October 2023 arXiv papers — page 180

Zijie Geng, Xijun Li, Jie Wang, Xiao Li

In the past few years, there has been an explosive surge in the use of machine learning (ML) techniques to address combinatorial optimization (CO) problems, especially mixed-integer linear programs (MILPs). Despite the achievements, the limited availability of real-world instances often leads to sub-optimal decisions and biased solver assessments, which moti

Zeyuan Song, Zheyu Jiang

The spatiotemporal water flow dynamics in unsaturated soils can generally be modeled by the Richards equation. To overcome the computational challenges associated with solving this highly nonlinear partial differential equation (PDE), we present a novel solution algorithm, which we name as the MP-FVM (Message Passing-Finite Volume Method), to holistically in

Lishu Zhang, Zhengping Yuan, Jie Yang, Jun Zhou

The technique of conventional ferromagnet/heavy-metal spin-orbit torque (SOT) offers significant potential for enhancing the efficiency of magnetic memories. However, it faces fundamental physical limitations, including hunting effects from the metallic layer, broken symmetry for enabling antidamping switching, spin scattering caused by interfacial defects,

Peifang Wang, Olga Golovneva, Armen Aghajanyan, Xiang Ren

Visual language reasoning requires a system to extract text or numbers from information-dense images like charts or plots and perform logical or arithmetic reasoning to arrive at an answer. To tackle this task, existing work relies on either (1) an end-to-end vision-language model trained on a large amount of data, or (2) a two-stage pipeline where a caption

Eric C. Andrade, Matthias Vojta

Motivated by the observation of partial magnetic order in kagome-based magnets, we study the classical kagome Ising antiferromagnet, known as kagome spin ice, including further-neighbor interactions at zero and finite temperature. While the nearest-neighbor model displays an extensive ground-state degeneracy, various symmetry-breaking states can appear upon

Ziqian Ning, Yuepeng Jiang, Zhichao Wang, Bin Zhang

This paper introduces the T23 team's system submitted to the Singing Voice Conversion Challenge 2023. Following the recognition-synthesis framework, our singing conversion model is based on VITS, incorporating four key modules: a prior encoder, a posterior encoder, a decoder, and a parallel bank of transposed convolutions (PBTC) module. We particularly lever

Li Cai, Yangyu Fan, Yong Jiang

In both local and global settings, we establish explicit relations between Ichino triple product period and Waldspurger toric periods for CM forms via the theta lifting and the see-saw principle.

Yichao Yuan, Haojie Ye, Sanketh Vedula, Wynn Kaza

Temporal motif mining is the task of finding the occurrences of subgraph patterns within a large input temporal graph that obey the specified structural and temporal constraints. Despite its utility in several critical application domains that demand high performance (e.g., detecting fraud in financial transaction graphs), the performance of existing softwar

Majd Soud, Gísli Hjálmtýsson, Mohammad Hamdaqa

Blockchain is a distributed ledger technology that gained popularity for enabling the transformation of cryptocurrency among peers without mediation by a centralized third-party authority. Smart contracts expand the applications of blockchain technology and have played a role in its widespread adoption. Smart contracts are immutable digital programs that are

Manfred Cuntz

When stars depart from the main-sequence, various changes occur including the loss of angular momentum owing to changes in the stellar interior and the impact of stellar winds. These processes affect the amount of outer atmospheric heating and emission as revealed by observations in the UV and X-ray spectral regimes. From a theoretical perspective, both magn

Fangting Zhou, Ala Arvidsson, Jiaming Wu, Balazs Kulcsar

In this paper, we develop a profit-sharing-based optimal routing mechanism to incentivize horizontal collaboration among urban goods distributors. This paper investigates a collaborative routing problem for urban logistics, in which the exchange of goods at meet points is optimally planned en route. We show that collaboration does not only reduce the total c

Philip Cuthbertson, Robert Schneider

We define integer multimodal sequences, which are generalizations of unimodal sequences having multiple local peaks of equal size. The generating functions for multimodal sequences represent novel types of $q$-series that combine generating functions for both integer partitions and integer compositions. We prove a bijection between multimodal sequences of eq

V. Roccatagliata, A. Sicilia-Aguilar, M. Kim, J. Campbell-White

Nearby associations are ideal regions to study coeval samples of protoplanetary and debris disks down to late M-type stars. Those aged 5-10,Myrs, where most of the disk should have already dissipated forming planets, are of particular interest. We present the first complete study of both protoplanetary and debris disks in a young region, using the $\eta$ Cha

