July 2022 arXiv papers — page 120

Andrea Angiuli, Nils Detering, Jean-Pierre Fouque, Mathieu Laurière

We propose a mean field control game model for the intra-and-inter-bank borrowing and lending problem. This framework allows to study the competitive game arising between groups of collaborative banks. The solution is provided in terms of an asymptotic Nash equilibrium between the groups in the infinite horizon. A three-timescale reinforcement learning algor

Yousef Yeganeh, Azade Farshad, Johann Boschmann, Richard Gaus

Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patients. Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number of patients, and devices, lead to distinctive data distr

Rajeev Yasarla, Vishal M. Patel

Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems by causing spatially and temporally random fluctuations in the index of refraction of the atmosphere. Variations in the refractive index causes the captured images to be geometrically distorted and blurry. Hence, it is important to compensate for the

Akanksha Gupta

Introduction of DevOps into the software development life cycle represents a cultural shift in the IT culture, amalgamating development and operations to improve delivery speed in a rapid and maintainable manner. At the same time, security threats and breaches are expected to grow as more enterprises move to new agile frameworks for rapid product delivery. M

Anna Sonnefeld, Geoffrey Bodenhausen, Kirill Sheberstov

Long-lived states (LLS) involving pairs of magnetically inequivalent but chemically equivalent proton spins in aliphatic (CH$_2$)$_n$ chains can be excited by simultaneous application of weak selective radio-frequency (RF) fields at n chemical shifts by polychromatic spin lock induced crossing (poly-SLIC). The LLS are delocalized throughout the aliphatic cha

Osama A. Hanna, Antonious M. Girgis, Christina Fragouli, Suhas Diggavi

In this paper, we propose differentially private algorithms for the problem of stochastic linear bandits in the central, local and shuffled models. In the central model, we achieve almost the same regret as the optimal non-private algorithms, which means we get privacy for free. In particular, we achieve a regret of $\tilde{O}(\sqrt{T}+\frac{1}{\epsilon})$ m

Laura Londoño, Juana Valeria Hurtado, Nora Hertz, Philipp Kellmeyer

Machine learning has significantly enhanced the abilities of robots, enabling them to perform a wide range of tasks in human environments and adapt to our uncertain real world. Recent works in various machine learning domains have highlighted the importance of accounting for fairness to ensure that these algorithms do not reproduce human biases and consequen

Dylan C. Jones, Ka Ho Lam, Zhi-Yun Li, Yisheng Tu

With the advent of ALMA, it is now possible to observationally constrain how disks form around deeply embedded protostars. In particular, the recent ALMA C3H2 line observations of the nearby protostar L1527 have been interpreted as evidence for the so-called "centrifugal barrier," where the protostellar envelope infall is gradually decelerated to a stop by t

Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell

Test-time adaptation harnesses test inputs to improve the accuracy of a model trained on source data when tested on shifted target data. Existing methods update the source model by (re-)training on each target domain. While effective, re-training is sensitive to the amount and order of the data and the hyperparameters for optimization. We instead update the

Nicola Morandi, Roel Leus, Jannik Matuschke, Hande Yaman

In the Traveling Salesman Problem with Drones (TSP-mD), a truck and multiple drones cooperate to serve customers in the minimum amount of time. The drones are launched and retrieved by the truck at customer locations, and each of their flights must not consume more energy than allowed by their batteries. Most problem settings in the literature restrict the f

Dalibor Djukanovic, Georg von Hippel, Jonna Koponen, Harvey B. Meyer

The isovector axial form factor of the nucleon plays a key role in interpreting data from long-baseline neutrino oscillation experiments. We perform a lattice-QCD based calculation of this form factor, introducing a new method to directly extract its $z$-expansion from lattice correlators. Our final parametrization of the form factor, which extends up to spa

Thomas Offergeld, Nils Mattus, Florian Schmidtke, Andreas Ulbig

In this paper we introduce a method for performance quantification of flexibility aggregation in flexibility coordination schemes (FCS), with a focus on privacy preserving hierarchical FCS. The quantification is based on two performance metrics: The aggregation error and the aggregation efficiency. We present the simulation framework and the modelling of one

Paolo Guasoni, Yu-Jui Huang

Federal student loans are fixed-rate debt contracts with three main special features: (i) borrowers can use income-driven schemes to make payments proportional to their income above subsistence, (ii) after several years of good standing, the remaining balance is forgiven but taxed as ordinary income, and (iii) accrued interest is simple, i.e., not capitalize

