November 2024 arXiv papers — page 89

Subhajit Sahu

Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions are critical in a number of applications. This report presents an optimized implementation of the Label Propagation Algorithm (LPA) for community detection, featuring an asynchronous LPA with a Pick-Less (PL) method ev

Geert Hofman

The increasing integration of artificial intelligence into various domains, including design and creative processes, raises significant ethical questions. While AI ethics is often examined from the perspective of technology developers, less attention has been paid to the practical ethical considerations faced by technology users, particularly in design conte

Amaury Wei, Olga Fink

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learning-based methods capable of capturing complex interactions beyond explicit physical models and simulation

Markus Schön, Michael Buchholz, Klaus Dietmayer

Monocular geometric scene understanding combines panoptic segmentation and self-supervised depth estimation, focusing on real-time application in autonomous vehicles. We introduce MGNiceNet, a unified approach that uses a linked kernel formulation for panoptic segmentation and self-supervised depth estimation. MGNiceNet is based on the state-of-the-art real-

Omar Naim, Guilhem Fouilhé, Nicholas Asher

In-context learning (ICL) has emerged as a powerful paradigm for easily adapting Large Language Models (LLMs) to various tasks. However, our understanding of how ICL works remains limited. We explore a simple model of ICL in a controlled setup with synthetic training data to investigate ICL of univariate linear functions. We experiment with a range of GPT-2-

Zhaoyu Xing, Wei Zhong

Reconstructing large-scale latent networks from observed dynamics is crucial for understanding complex systems. However, the existing methods based on compressive sensing are often rendered infeasible in practice by prohibitive computational and memory costs. To address this challenge, we introduce a new distributed computing framework for efficient large-sc

Ansgar Freyer, Ádám Sagmeister

P\'al's isominwidth theorem states that for a fixed minimal width, the regular triangle has minimal area. A spherical version of this theorem was proven by Bezdek and Blekherman, if the minimal width is at most $\tfrac \pi 2$. If the width is greater than $\tfrac \pi 2$, the regular triangle no longer minimizes the area at fixed minimal width. We show that t

Marco Mingione, Francesco Lagona, Priyanka Nagar, Francois von Holtzhausen

Motivated by a case study of vegetation patterns, we introduce a mixture model with concomitant variables to examine the association between the orientation of vegetation stripes and wind direction. The proposal relies on a novel copula-based bivariate distribution for mixed axial and circular observations and provides a parsimonious and computationally trac

Dani Szpruch

Let $F$ be a $p$-adic field containing the full group of $n^{th}$ roots of 1 and let $ \widetilde{SL_2(F)}$ be the $n$-fold cover of $SL_2(F)$ constructed by Kubota. In this paper we compute the dimension of the space of Whittaker functionals of the two irreducible summands inside a reducible unitary genuine principal series representation of $\widetilde{SL_

Ayush Moharana, K. G. Helminiak, T. Pawar, G. Pawar

Compact hierarchical triples (CHTs) are triple stars where the tertiary is in an orbit of a period less than 1000 d. They were thought to be rare but we are discovering more of these systems recently, thanks to space-based missions like TESS, Kepler, and GAIA. In this work, we use orbital parameters obtained from these missions to constrain the formation pro

Zhe Yang, Hao Peng, Yanling Jiang, Xingwei Li

Internet of Things (IoT) devices offer convenience through web interfaces, web VPNs, and other web-based services, all relying on the HTTP protocol. However, these externally exposed HTTP services resent significant security risks. Although fuzzing has shown some effectiveness in identifying vulnerabilities in IoT HTTP services, most state-of-the-art tools s

Joona Pohjonen, Abderrahim-Oussama Batouche, Antti Rannikko, Kevin Sandeman

Foundation models are trained on massive amounts of data to distinguish complex patterns and can be adapted to a wide range of downstream tasks with minimal computational resources. Here, we develop a foundation model for prostate cancer digital pathology called HistoEncoder by pre-training on 48 million prostate tissue tile images. We demonstrate that Histo

Juan Cardenas-Cartagena, Massimiliano Falzari, Marco Zullich, Matthia Sabatelli

Model-Free Reinforcement Learning (RL) algorithms either learn how to map states to expected rewards or search for policies that can maximize a certain performance function. Model-Based algorithms instead, aim to learn an approximation of the underlying model of the RL environment and then use it in combination with planning algorithms. Upside-Down Reinforce