Charlotte Vermeylen, Marc Van Barel

We implement an Augmented Lagrangian method to minimize a constrained least-squares cost function designed to find polyadic decompositions of the matrix multiplication tensor. We use this method to obtain new discrete decompositions and parameter families of decompositions. Using these parametrizations, faster and more stable matrix multiplication algorithms

Dan Xie, Zekai Yu

We classify four dimensional $\mathcal{N}=2$ SCFTs whose Seiberg-Witten (SW) geometries can be written as hyperelliptic families. By using special K\"ahler condition of SW geometry, we reduce the problem to one parameter quasi-homogeneous hyperelliptic families $y^2=f(x,t)$. The classification is given by further demanding that the complex algebraic surface

Chengkang Shen, Hao Zhu, You Zhou, Yu Liu

Myocardial motion tracking stands as an essential clinical tool in the prevention and detection of cardiovascular diseases (CVDs), the foremost cause of death globally. However, current techniques suffer from incomplete and inaccurate motion estimation of the myocardium in both spatial and temporal dimensions, hindering the early identification of myocardial

Kim Tien Ly, Valeriy Semenov, Mattia Risiglione, Wolfgang Merkt

This paper presents an optimization-based solution to task and motion planning (TAMP) on mobile manipulators. Logic-geometric programming (LGP) has shown promising capabilities for optimally dealing with hybrid TAMP problems that involve abstract and geometric constraints. However, LGP does not scale well to high-dimensional systems (e.g. mobile manipulators

Mubashir Munaf, Hammad Afzal, Naima Iltaf, Khawir Mahmood

With the advent of Deep Learning based Artificial Neural Networks models, Natural Language Processing (NLP) has witnessed significant improvements in textual data processing in terms of its efficiency and accuracy. However, the research is mostly restricted to high-resource languages such as English and low-resource languages still suffer from a lack of avai

Tsuyoshi Yamamoto, Yasuhiro Tokura

We investigate the heat flow of a qubit coupled to heat baths under continuous quantum measurement. In the steady-state limit, we show that heat always flows from the measurement apparatus into the qubit regardless of the measured qubit state and derive lower and upper bounds for the heat current between the qubit and the measurement apparatus. Furthermore,

Felipe Gambardella

In this article, we prove a Reocurrence Theorem over function fields of curves over $\mathbf{C}(\! (t)\! )$ and over finite extensions of the Laurent series field $\mathbf{C}(\! (x,y)\! )$. This provides a partial replacement to Chebotarev's Theorem over such fields. A concrete application to the study of weak approximation for homogeneous spaces under $\mat

Jacopo Ulivelli

In this short note, we prove the existence of solutions to a Monge-Amp\`ere equation of entire type derived by a weighted version of the classical Minkowski problem.

N. Galikyan, Sh. Khlghatyan, A. A. Kocharyan, V. G. Gurzadyan

We studied the dynamics of S-stars in the Galactic center using the physics-informed neural networks. The neural networks are considered for both, Keplerian and the General Relativity dynamics, the orbital parameters for stars S1, S2, S9, S13, S31, and S54 are obtained and the regression problem is solved. It is shown that the neural network is able to detec

X. Wang, P. Garg, M. S. Mirmoosa, A. G. Lamprianidis

The realization of photonic time crystals is a major opportunity but also comes with significant challenges. The most pressing one, potentially, is the requirement for a substantial modulation strength in the material properties to create a noticeable momentum bandgap. Reaching that noticeable bandgap in optics is highly demanding with current, and possibly

Anja Janßen, Max Ziegenbalg

We use the framework of multivariate regular variation to analyse the extremal behaviour of preferential attachment models. To this end, we follow a directed linear preferential attachment model for a random, heavy-tailed number of steps in time and treat the incoming edge count of all existing nodes as a random vector of random length. By combining martinga

Samuel Hsia, Alicia Golden, Bilge Acun, Newsha Ardalani

Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs. Our analysis, grounded in real-world large model training on datacenter-scale infrastructures, reveals that 14~32% of all GPU hours are spent on communication with no overlapping computat

Deniz Yılmaz

Let $\mathbb{F}$ be an algebraically closed field of characteristic zero. Recently, we proved that isotypic blocks are functorially equivalent over $\mathbb{F}$. In this article we provide an example of functorially equivalent blocks which are not perfectly isometric.

