October 2023 arXiv papers — page 13

Thakur Pranav G. Singh, Utkarsh Anand, Tanvi Agrawal, Srinivas G

A novel, non-pyrotechnic payload deployment mechanism tailored for sounding rockets is introduced in this research paper. The mechanism addresses the challenge of efficiently and compactly deploying payloads radially during a single launch, featuring a cylindrical carrier structure actuated by a rack-pinion mechanism. Powered by a servo motor, the carrier st

U. Lebreuilly, P. Hennebelle, T. Colman, A. Maury

Protostellar disks are the product of angular momentum conservation during the protostellar collapse. Understanding their formation is crucial because they are the birthplace of planets and because their formation is tightly related to star formation. Unfortunately, the initial properties of Class 0 disks and their evolution are still poorly constrained obse

Bram M. A. van Dijk, Tom Kouwenhoven, Marco R. Spruit, Max J. van Duijn

Current Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text. LLMs are appearing rapidly, and debates on LLM capacities have taken off, but reflection is lagging behind. Thus, in this position paper, we first zoom in on the debate and critically assess three points recurring in critiques of LLM capacit

Jorge de Heuvel, Xiangyu Zeng, Weixian Shi, Tharun Sethuraman

Foresighted robot navigation in dynamic indoor environments with cost-efficient hardware necessitates the use of a lightweight yet dependable controller. So inferring the scene dynamics from sensor readings without explicit object tracking is a pivotal aspect of foresighted navigation among pedestrians. In this paper, we introduce a spatiotemporal attention

J. Moran-Fraile, A. Holas, F. K. Roepke, R. Pakmor

We have computed a three-dimensional hydrodynamic simulation of the merger between a massive ($0.4\,M_\odot$) helium white dwarf (He WD) and a low-mass ($0.6\,M_\odot$) carbon-oxygen white dwarf (CO WD). Despite the low mass of the primary, the merger triggers a thermonuclear explosion as a result of a double detonation, producing a faint transient and leavi

Guowei Xu, Ruijie Zheng, Yongyuan Liang, Xiyao Wang

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the performance such as sample efficiency, asymptotic performance, and their robustness to the choice of random seeds. In this paper, we identify a major shortcoming in existing visua

Josua Grawitter, Holger Stark

Motivated by strategies for targeted microfluidic transport of droplets, we investigate how sessile droplets can be steered toward a preferred direction using travelling waves in substrate wettability or deformations of the substrate. To perform our numerical study, we implement the boundary-element method to solve the governing Stokes equations for the flui

Christoph Schoenfeld, Lennart Feuerer, Julian Bär, Lukas Dörfelt

Controlling macroscopic properties of quantum materials requires the ability to induce and manipulate excited states. The set of collective excitations of a solid is encoded in its dispersion relations. We find that the spectra of the low-momentum eigenmodes are renormalized by resonantly driving the high-momentum excitations. Our experimental data rule out

Zheng Wang, Shikai Fang, Shibo Li, Shandian Zhe

Tensor decomposition is an important tool for multiway data analysis. In practice, the data is often sparse yet associated with rich temporal information. Existing methods, however, often under-use the time information and ignore the structural knowledge within the sparsely observed tensor entries. To overcome these limitations and to better capture the unde

Orlin Stoytchev

We present a simple visual description of the topology of the space of three-dimensional rotations, requiring just intuition, imagination and no advanced math.

Bihag Dave, Gaurav Goswami

It is well-known that Dark Matter (DM) inside a satellite galaxy orbiting a host halo experiences a tidal potential. If DM is ultra-light, given its wave-like nature, one expects it to tunnel out of the satellite - if this happens sufficiently quickly, then the satellite will not survive over cosmological timescales, severely constraining this dark matter mo

Dianming Hou, Zhonghua Qiao, Lili Ju

In this paper, a linear second order numerical scheme is developed and investigated for the Allen-Cahn equation with a general positive mobility. In particular, our fully discrete scheme is mainly constructed based on the Crank-Nicolson formula for temporal discretization and the central finite difference method for spatial approximation, and two extra stabi

Francesco Giacomarra, Gianmarco Bet, Alessandro Zocca

Synthetic power grids enable secure, real-world energy system simulations and are crucial for algorithm testing, resilience assessment, and policy formulation. We propose a novel method for the generation of synthetic transmission power grids using Exponential Random Graph (ERG) models. Our two main contributions are: (1) the formulation of an ERG model tail

Alex Bols, Siddharth Vadnerkar

We give a complete classification of the anyon sectors of Kitaev's quantum double model on the infinite triangular lattice and for finite gauge group $G$, including the non-abelian case. As conjectured, the anyon sectors of the model correspond precisely to equivalence classes of irreducible representations of the quantum double algebra of $G$.