E. Elizalde, A. V. Yurov, A. V. Timoshkin

Bounce cosmological models containing a dark viscous fluid in a spatially flat Friedmann-Robertson-Walker (FRW) universe are considered. The universe evolution is described in terms of generalized equation of state (EoS) parameters, in presence of the bulk viscosity. Entropic cosmology plays a key role in the discussion, and the matter bounce behavior is des

Markus Schön, Jona Ruof, Thomas Wodtko, Michael Buchholz

Depth estimation is an essential task toward full scene understanding since it allows the projection of rich semantic information captured by cameras into 3D space. While the field has gained much attention recently, datasets for depth estimation lack scene diversity or sensor modalities. This work presents the ADUULM-360 dataset, a novel multi-modal dataset

Li Yu, Xuanzhe Sun, Pan Gao, Moncef Gabbouj

Audio data, often synchronized with video frames, plays a crucial role in guiding the audience's visual attention. Incorporating audio information into video saliency prediction tasks can enhance the prediction of human visual behavior. However, existing audio-visual saliency prediction methods often directly fuse audio and visual features, which ignore the

Farshad Rostami Ghadi, Kai-Kit Wong, F. Javier Lopez-Martinez, Lajos Hanzo

This letter considers a fluid antenna system (FAS)-aided rate-splitting multiple access (RSMA) approach for downlink transmission. In particular, a base station (BS) equipped with a single traditional antenna system (TAS) uses RSMA signaling to send information to several mobile users (MUs) each equipped with FAS. To understand the achievable performance, we

Bertram Bitsch, Andre Izidoro

Migration is a key ingredient for the formation of close-in super-Earth and mini-Neptune systems, as it sets in which resonances planets can be trapped. Slower migration rates result in wider resonance configurations compared to higher migration rates. We investigate the influence of different migration rates, set by the disc's viscosity, on the structure of

Marnix Suilen, Thom Badings, Eline M. Bovy, David Parker

Markov decision processes (MDPs) are a standard model for sequential decision-making problems and are widely used across many scientific areas, including formal methods and artificial intelligence (AI). MDPs do, however, come with the restrictive assumption that the transition probabilities need to be precisely known. Robust MDPs (RMDPs) overcome this assump

Jens Boos

Potentials arising in ultraviolet-completed field theories can be devoid of singularities, and hence render spacetimes simply connected. This challenges the notion of topological invariants considered in such scenarios. We explore the classical implications for (i) electrodynamics in flat spacetime, (ii) ultrarelativistic gyratonic solutions of weak-field gr

Hongjun Wang, Jiyuan Chen, Lingyu Zhang, Renhe Jiang

Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown significant promise in traffic forecasting by effectively modeling temporal and spatial correlations. However, rapid urbanization in recent years has led to dynamic shifts in traffic patterns and travel demand, posing major challenges for accurate long-term traffic prediction. The gen

Nishu Kumari, Anna Stokke

We establish new Murnaghan--Nakayama rules for symplectic, orthogonal and orthosymplectic Schur functions. The classical Murnaghan--Nakayama rule expresses the product of a power sum symmetric function with a Schur function as a linear combination of Schur functions. Symplectic and orthogonal Schur functions correspond to characters of irreducible representa

Shihong Liu, Yu Rong, Zichen Hua, Huijie Hu

Using a semi-analytic approach, we estimate halo spins for a large sample of HI-rich galaxies from the Arecibo Legacy Fast Alfa Survey and examine the correlation between HI mass fractions and halo spins. Our analysis reveals a strong correlation between halo spin and the HI-to-stellar mass ratio in both low-mass and massive galaxy samples. This finding sugg

P. Wójcik, R. Citro, B. Szafran

The progress in the fabrication of nanoscale systems based on the two-dimensional electron gas at the interface between LaAlO$_3$ and SrTiO$_3$ (LAO/STO) has created an increased demand for simulations of these nanostructures, which typically range in size from tens to hundreds of nanometers. Due to the low lattice constant of LAO/STO, approximately 0.394 nm

Fiorella Fionda

Heavy-flavour hadrons, containing at least one charm or beauty quark, are excellent probes of the deconfined medium created in ultra-relativistic heavy-ion collisions, known as quark-gluon plasma. Results in smaller collision systems, such as proton-proton and p-Pb collisions, besides representing an important baseline for interpreting heavy-ion measurements

Shihong Liu, Yu Rong

We employ a semi-analytical methodology to estimate the dark matter halo spin of HI-rich galaxies in the Arecibo Legacy Fast Alfa Survey and investigate the relationship between halo spin and the proximity of galaxies to cosmic filaments. We exclude galaxies with low HI signal-to-noise ratios, those potentially influenced by velocity dispersions, and those a

