), 2022. The 4th ABAW Competition is a continuation of the Competitions held at IEEE CVPR 2022, ICCV 2021, IEEE FG 2020 and IEEE CVPR 2017 Conferences, and aims at automatically analyzing affect. In

Eleanor Loh, Jalaj Khandelwal, Brian Regan, Duncan A. Little

Managing discount promotional events ("markdown") is a significant part of running an e-commerce business, and inefficiencies here can significantly hamper a retailer's profitability. Traditional approaches for tackling this problem rely heavily on price elasticity modelling. However, the partial information nature of price elasticity modelling, together wit

Lingyu Zhu, Esa Rahtu, Hang Zhao

This paper focuses on perceiving and navigating 3D environments using echoes and RGB image. In particular, we perform depth estimation by fusing RGB image with echoes, received from multiple orientations. Unlike previous works, we go beyond the field of view of the RGB and estimate dense depth maps for substantially larger parts of the environment. We show t

Halo Anwar Abdulkhalaq, Nzar Rauf Abdullah, Vidar Gudmundsson

We study electron transport through double quantum dots (DQD) coupled to a cavity with a single photon mode. The DQD is connected to two electron reservoirs, and the total system is under an external perpendicular magnetic field. The DQD system exhibits a complex multi-level energy spectrum. By varying the photon energy, several anti-crossings between photon

Daniel Dantchev, Joseph Rudnick

We obtain exact closed-form expressions for the partition function of the one-dimensional Ising model in the fixed-$M$ ensemble, for three commonly-used boundary conditions: periodic, antiperiodic and Dirichlet. These expressions allow for the determination of fluctuation-induced forces in the canonical ensemble, which we term Helmholtz forces. The thermodyn

Guanzhou Zhu, Peng Liang, Cheng-Liang Huang, Cheng-Chia Huang

Developing new types of high-capacity and high-energy density rechargeable battery is important to future generations of consumer electronics, electric vehicles, and mass energy storage applications. Recently we reported ~ 3.5 V sodium/chlorine $(Na/Cl_2)$ and lithium/chlorine $(Li/Cl_2)$ batteries with up to 1200 mAh $g^{-1}$ reversible capacity, using eith

Ilia L. Rasskazov, Alexander Moroz, Catherine J. Murphy, Todd D. Krauss

A novel concept of a \textit{mesocavity}, based on a simple yet counter-intuitive idea of covering metal nanoparticles with unusually thick ($\gtrsim 100$ nm) high-index dielectric shells, is demonstrated in technologically relevant and largely sought-after case of an upconversion (UC) enhancement. For readily available combinations of materials and geometri

Zhenyu Zhang, Anas Chaaban

This paper studies the capacity of the two-user intensity-modulation/direct-detection (IM/DD) interference channel (IC), which is relevant in the context of multi-user optical wireless communications. Despite some known single-letter capacity characterizations for general discrete-memoryless ICs, a computable capacity expression for the IM/DD IC is missing.

Giulia Perugia, Dominika Lisy

The discussion around the problematic practice of gendering humanoid robots has risen to the foreground in the last few years. To lay the basis for a thorough understanding of how robot's "gender" has been understood within the Human-Robot Interaction (HRI) community - i.e., how it has been manipulated, in which contexts, and which effects it has yield on pe

Shin-ichi Nakano

A combinatorial Gray code for a set of combinatorial objects is a sequence of all combinatorial objects in the set so that each object is derived from the preceding object by changing a small part. In this paper we design a Gray code for ordered trees with n vertices such that each ordered tree is derived from the preceding ordered tree by removing a leaf th

Michela Ceria, Francesco Pavese

In this paper we are concerned with $m$-ovoids of the symplectic polar space ${\cal W}(2n+1, q)$, $q$ even. In particular we show the existence of an elliptic quadric of ${\rm PG}(2n+1, q)$ not polarizing to ${\cal W}(2n+1, q)$ forming a $\left(\frac{q^n-1}{q-1}\right)$-ovoid of ${\cal W}(2n+1, q)$. A further class of $(q+1)$-ovoids of ${\cal W}(5, q)$ is ex

