April 2023 arXiv papers — page 137

Jun Zhang, Yiping Xie, Li Ling, John Folkesson

Side-scan sonar (SSS) is a lightweight acoustic sensor that is frequently deployed on autonomous underwater vehicles (AUVs) to provide high-resolution seafloor images. However, using side-scan images to perform simultaneous localization and mapping (SLAM) remains a challenge when there is a lack of 3D bathymetric information and discriminant features in the

Christian Ketterer

We characterize the null energy condition for an $(n+1)$-dimensional Lorentzian manifold in terms of convexity of the relative $(n-1)$-Renyi entropy along displacement interpolations on null hypersurfaces. More generally, we also consider Lorentzian manifolds with a smooth weight function and introduce the Bakry-Emery $N$-null energy condition that we charac

Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang

This paper presents a comprehensive survey of ChatGPT-related (GPT-3.5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and

E. M. Holmbeck, T. M. Sprouse, M. R. Mumpower

The rapid neutron capture or 'r process' of nucleosynthesis is believed to be responsible for the production of approximately half the natural abundance of heavy elements found on the periodic table above iron (with proton number $Z=26$) and all of the heavy elements above bismuth ($Z=83$). In the course of creating the actinides and potentially superheavies

Xu Guo, Yiyuan Qian, Hongwei Shi, Weichao Yang

In this paper, we propose semiparametric efficient estimators of genetic relatedness between two traits in a model-free framework. Most existing methods require specifying certain parametric models involving the traits and genetic variants. However, the bias due to model misspecification may yield misleading statistical results. Moreover, the semiparametric

Florian Muhr, Lorenzo Zaniboni, Saeid K. Dehkordi, Fernando Pedraza Nieto

In a typical communication system, in order to maintain a desired SNR level, initial beam alignment (BA) must be established prior to data transmission. In a setup where a Base Station (BS) Tx sends data via a digitally modulated waveform, we propose a User Equipment (UE) enhanced with a Hybrid Intelligent Reflecting Surface (HIRS) to aid beam alignment. A n

Yusuke Kawamoto

We study the relation between isolated hypersurface singularities (e.g. ADE) and the quantum cohomology ring by using spectral invariants, which are symplectic invariants coming from Floer theory. We prove, under the assumption that the quantum cohomology ring is semi-simple, that (1) if the smooth Fano variety (or the symplectic manifold) degenerates to a F

José D. Alvarado, Yoshiharu Kohayakawa, Patrick Morris, Guilherme O. Mota

The celebrated canonical Ramsey theorem of Erd\H{o}s and Rado implies that for a given $k$-uniform hypergraph (or $k$-graph) $H$, if $n$ is sufficiently large then any colouring of the edges of the complete $k$-graph $K^{(k)}_n$ gives rise to copies of $H$ that exhibit certain colour patterns. We are interested in sparse random versions of this result and th

Antonia Chmiela, Ambros Gleixner, Pawel Lichocki, Sebastian Pokutta

Mixed Integer Programming (MIP) is NP-hard, and yet modern solvers often solve large real-world problems within minutes. This success can partially be attributed to heuristics. Since their behavior is highly instance-dependent, relying on hard-coded rules derived from empirical testing on a large heterogeneous corpora of benchmark instances might lead to sub

Lavinia Corina Ciungu

We define and study the notions of q-deductive systems, p-deductive systems, deductive systems, maximal and strongly maximal q-deductive systems in quantum-Wajsberg algebras. We also introduce the notion of congruences induced by deductive systems of a quantum-Wajsberg algebra, and we show that there is a relationship between congruences and deductive system

Jingyi Feng, Chenming Zhang

Comprehending how the brain interacts with the external world through generated neural data is crucial for determining its working mechanism, treating brain diseases, and understanding intelligence. Although many theoretical models have been proposed, they have thus far been difficult to integrate and develop. In this study, we were inspired in part by grid

Ammar Rafique, Naveed Ul Hassan, Muhammad Zubair, Ijaz Haider Naqvi

The ability of reconfigurable intelligent surfaces (RIS) to produce complex radiation patterns in the far-field is determined by various factors, such as the unit-cell's size, shape, spatial arrangement, tuning mechanism, the communication and control circuitry's complexity, and the illuminating source's type (point/planewave). Research on RIS has been mainl