U. Özdem

To shed light on the properties of states whose nature, internal structure, and spin-parity quantum numbers are not fully elucidated, we systematically study their electromagnetic properties. In light of this concept, we present a comprehensive analysis of the magnetic dipole moment of the $P_{cs}(4459)$ pentaquark within the context of QCD light-cone sum ru

Mahesh S. Nagargoje, Eneko Lazpita, Jesús Garicano-Mena, Soledad Le Clainche

The heart is the central part of the cardiovascular network. Its role is to pump blood to various body organs. Many cardiovascular diseases occur due to an abnormal functioning of the heart. A diseased heart leads to severe complications and in some cases death of an individual. The medical community believes that early diagnosis and treatment of heart disea

Yuyang Zhou, Adrian Lee, Yuji Chinone

E to B mixing or "leakage" due to time-ordered data (TOD) filtering has become an important source of sensitivity loss that ground-based cosmic microwave background polarization experiments must address. However, it is a difficult problem for which very few viable solutions exist. In this paper, we expand upon satellite E-mode methods to cover E/B leakage sp

Vittoria Vecchiotti, Giada Peron, Elena Amato, Stefano Menchiari

Recently, the Large High-Altitude Air Shower Observatory (LHAASO) collaboration has obtained a measurement of the gamma-ray diffuse emission in the ultra-high energy range, $10-10^3$ TeV after masking the contribution of known sources. The measurement is provided in two Galactic regions and appears to be 2-3 times higher than the gamma-ray signal expected fr

Wenxiao Xue, Yu Rong

The formation sequence of bulges and disks in late-type galaxies (LTGs) remains debated. While some theories suggest bulges form before disks, others propose the reverse. To address this, we analyze a bulge+disk decomposition catalog from the Sloan Digital Sky Survey, examining the alignment between central bulge major axes and large-scale filaments. For LTG

Phan van Tin

We consider a generalized derivative nonlinear Schr\''odinger equation. We prove existence of wave operator under an explicit smallness of the given asymptotic states. Our method bases on studying the associated system used in \cite{Tinpaper4}. Moreover, we show that if the initial data is small enough in $H^2(\mathbb{R})$ then the associated solution scatte

Navita Goyal, Ivan Stelmakh, Nihar Shah, Hal Daumé

A large host of scientific journals and conferences solicit peer reviews from multiple reviewers for the same submission, aiming to gather a broader range of perspectives and mitigate individual biases. In this work, we reflect on the role of diversity in the slate of reviewers assigned to evaluate a submitted paper as a factor in diversifying perspectives a

Junwen He, Yifan Wang, Lijun Wang, Huchuan Lu

Text logo design heavily relies on the creativity and expertise of professional designers, in which arranging element layouts is one of the most important procedures. However, this specific task has received limited attention, often overshadowed by broader layout generation tasks such as document or poster design. In this paper, we propose a Vision-Language

Kareem Shehata, Aashish Kolluri, Prateek Saxena

As AI-generated images become widespread, reliable watermarking is essential for content verification, copyright enforcement, and combating disinformation. Existing techniques rely on heuristic approaches and lack formal guarantees of undetectability, making them vulnerable to steganographic attacks that can expose or erase the watermark. Additionally, these

Chun-Meng Tang, Chun-Gui Duan, Liang Tang, Cong-Feng Qiao

Inspired by the experimental measurement of the charmed hadronic state X(6900), we calculate the mass spectra of tetraquark hybrid states with configuration of \([8_{c}]_{Q\bar{Q}} \otimes [8_{c}]_{G} \otimes [8_{c}]_{Q\bar{Q}}\) in color, by virtue of QCD sum rules. The two feasible types of currents with quantum numbers $J^{PC} = 0^{++}$ and $0^{-+}$ are i

Timotej Šujan

In this paper, we argue that while the concept of a set-theoretic paradox (or paradoxical set) can be relatively well-defined within a formal setting, the concept of a set-theoretic hypodox (or hypodoxical set) remains significantly less clear--especially if the self-membership assertion of the co-Russell set, $\{x:x\in x\}$, is classified as hypodoxical, wh

Ilya Tyomkin

In this appendix, we summarize known results on the geometry of Severi varieties on toric surfaces - the varieties parameterizing integral curves of a given geometric genus in a given linear system. Till the last decade, Severi varieties were studied exclusively in characteristic zero. In particular, in the 80-s, Zariski proved that a general plane curve of

