April 2024 arXiv papers — page 62

Jiyong Ma, Wen Gao, Chunli Wang

In this paper, a novel approach to sign language recognition based on state tying in each of data streams is presented. In this framework, it is assumed that hand gesture signal is represented in terms of six synchronous data streams, i.e., the left/right hand position, left/right hand orientation and left/right handshape. This approach offers a very accurat

Jacob E. Turner, Timothy Dolch, James M. Cordes, Stella K. Ocker

We use cyclic spectroscopy to perform high frequency-resolution analyses of multi-hour baseband Arecibo observations of the millisecond pulsar PSR B1937+21. This technique allows for the examination of scintillation features in far greater detail than is otherwise possible under most pulsar timing array observing setups. We measure scintillation bandwidths a

Dimitris Cheliotis, Michail Louvaris

In this work we study symmetric random matrices with variance profile satisfying certain conditions. We establish the convergence of the operator norm of these matrices to the largest element of the support of the limiting empirical spectral distribution. We prove that it is sufficient for the entries of the matrix to have finite only the $4$-th moment or th

L. Hernández-Sánchez, I. A. Bocanegra-Garay, I. Ramos-Prieto, F. Soto-Eguibar

In the framework of the Jaynes-Cummings model, we investigate how atomic lineshapes are affected by coherently driving the atom-field interaction. We pay particular attention to the two-level atom interaction with a thermal cavity field, when both are influenced by external classical fields. Adopting a density matrix formalism, we calculate the average atomi

Mustafa Erolcan Er, Murathan Kurfalı, Deniz Zeyrek

In this work, we introduce a lightweight discourse connective detection system. Employing gradient boosting trained on straightforward, low-complexity features, this proposed approach sidesteps the computational demands of the current approaches that rely on deep neural networks. Considering its simplicity, our approach achieves competitive results while off

Donghuo Zeng, Roberto S. Legaspi, Yuewen Sun, Xinshuai Dong

Customizing persuasive conversations related to the outcome of interest for specific users achieves better persuasion results. However, existing persuasive conversation systems rely on persuasive strategies and encounter challenges in dynamically adjusting dialogues to suit the evolving states of individual users during interactions. This limitation restrict

Steven A. Grosz, Anil K. Jain

The utilization of synthetic data for fingerprint recognition has garnered increased attention due to its potential to alleviate privacy concerns surrounding sensitive biometric data. However, current methods for generating fingerprints have limitations in creating impressions of the same finger with useful intra-class variations. To tackle this challenge, w

In-Jee Jeong, Sung-Jin Oh

It has been shown in our previous work that the incompressible and irresistive Hall- and electron-magnetohydrodynamic (MHD) equations are illposed on flat domains $M = \mathbb{R}^k \times \mathbb{T}^{3-k}$ for $0 \le k \le 2$. The data and solutions therein were assumed to be independent of one coordinate, which not only significantly simplifies the systems

Donghuo Zeng, Yanan Wang, Kazushi Ikeda, Yi Yu

Metric learning minimizes the gap between similar (positive) pairs of data points and increases the separation of dissimilar (negative) pairs, aiming at capturing the underlying data structure and enhancing the performance of tasks like audio-visual cross-modal retrieval (AV-CMR). Recent works employ sampling methods to select impactful data points from the

Wenhao Wang, Yifan Sun, Zhentao Tan, Yi Yang

This paper explores in-context learning for image copy detection (ICD), i.e., prompting an ICD model to identify replicated images with new tampering patterns without the need for additional training. The prompts (or the contexts) are from a small set of image-replica pairs that reflect the new patterns and are used at inference time. Such in-context ICD has

Andrei Ionov

We compute the Fourier transform of some of the summands of the push-forward of the constant sheaf under the Hitchin map for $\mathrm{SL}_n$ restricted to the locus of cyclic spectral curves inside the Hitchin base (for $\mathrm{SL}_2$ all spectral curves are cyclic) and give an estimate on the support of the Fourier transforms of the other summands.