Rotem Arnon, Zvika Brakerski, Thomas Vidick

We initiate a rigorous study of computational entanglement theory, inspired by the emerging usefulness of ideas from quantum information theory in computational complexity. We define new operational computational measures of entanglement -- the computational one-shot entanglement cost and distillable entanglement. We then show that the computational measures

Matthew Thomas Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio

The past decade has seen vast progress in deep reinforcement learning (RL) on the back of algorithms manually designed by human researchers. Recently, it has been shown that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks. Despite impressive initial results from algorithms s

Jaewon La, Jaime Phadke, Matt Hutton, Marius Schwinning

It is critical for probes landing on foreign planetary bodies to be able to robustly identify and avoid hazards - as, for example, steep cliffs or deep craters can pose significant risks to a probe's landing and operational success. Recent applications of deep learning to this problem show promising results. These models are, however, often learned with expl

Asuka Igarashi, Masao Mori, Shin'ya Nitta

Galactic winds play a crucial role in the ejection of the interstellar medium (ISM) into intergalactic space. This study presents a model that classifies possible transonic solutions of galactic winds in the gravitational potential of the dark matter halo and stellar component under spherically symmetric and steady assumptions. Our model includes injections

Marco Jiralerspong, Bilun Sun, Danilo Vucetic, Tianyu Zhang

Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully applied to various structured object generation tasks, sampling a diverse set of high-reward objects quickly. We propose expected flow networks (EFlowNets), which extend GFlowNets to stochastic environments. We show that

Rui Yang, Edison Marrese-Taylor, Yuhe Ke, Lechao Cheng

Large language models (LLMs) have demonstrated powerful text generation capabilities, bringing unprecedented innovation to the healthcare field. While LLMs hold immense promise for applications in healthcare, applying them to real clinical scenarios presents significant challenges, as these models may generate content that deviates from established medical f

Chenwei Wu, Li Erran Li, Stefano Ermon, Patrick Haffner

Compositionality is a common property in many modalities including natural languages and images, but the compositional generalization of multi-modal models is not well-understood. In this paper, we identify two sources of visual-linguistic compositionality: linguistic priors and the interplay between images and texts. We show that current attempts to improve

Kaijun Gong, Zhuowen Yin, Yushu Li, Kailing Guo

The redundancy of Convolutional neural networks not only depends on weights but also depends on inputs. Shuffling is an efficient operation for mixing channel information but the shuffle order is usually pre-defined. To reduce the data-dependent redundancy, we devise a dynamic shuffle module to generate data-dependent permutation matrices for shuffling. Sinc

Xiao Ye, Jun Liu, Bingyin Zhang, Hongfei Fu

In this paper, we consider the variable-order time fractional mobile/immobile diffusion (TF-MID) equation in two-dimensional spatial domain, where the fractional order $\alpha(t)$ satisfies $0<\alpha_{*}\leq \alpha(t)\leq \alpha^{*}<1$. We combine the quadratic spline collocation (QSC) method and the $L1^+$ formula to propose a QSC-$L1^+$ scheme. It can be p

Angelica Simonetti, Ferdinando Zanchetta

Graph Neural Networks (GNNs) are becoming central in the study of time series, coupled with existing algorithms as Temporal Convolutional Networks and Recurrent Neural Networks. In this paper, we see time series themselves as directed graphs, so that their topology encodes time dependencies and we start to explore the effectiveness of GNNs architectures on t

Sylvain Barde, Rowan Cherodian, Guy Tchuente

This paper proposes a Lasso-based estimator which uses information embedded in the Moran statistic to develop a selection procedure called Moran's I Lasso (Mi-Lasso) to solve the Eigenvector Spatial Filtering (ESF) eigenvector selection problem. ESF uses a subset of eigenvectors from a spatial weights matrix to efficiently account for any omitted cross-secti

Ryuji Saiin, Tomoya Shirakawa, Sota Yoshihara, Yoshihide Sawada

In this article, we propose a new paradigm for training spiking neural networks (SNNs), spike accumulation forwarding (SAF). It is known that SNNs are energy-efficient but difficult to train. Consequently, many researchers have proposed various methods to solve this problem, among which online training through time (OTTT) is a method that allows inferring at

Thomas Hewitt, Tom Bertheas, Manan Jain, Yusuke Nishida

We implement an experimental architecture in which a single atom of K is trapped in an optical tweezer, and is immersed in a bath of Rb atoms at ultralow temperatures. In this regime, the motion of the single trapped atom is confined to the lowest quantum vibrational levels. This realizes an elementary and fully controllable quantum impurity system. For the