Jie Jiang, Philippe Laurençot

A fully parabolic chemotaxis model of Keller-Segel type with local sensing is considered. The system features a signal-dependent asymptotically non-degenerate motility function, which accounts for a repulsion-dominated chemotaxis. Global boundedness of classical solutions is proved for an initial Neumann boundary value problem of the system in any space dime

Christian Hirsch, Raphaël Lachièze-Rey

We consider Betti numbers of the excursion of a smooth Euclidean Gaussian field restricted to a rectangular window, in the asymptotics where the window grows to R^d . With motivations coming from Topological Data Analysis, we derive a functional Central Limit Theorem where the varying argument is the thresholding parameter, under assumptions of regularity an

Nick Bezhanishvili, Laura Bussi, Vincenzo Ciancia, David Gabelaia

The work described in this paper builds on the polyhedral semantics of the Spatial Logic for Closure Spaces (SLCS) and the geometric spatial model checker PolyLogicA. Polyhedral models are central in domains that exploit mesh processing, such as 3D computer graphics. A discrete representation of polyhedral models is given by cell poset models, which are amen

François Dubois

In this contribution, we construct a connection between two quantum voting models presented previously. We propose to try to determine the result of a vote from associated given opinion polls. We introduce a density operator relative to the family of all candidates to a particular election. From an hypothesis of proportionality between a family of coefficien

Katie Gittins, Asma Hassannezhad, Corentin Léna, David Sher

We consider the Courant-sharp eigenvalues of the Robin Laplacian for bounded, connected, open sets in $\mathbb{R}^n$, $n \geq 2$, with Lipschitz boundary. We prove Pleijel's theorem which implies that there are only finitely many Courant-sharp eigenvalues in this setting as well as an improved version of Pleijel's theorem, extending previously known results

Aleksander Figiel, Darya Melnyk, André Nichterlein, Arash Pourdamghani

Graph augmentation is a fundamental and well-studied problem that arises in network optimization. We consider a new variant of this model motivated by reconfigurable communication networks. In this variant, we consider a given physical network and the measured communication demands between the nodes. Our goal is to augment the given physical network with a m

Frank Redig, Berend van Tol

We investigate the structure of non-equilibrium steady states (NESS) for a class of exactly solvable models in the setting of a chain with left and right reservoirs. Inspired by recent results on the harmonic model, we focus on models in which the NESS is a mixture of equilibrium product measures, and where the probability measure which describes the mixture

Zixiong Wang, Gaoyang Liu, Yang Yang, Chen Wang

Recent advances in Large Language Models (LLMs) have enabled them to overcome their context window limitations, and demonstrate exceptional retrieval and reasoning capacities on longer context. Quesion-answering systems augmented with Long-Context Language Models (LCLMs) can automatically search massive external data and incorporate it into their contexts, e

Chulmin Lee, Jaewon Hur, Sangho Lee, Byoungyoung Lee

Confidential container is becoming increasingly popular as it meets both needs for efficient resource management by cloud providers, and data protection by cloud users. Specifically, confidential containers integrate the container and the enclave, aiming to inherit the design-wise advantages of both (i.e., resource management and data protection). However, c

Baptiste Serraille, Vukašin Stojisavljević

We study a number of questions related to the $C^0$-topology of contactomorphisms and contact homeomorphisms. In particular, we show a connection between Rokhlin property of contact homeomorphisms and contact non-squeezing, we define a new conjugation-invariant norm on contactomorphisms and explore its relation to the contact fragmentation norm and we introd

Yongyu Wang

DBSCAN is one of the most important non-parametric unsupervised data analysis tools. By applying DBSCAN to a dataset, two key analytical results can be obtained: (1) clustering data points based on density distribution and (2) identifying outliers in the dataset. However, the time complexity of the DBSCAN algorithm is $O(n^2 \beta)$, where $n$ is the number

A. Landry, D. D. McNutt

We elaborate on and further develop an approach to determining the teleparallel analogue of spacetimes in General Relativity (GR) by studying the Teleparallel analogue of pp-Wave (TppW) spacetimes. This relies on using the fact that these solutions belong to the Vanishing Scalar Invariant (VSI) subclass for which the explicit forms of the frame and spin-conn

Baishun Lai, Shihao Zhang

In this paper, we employ the localization technique in frequency space developed by Tao in \cite{MR4337421} to investigate the quantitative estimates for the MHD equations. With the help of quantitative Carleman inequalities given by Tao in \cite{MR4337421} and the pigeonhole principle, we establish the quantitative regularity for the critical $L^3$ norm bou

