May 2023 arXiv papers — page 44

Manuel Ederer, Stefan Löffler

A new material characterization technique is emerging for the transmission electron microscope (TEM). Using electron energy-loss spectroscopy, real space mappings of the underlying electronic transitions in the sample, so called orbital maps, can be produced. Thus, unprecedented insight into the electronic orbitals responsible for most of the electrical, mag

Zihan Wang, Arthur Jacot

The $L_{2}$-regularized loss of Deep Linear Networks (DLNs) with more than one hidden layers has multiple local minima, corresponding to matrices with different ranks. In tasks such as matrix completion, the goal is to converge to the local minimum with the smallest rank that still fits the training data. While rank-underestimating minima can be avoided sinc

Ibrahim Ethem Hamamci, Sezgin Er, Anjany Sekuboyina, Enis Simsar

GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts, incorporates a text encoder and three key components: a novel causal vision transformer for encoding 3D CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model. Without directly c

Shusheng Li, Hehu Xie, Qilong Zhai

This paper introduces the application of the weak Galerkin (WG) finite element method to solve the Steklov eigenvalue problem, focusing on obtaining lower bounds of the eigenvalues. The noncomforming finite element space of the weak Galerkin finite element method is the key to obtain lower bounds of the eigenvalues. The arbitary high order lower bound estima

Shuhai Zhang, Feng Liu, Jiahao Yang, Yifan Yang

Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimating or comparing two data distributions is extremely difficult, especially in high-dimension spaces. Recently, the gradient of log probability density (a.k.a., score) w.r.t. the sam

Thomas Eboli, Jean-Michel Morel, Gabriele Facciolo

Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks. We show that when extracting patches of similar underlying blur is possible, jointly processing the stack of patches yields superior accuracy than handling them separately. Our collaborative scheme

Jonathan Frazer, Takafumi Ono, Jonathan C. F. Matthews

Low loss and high speed processing of photons is central to architectures for photonic quantum information. High speed switching enables non-deterministic photon sources and logic gates to be made deterministic, while the speed with which quantum light sources can be turned on and off impacts the clock rate of photonic computers and the data rate of quantum

Li-Wen Zhang, Ya-Qing Yang, Jun Chen, Lei Zhang

Photogalvanic effect (PGE) occurs in materials with non-centrosymmetric structures when irradiated by linearly or circularly polarized light. Here, using non-equilibrium Green's function combined with density functional theory (NEGF-DFT), we investigated the linear photogalvanic effect (LPGE) in monolayers of group-V elements (As, Sb, and Bi) by first-princi

Daniel Saggau, Mina Rezaei, Bernd Bischl, Ilias Chalkidis

Learning quality document embeddings is a fundamental problem in natural language processing (NLP), information retrieval (IR), recommendation systems, and search engines. Despite recent advances in the development of transformer-based models that produce sentence embeddings with self-contrastive learning, the encoding of long documents (Ks of words) is stil

Johannes Eschner, Peter Mindek, Manuela Waldner

3D animations are an effective method to learn about complex dynamic phenomena, such as mesoscale biological processes. The animators' goals are to convey a sense of the scene's overall complexity while, at the same time, visually guiding the user through a story of subsequent events embedded in the chaotic environment. Animators use a variety of visual emph

Daniel Hutchings, Sergei M. Kuzenko, Emmanouil S. N. Raptakis

We propose a new gauge prepotential $\Upsilon_i$ describing the four-dimensional ${\mathcal N}=2$ superconformal gravitino multiplet. The former naturally arises via a superspace reduction of the ${\mathcal N}=3$ conformal supergravity multiplet. A locally superconformal chiral action for $\Upsilon_i$, which is gauge-invariant in arbitrary conformally-flat b

Keiichiro Furuya, Nima Lashkari, Mudassir Moosa, Shoy Ouseph

A manifestation of the black hole information loss problem is that the two-point function of probe operators in a large Anti-de Sitter black hole decays in time, whereas, on the boundary CFT, it is expected to be an almost periodic function of time. We point out that the decay of the two-point function (clustering in time) holds important clues to the nature

Xiaoshang Jin

The inscribed radius of a compact manifold with boundary is bounded above if its Ricci curvature and mean curvature are bounded from below. The rigidity result implies that the upper bound can be achieved only in space form. In this paper, we generalize this result to asymptotically hyperbolic Einstein manifold. We get an upper bound of the relative volume o