Guillermo Lara, Miguel Bezares, Marco Crisostomi, Enrico Barausse

We investigate neutron star solutions in scalar-tensor theories of gravity with first-order derivative self-interactions in the action and in the matter coupling. We assess the robustness of the kinetic screening mechanism present in these theories against general conformal couplings to matter. The latter include ones leading to the classical Damour-Esposito

V. Rokaj, S. I. Mistakidis, H. R. Sadeghpour

Cavity quantum electrodynamics provides an ideal platform to engineer and control light-matter interactions with polariton quasiparticles. In this work, we investigate collective phenomena in a system of many particles in a harmonic trap coupled to a homogeneous quantum cavity field. The system couples collectively to the cavity field, through its center of

Francesco Tassi, Francesco Iodice, Elena De Momi, Arash Ajoudani

The recognition of actions performed by humans and the anticipation of their intentions are important enablers to yield sociable and successful collaboration in human-robot teams. Meanwhile, robots should have the capacity to deal with multiple objectives and constraints, arising from the collaborative task or the human. In this regard, we propose vision tec

Chun-Han Yao, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein

Creating high-quality articulated 3D models of animals is challenging either via manual creation or using 3D scanning tools. Therefore, techniques to reconstruct articulated 3D objects from 2D images are crucial and highly useful. In this work, we propose a practical problem setting to estimate 3D pose and shape of animals given only a few (10-30) in-the-wil

Changrui Chen, Kurt Debattista, Jungong Han

Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing. However, handling confusing samples is nontrivial: discarding valuable confusing samples would compromise the model generalisation while using them for training would exacerbate the confirmation bias issue cause

Muslem Rahimi, K. Keri Vos

We present Standard Model predictions for lepton flavour universality ratios of inclusive $B\to X_{(c)} \ell \bar\nu_\ell$. For the $\ell=\mu,e$, these ratios are very close to unity as expected. For the $\tau$ mode, we update the SM prediction for the branching ratio including power-corrections in the heavy-quark expansion up to $1/m_b^3$. These inclusive r

Ana Estrada-Real, Abdourahman Khaireh-Walieh, Bernhard Urbaszek, Peter R. Wiecha

Deep learning is a promising, ultra-fast approach for inverse design in nano-optics, but despite fast advancement of the field, the computational cost of dataset generation, as well as of the training procedure itself remains a major bottleneck. This is particularly inconvenient because new data need to be generated and a new network needs to be trained for

Xiangxi Meng, Yuning Gu, Yongsheng Pan, Nizhuan Wang

Multi-modal medical image completion has been extensively applied to alleviate the missing modality issue in a wealth of multi-modal diagnostic tasks. However, for most existing synthesis methods, their inferences of missing modalities can collapse into a deterministic mapping from the available ones, ignoring the uncertainties inherent in the cross-modal re

D. Davis, T. B. Littenberg, I. M. Romero-Shaw, M. Millhouse

Data from ground-based gravitational-wave detectors contains numerous short-duration instrumental artifacts, called "glitches." The high rate of these artifacts in turn results in a significant fraction of gravitational-wave signals from compact binary coalescences overlapping glitches. In LIGO-Virgo's third observing run, $\approx 20\%$ of signals required

Andrzej Białecki, Natalia Jakubowska, Paweł Dobrowolski, Piotr Białecki

As a relatively new form of sport, esports offers unparalleled data availability. Despite the vast amounts of data that are generated by game engines, it can be challenging to extract them and verify their integrity for the purposes of practical and scientific use. Our work aims to open esports to a broader scientific community by supplying raw and pre-proce

Namiko Matsumoto, Arya Mazumdar

Compressed sensing has been a very successful high-dimensional signal acquisition and recovery technique that relies on linear operations. However, the actual measurements of signals have to be quantized before storing or processing. 1(One)-bit compressed sensing is a heavily quantized version of compressed sensing, where each linear measurement of a signal

Katharina Brazda, Martin Kružík, Ulisse Stefanelli

The gradient flow of the Canham-Helfrich functional is tackled via the Generalized Minimizing Movements approach. We prove the existence of solutions in Wasserstein spaces of varifolds, as well as upper and lower diameter bounds. In the more regular setting of multiply covered $C^{1,1}$ surfaces, we provide a Li-Yau-type estimate for the Canham-Helfrich ener