Josh Magnus Ludan, Qing Lyu, Yue Yang, Liam Dugan

Black-box deep neural networks excel in text classification, yet their application in high-stakes domains is hindered by their lack of interpretability. To address this, we propose Text Bottleneck Models (TBM), an intrinsically interpretable text classification framework that offers both global and local explanations. Rather than directly predicting the outp

Noah Ziems, Gang Liu, John Flanagan, Meng Jiang

Network intrusion detection (NID) systems which leverage machine learning have been shown to have strong performance in practice when used to detect malicious network traffic. Decision trees in particular offer a strong balance between performance and simplicity, but require users of NID systems to have background knowledge in machine learning to interpret.

Tao Yang, Stephan Sleziona, Erik Pollmann, Eckart Hasselbrink

Transition metal dichalcogenides (TMDCs) monolayers, as two-dimensional (2D) direct bandgap semiconductors, hold promise for advanced optoelectronic and photocatalytic devices. Interaction with three-dimensional (3D) metals, like Au, profoundly affects their optical properties, posing challenges in characterizing the monolayer's optical responses within the

Mostafa Jahanifar, Manahil Raza, Kesi Xu, Trinh Vuong

Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications. Nevertheless, the presence of out-of-distribution data (stemming from a multitude of sources such as disparate imaging devices and diverse tissue preparation methods) can cause

Peter Hintz, Christiane Klein

Recently, it was found that the energy flux of a free scalar quantum field on a Reissner-Nordstr\"om-de Sitter spacetime has a quadratic divergence towards the inner horizon of the black hole. Moreover, the leading divergence was found to be state independent as long as the spectral gap of the wave equation on the spacetime is sufficiently large. In this wor

Youbo Lei, Feifei He, Chen Chen, Yingbin Mo

Due to the success of large-scale visual-language pretraining (VLP) models and the widespread use of image-text retrieval in industry areas, it is now critically necessary to reduce the model size and streamline their mobile-device deployment. Single- and dual-stream model structures are commonly used in image-text retrieval with the goal of closing the sema

Tim Z. Xiao, Johannes Zenn, Robert Bamler

Variational autoencoders (VAEs) are deep probabilistic models that are used in scientific applications. Many works try to mitigate this problem from the probabilistic methods perspective by new inference techniques or training procedures. In this paper, we approach the problem instead from the deep learning perspective by investigating the effectiveness of u

Tim Z. Xiao, Johannes Zenn, Robert Bamler

The Street View House Numbers (SVHN) dataset is a popular benchmark dataset in deep learning. Originally designed for digit classification tasks, the SVHN dataset has been widely used as a benchmark for various other tasks including generative modeling. However, with this work, we aim to warn the community about an issue of the SVHN dataset as a benchmark fo

S. N. Filippov, E. N. Gushchin, A. A. Khudyakov, V. I. Kravtsov

The $K^{+} \to \pi^{+}\pi^{0}\pi^{0}\gamma$ decay is observed by the OKA collaboration. The branching ratio is measured to be $(4.1 \pm 0.9(stat) \pm 0.4(syst))\times 10^{-6}$. The branching ratio and $\gamma$ energy spectrum are consistent with ChPT prediction.

Chiyu Song, Zhanchao Zhou, Jianhao Yan, Yuejiao Fei

Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models (LLMs). While numerous studies have examined the impact of factors such as data volume and model size on English models, the scaling properties of instruction tuning in other languages remain largely unexplored. In this work, we systematically investigate th

Iftitahu Ni'mah, Samaneh Khoshrou, Vlado Menkovski, Mykola Pechenizkiy

Representing documents into high dimensional embedding space while preserving the structural similarity between document sources has been an ultimate goal for many works on text representation learning. Current embedding models, however, mainly rely on the availability of label supervision to increase the expressiveness of the resulting embeddings. In contra

