February 2024 arXiv papers — page 68

Huimin Cheng, Feng Zhou

In this article, we study a Besov regularity estimate of weak solutions to a class of nonlinear elliptic equations in divergence form. The main purpose is to establish Calderon-Zygmund type estimate in Besov spaces with more general assumptions on coefficients, non-homogeneous term and integrable index. By involving the Sharp maximal function, we establish a

Jaeseok Jeong, Junho Kim, Yunjey Choi, Gayoung Lee

In the evolving domain of text-to-image generation, diffusion models have emerged as powerful tools in content creation. Despite their remarkable capability, existing models still face challenges in achieving controlled generation with a consistent style, requiring costly fine-tuning or often inadequately transferring the visual elements due to content leaka

Jonas Schnidrig, Matthieu Souttre, Arthur Chuat, François Maréchal

The Paris agreement is the first-ever universally accepted and legally binding agreement on global climate change. It is a bridge between today's and climate-neutrality policies and strategies before the end of the century. Critical to this endeavor is energy system modeling, which, while adept at devising cost-effective carbon-neutral strategies, often over

Vikrant Yadav, Santosh Kumar Yadav, Rajpal

In this paper, we derive observational constraints on an anisotropic $w$CDM model from observational data including Baryonic Acoustic Oscillations (BAOs), Cosmic Chronometer (CC), Big Bang Nucleosynthesis (BBN), Pantheon Plus (PP) compilation of Type Ia supernovae, and SH0ES Cepheid host distance anchors. We find that anisotropy is of the order $10^{-13}$, a

Peter Caradonna

The Stokes-Mueller method is used to analyze the scattering of entangled photon pairs in a two-photon system. This study examines the scenario where one of the photons, part of a pair of maximally entangled annihilation photons, undergoes intermediate Compton scattering before both photons are detected using Compton polarimeters. The method also accounts for

Bjoern List, Li-Wei Chen, Kartik Bali, Nils Thuerey

Unrolling training trajectories over time strongly influences the inference accuracy of neural network-augmented physics simulators. We analyze this in three variants of training neural time-steppers. In addition to one-step setups and fully differentiable unrolling, we include a third, less widely used variant: unrolling without temporal gradients. Comparin

Ignacio Roldan, Andras Palffy, Julian F. P. Kooij, Dariu M. Gavrila

In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with multiple objects that appear as extended targets. We propose a data-driven radar target detector exploiting a highly efficient 2D CNN backbone inspired by the computer vision domain.

Ricardo Lopes, João Magalhães, David Semedo

Significant strides have been made in natural language tasks, largely attributed to the emergence of powerful large language models (LLMs). These models, pre-trained on extensive and diverse corpora, have become increasingly capable of comprehending the intricacies of language. Despite the abundance of LLMs for many high-resource languages, the availability

Fei Wang, Ruohui Zhang, Chenglin Chen, Min Yang

Multi-Object Tracking (MOT) aims to maintain stable and uninterrupted trajectories for each target. Most state-of-the-art approaches first detect objects in each frame and then implement data association between new detections and existing tracks using motion models and appearance similarities. Despite achieving satisfactory results, occlusion and crowds can

Leonhard Grosse, Sara Saeidian, Parastoo Sadeghi, Tobias J. Oechtering

We examine the relationship between privacy metrics that utilize information density to measure information leakage between a private and a disclosed random variable. Firstly, we prove that bounding the information density from above or below in turn implies a lower or upper bound on the information density, respectively. Using this result, we establish new

Robin Krebs, Mariami Gachechiladze

A deep understanding of quantum entanglement is vital for advancing quantum technologies. The strength of entanglement can be quantified by counting the degrees of freedom that are entangled, which results in a quantity called Schmidt number. A particular challenge is to identify the strength of entanglement in quantum states which remain positive under part

A. A. Mushtukov, A. Ingram, V. F. Suleimanov, N. DiLullo

Within the magnetospheric radius, the geometry of accretion flow in X-ray pulsars is shaped by a strong magnetic field of a neutron star. Starting at the magnetospheric radius, accretion flow follows field lines and reaches the stellar surface in small regions located close to the magnetic poles of a star. At low mass accretion rates, the dynamic of the flow

Mitsuhiro Nishijima, Bruno F. Lourenço

In this paper, we consider copositive cones over symmetric cones and show that they are never facially exposed when the underlying cone has dimension at least 2. We do so by explicitly exhibiting a non-exposed extreme ray. Our result extends the known fact that the cone of copositive matrices over the nonnegative orthant is not facially exposed in general.