Chongming Liu, Songting Li, Douglas Zhou

Incorporating biological neuronal properties into Artificial Neural Networks (ANNs) to enhance computational capabilities poses a formidable challenge in the field of machine learning. Inspired by recent findings indicating that dendrites adhere to quadratic integration rules for synaptic inputs, we propose a novel ANN model, Dendritic Integration-Based Quad

Damian Dąbrowski

We show that for any compact set $E\subset\mathbb{R}^d$ the visible part of $E$ has Hausdorff dimension at most $d-1/6$ for almost every direction. This improves recent estimates of Orponen and Matheus. If $E$ is $s$-Ahlfors regular, where $s>d-1$, we prove a much better estimate. In that case for almost every direction the Hausdorff dimension of the visible

Runsen Feng, Zongyu Guo, Weiping Li, Zhibo Chen

In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (R-D) performance. Recent state-of-the-art methods for neural image compression are mainly based on nonlinear transform coding (NTC) with uniform scalar quantization, overlooking the benefits of VQ due to its exponentially increased complexity. In t

Marco Rando, Cesare Molinari, Lorenzo Rosasco, Silvia Villa

Finite-difference methods are a class of algorithms designed to solve black-box optimization problems by approximating a gradient of the target function on a set of directions. In black-box optimization, the non-smooth setting is particularly relevant since, in practice, differentiability and smoothness assumptions cannot be verified. To cope with nonsmoothn

Eunseop Yoon, Hee Suk Yoon, John Harvill, Mark Hasegawa-Johnson

Automatic Speech Recognition (ASR) systems have attained unprecedented performance with large speech models pre-trained based on self-supervised speech representation learning. However, these pre-trained speech models suffer from representational bias as they tend to better represent those prominent accents (i.e., native (L1) English accent) in the pre-train

Yue Zhang, Bo Zhang, Haochen Jiang, Zhenghua Li

We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains. Previous CGEC research primarily focuses on correcting texts from a single domain, especially learner essays. To broaden the target domain, we annotate multiple references for 12,500 sentences from three nat

Ugo Bessi

Several authors have shown that Kusuoka's measure $\kappa$ on fractals is a scalar Gibbs measure; in particular, it maximises a pressure. There is also a different approach, in which one defines a matrix-valued Gibbs measure $\mu$ which induces both Kusuoka's measure $\kappa$ and Kusuoka's bilinear form. In the first part of the paper we show that one can de

Qian Chen, Dazhu Li

The logic of the hide and seek game LHS was proposed to reason about search missions and interactions between agents in pursuit-evasion environments. As proved in literature, having an equality constant in the language of LHS drastically increases its computational complexity: the satisfiability problem for LHS with multiple relations is undecidable. In this

Nigar Alata, Rıza Erdem, Gül Gülpınar

As a continuation of our preceding work [R. Erdem and N. Alata, Eur. Phys. J. Plus 135, 911 (2020), https://doi.org/10.1140/epjp/s13360-020-00934-3], we used the thermodynamic geometry in the Ruppeiner formalism to study the geometrical aspects of the multicritical phase diagrams for the spin-1 Blume-Emery- Griffiths model in the presence of crystal field. W

Zheng Sun, Yi Wei, Wenxiao Jia, Long Yu

Multivariate long-term time series forecasting is of great application across many domains, such as energy consumption and weather forecasting. With the development of transformer-based methods, the performance of multivariate long-term time series forecasting has been significantly improved, however, the study of spatial features extracting in transformer-b

Vera Uzunova, Boris A. Ivanov

We study a nonlinear spin dynamics of a ferromagnetic ring in a vortex state induced by the spin-polarized current. We also suggest to use the ferromagnetic ring as a free layer of a coreless vortex spin-transfer nano-oscillator. The calculated working frequency is about several GHz, that is much higher than the gyromode frequency of the disk-based vortex os

Sean Yiu, Li Su

Irregular longitudinal data with informative visit times arise when patients' visits are partly driven by concurrent disease outcomes. However, existing methods such as inverse intensity weighting (IIW), often overlook or have not adequately assess the influence of informative visit times on estimation and inference. Based on novel balancing weights estimato

Naga Prathibha Jasti, Igal Levine, Yishay Feldman, Gary Hodes

The term defect tolerance (DT) is used often to rationalize the exceptional optoelectronic properties of Halide Perovskites (HaPs) and their devices. Even though DT lacked direct experimental evidence, it became a "fact" in the field. DT in semiconductors implies that structural defects do not translate to electrical and optical effects (e.g., due to charge