Anselm Paulus, Arman Zharmagambetov, Chuan Guo, Brandon Amos

Large Language Models (LLMs) are vulnerable to jailbreaking attacks that lead to generation of inappropriate or harmful content. Manual red-teaming requires a time-consuming search for adversarial prompts, whereas automatic adversarial prompt generation often leads to semantically meaningless attacks that do not scale well. In this paper, we present a novel

Filipe Barroso, Diogo Gomes, Gareth J. Baxter

We propose a constraint-based algorithm, which automatically determines causal relevance thresholds, to infer causal networks from data. We call these topological thresholds. We present two methods for determining the threshold: the first seeks a set of edges that leaves no disconnected nodes in the network; the second seeks a causal large connected componen

Jay Seitz

In the last year or so and going back many decades there has been extensive claims by major computational scientists, engineers, and others that AGI, artificial general intelligence, is five or ten years away, but without a scintilla of scientific evidence, for a broad body of these claims. Computers will become conscious, have a theory of mind, think and re

Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim, David J. Love

Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational complexity coming from processing high-dimensional features can be prohibitive for mobile applications. In this w

Shuyao Shi, Neiwen Ling, Zhehao Jiang, Xuan Huang

Recently,smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of

Zhihang Li, Zhao Song, Weixin Wang, Junze Yin

Leverage score is a fundamental problem in machine learning and theoretical computer science. It has extensive applications in regression analysis, randomized algorithms, and neural network inversion. Despite leverage scores are widely used as a tool, in this paper, we study a novel problem, namely the inverting leverage score problem. We analyze to invert t

Ali Naseh, Katherine Thai, Mohit Iyyer, Amir Houmansadr

With the digital imagery landscape rapidly evolving, image stocks and AI-generated image marketplaces have become central to visual media. Traditional stock images now exist alongside innovative platforms that trade in prompts for AI-generated visuals, driven by sophisticated APIs like DALL-E 3 and Midjourney. This paper studies the possibility of employing

Jianhao M. Yang

Quantum theory of electron spin is developed here based on the extended least action principle and assumptions of intrinsic angular momentum of an electron with random orientations. The novelty of the formulation is the introduction of relative entropy for the random orientations of intrinsic angular momentum when extremizing the total actions. Applying recu

Robert E. Kent

Human knowledge is made up of the conceptual structures of many communities of interest. In order to establish coherence in human knowledge representation, it is important to enable communication between the conceptual structures of different communities The conceptual structures of any particular community is representable in an ontology. Such a ontology pr

Alireza Salemi, Hamed Zamani

Evaluating retrieval-augmented generation (RAG) presents challenges, particularly for retrieval models within these systems. Traditional end-to-end evaluation methods are computationally expensive. Furthermore, evaluation of the retrieval model's performance based on query-document relevance labels shows a small correlation with the RAG system's downstream p

Xiaobing Feng, Tingao Jiang

This paper is concerned with the PDE and numerical analysis of a modified one-dimensional intravascular stent model originally proposed in [4]. It is proved that the modified model has a unique weak solution using the Galerkin method combined with a compactness argument. A semi-discrete finite element method and a fully discrete scheme using the Euler time-s

Ziqing Guo

Biomedical literature is a rapidly expanding field of science and technology. Classification of biomedical texts is an essential part of biomedicine research, especially in the field of biology. This work proposes the fine-tuned DistilBERT, a methodology-specific, pre-trained generative classification language model for mining biomedicine texts. The model ha

Adilet Yerkin, Elnara Kadyrgali, Yerdauit Torekhan, Pakizar Shamoi

Watching movies is one of the social activities typically done in groups. Emotion is the most vital factor that affects movie viewers' preferences. So, the emotional aspect of the movie needs to be determined and analyzed for further recommendations. It can be challenging to choose a movie that appeals to the emotions of a diverse group. Reaching an agreemen

Mengjie Yu, Dustin Harris, Ian Jones, Ting Zhang

Gaze-based interactions offer a potential way for users to naturally engage with mixed reality (XR) interfaces. Black-box machine learning models enabled higher accuracy for gaze-based interactions. However, due to the black-box nature of the model, users might not be able to understand and effectively adapt their gaze behaviour to achieve high quality inter

Avner Ash, Jeremy Miller, Peter Patzt

We prove that the direct sum of all homology groups of the integral general linear groups with Steinberg module coefficients form a commutative Hopf algebra, in particular a free graded commutative algebra. We use this to construct new infinite families of unstable cohomology classes of $SL_n(\mathbb Z)$.

