October 2022 arXiv papers — page 29

Hao Sun, Ming-Lei Xiao, Jiang-Hao Yu

For the first time, we list the complete and independent set of operators at the next-to-next-to-leading order (NNLO) in the Higgs effective field theory (HEFT), also named as the electroweak chiral Lagrangian with the light Higgs boson. The Young tensor technique utilized in this work guarantees the completeness and independence of the on-shell amplitude ba

Nikhil Sarin, Paul D. Lasky, Rowina S. Nathan

Gamma-ray burst GRB 211211A may have been the result of a neutron star merger at $\approx350$ Mpc. However, none of the LIGO-Virgo detectors were operating at the time. We show that the gravitational-wave signal from a \grb-like binary neutron star inspiral in the next LIGO-Virgo-KAGRA observing run (O4) would be below the conventional detection threshold, h

Jonáš Dujava

We systematically develop the Hilbert series technique for counting independent operators in Effective Field Theories. In the hope of providing more approachable entry point to the subject we include a detailed introduction of all necessary group theoretic tools (in a rather mathematical definition-theorem-proof style). Finally, we apply the formalism in the

Jing Yang, Adolfo del Campo

The description of strongly correlated quantum many-body systems far from equilibrium presents a fundamental challenge due to the vast amount of information it requires. We introduce a generalization of the Jastrow ansatz for time-dependent wavefunctions that offers an efficient and exact description of the time evolution of various strongly correlated syste

Niklas Pirnay, Ryan Sweke, Jens Eisert, Jean-Pierre Seifert

Density modelling is the task of learning an unknown probability density function from samples, and is one of the central problems of unsupervised machine learning. In this work, we show that there exists a density modelling problem for which fault-tolerant quantum computers can offer a super-polynomial advantage over classical learning algorithms, given sta

Zhao-Di Liu, Olli Siltanen, Tom Kuusela, Rui-Heng Miao

Quantum entanglement and decoherence are the two counterforces of many quantum technologies and protocols. For example, while quantum teleportation is fueled by a pair of maximally entangled resource qubits, it is vulnerable to decoherence. In this Article, we propose an efficient quantum teleportation protocol in the presence of pure decoherence and without

Lorenzo Piga, Matteo Lucca, Nicola Bellomo, Valentì Bosch-Ramon

Should Primordial Black Holes (PBHs) exist in nature, they would inevitably accrete baryonic matter in their vicinity. In turn, the consequent emission of high-energy radiation could affect the thermal history of the universe to an extent that can be probed with a number of cosmological observables such as the Cosmic Microwave Background (CMB) anisotropies.

Lukia Mistryukova, Andrey Plotnikov, Aleksandr Khizhik, Irina Knyazeva

Magnetic fields are responsible for a multitude of Solar phenomena, including such destructive events as solar flares and coronal mass ejections, with the number of such events rising as we approach the peak of the 11-year solar cycle, in approximately 2025. High-precision spectropolarimetric observations are necessary to understand the variability of the Su

Da Ma, Arpit Arora, Giovanni Vignale, Justin C. W. Song

Berry curvature and skew-scattering play central roles in determining both the linear and nonlinear anomalous Hall effects. Yet in {\it PT}-symmetric antiferromagnetic metals, Hall effects from either intrinsic Berry curvature mediated anomalous velocity or the conventional skew-scattering process individually vanish. Here we reveal an unexpected nonlinear H

Théo Simon, Pierre Zhang, Vivian Poulin

We present cosmological results inferred from the effective-field theory (EFT) analysis of the full-shape of eBOSS quasars (QSO) power spectrum. We validate our analysis pipeline against simulations, and find overall good agreement between the analyses in Fourier and configuration space. Keeping the baryon abundance and the spectral tilt fixed, we reconstruc

Peizhi Mai, Nathan S. Nichols, Seher Karakuzu, Feng Bao

There is growing evidence that the hole-doped single-band Hubbard and $t$-$J$ models do not have a superconducting ground state reflective of the high-temperature cuprate superconductors but instead have striped spin- and charge-ordered ground states. Nevertheless, it is proposed that these models may still provide an effective low-energy model for electron-

Kangyu Chai, Jiang-Hao Yu, Hao Zhang

The Higgs self coupling measurement is quite essential for determining the shape of the Higgs potential and nature of the Higgs boson. We propose the di-Higgs plus jet final states at hadron colliders to increase the discovery sensitivity of the Higgs self coupling at the low invariant mass region. Our simulation indicates that the allowed region of the Higg