Vittorio Pippi, Silvia Cascianelli, Lorenzo Baraldi, Rita Cucchiara

In this work, we explore massive pre-training on synthetic word images for enhancing the performance on four benchmark downstream handwriting analysis tasks. To this end, we build a large synthetic dataset of word images rendered in several handwriting fonts, which offers a complete supervision signal. We use it to train a simple convolutional neural network

Meifeng Lin, Zhihua Dong, Tianle Wang, Mohammad Atif

The evolution of the computing landscape has resulted in the proliferation of diverse hardware architectures, with different flavors of GPUs and other compute accelerators becoming more widely available. To facilitate the efficient use of these architectures in a heterogeneous computing environment, several programming models are available to enable portabil

José Niño-Mora

This paper presents a new \emph{fast-pivoting} algorithm that computes the $n$ Gittins index values of an $n$-state bandit -- in the discounted and undiscounted cases -- by performing $(2/3) n^3 + O(n^2)$ arithmetic operations, thus attaining better complexity than previous algorithms and matching that of solving a corresponding linear-equation system by Gau

Géza Csima

In this work, we examine the isoptic surface of line segments in the $S^2\times R$ and $H^2\times R$ geometries, which are from the 8 Thurston geometries. Based on the procedure first described in [10], we are able to give the isoptic surface of any segment implicitly. We rely heavily on the calculations published in [41, 42]. As a special case, we examine t

Patrick Møller Jensen, Vedrana Andersen Dahl, Carsten Gundlach, Rebecca Engberg

Domain shift significantly influences the performance of deep learning algorithms, particularly for object detection within volumetric 3D images. Annotated training data is essential for deep learning-based object detection. However, annotating densely packed objects is time-consuming and costly. Instead, we suggest training models on individually scanned ob

Lucia A. Perez, Sangeeta Malhotra, James E. Rhoads, Isak G. B. Wold

We use large simulations of Lyman-Alpha Emitters with different fractions of ionized intergalactic medium to quantify the clustering of Ly$\alpha$ emitters as measured by the Void Probability function (VPF), and how it evolves under different ionization scenarios. We quantify how well we might be able to distinguish between these scenarios with a deep spectr

Elias Starchl, Lukas M. Sieberer

We study the quench dynamics of noninteracting fermionic quantum many-body systems that are subjected to Markovian drive and dissipation and are described by a quadratic Liouvillian which has parity-time (PT) symmetry. In recent work, we have shown that such systems relax locally to a maximum entropy ensemble that we have dubbed the PT-symmetric generalized

Ming Yang, Alceste Z. Bonanos, Biwei Jiang, Emmanouil Zapartas

We assemble the most complete and clean red supergiant (RSG) sample (2,121 targets) so far in the Small Magellanic Cloud (SMC) with 53 different bands of data to study the MLR of RSGs. In order to match the observed spectral energy distributions (SEDs), a theoretical grid of 17,820 Oxygen-rich models (``normal'' and ``dusty'' grids are half-and-half) is crea

Ntumba Elie Nsampi, Adarsh Djeacoumar, Hans-Peter Seidel, Tobias Ritschel

Neural fields are evolving towards a general-purpose continuous representation for visual computing. Yet, despite their numerous appealing properties, they are hardly amenable to signal processing. As a remedy, we present a method to perform general continuous convolutions with general continuous signals such as neural fields. Observing that piecewise polyno

Zahra Mirzaiyan, Giampiero Esposito

This paper investigates first the four branches of BMS transformations, motivated by the classification into elliptic, parabolic, hyperbolic and loxodromic proposed a few years ago in the literature. We first prove that to each normal elliptic transformation of the complex variable zeta used in the metric for cuts of null infinity there corresponds a BMS sup

Sam Hughes, Motiejus Valiunas

We prove groups acting cocompactly on locally finite trees with hyperbolic vertex stabilisers are asynchronously automatic. Combining this with previous work of the authors, we obtain an example of a group satisfying several non-positive curvature properties (being a $\mathrm{CAT}(0)$ group, an injective group, a hierarchically hyperbolic group, and having q

Richard S. Miller

Homogeneity is the hallmark of standard candle-based cosmology investigations. Thermonuclear supernovae (Type-Ia, SNeIa) violate this essential requirement if they develop along multiple evolutionary pathways. In this work, the impact of thermonuclear diversity on cosmological parameter constraints is quantified using Pantheon+, one of the largest ensembles