Ognjen Tošić

For a negatively curved manifold $M$ and a continuous map $\psi:\Sigma\to M$ from a closed surface $\Sigma$, we study complex submanifolds of Teichm\"uller space $\mathcal{S}\subset\mathcal{T}(\Sigma)$ such that the harmonic maps $\{h_X:X\to M\text{ for }X\in\mathcal{S}\}$ in the homotopy class of $\psi$ all have equal energy. When $M$ is real analytic with

Farrokh Razavinia

Based on our previous works, and in order to relate them with the theory of quantum graphs and the quantum computing principles, we once again try to introduce some newly developed technical structures just by relying on our toy example, the coordinate ring of $n\times n$ quantum matrix algebra $M_q(n)$, and the associated directed locally finite graphs $\ma

Rebeka Tóth, Tamas Bisztray, László Erdodi

This study evaluates the security of web application code generated by Large Language Models, analyzing 2,500 GPT-4 generated PHP websites. These were deployed in Docker containers and tested for vulnerabilities using a hybrid approach of Burp Suite active scanning, static analysis, and manual review. Our investigation focuses on identifying Insecure File Up

José Edson Sampaio, Euripedes Carvalho da Silva

The main goal of this present paper is to bring the results proved by Barbosa, Kenmotsu and Oshikiri (1991) and its ideas to a perspective where the Ricci curvature is bounded from below. For instance, for a foliation by CMC hypersurfaces on a compact (without boundary) Riemannian manifold $M^{n+1}$ with Ricci curvature bounded from below by $-nK_0\leq 0$ an

Toshiya Namikawa

We calculate the cosmic microwave background (CMB) $B$-mode power spectrum resulting from anisotropic cosmic birefringence, without relying on the thin approximation of the last scattering surface. Specifically, we consider the influence of anisotropic cosmic birefringence arising from massless axion-like particles. Comparing our results to those obtained us

Hasanul Mahmud, Kevin Desai, Palden Lama, Sushil K. Prasad

Image classification is a fundamental task in computer vision, and the quest to enhance DNN accuracy without inflating model size or latency remains a pressing concern. We make a couple of advances in this regard, leading to a novel EncodeNet design and training framework. The first advancement involves Converting Autoencoders, a novel approach that transfor

Takuto Tsuno, Shintaro Taie, Yosuke Takasu, Kazuya Yamashita

Recent advances in quantum technology have highlighted the importance of controlling quantum states, especially in open quantum systems, where the system interacts with the environment. Non-Hermitian quantum mechanics describes these systems. Photonic systems are a key platform for studying non-Hermitian quantum mechanics owing to their ability to engineer g

Yulin Liu, Luyao Zhang

Decentralized Autonomous Organizations (DAOs), utilizing blockchain technology to enable collective governance, are a promising innovation. This research addresses the ongoing query in blockchain governance: How can DAOs optimize human cooperation? Focusing on the Network Nervous System (NNS), a comprehensive on-chain governance framework underpinned by the

Michael Potter, Rahil Bhowal, Richard Zhao, Anuj Patel

In response to the critical need for effective reconnaissance in disaster scenarios, this research article presents the design and implementation of a complete autonomous robot system using the Turtlebot3 with Robotic Operating System (ROS) Noetic. Upon deployment in closed, initially unknown environments, the system aims to generate a comprehensive map and

Delfim F. M. Torres

Through duality it is possible to transform left fractional operators into right fractional operators and vice versa. In contrast to existing literature, we establish integration by parts formulas that exclusively involve either left or right operators. The emergence of these novel fractional integration by parts formulas inspires the introduction of a new c

Maria Mihaela Trusca, Wolf Nuyts, Jonathan Thomm, Robert Honig

Current diffusion models create photorealistic images given a text prompt as input but struggle to correctly bind attributes mentioned in the text to the right objects in the image. This is evidenced by our novel image-graph alignment model called EPViT (Edge Prediction Vision Transformer) for the evaluation of image-text alignment. To alleviate the above pr

Xingbo Wang, Samantha L. Huey, Rui Sheng, Saurabh Mehta

The explosion of scientific literature has made the efficient and accurate extraction of structured data a critical component for advancing scientific knowledge and supporting evidence-based decision-making. However, existing tools often struggle to extract and structure multimodal, varied, and inconsistent information across documents into standardized form

Li Siyan, Teresa Shao, Zhou Yu, Julia Hirschberg

Existing English-teaching chatbots rarely incorporate empathy explicitly in their feedback, but empathetic feedback could help keep students engaged and reduce learner anxiety. Toward this end, we propose the task of negative emotion detection via audio, for recognizing empathetic feedback opportunities in language learning. We then build the first spoken En

Da-Wei Luo, Ting Yu

We consider the Jaynes-Cummings (JC) model embedded in a structured environment, where the atom inside an optical cavity will be affected by a hierarchical environment consisting of the cavity and its environment. We propose several effective strategies to control and suppress the decoherence effects to protect the quantum coherence of the JC atom. We study

Tom Banks

We use the Kantor-Susskind\cite{kantsuss} model of fermions as "dumbbells" connecting points on a cubic lattice to points on its dual, to define a duality between local fermionic models invariant under a $Z_2$ gauge symmetry and models of bosonic variables (generalizations of Pauli matrices) defined on the lattice.