Nils Quetschlich, Lukas Burgholzer, Robert Wille

Quantum computing is fast evolving as a technology due to recent advances in hardware, software, as well as the development of promising applications. To use this technology for solving specific problems, a suitable quantum algorithm has to be determined, the problem has to be encoded in a form suitable for the chosen algorithm, it has to be executed, and th

Michael Pagano, Jing Liu, Adrian Liu, Nicholas S. Kern

Radio Frequency Interference (RFI) is one of the systematic challenges preventing 21cm interferometric instruments from detecting the Epoch of Reionization. To mitigate the effects of RFI on data analysis pipelines, numerous inpaint techniques have been developed to restore RFI corrupted data. We examine the qualitative and quantitative errors introduced int

Neil Dowling, Kavan Modi

Chaotic systems are highly sensitive to a small perturbation, and are ubiquitous throughout biological sciences, physical sciences and even social sciences. Taking this as the underlying principle, we construct an operational notion for quantum chaos. Namely, we demand that the future state of a many-body, isolated quantum system is sensitive to past multiti

Maximilian Zillinger, Bastian Kubis, Pablo Sánchez-Puertas

It has been pointed out recently that a certain set of dimension-6 scalar $P$- and $CP$-violating light-quark-muon operators may be tested in $\eta$ and $\eta'$ decays to various final states involving $\mu^+\mu^-$ pairs, at a level not yet excluded by constraints from electric dipole moments. We here work out the hadronic matrix elements required for the pr

Anna Hallin, Gregor Kasieczka, Tobias Quadfasel, David Shih

We introduce a new technique named Latent CATHODE (LaCATHODE) for performing "enhanced bump hunts", a type of resonant anomaly search that combines conventional one-dimensional bump hunts with a model-agnostic anomaly score in an auxiliary feature space where potential signals could also be localized. The main advantage of LaCATHODE over existing methods is

Jan Albert, Justin Kaidi, Ying-Hsuan Lin

The theory of topological modular forms (TMF) predicts that elliptic genera of physical theories satisfy a certain divisibility property, determined by the theory's gravitational anomaly. In this note we verify this prediction in Duncan's Supermoonshine module, as well as in tensor products and orbifolds thereof. Along the way we develop machinery for comput

Tom Rose, B. R. McNamara, F. Combes, A. C. Edge

Galaxies often contain large reservoirs of molecular gas which shape their evolution. This can be through cooling of the gas -- which leads to star formation, or accretion onto the central supermassive black hole -- which fuels AGN activity and produces powerful feedback. Molecular gas has been detected in early-type galaxies on scales of just a few tens to

T. Rick Perche, Boris Ragula, Eduardo Martín-Martínez

We study how quantum systems can harvest entanglement from the quantum degrees of freedom of the gravitational field. Concretely, we describe in detail the interaction of non-relativistic quantum systems with linearized quantum gravity, and explore how two spacelike separated probes can harvest entanglement from the gravitational field in this context. We pr

Nicholas Geiser, Lukas W. Lindwasser

We analyze so-called generalized Veneziano and generalized Virasoro amplitudes. Under some physical assumptions, we find that their spectra must satisfy an over-determined set of non-linear recursion relations. The recursion relation for the generalized Veneziano amplitudes can be solved analytically and yields a two-parameter family which includes the Venez

Mackenzie H. Shaw, Andrew C. Doherty, Arne L. Grimsmo

The Gottesman-Kitaev-Preskill (GKP) error correcting code encodes a finite dimensional logical space in one or more bosonic modes, and has recently been demonstrated in trapped ions and superconducting microwave cavities. In this work we introduce a new subsystem decomposition for GKP codes that we call the stabilizer subsystem decomposition, analogous to th

Ahmed Abouelkomsan, Emil J. Bergholtz, Shubhayu Chatterjee

Van der Waals heterostructures have recently emerged as an exciting platform for investigating the effects of strong electronic correlations, including various forms of magnetic or electrical orders. Here, we perform an unbiased exact diagonalization study of the effects of interactions on topological flat bands of twisted transition metal dichalcogenides (T

Tanner Trickle

Direct detection experiments utilizing electronic excitations are spearheading the search for light, sub-GeV, dark matter (DM). It is thus crucial to have accurate predictions for any DM-electron interaction rate in this regime. EXCEED-DM (EXtended Calculation of Electronic Excitations for Direct detection of Dark Matter) computes DM-electron interaction rat