Hao Zhang, Yuan Li, Chen Zhang, Tao Huang

One of the first problems of studying the quantum internet is how to realize quantum interconnection between users in a quantum network. To address above problem, by referencing the classical Internet, developing the packet switching of quantum networks is a promising way. In this paper, we propose a new hybrid packet switching for entanglement-based quantum

Ruturaj Reddy, Utkarsh Gupta, Ishaan Kale, Apoorva Shastri

A modified LAB algorithm is introduced in this paper. It builds upon the original LAB algorithm (Reddy et al. 2023), which is a socio-inspired algorithm that models competitive and learning behaviours within a group, establishing hierarchical roles. The proposed algorithm incorporates the roulette wheel approach and a reduction factor introducing inter-group

Pedro D. Alvarez, Cristóbal Corral, Jorge Zanelli

In this work, we study (anti-)self duality conditions in unconventional conformal supersymmetry. We focus on a theory constructed in a Townsend-MacDowell-Mansouri form for an $SU(2,2|N)$ gauge connection with matter fields in the adjoint representation. We found bosonic solutions that correspond to analytic gravitational instantons with nontrivial torsion. T

Fouzi Boukhalfa, Reda Alami, Mastane Achab, Eric Moulines

In today's era, autonomous vehicles demand a safety level on par with aircraft. Taking a cue from the aerospace industry, which relies on redundancy to achieve high reliability, the automotive sector can also leverage this concept by building redundancy in V2X (Vehicle-to-Everything) technologies. Given the current lack of reliable V2X technologies, this ide

V. Courtillot, F. Lopes, V. Kossobokov, P. Zuddas

In this lengthy letter, we wanted to discuss the concept of climate based on definitions established for over a century and direct observations that we have been collecting for more than a century as well. To do this, we present and discuss the remarkably stable maps over time of the various physical parameters that make up the climate corpus: solar temperat

Alberto Giaretta, Mauro Bisiacco, Gianluigi Pillonetto

One central theme in machine learning is function estimation from sparse and noisy data. An example is supervised learning where the elements of the training set are couples, each containing an input location and an output response. In the last decades, a substantial amount of work has been devoted to design estimators for the unknown function and to study t

Jacopo Lenti, Corrado Monti, Gianmarco De Francisci Morales

We show that a maximum likelihood approach for parameter estimation in agent-based models (ABMs) of opinion dynamics outperforms the typical simulation-based approach. Simulation-based approaches simulate the model repeatedly in search of a set of parameters that generates data similar enough to the observed one. In contrast, likelihood-based approaches deri

Max Sauerbrey

Recently, many existence results for the stochastic thin-film equation were established in the case of a quadratic mobility exponent $n=2$, in which the noise term $\partial_x(u^\frac{n}{2}\mathcal{W})$ becomes linear. In the case of a non-quadratic mobility exponent, results are only available in the situation that $n\ge \frac{8}{3}$ leaving the interval of

Indranil Biswas, Fatima Laytimi, D. S. Nagaraj, Werner Nahm

We give an algebraic-geometric proof of the fact that for a smooth fibration $\pi: X \longrightarrow Y$ of projective varieties, the direct image $\pi_*(L\otimes K_{X/Y})$ of the adjoint line bundle of an ample (respectively, nef and $\pi$-strongly big) line bundle $L$ is ample (respectively, nef and big).

Jakub Rembielinski, Jacek Ciborowski

Assuming that neutrinos are spacelike (tachyonic) fermions, we calculate width for the kinematically allowed, lepton number conserving, three-body decay $\nu_{\alpha}\rightarrow \nu_{\alpha} \; \nu_{\beta} \bar{\nu}_{\beta}$ in the Standard Model. Decays of tachyonic neutrinos over cosmological distances can lead to a reduction of the neutrino flux in the hi

Mohamed Amine Boutiche, Mohamed Mechacha, Mourad Rahmani

The main object of this paper is to investigate a new class of the generalized Hurwitz type poly-Bernoulli numbers and polynomials from which we derive some algorithms for evaluating the Hurwitz type poly-Bernoulli numbers and polynomials. By introducing a new generalization of the Stirling numbers of the second kind, we succeed to establish some combinatori

R. El Hage, D. Sánchez-Manzano, V. Humbert, S. J. Carreira

The normal-state conductivity and superconducting critical temperature of oxygen-deficient YBa2Cu3O7-x can be persistently enhanced by illumination. Strongly debated for years, the origin of those effects -- termed persistent photoconductivity (PPC) and photosuperconductivity (PPS) -- has remained an unsolved critical problem, whose comprehension may provide