Martin Markl

We develop a self-dual, bivariant extension of the concept of an operadic category, its associated operads and their algebras. Our new theory covers, besides all classical subjects, also generalized traces and bivariant versions of Kapranov's charades. It is, moreover, combinatorially rich and aesthetically pleasing.

Chunyang Meng, Haogang Tong, Tianyang Wu, Maolin Pan

Autoscaling is a technology that automatically scales resources for applications without human intervention to ensure runtime Quality of Service (QoS) while reducing costs. However, user-facing cloud applications serve dynamic workloads that often exhibit variability and contain bursts, posing challenges to autoscaling in maintaining QoS within Service-Level

Arup Majumdar, P. Sam Johnson

In this paper, we prove the relation $\frac{r_{A}(T) + r_{A}(T^{\diamond}) + |r_{A}(T^{\diamond}) - r_{A}(T)|}{2} = \sup \{ |\lambda|: \lambda \in \sigma_{A}(T)\}$, where $A$ is a positive semidefinite operator (not necessarily to have a closed range) and $r_{A}(T)$ is the $A$-spectral radius of $T$ in $B_{A^{\frac{1}{2}}}(H)$. Also we prove that $\sup \{ |\

Michael Hanus

Unintended failures during a computation are painful but frequent during software development. Failures due to external reasons (e.g., missing files, no permissions) can be caught by exception handlers. Programming failures, such as calling a partially defined operation with unintended arguments, are often not caught due to the assumption that the software i

Zeyang Sha, Yang Zhang

The increasing reliance on large language models (LLMs) such as ChatGPT in various fields emphasizes the importance of ``prompt engineering,'' a technology to improve the quality of model outputs. With companies investing significantly in expert prompt engineers and educational resources rising to meet market demand, designing high-quality prompts has become

Yichen Li, Yintong Huo, Renyi Zhong, Zhihan Jiang

Logging practices have been extensively investigated to assist developers in writing appropriate logging statements for documenting software behaviors. Although numerous automatic logging approaches have been proposed, their performance remains unsatisfactory due to the constraint of the single-method input, without informative programming context outside th

Xuefeng Han, Wen Chen, Jun Li, Ming Ding

Federated learning (FL) has been recognized as a viable distributed learning paradigm for training a machine learning model across distributed clients without uploading raw data. However, FL in wireless networks still faces two major challenges, i.e., large communication overhead and high energy consumption, which are exacerbated by client heterogeneity in d

Charles Fromonteil, Roberto Tricarico, Francesco Cesa, Hannes Pichler

We discuss time-optimal control problems for two setups involving globally driven Rydberg atoms in the blockade limit by deriving the associated Hamilton-Jacobi-Bellman equations. From these equations, we extract the globally optimal trajectories and the corresponding controls for several target processes of the atomic system, using a generalized method of c

M. A. Weber, M. F. Gely, R. K. Hanley, T. P. Harty

Microwave-driven logic is a promising alternative to laser control in scaling trapped-ion based quantum processors. However, such electronic gates have yet to match the speed offered by their laser-driven counterparts. Here, we implement M{\o}lmer-S{\o}rensen two-qubit gates on $^{43}\text{Ca}^+$ hyperfine clock qubits in a cryogenic ($\approx25~\text{K}$) s

Chongzhi Zhang, Zhiping Peng, Junhao Zheng, Qianli Ma

Complex Query Answering (CQA) over Knowledge Graphs (KGs) is a challenging task. Given that KGs are usually incomplete, neural models are proposed to solve CQA by performing multi-hop logical reasoning. However, most of them cannot perform well on both one-hop and multi-hop queries simultaneously. Recent work proposes a logical message passing mechanism base

Marta Bilkova, Sabine Frittella, Daniil Kozhemiachenko, Ondrej Majer

This paper is an extended version of an earlier submission to WoLLIC 2023. We discuss two-layered logics formalising reasoning with probabilities and belief functions that combine the Lukasiewicz $[0,1]$-valued logic with Baaz $\triangle$ operator and the Belnap--Dunn logic. We consider two probabilistic logics that present two perspectives on the probabilit

Nicholas Hale

A framework for Chebyshev spectral collocation methods for the numerical solution of functional and delay differential equations (FDEs and DDEs) is described. The framework combines interpolation via the barycentric resampling matrix with a multidomain approach used to resolve isolated discontinuities propagated by non-smooth initial data. Geometric converge