Jorge Calero-Sanz, Bartolo Luque, Lucas Lacasa

This paper introduces Haros graphs, a construction which provides a graph-theoretical representation of real numbers in the unit interval reached via paths in the Farey binary tree. We show how the topological structure of Haros graphs yields a natural classification of the reals numbers into a hierarchy of families. To unveil such classification, we introdu

Ettore Saetta, Renato Tognaccini, Gianluca Iaccarino

A convolutional autoencoder is trained using a database of airfoil aerodynamic simulations and assessed in terms of overall accuracy and interpretability. The goal is to predict the stall and to investigate the ability of the autoencoder to distinguish between the linear and non-linear response of the airfoil pressure distribution to changes in the angle of

Fabio Cavalletti, Davide Manini

The sharp isoperimetric inequality for non-compact Riemannian manifolds with non-negative Ricci curvature and Euclidean volume growth has been obtained in increasing generality with different approaches in a number of contributions [arXiv:1812.05022, arXiv:2012.09490, arXiv:2009.13717, arXiv:2103.08496] culminated by Balogh and Kristaly [arXiv:2012.11862] co

Dhrubajyoti Pathak, Sukumar Nandi, Priyankoo Sarmah

We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The proposed NER dataset is likely to be a signifi

Shweta Wadhera, Deepa Kamra, Ankit Rajpal, Aruna Jain

The advent of the Internet led to the easy availability of digital data like images, audio, and video. Easy access to multimedia gives rise to the issues such as content authentication, security, copyright protection, and ownership identification. Here, we discuss the concept of digital image watermarking with a focus on the technique used in image watermark

Silvia Paparini, Epifanio G. Virga

Chromonic liquid crystals constitute a novel lyotropic phase, whose elastic properties have so far been modeled within the classical Oseen-Frank theory, provided that the twist constant is assumed to be considerably smaller than the saddle-splay constant, in violation of one Ericksen inequality. This paper shows that paradoxical consequences follow from such

Claudia Capone, Agnieszka Kałamajska

We give analytic description for the completion of $C_0^\infty ( \mathbf{R}_+)$ in Dirichlet space $D^{1,p}(\mathbf{R}_+, \omega):= \{ u:\mathbf{R}_+\rightarrow \mathbf{R}: u\ \hbox{ is locally absolutely continuous on} \ \mathbf{R}_+ \ {\rm and}\ \| u^{'}\|_{L^p(\mathbf{R}_+, \omega)}<\infty \}$, for given continuous weight $\omega$, in terms of the local $

Gene Abrams, Francesca Mantese, Alberto Tonolo

Let $K$ be any field, and let $E$ be a finite graph with the property that every vertex in $E$ is the base of at most one cycle (we say such a graph satisfies Condition (AR)). We explicitly construct the injective envelope of each simple left module over the Leavitt path algebra $L_K(E)$. The main idea girding our construction is that of a "formal power seri

Tom Smeding, Matthijs Vákár

Where dual-numbers forward-mode automatic differentiation (AD) pairs each scalar value with its tangent value, dual-numbers reverse-mode AD attempts to achieve reverse AD using a similarly simple idea: by pairing each scalar value with a backpropagator function. Its correctness and efficiency on higher-order input languages have been analysed by Brunel, Mazz

K. Vávra, L. Kolesniková, A. Belloche, R. T. Garrod

The interstellar detections of isocyanic acid, methyl isocyanate, and very recently also ethyl isocyanate, open the question of the possible detection of vinyl isocyanate in the interstellar medium. The aim of this study is to extend the laboratory rotational spectrum of vinyl isocyanate into the millimeter wave region and to undertake a check for its presen

Daniel W. Boutros, Edriss S. Titi

The first half of Onsager's conjecture states that the Euler equations of an ideal incompressible fluid conserve energy if $u (\cdot ,t) \in C^{0, \theta} (\mathbb{T}^3)$ with $\theta > \frac{1}{3}$. In this paper, we prove an analogue of Onsager's conjecture for several subgrid scale $\alpha$-models of turbulence. In particular we find the required H\"older

Gabriella Tarantello

It has been shown in by Huang-Lucia-Tarantello [17] that, for given $\vert c \vert <1$, the moduli space of constant mean curvature (CMC) $c$-immersions of a closed orientable surface of genus $\mathfrak{g} \geq 2$ into a hyperbolic $3$-manifold can be parametrized by elements of the tangent bundle of the corresponding Teichm\"uller space. This is attained b