Noam Gottlieb, Michael Werman

Deep neural networks (DNNs) and decision trees (DTs) are both state-of-the-art classifiers. DNNs perform well due to their representational learning capabilities, while DTs are computationally efficient as they perform inference along one route (root-to-leaf) that is dependent on the input data. In this paper, we present DecisioNet (DN), a binary-tree struct

Dante Mata, Harold A. Moreno-Franco, Kei Noba, José-Luis Pérez

This paper studies the bailout optimal dividend problem with regime switching under the constraint that dividend payments can be made only at the arrival times of an independent Poisson process while capital can be injected continuously in time. We show the optimality of the regime-modulated Parisian-classical reflection strategy when the underlying risk mod

Rixin Li, Dong Lai

Stellar-mass binary black holes (BBHs) embedded in active galactic nucleus (AGN) discs offer a promising dynamical channel to produce black-hole mergers that are detectable by LIGO/Virgo. Modeling the interactions between the disc gas and the embedded BBHs is crucial to understand their orbital evolution. Using a suite of 2D high-resolution simulations of pr

Mang Yu, Sitao Huang, Deming Chen

In recent years, hardware accelerators based on field-programmable gate arrays (FPGAs) have been widely adopted, thanks to FPGAs' extraordinary flexibility. However, with the high flexibility comes the difficulty in design and optimization. Conventionally, these accelerators are designed with low-level hardware descriptive languages, which means creating lar

Mohayeminul Islam, Ajay Kumar Jha, Ildar Akhmetov, Sarah Nadi

Developers heavily rely on Application Programming Interfaces (APIs) from libraries to build their software. As software evolves, developers may need to replace the used libraries with alternate libraries, a process known as library migration. Doing this manually can be tedious, time-consuming, and prone to errors. Automated migration techniques can help all

Ben Lambert, Elena Mäder-Baumdicker

We obtain estimates on nonlocal quantities appearing in the Volume Preserving Mean Curvature Flow (VPMCF) in the closed, Euclidean setting. As a result we demonstrate that blowups of finite time singularities of VPMCF are ancient solutions to Mean Curvature Flow (MCF), prove that monotonicity methods may always be applied at finite times and obtain informati

Lie Fu, Ben Moonen

We study Gushel-Mukai (GM) varieties of dimension 4 or 6 in characteristic $p$. Our main result is the Tate conjecture for all such varieties over finitely generated fields of characteristic $p\geq 5$. In the case of GM sixfolds, we follow the method used by Madapusi Pera in his proof of the Tate conjecture for K3 surfaces. As input for this, we prove a numb

Qi Wang, Roman Verba, Björn Heinz, Michael Schneider

Spin waves are ideal candidates for wave-based computing, but the construction of magnetic circuits is blocked by a lack of an efficient mechanism to excite long-running exchange spin waves with normalised amplitudes. Here, we solve the challenge by exploiting the deeply nonlinear phenomena of forward-volume spin waves in 200 nm wide nanoscale waveguides and

V. Ukleev, C. Luo, R. Abrudan, A. Aqeel

Resonant elastic soft x-ray magnetic scattering (XRMS) is a powerful tool to explore long-periodic spin textures in single crystals. However, due to the limited momentum transfer range imposed by long wavelengths of photons in the soft x-ray region, Bragg diffraction is restricted to crystals with the large lattice parameters. Alternatively, small angle x-ra

Roohollah Aliabadi, Soudabe Nasirimoghadam, Henricus Herman Wensink

We use the Parsons Lee modification of Onsagers second virial theory within the restricted orientation approximation to analyse the phase behaviour of hard cylindrical rods confined in narrow pores Depending on the wall to wall separation we predict a number of distinctly different surface generated nematic phases including a biaxial planar nematic with vari

Guangji Bai, Liang Zhao

Multitask learning is a framework that enforces multiple learning tasks to share knowledge to improve their generalization abilities. While shallow multitask learning can learn task relations, it can only handle predefined features. Modern deep multitask learning can jointly learn latent features and task sharing, but they are obscure in task relation. Also,