Yongjun Ahn, Viktor Jahnke, Hyun-Sik Jeong, Chang-Woo Ji

We study the pole-skipping phenomenon within holographic axion theories, a common framework for studying strongly coupled systems with chemical potential ($\mu$) and momentum relaxation ($\beta$). Considering the backreaction characterized by $\mu$ and $\beta$, we encounter coupled equations of motion for the metric, gauge, and axion field, which are classif

Jinjing Shi, Zimeng Xiao, Heyuan Shi, Yu Jiang

Quantum Neural Network (QNN) combines the Deep Learning (DL) principle with the fundamental theory of quantum mechanics to achieve machine learning tasks with quantum acceleration. Recently, QNN systems have been found to manifest robustness issues similar to classical DL systems. There is an urgent need for ways to test their correctness and security. Howev

I. Todoshchenko, M. Kamada, J. -P. Kaikkonen, Y. Liao

The ground-state phonon pressure is an analogue to the famous Casimir pressure of vacuum produced by zero-point photons. The acoustic Casimir forces are, however, many orders of magnitude weaker than the electromagnetic Casimir forces, as the typical speed of sound is 100 000 times smaller than the speed of light. Because of its weakness, zero-point acoustic

Jiayi Fu, Xuandong Zhao, Ruihan Yang, Yuansen Zhang

Large language models (LLMs) excellently generate human-like text, but also raise concerns about misuse in fake news and academic dishonesty. Decoding-based watermark, particularly the GumbelMax-trick-based watermark(GM watermark), is a standout solution for safeguarding machine-generated texts due to its notable detectability. However, GM watermark encounte

Yuxuan Chen, Garth N. Wells

The convergence of multigrid methods degrades significantly if a small number of low quality cells are present in a finite element mesh, and this can be a barrier to the efficient and robust application of multigrid on complicated geometric domains. The degraded performance is observed also if intermediate levels in a non-nested geometric multigrid problem h

Wei Lou, Guanbin Li, Xiang Wan, Haofeng Li

Nuclei classification is a critical step in computer-aided diagnosis with histopathology images. In the past, various methods have employed graph neural networks (GNN) to analyze cell graphs that model inter-cell relationships by considering nuclei as vertices. However, they are limited by the GNN mechanism that only passes messages among local nodes via fix

Srihari P, Anik Kumar Paul, Bharath Bhikkaji

This paper considers the Federated learning (FL) in a stochastic approximation (SA) framework. Here, each client $i$ trains a local model using its dataset $\mathcal{D}^{(i)}$ and periodically transmits the model parameters $w^{(i)}_n$ to a central server, where they are aggregated into a global model parameter $\bar{w}_n$ and sent back. The clients continue

G. Homa, A. Ortega, M. Koniorczyk

The Choi representation of completely positive (CP) maps, i.e. quantum channels is often used in the context of quantum information and computation as it is easy to work with. It is a correspondence between CP maps and quantum states also termed as the Choi-Jamio\l kowski isomorphism. It is especially useful if a parametrization of the set of CP maps is need

Giuseppe Gaeta

We present a version of the classical Moran model, in which mutations are taken into account; the possibility of mutations was introduced by Moran in his seminal paper, but it is more often overlooked in discussing the Moran model. For this model, fixation is prevented by mutation, and we have an ergodic Markov process; the equilibrium distribution for such

Robert Blaga

Low-cost particulate matter sensors (LCS) are an important source of air quality data, improving the spatial and temporal resolution of data gathered by sparsely placed official monitoring stations. Their readings, however, are subject to bias due to unaccounted for effects pertaining to both the physical properties of the aerosol particles and design limita

Ignacio Figueruelo-Campanero, Adolfo del Campo, Gladys Nieva, Elvira M. González

Studying and controlling the properties of individual exfoliated materials is one of the first steps towards the fabrication of complex van der Waals systems. However, prolonged exposure to ambient conditions can affect the properties of very thin exfoliated materials altering their physical properties. For this reason, it is imperative to employ versatile c

Houcemeddine Turki, Kawthar Ellouze, Hager Ben Ammar, Mohamed Ali Hadj Taieb

Tunisian Arabic (ISO 693-3: aeb) isa distinct variety native to Tunisia, derived from Arabic and enriched by various historical influences. This research introduces the "Normalized Orthography for Tunisian Arabic" (NOTA), an adaptation of CODA* guidelines for transcribing Tunisian Arabic using Arabic script. The aim is to enhance language resource developmen