Julien Barrat, Pedro Liendo, Giulia Peveri

We continue our study of multipoint correlators of scalar fields on the $1d$ defect CFT generated by inserting operators along the Maldacena-Wilson line in $\mathcal{N} = 4$ SYM. We present a weak-coupling recursion relation that captures correlators at next-to-leading order involving an arbitrary number of the elementary scalar fields $\phi^i$ and $\phi^6$,

Moritz Haslbauer, Pavel Kroupa, Akram Hasani Zonoozi, Hosein Haghi

The James Webb Space Telescope (JWST) discovered several luminous high-redshift galaxy candidates with stellar masses of $M_{*} \gtrsim 10^{9} \, \rm{M_{\odot}}$ at photometric redshifts $z_{\mathrm{phot}} \gtrsim 10$ which allows to constrain galaxy and structure formation models. For example, Adams et al. identified the candidate ID 1514 with $\log_{10}(M_

Oksana Chelpanova, Shane P. Kelly, Giovanna Morigi, Ferdinand Schmidt-Kaler

Topological defects have strong impact on both elastic and inelastic properties of materials. In this article, we investigate the possibility to controllably inject topological defects in quantum simulators of solid state lattice structures. We investigate the quench dynamics of a Frenkel-Kontorova chain, which is used to model discommensurations of particle

J. E. Pascoe

Many theorems in complex analysis propagate analyticity, such as the Forelli theorem, edge-of-the-wedge theorem and so on. We give a germination theorem which allows for general analytic propagation in complete normed fields. In turn, we develop general analogs of the Forelli theorem, edge-of-the-wedge theorem, and the royal road theorem, and gain insight in

Patrick C. Breysse, Dongwoo T. Chung, Håvard T. Ihle

We introduce a novel unbiased, cross-correlation estimator for the one-point statistics of cosmological random fields. One-point statistics are a useful tool for analysis of highly non-Gaussian density fields, while cross-correlations provide a powerful method for combining information from pairs of fields and separating them from noise and systematics. We d

Zahra Essack, Avi Shporer, Jennifer A. Burt, Sara Seager

Populating the exoplanet mass-radius diagram in order to identify the underlying relationship that governs planet composition is driving an interdisciplinary effort within the exoplanet community. The discovery of hot super-Earths - a high temperature, short-period subset of the super-Earth planet population - has presented many unresolved questions concerni

Zhiyuan Zhang, Yuchao Dai, Bin Fan, Jiadai Sun

Existing learning-based point feature descriptors are usually task-agnostic, which pursue describing the individual 3D point clouds as accurate as possible. However, the matching task aims at describing the corresponding points consistently across different 3D point clouds. Therefore these too accurate features may play a counterproductive role due to the in

Yufei Wang, Yuchao Dai, Qi Liu, Peng Yang

LiDAR depth-only completion is a challenging task to estimate dense depth maps only from sparse measurement points obtained by LiDAR. Even though the depth-only methods have been widely developed, there is still a significant performance gap with the RGB-guided methods that utilize extra color images. We find that existing depth-only methods can obtain satis

Zhiyuan Zhang, Jiadai Sun, Yuchao Dai, Bin Fan

Although 3D point cloud data has received widespread attentions as a general form of 3D signal expression, applying point clouds to the task of dense correspondence estimation between 3D shapes has not been investigated widely. Furthermore, even in the few existing 3D point cloud-based methods, an important and widely acknowledged principle, i.e . one-to-one

Zijie J. Wang, Evan Montoya, David Munechika, Haoyang Yang

With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introd

Jirayu Mongkolkiattichai, Liyu Liu, Davis Garwood, Jin Yang

Geometrically frustrated many-particle quantum systems are notoriously hard to study numerically but are of profound interest because of their unusual properties and emergent phenomena. In these systems energetic constraints cannot be minimized simultaneously, leading to large ground-state degeneracy and a variety of exotic quantum phases. Here, we present a

Hsin-Yuan Huang, Sitan Chen, John Preskill

We present an efficient machine learning (ML) algorithm for predicting any unknown quantum process $\mathcal{E}$ over $n$ qubits. For a wide range of distributions $\mathcal{D}$ on arbitrary $n$-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process~$\mathcal{E}$, with a small average error

Fabian Offert, Thao Phan

In this paper, we examine how generative machine learning systems produce a new politics of visual culture. We focus on DALL-E 2 and related models as an emergent approach to image-making that operates through the cultural techniques of feature extraction and semantic compression. These techniques, we argue, are inhuman, invisual, and opaque, yet are still c