Miguel A. Benitez-Rathgeb, Diogo Boito, André H. Hoang, Matthias Jamin

In a previous article, we have shown that the discrepancy between the fixed-order (FOPT) and contour-improved (CIPT) perturbative expansions for $\tau$ hadronic spectral function moments, which had affected the precision of $\alpha_s$ determinations for many years, may be reconciled by employing a renormalon-free (RF) scheme for the gluon condensate (GC) mat

Liam Schramm, Yunfu Deng, Edgar Granados, Abdeslam Boularias

Dealing with sparse rewards is a long-standing challenge in reinforcement learning (RL). Hindsight Experience Replay (HER) addresses this problem by reusing failed trajectories for one goal as successful trajectories for another. This allows for both a minimum density of reward and for generalization across multiple goals. However, this strategy is known to

Shuheng Liu, Xiyue Huang, Pavlos Protopapas

There have been extensive studies on solving differential equations using physics-informed neural networks. While this method has proven advantageous in many cases, a major criticism lies in its lack of analytical error bounds. Therefore, it is less credible than its traditional counterparts, such as the finite difference method. This paper shows that one ca

Mani Kumar Tellamekala, Ömer Sümer, Björn W. Schuller, Elisabeth André

Recognising continuous emotions and action unit (AU) intensities from face videos requires a spatial and temporal understanding of expression dynamics. Existing works primarily rely on 2D face appearances to extract such dynamics. This work focuses on a promising alternative based on parametric 3D face shape alignment models, which disentangle different fact

Vahid Reza Khazaie, Anthony Wong, Yalda Mohsenzadeh

Anomaly detection is to recognize samples that differ in some respect from the training observations. These samples which do not conform to the distribution of normal data are called outliers or anomalies. In real-world anomaly detection problems, the outliers are absent, not well defined, or have a very limited number of instances. Recent state-of-the-art d

David Wagner, Nora Weickgenannt, Enrico Speranza

We derive an expression for the tensor polarization of a system of massive spin-1 particles in a hydrodynamic framework. Starting from quantum kinetic theory based on the Wigner-function formalism, we employ a modified method of moments which also takes into account all spin degrees of freedom. It is shown that the tensor polarization of an uncharged fluid i

Adam Wunderlich, Jack Sklar

Random noise arising from physical processes is an inherent characteristic of measurements and a limiting factor for most signal processing and data analysis tasks. Given the recent interest in generative adversarial networks (GANs) for data-driven modeling, it is important to determine to what extent GANs can faithfully reproduce noise in target data sets.

Ayan Chakraborty, Cosmin Anitescu, Somdatta Goswami, Xiaoying Zhuang

Modeling fracture is computationally expensive even in computational simulations of two-dimensional problems. Hence, scaling up the available approaches to be directly applied to large components or systems crucial for real applications become challenging. In this work. we propose domain decomposition framework for the variational physics-informed neural net

Václav Blažej, Pratibha Choudhary, Dušan Knop, Šimon Schierreich

For many problems, the important instances from practice possess certain structure that one should reflect in the design of specific algorithms. As data reduction is an important and inextricable part of today's computation, we employ one of the most successful models of such precomputation -- the kernelization. Within this framework, we focus on Traveling S

Sujoy Bhore, Fabian Klute, Jelle J. Oostveen

We study the maximum geometric independent set and clique problems in the streaming model. Given a collection of geometric objects arriving in an insertion only stream, the aim is to find a subset such that all objects in the subset are pairwise disjoint or intersect respectively. We show that no constant factor approximation algorithm exists to find a maxim

E. Tavabi, S. Zeighami, M. Heydari

In this research, we investigate Explosive Events (EEs) in the off-limb solar atmosphere, with simultaneous observations from the Si IV, Mg II k, and slit-jaw images (SJI) of the Interface Region Imaging Spectrograph (IRIS), on 17 August 2014, and 19 February. IRIS data can be investigated to observe the motion of matter, fluctuations, energy absorption, and