Jérôme Bétrisey, Gaël Buldgen, Daniel R. Reese, Georges Meynet

*Context*. In the framework of the PLATO mission, to be launched in late 2026, seismic inversion techniques will play a key role in the mission precision requirements of the stellar mass, radius, and age. It is therefore relevant to discuss the challenges of the automation of seismic inversions, which were originally developed for individual modelling.\\ *Ai

Joe Boninger, Joshua Evan Greene

We prove that a special alternating knot does not decompose as a non-trivial band sum. This restricts concordances from special alternating knots, and we conjecture that special alternating knots are ribbon concordance minimal. We verify our conjecture in many cases. This work is motivated by another conjecture of Owens and the second author, which posits th

Binghui Peng, Aviad Rubinstein

We give a simple and computationally efficient algorithm that, for any constant $\varepsilon>0$, obtains $\varepsilon T$-swap regret within only $T = \mathsf{polylog}(n)$ rounds; this is an exponential improvement compared to the super-linear number of rounds required by the state-of-the-art algorithm, and resolves the main open problem of [Blum and Mansour

Hauke Gravenkamp, Albert A. Saputra, Sascha Eisenträger

The scaled boundary finite element method (SBFEM) has recently been employed as an efficient means to model three-dimensional structures, in particular when the geometry is provided as a voxel-based image. To this end, an octree decomposition of the computational domain is deployed and each cubic cell is treated as an SBFEM subdomain. The surfaces of each su

Yi-Fan Wang, Collin D. Capano, Jahed Abedi, Shilpa Kastha

We revisit the recent debate on the evidence for an overtone in the black hole ringdown of GW150914 using an independent data-analysis pipeline. By gating and inpainting the data, we discard the contamination from earlier parts of the gravitational wave signal before ringdown. This enables parameter estimation to be conducted in the frequency domain, which i

Zexu Pan, Gordon Wichern, Yoshiki Masuyama, Francois G. Germain

Target speech extraction aims to extract, based on a given conditioning cue, a target speech signal that is corrupted by interfering sources, such as noise or competing speakers. Building upon the achievements of the state-of-the-art (SOTA) time-frequency speaker separation model TF-GridNet, we propose AV-GridNet, a visual-grounded variant that incorporates

Yasuko Kawahata

In this paper, we will review some of the issues that have been raised by opinion dynamics theory to date. In particular, we conducted a hypothesis-based simulation using a socio-physical approach regarding the filter bubble phenomenon that tends to occur under special conditions such as (1) Distance, (2) Time, and (3) Existence of strong opinion clusters in

Paraschos Koutris, Xiating Ouyang, Jef Wijsen

We study the data complexity of consistent query answering (CQA) on databases that may violate the primary key constraints. A repair is a maximal subset of the database satisfying the primary key constraints. For a Boolean query q, the problem CERTAINTY(q) takes a database as input, and asks whether or not each repair satisfies q. The computational complexit

Jean Ollion, Martin Maliet, Caroline Giuglaris, Elise Vacher

Extracting long tracks and lineages from videomicroscopy requires an extremely low error rate, which is challenging on complex datasets of dense or deforming cells. Leveraging temporal context is key to overcoming this challenge. We propose DistNet2D, a new deep neural network (DNN) architecture for 2D cell segmentation and tracking that leverages both mid-

Mohsen Nafar, Hamed Azami Zenouzagh

We present an algorithmic technique for visualizing the co-authorship networks and other networks modeled with hypergraphs (set systems). As more than two researchers can co-author a paper, a direct representation of the interaction of researchers through their joint works cannot be adequately modeled with direct links between the author-nodes. A hypergraph

Joel Chaskalovic, Franck Assous

Based on a new Taylor-like formula, we derived an improved interpolation error estimate in $W^{1,p}$. We compare it with the classical error estimates based on the standard Taylor formula, and also with the corresponding interpolation error estimate, derived from the mean value theorem. We then assess the improvement in accuracy we can get from this formula,

Yasuko Kawahata

The simulations in this paper are based on the theory of opinion dynamics, which incorporates both Opinion A and Opinion B, a case that is the inverse of Opinion A, in human relationships. It was confirmed that aspects of consensus building depend on the ratio of the trust coefficient to the distrust coefficient. In this study, the ratio of trust to distrust

Andrii Anataichuk, Sven-Olaf Moch, Hamed Abdolmaleki, Simone Amoroso

Neutral current Drell-Yan (DY) lepton-pair production is considered in the framework of the Standard Model Effective Field Theory (SMEFT). Using the open-source fit platform xFitter, we investigate the impact of high-statistics measurements of the neutral current DY (NCDY) forward-backward asymmetry $A_{\rm{FB}}$ near the weak boson mass scale in the present