Gourav Jhanwar, Navdeep Dahiya, Parmida Ghahremani, Masoud Zarepisheh

Dose volume histogram (DVH) metrics are widely accepted evaluation criteria in the clinic. However, incorporating these metrics into deep learning dose prediction models is challenging due to their non-convexity and non-differentiability. We propose a novel moment-based loss function for predicting 3D dose distribution for the challenging conventional lung i

Vladimir R. Sidorenko, Christian Deppe

Ahlswede and Dueck showed possibility to identify with high probability one out of $M$ messages by transmitting $1/C\log\log M$ bits only, where $C$ is the channel capacity. It is known that this identification can be based on error-correcting codes. We propose an identification procedure based on random codes that achieves channel capacity. Then we show tha

V. V. Bobylev, A. T. Bajkova

We have analyzed the kinematics of 9750 OB2 stars with proper motions and parallaxes selected by Xu et al. from the Gaia EDR3 catalogue. The relative parallax errors for these stars do not exceed 10\%. Based on the entire sample of stars, we have found the velocities $(U,V)_\odot=(7.17,7.37)\pm(0.16,0.24)$ km s$^{-1}$ and the components of the angular veloci

Yusuf Dalva, Said Fahri Altindis, Aysegul Dundar

We propose VecGAN, an image-to-image translation framework for facial attribute editing with interpretable latent directions. Facial attribute editing task faces the challenges of precise attribute editing with controllable strength and preservation of the other attributes of an image. For this goal, we design the attribute editing by latent space factorizat

D. Munshi, R. Takahashi, J. D. McEwen

We introduce the response function (RFs) approach to model the weak lensing statistics in the context of separate universe formalism. Numerical results for the RFs are presented for various semi-analytical models that include perturbative modelling and variants of halo models. These results extend the recent studies of the Integrated Bispectrum (IB) and Tris

Yangming Zhou, Qichao Ying, Xiangyu Zhang, Zhenxing Qian

Videos are prone to tampering attacks that alter the meaning and deceive the audience. Previous video forgery detection schemes find tiny clues to locate the tampered areas. However, attackers can successfully evade supervision by destroying such clues using video compression or blurring. This paper proposes a video watermarking network for tampering localiz

Kartik Sharma, Mohit Raghavendra, Yeon Chang Lee, Anand Kumar M

Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of the signed structure (i.e., link signs and

Zhu-Xiong Ye, Alberto Canali, Elisa Soave, Marian Kreyer

We report on the observation of Feshbach resonances at low magnetic field strength (below 10 G) in the Fermi-Fermi mixture of $^{161}$Dy and $^{40}$K. We characterize five resonances by measurements of interspecies thermalization rates and molecular binding energies. As a case of particular interest for applications, we consider a resonance near 7.29 G, whic

Matthew Repasky, Xiuyuan Cheng, Yao Xie

Learning to differentiate model distributions from observed data is a fundamental problem in statistics and machine learning, and high-dimensional data remains a challenging setting for such problems. Metrics that quantify the disparity in probability distributions, such as the Stein discrepancy, play an important role in high-dimensional statistical testing

Judith S. Heinisch, Nan Gao, Christoph Anderson, Shohreh Deldari

Notifications are one of the most prevailing mechanisms on smartphones and personal computers to convey timely and important information. Despite these benefits, smartphone notifications demand individuals' attention and can cause stress and frustration when delivered at inopportune timings. This paper investigates the effect of individuals' smartphone usage

Mario Mastriani

Quantum Fourier gates (QFG) constitute a family of quantum gates that result from an exact combination of the quantum Fourier transform (QFT) and the SWAP gate. As a direct consequence of this, the Feynman gate is a particular case of that family, just as the Bell states are particular cases of the states that are also derived from the aforementioned family.