Vahid Reza Khazaie, Anthony Wong, John Taylor Jewell, Yalda Mohsenzadeh

Anomaly detection is to identify samples that do not conform to the distribution of the normal data. Due to the unavailability of anomalous data, training a supervised deep neural network is a cumbersome task. As such, unsupervised methods are preferred as a common approach to solve this task. Deep autoencoders have been broadly adopted as a base of many uns

Yun Liao, Seyyed Ali Hashemi, Hengjie Yang, John M. Cioffi

While constructing polar codes for successive-cancellation decoding can be implemented efficiently by sorting the bit-channels, finding optimal polar codes for cyclic-redundancy-check-aided successive-cancellation list (CA-SCL) decoding in an efficient and scalable manner still awaits investigation. This paper first maps a polar code to a unique heterogeneou

E. Barredo, L. F. Urrutia

In the framework of Carrol-Field-Jackiw electrodynamics we calculate the spectral distribution of the Cherenkov radiation (CHR) produced by a charge moving at constant velocity in a chiral medium. We find zero, one or two Cherenkov angles according to the relation between the velocity of the particle and the refraction index of the medium.

Yosef Ashkenazy, Hezi Yizhaq

Winds play an essential role in the climate system. In this study, we analyze the global pattern of the diurnal cycle of surface (10 m) winds from the ERA5 reanalysis data. We find that over the land and especially over sand dune regions, the maximal wind speed and wind drift potential (DP) occur during the hours around midday. However, over the ocean, the w

Miguel Ferrer, María de Diego, Gema Piñero, Amin Hassani

Multi-channel Multi-tone Active Noise Equalizers can achieve different user-selected noise spectrum profiles even at different space positions. They can apply a different equalization factor at each noise frequency component and each control point. Theoretically, the value of the transfer function at the frequencies where the noise signal has energy is deter

Richard F. Lebed, Steven R. Martinez

We apply the diabatic formalism, an extension of the adiabatic approximation inherent to the Born-Oppenheimer (BO) approach of atomic physics, to the problem of mixing between exotic multiquark hadrons and their nearby di-hadron thresholds. The unperturbed BO eigenstates are obtained using the dynamical diquark model, while the diabatic calculation introduce

Minghui Du, Qiong Deng, Yifan Bian, Ziren Luo

Coalescing supermassive black hole binaries (SMBHBs) are the primary source candidates for low frequency gravitational wave (GW) detections, which could bring us deep insights into galaxy evolutions over cosmic time and violent processes of spacetime dynamics. Promising candidates had been found based on optical and X-ray observations, which claims for new a

David Moya, Joaquín Pérez

We generalize the classical Henneberg minimal surface by giving an infinite family of complete, finitely branched, non-orientable, stable minimal surfaces in $\mathbb{R}^3$. These surfaces can be grouped into subfamilies depending on a positive integer (called the complexity), which essentially measures the number of branch points. The classical Henneberg su

Sushmitha Sajeevu, Sakthivel Vellaisamy

Cognitive radio is a potential solution to meet the upcoming spectrum crunch issue. In a cognitive radio, spectrum holes can be identified using spectrum sensing techniques. A high resolution spectrum hole detection can ensure even the smallest inactive portion in the spectrum is efficiently utilized. In this paper, a spectrum hole detection technique is pro

Brian Cook, Kevin Hughes, Zane Kun Li, Akshat Mudgal

We interpret into decoupling language a refinement of a 1973 argument due to Karatsuba on Vinogradov's mean value theorem. The main goal of our argument is to answer what precisely does solution counting in older partial progress on Vinogradov's mean value theorem correspond to in Fourier decoupling theory.

