April 2024 arXiv papers — page 28

Fredrik Engström

Dependence logic provides an elegant approach for introducing dependencies between variables into the object language of first-order logic. In [1] generalized quantifiers were introduced in this context. However, a satisfactory account was only achieved for monotone increasing generalized quantifiers. In this paper, we modify the fundamental semantical guide

E. I. Marchenko, V. V. Korolev, E. A. Kobeleva, N. A. Belich

Identification of crystal structures is a crucial stage in the exploration of novel functional materials. This procedure is usually time-consuming and can be false-positive or false-negative. This necessitates a significant level of expert proficiency in the field of crystallography and, especially, requires deep experience in perovskite - related structures

Vanesa Gómez-Martínez, Francisco J. Lara-Abelenda, Pablo Peiro-Corbacho, David Chushig-Muzo

Tabular data have been extensively used in different knowledge domains. Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features (images), outperforming predictive results of traditional models. Recently, several researchers have proposed transforming tab

Jan Simson, Alessandro Fabris, Christoph Kern

Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field by highlighting shortcomings and proposing recommendations for improvement. In this work, we present a comprehensive analysis of fair ML datasets, demonstrating how

Sergey Kurapov, Maxim Davidovsky

The Four color problem is closely related to other branches of mathematics and practical applications. More than 20 of its reformulations are known, which connect this problem with problems of algebra, statistical mechanics and planning. And this is also typical for mathematics: the solution to a problem studied out of pure curiosity turns out to be useful i

Gabriel Kronberger, Fabricio Olivetti de Franca, Harry Desmond, Deaglan J. Bartlett

We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings, allowing exhaustive enumeration of all solutions. This enables us to quantify the success probability of finding the best possible expressions, and to compare the search efficiency of genetic programming to random search in the space o

Stuart F. Knowles, Eleanor K. R. Mackay, Alice L. Thorneywork

The transport of molecules through biological and synthetic nanopores is governed by multiple stochastic processes that lead to noisy, fluctuating currents. Disentangling the characteristics of different noise-generating mechanisms is central to better understanding molecular transport at a fundamental level but is extremely challenging in molecular systems,

Weijie Shen, Weiwei Xu, Lei Zhu

Low-rank matrix approximation play a ubiquitous role in various applications such as image processing, signal processing, and data analysis. Recently, random algorithms of low-rank matrix approximation have gained widespread adoption due to their speed, accuracy, and robustness, particularly in their improved implementation on modern computer architectures.

Andrew K. J. Pritchard, David Seifert

We study the asymptotic behaviour of orbits $(T^nx)_{n\ge0}$ of the classical Ces\`aro operator $T$ for sequences $x$ in the Banach space $c$ of convergent sequences. We give new non-probabilistic proofs, based on the Katznelson-Tzafriri theorem and one of its quantified variants, of results which characterise the set of sequences $x\in c$ that lead to conve

Christoph Draxler, Henk van den Heuvel, Arjan van Hessen, Pavel Ircing

Oral history is about oral sources of witnesses and commentors on historical events. Speech technology is an important instrument to process such recordings in order to obtain transcription and further enhancements to structure the oral account In this contribution we address the transcription portal and the webservices associated with speech processing at B

Wenhao Zhang, Mengqi Zhang, Shiguang Wu, Jiahuan Pei

Exclusion is an important and universal linguistic skill that humans use to express what they do not want. However, in information retrieval community, there is little research on exclusionary retrieval, where users express what they do not want in their queries. In this work, we investigate the scenario of exclusionary retrieval in document retrieval for th

Shuchang Tao, Liuyi Yao, Hanxing Ding, Yuexiang Xie

Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlights the importance of discerning when to trust LLMs, especially in safety-critical domains. Existing methods often express reliability by confidence level, however, their effective

Nejc Čeplak, Hong Liu, Andrei Parnachev, Samuel Valach

Eternal asymptotically AdS black holes are dual to thermofield double states in the boundary CFT. It has long been known that black hole singularities have certain signatures in boundary thermal two-point functions related to null geodesics bouncing off the singularities (bouncing geodesics). In this paper we shed light on the manifestations of black hole si

Hans Langva Skarsvåg, Eirik Holm Fyhn, Ailo Aasen

Ammonia is expected to play an important role in the green transition, both as a hydrogen carrier and a zero-emission fuel. The use of refrigerated ammonia is attractive due to its relatively high volumetric energy density and increased safety compared to pressurized solutions. Ammonia is highly toxic, and with new applications and increased global demand co