Rishi Sreedhar, Pontus Vikstål, Marika Svensson, Andreas Ask

Variational quantum algorithms constitute one of the most widespread methods for using current noisy quantum computers. However, it is unknown if these heuristic algorithms provide any quantum-computational speedup, although we cannot simulate them classically for intermediate sizes. Since entanglement lies at the core of quantum computing power, we investig

Sumeet Khatri

The quantum internet is one of the frontiers of quantum information science research. It will revolutionize the way we communicate and do other tasks, and it will allow for tasks that are not possible using the current, classical internet. The backbone of a quantum internet is entanglement distributed globally in order to allow for such novel applications to

Martin Odersky, Aleksander Boruch-Gruszecki, Edward Lee, Jonathan Brachthäuser

Type systems usually characterize the shape of values but not their free variables. However, many desirable safety properties could be guaranteed if one knew the free variables captured by values. We describe CCsubBox, a calculus where such captured variables are succinctly represented in types, and show it can be used to safely implement effects and effect

Raed Jaberi

Wu and Grumbach introduced the concept of strongly biconnected directed graphs. A directed graph $G=(V,E)$ is called strongly biconnected if the directed graph $G$ is strongly connected and the underlying undirected graph of $G$ is biconnected. A strongly biconnected directed graph $G=(V,E)$ is said to be $2$- edge strongly biconnected if it has at least thr

Seongjin Park, Haedong Jeong, Tair Djanibekov, Giyoung Jeon

In general, Deep Neural Networks (DNNs) are evaluated by the generalization performance measured on unseen data excluded from the training phase. Along with the development of DNNs, the generalization performance converges to the state-of-the-art and it becomes difficult to evaluate DNNs solely based on this metric. The robustness against adversarial attack

A. Raghuram

This is an expository article that concerns the various related notions of algebraic idele class characters, the Groessencharaktere of Hecke, and cohomological automorphic representations of GL(1), all under the general title of algebraic Hecke characters. The first part of the article systematically lays the foundations of algebraic Hecke characters. The on

Davis Wertheimer, Luming Tang, Bharath Hariharan

Few-shot recognition involves training an image classifier to distinguish novel concepts at test time using few examples (shot). Existing approaches generally assume that the shot number at test time is known in advance. This is not realistic, and the performance of a popular and foundational method has been shown to suffer when train and test shots do not m

William R. Zame

The continuous time model of dynamic asset trading is the central model of modern finance. Because trading cannot in fact take place at every moment of time, it would seem desirable to show that the continuous time model can be viewed as the limit of models in which trading can occur only at (many) discrete moments of time. This paper demonstrates that, if w

Adam B. Birchfield

If a disturbance rocks a low-inertia power system, the frequency decline may be too rapid to arrest before it triggers undesirable responses from generators and loads. In the worst case, this instability could lead to blackout and major equipment damage. Electric utilities, to combat this, study inertia adequacy in systems that are particularly vulnerable. T

Shaunak A. Mehta, Dylan P. Losey

Humans can leverage physical interaction to teach robot arms. This physical interaction takes multiple forms depending on the task, the user, and what the robot has learned so far. State-of-the-art approaches focus on learning from a single modality, or combine multiple interaction types by assuming that the robot has prior information about the human's inte

Maximilian Neumann, Michael Bleher, Lukas Hahn, Samuel Braun

Multi-parameter persistent homology naturally arises in applications of persistent topology to data that come with extra information depending on additional parameters, like for example time series data. We introduce the concept of a Vietoris-Rips transformation, a method that reduces the computation of the one-parameter persistent homology of pathwise subco

A. Raghuram

Rationality results are proved for the ratios of critical values of Rankin-Selberg L-functions of GL(n) x GL(n') over a totally imaginary field F, by studying rank-one Eisenstein cohomology for the group GL(N)/F, where N = n+n', generalizing the methods and results of previous work with Guenter Harder where the base field was totally real. In contrast to the

Davide Turco, Conor Houghton

Bayesian hierarchical models are well-suited to analyzing the often noisy data from electroencephalography experiments in cognitive neuroscience: these models provide an intuitive framework to account for structures and correlations in the data, and they allow a straightforward handling of uncertainty. In a typical neurolinguistic experiment, event-related p

Muhammad Umar Farooq, Darshan Adiga Haniya Narayana, Thomas Hain

Multilingual speech recognition has drawn significant attention as an effective way to compensate data scarcity for low-resource languages. End-to-end (e2e) modelling is preferred over conventional hybrid systems, mainly because of no lexicon requirement. However, hybrid DNN-HMMs still outperform e2e models in limited data scenarios. Furthermore, the problem

Muhammad Umar Farooq, Thomas Hain

Multilingual automatic speech recognition (ASR) systems mostly benefit low resource languages but suffer degradation in performance across several languages relative to their monolingual counterparts. Limited studies have focused on understanding the languages behaviour in the multilingual speech recognition setups. In this paper, a novel data-driven approac