Emil Génetay Johansen, Tapio Simula

General relativity predicts that the curvature of spacetime induces spin rotations on a parallel transported particle. We deploy Unruh's analogue gravity picture and consider a quantised vortex embedded in a two-dimensional superfluid Bose--Einstein condensate. We show that such a vortex behaves dynamically like a charged particle with a spin in a gravitatio

Daniel Ratliff, Oliver Allanson

We review the modulation stability of parallel propagating/field aligned Whistler Mode Chorus waves propagating in a warm plasma from a formal perspective with a focus on wave-particle interactions. The modulation instability criteria is characterised by a curvature of the dispersion relation for Whistler mode waves and a condition on the ratio between the g

Vivien Cabannes, Stefano Vigogna

A core principle in statistical learning is that smoothness of target functions allows to break the curse of dimensionality. However, learning a smooth function seems to require enough samples close to one another to get meaningful estimate of high-order derivatives, which would be hard in machine learning problems where the ratio between number of data and

Fabian Spaeh, Alina Ene, Huy L. Nguyen

Constrained $k$-submodular maximization is a general framework that captures many discrete optimization problems such as ad allocation, influence maximization, personalized recommendation, and many others. In many of these applications, datasets are large or decisions need to be made in an online manner, which motivates the development of efficient streaming

Bo Wei, Zhenyun Qin, Gui Mu

General rational solutions for the nonlocal resonant nonlinear Schrodinger equations are derived by using the Hirota bilinear method and the KP hierarchy reduction method. These rational solutions are presented in terms of determinants in which the elements are algebraic expressions. A weaker condition is given for KP reduction in the nonlocal case. The dyna

Jan Smit

This is a companion article to `Using massless fields for observing black hole features in the collapsed phase of Euclidean dynamical triangulations' [1]. It clarifies a singular co\"{o}rdinate transformation of an $SO(4)$ invariant metric to the usual spherical co\"{o}rdinates in which, at an instant of time called zero, the metric takes the form of a black

Olga S. Rozanova

The Kolmogorov-Feller equation for the probability density of a Markov process on a half-axis, which arises in important problems of biology, is considered. This process consists of random jumps distributed according to Laplace's law and a deterministic return to zero. It is shown that Green's function for such an equation can be found both in the form of a

Christine C. Dantas

We consider, from a dynamical systems point of view, a trivalent spin network model in Loop Quantum Gravity presenting self-organized criticality (SOC), arising from a spin propagation dynamics. We obtain a partition function for the domains of stability connecting gauge non-invariant avalanches, leading to an entropy formula for the asymptotic SOC state. Th

Phuoc Nguyen Thuan, Tomi Westerlund, Jorge Peña Queralta

The remarkable growth of unmanned aerial vehicles (UAVs) has also sparked concerns about safety measures during their missions. To advance towards safer autonomous aerial robots, this work presents a vision-based solution to ensuring safe autonomous UAV landings with minimal infrastructure. During docking maneuvers, UAVs pose a hazard to people in the vicini

Siddhartha Karmakar, Susobhan Chattopadhyay, Amol Dighe

In the Standard Model Effective Field Theory (SMEFT), the $SU(2)_L\times U(1)_Y$ symmetry of the Standard Model is linearly realized. However, it is possible that more general effective field theories, such as the Higgs Effective Field Theory (HEFT) where this symmetry is realized non-linearly, are needed to describe the data. Identifying physics beyond SMEF

Jintao Shuai, Luis Lopez-Diaz, John E. Cunningham, Thomas A. Moore

Magnetic skyrmions in thin films with perpendicular magnetic anisotropy are promising candidates for magnetic memory and logic devices, making the development of ways to transport skyrmions efficiently and precisely of significant interest. Here, we investigate the transport of skyrmions by surface acoustic waves (SAWs) via several modalities using micromagn

Pengyu Le

We provide a proof of effective uniformization for nearly round 2-spheres, utilizing an identity related to the third-order differential of the conformal factor. This identity is connected to the geometry of the embedded spacelike surface within the Minkowski lightcone. Additionally, we investigate the stability of the effective uniformization introduced by