Anirudh Narayan D, Akshat Johar, Divye Kalra, Bhavya Ardeshna

Accurate prediction of battery temperature rise is very essential for designing an efficient thermal management scheme. In this paper, machine learning (ML) based prediction of Vanadium Redox Flow Battery (VRFB) thermal behavior during charge-discharge operation has been demonstrated for the first time. Considering different currents with a specified electro

Xuan Zhang, Wei Gao

Retrieval-augmented language models have exhibited promising performance across various areas of natural language processing (NLP), including fact-critical tasks. However, due to the black-box nature of advanced large language models (LLMs) and the non-retrieval-oriented supervision signal of specific tasks, the training of retrieval model faces significant

S. Grouffal, A. Santerne, N. C. Hara, I. Boisse

High precision spectrographs might exhibit temporal variations of their reference velocity or nightly zero point (NZP). One way to monitor the NZP is to measure bright stars, which are assumed to have an intrinsic radial velocity variation much smaller than the instrument's precision. While this method is effective in most cases, it does not fully propagate

Jun Wang, Jie Liu, Peng Hu, Qiao Jiang

A Dirac point in the Hermitian photonic system will split into a pair of exceptional points (EPs) or even spawn a ring of EPs if non-Hermiticity is involved. Here, we present a new type of non-Hermitian Dirac point which is situated in the complex plane of eigenfrequency. When there is differential loss, the Dirac point exhibits a dual behavior: it not only

Mingrui He, Longting Xu, Han Wang, Mingjun Zhang

The most common spoofing attacks on automatic speaker verification systems are replay speech attacks. Detection of replay speech heavily relies on replay configuration information. Previous studies have shown that graph Fourier transform-derived features can effectively detect replay speech but ignore device and environmental noise effects. In this work, we

Indrajit Paul, Ashok Kumar Das

The concept of graph powers has been extensively studied in graph theory. Analogous to graph powers, Chandran et al. [3] introduced the notion of bipartite powers for bipartite graphs. In this paper, we show that the class of interval bigraphs, as well as the class of proper interval bigraphs are closed under the operation of taking bipartite powers. Finally

Agelos Georgakopoulos

We introduce notions of dimension of an infinite group, or more generally, a metric space, defined using percolation. Roughly speaking, the percolation dimension $pdim(G)$ of a group $G$ is the fastest rate of decay of a symmetric probability measure $\mu$ on $G$, such that Bernoulli percolation on $G$ with connection probabilities proportional to $\mu$ beha

A. S. Alam, L. C. Andaru, B. N. Jayawiguna, H. S. Ramadhan

We conduct a comprehensive study on spherical orbits around two types of black holes: Kerr-Newman black holes, which are charged, and Ghosh black holes, which are nonsingular. In this work, we consider both null and timelike cases of orbits. Utilizing the Mino formalism, all analytical solutions for the geodesics governing these orbits can be obtained. It tu

Charalampos Symeonidis, Nikos Nikolaidis

Electricity generated from renewable energy sources has been established as an efficient remedy for both energy shortages and the environmental pollution stemming from conventional energy production methods. Solar and wind power are two of the most dominant renewable energy sources. The accurate forecasting of the energy generation of those sources facilitat

Xiang Gu, Xi Yu, Yan Yang, Jian Sun

The practical Domain Adaptation (DA) tasks, e.g., Partial DA (PDA), open-set DA, universal DA, and test-time adaptation, have gained increasing attention in the machine learning community. In this paper, we propose a novel approach, dubbed Adversarial Reweighting with $\alpha$-Power Maximization (ARPM), for PDA where the source domain contains private classe

Benedikt Fesl, Aziz Banna, Wolfgang Utschick

Channel estimation in quantized systems is challenging, particularly in low-resolution systems. In this work, we propose to leverage a Gaussian mixture model (GMM) as generative prior, capturing the channel distribution of the propagation environment, to enhance a classical estimation technique based on the expectation-maximization (EM) algorithm for one-bit

Klaus Jansen, Kai Kahler, Esther Zwanger

Goemans and Rothvoss (SODA'14) gave a framework for solving problems which can be described as finding a point in int$.$cone$(P\cap\mathbb{Z}^N)\cap Q$, where $P,Q\subset\mathbb{R}^N$ are (bounded) polyhedra. The running time for solving such a problem is $enc(P)^{2^{O(N)}}enc(Q)^{O(1)}$. This framework can be used to solve various scheduling problems, but t