Jared Miller, Tianyu Dai, Mario Sznaier

This paper applies a polynomial optimization based framework towards the superstabilizing control of an Autoregressive with Exogenous Input (ARX) model given noisy data observations. The recorded input and output values are corrupted with L-infinity bounded noise where the bounds are known. This is an instance of Error in Variables (EIV) in which true intern

Sam McArdle, András Gilyén, Mario Berta

We introduce a versatile method for preparing a quantum state whose amplitudes are given by some known function. Unlike existing approaches, our method does not require handcrafted reversible arithmetic circuits, or quantum table reads, to encode the function values. Instead, we use a template quantum eigenvalue transformation circuit to convert a low cost b

Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger

We present a smoothly broken power law functional form (that we refer to as a Broken Neural Scaling Law (BNSL)) that accurately models & extrapolates the scaling behaviors of deep neural networks (i.e. how the evaluation metric of interest varies as amount of compute used for training (or inference), number of model parameters, training dataset size, model i

Rogelio A. Mancisidor, Kjersti Aas

Textual data from financial filings, e.g., the Management's Discussion & Analysis (MDA) section in Form 10-K, has been used to improve the prediction accuracy of bankruptcy models. In practice, however, we cannot obtain the MDA section for all public companies, which limits the use of MDA data in traditional bankruptcy models, as they need complete data to m

Dongwoo T. Chung, Ishika Bangari, Patrick C. Breysse, Håvard T. Ihle

We present the deconvolved distribution estimator (DDE), an extension of the voxel intensity distribution (VID), in the context of future observations proposed as part of the CO Mapping Array Project (COMAP). The DDE exploits the fact that the observed VID is a convolution of correlated signal intensity distributions and uncorrelated noise or interloper inte

Jefferson Abrantes Santos, Claudianor O. Alves, Eugenio Massa

This paper concerns the existence of a solution for the following class of semipositone quasilinear problems \begin{equation*} \left \{ \begin{array}{rclcl} -\Delta_p u = h(x)(f(u)-a),\ & u > 0 & \mbox{in} & \mathbb{R}^N, \end{array} \right. \end{equation*} where $1<p<N$, $a>0$, $ f:[0,+\infty) \to [0,+\infty)$ is a function with subcritical growth and $f(0)

Przemysław Berk, Frank Trujillo

The goal of this article is to show a rigidity property of conjugacies of generalized interval exchange transformations (GIETs). More precisely, we show that if two piecewise $C^3$ GIETs $f$ and $g$ of generic rotation number with mean-non-linearity 0 are homeomorphic, boundary-equivalent and their renormalizations approach in an appropriate way the set of a

M. R. Ghoreyshi, C. E. Jones, A. Granada

Circumstellar discs around Be stars are formed by the material ejected by the central star. This process removes excess angular momentum from the star as viscosity facilitates the mass and angular momentum transfer within the disc and its growth. The angular momentum loss rates (AMLR) of Be stars is a subject of debate in the literature. Through the modellin

Slava Naprienko

We study the Yang-Baxter equation for the $R$-matrices of the six-vertex model. We analyze the solutions and give new parametrizations of the Yang-Baxter equation. In particular, we find the maximal commutative families of parametrized solutions which generalize the $R$-matrices from the affine quantum (super)-groups. Then we give a new parametrization of th

P. I. Gerevenkov, V. D. Bessonov, V. S. Teplov, A. V. Telegin

Nonreciprocity, i.e. inequivalence in amplitudes and frequencies of spin waves propagating in opposite directions, is a key property underlying functionality in prospective magnonic devices. Here we demonstrate experimentally and theoretically a simple approach to induce frequency nonreciprocity in a magnetostatically coupled ferromagnetic bilayer structure

Peter Vieting, Christoph Lüscher, Julian Dierkes, Ralf Schlüter

Unsupervised representation learning has recently helped automatic speech recognition (ASR) to tackle tasks with limited labeled data. Following this, hardware limitations and applications give rise to the question how to take advantage of large pre-trained models efficiently and reduce their complexity. In this work, we study a challenging low resource conv

Dhruv Kedar, Jialiang Yu, Eric Oelker, Alexander Staron

State-of-the-art optical oscillators employing cryogenic reference cavities are limited in performance by the Brownian thermal noise associated with the mechanical dissipation of the mirror coatings. Recently, crystalline Al$_{1-x}$Ga$_{x}$As/GaAs coatings have emerged as a promising candidate for improved coating thermal noise. We present measurements of th