Máté Gerencsér, Gerald Lampl, Chengcheng Ling

We study the $L^p$ rate of convergence of the Milstein scheme for SDEs when the drift coefficients possess only H\"older regularity. If the diffusion is elliptic and sufficiently regular, we obtain rates consistent with the additive case. The proof relies on regularisation by noise techniques, particularly stochastic sewing, which in turn requires (at least

Shlomo Dado, Arnon Dar

Recent observations provide compelling evidence that the bulk of the high energy cosmic rays (CRs) and gamma-ray bursts (GRBs) are co-produced by highly relativistic jets of plasmoids of stellar matter. These jets are launched by fall back matter on newly born neutron stars and stellar black holes in core collapse of stripped envelope massive stars with or w

John Bourke, Stephen Lack

Recently Riehl and Verity have introduced $\infty$-cosmoi, which are certain simplicially enriched categories with additional structure. In this paper we investigate those $\infty$-cosmoi which are in fact $2$-categories; we shall refer to these as $2$-cosmoi. We show that each $2$-category with flexible limits gives rise to a $2$-cosmos whose distinguished

Lutz Oettershagen, Nils M. Kriege, Petra Mutzel

The H-index of a node in a static network is the maximum value $h$ such that at least $h$ of its neighbors have a degree of at least $h$. Recently, a generalized version, the $n$-th order H-index, was introduced, allowing to relate degree centrality, H-index, and the $k$-core of a node. We extend the $n$-th order H-index to temporal networks and define corre

Hao Li, Viktor Schlegel, Riza Batista-Navarro, Goran Nenadic

Argument summarisation is a promising but currently under-explored field. Recent work has aimed to provide textual summaries in the form of concise and salient short texts, i.e., key points (KPs), in a task known as Key Point Analysis (KPA). One of the main challenges in KPA is finding high-quality key point candidates from dozens of arguments even in a smal

Zain Ul Abideen, Sumathi Gokulanathan, Muayad J. Aljafar, Samuel Pagliarini

Building and maintaining a silicon foundry is a costly endeavor that requires substantial financial investment. From this scenario, the semiconductor business has largely shifted to a fabless model where the Integrated Circuit supply chain is globalized but potentially untrusted. In recent years, several hardware obfuscation techniques have emerged to thwart

Jean-Baptiste Fouvry, María José Bustamante-Rosell, Aaron Zimmerman

Stars evolving around a supermassive black hole see their orbital orientations diffuse efficiently, a process called "vector resonant relaxation". In particular, stars within the same disc, i.e. neighbors in orientations, will slowly diffuse away from one another through this stochastic process. We use jointly (i) detailed kinetic predictions for the efficie

Adrien Courtois, Damien Scieur, Jean-Michel Morel, Pablo Arias

We propose SING (StabIlized and Normalized Gradient), a plug-and-play technique that improves the stability and generalization of the Adam(W) optimizer. SING is straightforward to implement and has minimal computational overhead, requiring only a layer-wise standardization of the gradients fed to Adam(W) without introducing additional hyper-parameters. We su

Kris Aerts, Wouter Groeneveld

Feedback beyond the grade is an important part of the learning process. However, because of the large student groups, many teachers in higher education are faced with practicalities such as the limited time to prepare and communicate the feedback to individual students. We have set up an experiment, titled Pro-f-quiz, in which over two years 236 students par

Neelam Shah Jahan

The aim of this research is to introduce the notion of multi-transitivity in non-autonomous discrete dynamical systems(NDDS) with respect to a vector. Necessary and sufficient conditions are obtained under which a NDDS is multitransitive and strongly multi-transitive. Examples and counter-examples are constructed to justify that results in Remark 3.7 [1] wha

Mariko Kimura, Kazumi Kashiyama, Toshikazu Shigeyama, Yusuke Tampo

We present timing and spectral analysis results of the {\it NICER} and {\it NuSTAR} observations of the dwarf nova MASTER OT J030227.28$+$191754.5 during the 2021--2022 outburst. The soft X-ray component was found to be dominated by blackbody radiation with a temperature of $\sim$30 eV and also showed prominent oxygen and neon emission lines. The blackbody l

Kubilay Can Demir, Tobias Weise, Matthias May, Axel Schmid

Surgical phase recognition is a challenging and necessary task for the development of context-aware intelligent systems that can support medical personnel for better patient care and effective operating room management. In this paper, we present a surgical phase recognition framework that employs a Multi-Stage Temporal Convolution Network using speech and X-