Sindre Benjamin Remman, Anastasios M. Lekkas

Understanding the behavior of deep reinforcement learning (DRL) agents is crucial for improving their performance and reliability. However, the complexity of their policies often makes them challenging to understand. In this paper, we introduce a new approach for investigating the behavior modes of DRL policies, which involves utilizing dimensionality reduct

Junjia Huang, Haofeng Li, Xiang Wan, Guanbin Li

The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis. Numerous pathological datasets are currently available, but their annotations are inconsistent. Most existing methods require individual training on each dataset to deduce the relevant labels and lack the use of common knowledge across datasets

Anuj Kumar Sirohi, Anjali Gupta, Sandeep Kumar, Amitabha Bagchi

Graph Neural Networks (GNNs) have demonstrated impressive performance across various tasks, leading to their increased adoption in high-stakes decision-making systems. However, concerns have arisen about GNNs potentially generating unfair decisions for underprivileged groups or individuals when lacking fairness constraints. This work addresses this issue by

Aftab Hussain, Md Rafiqul Islam Rabin, Navid Ayoobi, Mohammad Amin Alipour

Large language models (LLMs) have revolutionized software development practices, yet concerns about their safety have arisen, particularly regarding hidden backdoors, aka trojans. Backdoor attacks involve the insertion of triggers into training data, allowing attackers to manipulate the behavior of the model maliciously. In this paper, we focus on analyzing

Eugenia Franco, Bernhard Kepka, Juan J. L. Velázquez

In this paper we study how to determine if a linear biochemical network satisfies the detailed balance condition, without knowing the details of all the reactions taking place in the network. To this end, we use the formalism of response functions $R_{ij} (t) $ that measure how the system reacts to the injection of the substance $j$ at time $t=0$, by measuri

Kefan Pan, Shixin Wen, Jing Yang

In this paper, we are concerned with the critical Hartree equation \begin{equation*} \begin{cases} -\Delta u=\left(\displaystyle{\displaystyle{\int_{\Omega}}}\frac{u^{2^{*}_{\mu}}(y)}{|x-y|^{\mu}}dy\right)u^{2^{*}_{\mu}-1}+\varepsilon u,\quad u>0,\quad &\text{in $\Omega$,}\\ u=0,\quad &\text{on $\partial\Omega$,} \end{cases} \end{equation*} where $\Omega\sub

Alessandro Licciardi, Davide Carbone

Marine mammal communication is a complex field, hindered by the diversity of vocalizations and environmental factors. The Watkins Marine Mammal Sound Database (WMMD) constitutes a comprehensive labeled dataset employed in machine learning applications. Nevertheless, the methodologies for data preparation, preprocessing, and classification documented in the l

I. F. Llovo, J. Mosqueira, Ding Hu, Huiqian Luo

The critical current $I_c$ of single crystals of the iron pnictide superconductor BaFe$_2$(As$_{1-x}$P$_x$)$_2$, has been studied through measurements of magnetic hysteresis cycles. We show that the introduction of surface irregularities in the $\mu$m scale significantly increase $I_c$, primarily near the irreversibility magnetic field $H_{irr}$, where the s

Giorgia Greco, Giuseppe Antonio Elia, Daniel Hermida-Merino, Robert Hahn

The process of anion intercalation in graphite and its reversibility plays a crucial role in the next generation energy-storage devices. Herein the reaction mechanism of the aluminum graphite dual ion cell by operando X-ray scattering from small angles to wide angles is investigated. The staging behavior of the graphite intercalation compound (GIC) formation

Krzysztof A. Krawczyk

This paper is a mathematical investigation on Epstein semantics. One of the main tools of the present paper is the model-theoretic S-set construction introduced in (Krawczyk 2022). We use it to prove several results: 1) that each Epstein model has uncountably many equivalent Epstein models, 2) that the logic of generalised Epstein models is the S-set invaria

Sascha Xu, Nils Philipp Walter, Janis Kalofolias, Jilles Vreeken

Finding and describing sub-populations that are exceptional regarding a target property has important applications in many scientific disciplines, from identifying disadvantaged demographic groups in census data to finding conductive molecules within gold nanoparticles. Current approaches to finding such subgroups require pre-discretized predictive variables

Jiyoung Han

In this concise article, we compute the weight decomposition of $\mathfrak{sl}_d(\mathbb R)$ with respect to the adjoint representation of $\mathfrak{so}(p,q)$, where $d=p+q$ and demonstrate in detail that $\mathfrak{sl}_d(\mathbb R)$ comprises two irreducible $\mathfrak{so}(p,q)$-invariant subspaces. This can be employed to establish the well-known fact tha