Dongfang Xu, Ata Khalili, Xianghao Yu, Derrick Wing Kwan Ng

In this paper, we investigate the resource allocation design for integrated sensing and communication (ISAC) in distributed antenna networks (DANs). In particular, coordinated by a central processor (CP), a set of remote radio heads (RRHs) provide communication services to multiple users and sense several target locations within an ISAC frame. To avoid the s

Taishi Kotsuka, Yutaka Hori

Molecular communication (MC) enables cooperation of spatially dispersed molecular robots through the feedback control mediated by diffusing signal molecules. However, conventional analysis frameworks for the MC channels mostly consider the dynamics of unidirectional communication, lacking the effect of feedback interactions. In this paper, we propose a gener

Shahriar Talebi, Amirhossein Taghvaei, Mehran Mesbahi

Duality of control and estimation allows mapping recent advances in data-guided control to the estimation setup. This paper formalizes and utilizes such a mapping to consider learning the optimal (steady-state) Kalman gain when process and measurement noise statistics are unknown. Specifically, building on the duality between synthesizing optimal control and

Shang Yu, Zhi-Peng Zhong, Yuhua Fang, Raj B. Patel

Gaussian Boson Sampling (GBS) exhibits a unique ability to solve graph problems, such as finding cliques in complex graphs. It is noteworthy that many drug discovery tasks can be viewed as the clique-finding process, making them potentially suitable for quantum computation. However, to perform these tasks in their quantum-enhanced form, a large-scale quantum

Samuel Yen-Chi Chen

Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of quantum machine learning (QML). Reinforcement learning (RL) is one of the ML paradigms which can be used to solve complex sequential decision making problems. Classical RL has been shown to be capable to solve various challenging tasks.

Guilherme Franzmann, Sebastian M. D. Jovancic, Matthew Lawson

Spacetime emergence from entanglement proposes an alternative to quantizing gravity and typically derives a notion of distance based on the amount of mutual information shared across sub-systems. Albeit promising, this program still faces challenges to describe simple physical systems, such as a maximally entangled Bell pair that is taken apart while preserv

Arnav Kundu, Mohammad Samragh Razlighi, Minsik Cho, Priyanka Padmanabhan

Streaming keyword spotting is a widely used solution for activating voice assistants. Deep Neural Networks with Hidden Markov Model (DNN-HMM) based methods have proven to be efficient and widely adopted in this space, primarily because of the ability to detect and identify the start and end of the wake-up word at low compute cost. However, such hybrid system

Wei Huang, Michelangelo Valsecchi, Michael Multerer

Generative Adversarial Networks (GANs) have paved the path towards entirely new media generation capabilities at the forefront of image, video, and audio synthesis. However, they can also be misused and abused to fabricate elaborate lies, capable of stirring up the public debate. The threat posed by GANs has sparked the need to discern between genuine conten

Alexander Elgart, Abel Klein

We study the many-body localization (MBL) properties of the Heisenberg XXZ spin-$\frac12$ chain in a random magnetic field. We prove that the system exhibits localization in any given energy interval at the bottom of the spectrum in a nontrivial region of the parameter space. This region, which includes weak interaction and strong disorder regimes, is indepe

Kiran S. Kedlaya, Daxin Xu

We prove a Tannakian form of Drinfeld's lemma for isocrystals on a variety over a finite field, equipped with actions of partial Frobenius operators. This provides an intermediate step towards transferring V. Lafforgue's work on the Langlands correspondence over function fields from $\ell$-adic to $p$-adic coefficients. We also discuss a motivic variant and

Eric Schares

On August 25, 2022, the White House Office of Science and Technology Policy (OSTP) released a memo regarding public access to scientific research. Signed by Director Alondra Nelson, this updated guidance eliminated the 12-month embargo period on publications arising from U.S. federal funding that had been allowed from a previous 2013 OSTP memo. While reactio

A. T. M Mustafa Masud Chowdhury, Jeenat Sultana, Md Sakib Ullah Sourav

A huge wastage of electricity can be seen in Bangladesh due to improper street light management which leads to an enormous financial loss every year. Many noteworthy works have been done by researchers from different parts of the world in tackling this issue by using the Internet of Things yet very few in Bangladeshi perspective. In this work, we propose an

Rahul Gulati, Shiva Rudraraju

Action potential propagation along the axons and across the dendrites is the foundation of the electrical activity observed in the brain and the rest of the central nervous system. Theoretical and numerical modeling of this action potential activity has long been a key focus area of electro-chemical neuronal modeling. Specifically, considering the presence o

Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li

We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages and are generated using a scalable conversion framework that transpiles prompts and test cases from the original Python datasets into the corresponding data in the target language. Using these bench

Barun Patra, Saksham Singhal, Shaohan Huang, Zewen Chi

In this paper, we elaborate upon recipes for building multilingual representation models that are not only competitive with existing state-of-the-art models but are also more parameter efficient, thereby promoting better adoption in resource-constrained scenarios and practical applications. We show that going beyond English-centric bitexts, coupled with a no

Kiran S. Kedlaya

We prove that in either the convergent or overconvergent setting, an absolutely irreducible $F$-isocrystal on the absolute product of two or more smooth schemes over perfect fields of characteristic $p$, further equipped with actions of the partial Frobenius maps, is an external product of $F$-isocrystals over the multiplicands. The corresponding statement f

Qinglai Wei, Diancheng Chen, Beiming Yuan

Great endeavors have been made to study AI's ability in abstract reasoning, along with which different versions of RAVEN's progressive matrices (RPM) are proposed as benchmarks. Previous works give inkling that without sophisticated design or extra meta-data containing semantic information, neural networks may still be indecisive in making decisions regardin

Alex Edison, Micah Tegevi

The $\alpha'$-expansion of string theory provides a rich set of higher-dimension operators, indexed by $\zeta$ values, which can be used to study color-kinematics duality and the double copy. These two powerful properties, actually first noticed in tree-level string amplitudes, simplify the construction of both gauge and gravity amplitudes. However, their ap

S. A. Zvyagin, A. N. Ponomaryov, J. Wosnitza, D. Hirai

The observation of spinon excitations in the $S=\frac{1}{2}$ triangular antiferromagnet Ca$_3$ReO$_5$Cl$_2$ reveals a quasi-one-dimensional (1D) nature of magnetic correlations, in spite of the nominally 2D magnetic structure. This phenomenon is known as frustration-induced dimensional reduction. Here, we present high-field electron spin resonance spectrosco

Leon M. G. de la Vega, Eduardo Peinado, Jose Wudka

New light gauge bosons can affect several low-energy experiments, such as atomic parity violation or colliders. Here, we explore the possibility that a dark sector is charged under a new $U(1)$ gauge symmetry, and the portal to the Standard Model is through a $Z-Z'$ mass mixing. In our approach, breaking the new gauge symmetry is crucial to generate neutrino

Mohamed Ashraf Abdelsalam, Zhan Shi, Federico Fancellu, Kalliopi Basioti

The success of scene graphs for visual scene understanding has brought attention to the benefits of abstracting a visual input (e.g., image) into a structured representation, where entities (people and objects) are nodes connected by edges specifying their relations. Building these representations, however, requires expensive manual annotation in the form of

Giacomo De Nicola, Cornelius Fritz, Marius Mehrl, Göran Kauermann

Networks are ubiquitous in economic research on organizations, trade, and many other areas. However, while economic theory extensively considers networks, no general framework for their empirical modeling has yet emerged. We thus introduce two different statistical models for this purpose -- the Exponential Random Graph Model (ERGM) and the Additive and Mult

Dionysios Moutevelis, Javier Roldan-Perez, Njegos Jankovic, Milan Prodanovic

This paper proposes a recursive, virtual admittance based, secondary controller for DG units that improves the voltage profile in distribution networks. First, the adaptation of the virtual admittance concept for the goal of voltage regulation is explained. Then, a recursive secondary controller is developed to periodically update the virtual admittance gain

Zhen Guo, Zheng-Tao Xu, Meng Li, Li You

Non-Hermitian systems exhibiting topological properties are attracting growing interest. In this work, we propose an algorithm for solving the ground state of a non-Hermitian system in the matrix product state (MPS) formalism based on a companion Hermitian Hamiltonian. If the eigenvalues of the non-Hermitian system are known, the companion Hermitian Hamilton

Aswin Govindan Sheri

We consider the $L^p \rightarrow L^p$ boundedness of a Nikodym maximal function associated to a one-parameter family of tubes in $\mathbb{R}^{d+1}$ whose directions are determined by a non-degenerate curve $\gamma$ in $\mathbb{R}^d$. These operators arise in the analysis of maximal averages over space curves. The main theorem generalises the known results fo

Meral Süer, Mehmet Yeşil

In this paper, we investigate the row-factorization matrices of Arf numerical semigroups, and we provide the full list of such matrices of certain Arf numerical semigroups. We use the information of row-factorization matrices to detect the generic nature and to find generators of the defining ideals.