Yongwei Guo, Wenliang Li

As basic quantum mechanical models, anharmonic oscillators are recently revisited by bootstrap methods. An effective approach is to make use of the positivity constraints in Hermitian theories. There exists an alternative avenue based on the null state condition, which applies to both Hermitian and non-Hermitian theories. In this work, we carry out an analyt

Felix Kuchelmeister, Sara van de Geer

The logistic regression estimator is known to inflate the magnitude of its coefficients if the sample size $n$ is small, the dimension $p$ is (moderately) large or the signal-to-noise ratio $1/\sigma$ is large (probabilities of observing a label are close to 0 or 1). With this in mind, we study the logistic regression estimator with $p\ll n/\log n$, assuming

Francesco Brandolin, Matteo Ravasi, Tariq Alkhalifah

Interpolation of aliased seismic data constitutes a key step in a seismic processing workflow to obtain high quality velocity models and seismic images. Building on the idea of describing seismic wavefields as a superposition of local plane waves, we propose to interpolate seismic data by utilizing a physics informed neural network (PINN). In the proposed fr

Pawel Sarkowicz

We show that continuous group homomorphisms between unitary groups of unital C*-algebras induce maps between spaces of continuous real-valued affine functions on the trace simplices. Under certain $K$-theoretic regularity conditions, these maps can be seen to commute with the pairing between $K_0$ and traces. If the homomorphism is contractive and sends the

Tim Tsz-Kit Lau, Han Liu, Thomas Pock

We study the problem of approximate sampling from non-log-concave distributions, e.g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodality. We focus on performing this task via Markov chain Monte Carlo (MCMC) methods derived from discretizations of the overdamped Langevin diffusions, which are commonly known as Lan

Ron Levie

We present an approach for analyzing message passing graph neural networks (MPNNs) based on an extension of graphon analysis to a so called graphon-signal analysis. A MPNN is a function that takes a graph and a signal on the graph (a graph-signal) and returns some value. Since the input space of MPNNs is non-Euclidean, i.e., graphs can be of any size and top

Supraja Sridhara, Andrin Bertschi, Benedict Schlüter, Mark Kuhne

Trusted execution environments in several existing and upcoming CPUs demonstrate the success of confidential computing, with the caveat that tenants cannot securely use accelerators such as GPUs and FPGAs. In this paper, we reconsider the Arm Confidential Computing Architecture (CCA) design, an upcoming TEE feature in Armv9-A, to address this gap. We observe

Jiafei Fu, Pengcheng Zhu, Bo Ai, Jiangzhou Wang

The explosive growth of data results in more scarce spectrum resources. It is important to optimize the system performance under limited resources. In this paper, we investigate how to achieve weighted throughput (WTP) maximization for cell-free (CF) multiuser MIMO (MU-MIMO) multicarrier (MC) systems through resource allocation (RA), in the cases of finite b

Vinod Kumar Chauhan, Jiandong Zhou, Ghadeer Ghosheh, Soheila Molaei

Estimation of individualized treatment effects (ITE) from observational studies is a fundamental problem in causal inference and holds significant importance across domains, including healthcare. However, limited observational datasets pose challenges in reliable ITE estimation as data have to be split among treatment groups to train an ITE learner. While in

Olha Bodnar, Taras Bodnar

In this paper, we present the Bayesian inference procedures for the parameters of the multivariate random effects model derived under the assumption of an elliptically contoured distribution when the Berger and Bernardo reference and the Jeffreys priors are assigned to the model parameters. We develop a new numerical algorithm for drawing samples from the po

T. J. Meijer, V. S. Dolk, W. P. M. H. Heemels

By computing Lyapunov functions of a certain, convenient structure, Lyapunov-based methods guarantee stability properties of the system or, when performing synthesis, of the relevant closed-loop or error dynamics. In doing so, they provide conclusive affirmative answers to many analysis and design questions in systems and control. When these methods fail to

Marc T. Lehner, Paul Katzberger, Niels Maeder, Carl C. G. Schiebroek

We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was trained to predict atomic partial charges from accurate quantum-mechanical (QM) calculations. The resulting dynamic

Aditya Shende, Shivendra Kumar Gupta, Ashish Kore, Poorva Singh

Using DFT-based first-principles calculations, we demonstrate the tuning of the electronic structure of Weyl semimetal SrSi$_{2}$ via external uniaxial strain. The uniaxial strain facilitates the opening of bandgap along $\Gamma$-X direction and subsequent band inversion between Si $p$ and Sr $d$ orbitals. Z$_{2}$ invariants and surface states reveal conclus