Lamia Berriche, Maha Driss, Areej Ahmed Almuntashri, Asma Mufreh Lghabi

This paper introduces a new application named ArPA for Arabic kids who have trouble with pronunciation. Our application comprises two key components: the diagnostic module and the therapeutic module. The diagnostic process involves capturing the child's speech signal, preprocessing, and analyzing it using different machine learning classifiers like K-Nearest

Francisco Javier Valverde-Muñoz, Ricardo Guillermo Torres Ramírez, Elzbieta Trzop, Thierry Bataille

Symmetry-breaking is pivotal for controlling ferroelectric, ferroelastic and/or ferromagnetic functions of materials, which enables applications in sensors, memories, transducers or actuators. Commonly, ferroic phases emerge from descending symmetry-breaking, as the laws of thermodynamics dictate that the ordered low entropy phases form at low temperature, w

Claudia Schoemann, Skylar Werner

Let $k$ be an uncountable algebraically closed field of positive characteristic and let $S$ be a smooth projective connected surface over $k$. We extend the theorem on the Gysin kernel from [20, Theorem 5.1] to also be true over $k$, where it was proved over $\mathbb{C}$. This is done by showing that almost all results still hold true over $k$ via the same a

Akshay Patil, Clara García-Sánchez

In this study, we introduced a simple yet innovative method to trigger turbulence in a channel flow to achieve statistically stationary flow conditions. We compare this new method based on synthetically generated three-dimensional turbulence with two other well-established methods, namely, linear profile superposed with random noise and descending counter-ro

Sinho Chewi, Austin J. Stromme

The Polyak-Lojasiewicz (PL) constant of a function $f \colon \mathbb{R}^d \to \mathbb{R}$ characterizes the best exponential rate of convergence of gradient flow for $f$, uniformly over initializations. Meanwhile, in the theory of Markov diffusions, the log-Sobolev (LS) constant plays an analogous role, governing the exponential rate of convergence for the L

Jordan Koechler

In this thesis, we present a comprehensive and pedagogical overview of dark matter (DM). Chapter 1 discusses the main evidences for its existence, its properties, and potential candidates. We then explore major detection strategies, with Chapter 2 specifically dedicated to indirect detection. In the following chapters, we study the emission of secondary phot

Anmol Biswas, Sharvari Ashok Medhe, Raghav Singhal, Udayan Ganguly

Reservoir computing (RC), is a class of computational methods such as Echo State Networks (ESN) and Liquid State Machines (LSM) describe a generic method to perform pattern recognition and temporal analysis with any non-linear system. This is enabled by Reservoir Computing being a shallow network model with only Input, Reservoir, and Readout layers where inp

Sonali Patnaik

The $B_c$ meson, a unique bound state comprising of two open heavy flavors, charm and bottom, offers a rich avenue for probing the predictions of the Next Decade - Standard Model (ND-SM) physics properties due to its heavy mass. With recent observations of its excited states, interest in understanding $B_c$ production mechanisms and decay modes has surged. T

Sira Gratz, Henrik Holm, Peter Jorgensen, Greg Stevenson

The main result of this paper is that there is sometimes a triangulated equivalence between $D_Q( A )$, the $Q$-shaped derived category of an algebra $A$, and $D( B )$, the classic derived category of a different algebra $B$. By construction, $D_Q( A )$ consists of $Q$-shaped diagrams of $A$-modules for a suitable small category $Q$. Our result concerns the

P Raghavendra Rao, Pooja Vyavahare

This work studies the distributed learning process on a network of agents. Agents make partial observation about an unknown hypothesis and iteratively share their beliefs over a set of possible hypotheses with their neighbors to learn the true hypothesis. We present and analyze a distributed learning algorithm in which agents share belief on only one randoml

Xiufeng Xu, Fuman Xie, Chenguang Zhu, Guangdong Bai

Modern AI- and Data-intensive software systems rely heavily on data science and machine learning libraries that provide essential algorithmic implementations and computational frameworks. These libraries expose complex APIs whose correct usage has to follow constraints among multiple interdependent parameters. Developers using these APIs are expected to lear

Yunong Liu, Cristobal Eyzaguirre, Manling Li, Shubh Khanna

Shape assembly is a ubiquitous task in daily life, integral for constructing complex 3D structures like IKEA furniture. While significant progress has been made in developing autonomous agents for shape assembly, existing datasets have not yet tackled the 4D grounding of assembly instructions in videos, essential for a holistic understanding of assembly in 3