Jose Miguel Ramos, Luis Sa-Couto, Andreas Wichert

A vast majority of the current research in the field of Machine Learning is done using algorithms with strong arguments pointing to their biological implausibility such as Backpropagation, deviating the field's focus from understanding its original organic inspiration to a compulsive search for optimal performance. Yet, there have been a few proposed models

Simon Hediger, Jeffrey Näf, Michael Wolf

We combine Tyler's robust estimator of the dispersion matrix with nonlinear shrinkage. This approach delivers a simple and fast estimator of the dispersion matrix in elliptical models that is robust against both heavy tails and high dimensions. We prove convergence of the iterative part of our algorithm and demonstrate the favorable performance of the estima

Walter Dehnen, Hossam Aly

We present a family of analytical potential-density pairs for barred discs, which can be combined to describe galactic bars in a realistic way, including boxy/peanut components. We illustrate this with two reasonable compound models. Computer code for the evaluation of potential, forces, density, and projected density is freely provided.

Quynh Anh Nguyen, Arka Mitra

The paper describes the work that has been submitted to the 5th workshop on Challenges and Applications of Automated Extraction of socio-political events from text (CASE 2022). The work is associated with Subtask 1 of Shared Task 3 that aims to detect causality in protest news corpus. The authors used different large language models with customized cross-ent

Flavio Piccoli, Micol Rossini, Roberto Colombo, Raimondo Schettini

In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method overcomes the limitations of previous methods in the state of art: (i) it allows to predict multiple soil variables at once; (ii) it permits to backtrace the spectral bands that m

Catalina Freijo, Pedro Duarte

In this paper we establish uniform large deviations estimates of exponential type and H\"older continuity of the Lyapunov exponents for random non-invertible cocycles with constant rank.

Kentaro Seki, Shinnosuke Takamichi, Takaaki Saeki, Hiroshi Saruwatari

This paper proposes a method for selecting training data for text-to-speech (TTS) synthesis from dark data. TTS models are typically trained on high-quality speech corpora that cost much time and money for data collection, which makes it very challenging to increase speaker variation. In contrast, there is a large amount of data whose availability is unknown

G. Vicente, A. Adin, T. Goicoa, M. D. Ugarte

Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains limited due to difficulties in implementation and computational burden. These problems are exacerbated when the number of small areas is very large. In this paper, we introduce an order-free multivariate scalable Bayesian modelling

Eric A. Bergshoeff, Joaquim Gomis, Axel Kleinschmidt

We exhibit a new method of constructing non-Lorentzian models by applying a method we refer to as starting from a so-called seed Lagrangian. This method typically produces additional constraints in the system that can drastically alter the physical content of the model. We demonstrate our method for particles, scalars and vector fields.

Arturs Cinins, Martins Bruvelis, Nikolai N. Bezuglov

We present an analysis of the stimulated Raman adiabatic passage processes based on the methods of differential geometry. The present work was inspired by an excellent article by Bruce W. Shore et al. (R. G. Unanyan, B. W. Shore, and K. Bergmann Phys. Rev. A \textbf{59}, 2910 (1999)). We demonstrate how a purely geometric interpretation of the adiabatic pass

Gabriel Amaral, Odinaldo Rodrigues, Elena Simperl

Knowledge Graphs are repositories of information that gather data from a multitude of domains and sources in the form of semantic triples, serving as a source of structured data for various crucial applications in the modern web landscape, from Wikipedia infoboxes to search engines. Such graphs mainly serve as secondary sources of information and depend on w

Qixin Hu, Junfei Xiao, Yixiong Chen, Shuwen Sun

We develop a novel strategy to generate synthetic tumors. Unlike existing works, the tumors generated by our strategy have two intriguing advantages: (1) realistic in shape and texture, which even medical professionals can confuse with real tumors; (2) effective for AI model training, which can perform liver tumor segmentation similarly to a model trained on

S. B. Iplikcioglu, M. I. Aksun

A simple yet accurate model for the transmission of surface plasmon polaritons (SPPs) in a finite metal-insulator-metal (MIM) waveguide to the sides of the apertures is proposed and demonstrated to be more accurate than available models. It is as simple as using a magnetic current density across the plane of the aperture whose value is defined by SPPs with a

