May 2023 arXiv papers — page 6

Antonino Del Popolo, Saeed Fakhry

In this work, we extend the model proposed by White concerning the post-collapse evolution of density peaks while considering the role of angular momentum. On a timescale smaller than the peak collapse, $t_{0}$, the inner regions of the peak reach the equilibrium forming a cuspy profile, as in White's paper, but the power-law density profile is flatter, name

Gunter Malle, Alexander Moretó, Noelia Rizo

Conjecture A of \cite{EM14} predicts the equality between the smallest positive height of the irreducible characters in a $p$-block of a finite group and the smallest positive height of the irreducible characters in its defect group. Hence, it can be seen as a generalization of Brauer's famous height zero conjecture. One inequality was shown to be a conseque

Yong-Li Wen, Yunfei Wang, Li-Man Tian, Shanchao Zhang

The principle of least action is arguably the most fundamental principle in physics as it can be used to derive the equations of motion in various branches of physics. However, this principle has not been experimentally demonstrated at the quantum level because the propagators for Feymann's path integrals have never been observed. The propagator is a fundame

Boris Latosh, Miok Park

We study hairy black hole solutions in Einstein(--Maxwell)--scalar--Gauss--Bonnet theory. The scalar coupling function includes quadratic and quartic terms, so the gravitational action has a U(1) symmetry. We argued that when the effective mass of the scalar field is at the critical value, the non-hairy black holes transform into hairy black holes in a symme

Wanxin Li, Collin Meese, Hao Guo, Mark Nejad

Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. This paper introduces an aggregated zero-knowledge proof and blockchain-empowered system for privacy-preserving identity verification in the mixed fleet platooning environment. The correctness

Aoran Xiao, Xiaoqin Zhang, Ling Shao, Shijian Lu

In the past decade, deep neural networks have achieved significant progress in point cloud learning. However, collecting large-scale precisely-annotated training data is extremely laborious and expensive, which hinders the scalability of existing point cloud datasets and poses a bottleneck for efficient exploration of point cloud data in various tasks and ap

Aitor Alaña, Iñigo L. Egusquiza, Michele Modugno

We characterize the role of the roton instability in the formation of a supersolid state of an elongated dipolar condensate, following a quench of the contact interactions across the superfluid-supersolid transition, as observed in recent experiments. We perform dynamical simulations by means of the extended Gross-Pitaevskii equation including quantum correc

Junsong Sun, Chang-An Li, Shiping Feng, Huaiming Guo

We investigate the non-Hermitian Haldane model on hyperbolic $\{8, 3\}$ and $\{12, 3\}$ lattices, and showcase its intriguing topological properties in the simultaneous presence of non-Hermitian effect and hyperbolic geometry. From bulk descriptions of the system, we calculate the real space non-Hermitian Chern numbers by generalizing the method from its Her

Hyungjin Chung, Jeongsol Kim, Jong Chul Ye

Diffusion model-based inverse problem solvers have shown impressive performance, but are limited in speed, mostly as they require reverse diffusion sampling starting from noise. Several recent works have tried to alleviate this problem by building a diffusion process, directly bridging the clean and the corrupted for specific inverse problems. In this paper,

Y. Sifontes, D. Tejada

In \cite{GCF} it is proved that any quadratic irrational number has a representation as a continuous, infinite and periodic fraction. In 1848, Charles Hermite through a letter Jacobi \cite{Per} wondered if this fact could be generalized to cubic irrational numbers. In This research addresses the Hermite problem, through the study of continuous functions by p

Ana Nikolikj, Gjorgjina Cenikj, Gordana Ispirova, Diederick Vermetten

A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive model. The predictive model uses the feature representation of a set of problem instances as input data and predicts the algorithm performance achieved on them. Common machine lea

Xu-Dan Xie, Zheng-Yuan Xue, Dan-Bo Zhang

Solving non-Hermitian quantum many-body systems on a quantum computer by minimizing the variational energy is challenging as the energy can be complex. Here, based on energy variance, we propose a variational method for solving the non-Hermitian Hamiltonian, as zero variance can naturally determine the eigenvalues and the associated left and right eigenstate

Jindong Wang, Shuonan Wu

We propose and analyze a hybridizable discontinuous Galerkin (HDG) method for solving a mixed magnetic advection-diffusion problem within a more general Friedrichs system framework. With carefully constructed numerical traces, we introduce two distinct stabilization parameters: $\tau_t$ for the tangential trace and $\tau_n$ for the normal trace. These parame