Silke Möbius

After Run III the ATLAS detector will undergo a series of upgrades to cope with the harsher radiation environment and increased number of proton interactions in the High Luminosity- LHC. One of the key projects in this suite of upgrades is the ATLAS Inner Tracker (ITk). The pixel detector of the ITk must be read out accurately and with extremely high rate. T

Yuhang Zhang, Yaqi Li, Lixiong Qin, Xuannan Liu

Facial expression data is characterized by a significant imbalance, with most collected data showing happy or neutral expressions and fewer instances of fear or disgust. This imbalance poses challenges to facial expression recognition (FER) models, hindering their ability to fully understand various human emotional states. Existing FER methods typically repo

Keegan Quigley, Miriam Cha, Josh Barua, Geeticka Chauhan

Vision-language pretraining has been shown to produce high-quality visual encoders which transfer efficiently to downstream computer vision tasks. Contrastive learning approaches have increasingly been adopted for medical vision language pretraining (MVLP), yet recent developments in generative AI offer new modeling alternatives. This paper introduces RadTex

Angeliki Aktypi, Kasper Rasmussen

In structured peer-to-peer networks, like Chord, users find data by asking a number of intermediate nodes in the network. Each node provides the identity of the closet known node to the address of the data, until eventually the node responsible for the data is reached. This structure means that the intermediate nodes learn the address of the sought after dat

Oscar Kivinen, Minh-Tâm Quang Trinh

Let $R$ be the complete local ring of a complex plane curve germ and $S$ its normalization. We propose a "Hilb-vs-Quot" conjecture relating the virtual weight polynomials of the Hilbert schemes of $R$ to those of the Quot schemes that parametrize $R$-submodules of $S$. By relating the Quot side to a type of compactified Picard scheme, we show that our conjec

Jay Pantone

We derive the algebraic generating function for inversion sequences avoiding the patterns $201$ and $210$ by describing a set of succession rules, converting them to a system of generating function equations with one catalytic variable, and then solving the system with kernel method techniques.

Timur Pryadilin, Daniil Zhitov, Vitalii Vertogradov

The process of particle collision in the vicinity of black holes is known to generate unbounded energies in the center-of-mass frame (the Banados-Silk-West (BSW) effect) under specific conditions. We consider this process in the charged black hole metrics, namely, the Reissner-Nordstrom (RN) and Majumdar-Papapetrou (MP) metrics. We consider the energy extrac

Shiwei Liu, Tian Zhu, Milong Ren, Chungong Yu

Many crucial biological processes rely on networks of protein-protein interactions. Predicting the effect of amino acid mutations on protein-protein binding is vital in protein engineering and therapeutic discovery. However, the scarcity of annotated experimental data on binding energy poses a significant challenge for developing computational approaches, pa

Olivier Rukundo, Andrea Behanova, Riccardo De Feo, Seppo Ronkko

Regular monitoring of the primary particles and purity profiles of a drug product during development and manufacturing processes is essential for manufacturers to avoid product variability and contamination. Transmission electron microscopy (TEM) imaging helps manufacturers predict how changes affect particle characteristics and purity for virus-based gene t

Zhuoman Liu, Bo Yang, Yan Luximon, Ajay Kumar

In this paper, we study the problem of continuous 3D shape representations. The majority of existing successful methods are coordinate-based implicit neural representations. However, they are inefficient to render novel views or recover explicit surface points. A few works start to formulate 3D shapes as ray-based neural functions, but the learned structures

Yasuko Kawahata

The proliferation of public networks has enabled instantaneous and interactive communication that transcends temporal and spatial constraints. The vast amount of textual data on the Web has facilitated the study of quantitative analysis of public opinion, which could not be visualized before. In this paper, we propose a new theory of opinion dynamics. This t

Jiazheng Dou, Shamik Ghosh, Larissa Santos, Wen Zhao

One of the main goals of most future CMB experiments is the precise measurement of CMB B-mode polarization, whose major obstacle is the Galactic foregrounds. In this paper, we evaluate the foreground cleaning performance of the variants of the ILC method on partial sky B-modes and analyze the main sources of biases on the BB power spectrum. Specially, we com