Xuri Ge, Songpei Xu, Fuhai Chen, Jie Wang

In this paper, we propose a novel visual Semantic-Spatial Self-Highlighting Network (termed 3SHNet) for high-precision, high-efficiency and high-generalization image-sentence retrieval. 3SHNet highlights the salient identification of prominent objects and their spatial locations within the visual modality, thus allowing the integration of visual semantics-sp

ALICE Collaboration

The inclusive production of the charm-strange baryon $\Omega^{0}_{\rm c}$ is measured for the first time via its semileptonic decay into $\Omega^{-}\rm e^{+}\nu_{e}$ at midrapidity ($|y|<0.8$) in proton$-$proton (pp) collisions at the centre-of-mass energy $\sqrt{s}=13$ TeV with the ALICE detector at the LHC. The transverse momentum ($p_{\rm T}$) differentia

Erik-Jan van Kesteren

For over 30 years, synthetic data has been heralded as a promising solution to make sensitive datasets accessible. However, despite much research effort and several high-profile use-cases, the widespread adoption of synthetic data as a tool for open, accessible, reproducible research with sensitive data is still a distant dream. In this opinion, Erik-Jan van

Haiyang Miao, Jianhua Zhang, Pan Tang, Lei Tian

In the sixth-generation (6G), the extremely large-scale multiple-input-multiple-output (XL-MIMO) is considered a promising enabling technology. With the further expansion of array element number and frequency bands, near-field effects will be more likely to occur in 6G communication systems. The near-field radio communications (NFRC) will become crucial in 6

Christoph Zelch, Jan Peters, Oskar von Stryk

Clustering of motion trajectories is highly relevant for human-robot interactions as it allows the anticipation of human motions, fast reaction to those, as well as the recognition of explicit gestures. Further, it allows automated analysis of recorded motion data. Many clustering algorithms for trajectories build upon distance metrics that are based on poin

Vinicius Bernardes, Ulisses Portugal

We introduce a two parameter family of string field theory vertices, which we refer to as hyperbolic Kaku vertices. It is defined in terms of hyperbolic metrics on the Riemann surface, but the geometry is allowed to depend on inputs of the states. The vertices are defined for both open and closed strings. In either case, the family contains the hyperbolic ve

Xiang Li, Hu Yang

Since the chemical industry index is one of the important indicators to measure the development of the chemical industry, forecasting it is critical for understanding the economic situation and trends of the industry. Taking the multivariable nonstationary series-synthetic material index as the main research object, this paper proposes a new prediction model

Ivan Arraut

We demonstrate that the black hole evaporation can be modelled as a process where one symmetry of the system is spontaneously broken continuously. We then identify three free-parameters of the system. The sign of one of the free-parameters, governs whether the particles emitted by the black-hole are fermions or bosons. The present model explains why the Blac

Salvatore Capozziello, Silvia De Bianchi, Emmanuele Battista

We investigate a Schwarzschild metric exhibiting a signature change across the event horizon, which gives rise to what we term a Lorentzian-Euclidean black hole. The resulting geometry is regularized by employing the Hadamard partie finie technique, which allows us to prove that the metric represents a solution of vacuum Einstein equations. In this framework

Arkadii Levskii, Alexander Shlapunov

We describe the strong dual space $({\mathcal O}^s (D))^*$ for the space ${\mathcal O}^s (D) = H^s (D) \cap {\mathcal O} (D)$ of holomorphic functions from the Sobolev space $H^s(D)$, $s \in \mathbb Z$, over a bounded simply connected plane domain $D$ with infinitely differential boundary $\partial D$. We identify the dual space with the space of holomorhic

Saptarshi Roy

Trends of genuine entanglement in Haar uniformly generated multimode pure Gaussian states with fixed average energy per mode are explored. A distance-based metric known as the generalized geometric measure (GGM) is used to quantify genuine entanglement. The GGM of a state is defined as its minimum distance from the set of all non-genuinely entangled states.