Sarah Parisot, Yongxin Yang, Steven McDonagh

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing t

Liu Yang, Di Chai, Junxue Zhang, Yilun Jin

Vertical federated learning (VFL) is a promising category of federated learning for the scenario where data is vertically partitioned and distributed among parties. VFL enriches the description of samples using features from different parties to improve model capacity. Compared with horizontal federated learning, in most cases, VFL is applied in the commerci

Chris Verhoek, Ruigang Wang, Roland Tóth

This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for all parameter values during training, the allowed models are stable in the contraction sense or have their Lipschitz constant bounded by a user-defined value $\gamma$. Furthermore,

Vineel Nagisetty, Laura Graves, Guanting Pan, Piyush Jha

In this paper, we propose a new Deep Neural Network (DNN) testing algorithm called the Constrained Gradient Descent (CGD) method, and an implementation we call CGDTest aimed at exposing security and robustness issues such as adversarial robustness and bias in DNNs. Our CGD algorithm is a gradient-descent (GD) method, with the twist that the user can also spe

Jenia Tevelev

Let N be the moduli space of stable rank 2 vector bundles on a smooth projective curve of genus g>1 with fixed odd determinant. With Sebastian Torres, we previously found a semi-orthogonal decomposition of the bounded derived category of N into bounded derived categories of symmetric powers of the curve and, possibly, a phantom block. In this work, we employ

Mikhail D. Minin, Andrei G. Pronko, Vitaly O. Tarasov

We consider the problem of construction of determinant formulas for the partition function of the six-vertex model with domain wall boundary conditions. In pioneering works of Korepin and Izergin a determinant formula was proposed and proved using a recursion relation. In later works, another determinant formulas were given by Kostov for the rational case an

Louis Esperet, Ugo Giocanti, Clément Legrand-Duchesne

An infinite graph is quasi-transitive if its vertex set has finitely many orbits under the action of its automorphism group. In this paper we obtain a structure theorem for locally finite quasi-transitive graphs avoiding a minor, which is reminiscent of the Robertson-Seymour Graph Minor Structure Theorem. We prove that every locally finite quasi-transitive g

Johanna Sommer, Leon Hetzel, David Lüdke, Fabian Theis

Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules. Deep generative models have shown promising results for molecule generation, but the benefits of specific inductive biases for learning distributions o

Garima Singh, Jonas Rembser, Lorenzo Moneta, David Lange

RooFit is a toolkit for statistical modeling and fitting used by most experiments in particle physics. Just as data sets from next-generation experiments grow, processing requirements for physics analysis become more computationally demanding, necessitating performance optimizations for RooFit. One possibility to speed-up minimization and add stability is th

Dalimil Peša

In this paper we consider the question of smoothness of slowly varying functions satisfying the modern definition that, in the last two decades, gained prevalence in the applications concerning function spaces and interpolation. We show, that every slowly varying function of this type is equivalent to a slowly varying function that has continuous classical d

Da Li, Bo Tang, Xuyang Wang, Wenjun Wu

Reconfigurable intelligent surface (RIS) refers to a signal reflection surface containing a large number of low-cost passive reflecting elements. RIS can improve the performance of radar and communication systems by dynamically modulating the wireless channels. In this paper, we consider the co-design for improving the co-existence between multiple-input-mul

Emil Njor, Jan Madsen, Xenofon Fafoutis

Neural Architecture Search (NAS) is a popular tool for automatically generating Neural Network (NN) architectures. In early NAS works, these tools typically optimized NN architectures for a single metric, such as accuracy. However, in the case of resource constrained Machine Learning, one single metric is not enough to evaluate a NN architecture. For example

Izzatjon Allayarov, Andrey B. Evlyukhin, Diane J. Roth, Boris Chichkov

Dynamic control of metamaterials and metasurfaces is crucial for many photonic technologies, such as flat lenses, displays, augmented reality devices, and beam steering, to name a few. The dynamic response is typically achieved by controlling the phase and/or amplitude of individual meta-atom resonances using electro-optic, phase-change or nonlinear effects.