Margarita Veshchezerova, Mikhail Somov, David Bertsche, Steffen Limmer

We suggest a hybrid quantum-classical routine for the NP-hard Electric Vehicle Fleet Charging and Allocation Problem. The original formulation is a Mixed Integer Linear Program with continuous variables and inequality constraints. To separate inequality constraints that are difficult for quantum routines we use a decomposition in master and pricing problems:

Bagiya Lakshmi S, Sanjjushri Varshini R, Rohith Mahadevan, Raja CSP Raman

Automated Essay Score (AES) is proven to be one of the cutting-edge technologies. Scoring techniques are used for various purposes. Reliable scores are calculated based on influential variables. Such variables can be computed by different methods based on the domain. The research is concentrated on the user's understanding of a given topic. The analysis is b

N. Unger, D. Ségransan, D. Barbato, J. -B. Delisle

Context. The observed scarcity of brown dwarfs in close orbits (within 10 au) around solar-type stars poses significant questions about the origins of these substellar companions. These questions impact our broader understanding of planetary formation processes. However, to resolve these formation mechanisms, accurate observational constraints are essential.

Marlena Dziurawiec, Jessica O. de Almeida, Mohit Lal Bera, Marcin Płodzień

Quasicrystals are fascinating and important because of their unconventional atomic arrangements, which challenge traditional notions of crystalline structures. Unlike regular crystals, they lack translational symmetry and generate unique mechanical, thermal, and electrical properties, holding promise for numerous applications. In order to probe the electroni

Daniel R. Bayer, Marco Pruckner

Currently, gas furnaces are common heating systems in Europe. Due to the efforts for decarbonizing the complete energy sector, heat pumps should continuously replace existing gas furnaces. At the same time, the electrification of the heating sector represents a significant challenge for the power grids and their operators. Thus, new approaches are required t

Ghania Guettai, Diffalah Laissaoui, Mohamed Amine Boutiche, Mourad Rahmani

The main objective of this paper is to present recurrence relations for the generalized poly-Cauchy numbers and polynomials. This is accomplished by introducing the concept of generalized m-poly-Cauchy numbers and polynomials. Additionally, the paper delves into the discussion of the corresponding generalized m-poly-Bernoulli numbers and polynomials that are

Paul Tardy, Charlotte Roze, Paul Poupet

Being able to read and understand written text is critical in a digital era. However, studies shows that a large fraction of the population experiences comprehension issues. In this context, further initiatives in accessibility are required to improve the audience text comprehension. However, writers are hardly assisted nor encouraged to produce easy-to-unde

Debayan Deb, Suvidha Tripathi, Pranit Puri

The automated generation of 3D human heads has been an intriguing and challenging task for computer vision researchers. Prevailing methods synthesize realistic avatars but with limited control over the diversity and quality of rendered outputs and suffer from limited correlation between shape and texture of the character. We propose a method that offers qual

James Brookhouse, Alex Freitas

Machine learning classifiers are widely used to make decisions with a major impact on people's lives (e.g. accepting or denying a loan, hiring decisions, etc). In such applications,the learned classifiers need to be both accurate and fair with respect to different groups of people, with different values of variables such as sex and race. This paper focuses o

JunHoo Lee, Jayeon Yoo, Nojun Kwak

In this paper, we hypothesize that gradient-based meta-learning (GBML) implicitly suppresses the Hessian along the optimization trajectory in the inner loop. Based on this hypothesis, we introduce an algorithm called SHOT (Suppressing the Hessian along the Optimization Trajectory) that minimizes the distance between the parameters of the target and reference

A. Ruzzon, M. Maggiore, C. Roncolato, G. Ban

In order to allow a good separation of isotopes in a High Resolution Mass Spectrometer (HRMS), the transverse emittance and the energy spread of the beam should have very low values, for this reason a Beam Cooler (BC) is planned to be located between the ISOL target, i.e. the beam source, and the HRMS in the new project Selective Production of Exotic Species

Paul Hagemann, Johannes Hertrich, Fabian Altekrüger, Robert Beinert

We propose conditional flows of the maximum mean discrepancy (MMD) with the negative distance kernel for posterior sampling and conditional generative modeling. This MMD, which is also known as energy distance, has several advantageous properties like efficient computation via slicing and sorting. We approximate the joint distribution of the ground truth and

Arturo Martínez-Celis

A Michael space is a Lindel\"of space which has a non-Lindel\"of product with the Baire space. In this work, we present the notion of Michael ultrafilter and we use it to construct a Michael space under the existence of a selective ultrafilter and $\max \{ \mathfrak{b}, \mathfrak{g} \} =\mathfrak{d}$.