Thomas A. Henzinger, Mahyar Karimi, Konstantin Kueffner, Kaushik Mallik

Machine-learned systems are in widespread use for making decisions about humans, and it is important that they are fair, i.e., not biased against individuals based on sensitive attributes. We present runtime verification of algorithmic fairness for systems whose models are unknown, but are assumed to have a Markov chain structure. We introduce a specificatio

Ning Wang, Fei Gao, Song Lin

Based on $d$-dimensional quantum full homomorphic encryption, an efficient and secure quantum network coding protocol is proposed in this paper. First, a quantum full homomorphic encryption protocol is constructed utilizing $d$-dimensional universal quantum gates. On this basis, an efficient quantum network coding protocol is proposed. In the protocol, two s

L. V. Bekenov, S. V. Moklyak, B. F. Zhuravlev, Yu. N. Kucherenko

We study the electronic and magnetic properties of T$_2$AlC (T=Ti and Cr) compounds in the density-functional theory using the generalized gradient approximation (GGA) with consideration of strong Coulomb correlations (GGA+$U$) in the framework of the fully relativistic spin-polarized Dirac linear muffin-tin orbital (LMTO) band-structure method. The X-ray ab

Tai-Ping Sun, Zhao-Yun Chen, Cheng Xue, Huan-Yu Liu

The increasing focus on long-term time series prediction across various fields has been significantly strengthened by advancements in quantum computation. In this paper, we introduce a data-driven method designed for time series prediction with quantum dynamical embedding (QDE). This approach enables a trainable embedding of the data space into an extended s

Xijun Wang, Dongyang Liu, Meina Kan, Chunrui Han

In Knowledge Distillation, the teacher is generally much larger than the student, making the solution of the teacher likely to be difficult for the student to learn. To ease the mimicking difficulty, we introduce a triplet knowledge distillation mechanism named TriKD. Besides teacher and student, TriKD employs a third role called anchor model. Before distill

Takashi Imoto, Yuki Susa, Ryoji Miyazaki, Tadashi Kadowaki

Quantum annealing is a way to prepare an eigenstate of the problem Hamiltonian. Starting from an eigenstate of a trivial Hamiltonian, we slowly change the Hamiltonian to the problem Hamiltonian, and the system remains in the eigenstate of the Hamiltonian as long as the so-called adiabatic condition is satisfied. By using devices provided by D-Wave Systems In

Hamid Bounit, Abderrahim Driouich, Said Hadd

This paper focuses on the study of integro-differential equations with delays, presenting a novel perturbation approach. The primary objective is to introduce the concepts of classical and mild solutions for these equations and establish their existence and uniqueness, under suitable assumptions. Furthermore, we provide a variation of constants formula that

Yangsen Ye, Tan He, He-Liang Huang, Zuolin Wei

Fault-tolerant quantum computing based on surface code has emerged as an attractive candidate for practical large-scale quantum computers to achieve robust noise resistance. To achieve universality, magic states preparation is a commonly approach for introducing non-Clifford gates. Here, we present a hardware-efficient and scalable protocol for arbitrary log

Takafumi Moriya, Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix

Neural transducer (RNNT)-based target-speaker speech recognition (TS-RNNT) directly transcribes a target speaker's voice from a multi-talker mixture. It is a promising approach for streaming applications because it does not incur the extra computation costs of a target speech extraction frontend, which is a critical barrier to quick response. TS-RNNT is trai

C. Escribano, R. Gonzalo

The main aim of this work is to apply the study of the asymptotic behaviour of generalized eigenvalues between infinite Hermitian definite positive matrices in an important question regarding the location of zeros of Sobolev orthogonal polynomials. In order to do it we introduce matrix Sobolev inner products associated with a set of infinite Hermitian positi

Nhan Nguyen

In this paper, we introduce the notion of local quasi-isometry for metric germs and prove that two definable germs are quasi-isometric if and only if their tangent cones are bi-Lipschitz homeomorphic. Since bi-Lipschitz equivalence is a particular case of local quasi-isometric equivalence, we obtain Sampaio's tangent cone theorem as a corollary. As an applic