Penghai Zhao, Xin Zhang, Jiayue Cao, Ming-Ming Cheng

The rapid growth of research in Pattern Analysis and Machine Intelligence (PAMI) has rendered literature reviews essential for consolidating and interpreting knowledge across its many subfields. In this work, we present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions: (i) identifying structural and statistical regularit

Sohail Ahmed Khan, Duc-Tien Dang-Nguyen

The recent advancements in Generative Adversarial Networks (GANs) and the emergence of Diffusion models have significantly streamlined the production of highly realistic and widely accessible synthetic content. As a result, there is a pressing need for effective general purpose detection mechanisms to mitigate the potential risks posed by deepfakes. In this

Shen-Shi Du, Xiao-Jin Liu, Zu-Cheng Chen, Zhi-Qiang You

We derive the initial spin period distribution of neutron stars by studying the population of young pulsars associated with supernova remnants. Our hierarchical Bayesian approach accounts for the measurement uncertainties of individual observations and selection effects. Without correcting for selection effects, as done in previous studies, we find that puls

Luis Martínez, Antonio Vera López, Antonio Vera Pérez, Beatriz Vera Pérez

We revisit the concepts of acyclic orderings and number of acyclic orderings of acyclic digraphs in terms of dispositions and counters for arbitrary multidigraphs. We prove that when we add a sequence of nested directed paths to a directed graph there is a unique polynomial such that the generatrix function of the family of counters is the product of the pol

Afshin Akhshani, Małgorzata Białous, Leszek Sirko

We investigate properties of the transmission amplitude of quantum graphs and microwave networks composed of regular polygons such as triangles and squares. We show that for the graphs composed of regular polygons with the edges of the length $l$ the transmission amplitude displays a band of transmission suppression with some narrow peaks of full transmissio

Seunggyu Kim, Eunwoo Lee

It has been proposed that the superconformal index admits a novel reformulation, called giant graviton expansion. In this paper, we investigate the properties of dual $AdS_5$ black holes using the giant graviton expansion framework. First, we compute the entropy of black holes in $AdS_5\times S^5$ with fixed charges through a large $N$ saddle point analysis

Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic

In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse applications across various fields, such as computer vision (CV), condition monitoring (CM), virtual reality, robotics, autonomous driving, etc. Deep learning (DL) has proven effective in leveraging 3D PCs to address various challenges encountered in 2D vision. Howe

Hamed Kouhi, Mansour Kabganian, Farhad Fani Saberi, Fatemeh Ghorbani

Attitude control of spacecraft during an impulsive orbital maneuver is a vital task. Many spacecraft and launchers use the gimbaled thrust vector control (TVC) in their attitude control system during an orbital maneuver. Mathematical modeling of the gimbal actuator is an important task because we should show the applicability of the gimbaled-TVC in a spacecr

Maurice Kraus, David Steinmann, Antonia Wüst, Andre Kokozinski

Deep time series models often suffer from reliability issues due to their tendency to rely on spurious correlations, leading to incorrect predictions. To mitigate such shortcuts and prevent "Clever-Hans" moments in time series models, we introduce Right on Time (RioT), a novel method that enables interacting with model explanations across both the time and f

Kun Wang, Zheng Chen, Fangmin Lu, Jun Li

This paper addresses an optimal guidance problem concerning the vertical landing of a lunar lander with the objective of minimizing fuel consumption. The vertical landing imposes a final attitude constraint, which is treated as a final control constraint. To handle this constraint, we propose a nonnegative small regularization term to augment the original co

Sudip Sasmal, Markus Schröder, Oriol Vendrell

We propose an approach to represent the second-quantized electronic Hamiltonian in a compact sum-of-products (SOP) form. The approach is based on the canonical polyadic decomposition (CPD) of the original Hamiltonian projected onto the sub-Fock spaces formed by groups of spin orbitals. The algorithm for obtaining the canonical polyadic form starts from an ex

Mohsen Hafez-Torbati, Götz S. Uhrig

Despite the discovery of multiple intrinsic magnetic topological insulators in recent years the observation of Chern insulators is still restricted to very low temperatures due to the negligible charge gaps. Here, we uncover the potential of heavy transition-metal compounds for realizing a collinear antiferromagnetic Chern insulator (AFCI) with a charge gap

Emerick Y. Guillaume, Danny E. P. Vanpoucke, Rozita Rouzbahani, Luna Pratali Maffei