Yitian Dai, Robin Preece, Mathaios Panteli

Time-based dynamic models of cascading failures have been recognized as one of the most comprehensive methods of representing detailed cascading information and are often used for benchmarking and validation. This paper provides an overview of the progress in the field of dynamic analysis of cascading failures in power systems and outlines the benefits and c

Stefan Steinerberger

Let $A \in \mathbb{R}^{n \times n}$ be invertible, $x \in \mathbb{R}^n$ unknown and $b =Ax $ given. We are interested in approximate solutions: vectors $y \in \mathbb{R}^n$ such that $\|Ay - b\|$ is small. We prove that for all $0< \varepsilon <1 $ there is a composition of $k$ orthogonal projections onto the $n$ hyperplanes generated by the rows of $A$, whe

Tanya Berry, Michael Nicklas, Qun Yang, Walter Schnelle

Double Dirac materials are a topological phase of matter in which a non-symmorphic symmetry enforces greater electronic degeneracy than normally expected up to eightfold. The cubic palladium bronzes NaPd$_3$O$_4$ and LaPd$_3$S$_4$ are built of Pd$_3$X$_4$ (X = O, S) anionic frameworks that are ionically bonded to A cations (A = Na, La). These materials were

Yuhang Hu, Boyuan Chen, Hod Lipson

The ability of robots to model their own dynamics is key to autonomous planning and learning, as well as for autonomous damage detection and recovery. Traditionally, dynamic models are pre-programmed or learned from external observations. Here, we demonstrate for the first time how a task-agnostic dynamic self-model can be learned using only a single first-p

Seungone Kim

Abductive Reasoning is a task of inferring the most plausible hypothesis given a set of observations. In literature, the community has approached to solve this challenge by classifying/generating a likely hypothesis that does not contradict with a past observation and future observation. Some of the most well-known benchmarks that tackle this problem are aNL

J. S. Dowker

Some elementary algebraic points regarding the Green function for a localised flux tube are developed. A calculation of the effective action density is included.

Ehud Aharoni, Moran Baruch, Pradip Bose, Alper Buyuktosunoglu

Privacy-preserving machine learning (PPML) solutions are gaining widespread popularity. Among these, many rely on homomorphic encryption (HE) that offers confidentiality of the model and the data, but at the cost of large latency and memory requirements. Pruning neural network (NN) parameters improves latency and memory in plaintext ML but has little impact

Claudio Ternullo, Isabella Fascitiello

While Peano's negative attitude towards infinitesimals, in particular, geometric infinitesimals, is widely documented, his conception of a single infinite cardinality and, more generally, his views on the infinite, are a lot less known. The paper reconstructs the evolution of Peano's ideas on these questions, and formulates several hypotheses about their und

Claire Rader, Zachary B. Zaccardi, Sin Hang, Ho

We report the full characterization of a new organic nonlinear optical (NLO) crystal for intense THz generation: PNPA ((E)-4-((4-nitrobenzylidene)amino)-N-phenylaniline). We discuss crystal growth and structural characteristics. We present the wavelength dependence of THz generation, the thickness dependence of the THz spectrum for PNPA crystals, and measure

Bing Yao, Xiaohui Zhang, Hui Sun, Jing Su

The coming quantum computation is forcing us to reexamine the cryptosystems people use. We are applying graph colorings of topological coding to modern information security and future cryptography against supercomputer and quantum computer attacks in the near future. Many of techniques introduced here are associated with many mathematical conjecture and NP-p

Alexandre Pasquiou, Yair Lakretz, John Hale, Bertrand Thirion

Neural Language Models (NLMs) have made tremendous advances during the last years, achieving impressive performance on various linguistic tasks. Capitalizing on this, studies in neuroscience have started to use NLMs to study neural activity in the human brain during language processing. However, many questions remain unanswered regarding which factors determ

Jacob V. Spertus, Philip B. Stark

Stratified sampling can be useful in risk-limiting audits (RLAs), for instance, to accommodate heterogeneous voting equipment or laws that mandate jurisdictions draw their audit samples independently. We combine the union-intersection tests in SUITE, the reduction of RLAs to testing whether the means of a collection of lists are all $\leq 1/2$ of SHANGRLA, a