H. Espinosa-Jiménez, A. B. Salazar-Arriaga, H. Dominguez

The applicability of the three steps systematic parametrization procedure (3SSPP) to develop a force field for primary amines was evaluated in the present work. Previous simulations of primary amines show that current force fields (FF) can underestimate some experimental values under room conditions. Therefore, we propose a new set of parameters, for an unit

Tiziana Trombetti, Carlo Burigana, Prospero De Martino, Sergio Guardato

Seafloor deformation monitoring is now performed in the marine sector of the Campi Flegrei volcanic area. MEDUSA infrastructure consists of 4 buoys at depths of 40-96m equipped with cGPS receivers, accelerometers and magnetic compasses to monitor buoy status and a seafloor module with a bottom pressure recorder. We study the seafloor deformation in the calde

Isabelle Tulleners, Tobias Moers, Thomas Schulik, Martin Sedlacek

In order to deploy automated vehicles to the public, it has to be proven that the vehicle can safely and robustly handle traffic in many different scenarios. One important component of automated vehicles is the perception system that captures and processes the environment around the vehicle. Perception systems require large datasets for training their deep n

M. V. Tkach, Ju. O. Seti, O. M. Voitsekhivska, V. V. Hutiv

A quantum theory of spectral parameters and oscillator strengths of quantum transitions in an active region, which contains cascades of wide quantum wells with a complicated potential profile is developed. A new spatial design of the cascade is calculated and proposed with such an asymmetric arrangement of the wells and barriers, in which, without an applied

Huan Qiu, Keng Li, Guoping Wang

The least eigenvalue of a graph $G$ is the least eigenvalue of adjacency matrix of $G$. In this paper we determine the graphs which attain the minimum least eigenvalue among all complements of connected simple graphs with given connectivity.

Zhi-Cong Ong, Meng-Chwan Tan

We perform a topological-holomorphic twist of $\mathcal{N}=4$ supersymmetric gauge theory on a four-manifold of the form $M_4=\Sigma_1 \times \Sigma_2$ with Riemann surfaces $\Sigma_{1,2}$, and unravel the mathematical implications of its physics. In particular, we consider different linear combinations of the resulting scalar supercharges under $S$-duality,

Paul Stahlhofen, André Artelt, Luca Hermes, Barbara Hammer

Many Machine Learning models are vulnerable to adversarial attacks: There exist methodologies that add a small (imperceptible) perturbation to an input such that the model comes up with a wrong prediction. Better understanding of such attacks is crucial in particular for models used in security-critical domains, such as monitoring of water distribution netwo

Zihao Zhao, Sheng Wang, Jinchen Gu, Yitao Zhu

The integration of Computer-Aided Diagnosis (CAD) with Large Language Models (LLMs) presents a promising frontier in clinical applications, notably in automating diagnostic processes akin to those performed by radiologists and providing consultations similar to a virtual family doctor. Despite the promising potential of this integration, current works face a

R. Ya. Leshko, I. V. Bilynskyi, O. V. Leshko, V. B. Hols'kyi

The model of a spherical quantum dot with several donor impurities on its surface is suggested. The electron energy spectra are studied as a function of the quantum dot radius and the number of impurities. Several cases of the location of impurities on the quantum dot surface are considered. The plane wave functions method has been applied to calculate the e

The ATLAS Collaboration

A search for leptoquarks decaying into the $b\tau$ final state is performed using Run 2 proton-proton collision data from the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb$^{-1}$ at $\sqrt{s} = 13$ TeV recorded by the ATLAS detector. The benchmark models considered in this search are vector leptoquarks with electric charge of 2/3

Jonas Teufel, Luca Torresi, Pascal Friederich

Despite the increasing relevance of explainable AI, assessing the quality of explanations remains a challenging issue. Due to the high costs associated with human-subject experiments, various proxy metrics are often used to approximately quantify explanation quality. Generally, one possible interpretation of the quality of an explanation is its inherent valu

Yingqian Cui, Jie Ren, Han Xu, Pengfei He

Recently, Generative Diffusion Models (GDMs) have showcased their remarkable capabilities in learning and generating images. A large community of GDMs has naturally emerged, further promoting the diversified applications of GDMs in various fields. However, this unrestricted proliferation has raised serious concerns about copyright protection. For example, ar

I. N. Karnaukhov

Applying a unified approach, we study the integer quantum Hall effect (IQHE) and fractional quantum Hall effect (FQHE) in the Hofstadter model with short range interactions between fermions. An effective field, that takes into account the interaction between fermions, is determined by both amplitude and phase. Its amplitude is proportional to the interaction