Matthias Laschke, Lenneke Kuijer

In this position paper we present Municipan, an artefact resulting from a post-growth design experiment, applied in a student design project. In contrast to mainstream human-centered design directed at efficiency and convenience, which we argue leads to deskilling, dependency, and the progression of the climate crisis, we challenged students to envision an o

Mohamed Elfiatoure, Mohammadali Mohammadi, Hien Quoc Ngo, Hyundong Shin

We propose a distributed implementation for integrated sensing and communication (ISAC) backed by a massive multiple input multiple output (CF-mMIMO) architecture without cells. Distributed multi-antenna access points (APs) simultaneously serve communication users (UEs) and emit probing signals towards multiple specified zones for sensing. The APs can switch

Panagiotis Spanos, Matthew Tointon

Let $G$ be a vertex-transitive graph of superlinear polynomial growth. Given $r>0$, let $G_r$ be the graph on the same vertex set as $G$, with two vertices joined by an edge if and only if they are at graph distance at most $r$ apart in $G$. We show that the critical probability $p_c(G_r)$ for Bernoulli bond percolation on $G_r$ satisfies $p_c(G_r) \sim 1/\m

Ehsan Ghaderi, MohammadAli Bijarchi, Siamak Kazemzadeh Hannani, Ali Nouri-Borujerdi

In this study, fluid flow inside of an obstructed channel under the influence of magnetic field has been analyzed using physics informed neural networks(PINNs). Governing equations have been utilized in low-order form and the solution has been obtained in dimensionless form. Geometric and physics-related dimensionless parameters have been used as input varia

Maurício Collares, Joseph Doolittle, Joshua Erde

In their seminal paper introducing the theory of random graphs, Erd\H{o}s and R\'{e}nyi considered the evolution of the structure of a random subgraph of $K_n$ as the density increases from $0$ to $1$, identifying two key points in this evolution -- the \emph{percolation threshold}, where the order of the largest component seemingly jumps from logarithmic to

J. M. Y. Woo, D. Nesvorny, J. Scora, A. Morbidelli

The process leading to the formation of the terrestrial planet remains elusive. In a previous publication, we have shown that, if the first generation of planetesimals forms in a ring at about 1 AU and the gas disk's density peaks at the ring location, planetary embryos of a few martian masses can grow and remain in the ring. In this work, we extend our simu

Alfred Geroldinger, Florian Kainrath

Let $G$ be an additive abelian group. A sequence $S = g_1 \cdot \ldots \cdot g_{\ell}$ of terms from $G$ is a plus-minus weighted zero-sum sequence if there are $\varepsilon_1, \ldots, \varepsilon_{\ell} \in \{-1, 1\}$ such that $\varepsilon_1 g_1 + \ldots + \varepsilon_{\ell} g_{\ell}=0$. We study sets of lengths in the monoid $\mathcal B_{\pm} (G)$ of plus

R. Somogyfoki, A. Famà, L. Restuccia, P. Ván

The fundamental dynamic stability of heat conduction theories beyond Fourier is analyzed in the framework of nonequilibrium thermodynamics. It is shown, that the thermodynamic framework, concave entropy and nonnegative entropy production, can ensure the stability of homogeneous thermodynamic equilibrium. Various special heat conduction theories of Extended T

Ben Blum-Smith

We study degree bounds on rational but not necessarily polynomial generators for the field $\mathbf{k}(V)^G$ of rational invariants of a linear action of a finite abelian group. We show that lattice-theoretic methods used recently by the author and collaborators to study polynomial generators for the same field largely carry over, after minor modifications t

Georgia Baltsou, Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos

AI systems rely on extensive training on large datasets to address various tasks. However, image-based systems, particularly those used for demographic attribute prediction, face significant challenges. Many current face image datasets primarily focus on demographic factors such as age, gender, and skin tone, overlooking other crucial facial attributes like

Jiawei Song, Dengpan Ye, Yunming Zhang

Artificial Intelligence Generated Content (AIGC) techniques, represented by text-to-image generation, have led to a malicious use of deep forgeries, raising concerns about the trustworthiness of multimedia content. Adapting traditional forgery detection methods to diffusion models proves challenging. Thus, this paper proposes a forgery detection method expli

Glen Pouliquen, Guillaume Chiron, Joseph Chazalon, Thierry Géraud

We propose a method to remotely verify the authenticity of Optically Variable Devices (OVDs), often referred to as ``holograms'', in identity documents. Our method processes video clips captured with smartphones under common lighting conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020. Thanks to a weakly-supervised training, we optim

Houtan Ghaffari, Paul Devos

Transferring the weights of a pre-trained model to assist another task has become a crucial part of modern deep learning, particularly in data-scarce scenarios. Pre-training refers to the initial step of training models outside the current task of interest, typically on another dataset. It can be done via supervised models using human-annotated datasets or s