Luohan Wang, Bo-Qiang Ma

The newly observed gamma ray burst GRB221009A exhibits the existence of 10~TeV-scale photons, and the axion-photon conversion has been suggested as a candidate to explain such energetic features of GRB221009A. In this work we adopt a model to calculate the conversion probability of the energetic photons from GRB221009A to the Earth. The result shows that the

Bin Zheng, Yael Avni, David Andelman, Rudolf Podgornik

We study the effects of charge-regulated acid/base equilibrium on the swelling of polyelectrolyte gels, by considering a combination of the Poisson-Boltzmann theory and a two-site charge-regulation model based on the Langmuir adsorption isotherm. By exploring the volume change as a function of salt concentration for both nano-gels and micro-gels, we identify

Benjamin Kenwright

Fourier transform (FT) plays a crucial role in a broad range of applications, from enhancement, restoration and analysis through to security, compression and manipulation. The Fourier transform (FT) is a process that converts a function into a form that describes the frequencies. This process has been extended to many domains and numerical representations (i

M. Chamseddine, J. Margueron, G. Chanfray, H. Hansen

A relativistic Hartree-Fock Lagrangian including a chiral potential and nucleon polarisation is investigated in hopes of providing a better description of dense nuclear matter. We fully consider the contribution of the exchange Fock term to the energy and the self-energies, and in addition we investigate the nucleon's compositeness and finite size effects (c

Mayu Otani, Riku Togashi, Yu Sawai, Ryosuke Ishigami

Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process requires deep comprehension of text and images. However, our survey of 37 recent papers reveals that many works rely solely on automatic measures (e.g., FID) or perform poorly described human evaluations that are not reliable or re

Mitchell Black, Dimitra Panagou

In this paper, we develop a novel adaptation-based approach to constrained control design under multiple state and input constraints. Specifically, we introduce a method for synthesizing any number of time-varying candidate control barrier functions (CBF) into one consolidated CBF (C-CBF) candidate, and propose a predictor-corrector optimization-based adapta

Qi Gao, Zilong Li, Junping Zhang, Yi Zhang

Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models to address the over-smoothness and training instability encountered by previous deep-learning-based denoising models. However, diffusion models suffer from long inference times due

Qian-Qian Guo, Hai-Qing Zhou

In this work, the $\gamma Z$-exchange contributions in the low-energy elastic parity-violating $ep$ scattering are discussed with the approximation $m_e=0$. By expanding the $\gamma pp$ and $Zpp$ interactions on the momentum of photon and considering both the leading-order and the next-to-leading order interactions, we calculate the amplitudes of the $\gamma

G. Leroy, S. Pires, G. W. Pratt, C. Giocoli

The sensitivity and wide area reached by ongoing and future wide-field optical surveys allows for the detection of an increasing number of galaxy clusters uniquely through their weak lensing (WL) signal. This motivates the development of new methods to analyse the unprecedented volume of data faster and more efficiently. Here we introduce a new multi-scale W

Lu Chen, Siyu Lou, Keyan Zhang, Jin Huang

The Shapley value is widely regarded as a trustworthy attribution metric. However, when people use Shapley values to explain the attribution of input variables of a deep neural network (DNN), it usually requires a very high computational cost to approximate relatively accurate Shapley values in real-world applications. Therefore, we propose a novel network a

Piotr Lebiedowicz, Antoni Szczurek, Otto Nachtmann

The two-tensor-pomeron model is applied to deeply virtual Compton scattering (DVCS) on a proton. A good description of the DVCS HERA data at small Bjorken-$x$ is achieved due to a sizeable interference of soft and hard pomeron contributions. We present two fits which differ somewhat in the strength of the hard pomeron contribution. We describe, in the same f

Marius Müller, Fabian Rupp, Christian Scharrer

We prove a lower bound on the length of closed geodesics for spheres with Willmore energy below $6\pi$. The energy threshold is optimal and the inequality cannot be extended to surfaces of higher genus. Moreover, we discuss consequences for the injectivity radius.

Jiming Ma, Zixi Wang

There are many results showing the connection and phenomenon between some low-dimensional manifolds with the profinite completions of their fundamental groups. We focus on some Seifert 4-manifolds about the extent of their profinite completion to detect one, giving classification of monodromies and conditions for them to be profinitely rigid.

Matteo Baggioli, Sera Cremonini, Laura Early, Li Li

We revisit the computation of the shear viscosity to entropy ratio in a holographic p-wave superfluid model, focusing on the role of rotational symmetry breaking. We study the interplay between explicit and spontaneous symmetry breaking and derive a simple horizon formula for $\eta/s$, which is valid also in the presence of explicit breaking of rotations and

Evgeny R. Gafarov, Frank Werner

In this paper, we consider scheduling problems that arise in connected and autonomous vehicle systems. For four variants of such problems, mathematical models and solution algorithms are presented. In particular, three polynomial algorithms and a branch and bound algorithms are developed.