Hydrogen radical attacks and subsequent hydrogen migrations are considered to play an important role in the atomic-scale mechanisms of diamond chemical vapour deposition growth. We perform a comprehensive analysis of the reactions involving H-radical and vacancies on H-passivated diamond surfaces exposed to hydrogen radical-rich atmosphere. By means of first

Jiang Wu, Hongbo Wang, Chunhe Ni, Chenwei Zhang

Data Pipeline plays an indispensable role in tasks such as modeling machine learning and developing data products. With the increasing diversification and complexity of Data sources, as well as the rapid growth of data volumes, building an efficient Data Pipeline has become crucial for improving work efficiency and solving complex problems. This paper focuse

Aida Abiad, Cristina Dalfó, Miquel Àngel Fiol

In this note, we use eigenvalue interlacing to derive an inequality between the maximum degree of a graph and its maximum and minimum adjacency eigenvalues. The case of equality is fully characterized.

Xueyang Feng, Zhi-Yuan Chen, Yujia Qin, Yankai Lin

In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite this, LLM-based agents frequently demonstrate notable shortcomings in adjusting to dynamic environments and fully grasping human needs. In this work, we introduce the problem of L

Chengcheng Wei, Ze Chen, Songtan Fang, Jiarong He

This paper mainly describes a unified system for hallucination detection of LLMs, which wins the second prize in the model-agnostic track of the SemEval-2024 Task 6, and also achieves considerable results in the model-aware track. This task aims to detect hallucination with LLMs for three different text-generation tasks without labeled training data. We util

Xiao-Xiao Long, Gao-Feng Wei

Within the possible least uncertainty on the nuclear incompressibility $K_{0}$, we examine effects of $K_{0}$ in heavy-ion collisions at intermediate energies. Based on simulations of Au + Au collision at 400 MeV/nucleon using an isospin- and momentum-dependent transport model, we find that the incompressibility $K_{0}$ indeed affects significantly the attai

Katarzyna Kunio, Jakub Bogusławski, Grzegorz Soboń

Multiphoton microscopes employ femtosecond lasers as light sources because the high peak power of the ultrashort pulse allows for multiphoton excitation of fluorescence in the examined sample. However, such short pulses are susceptible to broadening in a microscope's highly dispersive optical elements and require careful dispersion management, otherwise decr

Dominique Levesque, Nicolas Sourlas

One of the important questions in statistical mechanics is how irreversibility (time's arrow) occurs when Newton equations of motion are time reversal invariant. One objection to irreversibility is based on Poincar\'e's recursion theorem: a classical hamiltonian confined system returns after some time, so-called Poincar\'e recurrence time (PRT), close to its

Kentaro Yamaguchi

We studied the closure of a complex subtorus given from an affine subspace in $\mathfrak{t}^{n} \cong \mathbb{R}^{n}$ in a toric manifold. If the closure of the complex subtorus is a smooth complex submanifold in the toric manifold, then we call such submanifold a torus-equivariantly embedded toric manifold with respect to the subtorus action determined by t

Shunsuke Kasao, Yu Kawakami

There exists the duality between normal family theory and value distribution theory of meromorphic functions, which is called the Bloch principle. Zalcman formulated a more precise statement on it. In this paper, based on the Zalcman and Ros work, we comprehend the phenomenon of the trinity among normal family theory, value distribution theory and minimal su

Xinchen Zhang, Ling Yang, Yaqi Cai, Zhaochen Yu

Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositional generation. In this paper, we propose RealCompo, a new training-free and transferred-friendly text-to-image generation framework, which aims to leverage the respective advantag

Massimo Blasone, Fabrizio Illuminati, Luciano Petruzziello, Kyrylo Simonov

Macrorealism formalizes the intuitive notion that at any given time the system occupies a definite state and that the evolution of the system is independent of the measurements performed on it, in contrast to the principles of quantum mechanics. In this study, we carry out a comparative analysis between three-time Leggett--Garg-type inequalities and the cond

Zhaowei Zhang, Fengshuo Bai, Mingzhi Wang, Haoyang Ye

The burgeoning integration of artificial intelligence (AI) into human society brings forth significant implications for societal governance and safety. While considerable strides have been made in addressing AI alignment challenges, existing methodologies primarily focus on technical facets, often neglecting the intricate sociotechnical nature of AI systems,

Aditya Kumar, Satish Narayana Srirama

The amount of data being produced at every epoch of second is increasing every moment. Various sensors, cameras and smart gadgets produce continuous data throughout its installation. Processing and analyzing raw data at a cloud server faces several challenges such as bandwidth, congestion, latency, privacy and security. Fog computing brings computational res