Elisa Bassignana, Viggo Unmack Gascou, Frida Nøhr Laustsen, Gustav Kristensen

Current language models require a lot of training data to obtain high performance. For Relation Classification (RC), many datasets are domain-specific, so combining datasets to obtain better performance is non-trivial. We explore a multi-domain training setup for RC, and attempt to improve performance by encoding domain information. Our proposed models impro

Kiernan E. Arledge, Chase T. Ellis, Nazli Rasouli Sarabi, Vincent R. Whiteside

Phonon polaritons (PhPs) are hybrid photon-phonon waves which enable strong light-matter interactions and subdiffractional confinement, potentially empowering applications in sensing, nonlinear optics and nanoscale energy manipulation. In this work, we use confocal Raman microscopy to investigate the coupling between bulk phonon modes and localized surface p

Marco Castelli

We extend the so-called retract relation given in [6] for involutive set-theoretic solutions of the Pentagon Equation and we introduce the notion of associated permutation group to study the family of the commutative non-degenerate ones. Moreover, we develop a machinery to construct all these solutions and we use it to give a quite explicit classification of

Cameron Musco, Kshiteej Sheth

We present a sublinear time algorithm for computing a near optimal low-rank approximation to any positive semidefinite (PSD) Toeplitz matrix $T\in \mathbb{R}^{d\times d}$, given noisy access to its entries. In particular, given entrywise query access to $T+E$ for an arbitrary noise matrix $E\in \mathbb{R}^{d\times d}$, integer rank $k\leq d$, and error param

Anthony Bilic, Chen Chen

Binary breast cancer tumor segmentation with Magnetic Resonance Imaging (MRI) data is typically trained and evaluated on private medical data, which makes comparing deep learning approaches difficult. We propose a benchmark (BC-MRI-SEG) for binary breast cancer tumor segmentation based on publicly available MRI datasets. The benchmark consists of four datase

Maya Keely, Yeunhee Kim, Shaunak A. Mehta, Joshua Hoegerman

For robot arms to perform everyday tasks in unstructured environments, these robots must be able to manipulate a diverse range of objects. Today's robots often grasp objects with either soft grippers or rigid end-effectors. However, purely rigid or purely soft grippers have fundamental limitations: soft grippers struggle with irregular, heavy objects, while

Bastien Baldacci, Philippe Bergault, Olivier Guéant

This paper introduces a novel methodology for the pricing and management of share buyback contracts, overcoming the limitations of traditional optimal control methods, which frequently encounter difficulties with high-dimensional state spaces and the intricacies of selecting appropriate risk penalty or risk aversion parameter. Our methodology applies optimiz

José E. Chacón, Carlos Tenreiro

In density estimation, the mean integrated squared error (MISE) is commonly used as a measure of performance. In that setting, the cross-validation criterion provides an unbiased estimator of the MISE minus the integral of the squared density. Since the minimum MISE is known to converge to zero, this suggests that the minimum value of the cross-validation cr

Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these repr

Kevin Scaria, Abyn Scaria, Ben Scaria

Aspect Based Sentiment Analysis (ABSA) tasks involve the extraction of fine-grained sentiment tuples from sentences, aiming to discern the author's opinions. Conventional methodologies predominantly rely on supervised approaches; however, the efficacy of such methods diminishes in low-resource domains lacking labeled datasets since they often lack the abilit

Oscar Barragán, Haochuan Yu, Alix Violet Freckelton, Annabella Meech

We present an exhaustive photometric and spectroscopic analysis of TOI-837, a F9/G0 35 Myr young star, hosting a transiting exoplanet, TOI-837b, with an orbital period of 8.32d. Utilising data from TESS and ground-based observations, we determine a planetary radius of 0.82 R_J for TOI-837b. Through detailed HARPS spectroscopic time series analysis, we derive