Haram Choi, Cheolwoong Na, Jinseop Kim, Jihoon Yang

As multimedia content often contains noise from intrinsic defects of digital devices, image denoising is an important step for high-level vision recognition tasks. Although several studies have developed the denoising field employing advanced Transformers, these networks are too momory-intensive for real-world applications. Additionally, there is a lack of r

Youngwook Kim, Jae Myung Kim, Jieun Jeong, Cordelia Schmid

Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer vision. One baseline approach to this task is to assume unobserved labels as negative labels, but this assumption induces label noise as a form of false negative. To understand the neg

Anders Björn, Jana Björn

We obtain precise estimates, in terms of the measure of balls, for the Besov capacity of annuli and singletons in complete metric spaces. The spaces are only assumed to be uniformly perfect with respect to the centre of the annuli and equipped with a doubling measure.

Tomas Teijeiro, Jamie M. Taylor, Ali Hashemian, David Pardo

We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially consider 1D spline spaces of arbitrary degree spanned over uniform and non-uniform knot sequences, and then the generated optimal rules are used for integration over higher-dimensi

Oded Agam, Erez Braun

We utilize whole-body Hydra regeneration from a small tissue segment to develop a physics framework for animal morphogenesis. Introducing experimental controls over this process, an external electric field and a drug that blocks gap junctions, allows us to characterize the essential step in the morphological transition - from a spherical shape to an elongate

Fuyuki Kitagawa, Tomoyuki Morimae, Ryo Nishimaki, Takashi Yamakawa

We construct quantum public-key encryption from one-way functions. In our construction, public keys are quantum, but ciphertexts are classical. Quantum public-key encryption from one-way functions (or weaker primitives such as pseudorandom function-like states) are also proposed in some recent works [Morimae-Yamakawa, eprint:2022/1336; Coladangelo, eprint:20

Gavin Mischler, Vinay Raghavan, Menoua Keshishian, Nima Mesgarani

Recently, the computational neuroscience community has pushed for more transparent and reproducible methods across the field. In the interest of unifying the domain of auditory neuroscience, naplib-python provides an intuitive and general data structure for handling all neural recordings and stimuli, as well as extensive preprocessing, feature extraction, an

Dinesh P. R.

Normalizing flow-based sampling methods have been successful in tackling computational challenges traditionally associated with simulating lattice quantum field theories. Further works have incorporated gauge and translational invariance of the action integral in the underlying neural networks, which have led to efficient training and inference in those mode

Baiwei Guo, Yang Wang, Yuning Jiang, Maryam Kamgarpour

To solve unmodeled optimization problems with hard constraints, this paper proposes a novel zeroth-order approach called Safe Zeroth-order Optimization using Linear Programs (SZO-LP). The SZO-LP method solves a linear program in each iteration to find a descent direction, followed by a step length determination. We prove that, under mild conditions, the iter

Lei Li, Julia Camps, Zhinuo, Wang

The interplay between structural and electrical changes in the heart after myocardial infarction (MI) plays a key role in the initiation and maintenance of arrhythmia. The anatomical and electrophysiological properties of scar, border zone, and normal myocardium modify the electrocardiographic morphology, which is routinely analysed in clinical settings. How

Giorgia Disarò, Maria Elena Valcher

In this paper we propose an extended version of the Friedkin-Johnsen (FJ) model that accounts for the effects of homophily mechanisms on the agents' mutual appraisals. The proposed model consists of two difference equations. The first one describes the opinions' evolution, namely how agents modify their opinions taking into account both their personal belief

Qianqian Yu, Siqi Liu, Xueke Wang, Dong Sheng

Magnetic gradiometers have wide practical and academic applications, and two important types of field gradient observables are the total field gradient and field gradient tensor. However, measurements of the field gradient tensor have not been the focus of previous researches on atomic magnetic gradiometers. In this work, we develop an atomic magnetic gradio

Matteo Rinaldi, Vicente Vento

A Hawking-Page phase transition between AdS thermal and AdS Black Hole was presented as a mechanism for explaining the QCD deconfinement phase transition within holographic models. In order to implement temperature dependence in the confined phase we use a hard-wall AdS/QCD model, where the geometry at low temperatures is described also by a Black Hole metri