Samuel Cerezo, Javier Civera

In this paper, we introduce a novel formulation for camera motion estimation that integrates RGB-D images and inertial data through scene flow. Our goal is to accurately estimate the camera motion in a rigid 3D environment, along with the state of the inertial measurement unit (IMU). Our proposed method offers the flexibility to operate as a multi-frame opti

Maria Francisca Madeira, Alessandro Poggiali, Jeanette Miriam Lorenz

Quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level and can be addressed with both supervised and unsupervised methods. Autoencoders are commonly used in unsupervised tasks, where models are tr

Zhonghua Li, Shengbo Zhao

We establish an omega theorem for logarithmic derivative of the Riemann zeta function near the 1-line by resonance method. We show that the inequality $\left| \zeta^{\prime}\left(\sigma_A+it\right)/\zeta\left(\sigma_A+it\right) \right| \geqslant \left(\left(e^A-1\right)/A\right)\log_2 T + O\left(\log_2 T / \log_3 T\right)$ has a solution $t \in [T^{\beta}, T

Freddie Bickford Smith, Adam Foster, Tom Rainforth

Fully supervised models are predominant in Bayesian active learning. We argue that their neglect of the information present in unlabelled data harms not just predictive performance but also decisions about what data to acquire. Our proposed solution is a simple framework for semi-supervised Bayesian active learning. We find it produces better-performing mode

N. K. Patra

A Bayesian method is used in this extensive work to generate a large set of minimally constrained equations of state (EOSs) for matters in neutron stars (NS). These EOSs are analyzed for their correlations with key NS properties, such as the tidal deformability, radius, and maximum mass, within the mass range of $1.2-2M_\odot$. The observed connections betwe

Kaito Iwamura, Takayuki Suzuki

A defect density which quantifies the deviation from the spin ground state characterizes non-equilibrium dynamics during phase transitions. The widely recognized Kibble-Zurek scaling predicts how the defect density evolves during phase transitions. However, it can be perturbed by a noise, leading to the anti-Kibble-Zurek scaling. In this research, we analyti

Tian-Ge Li, Sheng-Chao Zhang, Guan-Ying Wang, Qi-Fang Lü

Inspired by the newly observed $X(2085)$ by the BESIII Collaboration, we study the strong decay behaviors of excited axialvector strange mesons within the quark pair creation model. Our results indicate that the $K_1(1793)/K_1(1861)$ can be regarded as the same $K_1(2P)$ state, and the $K_1(1911)$ is assigned as the $K_1(2P^\prime)$ state. Considering the ma

Reza Akbarian Bafghi, Nidhin Harilal, Claire Monteleoni, Maziar Raissi

Artificial neural networks often suffer from catastrophic forgetting, where learning new concepts leads to a complete loss of previously acquired knowledge. We observe that this issue is particularly magnified in vision transformers (ViTs), where post-pre-training and fine-tuning on new tasks can significantly degrade the model's original general abilities.

Abdullatif AlShriaf, Hans-Martin Heyn, Eric Knauss

This work introduces a tool for generating runtime configurations automatically from textual requirements stored as artifacts in git repositories (a.k.a. T-Reqs) alongside the software code. The tool leverages T-Reqs-modelled architectural description to identify relevant configuration properties for the deployment of artificial intelligence (AI)-enabled sof

Fabio Quattrini, Vittorio Pippi, Silvia Cascianelli, Rita Cucchiara

Document Image Binarization is a well-known problem in Document Analysis and Computer Vision, although it is far from being solved. One of the main challenges of this task is that documents generally exhibit degradations and acquisition artifacts that can greatly vary throughout the page. Nonetheless, even when dealing with a local patch of the document, tak

Victor Fischer, Maximilian Passek, Friedrich Wagner, Jernej Rudi Finžgar

Understanding the benefits of quantum computing for solving combinatorial optimization problems (COPs) remains an open research question. In this work, we extend and analyze algorithms that solve COPs by recursively shrinking them. The algorithms leverage correlations between variables extracted from quantum or classical subroutines to recursively simplify t

Michelle K Croughan, David M Paganin, Samantha J Alloo, Jannis N Ahlers

In recent years, a novel x-ray imaging modality has emerged that reveals unresolved sample microstructure via a "dark-field image", which provides complementary information to conventional "bright-field" images, such as attenuation and phase-contrast modalities. This x-ray dark-field signal is produced by unresolved microstructures scattering the x-ray beam