Xinyu Huang, Shisheng Hu, Mushu Li, Cheng Huang

In this paper, we propose a novel efficient digital twin (DT) data processing scheme to reduce service latency for multicast short video streaming. Particularly, DT is constructed to emulate and analyze user status for multicast group update and swipe feature abstraction. Then, a precise measurement model of DT data processing is developed to characterize th

Wencheng Bao, Shi Feng, Kaiwen Zhang

In areas such as finance, engineering, and science, we often face situations that change quickly and unpredictably. These situations are tough to handle and require special tools and methods capable of understanding and predicting what might happen next. Stochastic Differential Equations (SDEs) are renowned for modeling and analyzing real-world dynamical sys

Shakti Singh, Sanjay Kumar Mishra, Akhilesh Kumar Mishra

An optical vortex beam propagating through turbulent atmosphere encounters distortions in the wavefront that results in modal scattering. Abruptly autofocussing (AAF) beams with orbital angular momentum have gained significant attention due to their non-diffracting and self-healing nature. These warrants understanding of the behaviour of these beams through

Nikolaos Georgakarakos, Siegfried Eggl, Mohamad Ali-Dib, Ian Dobbs-Dixon

In this work we revisit the problem of the dynamical stability of hierarchical triple systems with applications to circumbinary planetary orbits. We carry out more than 3 10^8 numerical simulations of planets between the size of Mercury and the lower fusion boundary (13 Jupiter masses) which revolve around the center of mass of a stellar binary over long tim

Mauro Camporeale, Giovanni Dimauro, Matteo Gelardi, Giorgia Iacobellis

Nasal Cytology is a new and efficient clinical technique to diagnose rhinitis and allergies that is not much widespread due to the time-consuming nature of cell counting; that is why AI-aided counting could be a turning point for the diffusion of this technique. In this article we present the first dataset of rhino-cytological field images: the NCD (Nasal Cy

Shuai Jiang, Christian Glusa

We propose a method to couple local and nonlocal diffusion models. By inheriting desirable properties such as patch tests, asymptotic compatibility and unintrusiveness from related splice and optimization-based coupling schemes, it enables the use of weak (or variational) formulations, is computationally efficient and straightforward to implement. We prove w

Kamila Kowalska, Enrico Maria Sessolo

We investigate quantum entanglement in high-energy $2\to 2$ scalar scattering, where the scalars are characterized by an internal flavor quantum number acting like a qubit. Working at the 1-loop order in perturbation theory, we build the final-state density matrix as a function of the scattering amplitudes connecting the initial to the outgoing state. In thi

Nadav Cohen, Itzik Klein

Autonomous Underwater Vehicles (AUVs) commonly utilize an inertial navigation system (INS) and a Doppler velocity log (DVL) for underwater navigation. To that end, their measurements are integrated through a nonlinear filter such as the extended Kalman filter (EKF). The DVL velocity vector estimate depends on retrieving reflections from the seabed, ensuring

Sebastián Barría

Let $\mathbb{A}$ and $\mathbb{S}$ denote the double arrow of Alexandroff and the Sorgenfrey line, respectively. We show that for any $n\geq 1$, the space of all unions of at most $n$ closed intervals of $\mathbb{A}$ is not homogeneous. We also prove that the spaces of non-trivial convergent sequences of $\mathbb{A}$ and $\mathbb{S}$ are homogeneous. This par

Prateek Dwivedi, Sobiya Ashraf, Pawan Kumar, Dipin Pillai

Interactions among biologically active agents is facilitated by their self-generated chemical and hydrodynamic fields. In order to elucidate the pair-wise interactions between such micro-organisms, we employ active droplets as a model system, capable of self-generating chemical and hydrodynamic fields. We demonstrate that the solute P\'eclet number ($Pe$), c

Christopher D. Sogge

We discuss problems that relate curvature and concentration properties of eigenfunctions and quasimodes on compact boundaryless Riemannian manifolds. These include new sharp $L^q$-estimates, $q\in (2,q_c]$, $q_c=2(n+1)/(n-1)$, of log-quasimodes that characterize compact connected space forms in terms of the growth rate of $L^q$-norms of such quasimode for th

Stuti Pandey, Josh Myers-Dean, Jarek Reynolds, Danna Gurari

Lateral flow tests (LFTs) enable rapid, low-cost testing for health conditions including Covid, pregnancy, HIV, and malaria. Automated readers of LFT results can yield many benefits including empowering blind people to independently learn about their health and accelerating data entry for large-scale monitoring (e.g., for pandemics such as Covid) by using on