Leo Sünkel, Darya Martyniuk, Julia J. Reichwald, Andrei Morariu

Practical quantum computing (QC) is still in its infancy and problems considered are usually fairly small, especially in quantum machine learning when compared to its classical counterpart. Image processing applications in particular require models that are able to handle a large amount of features, and while classical approaches can easily tackle this, it i

Patrick Arras, Felix Joos

It is a well known result due to Korshunov and Sapozhenko that the hypercube in $n$ dimensions has $(1 + o(1)) \cdot 2 \sqrt e \cdot 2^{2^{n-1}}$ independent sets. Jenssen and Keevash investigated in depth Cartesian powers of cycles of fixed even lengths far beyond counting independent sets. They wonder to which extent their results extend to cycles of odd l

Philipp Reiser, David J. Wraith

We consider the problem of performing connected sums in the context of positive $k^{th}$ intermediate Ricci curvature. We show that such connected sums are possible if the manifolds involved possess `$k$-core metrics' for some $k$. Here, a $k$-core metric is a generalization of the notion of core metric introduced by Burdick for positive Ricci curvature. Fur

Xun Li, Liangquan Zhang

In this note, we study a class of indefinite stochastic McKean-Vlasov linear-quadratic (LQ in short) control problem under the control taking nonnegative values. In contrast to the conventional issue, both the classical dynamic programming principle (DPP in short) and the usual Riccati equation approach fail. We tackle these difficulties by extending the sta

Kathryn E. Kirchoff, Travis Maxfield, Alexander Tropsha, Shawn M. Gomez

In deep learning for drug discovery, chemical data are often represented as simplified molecular-input line-entry system (SMILES) sequences which allow for straightforward implementation of natural language processing methodologies, one being the sequence-to-sequence autoencoder. However, we observe that training an autoencoder solely on SMILES is insufficie

Thomas Coste, Usman Anwar, Robert Kirk, David Krueger

Reinforcement learning from human feedback (RLHF) is a standard approach for fine-tuning large language models to follow instructions. As part of this process, learned reward models are used to approximately model human preferences. However, as imperfect representations of the "true" reward, these learned reward models are susceptible to overoptimization. Ga

Jayasudha M, Ayesha Shaik, Gaurav Pendharkar, Soham Kumar

Cybersecurity is a major concern due to the increasing reliance on technology and interconnected systems. Malware detectors help mitigate cyber-attacks by comparing malware signatures. Machine learning can improve these detectors by automating feature extraction, identifying patterns, and enhancing dynamic analysis. In this paper, the performance of six mult

Alfredo D. Millano, Genly Leon

This research focuses on scalar field cosmologies with a generalized harmonic potential. Our attention is centred on the anisotropic LRS Bianchi I and III metrics, Bianchi V metrics, and their isotropic limits. We provide a comprehensive overview of the first two metrics classes and offer new findings for Bianchi V metrics. We show that the Hubble parameter

S. Aravinda, Shilpak Banerjee, Ranjan Modak

The development of classical ergodic theory has had a significant impact in the areas of mathematics, physics, and, in general, applied sciences. The quantum ergodic theory of Hamiltonian dynamics has its motivations to understand thermodynamics and statistical mechanics. Quantum channel, a completely positive trace-preserving map, represents a most general

Hussam Azzuni, Sharim Jamal, Abdulmotaleb Elsaddik

Large Language Models (LLMs) have revolutionized various industries by harnessing their power to improve productivity and facilitate learning across different fields. One intriguing application involves combining LLMs with visual models to create a novel approach to Human-Computer Interaction. The core idea of this system is to create a user-friendly platfor

Hamid Rahimpoor, Saeed H. Abedinpour

Topological nodal-line semimetals are characterized by symmetry-protected one-dimensional band-touching lines or loops, which give rise to their peculiar Fermi surfaces at low energies. Furthermore, if time-reversal or inversion symmetry breaking tilts the bands, anisotropic Fermi surfaces hosting electron and hole carriers simultaneously can also appear. We