Weihua Hu, Kaidi Cao, Kexin Huang, Edward W Huang

Despite recent advances in Graph Neural Networks (GNNs), their training strategies remain largely under-explored. The conventional training strategy learns over all nodes in the original graph(s) equally, which can be sub-optimal as certain nodes are often more difficult to learn than others. Here we present TuneUp, a simple curriculum-based training strateg

Sven Lilge, Timothy D. Barfoot, Jessica Burgner-Kahrs

Continuum robots have the potential to enable new applications in medicine, inspection, and countless other areas due to their unique shape, compliance, and size. Excellent progess has been made in the mechanical design and dynamic modelling of continuum robots, to the point that there are some canonical designs, although new concepts continue to be explored

Thomas Hummel, Alex Widhalm, Jan Philipp HÖpker, Klaus D. JÖns

Superconducting nanowire single-photon detectors (SNSPDs) show near unity efficiency, low dark count rate, and short recovery time. Combining these characteristics with temporal control of SNSPDs broadens their applications as in active de-latching for higher dynamic range counting or temporal filtering for pump-probe spectroscopy or LiDAR. To that end, we d

Ievgen I. Arkhipov, Adam Miranowicz, Fabrizio Minganti, Şahin K. Özdemir

Nontrivial spectral properties of non-Hermitian systems can lead to intriguing effects with no counterparts in Hermitian systems. For instance, in a two-mode photonic system, by dynamically winding around an exceptional point (EP) a controlled asymmetric-symmetric mode switching can be realized. That is, the system can either end up in one of its eigenstates

Ron M. Adin, Yuval Roichman

A new descent set statistic on involutions, defined geometrically via their interpretation as matchings, is introduced in this paper, and shown to be equi-distributed with the standard one. This concept is then applied to construct explicit cyclic descent extensions on involutions, standard Young tableaux and Motzkin paths. Schur-positivity of the associated

Susanna Azzoni, David Alonso, Maximilian H. Abitbol, Josquin Errard

The observation of the polarised emission from the Cosmic Microwave Background (CMB) from future ground-based and satellite-borne experiments holds the promise of indirectly detecting the elusive signal from primordial tensor fluctuations in the form of large-scale $B$-mode polarisation. Doing so, however, requires an accurate and robust separation of the si

Thales Sales Almeida, Thiago Laitz, João Seródio, Luiz Henrique Bonifacio

The widespread availability of search API's (both free and commercial) brings the promise of increased coverage and quality of search results for metasearch engines, while decreasing the maintenance costs of the crawling and indexing infrastructures. However, merging strategies frequently comprise complex pipelines that require careful tuning, which is often

Pierpaolo Fontana, Joao C. Pinto Barros, Andrea Trombettoni

We propose a scheme for the quantum simulation of quantum link models in two-dimensional lattices. Our approach considers spinor dipolar gases on a suitably shaped lattice, where the dynamics of particles in the different hyperfine levels of the gas takes place in one-dimensional chains coupled by the dipolar interactions. We show that at least four levels a

D. D. Nematov, A. S. Burhonzoda, F. Shokir

Structural and optoelectronic properties of {\alpha}, \{beta}, {\gamma} phases of calcium titanate are studied with the implementation of first-principles quantum-chemical calculations in the framework of DFT. When optimizing the geometry, the GGA approximation was used. The relaxed lattice parameters obtained by us are identical with the experimental analog

I. T. Khan, M. Tudorovskaya, J. J. M. Kirsopp, D. Muñoz Ramo

Circuit depth reduction is of critical importance for quantum chemistry simulations on current and near term quantum computers. This issue is tackled by introducing a chemically aware strategy for the Unitary Coupled Cluster ansatz. The objective is to use the chemical description of a system to aid in the synthesis of a quantum circuit. We combine this appr

Yeongmin Kim, Huiwon Jang, DongKeon Lee, Ho-Jin Choi

Unsupervised anomaly detection is coming into the spotlight these days in various practical domains due to the limited amount of anomaly data. One of the major approaches for it is a normalizing flow which pursues the invertible transformation of a complex distribution as images into an easy distribution as N(0, I). In fact, algorithms based on normalizing f

Prashant Agrawal, Abhinav Nakarmi, Mahabir Prasad Jhanwar, Subodh Sharma

We study the problem of simultaneously addressing both ballot stuffing and participation privacy for pollsite voting systems. Ballot stuffing is the attack where fake ballots (not cast by any eligible voter) are inserted into the system. Participation privacy is about hiding which eligible voters have actually cast their vote. So far, the combination of ball