Yoshitaka Hatta, Jian Zhou

We study the small-$x$ evolution equation for the gluon generalized parton distribution (GPD) $E_g$ of the nucleon. It is shown that $E_g$ at vanishing skewness exhibits the Regge behavior identical to the BFKL Pomeron, despite its association with nucleon helicity-flip processes. We also consider the effect of gluon saturation and demonstrate that $E_g$ get

Lexin Ding, Zoltan Zimboras, Christian Schilling

Entanglement is one of the most fascinating concepts of modern physics. In striking contrast to its abstract, mathematical foundation, its practical side is, however, remarkably underdeveloped. Even for systems of just two orbitals or sites no faithful entanglement measure is known yet. By exploiting the spin symmetries of realistic many-electron systems, we

Qinghong Yang, Yi Zuo, Dong E. Liu

Quantum entanglement phase transitions have provided new insights to quantum many-body dynamics. Both disorders and measurements are found to induce similar entanglement transitions. Here, we provide a theoretical framework that unifies these two seemingly disparate concepts and discloses their internal connections. Specifically, we analytically analyze a $d

Devon M. Powell, Simona Vegetti, J. P. McKean, Cristiana Spingola

We investigate the mass structure of a strong gravitational lens galaxy at $z=0.350$, taking advantage of the milli-arcsecond (mas) angular resolution of very long baseline interferometric (VLBI) observations. In the first analysis of its kind at this resolution, we jointly infer the lens model parameters and pixellated radio source surface brightness. We co

Kirandeep Kaur, Shishram Rebari, Varinder Singh

We investigate the optimal performance of quantum Otto engine and refrigeration cycles of a time-dependent harmonic oscillator under a trade-off figure of merit for both adiabatic and nonadiabatic (sudden-switch) frequency modulations. For heat engine (refrigerator), the chosen trade-off figure of merit is an objective function defined by the product of effi

J. -B. Durrive, M. Changmai, R. Keppens, P. Lesaffre

Magnetohydrodynamic turbulence is central to laboratory and astrophysical plasmas, and is invoked for interpreting many observed scalings. Verifying predicted scaling law behaviour requires extreme-resolution direct numerical simulations (DNS), with needed computing resources excluding systematic parameter surveys. We here present an analytic generator of re

Ziwei Zhu, Yun He, Xing Zhao, James Caverlee

Popularity bias is a long-standing challenge in recommender systems. Such a bias exerts detrimental impact on both users and item providers, and many efforts have been dedicated to studying and solving such a bias. However, most existing works situate this problem in a static setting, where the bias is analyzed only for a single round of recommendation with

Chang-Hong Wu, Dongyuan Xiao, Maolin Zhou

In this paper, we mainly consider the speed selection problem for the classical Lotka-Volterra competition system. For the first time, we propose a sufficient and necessary condition for this long-standing problem from a new point of view. Moreover, our results can also reveal the essence of the linearly selected problem for the monostable dynamical system f

J. M. Pittard

We investigate the effect of mass-loading from embedded clouds on the evolution of wind-blown bubbles. We use 1D hydrodynamical calculations and assume that the clouds are numerous enough that they can be treated in the continuous limit, and that rapid mixing occurs so that the injected mass quickly merges with the global flow. The destruction of embedded cl

Natã Machado, Johan Öinert, Stefan Wagner

We present a geometrically oriented classification theory for non-Abelian extensions of groupoids generalizing the classification theory for Abelian extensions of groupoids by Westman as well as the familiar classification theory for non-Abelian extensions of groups by Schreier and Eilenberg-MacLane. As an application of our techniques we demonstrate that ea

Hua Tian, Lirong Zhang, Youjin Deng, Wanzhou Zhang

Percolation is an important topic in climate, physics, materials science, epidemiology, finance, and so on. Prediction of percolation thresholds with machine learning methods remains challenging. In this paper, we build a powerful graph convolutional neural network to study the percolation in both supervised and unsupervised ways. From a supervised learning

Gang Xu, Yu-chen Yang, Liang Wang, Xian-Tong Zhen

Joint Super-Resolution and Inverse Tone-Mapping (joint SR-ITM) aims to increase the resolution and dynamic range of low-resolution and standard dynamic range images. Recent networks mainly resort to image decomposition techniques with complex multi-branch architectures. However, the fixed decomposition techniques would largely restricts their power on versat