Kumiko Nomura, Yoshifumi Nishi

We propose hardware-oriented models of intrinsic plasticity (IP) and synaptic plasticity (SP) for spiking randomly connected recursive neural network (RNN). Although the potential of RNNs for temporal data processing has been demonstrated, randomness of the network architecture often causes performance degradation. Self-organization mechanism using IP and SP

Indrani Ray, Argha Deb

TALYS calculations were performed to obtain the theoretical proton capture cross-sections on the p-nuclei. A short review on the status of related experimental studies was also conducted. Some basic properties such as Q-values, Coulomb barrier, Gamow peak, Gamow Window, and decay properties of the parent and daughter nuclei were studied. Various experimental

P. T. How, S. K. Yip

We consider the vestigial phase with broken time-reversal symmetry above the superconducting transition temperature of a two-component superconductor in two spatial dimensions. We show that, in contrast to 3D, a vestigial phase is in general allowed within Ginzburg-Landau theory. The vestigial phase occupies an increasing temperature region if the parameters

Meng Yan, Haibin Huang, Ying Liu, Juan Zhao

Sequential recommender systems explore users' preferences and behavioral patterns from their historically generated data. Recently, researchers aim to improve sequential recommendation by utilizing massive user-generated multi-modal content, such as reviews, images, etc. This content often contains inevitable noise. Some studies attempt to reduce noise inter

Nguyen Tat Thang, Pham Thu Thuy

In this note, we consider a complete intersection $X=\{x\in \mathbb{R}^n : f_1(x)= \ldots = f_m(x)=0\}, n>m$ and study its Euclidean distance degree in terms of the mixed volume of the Newton polytopes. We show that if the Newton polytopes of $f_j,j=1,\ldots, m$ contain the origin then when these polynomials are generic with respect to their Newton polytopes

Renqiang Luo, Tao Tang, Feng Xia, Jiaying Liu

Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups. These concerns have manifested in legal, ethical, and societal challenges, including the erosion of trust in intelligent systems. In resp

David Criens

We consider controlled stochastic differential equations (SDEs) with measurable coefficients, a uniformly elliptic diffusion coefficient and an $L_d$-drift. No space-regularity will be assumed for the coefficients. In this framework we investigate the relation of value functions, partial differential equations (PDEs) and operator semigroups. First, for a cos

Kazi Shahriar Sanjid, Md. Tanzim Hossain, Md. Shakib Shahariar Junayed, M. Monir Uddin

Deep learning has revolutionized medical imaging by providing innovative solutions to complex healthcare challenges. Traditional models often struggle to dynamically adjust feature importance, resulting in suboptimal representation, particularly in tasks like semantic segmentation crucial for accurate structure delineation. Moreover, their static nature incu

Joshua L. Botha, Bertus van Heerden, Tjaart P. J. Krüger

We present Full SMS, a multipurpose graphical user interface (GUI)-based software package for analysing single-molecule spectroscopy (SMS) data. SMS typically delivers multiparameter data -- such as fluorescence brightness, lifetime, and spectra -- of molecular- or nanometre-scale particles such as single dye molecules, quantum dots, or fluorescently labelle

Fangze Sheng

The Witten-Dijkgraaf-Verlinde-Verlinde(WDVV) equations appeared in the study of two-dimensional topological field theoies in the early 1990s. An extension of the WDVV equations, called the open WDVV equations, was introduced by A.Horev and J.P.Solomon (arXiv:1210.4034). In this paper, we give some particular solutions to the open WDVV equations.

Will Johnson

We prove generic differentiability in $P$-minimal theories, strengthening an earlier result of Kuijpers and Leenknegt. Using this, we prove Onshuus and Pillay's $P$-minimal analogue of Pillay's conjectures on o-minimal groups. Specifically, let $G$ be an $n$-dimensional definable group in a highly saturated model $M$ of a $P$-minimal theory. Then there is an

Po-Shen Hsin

Anomalies of global symmetry provide powerful tool to constrain the dynamics of quantum systems, such as anomaly matching in the renormalization group flow and obstruction to symmetric mass generation. In this note we compute the anomalies in 2+1d time-reversal symmetric gauge theories with massless fermions in the fundamental and rank-two tensor representat

L. A. Bokut, P. S. Kolesnikov

The Dong Lemma in the theory of vertex algebras states that the locality property of formal distributions over a Lie algebra is preserved under the action of a vertex operator. A~similar statement is known for associative algebras. We study local formal distributions over pre-Lie (right-symmetric), pre-associative (dendriform), and Novikov algebras to show t