Chiara Devescovi, Antonio Morales-Pérez, Yoonseok Hwang, Mikel García-Díez

Axion insulators are 3D magnetic higher-order topological insulators protected by inversion-symmetry that exhibit hinge-localized chiral channels and induce quantized topological magnetoelectric effects. Recent research has suggested that axion insulators may be capable of detecting dark-matter axion-like particles by coupling to their axionic excitations. B

Katarina Firdova, Céline Labart, Arthur Martel

This paper presents a new filter method for unsupervised feature selection. This method is particularly effective on imbalanced multi-class dataset, as in case of clusters of different anomaly types. Existing methods usually involve the variance of the features, which is not suitable when the different types of observations are not represented equally. Our m

Aimee Norton, Rachel Howe, Lisa Upton, Ilya Usoskin

We describe the defining observations of the solar cycle that provide constraints for the dynamo processes operating within the Sun. Specifically, we report on the following topics: historical sunspot numbers and revisions; active region (AR) flux ranges and lifetimes; bipolar magnetic region tilt angles; Hale and Joy's law; the impact of rogue ARs on cycle

Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus

Factor analysis (FA) is a statistical method for explaining how mutually dependent observed variables can be represented in terms of mutually independent latent factors, and it is widely used in the psychological, biological, and physical sciences. We revisit this classic method from the perspective of recent advances in causal structure learning and deep ge

Dmitri Orlov

In this paper, twisted tensor product of DG algebras is studied and sufficient conditions for smoothness of such a product are given. It is shown that in the case of finite-dimensional DG algebras, applying this operation offers great possibilities for constructing new examples of smooth DG algebras and algebras. In particular, examples are given of families

Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens

Recently, a new line of works has emerged to understand and improve self-attention in Transformers by treating it as a kernel machine. However, existing works apply the methods for symmetric kernels to the asymmetric self-attention, resulting in a nontrivial gap between the analytical understanding and numerical implementation. In this paper, we provide a ne

Hao Guo, Wanxin Li, Mark Nejad, Chien-Chung Shen

This paper presents a hybrid blockchain-edge architecture for managing Electronic Health Records (EHRs) with attribute-based cryptographic mechanisms. The architecture introduces a novel attribute-based signature aggregation (ABSA) scheme and multi-authority attribute-based encryption (MA-ABE) integrated with Paillier homomorphic encryption (HE) to protect p

Shishir Biswas, Rajaraman Ganesh

Understanding the origin and structure of mean magnetic fields in astrophysical conditions is a major challenge. Shear flows often coexist in such astrophysical conditions and the role of flow shear on dynamo mechanism is only beginning to be investigated. Here, we present a direct numerical simulation (DNS) study of the effect of flow shear on dynamo instab

Harsh Bhatt, Lavanya Negi

Ni thin films grown by thermal evaporation and sputtering under different deposition conditions are characterized for structural and morphological properties using X-ray diffraction (XRD) and atomic force microscopy (AFM) techniques. XRD results suggested the growth of polycrystalline fcc Ni phase for all the samples. Morphological characteristics of the fil

Daizhan Cheng

A new matrix product, called dimension-keeping semi-tensor product (DK-STP), is proposed. Under DK-STP, the set of $m\times n$ matrices becomes a semi-group $G({m\times n},\mathbb{F})$, and a ring, denoted by $R(m\times n,\mathbb{F})$. Moreover, the Lie bracket can also be defined, which turns the ring into a Lie algebra, called non-square (or STP) general l

Niranjan Sitapure, Joseph S. Kwon

For prediction and real-time control tasks, machine-learning (ML)-based digital twins are frequently employed. However, while these models are typically accurate, they are custom-designed for individual systems, making system-to-system (S2S) transferability difficult. This occurs even when substantial similarities exist in the process dynamics across differe

L. Grimanellis, G. Papadopoulos, E. Perez-Bolanos

We show that the sigma models with target spaces supersymmetric heterotic backgrounds with $SU(2)$ and $SU(3)$ holonomy are invariant under a W-symmetry algebra generated by the covariantly constant forms of these backgrounds. The closure of the W-algebra requires additional generators which we identify. We prove that the chiral anomalies of all these symmet