Zhengliang Liu, Yiwei Li, Qian Cao, Junwen Chen

Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities. However, the swift evolution of AGI has also raised critical questions about its responsible deployment in these culturally significant

Tomasz Mańdziuk, Emanuele Ventura

We study border varieties of sums of powers ($\underline{\mathrm{VSP}}$'s for short), recently introduced by Buczy\'nska and Buczy\'nski, parameterizing border rank decompositions of a point (e.g. of a tensor or a homogeneous polynomial) with respect to a smooth projective toric variety and living in the Haiman-Sturmfels multigraded Hilbert scheme. Their imp

Shuang Peng, Fei Yang, Ning Sun, Sheng Chen

Recent advancements in unsupervised protein language models (ProteinLMs), like ESM-1b and ESM-2, have shown promise in different protein prediction tasks. However, these models face challenges due to their high computational demands, significant memory needs, and latency, restricting their usage on devices with limited resources. To tackle this, we explore p

Jesse Franklin

We give a geometric perspective on the algebra of Drinfeld modular forms for congruence subgroups $\Gamma\leq \GL_2(\bbF_q[T]).$ In particular, we describe an isomorphism between the section ring of a line bundle on the stacky modular curve for $\Gamma_2$ and the algebra of Drinfeld modular forms for $\Gamma_2,$ where $\Gamma_2$ is the subgroup of square-det

Jin-Fu Chen, H. T. Quan

The pursuit of achieving the maximum power in microscopic thermal engines has gained increasing attention in recent studies of stochastic thermodynamics. We employ the optimal control theory to study the performance of Brownian heat engines and determine the optimal heat-engine cycles in generic damped situation, which were previously known only in the overd

Siddhesh Kulkarni, Subhadip Pal, Jeremy T. Gaskins

It can be challenging to perform an integrative statistical analysis of multi-view high-dimensional data acquired from different experiments on each subject who participated in a joint study. Canonical Correlation Analysis (CCA) is a statistical procedure for identifying relationships between such data sets. In that context, Structured Sparse CCA (ScSCCA) is

Qiao Sun, Shiduo Zhang, Danjiao Ma, Jingzhe Shi

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired

Ziqiao Ma, Jacob Sansom, Run Peng, Joyce Chai

Large Language Models (LLMs) have generated considerable interest and debate regarding their potential emergence of Theory of Mind (ToM). Several recent inquiries reveal a lack of robust ToM in these models and pose a pressing demand to develop new benchmarks, as current ones primarily focus on different aspects of ToM and are prone to shortcuts and data lea

Roselle Ngaloy, Bing Zhao, Soheil Ershadrad, Rahul Gupta

Van der Waals (vdW) magnets are promising owing to their tunable magnetic properties with doping or alloy composition, where the strength of magnetic interactions, their symmetry, and magnetic anisotropy can be tuned according to the desired application. However, most of the vdW magnet based spintronic devices are so far limited to cryogenic temperatures wit

J. H. Guo, Y. W. Ni, Y. Guo, C. Xia

Magnetic flux ropes are a bundle of twisted magnetic field lines produced by internal electric currents, which are responsible for solar eruptions and are the major drivers of geomagnetic storms. As such, it is crucial to develop a numerical model that can capture the entire evolution of a flux rope, from its birth to death, in order to predict whether adver

Hiromasa Suzuki, Aya Bamba, Ryo Yamazaki, Yutaka Ohira

Supernova remnants (SNRs) are thought to be the most plausible sources of Galactic cosmic rays. One of the principal questions is whether they are accelerating particles up to the maximum energy of Galactic cosmic rays ($\sim$PeV). In this paper, we summarize our recent studies on gamma-ray-emitting SNRs. We first evaluated the reliability of SNR age estimat

Makiko Ban

A microlensing event is mainly used to search for free-floating planets (FFPs). To estimate the FFP mass and distance via the microlensing effect, a microlensing parallax is one of the key parameters. A short duration of FFP microlensing is difficult to yield a parallax by the observer's motion at a recognisable level, so the FFP microlensing parallax is exp

Julian Rossbroich, Friedemann Zenke

How neuronal circuits achieve credit assignment remains a central unsolved question in systems neuroscience. Various studies have suggested plausible solutions for back-propagating error signals through multi-layer networks. These purely functionally motivated models assume distinct neuronal compartments to represent local error signals that determine the si