Till Fluschnik, Leon Kellerhals, Malte Renken

We introduce the algorithmic problem of finding a locally rainbow path of length $\ell$ connecting two distinguished vertices $s$ and $t$ in a vertex-colored directed graph. Herein, a path is locally rainbow if between any two visits of equally colored vertices, the path traverses consecutively at least $r$ differently colored vertices. This problem generali

T. H. Freitas, J. A. Lima

In this paper we introduce new classes of gluing of complex analytic spaces germs, called weakly large, large and strongly large. We give a description of their Poincar\'e series and, as applications, we give numerical criteria to determine when these classes of gluing of germs of complex analytic spaces are smooth, singular, complete intersections and Goren

Katya Krupchyk, Shiqi Ma, Suman Kumar Sahoo, Mikko Salo

We study inverse boundary problems for semilinear Schr\"odinger equations on smooth compact Riemannian manifolds of dimensions $\ge 2$ with smooth boundary, at a large fixed frequency. We show that certain classes of cubic nonlinearities are determined uniquely from the knowledge of the nonlinear Dirichlet--to--Neumann map at a large fixed frequency on quite

S. E. Chorfi, G. El Guermai, L. Maniar, W. Zouhair

In this paper, we present a refined approach to establish a global Lipschitz stability for an inverse source problem concerning the determination of forcing terms in the wave equation with mixed boundary conditions. It consists of boundary conditions incorporating a dynamic boundary condition and Dirichlet boundary condition on disjoint subsets of the bounda

Martin Hanik, Hans-Christian Hege, Christoph von Tycowicz

Data sets sampled in Lie groups are widespread, and as with multivariate data, it is important for many applications to assess the differences between the sets in terms of their distributions. Indices for this task are usually derived by considering the Lie group as a Riemannian manifold. Then, however, compatibility with the group operation is guaranteed on

Elodie Germani, Xavier Rolland, Pierre Maurel, Camille Maumet

In recent years, the lack of reproducibility of research findings has become an important source of concerns in many scientific fields, including functional Magnetic Resonance Imaging (fMRI). The low statistical power often observed in fMRI studies was identified as one of the leading causes of irreproducibility. The development of data sharing opens up new

Raphaël Achddou, Yann Gousseau, Saïd Ladjal

In order to evaluate the capacity of a camera to render textures properly, the standard practice, used by classical scoring protocols, is to compute the frequential response to a dead leaves image target, from which is built a texture acutance metric. In this work, we propose a mixed training procedure for image restoration neural networks, relying on both n

Luka Medic, Jernej Mravlje, Anton Ramšak, Tomaž Rejec

We explore anomalous high-harmonic generation in a model that realizes a transition from a broken time-reversal symmetry Weyl-semimetal to a semi-Dirac regime, i.e. a gapless semimetal with dispersion that is parabolic in one direction and conical in the other two. We point out the intensity of the induced anomalous high harmonics is high in the semi-Dirac r

Xi Chen

In this paper, we extend G{\'e}rard's formula for the solution of the Benjamin--Ono equation on the line to square integrable and real valued initial data. Combined with this formula, we also extend the G{\'e}rard's formula for the zero dispersion limit of the Benjamin--Ono equation on the line to more singular initial data. In the derivation of the extensio

K. S. C. Decker, C. Karrasch

We document the ground state phase diagram of the one-dimensional Kitaev chain with quasi-periodic disorder in the presence of two-body interactions. Our data was obtained for systems of $L=1000$ sites using large-scale density matrix renormalization group numerics and is benchmarked against known results for the clean system. We demonstrate that moderate qu

L. M. Bernabò, Sz. Csizmadia, A. M. S. Smith, H. Rauer

Love numbers measure the reaction of a celestial body to perturbing forces, such as the centrifugal force caused by rotation, or tidal forces resulting from the interaction with a companion body. These parameters are related to the interior density profile. The non-point mass nature of the host star and a planet orbiting around each other contributes to the

Kossi Tepe, Yann Verchier, Yetongnon Kokou

Like many developing countries, Togo faces the challenge of massification in higher education resulting from a large increase in the number of students enrolled in its public universities. Encouraged by the public authorities, with the support of the United Nations and Unesco, the number of students to be trained continues to grow to provide the country with

Coline Emprin, Dana Hunter, Muriel Livernet, Christine Vespa

Motivated by its link with functor homology, we study the prop freely generated by the operadic suspension of the operad Com. We exhibit a particular family of generators, for which the composition and the symmetric group actions admit simple descriptions. We highlight associated subcategories of its Karoubi envelope which allows us to compute extensions gro