Gaogao Dong, Nannan Sun, Fan Wang, Renaud Lambiotte

The shell structure holds significant importance in various domains such as information dissemination, supply chain management, and transportation. This study focuses on investigating the shell structure of hub and non-hub nodes, which play important roles in these domains. Our framework explores the topology of Erd\"{o}s-R\'{e}nyi (ER) and Scale-Free (SF) n

Ziyue Zhang, Mingbao Lin, Quanjian Song, Yuxin Zhang

We introduce ObjectAdd, a training-free diffusion modification method to add user-expected objects into user-specified area. The motive of ObjectAdd stems from: first, describing everything in one prompt can be difficult, and second, users often need to add objects into the generated image. To accommodate with real world, our ObjectAdd maintains accurate ima

Cong Fan, Shengkai Zhang, Kezhong Liu, Shuai Wang

Complementary to prevalent LiDAR and camera systems, millimeter-wave (mmWave) radar is robust to adverse weather conditions like fog, rainstorms, and blizzards but offers sparse point clouds. Current techniques enhance the point cloud by the supervision of LiDAR's data. However, high-performance LiDAR is notably expensive and is not commonly available on veh

Zexing Li, Tao Zhou

While finite-time blowup solutions have been studied in depth for the Keller-Segel equation, a fundamental model describing chemotaxis, the existence of finite-time blowup solutions to chemotaxis-fluid models remains largely unexplored. To fill this gap in the literature, we use a quantitative method to directly construct a smooth finite-time blowup solution

Xintong Wu, Wanlin Deng, Yutong Quan, Lin William Cong

Trust mechanisms diverge between centralized and decentralized exchanges, representing distinct sociotechnical governance paradigms. However, quantifying trust dynamics and their redistribution between these architectures remains empirically challenging, limiting understanding of how institutional shocks affect market behavior. The FTX collapse offers a natu

Jens Moesgaard, Tomoki Fujita, Shuai Wei

The Boson peak is a universal phenomenon in amorphous solids. It can be observed as an anomalous contribution to the low-temperature heat capacity over the Debye model. Amorphous phase-change materials (PCMs) such as Ge-Sb-Te are a family of poor glass formers with fast crystallization kinetics, being of interest for phase-change memory applications. So far,

Vatsal Aggarwal, Arjun Ramesh Kaushik, Charanjit Jutla, Nalini Ratha

Autonomous Unmanned Aerial Vehicles (UAVs) have become essential tools in defense, law enforcement, disaster response, and product delivery. These autonomous navigation systems require a wireless communication network, and of late are deep learning based. In critical scenarios such as border protection or disaster response, ensuring the secure navigation of

Maximilian Zipfl, Barbara Schütt, J. Marius Zöllner

Verifying highly automated driving functions can be challenging, requiring identifying relevant test scenarios. Scenario-based testing will likely play a significant role in verifying these systems, predominantly occurring within simulation. In our approach, we use traffic scenes as a starting point (seed-scene) to address the individuality of various highly

Dimitri Watel, Marc-Antoine Weisser, Dominique Barth, Ylène Aboulfath

We address a specific case of the matroid intersection problem: given a set of graphs sharing the same set of vertices, select a minimum cycle basis for each graph to maximize the size of their intersection. We provide a comprehensive complexity analysis of this problem, which finds applications in chemoinformatics. We establish a complete partition of subca

Lucas Reding, Andrés F. López-Lopera, François Bachoc

We consider covariance parameter estimation for Gaussian processes with functional inputs. From an increasing-domain asymptotics perspective, we prove the asymptotic consistency and normality of the maximum likelihood estimator. We extend these theoretical guarantees to encompass scenarios accounting for approximation errors in the inputs, which allows robus

Marco Peer, Florian Kleber, Robert Sablatnig

This paper introduces SAGHOG, a self-supervised pretraining strategy for writer retrieval using HOG features of the binarized input image. Our preprocessing involves the application of the Segment Anything technique to extract handwriting from various datasets, ending up with about 24k documents, followed by training a vision transformer on reconstructing ma

Christian Kuehn, Pascal Lehner, Jan-Eric Sulzbach

The aim of this expository paper is twofold. We start with a concise overview of the theory of invariant slow manifolds for fast-slow dynamical systems starting with the work by Tikhonov and Fenichel to the most recent works on infinite-dimensional fast-slow systems. The main part focuses on a class of linear fast-reaction PDE, which are particular forms of