P G Romeo, A Anju

In this article, we introduce the normal category L(S) [R(S)] of principal left [right] ideals of the normed algebra S of all finite rank bounded operators on a Hilbert space H and is shown that they are isomorphic, using Hilbert space duality. We also described the semigroup of all normal cones in L(S) which is isomorphic to the semigroup of all finite rank

Marion Guelfand, Simon Chiche, Kumiko Kotera, Simon Prunet

The reconstruction of very inclined air showers is a new challenge for next-generation radio experiments such as the AugerPrime radio upgrade, BEACON, and GRAND, which focus on the detection of ultra-high-energy particles. To tackle this, we study the electromagnetic particle content of very inclined air showers, which has scarcely been studied so far. Using

Paolo Baldi, Domenico Marinucci, Stefano Trapani

In this note we prove that a finite family $\{X_1,\dots,X_d\}$ of real r.v.'s that is exchangeable and such that $(X_1,\dots,X_d)$ is invariant with respect to a subgroup of $SO(d)$ acting irreducibly, is actually invariant with respect to the action of the full group $SO(d)$. Three immediate consequences are deduced: a characterization of isotropic spherica

Lenart Treven, Jonas Hübotter, Bhavya Sukhija, Florian Dörfler

Reinforcement learning algorithms typically consider discrete-time dynamics, even though the underlying systems are often continuous in time. In this paper, we introduce a model-based reinforcement learning algorithm that represents continuous-time dynamics using nonlinear ordinary differential equations (ODEs). We capture epistemic uncertainty using well-ca

Anca Măcinic, Piotr Pokora

In the recent paper A. Dimca proves that when one adds to or deletes a line from a free curve the resulting curve is either free or plus-one generated. We prove the converse statements, we give an additional insight into the original deletion result, and derive a characterisation of free curves in terms of behaviour to addition/deletion of lines. We describe

Roghayeh Maleki

In 1992, Terwilliger introduced the notion of the \emph{Terwilliger algebra} in order to study association schemes. The Terwilliger algebra of an association scheme $\mathcal{A}$ is the subalgebra of the complex matrix algebra, generated by the \emph{Bose-Mesner algebra} of $\mathcal{A}$ and its dual idempotents with respect to a point $x$. In [{\em Kyushu J

Zheng Zhao, Sebastian Mair, Thomas B. Schön, Jens Sjölund

Recently, partial Bayesian neural networks (pBNNs), which only consider a subset of the parameters to be stochastic, were shown to perform competitively with full Bayesian neural networks. However, pBNNs are often multi-modal in the latent variable space and thus challenging to approximate with parametric models. To address this problem, we propose an effici

Guilherme Paulino-Passos, Francesca Toni

Case-based reasoning is known to play an important role in several legal settings. In this paper we focus on a recent approach to case-based reasoning, supported by an instantiation of abstract argumentation whereby arguments represent cases and attack between arguments results from outcome disagreement between cases and a notion of relevance. In this contex

Magnus C. Schaaf

Effective Field Theories (EFTs) provide a framework for capturing the effects of yet unseen heavy degrees of freedom in a model-independent manner. However, constructing a complete and minimal set of operators, especially at higher mass dimensions, is challenging. We present AutoEFT, an implementation of an algorithm that systematically handles redundancies

Sreevarsha Sreejith, Anže Slosar, Hong Wang

A major issue in optical astronomical image analysis is the combined effect of the instrument's point spread function (PSF) and the atmospheric seeing that blurs images and changes their shape in a way that is band and time-of-observation dependent. In this work we present a very simple neural network based approach to non-blind image deconvolution that reli

Alejandro López-Nieto, Phillipo Lappicy, Nicola Vassena, Hannes Stuke

We describe a new mechanism that triggers periodic orbits in smooth dynamical systems. To this end, we introduce the concept of hybrid bifurcations: Such bifurcations occur when a line of equilibria with an exchange point of normal stability vanishes. Our main result is the existence and stability criteria of periodic orbits that bifurcate from breaking a li

Blanka Horvath, Anastasis Kratsios, Yannick Limmer, Xuwei Yang

The use of attention-based deep learning models in stochastic filtering, e.g. transformers and deep Kalman filters, has recently come into focus; however, the potential for these models to solve stochastic filtering problems remains largely unknown. The paper provides an affirmative answer to this open problem in the theoretical foundations of machine learni