Julio Backhoff, Edoardo Kimani Bellotto

In continuous time, the laws of martingales tend to be singular to each other. Notably, N. Gantert introduced the concept of specific relative entropy between real-valued continuous martingales, defined as a scaling limit of finite-dimensional relative entropies, and showed that this quantity is non-trivial despite the aforementioned mutual singularity of ma

Xikang Yang, Xuehai Tang, Jizhong Han, Songlin Hu

The widespread deployment of large language models (LLMs) across various domains has showcased their immense potential while exposing significant safety vulnerabilities. A major concern is ensuring that LLM-generated content aligns with human values. Existing jailbreak techniques reveal how this alignment can be compromised through specific prompts or advers

Haizhou Ge, Ruixiang Wang, Zhu-ang Xu, Hongrui Zhu

Advanced imitation learning with structures like the transformer is increasingly demonstrating its advantages in robotics. However, deploying these large-scale models on embedded platforms remains a major challenge. In this paper, we propose a pipeline that facilitates the migration of advanced imitation learning algorithms to edge devices. The process is ac

Hadi Beik Mohammadi, Søren Hauberg, Georgios Arvanitidis, Gerhard Neumann

Stability guarantees are crucial when ensuring that a fully autonomous robot does not take undesirable or potentially harmful actions. We recently proposed the Neural Contractive Dynamical Systems (NCDS), which is a neural network architecture that guarantees contractive stability. With this, learning-from-demonstrations approaches can trivially provide stab

James A. Sellers, Roberto Tauraso

In this paper, we prove several new infinite families of Ramanujan--like congruences satisfied by the coefficients of the generating function $U_t(a,q)$ which is an extension of MacMahon's generalized sum-of-divisors function. As a by-product, we also show that, for all $n\geq 0$, $\overline{B}_3(15n+7)\equiv 0 \pmod{5}$ where $\overline{B}_3(n)$ is the numb

Ivan Cheltsov, Federico Cornalba, Clarice Poon, Tony Shardlow

Priors with non-smooth log-densities, such as the l1-prior, are widely used in Bayesian inverse problems for their sparsity-inducing properties. Existing Langevin-based sampling methods typically rely on proximal mappings or smooth approximations, which alter the target distribution. We propose an alternative approach based on a Hadamard product parameteriza

Kenta Ohira

We derive an exact solution for a simple non-autonomous delay differential equation (DDE) over the entire real-time axis, representing it as a sum of Gaussian-shaped dynamics with distinct peak positions. This marks the first explicit solution for non-autonomous DDEs and is a rare example even among general DDEs. The constructed solution offers key physical

Rosalia Tufano, Alberto Martin-Lopez, Ahmad Tayeb, Ozren Dabić

Several techniques have been proposed to automate code review. Early support consisted in recommending the most suited reviewer for a given change or in prioritizing the review tasks. With the advent of deep learning in software engineering, the level of automation has been pushed to new heights, with approaches able to provide feedback on source code in nat

Emir Baysazan, Ayse Humeyra Bilge, Tolga Birkandan, Tekin Dereli

The Newman-Unti-Tamburino (NUT) solution is characterized as the unique Petrov Type $D$ vacuum metric such that the two double principal null directions form an integrable distribution. The uniqueness of the NUT is established by evaluating the integrability conditions of the Newman-Penrose equations up to $SL(2,\mathbb{C})$ transformations, resulting in a c

Maitry Joshi, Trupti K Gajaria, Prafulla K. Jha

Electron-phonon (E-p) coupling incorporated density functional theory (DFT) based investigation of structural, electronic and vibrational properties of bulk MgSe and MgTe is presented. Electron-phonon coupling is incorporated to understand its effect on charge carrier dynamics. It is observed that the MgTe possesses room temperature hole and electron mobilit

Ze-Fang Jiang, Shuo-Yan Liu, Tian-Yu Hu, Huang-Jing Zheng

We study 1+1 dimensional relativistic non-resistive magnetohydrodynamics (MHD) with longitudinal boost invariance and shear stress tensor. Several analytical solutions that describe the fluid temperature evolution under the equation of state (EoS) $\varepsilon=3p$ are derived, relevant to relativistic heavy-ion collisions. Extending the Victor-Bjorken ideal

Anjia Cao, Xing Wei, Zhiheng Ma

Language-image pre-training faces significant challenges due to limited data in specific formats and the constrained capacities of text encoders. While prevailing methods attempt to address these issues through data augmentation and architecture modifications, they continue to struggle with processing long-form text inputs, and the inherent limitations of tr