Juliette Dubois, Sébastien Imperiale, Anne Mangeney, Jacques Sainte-Marie

We study a linear model for the propagation of hydro-acoustic waves and tsunami in a stratified free-surface ocean. A formulation was previously obtained by linearizing the compressible Euler equations. The new formulation is obtained by studying the functional spaces and operators associated to the model. The mathematical study of this new formulation is ea

Mahammed Kamruzzaman, Gene Louis Kim

Dual process theory posits that human cognition arises via two systems. System 1, which is a quick, emotional, and intuitive process, which is subject to cognitive biases, and System 2, is a slow, onerous, and deliberate process. Prior research in LLMs found that using chain-of-thought (CoT) prompting in LLMs, which has been often compared to System 2 reason

Jordi Grau-Escolano, Aleix Bassolas, Julian Vicens

Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehe

Michelle Terblanche, Kayode Olaleye, Vukosi Marivate

Many multilingual communities, including numerous in Africa, frequently engage in code-switching during conversations. This behaviour stresses the need for natural language processing technologies adept at processing code-switched text. However, data scarcity, particularly in African languages, poses a significant challenge, as many are low-resourced and und

Christoph Dürr, Arturo Merino, José A. Soto, José Verschae

We study set selection problems where the weights are uncertain. Instead of its exact weight, only an uncertainty interval containing its true weight is available for each element. In some cases, some solutions are universally optimal; i.e., they are optimal for every weight that lies within the uncertainty intervals. However, it may be that no universal opt

Xavier Salas-Barzola, Guillaume Maîtrejean, Clément de Loubens, Antoine Naillon

The imbalance of normal stress around a particle induces its transverse migration in pressure-driven viscoelastic flow, offering possibilities for particle manipulation in microfluidic devices. Theoretical predictions align with experimental evidence of particles migrating towards the center-line of the flow. However, these arguments have been challenged by

Yawar Ali, Krishnan K N, Debashis Ray Sarkar, K. Ramachandra Rao

Traffic data collection has been an overwhelming task for researchers as well as authorities over the years. With the advancement in technology and introduction of various tools for processing and extracting traffic data the task has been made significantly convenient. Data from Sky (DFS) is one such tool, based on image processing and artificial intelligenc

Ariane Cwiling, Vittorio Perduca, Olivier Bouaziz

In the context of right-censored data, we study the problem of predicting the restricted time to event based on a set of covariates. Under a quadratic loss, this problem is equivalent to estimating the conditional Restricted Mean Survival Time (RMST). To that aim, we propose a flexible and easy-to-use ensemble algorithm that combines pseudo-observations and

Yan Wang, Xiaosong Bai, Wujun Shi, Wenjian Liu

In recent years, a type of topological semimetals (TSMs) that can host new fermions with high-fold degeneracy has attracted considerable interest. Among them, ones with chiral structrue particularly catch our attention. Such chiral high-fold degenerate semimetals always have a larger topological charge and longer Fermi arcs which bringing about some special

Julien Chhor, Olga Klopp, Alexandre Tsybakov

We study the problem of bivariate discrete or continuous probability density estimation under low-rank constraints.For discrete distributions, we assume that the two-dimensional array to estimate is a low-rank probability matrix. In the continuous case, we assume that the density with respect to the Lebesgue measure satisfies a generalized multi-view model,

Mrinnoy M. Gohain, Kalyan Bhuyan, Chayanika Chetia

We explore the possibility of formation of a traversable wormhole in General Relativity supported by particle creation mechanism. The repulsive back-reaction pressure generated through this mechanism can be thought of as a source of sustaining a traversable wormhole. In the first part of this paper, we model a wormhole geometry by assuming an inverse powerla

Kazu Bodaiji, Katsuhiro Morita, Yoshiyuki Fukumoto

A recently discovered kagome antiferromagnet $\rm{Y}_3\rm{Cu}_9(\rm{OH})_{19}\rm{Cl}_8$ has attracted significant interest due to its unique kagome lattice structure and magnetic properties. The kagome lattice has three types of exchange interactions: one hexagonal coupling and two different triangular couplings. Previous studies have shown that its ground s

ChulMin Oh, Herve Hugonnet, Juheon Lee, YongKeun Park

Analyzing 3D anisotropic materials presents significant challenges, especially when assessing 3D orientations, material distributions, and anisotropies through scattered light, due to the inherently vectorial nature of light-matter interactions. In this study, we formulate a scattering theory based on the Rytov approximation, commonly employed in scalar wave