Jialu Li, Junhui Li, Pu Wang, Youshan Zhang

Most of the current deep learning-based approaches for speech enhancement only operate in the spectrogram or waveform domain. Although a cross-domain transformer combining waveform- and spectrogram-domain inputs has been proposed, its performance can be further improved. In this paper, we present a novel deep complex hybrid transformer that integrates both s

Andrea Bondesan, Giuseppe Toscani, Mattia Zanella

We propose a kinetic model for understanding the link between opinion formation phenomena and epidemic dynamics. The recent pandemic has brought to light that vaccine hesitancy can present different phases and temporal and spatial variations, presumably due to the different social features of individuals. The emergence of patterns in societal reactions permi

Rebecca G. Martin, Stephen H. Lubow, David Vallet, Narsireddy Anugu

We examine the geometry of the post-asymptotic giant branch (AGB) star binary AC Her and its circumbinary disk. We show that the observations describe a binary orbit that is perpendicular to the disk with an angular momentum vector that is within $9^\circ$ of the binary eccentricity vector, meaning that the disk is close to a stable polar alignment. The most

Salvador Bará, Fabio Falchi

Light pollution is the alteration of the natural levels of darkness by an increased concentration of light particles in the nighttime environment, resulting from human activity. Light pollution is changing in a deep way the environmental conditions of the night in wide areas of the planet, and is a relevant stressor whose effects on life are being unveiled b

A. Ya. Dzyublik, V. Yu. Spivak

The absorption cross section of M\"{o}ssbauer radiation in magnetic liquids is calculated, taking into consideration both translational and rotational Brownian motion of magnetic nanoparticles. Stochastic reversals of their magnetization are also regarded in the absence of external magnetic field. The role of Brownian motion in ferrofluids is considered in t

Yasong Feng, Yifan Jiang, Tianyu Wang, Zhiliang Ying

We study the stochastic optimization problem from a continuous-time perspective, with a focus on the Stochastic Gradient Descent with Momentum (SGDM) method. We show that the trajectory of SGDM, despite its \emph{stochastic} nature, converges in $L_2$-norm to a \emph{deterministic} second-order Ordinary Differential Equation (ODE) as the stepsize goes to zer

Ruoyu Zhang, Yanzeng Li, Yongliang Ma, Ming Zhou

Prevalent supervised learning methods in natural language processing (NLP) are notoriously data-hungry, which demand large amounts of high-quality annotated data. In practice, acquiring such data is a costly endeavor. Recently, the superior few-shot performance of large language models (LLMs) has propelled the development of dataset generation, where the tra

R. Grimaudo, G. Falci, A. Messina, E. Paladino

The occurrence of a second-order superradiant quantum phase transition is brought to light in a quantum system consisting of two interacting qubits coupled to the same quantized field mode. We introduce an appropriate thermodynamic-like limit for the integrable two-qubit quantum Rabi model with spin-spin interaction. Namely, it is determined by the infinite

Lukas Michel, Alex Scott

Local search algorithms for NP-hard problems such as Max-Cut frequently perform much better in practice than worst-case analysis suggests. Smoothed analysis has proved an effective approach to understanding this: a substantial literature shows that when a small amount of random noise is added to input data, local search algorithms typically run in polynomial

Gregory S. H. Paek, Myungshin Im, Joonho Kim, Gu Lim

One of the keys to the success of multimessenger astronomy is the rapid identification of the electromagnetic wave counterpart, kilonova (KN), of the gravitational-wave (GW) event. Despite its importance, it is hard to find a KN associated with a GW event, due to a poorly constrained GW localization map and numerous signals that could be confused as a KN. He

Chuanxin Cui, Sirui Ning

In this paper we present a realization of dark dimension. We consider the 5D standard model coupling to gravity with one dimension compactified on an orbifold, which is seen as dark dimension of size R. We stabilize the radion by casimir effect wrapping around compact dimension and recover the neutrino mass and 4D cosmological constant with the observed valu

Vladimir V'yugin, Vladimir Trunov

The problem of continuous machine learning is studied. Within the framework of the game-theoretic approach, when for calculating the next forecast, no assumptions about the stochastic nature of the source that generates the data flow are used -- the source can be analog, algorithmic or probabilistic, its parameters can change at random times, when building a

Bin Lin, Zhiping Mao, Zhicheng Wang, George Em Karniadakis

Physics-informed Neural Networks (PINNs) have been shown as a promising approach for solving both forward and inverse problems of partial differential equations (PDEs). Meanwhile, the neural operator approach, including methods such as Deep Operator Network (DeepONet) and Fourier neural operator (FNO), has been introduced and extensively employed in approxim