Avinash Kumar, Saurabh Kumar Singh, Pranav R. Shirhatti

We describe the design and characterization of a versatile pulsed (5 ns, 10 Hz repetition rate) optical parametric oscillator and amplifier system capable of generating single longitudinal mode, narrow linewidth (0.01 cm$^{-1}$) radiation in the wavelength range of 680 - 870 nm and 1380 - 4650 nm. Using a combination of power-normalized photoacoustic signal

Amin Esfahani, Hichem Hajaiej, Yongming Luo, Linjie Song

In this paper, we consider the focusing fractional nonlinear Schr\"{o}dinger equation (FNLS) on the waveguide manifolds $\mathbb{R}^d\times\mathbb{T}^m$ both in the isotropic and anisotropic case. Under different conditions, we establish the existence and periodic dependence of the ground states of the focusing FNLS. In the intercritical regime, we also esta

Vandana Rani, Jasleen Kaur

This paper studies the geometric properties of contact CR-submanifolds of Sasakian statistical manifold. The integrability of invariant and anti-invariant distributions of contact CR-submanifolds has been characterized. Results on D-totally geodesic, mixed totally geodesic and D-umbilic contact CR submanifolds with regard to dual connections in statistical m

M. V. Belov, S. A. Koutovoi, V. A. Kozlov, N. V. Pestovskii

Photoluminescence (PL) of Lu$_{2}$SiO$_{5}$ crystal and ceramics with a high concentration of oxygen vacancies (about ~0.5 at.%) is studied. Oxygen vacancies were created using two ways. The first method is a growth of crystal from the non-stoichiometric Lu$_{2}$Si$_{0.98}$O$_{4.96}$ melt and the second one is a doping of Lu$_{2}$SiO$_{5}$ matrix with divale

Erik Jansson, Klas Modin

We investigate a matrix dynamical system related to optimal mass transport in the linear category, namely, the problem of finding an optimal invertible matrix by which two covariance matrices are congruent. We first review the differential geometric structure of the problem in terms of a principal fiber bundle. The dynamical system is a gradient flow restric

Xianwei Lv, Claudio Persello, Wangbin Li, Xiao Huang

Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land objects with diverse shapes and sizes. Additionally, the determination of segmentation scale parameters frequently adheres t

Stephan Dempe, Markus Friedemann, Felix Harder, Patrick Mehlitz

In this chapter, we are concerned with inverse optimal control problems, i.e., optimization models which are used to identify parameters in optimal control problems from given measurements. Here, we focus on linear-quadratic optimal control problems with control constraints where the reference control plays the role of the parameter and has to be reconstruct

Yasmine Ezzeddine, Petra Saskia Bayerl, Helen Gibson

In the light of modern technological advances, Artificial Intelligence (AI) is relied upon to enhance performance, increase efficiency, and maximize gains. For Law Enforcement Agencies (LEAs), it can prove valuable in optimizing evidence analysis and establishing proactive prevention measures. Nevertheless, citizens raise legitimate concerns around privacy i

Friedemann Kemm

With this short note, we close a gap in the linear stability theory of block predictor-corrector Runge-Kutta schemes originally proposed for the parallel solution of ODEs.

Marcin Przewięźlikowski, Mateusz Pyla, Bartosz Zieliński, Bartłomiej Twardowski

Self-supervised learning (SSL) is a powerful technique for learning from unlabeled data. By learning to remain invariant to applied data augmentations, methods such as SimCLR and MoCo can reach quality on par with supervised approaches. However, this invariance may be detrimental for solving downstream tasks that depend on traits affected by augmentations us

Liam Mazurowski, Xuan Yao

Consider a complete asymptotically flat 3-manifold $M$ with non-negative scalar curvature and non-empty minimal boundary $\Sigma$. Fix a number $1 < p < 3$. We derive monotone quantities for $p$-harmonic functions on $M$ which become constant on Schwarzschild. These monotonicity formulas imply a sharp mass-capacity estimate relating the ADM mass of $M$ with

Mahdi Taheri, Saeideh Sheikhpour, Ali Mahani, Maksim Jenihhin

We introduce a novel logic style with self-checking capability to enhance hardware reliability at logic level. The proposed logic cells have two-rail inputs/outputs, and the functionality for each rail of outputs enables construction of faulttolerant configurable circuits. The AND and OR gates consist of 8 transistors based on CNFET technology, while the pro