Zihao Zhao, Zhenpeng Shi, Yang Liu, Wenbo Ding

Federated Learning (FL) is often impeded by communication overhead issues. Prompt tuning, as a potential solution, has been introduced to only adjust a few trainable parameters rather than the whole model. However, current single-modality prompt tuning approaches fail to comprehensively portray local clients' data. To overcome this limitation, we present Twi

Hans A. Weidenmüller

A closed quantum system thermalizes if for time $t \to \infty$, the function ${\rm Tr} (A \rho(t))$ tends asymptotically to ${\rm Tr} (A \rho_{\rm eq})$. Here $A$ is an operator that represents an observable, $\rho(t)$ is the time-dependent density matrix, and $\rho_{\rm eq}$ its equilibrium value. We investigate thermalization of a chaotic many-body quantum

Benjamin Arthur Hugo Meunier, Maxime Christophe Nicolas Roux

This bachelor project presents a theoretical model describing the resonant frequencies in rectangular and cylindrical tanks. It presents an experimental validation in the rectangular case. The resonant frequencies are determined and then used to construct a theoretical model for the free damping of viscous fluids. The predictions of the model are qualitative

E. N. Antonov, A. Yu. Orlov

The generating series for the instanton contribution to Green functions of the $2D$ sigma model was found in the works of Schwarz, Fateev and Frolov. We show that this series can be written as a formal tau function of the two-sided two-component KP hierarchy. We call it formal singular tau function because this tau function is a sum where each term is the in

A. Arhrib, S. Moretti, S. Semlali, C. H. Shepherd-Themistocleous

Unlike other realisations of the 2-Higgs Doublet Model (2HDM), the so-called Type-I allows for a very light Higgs boson spectrum. Specifically, herein, the heaviest of the two CP-even neutral Higgs states, $H$, can be the one discovered at the Large Hadron Collider (LHC) in 2012, with a mass of $\approx 125$ GeV and couplings consistent with those predicted

Majid Rafiei, Duygu Bayrak, Mahsa Pourbafrani, Gyunam Park

In this study, we examine how event data from campus management systems can be used to analyze the study paths of higher education students. The main goal is to offer valuable guidance for their study planning. We employ process and data mining techniques to explore the impact of sequences of taken courses on academic success. Through the use of decision tre

V. T. Mai, R. Chatelin, E. -J. Courtial, C. Boulocher

Extrusion-based 3D printing is a widely utilized tool in tissue engineering, offering precise 3D control of bioinks to construct organ-sized biomaterial objects with hierarchically organized cellularized scaffolds. The internal organization of scaffold constituents must replicate the structural anisotropy of the targeted tissue to effectively promote cellula

Aster G. Taylor, Davide Farnocchia, David Vokrouhlicky, Darryl Z. Seligman

Significant nonradial, nongravitational accelerations with magnitudes incompatible with radiation-driven effects have been reported in seven small, photometrically inactive near-Earth objects. Two of these objects exhibit large transverse accelerations (i.e., within the orbital plane but orthogonal to the radial direction), and six exhibit significant out-of

Dominik Klement, Mireia Diez, Federico Landini, Lukáš Burget

Bayesian HMM clustering of x-vector sequences (VBx) has become a widely adopted diarization baseline model in publications and challenges. It uses an HMM to model speaker turns, a generatively trained probabilistic linear discriminant analysis (PLDA) for speaker distribution modeling, and Bayesian inference to estimate the assignment of x-vectors to speakers

Yiliang Li, Hongli Lyu, Jun-e Feng, Abdelhamid Tayebi

A state feedback control strategy is proposed for input-output (IO) decoupling of a class of fully output controllable Boolean control networks (BCNs). Some necessary and sufficient conditions for BCN IO-decoupling are presented. As an instrumental tool in our design, we introduce a canonical form for IO-decoupled BCNs along with some conditions guaranteeing

Fabio Della Rossa, Davide Liuzza, Francesco Lo Iudice, Pietro De Lellis

The emergence of collective behaviors in networks of dynamical units in pairwise interaction has been explained as the effect of diffusive coupling. How does the presence of higher-order interaction impact the onset of spontaneous or induced synchronous behavior? Inspired by actuation and measurement constraints typical of physical and engineered systems, we

Nanda Kishor Panda, Simon H. Tindemans

Aggregation is crucial to the effective use of flexibility, especially in the case of electric vehicles (EVs) because of their limited individual battery sizes and large aggregate impact. This research proposes a novel method to quantify and represent the aggregate charging flexibility of EV fleets within a fixed flexibility request window. These windows can