Ziv Ran

Let $X$ be either a general hypersurface of degree $n+1$ in $\mathbb P^n$ or a general $(2,n)$ complete intersection in $\mathbb P^{n+1}, n\geq 4$. We construct balanced rational curves on $X$ of all high enough degrees. If $n=3$ or $g=1$, we construct rigid curves of genus $g$ on $X$ of all high enough degrees. As an application we construct some rigid bund

Manasa Kaniselvan, Mathieu Luisier, Marko Mladenović

We present a framework dedicated to modelling the resistive switching operation of Valence Change Memory (VCM) cells. The method combines an atomistic description of the device structure, a Kinetic Monte Carlo (KMC) model for the creation and diffusion of oxygen vacancies in the central oxide under an external field, and an ab-initio quantum transport method

David Cushing, George W. Stagg, David I. Stewart

We describe some recent computer investigations with the `Constraint Logic Programming over Finite Domains' -- CLP(FD) -- library in the Prolog programming environment to search for new simple Lie algebras over the field $\GF(2)$ of $2$ elements. Motivated by a paper of Grishkov et. al., we specifically look for those with a `thin decomposition', and we sett

Nicolas García Trillos, Daniel Sanz-Alonso, Ruiyi Yang

In recent decades, science and engineering have been revolutionized by a momentous growth in the amount of available data. However, despite the unprecedented ease with which data are now collected and stored, labeling data by supplementing each feature with an informative tag remains to be challenging. Illustrative tasks where the labeling process requires e

Marek Szykuła, Adam Zyzik

A synchronizing word of a deterministic finite complete automaton is a word whose action maps every state to a single one. Finding a shortest or a short synchronizing word is a central computational problem in the theory of synchronizing automata and is applied in other areas such as model-based testing and the theory of codes. Because the problem of finding

Alexander Schäfer, Gerd Reis, Didier Stricker

Natural user interfaces are on the rise. Manufacturers for Augmented, Virtual, and Mixed Reality head mounted displays are increasingly integrating new sensors into their consumer grade products, allowing gesture recognition without additional hardware. This offers new possibilities for bare handed interaction within virtual environments. This work proposes

Fausto Giunchiglia, Mayukh Bagchi

Semantic Heterogeneity is conventionally understood as the existence of variance in the representation of a target reality when modelled, by independent parties, in different databases, schemas and/ or data. We argue that the mere encoding of variance, while being necessary, is not sufficient enough to deal with the problem of representational heterogeneity,

Minh Nguyen, Nicolas Wu

Neural networks are typically represented as data structures that are traversed either through iteration or by manual chaining of method calls. However, a deeper analysis reveals that structured recursion can be used instead, so that traversal is directed by the structure of the network itself. This paper shows how such an approach can be realised in Haskell

Caleb Eckhardt, Tatiana Shulman

We examine Hilbert-Schmidt stability (HS-stability) of discrete amenable groups from several angles. We give a short, elementary proof that finitely generated nilpotent groups are HS-stable. We investigate the permanence of HS-stability under central extensions by showing HS-stability is preserved by finite central quotients, but is not preserved in general.

Nathan Hubens

FasterAI is a PyTorch-based library, aiming to facilitate the utilization of deep neural networks compression techniques such as sparsification, pruning, knowledge distillation, or regularization. The library is built with the purpose of enabling quick implementation and experimentation. More particularly, compression techniques are leveraging Callback syste

Yu Wang, Yuying Zhao, Yi Zhang, Tyler Derr

Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless, most of the existing message-passing mechanisms for recommendation are directly inherited from GNNs without scrutinizing whether the captured collaborative effect would benefit the pr

Mingwei Shi, Jiang Zhou

In this paper, we introduce homogeneous mixed Herz-Morrey spaces $M\dot{K}_{p,\vec{q}}^{\alpha,\lambda}(\mathbb{R}^n)$ and show it's some properties. Firstly, the boundedness of sublinear operators, fractional type operators in homogeneous mixed Herz-Morrey spaces is investigated. In particular, the above results are still valid for Calder$\acute{o}$n-Zygmun

José Ángel Peláez, Jouni Rättyä

The Bergman projection $P_\alpha$, induced by a standard radial weight, is bounded and onto from $L^\infty$ to the Bloch space $\mathcal{B}$. However, $P_\alpha: L^\infty\to \mathcal{B}$ is not a projection. This fact can be emended via the boundedness of the operator $P_\alpha:BMO_2(\Delta)\to\mathcal{B}$, where $BMO_2(\Delta)$ is the space of functions of