Maarten De Raedt, Fréderic Godin, Thomas Demeester, Chris Develder

Intent discovery is the task of inferring latent intents from a set of unlabeled utterances, and is a useful step towards the efficient creation of new conversational agents. We show that recent competitive methods in intent discovery can be outperformed by clustering utterances based on abstractive summaries, i.e., "labels", that retain the core elements wh

Faustin Adiceam, Oscar Marmon

Given a set of inequalities determined by homogeneous forms, the following intertwined results are established: (1) the volume of the real semi-algebraic domain determined by these inequalities is explicitly determined; it is shown to be related to the largest root of the so-called Sato-Bernstein polynomial associated to a multivarite polynomial derived from

Debdip Ganguly, Diksha Gupta, K. Sreenadh

We study the existence and non-existence of positive solutions for the following class of nonlinear elliptic problems in the hyperbolic space $$ -\Delta_{\mathbb{B}^N} u-\lambda u=a(x)u^{p-1} \, + \, \varepsilon u^{2^*-1} \,\;\;\text{in}\;\mathbb{B}^{N}, \quad u \in H^{1}{(\mathbb{B}^{N})}, $$ where $\mathbb{B}^N$ denotes the hyperbolic space, $2<p<2^*:=\fra

Michaël Fonder, Marc Van Droogenbroeck

When used by autonomous vehicles for trajectory planning or obstacle avoidance, depth estimation methods need to be reliable. Therefore, estimating the quality of the depth outputs is critical. In this paper, we show how M4Depth, a state-of-the-art depth estimation method designed for unmanned aerial vehicle (UAV) applications, can be enhanced to perform joi

Elizaveta Semenova, Swapnil Mishra, Samir Bhatt, Seth Flaxman

Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the state-of-the-art approach for disease mapping since they are able to both capture structure in the data and robustly characterise uncertainty. When working with areal data, e.g.~aggr

Kirti Gupta, Subham Sahoo, Bijaya Ketan Panigrahi, Charalambos Konstantinou

The development of power electronics-based medium voltage direct current (MVDC) networks has revolutionized the marine industry by enabling all-electric ships (AES). This technology facilitates the integration of heterogeneous resources and improves efficiency. The independent shipboard power system (SPS) is controlled by exchanging measurements and control

Yael Eisenberg, Itamar Rot, Muli Safra

Given an arbitrary basis for a mathematical lattice, to find a ``good" basis for it is one of the classic and important algorithmic problems. In this note, we give a new and simpler proof of a theorem by Regavim (arXiv:2106.03183): we construct a 18-dimensional lattice that does not have a basis that satisfies the following two properties simultaneously: 1.

Benedikt Lorch

J-UNIWARD is a popular steganography method for hiding secret messages in JPEG cover images. As a content-adaptive method, J-UNIWARD aims to embed into textured image regions where changes are difficult to detect. To this end, J-UNIWARD first assigns to each DCT coefficient an embedding cost calculated based on the image's Wavelet residual, and then uses a c

Leo Francoso Dal Piccol Sotto, Sebastian Mayer, Hemanth Janarthanam, Alexander Butz

We consider optimizing for different production requirements from the viewpoint of a bio-inspired framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks, which also relates to dynamic evolutionary optimization. Optimizing manufacturing process parameters is typically a multi

Davide Evangelista, Elena Morotti, Elena Loli Piccolomini, James Nagy

In recent years, large convolutional neural networks have been widely used as tools for image deblurring, because of their ability in restoring images very precisely. It is well known that image deblurring is mathematically modeled as an ill-posed inverse problem and its solution is difficult to approximate when noise affects the data. Really, one limitation

Matteo Nerini, Bruno Clerckx

Reconfigurable intelligent surface (RIS) is an emerging technology allowing to control the propagation environment in wireless communications. Recently, beyond diagonal RIS (BD-RIS) has been proposed to reach higher performance than conventional RIS, at the expense of higher circuit complexity. Multiple BD-RIS architectures have been developed with the goal

Allan Freitas, Alberto Roncoroni, Márcio Santos

In this paper, we deal with Serrin-type problems in Riemannian manifolds. First, we obtain a Heintze-Karcher inequality and a Soap Bubble result, with its respective rigidity, when the ambient space has a Ricci tensor bounded below. After, we approach a Serrin problem in bounded domains of manifolds endowed with a closed conformal vector field. Our primary t