Sanjay Vishwakarma, Shalini D, Srinjoy Ganguly, Sai Nandan Morapakula

Quantum technology is an emerging cutting-edge field which offers a new paradigm for computation and research in the field of physics, mathematics and other scientific disciplines. This technology is of strategic importance to governments globally and heavy investments and budgets are being sanctioned to gain competitive advantage in terms of military, space

Takafumi Moriya, Takanori Ashihara, Hiroshi Sato, Kohei Matsuura

The recurrent neural network-transducer (RNNT) is a promising approach for automatic speech recognition (ASR) with the introduction of a prediction network that autoregressively considers linguistic aspects. To train the autoregressive part, the ground-truth tokens are used as substitutions for the previous output token, which leads to insufficient robustnes

Sitian Shen, Zilin Zhu, Linqian Fan, Harry Zhang

Large pre-trained models have had a significant impact on computer vision by enabling multi-modal learning, where the CLIP model has achieved impressive results in image classification, object detection, and semantic segmentation. However, the model's performance on 3D point cloud processing tasks is limited due to the domain gap between depth maps from 3D p

Arian Mousakhan, Thomas Brox, Jawad Tayyub

Traditional reconstruction-based methods have struggled to achieve competitive performance in anomaly detection. In this paper, we introduce Denoising Diffusion Anomaly Detection (DDAD), a novel denoising process for image reconstruction conditioned on a target image. This ensures a coherent restoration that closely resembles the target image. Our anomaly de

Ramón L. Panadés-Barrueta, Dorothea Golze

In recent years, the $GW$ method has emerged as a reliable tool for computing core-level binding energies. The contour deformation (CD) technique has been established as an efficient, scalable, and numerically stable approach to compute the $GW$ self-energy for deep core excitations. However, core-level $GW$ calculations with CD face the challenge of higher

M. D. Kassa, N. G. Debelo, M. M. Woldemariam

Chalcogenide perovskites offer superior thermal and aqueous stability as well as a benign elemental composition compared to organic halide perovskites for optoelectronic applications. In this study, the structural, electrical, elastic, phonon dispersion, and thermodynamic features of the orthorhombic phase of chalcogenide perovskite CaZrS$_3$ (space group Pn

Manoel Jarra

We introduce strong congruence spaces, which are topological spaces that provide a useful concept of dimension for monoid schemes. We study their properties and show that, given a toric monoid scheme over an algebraically closed basis, its strong congruence space and the complex toric variety associated to its fan have the same dimension.

AbdulRahman M. Alharbi, Yuri Ashrafyan, Diogo Gomes

Entry-exit dynamics are crucial in modeling crowd movement. Here, we present a novel first-order, stationary mean-field game model on a bounded domain that accurately captures these dynamics. The interior dynamics of the system are governed by a standard first-order stationary MFG system consisting of a Hamilton-Jacobi equation coupled with a transport equat

Emily C. Hector, Brian J. Reich, Ani Eloyan

Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gaussian process model parameters with ultra-high-dimensional likelihoods. We propose a shift in viewpoint from whole to local data perspectives that is rooted in distributed model buil

Paul Häusner, Ozan Öktem, Jens Sjölund

The convergence of the conjugate gradient method for solving large-scale and sparse linear equation systems depends on the spectral properties of the system matrix, which can be improved by preconditioning. In this paper, we develop a computationally efficient data-driven approach to accelerate the generation of effective preconditioners. We, therefore, repl

Leif Eriksson, Victor Lagerkvist

Allen's interval algebra is one of the most well-known calculi in qualitative temporal reasoning with numerous applications in artificial intelligence. Recently, there has been a surge of improvements in the fine-grained complexity of NP-hard reasoning tasks, improving the running time from the naive $2^{O(n^2)}$ to $O^*((1.0615n)^{n})$, with even faster alg

Cedric Aaron Beschle, Andrea Barth

Continuous level Monte Carlo is an unbiased, continuous version of the celebrated multilevel Monte Carlo method. The approximation level is assumed to be continuous resulting in a stochastic process describing the quantity of interest. Continuous level Monte Carlo methods allow naturally for samplewise adaptive mesh refinements, which are indicated by goal-o

Meng-Chwan Tan

This is a concise version of the original article in [arXiv:2203.17115] that will be published in the String Math 2022 Proceedings by the American Mathematical Society.