Chengfeng Zhou, Songchang Chen, Chenming Xu, Jun Wang

Since data scarcity and data heterogeneity are prevailing for medical images, well-trained Convolutional Neural Networks (CNNs) using previous normalization methods may perform poorly when deployed to a new site. However, a reliable model for real-world clinical applications should be able to generalize well both on in-distribution (IND) and out-of-distribut

Harish K. Singh, Amit Sehrawat, Chen Shen, Ilias Samathrakis

Half-antiperovskites (HAPs) are a class of materials consisting of stacked kagome lattices and thus host exotic magnetic and electronic states. We perform high-throughput calculations based on density functional theory (DFT) and atomistic spin dynamics (ASD) simulations to predict stable magnetic HAPs M$_3$X$_2$Z$_2$ (M = Cr, Mn, Fe, Co, and Ni; X is one of

Shaojie Tang, Jing Yuan

In this paper, we study the classic submodular maximization problem subject to a group equality constraint under both non-adaptive and adaptive settings. It has been shown that the utility function of many machine learning applications, including data summarization, influence maximization in social networks, and personalized recommendation, satisfies the pro

Felix Küng

We apply computations of twisted Hodge diamonds to construct an infinite number of non-Fourier-Mukai functors with well behaved target and source spaces. To accomplish this we first study the characteristic morphism in order to control it for tilting bundles. Then we continue by applying twisted Hodge diamonds of hypersurfaces embedded in projective space to

Ashot Minasyan, Lawk Mineh

A relatively hyperbolic group $G$ is said to be QCERF if all finitely generated relatively quasiconvex subgroups are closed in the profinite topology on $G$. Assume that $G$ is a QCERF relatively hyperbolic group with double coset separable (e.g., virtually polycyclic) peripheral subgroups. Given any two finitely generated relatively quasiconvex subgroups $Q

Tomer Ezra, Stefano Leonardi, Rebecca Reiffenhäuser, Matteo Russo

We consider prophet inequalities under downward-closed constraints. In this problem, a decision-maker makes immediate and irrevocable choices on arriving elements, subject to constraints. Traditionally, performance is compared to the expected offline optimum, called the \textit{Ratio of Expectations} (RoE). However, RoE has limitations as it only guarantees

Ugo Dal Lago, Giulia Giusti

A system of session types is introduced as induced by a Curry Howard correspondence applied to Bounded Linear Logic, and then extending the thus obtained type system with probabilistic choices and ground types. The obtained system satisfies the expected properties, like subject reduction and progress, but also unexpected ones, like a polynomial bound on the

Julius Degünther, Udo Seifert

The Mpemba effect denotes an anomalous relaxation phenomenon where a system initially at a hot temperature cools faster than a system that starts at a less elevated temperature. We introduce an isothermal analog of this effect for a system prepared in a non-equilibrium steady state that then relaxes towards equilibrium. Here, the driving strength, which dete

Yuchen He, Sung Ha Kang, Wenjing Liao, Hao Liu

Aggregation equations are broadly used to model population dynamics with nonlocal interactions, characterized by a potential in the equation. This paper considers the inverse problem of identifying the potential from a single noisy spatial-temporal process. The identification is challenging in the presence of noise due to the instability of numerical differe

Rui Song, Qiongxiang Huang

A topological index reflects the physical, chemical and structural properties of a molecule, and its study has an important role in molecular topology, chemical graph theory and mathematical chemistry. It is a natural problem to characterize non-isomorphic graphs with the same topological index value. By introducing a relation on trees with respect to edge d

Tobias Hänel, Nishant Kumar, Dmitrij Schlesinger, Mengze Li

The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes. We address this problem by introducing fairness-aware regularization losses based on batch estimates of Demographic Parity, Equalized Odds, and a novel Intersection-over-Union measur

O. Auriacombe, S. Rea, S. Ioppolo, M. Oldfield

We present an experimental instrument that performs laboratory-based gas-phase Terahertz Desorption Emission Spectroscopy (THz-DES) experiments in support of astrochemistry. The measurement system combines a terahertz heterodyne radiometer that uses room temperature semiconductor mixer diode technology previously developed for the purposes of Earth observati

Ghulam Jilani Quadri, Jennifer Adorno Nieves, Brenton M. Wiernik, Paul Rosen

Scatterplots are among the most widely used visualization techniques. Compelling scatterplot visualizations improve understanding of data by leveraging visual perception to boost awareness when performing specific visual analytic tasks. Design choices in scatterplots, such as graphical encodings or data aspects, can directly impact decision-making quality fo