V. Cirigliano, Z. Davoudi, J. Engel, R. J. Furnstahl

We present the results of a National Science Foundation (NSF) Project Scoping Workshop, the purpose of which was to assess the current status of calculations for the nuclear matrix elements governing neutrinoless double-beta decay and determine if more work on them is required. After reviewing important recent progress in the application of effective field t

Jinfeng Lin, Amrit Poudel, Wenhao Yu, Qingkai Zeng

Software requirements traceability is a critical component of the software engineering process, enabling activities such as requirements validation, compliance verification, and safety assurance. However, the cost and effort of manually creating a complete set of trace links across natural language artifacts such as requirements, design, and test-cases can b

Sebastian Hammer, Theresa Linderl, Kristofer Tvingstedt, Wolfgang Brütting

Multi-molecular excited states accompanied by an intra- and inter-molecular geometric relaxation are commonly encountered in optical and electrooptical studies and applications of organic semiconductors as, for example excimers or charge transfer states. Understanding the dynamics of these states is crucial to improve organic devices such as light emitting d

Stavros Nousias

Asthma is a common chronic disease of the respiratory system causing significant disability and societal burden. It affects more than 300 million people worldwide, while more than 100 million people will likely have asthma by 2025. The price of asthma varies greatly from nation to nation. Mean yearly cost can be estimated to 1900 EUR in Europe and $3100 in t

Boris Rotstein, Eric Akkermans

Non-equilibrium radiation is addressed theoretically by means of a stochastic lattice-gas model. We consider a resonating transmission line composed of a chain of radiation resonators, each at a local equilibrium, whose boundaries are in thermal contact with two blackbody reservoirs at different temperatures. In the long chain limit, the stationary state of

Abhishek Mathur, Sumati Surya, Nomaan X

We review some recent results on Sorkin's spacetime formulation of the entanglement entropy (SSEE) for a free quantum scalar field both in the continuum and in manifold-like causal sets. The SSEE for a causal diamond in a 2d cylinder spacetime has been shown to have a Calabrese-Cardy form, while for de Sitter and Schwarzschild de Sitter horizons in dimension

Tanishq Gupta, Mohd Zaki, Devanshi Khatsuriya, Kausik Hira

A crucial component in the curation of KB for a scientific domain (e.g., materials science, foods & nutrition, fuels) is information extraction from tables in the domain's published research articles. To facilitate research in this direction, we define a novel NLP task of extracting compositions of materials (e.g., glasses) from tables in materials science p

Kenneth Ooi, Zhen-Ting Ong, Karn N. Watcharasupat, Bhan Lam

Choosing optimal maskers for existing soundscapes to effect a desired perceptual change via soundscape augmentation is non-trivial due to extensive varieties of maskers and a dearth of benchmark datasets with which to compare and develop soundscape augmentation models. To address this problem, we make publicly available the ARAUS (Affective Responses to Augm

Renrui Zhang, Ziyao Zeng, Ziyu Guo, Yafeng Li

Besides image classification, Contrastive Language-Image Pre-training (CLIP) has accomplished extraordinary success for a wide range of vision tasks, including object-level and 3D space understanding. However, it's still challenging to transfer semantic knowledge learned from CLIP into more intricate tasks of quantified targets, such as depth estimation with

Mingzhe Guo, Zhipeng Zhang, Heng Fan, Liping Jing

Relying on Transformer for complex visual feature learning, object tracking has witnessed the new standard for state-of-the-arts (SOTAs). However, this advancement accompanies by larger training data and longer training period, making tracking increasingly expensive. In this paper, we demonstrate that the Transformer-reliance is not necessary and the pure Co

Jae Woong Soh, Nam Ik Cho

There have been numerous image restoration methods based on deep convolutional neural networks (CNNs). However, most of the literature on this topic focused on the network architecture and loss functions, while less detailed on the training methods. Hence, some of the works are not easily reproducible because it is required to know the hidden training skills

Jae Woong Soh, Nam Ik Cho

This paper presents a new variational inference framework for image restoration and a convolutional neural network (CNN) structure that can solve the restoration problems described by the proposed framework. Earlier CNN-based image restoration methods primarily focused on network architecture design or training strategy with non-blind scenarios where the deg