Akansh Agrawal, Akshan Agrawal, Shashwat Gupta, Priyanka Bagade

Medical image segmentation is essential for applications like robotic surgeries, disease diagnosis, and treatment planning. Recently, various deep-learning models have been proposed to enhance medical image segmentation. One promising approach utilizes Kolmogorov-Arnold Networks (KANs), which better capture non-linearity in input data. However, they are unab

Jikang Cheng, Zhiyuan Yan, Ying Zhang, Li Hao

The rapid advancement of face forgery techniques has introduced a growing variety of forgeries. Incremental Face Forgery Detection (IFFD), involving gradually adding new forgery data to fine-tune the previously trained model, has been introduced as a promising strategy to deal with evolving forgery methods. However, a naively trained IFFD model is prone to c

Weiyao Zhao, Kaijian Xing, Yufei Zhao, Lei Chen

Quantum geometry, including Berry curvature and the quantum metric, of the electronic Bloch bands has been studied via nonlinear responses in topological materials. Naturally, these material systems with intrinsic strong nonlinear responses also form the key component in nonlinear electronic devices. However, the previous reported quantum geometry effects ar

Yu Yan, Rongtao Xu, Jiazhao Zhang, Peiyang Li

Recent research on Vision-and-Language Navigation (VLN) indicates that agents suffer from poor generalization in unseen environments due to the lack of realistic training environments and high-quality path-instruction pairs. Most existing methods for constructing realistic navigation scenes have high costs, and the extension of instructions mainly relies on

Boy Schultz

We conduct an investigation of the differentiability and continuity of reward functionals associated to Markovian randomized stopping times. Our focus is mostly on the differentiability, which is a crucial ingredient for a common approach to derive analytic expressions for the reward function.

Hy Lam

In this paper, we establish the spectral decomposition of the Koopman operator and determine the flat-trace distribution associated with the geodesic flow on the co-circle bundle over the compactification of Poincar\'e upper half-plane $\mathbf{H}^2 = \{z \in \mathbb{C} : \Im(z) > 0\}$, equipped with the hyperbolic metric $ds^2 = \frac{dz^2}{\Im(z)^2}$.

Salvatore Calderaro, Domenico Amato, Giosuè Lo Bosco, Riccardo Rizzo

Graph Neural Networks (GNNs) are powerful models that can manage complex data sources and their interconnection links. One of GNNs' main drawbacks is their lack of interpretability, which limits their application in sensitive fields. In this paper, we introduce a new methodology involving graph communities to address the interpretability of graph classificat

Takashi Sakajo, Changjun Zou

We construct a series of classic vorticity solutions for incompressible Euler equation on $\mathbb S^2$, which constitute the $C^1$ type regularization for a general traveling point vortex system. The construction is accomplished by applying tangent mapping on $\mathbb S^2$ and Lyapunov--Schmidt reduction argument. Using the fixed-point theorem and a finite

Chaogui Kang, Xiaxin Wu, Jialei Shi, Chao Yang

School-escorted trips are a significant contributor to traffic congestion. Existing studies mainly compare road traffic during student pick-up/drop-off hours with off-peak times, often overlooking the fact that school-run traffic congestion is unevenly distributed across areas with different built environment characteristics. We examine the relationship betw

Fengchao Chen, Tingmin Wu, Van Nguyen, Shuo Wang

Phishing remains a pervasive cyber threat, as attackers craft deceptive emails to lure victims into revealing sensitive information. While Artificial Intelligence (AI), in particular, deep learning, has become a key component in defending against phishing attacks, these approaches face critical limitations. The scarcity of publicly available, diverse, and up

Takashi Sakajo, Changjun Zou

We construct a series of patch type solutions for incompressible Euler equation on $\mathbb S^2$, which constitutes the regularization for steady or traveling point vortex systems. We first prove the existence of $k$-fold symmetric patch solutions, whose limit is the well-known von K\'arm\'an point vortex street on $\mathbb S^2$; then we consider the general

Thomas Creutzig, Robert McRae, Jinwei Yang

We show that the braided tensor category of finitely-generated weight modules for the simple affine vertex operator algebra $L_k(\mathfrak{sl}_2)$ of $\mathfrak{sl}_2$ at any admissible level $k$ is rigid and hence a braided ribbon category. The proof uses a recent result of the first two authors with Shimizu and Yadav on embedding a braided Grothendieck-Ver