S. Semak, V. Kapustianyk, Yu. Eliyashevskyy, O. Bovgyra

Despite symmetrical polarization, the magnitude of a light-induced voltage is known to be asymmetric with respect to poling sign in many photovoltaic (PV) ferroelectrics (FEs). This asymmetry remains unclear and is often attributed to extrinsic effects. We show here for the first time that such an asymmetry can be intrinsic, steaming from the superposition o

José Camacho, Katarzyna Wasielewska, Pablo Espinosa, Marta Fuentes-García

Autonomous or self-driving networks are expected to provide a solution to the myriad of extremely demanding new applications with minimal human supervision. For this purpose, the community relies on the development of new Machine Learning (ML) models and techniques. %, like the celebrated Deep Learning (DL). However, ML can only be as good as the data it is

Erik Fubel, Niclas Michael Groll, Patrick Gundlach, Qiwei Han

Search Engine Result Pages (SERPs) serve as the digital gateways to the vast expanse of the internet. Past decades have witnessed a surge in research primarily centered on the influence of website ranking on these pages, to determine the click-through rate (CTR). However, during this period, the landscape of SERPs has undergone a dramatic evolution: SERP fea

Parthasaarathy Sudarsanam, Tuomas Virtanen

Audio question answering (AQA) is the task of producing natural language answers when a system is provided with audio and natural language questions. In this paper, we propose neural network architectures based on self-attention and cross-attention for the AQA task. The self-attention layers extract powerful audio and textual representations. The cross-atten

G. Heinrich, S. P. Jones, M. Kerner, V. Magerya

We present a major update of the program pySecDec, a toolbox for the evaluation of dimensionally regulated parameter integrals. The new version enables the evaluation of multi-loop integrals as well as amplitudes in a highly distributed and flexible way, optionally on GPUs. The program has been optimised and runs up to an order of magnitude faster than the p

Zhisheng Wang, Yue Liu, Shunli Wang, Xingyuan Bian

This paper is to investigate the high-quality analytical reconstructions of multiple source-translation computed tomography (mSTCT) under an extended field of view (FOV). Under the larger FOVs, the previously proposed backprojection filtration (BPF) algorithms for mSTCT, including D-BPF and S-BPF (their differences are different derivate directions along the

Faedi Loulidi, Ion Nechita, Clément Pellegrini

In this paper we introduce a novel noise model for quantum measurements motivated by an indirect measurement scheme with faulty preparation. Averaging over random dynamics governing the interaction between the quantum system and a probe, a natural, physical noise model emerges. We compare it to existing noise models (uniform and depolarizing) in the framewor

Elisa Nguyen, Minjoon Seo, Seong Joon Oh

Training data attribution (TDA) techniques find influential training data for the model's prediction on the test data of interest. They approximate the impact of down- or up-weighting a particular training sample. While conceptually useful, they are hardly applicable to deep models in practice, particularly because of their sensitivity to different model ini

Ru Zhou, Wenya Guo, Xumeng Liu, Shenglong Yu

Multimodal aspect-based sentiment analysis (MABSA) aims to extract aspects from text-image pairs and recognize their sentiments. Existing methods make great efforts to align the whole image to corresponding aspects. However, different regions of the image may relate to different aspects in the same sentence, and coarsely establishing image-aspect alignment w

Federico Pichi, Gianluigi Rozza

In this paper, we discuss reduced order modelling approaches to bifurcating systems arising from continuum mechanics benchmarks. The investigation of the beam's deflection is a relevant topic of investigation with fundamental implications on their design for structural analysis and health. When the beams are exposed to external forces, their equilibrium stat

Garima Varshney, Anikesh Pal

We perform three-dimensional numerical simulations to understand the role of viscous fingering in sweeping a high-viscous fluid (HVF). These fingers form due to the injection of a low-viscous fluid (LVF) into a porous media containing the high-viscous fluid. We find that the sweeping of HVF depends on different parameters such as the Reynolds number ($Re$) b

Min Zhao, Rong Yuan

In this paper, we study the existence and stability of random transition waves for time heterogeneous Fisher-KPP Equations with nonlocal diffusion. More specifically, we consider general time heterogeneities both for the nonlocal diffusion kernel and the reaction term. We use the comparison principle of the scalar equation and the method of upper and lower s

Jun Inukai, Tadahiro Taniguchi, Akira Taniguchi, Yoshinobu Hagiwara

In the studies on symbol emergence and emergent communication in a population of agents, a computational model was employed in which agents participate in various language games. Among these, the Metropolis-Hastings naming game (MHNG) possesses a notable mathematical property: symbol emergence through MHNG is proven to be a decentralized Bayesian inference o