Ke-Shuang Cui, Xiao-Jun Zhang, Jin-Hui Wu

The generation of the narrowband strong-correlated biphotons via spontaneous four-wave mixing can be effectively controlled and enhanced by an additional driving field which drives a transition with its upper level being a Rydberg state. We study the properties of the noise of the generated biphotons and show that in the region of weak pumping and low atomic

Yingfan Liu, Renyu Zhu, Ming Gao

With the rapid development of big data and AI technology, programming is in high demand and has become an essential skill for students. Meanwhile, researchers also focus on boosting the online judging system's guidance ability to reduce students' dropout rates. Previous studies mainly targeted at enhancing learner engagement on online platforms by providing

Rikkert Frederix, Leif Gellersen, Jasmina Nasufi

We present a method that allows enabling Matrix Element Corrections (MECs) in Pythia8 with MC@NLO matching, without incurring double counting. MECs are an interesting feature that may contribute to the accuracy of theoretical predictions, alongside matching and merging. We directly compare our method to a specific choice of settings in Pythia8, which can rem

Saebyeok Ahn, JinMyeong Kim, Boris I. Ivanov, Ohjoon Kwon

We report an extensive high-sensitivity search for axion dark matter above 1\,GHz at the Center for Axion and Precision Physics Research (CAPP). The cavity resonant search, exploiting the coupling between axions and photons, explored the frequency (mass) range of 1.025\,GHz (4.24\,$\mu$eV) to 1.185\,GHz (4.91\,$\mu$eV). We have introduced a number of innovat

Tim Michels, Daniel Mäckelmann, Reinhard Koch

Among the common applications of plenoptic cameras are depth reconstruction and post-shot refocusing. These require a calibration relating the camera-side light field to that of the scene. Numerous methods with this goal have been developed based on thin lens models for the plenoptic camera's main lens and microlenses. Our work addresses the often-overlooked

Chakib Fettal, Lazhar Labiod, Mohamed Nadif

This paper explores an empirical approach to learn more discriminantive sentence representations in an unsupervised fashion. Leveraging semantic graph smoothing, we enhance sentence embeddings obtained from pretrained models to improve results for the text clustering and classification tasks. Our method, validated on eight benchmarks, demonstrates consistent

Hechuan Guo, Minghui Xu, Jiahao Zhang, Chunchi Liu

With the rapid development of blockchain and its applications, the amount of data stored on decentralized storage networks (DSNs) has grown exponentially. DSNs bring together affordable storage resources from around the world to provide robust, decentralized storage services for tens of thousands of decentralized applications (dApps). However, existing DSNs

Yi-Hsin Chen, Kuan-Wei Ho, Shiau-Rung Tsai, Guan-Hsun Lin

This work introduces a Transformer-based image compression system. It has the flexibility to switch between the standard image reconstruction and the denoising reconstruction from a single compressed bitstream. Instead of training separate decoders for these tasks, we incorporate two add-on modules to adapt a pre-trained image decoder from performing the sta

Steven Mascaro, Owen Woodberry, Yue Wu, Ann E. Nicholson

The typical phases of Bayesian network (BN) structured development include specification of purpose and scope, structure development, parameterisation and validation. Structure development is typically focused on qualitative issues and parameterisation quantitative issues, however there are qualitative and quantitative issues that arise in both phases. A com

Tiansong Zhou, Yebin Liu, Xuangeng Chu, Chengkun Cao

Rendering photo-realistic novel-view images of complex scenes has been a long-standing challenge in computer graphics. In recent years, great research progress has been made on enhancing rendering quality and accelerating rendering speed in the realm of view synthesis. However, when rendering complex dynamic scenes with sparse views, the rendering quality re

Paul Dommel

Kernel ridge regression, in general, is expensive in memory allocation and computation time. This paper addresses low rank approximations and surrogates for kernel ridge regression, which bridge these difficulties. The fundamental contribution of the paper is a lower bound on the minimal rank such that the prediction power of the approximation remains reliab

Saieed Akbari, Sina Ghasemi Nezhad, Reyhane Ghazizadeh, John Haslegrave

We investigate how small the Randi\'c index of a graph can be in terms of its matching number, and prove several results. We give best-possible linear bounds for graphs of small excess and for subcubic graphs; in the former case the size of excess we permit is qualitatively the best possible. We show that a linear bound holds for any sparse hereditary graph