Taeho Ryu, Ruggero Valli, Rudiger Pakmor, Rosalba Perna

Dynamical interactions involving binaries play a crucial role in the evolution of star clusters and galaxies. We continue our investigation of the hydrodynamics of three-body encounters, focusing on binary black hole (BBH) formation, stellar disruption, and electromagnetic (EM) emission in dynamical interactions between a BH-star binary and a stellar-mass BH

R. Schoedel, F. Nogueras-Lara, M. Hosek, T. Do

Until recently it was thought that the nuclear stellar disc at the centre of our Galaxy was formed via quasi-continuous star formation over billions of years. However, an analysis of GALACTICNUCLEUS survey data indicates that >80% of the mass of the stellar disc formed at least 8 Gyr ago and about 5% roughly 1 Gyr ago. Our aim is to derive new constraints on

Hung Le, Christian Wulff-Nilsen

A recent line of work on VC set systems in minor-free (undirected) graphs, starting from Li and Parter, who constructed a new VC set system for planar graphs, has given surprising algorithmic results. In this work, we initialize a more systematic study of VC set systems for minor-free graphs and their applications in both undirected graphs and directed graph

Claudia R Schneider, John R Kerr, Sarah Dryhurst, John A D Aston

This review provides an overview of concepts relating to the communication of statistical and empirical evidence in times of crisis, with a special focus on COVID-19. In it, we consider topics relating both to the communication of numbers -- such as the role of format, context, comparisons, and visualization -- and the communication of evidence more broadly

Fabio Veronese, Piero Agostinetti, Christian Hopf, Giuseppe Starnella

The Beam Driven Plasma Neutralizer (BDPN) has been proposed as a more efficient alternative to the gas neutralizer for negative-ion based Neutral Beam Injection (NNBI). In this paper we model the performance of an entire NNBI beamline with a BDPN. We simultaneously consider all the relevant physics and engineering aspects, the most important being the plasma

Shweta Agrawal, Sagnik Saha, Nikolaj I. Schwartzbach, and Akhil Vanukuri

In the average-case $k$-SUM problem, given $r$ integers chosen uniformly at random from $\{0,\dots,M-1\}$, the objective is to find a ``solution'' set of $k$ numbers that sum to $0$ modulo $M$. In the dense regime of $M \leq r^k$, where solutions exist with high probability, the complexity of these problems is well understood. Much less is known in the spars

George Pantazis, Barbara Franci, Sergio Grammatico, Kostas Margellos

We consider multi-agent coalitional games with uncertainty in the coalitional values. We provide a novel methodology to study the stability of the grand coalition in the case where each coalition constructs ambiguity sets for the (possibly) unknown probability distribution of the uncertainty. As a less conservative solution concept compared to worst-case app

Paul Biran, Octav Cornea, Jun Zhang

This paper introduces a new algebraic notion - triangulated persistence category (TPC) - that refines that of triangulated category in the same sense that a persistence module is a refinement of the notion of a vector space. The spaces of morphisms of such a TPC are persistence modules and this category is endowed with a class of weighted distinguished trian

Antonios Antoniadis, Christian Coester, Marek Eliáš, Adam Polak

A major technique in learning-augmented online algorithms is combining multiple algorithms or predictors. Since the performance of each predictor may vary over time, it is desirable to use not the single best predictor as a benchmark, but rather a dynamic combination which follows different predictors at different times. We design algorithms that combine pre

Arnau Bayón, Daniel Valero, Mário J. Franca

Extreme floods threaten lives, assets and ecosystems, with the largest impacts occurring in urbanised areas. However, flood mitigation schemes generally neglect the fact that urban floods carry a considerable amount of solid load. In this study, we define Urban Flood Drifters (UFDs) as loose objects present in the urban landscape that can become mobile under

Antai Xie, Xinlei Yi, Xiaofan Wang, Ming Cao

This paper addresses the problem of differentially private distributed optimization under limited communication, where each agent aims to keep their cost function private while minimizing the sum of all agents' cost functions. In response, we propose a novel Compressed differentially Private distributed Gradient Tracking algorithm (CPGT). We demonstrate that

Zbyněk Koldovský, Jaroslav Čmejla, Tülay Adalı, Stephen O'Regan

Independent Vector Analysis (IVA) is a popular extension of Independent Component Analysis (ICA) for joint separation of a set of instantaneous linear mixtures, with a direct application in frequency-domain speaker separation or extraction. The mixtures are parameterized by mixing matrices, one matrix per mixture. This means that the IVA mixing model does no