Marc Ohm, Timo Pohl, Felix Boes

Maliciously prepared software packages are an extensively leveraged weapon for software supply chain attacks. The detection of malicious packages is undoubtedly of high priority and many academic and commercial approaches have been developed. In the inevitable case of an attack, one needs resilience against malicious code. To this end, we present a runtime p

Shuyue Stella Li, Cihan Xiao, Tianjian Li, Bismarck Odoom

Code-switching, also called code-mixing, is the linguistics phenomenon where in casual settings, multilingual speakers mix words from different languages in one utterance. Due to its spontaneous nature, code-switching is extremely low-resource, which makes it a challenging problem for language and speech processing tasks. In such contexts, Code-Switching Lan

Tengfei Wu, Marc Guillon, Gilles Tessier, Pascal Berto

In astronomy or biological imaging, refractive index inhomogeneities of e.g. atmosphere or tissues induce optical aberrations which degrade the desired information hidden behind the medium. A standard approach consists in measuring these aberrations with a wavefront sensor (e.g Shack-Hartmann) located in the pupil plane, and compensating them either digitall

Malina Chichirau, Rik van Noord, Antonio Toral

We tackle the task of automatically discriminating between human and machine translations. As opposed to most previous work, we perform experiments in a multilingual setting, considering multiple languages and multilingual pretrained language models. We show that a classifier trained on parallel data with a single source language (in our case German-English)

Yuting Liang, Ke Yi

This paper proposes concentrated geo-privacy (CGP), a privacy notion that can be considered as the counterpart of concentrated differential privacy (CDP) for geometric data. Compared with the previous notion of geo-privacy [ABCP13, CABP13], which is the counterpart of standard differential privacy, CGP offers many benefits including simplicity of the mechani

Chee Sheng Fong

Neutrino oscillation phenomenon is a definite evidence of physics beyond the Standard Model (SM) and high precision measurement of neutrino properties will certainly give us clue about what lies beyond the SM. In particular, precise measurements of the mixing matrix elements $U_{\alpha i}$ which relate the neutrino flavor $\alpha$ and mass $i$ eigenstates ar

Kang Liu, Jipeng Qiang

Neural sentence simplification method based on sequence-to-sequence framework has become the mainstream method for sentence simplification (SS) task. Unfortunately, these methods are currently limited by the scarcity of parallel SS corpus. In this paper, we focus on how to reduce the dependence on parallel corpus by leveraging a careful initialization for ne

Wojciech Masarczyk, Mateusz Ostaszewski, Ehsan Imani, Razvan Pascanu

Deep neural networks are widely known for their remarkable effectiveness across various tasks, with the consensus that deeper networks implicitly learn more complex data representations. This paper shows that sufficiently deep networks trained for supervised image classification split into two distinct parts that contribute to the resulting data representati

Guglielmo Frigo, Paolo Attilio Pegoraro, Sergio Toscani

Fast dynamics and transient events are becoming more and more frequent in power systems, due to the high penetration of renewable energy sources and the consequent lack of inertia. In this scenario, Phasor Measurement Units (PMUs) are expected to track the monitored quantities. Such functionality is related not only to the PMU accuracy (as per the IEC/IEEE 6

A. K. Maity, L. K. Dewangan, N. K. Bhadari, D. K. Ojha

We present a multi-scale and multi-wavelength study to unveil massive star formation (MSF) processes around sites AFGL 5180, and AFGL 6366S, both hosting a Class II 6.7 GHz methanol maser emission. The radio continuum map at 8.46 GHz reveals a small cluster of radio sources toward AFGL 5180. Signatures of the early stages of MSF in our target sites are spati

Tobias Bollinger, Jan Deriu, Manfred Vogel

In this work, we studied the synthesis of Swiss German speech using different Text-to-Speech (TTS) models. We evaluated the TTS models on three corpora, and we found, that VITS models performed best, hence, using them for further testing. We also introduce a new method to evaluate TTS models by letting the discriminator of a trained vocoder GAN model predict

Cristian F. Jiménez-Varón, Ying Sun, Han Lin Shang

We study the modeling and forecasting of high-dimensional functional time series (HDFTS), which can be cross-sectionally correlated and temporally dependent. We introduce a decomposition of the HDFTS into two distinct components: a deterministic component and a residual component that varies over time. The decomposition is derived through the estimation of t