Shuqin Pang, Wenyi Zhang

Information transmission over discrete-time channels with memoryless additive noise obeying a Cauchy, rather than Gaussian, distribution, are studied. The channel input satisfies an average power constraint. Upper and lower bounds to such additive white Cauchy noise (AWCN) channel capacity are established. In the high input power regime, the gap between uppe

Giovanni Brignone, Roberto Bosio, Fabrizio Ottati, Claudio Sansoè

High-level synthesis (HLS) aims at democratizing custom hardware acceleration with highly abstracted software-like descriptions. However, efficient accelerators still require substantial low-level hardware optimizations, defeating the HLS intent. In the context of field-programmable gate arrays, digital signal processors (DSPs) are a crucial resource that ty

Thomas Creutzig

Verlinde's formula for rational vertex operator algebras computes the fusion rules from the modular transformations of characters. In the non semisimple and non finite case, a logarithmic Verlinde formula has been proposed together with David Ridout. In this formula one replaces simple modules by their resolutions by standard modules. Here and under certain

Mudassir Ibrahim Awan, Ahsan Raza, Waseem Hassan, Ki-Uk Kyung

Haptic affection plays a crucial role in user experience, particularly in the automotive industry where the tactile quality of components can influence customer satisfaction. This study aims to accurately predict the affective property of a car door by only watching the force or torque profile of it when opening. To this end, a deep learning model is designe

Xiang-Fei Guo, Jian-Wei Liu, Hong-Xiang Chen, Fu-Long Shi

Research on two-dimensional van der Waals materials has demonstrated that the layer degree of freedom can significantly alter the physical properties of materials due to the substantial modification of bulk bands. Inspired by this concept, layered photonic systems have been proposed and realized, revealing novel phenomena absent in their monolayer counterpar

Marcin Dumnicki, Mikolaj Le Van, Grzegorz Malara, Tomasz Szemberg

The purpose of the present note is to provide a new proof ot the well-known result due to Hartshorne and Hirschowitz to the effect that general lines in projective spaces have good postulation. Our approach uses specialization to a hyperplane and thus opens door to study postulation of general codimension 2 linear subspaces in projective spaces.

Dao Thanh Hai, Isaac Woungang

From an architectural perspective with the main goal of reducing the effective traffic load in the network and thus gaining more operational efficiency, optical networks have been essentially remained the same in the recent two decades since the year 2000s with the success and then dominance of optical-bypass mode. In the optical-bypass-enabled network, the

Laura d'Avossa, Caitao Zhan, Joaquin Chung, Rajkumar Kettimuthu

The Quantum Internet would likely be composed of diverse qubit technologies that interact through a heterogeneous quantum network. Thus, quantum transduction has been identified as a key enabler of the Quantum Internet. To better study heterogeneous quantum networks, the integration of a quantum transducer component into quantum network simulators has become

Baljinnyam Dayan

Recent advancements in medical image analysis have predominantly relied on Convolutional Neural Networks (CNNs), achieving impressive performance in chest X-ray classification tasks, such as the 92% AUC reported by AutoThorax-Net and the 88% AUC achieved by ChexNet in classifcation tasks. However, in the medical field, even small improvements in accuracy can

Dmytro Lopushanskyy, Borun Shi

Training graph neural networks on large datasets has long been a challenge. Traditional approaches include efficiently representing the whole graph in-memory, designing parameter efficient and sampling-based models, and graph partitioning in a distributed setup. Separately, graph databases with native graph storage and query engines have been developed, whic

Zhenxing Mi, Dan Xu

In NeRF, a critical problem is to effectively estimate the occupancy to guide empty-space skipping and point sampling. Grid-based methods work well for small-scale scenes. However, on large-scale scenes, they are limited by predefined bounding boxes, grid resolutions, and high memory usage for grid updates, and thus struggle to speed up training for large-sc

Shangchun Xie, Zhihao Ouyang, Jingyu Wu, Hubing Xiao

The origin of jet launching mainly comes from two mechanisms: the BZ mechanism and the BP mechanism. However, it is in debate which one is dominating in blazars. In this work, we used a sample of 937 Fermi blazars to study the jet formation mechanism. We studied the correlation between the jet power and the accretion rate, as well as the comparison between j

Roger Arnau, Jose M. Calabuig, Enrique A. Sánchez-Pérez

Given a Banach lattice $L,$ the space of lattice Lipschitz operators on $L$ has been introduced as a natural Lipschitz generalization of the linear notions of diagonal operator and multiplication operator on Banach function lattices. It is a particular space of superposition operators on Banach lattices. Motivated by certain procedures in Reinforcement Learn