Norbert Hegyvári

A given subset $A$ of natural numbers is said to be complete if every element of $\mathbb{N}$ is the sum of distinct terms taken from $A$. This topic is strongly connected to the knapsack problem which is known to be NP complete. Interestingly if $A$ and $B$ are complete sequences then $A\times B$ is not necessarily complete in $\mathbb{N}^2$. In this paper

Song He, Yichao Tang

We derive an algorithm for computing the total differentials of multi-loop integrals expressed as one-fold integrals of multiple polylogarithms, which can involve square roots of polynomials up to degree four and may evaluate to (elliptic) multiple polylogarithms ((e)MPL). This gives simple algebraic rules for computing the $(W{-}1, 1)$-coproduct of the resu

Nikola Adžaga, Goran Dražić, Andrej Dujella, Attila Pethő

Let $q$ be an integer. A $D(q)$-$m$-tuple is a set of $m$ distinct positive integers ${a_1, a_2, . . . , a_m}$ such that $a_ia_j + q$ is a perfect square for all $1 \leq i < j \leq m$. By counting integer solutions $x \in [1, b]$ of congruences $x^2 \equiv q (\mod b)$ with $b \leq N$, we count $D(q)$-pairs with both elements up to $N$, and give estimates on

Zheng Fang, Lama Alqazlan, Du Liu, Yulan He

Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the lack of evaluation based on real-world ex

Francesco Antonio Denisi, Ángel David Ríos Ortiz

Given a projective hyper-K\"ahler manifold $X$, we study the asymptotic base loci of big divisors on $X$. We provide a numerical characterization of these loci and study how they vary while moving a big divisor class in the big cone, using the divisorial Zariski decomposition, and the Beauville-Bogomolov-Fujiki form. We determine the dual of the cones of $k$

Tiago de Souza Farias, Vitor Vaz Schultz, José Carlos Merino Mombach, Jonas Maziero

We introduce a novel framework for simulating spin models using differentiable programming, an approach that leverages the advancements in machine learning and computational efficiency. We focus on three distinct spin systems: the Ising model, the Potts model, and the Cellular Potts model, demonstrating the practicality and scalability of our framework in mo

Fangzhen Lin, Ziyi Shou, Chengcai Chen

For a natural language problem that requires some non-trivial reasoning to solve, there are at least two ways to do it using a large language model (LLM). One is to ask it to solve it directly. The other is to use it to extract the facts from the problem text and then use a theorem prover to solve it. In this note, we compare the two methods using ChatGPT an

Yves Kini, Tuomo Salmi, Anna L. Watts, Serena Vinciguerra

We study the effects of the time-variable properties of thermonuclear X-ray bursts on modeling their millisecond-period burst oscillations. We apply the pulse profile modeling technique that is being used in the analysis of rotation-powered millisecond pulsars by the Neutron Star Interior Composition Explorer (NICER) to infer masses, radii, and geometric par

Jintian Zhu

Based on the $\mu$-bubble method we are able to prove the following version of Riemannian Penrose inequality without horizon: if $g$ is a complete metric on $\mathbb R^3\setminus\{O\}$ with nonnegative scalar curvature, which is asymptotically flat around the infinity of $\mathbb R^3$, then the ADM mass $m$ at the infinity of $\mathbb R^3$ satisfies $m\geq \

Simon Ruetz, Karin Schnass

In this paper we derive sufficient conditions for the convergence of two popular alternating minimisation algorithms for dictionary learning - the Method of Optimal Directions (MOD) and Online Dictionary Learning (ODL), which can also be thought of as approximative K-SVD. We show that given a well-behaved initialisation that is either within distance at most

Catherine Witteveen, Elisabetta Nocerino, Sara A. López-Paz, Harald O. Jeschke

We report on the synthesis of LiCrTe$_2$ single crystals and on their anisotropic magnetic properties. We have obtained these single crystals by employing a Te/Li-flux synthesis method. We find LiCrTe$_2$ to crystallize in a TlCdS$_2$ -type structure with cell parameters of $a$ = 3.9512(5) \r{A} and $c$ = 6.6196(7) \r{A} at $T$ = 175 K. The content of lithiu