Haishuo Fang, Haritz Puerto, Iryna Gurevych

The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course. Additionally, the breadth of QA derived from this exponential growth makes it an ideal scenario for teaching related NLP topics such as information retrieval, explainability, and adversarial attacks among others. In this paper

Cesar Gonzalez-Gutierrez, Audi Primadhanty, Francesco Cazzaro, Ariadna Quattoni

Textual representations based on pre-trained language models are key, especially in few-shot learning scenarios. What makes a representation good for text classification? Is it due to the geometric properties of the space or because it is well aligned with the task? We hypothesize the second claim. To test it, we develop a task alignment score based on hiera

Kyowoon Lee, Seongun Kim, Jaesik Choi

For robotic vehicles to navigate robustly and safely in unseen environments, it is crucial to decide the most suitable navigation policy. However, most existing deep reinforcement learning based navigation policies are trained with a hand-engineered curriculum and reward function which are difficult to be deployed in a wide range of real-world scenarios. In

Filipe Fernandes, Zbigniew Jelonek

In this note we provide two special examples of non-injective polynomial maps from $\mathbb{R}^2$ to $\mathbb{R}^2$ with non-vanishing Jacobian: the first one is surjective, the second one has non-dense image.

Archi Gupta, Priya Ghosh, Kornikar Sen, Ujjwal Sen

The Bernstein-Vazirani (BV) algorithm offers exceptional accuracy in finding the hidden bit string of a function. We explore how the algorithm performs in real-world situations where noise can potentially interfere with its performance. In order to assess the impact of imperfect equipments, we introduce various forms of glassy disorders into the effect of th

Patrick Seifner, Ramses J. Sanchez

Markov jump processes are continuous-time stochastic processes with a wide range of applications in both natural and social sciences. Despite their widespread use, inference in these models is highly non-trivial and typically proceeds via either Monte Carlo or expectation-maximization methods. In this work we introduce an alternative, variational inference a

Antonin Sulc, Imari Sato, Bastian Goldluecke, Tali Treibitz

Refraction is a common physical phenomenon and has long been researched in computer vision. Objects imaged through a refractive object appear distorted in the image as a function of the shape of the interface between the media. This hinders many computer vision applications, but can be utilized for obtaining the geometry of the refractive interface. Previous

Jonas Schweisthal, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel

Decision-making in personalized medicine such as cancer therapy or critical care must often make choices for dosage combinations, i.e., multiple continuous treatments. Existing work for this task has modeled the effect of multiple treatments independently, while estimating the joint effect has received little attention but comes with non-trivial challenges.

Yi Zhou, Ao Huang, Satoshi Hattori

In the analysis of prognosis studies with time-to-event outcomes, dichotomization of patients is often made. As the evaluations of prognostic capacity, the survivals of groups with high/low expression of the biomarker are often estimated by the Kaplan-Meier method, and the difference between groups is summarized via the hazard ratio (HR). The high/low expres

Jin Wang, Ali Khosravi, Andrea Vanossi, Erio Tosatti

A plethora of two-dimensional (2D) materials entered the physics and engineering scene in the last two decades. Their robust, membrane-like sheet permit -- mostly require -- deposition, giving rise to solid-solid dry interfaces whose bodily mobility, pinning, and general tribological properties under shear stress are currently being understood and controlled

Isabel Haasler, Pascal Frossard

Finding the mean of sampled data is a fundamental task in machine learning and statistics. However, in cases where the data samples are graph objects, defining a mean is an inherently difficult task. We propose a novel framework for defining a graph mean via embeddings in the space of smooth graph signal distributions, where graph similarity can be measured

Tishtrya Mehta, Anne-Marie Broomhall, Laura Hayes

Quasi-periodic pulsations (QPPs) are frequently observed in solar and stellar flare emission, with recent studies suggesting that an increasing instantaneous period is a common characteristic of QPPs. Determining the prevalence of non-stationarity in QPPs contributes to a better understanding of which mechanisms are responsible in QPP generation. We obtain t

Alexis Carlotti, Alexis Bidot, David Mouillet, Jean-Jacques Correia

A near-IR high-resolution, R=80000 spectrometer has been developed at IPAG to directly characterize the atmosphere of exoplanets using adaptive optics (AO) assisted telescopes, and a single-mode fiber-injection unit. A first technical test with the 200' Hale telescope at Palomar Observatory occurred in March 2022 using the PALM3000 AO system offered by this