Alexandre Adam, Laurence Perreault-Levasseur, Yashar Hezaveh

Modeling strong gravitational lenses in order to quantify the distortions in the images of background sources and to reconstruct the mass density in the foreground lenses has traditionally been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computat

Shuhan LI, Xiaomeng Li, Xiaowei Xu, Kwang-Ting Cheng

This paper addresses the problem of few-shot skin disease classification by introducing a novel approach called the Sub-Cluster-Aware Network (SCAN) that enhances accuracy in diagnosing rare skin diseases. The key insight motivating the design of SCAN is the observation that skin disease images within a class often exhibit multiple sub-clusters, characterize

Xiaoke Shen, Ioannis Stamos

Traditional systems typically require different models for processing different modalities, such as one model for RGB images and another for depth images. Recent research has demonstrated that a single model for one modality can be adapted for another using cross-modality transfer learning. In this paper, we extend this approach by combining cross/inter-moda

Alexey Yamilov, Nicholas Bender, Hui Cao

In a random-scattering system, the deposition matrix maps the incident wavefront to the internal field distribution across a target volume. The corresponding eigenchannels have been used to enhance the wave energy delivered to the target. Here we find the sum rules for the eigenvalues and eigenchannels of the deposition matrix in any system geometry: includi

Jesús M. F. Castillo, Manuel González, Raúl Pino

We study operators on the Kalton-Peck Banach space $Z_2$ from various points of view: matrix representations, examples, spectral properties and operator ideals. For example, we prove that there are non-compact, strictly singular operators acting on $Z_2$, but the product of two of them is a compact operator. Among applications, we show that every copy of $Z_

Michael Dine

Any would-be Peccei-Quinn (PQ) symmetry is vulnerable to various types of explicit breaking. It has long been recognized that these can disrupt the axion solution to the strong CP problem. There have also been suggestions that, under certain circumstances, these can lead to a surprisingly large mass for the axion. Two types of corrections to this computation

Lukas W. Lindwasser, E. T. Tomboulis

Recently it has been explicitly shown how a theory with global $GL(d,\mathbb{R})$ coordinate (affine) invariance which is spontaneously broken down to its Lorentz subgroup will have as its Goldstone fields enough degrees of freedom to create a metric and a covariant derivative arXiv:1105.5848. Such a theory would constitute an effective theory of gravity. So

Jianhui Zhang, Xiaosheng Chen, Zhenzhen Gui, Zhenlin Chen

For over 200 years, wettability has made significant contributions to understanding the properties of objects, advancing technological progress. Theoretical model of the contact angle (CA) for evaluating wettability has constantly been modified to address relevant emerging issues. However, these existing models disregard the difference in the CA along the co

Jianfeng Wang, Thomas Lukasiewicz, Daniela Massiceti, Xiaolin Hu

Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match is suited to this task for two reasons. Fir

Ra'ad David Mahmoud, Chris Done, Delphine Porquet, Andrew Lobban

We explore the accretion geometry in Arakelian 120 using intensive UV and X-ray monitoring from \textit{Swift}. The hard X-rays ($1-10$ keV) show large amplitude, fast (few-day) variability, so we expect reverberation from the disc to produce UV variability from the varying hard X-ray illumination. We model the spectral energy distribution including an outer

Heather E. Logan

These lectures are a pedagogical introduction to the application of perturbative unitarity to Higgs physics within and beyond the Standard Model (SM). I begin with a review of how perturbative unitarity arises from quantum mechanical scattering theory and apply it to the classic problem of longitudinal vector boson scattering in the SM to derive the famous u

Keon Lee, Kyumin Park, Daeyoung Kim

The majority of current Text-to-Speech (TTS) datasets, which are collections of individual utterances, contain few conversational aspects. In this paper, we introduce DailyTalk, a high-quality conversational speech dataset designed for conversational TTS. We sampled, modified, and recorded 2,541 dialogues from the open-domain dialogue dataset DailyDialog inh