Paul Manuel Schindler, Marin Bukov

Periodically driven systems have emerged as a useful technique to engineer the properties of quantum systems, and are in the process of being developed into a standard toolbox for quantum simulation. An outstanding challenge that leaves this toolbox incomplete is the manipulation of the states dressed by strong periodic drives. The state-of-the-art in Floque

Bernhard Nessler, Thomas Doms, Sepp Hochreiter

The authors are concerned about the safety, health, and rights of the European citizens due to inadequate measures and procedures required by the current draft of the EU Artificial Intelligence (AI) Act for the conformity assessment of AI systems. We observe that not only the current draft of the EU AI Act, but also the accompanying standardization efforts i

Andrei Ol'khovatov

Historically there were two main reasons to assign the 1908 Tunguska event to a spacebody infall: a) newspaper notes about a fall of a meteorite near the town of Kansk (later claimed to be false); b) eyewitnesses reports about seeing luminous phenomena in the sky. This paper examines accounts of the Siberian eyewitnesses about luminous phenomena in the sky,

Akash Kopparam Sreedhara, Deepesh Padala, Shashank Mahesh, Kai Cui

Swarms of aerial drones have recently been considered for last-mile deliveries in urban logistics or automated construction. At the same time, collaborative transportation of payloads by multiple drones is another important area of recent research. However, efficient coordination algorithms for collaborative transportation of many payloads by many drones rem

Rachel Nicks, Robert Allen, Stephen Coombes

Networks of coupled nonlinear oscillators can display a wide range of emergent behaviours under variation of the strength of the coupling. Network equations for pairs of coupled oscillators where the dynamics of each node is described by the evolution of its phase and slowest decaying isostable coordinate have previously been shown to capture bifurcations an

Daniel Mann, Tina Raissi, Wilfried Michel, Ralf Schlüter

We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM) where the transition probabilities between hidden states are modeled and learned explicitly. Most contemporary sequence-to-sequence models allow for from-scratch training by summing over all possible label segmentations in a given topology. In our

Ramis Khasyanov

The concept of the Bohr radius of a pair of operators is introduced. In terms of the convolution function, a general formula for calculating the Bohr radius of the Hadamard convolution type operator with a fixed initial coefficient is obtained. We apply this formula to the problems of the Bohr radius of the operators of differentiation and integration. Using

M. N. Jayakody, Priodyuti Pradhan, Dana Ben Porath, E. Cohen

Multilayer network is a potent platform which paves a way to study the interactions among entities in various networks with multiple types of relationships. In this study, the dynamics of discrete-time quantum walk on a multilayer network are explored in detail. We derive recurrence formulae for the coefficients of the wave function of a quantum walker on an

Or Feldman, Chaim Baskin

Modern approaches for learning on dynamic graphs have adopted the use of batches instead of applying updates one by one. The use of batches allows these techniques to become helpful in streaming scenarios where updates to graphs are received at extreme speeds. Using batches, however, forces the models to update infrequently, which results in the degradation

Jiatong Shi, Hirofumi Inaguma, Xutai Ma, Ilia Kulikov

Existing Self-Supervised Learning (SSL) models for speech typically process speech signals at a fixed resolution of 20 milliseconds. This approach overlooks the varying informational content present at different resolutions in speech signals. In contrast, this paper aims to incorporate multi-resolution information into speech self-supervised representation l

Hongyi Fan, Joe Kileel, Benjamin Kimia

In this paper, we introduce a general framework for analyzing the numerical conditioning of minimal problems in multiple view geometry, using tools from computational algebra and Riemannian geometry. Special motivation comes from the fact that relative pose estimation, based on standard 5-point or 7-point Random Sample Consensus (RANSAC) algorithms, can fail

Shiqi Liu, Yihua Tan, Yutong Bai, Alan Yuille

Pan-sharpening algorithms utilize a panchromatic image and a multispectral image to generate a high spatial and high spectral image. However, the optimizations of the algorithms are designed with different standards. We employ a simple matrix equation to describe the Pan-sharpening problem. The conditions for the existence of a solution and the acquisition o

Zhiyong Wang, Jize Xie, Xutong Liu, Shuai Li

The contextual linear bandit is an important online learning problem where given arm features, a learning agent selects an arm at each round to maximize the cumulative rewards in the long run. A line of works, called the clustering of bandits (CB), utilize the collaborative effect over user preferences and have shown significant improvements over classic lin