Andreas Frommer, Michael Günther, Björn Liljegren-Sailer, Nicole Marheineke

The port-Hamiltonian approach presents an energy-based modeling of dynamical systems with energy-conservative and energy-dissipative parts as well as an interconnection over the so-called ports. In this paper, we apply an operator splitting that treats the energy-conservative and energy-dissipative parts separately. This paves the way for linear equation sol

S. Ardizzoni, I. Saccani, L. Consolini, M. Locatelli

Among sub-optimal MAPF solvers, rule-based algorithms are particularly appealing since they are complete. Even in crowded scenarios, they allow finding a feasible solution that brings each agent to its target, preventing deadlock situations. However, generally, rule-based algorithms provide solutions that are much longer than the optimal one. The main contri

Kai Gao, Si Wei Feng, Baichuan Huang, Jingjin Yu

For rearranging objects on tabletops with overhand grasps, temporarily relocating objects to some buffer space may be necessary. This raises the natural question of how many simultaneous storage spaces, or "running buffers", are required so that certain classes of tabletop rearrangement problems are feasible. In this work, we examine the problem for both lab

Pravir Kumar, Rui Luo, Danny C. Price, Ryan M. Shannon

As the sample size of repeating fast radio bursts (FRBs) has grown, an increasing diversity of phenomenology has emerged. Through long-term multi-epoch studies of repeating FRBs, it is possible to assess which phenomena are common to the population and which are unique to individual sources. We present a multi-epoch monitoring campaign of the repeating FRB s

Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin

Conventional Bayesian Neural Networks (BNNs) are unable to leverage unlabelled data to improve their predictions. To overcome this limitation, we introduce Self-Supervised Bayesian Neural Networks, which use unlabelled data to learn models with suitable prior predictive distributions. This is achieved by leveraging contrastive pretraining techniques and opti

Laurent Cantier

In [5] the author conjectures and partially shows that the Cuntz semigroup classifies unitary elements of unital AF-algebras. We provide a complete proof by addressing the existence part of the conjecture, under a mild adjustment of both domain and codomain of the functor Cu. We also tackle the classification beyond the AF case and more particularly, we look

Lakhan V. Jaybhaye, Snehasish Bhattacharjee, P. K. Sahoo

This paper aims to recreate the gravitational baryogenesis epoch in the framework of the $f(R,L_m)$ theory of gravity, where $R$ and $L_m$ are the curvature scalar and the matter Langragian, respectively. In particular, we examine the model, $f(R,L_m) = \frac{R}{2} + L_m ^{\alpha} + \zeta$, under the supposition that the universe is saturated with dark energ

Francesco Zagaria, Stefano Facchini, Anna Miotello, Carlo F. Manara

The Atacama Large Millimeter/submillimeter Array (ALMA) revolutionised our understanding of protoplanetary discs. However, the available data have not given conclusive answers yet on the underlying disc evolution mechanisms (viscosity or MHD winds). Improving upon the current results, mostly based on the analysis of disc sizes, is difficult because larger, d

Tengfei Wu, Yixuan Zhang, Baptiste Blochet, Payvand Arjmand

Aberrations and multiple scattering in biological tissues critically distort light beams into highly complex speckle patterns. In this regard, digital optical phase conjugation (DOPC) is a promising technique enabling in-depth focusing. However, DOPC becomes challenging when using fluorescent guide-stars for four main reasons: The low photon budget available

Michael R. A. Abdelmalik, Zhenning Cai, Teddy Pichard

The method of moments is widely used for the reduction of kinetic equations into fluid models. It consists in extracting the moments of the kinetic equation with respect to a velocity variable, but the resulting system is a priori underdetermined and requires a closure relation. In this paper, we adapt the $\varphi$-divergence based closure, recently develop

Kaho Morii, Patricio Sanhueza, Fumitaka Nakamura, Qizhou Zhang

The initial conditions found in infrared dark clouds (IRDCs) provide insights on how high-mass stars and stellar clusters form. We have conducted high-angular resolution and high-sensitivity observations toward thirty-nine massive IRDC clumps, which have been mosaicked using the 12m and 7m arrays from the Atacama Large Millimeter/submillimeter Array (ALMA).

Zhihao Cheng, Kaining Zhang, Li Shen, Dacheng Tao

Despite remarkable successes in solving various complex decision-making tasks, training an imitation learning (IL) algorithm with deep neural networks (DNNs) suffers from the high computation burden. In this work, we propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. Concretely, we develop two QIL algorithms, qu