Jung Yeon Park, Lawson L. S. Wong, Robin Walters

Data over non-Euclidean manifolds, often discretized as surface meshes, naturally arise in computer graphics and biological and physical systems. In particular, solutions to partial differential equations (PDEs) over manifolds depend critically on the underlying geometry. While graph neural networks have been successfully applied to PDEs, they do not incorpo

Junhui Li, Pu Wang, Jialu Li, Xinzhe Wang

Recent high-performance transformer-based speech enhancement models demonstrate that time domain methods could achieve similar performance as time-frequency domain methods. However, time-domain speech enhancement systems typically receive input audio sequences consisting of a large number of time steps, making it challenging to model extremely long sequences

Mihir Dass, Lena Raab, Christoph Pauer, Christoph Sikeler

Counterfeiting threatens human health, social equity, national security and global and local economies. Hardware-based cryptography that exploits physical unclonable functions (PUFs) provides the means for secure identification and authentication of products. While optical PUFs are among the hardest to replicate, they suffer from low encoding capacity and of

Shilei Li, Dawei Shi, Yunjiang Lou, Wulin Zou

Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance simultaneously, and is optimal for a linear system with Gaussian noises. Unfortunately, The noise in the disturbanc

Nathan Schroeder, Weaam Alhejaili, Chiu-Yen Kao

We consider Steklov eigenvalues on nearly spherical and nearly annular domains in $d$ dimensions. By using the Green-Beltrami identity for spherical harmonic functions, the derivatives of Steklov eigenvalues with respect to the domain perturbation parameter can be determined by the eigenvalues of a matrix involving the integral of the product of three spheri

A. N. W. Hone, J. A. G. Roberts, P. Vanhaecke, F. Zullo

In recent work, we presented the construction of a family of difference equations associated with the Stieltjes continued fraction expansion of a certain function on a hyperelliptic curve of genus $g$. As well as proving that each such discrete system is an integrable map in the Liouville sense, we also showed it to be an algebraic completely integrable syst

Vibhas K. Vats, Sripad Joshi, David J. Crandall, Md. Alimoor Reza

Traditional multi-view stereo (MVS) methods rely heavily on photometric and geometric consistency constraints, but newer machine learning-based MVS methods check geometric consistency across multiple source views only as a post-processing step. In this paper, we present a novel approach that explicitly encourages geometric consistency of reference view depth

Darya Baranouskaya, Andrea Cavallaro

Privacy is a complex, subjective and contextual concept that is difficult to define. Therefore, the annotation of images to train privacy classifiers is a challenging task. In this paper, we analyse privacy classification datasets and the properties of controversial images that are annotated with contrasting privacy labels by different assessors. We discuss

Suyeon Lee, Chaeyoung Jung, Youngjoon Jang, Jaehun Kim

The objective of this work is to extract target speaker's voice from a mixture of voices using visual cues. Existing works on audio-visual speech separation have demonstrated their performance with promising intelligibility, but maintaining naturalness remains a challenge. To address this issue, we propose AVDiffuSS, an audio-visual speech separation model b

Christopher Otto, Prashanth Chandran, Gaspard Zoss, Markus Gross

Monocular 3D face reconstruction is a wide-spread topic, and existing approaches tackle the problem either through fast neural network inference or offline iterative reconstruction of face geometry. In either case carefully-designed energy functions are minimized, commonly including loss terms like a photometric loss, a landmark reprojection loss, and others

Roman Lakenbrink, Markus Müller-Olm, Christoph Ohrem, Jens Gutsfeld

Dynamic Pushdown Networks (DPNs) are a model for multithreaded programs with recursion and dynamic creation of threads. In this paper, we propose a temporal logic called NTL for reasoning about the call- and return- as well as thread creation behaviour of DPNs. Using tree automata techniques, we investigate the model checking problem for the novel logic and

Rafael Sayous

Using a standard definition of fractional powers on the universal cover $\exp:S\to \mathbb{C}^*$ seen as an infinite helicoid embedded in $\mathbb{R}^3$, we study the statistics of pairs from the countable family $\{n^\alpha \, : \, n \in \exp^{-1}(\Lambda) \}$ for every complex grid $\Lambda$ and every real parameter $\alpha \in \, ]0,1[\,$. We prove the co