Georg Schäfer, Hannes Waclawek, Sarah Riedmann, Christoph Binder

The four Industry 4.0 design principles information transparency, technical assistance, interconnection, and decentralized decisions pose challenges in integrating information technology (IT) and operational technology (OT) solutions in industrial systems. These different solutions have conflicting requirements, making interfaces between them problematic for

Paul Darm, Antonio Valerio Miceli-Barone, Shay B. Cohen, Annalisa Riccardi

Space agencies execute complex satellite operations that need to be supported by the technical knowledge contained in their extensive information systems. Knowledge bases (KB) are an effective way of storing and accessing such information at scale. In this work we present a system, developed for the European Space Agency (ESA), that can answer complex natura

Mahdi Taheri, Mohammad Hasan Ahmadilivani, Maksim Jenihhin, Masoud Daneshtalab

Nowadays, the extensive exploitation of Deep Neural Networks (DNNs) in safety-critical applications raises new reliability concerns. In practice, methods for fault injection by emulation in hardware are efficient and widely used to study the resilience of DNN architectures for mitigating reliability issues already at the early design stages. However, the sta

Kien Huu Nguyen

In 2006, Budur, Musta\c{t}\v{a} and Saito introduced the notion of Bernstein-Sato polynomial of an arbitrary scheme of finite type over fields of characteristic zero. Because of the strong monodromy conjecture, it should have a corresponding picture on the arithmetic side of ideals in polynomial rings. In this paper, we try to address this problem. Motivated

Saikat Panja, Prachi Saini, Anupam Singh

For $n\geq 2$, we consider the map on $M_n(\mathbb K)$ given by evaluation of a polynomial $f(X_1, \ldots, X_m)$ over the field $\mathbb K$. In this article, we explore the image of the diagonal map given by $f=\delta_1 X_1^{k_1} + \delta_2 X_2^{k_2} + \cdots +\delta_m X_m^{k_m}$ in terms of the solution of certain equations over $\mathbb K$. In particular,

Ilya Kaufman, Omri Azencot

Deep neural networks have been demonstrated to achieve phenomenal success in many domains, and yet their inner mechanisms are not well understood. In this paper, we investigate the curvature of image manifolds, i.e., the manifold deviation from being flat in its principal directions. We find that state-of-the-art trained convolutional neural networks for ima

Ville P. Saarinen, Ted Hsuan Yun Chen, Mikko Kivelä

We propose a hybrid heuristic algorithm for solving the Heaviest k-Subgraph Problem in online social networks -- a combinatorial graph optimization problem central to many important applications in weighted social networks, including detection of coordinated behavior, maximizing diversity of a group of users, and detecting social groups. Our approach builds

Dongchen Jiao, Pascale Voegtli

In 2007 Kawamata proved that two different minimal models can be connected by a sequence of flops. The aim of this paper is to show that the same holds true for 2 foliated minimal models descending from a common 3-fold pair equipped with a F-dlt foliation of corank 1.

Meyer Scetbon, Michal Klein, Giovanni Palla, Marco Cuturi

The relevance of optimal transport methods to machine learning has long been hindered by two salient limitations. First, the $O(n^3)$ computational cost of standard sample-based solvers (when used on batches of $n$ samples) is prohibitive. Second, the mass conservation constraint makes OT solvers too rigid in practice: because they must match \textit{all} po

Zizhuo Zhang, Lian Wen, Shaoyang Zhang, David Chen

In the burgeoning field of artificial intelligence (AI), understanding the capabilities and limitations of programming-oriented models is crucial. This paper presents a novel evaluation of the programming proficiency of Generative Pretrained Transformer (GPT) models, specifically GPT-3.5 and GPT-4, against coding problems of varying difficulty levels drawn f

Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar

This paper addresses unsupervised representation learning on tabular data containing multiple views generated by distinct sources of measurement. Traditional methods, which tackle this problem using the multi-view framework, are constrained by predefined assumptions that assume feature sets share the same information and representations should learn globally

Yufei Guo, Xuhui Huang, Zhe Ma

The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the

Konstantinos Gavriilidis, Andrea Munafo, Wei Pang, Helen Hastie

Adoption and deployment of robotic and autonomous systems in industry are currently hindered by the lack of transparency, required for safety and accountability. Methods for providing explanations are needed that are agnostic to the underlying autonomous system and easily updated. Furthermore, different stakeholders with varying levels of expertise, will req