Xiaoqi Huang, Christopher D. Sogge

We show that the upper bounds for the $L^2$-norms of $L^1$-normalized quasimodes that we obtained in [9] are always sharp on any compact space form. This allows us to characterize compact manifolds of constant sectional curvature using the decay rates of lower bounds of $L^1$-norms of $L^2$-normalized log-quasimodes fully resolving a problem initiated by the

Vincenzo Auletta, Diodato Ferraioli, Cosimo Vinci

In this work, we study the Stochastic Budgeted Multi-round Submodular Maximization (SBMSm) problem, where we aim to adaptively maximize the sum, over multiple rounds, of a monotone and submodular objective function defined on subsets of items. The objective function also depends on the realization of stochastic events, and the total number of items we can se

Junqi Jiang, Francesco Leofante, Antonio Rago, Francesca Toni

Counterfactual Explanations (CEs) have emerged as a major paradigm in explainable AI research, providing recourse recommendations for users affected by the decisions of machine learning models. However, CEs found by existing methods often become invalid when slight changes occur in the parameters of the model they were generated for. The literature lacks a w

Shaomin Wu

In this paper, I study the nonparametric identification and estimation of the marginal effect of an endogenous variable $X$ on the outcome variable $Y$, given a potentially mismeasured instrument variable $W^*$, without assuming linearity or separability of the functions governing the relationship between observables and unobservables. To address the challen

Xiaoqi Huang, Christopher D. Sogge

We obtain new optimal estimates for the $L^2(M)\to L^q(M)$, $q\in (2,q_c]$, $q_c=2(n+1)/(n-1)$, operator norms of spectral projection operators associated with spectral windows $[\lambda,\lambda+\delta(\lambda)]$, with $\delta(\lambda)=O((\log\lambda)^{-1})$ on compact Riemannian manifolds $(M,g)$ of dimension $n\ge2$ all of whose sectional curvatures are no

Shitong Shao, Zikai Zhou, Huanran Chen, Zhiqiang Shen

Dataset condensation, a concept within data-centric learning, efficiently transfers critical attributes from an original dataset to a synthetic version, maintaining both diversity and realism. This approach significantly improves model training efficiency and is adaptable across multiple application areas. Previous methods in dataset condensation have faced

Cristina Oancea, Katerina Sykorova, Jan Jakubek, Jiri Pivec

FLASH radiotherapy necessitates the development of advanced Quality Assurance methods and detectors for accurate and online monitoring of the radiation field. This study introduces enhanced time-resolution detection systems and methods tailored for single-pulse detection. The goal of this work was to measure the delivered number of pulses, investigate tempor

Mehrdad Pournaderi, Yu Xiang

The training-conditional coverage performance of the conformal prediction is known to be empirically sound. Recently, there have been efforts to support this observation with theoretical guarantees. The training-conditional coverage bounds for jackknife+ and full-conformal prediction regions have been established via the notion of $(m,n)$-stability by Liang

Christoph Hauert, Alex McAvoy

When individuals interact in groups, the evolution of cooperation is traditionally modeled using the framework of public goods games. These models often assume that the return of the public good depends linearly on the fraction of contributors. In contrast, in real life public goods interactions, the return can depend on the size of the investor pool as well

Dongryul M. Kim

In this paper, we prove two main theorems: conformal measure rigidity and ergodicity of horospherical foliations, especially in higher rank. Both theorems are new even for relatively Anosov groups. First, we establish a higher rank extension of rigidity theorems of Sullivan, Tukia, Yue, and Kim-Oh for representations of rank one discrete subgroups of diverge

Stefano Montaldo, Andrea Ratto

The main aim of this paper is to investigate the existence of Frenet helices which are polyharmonic of order $r$, shortly, $r$-harmonic. We shall obtain existence, non-existence and classification results. More specifically, we obtain a complete classification of proper $r$-harmonic helices into the $3$-dimensional solvable Lie group Sol$_3$. Next, we invest

Paul M. Alsing, Richard J. Birrittella

We show numerical evidence for a bipartite $d\times d$ pure state entanglement witness that is readily calculated from the wavefunction coefficients directly, without the need for the numerical computation of eigenvalues. This is accomplished by using an approximate analytic diagonalization of the bipartite state that captures dominant contributions to the n

S. Vignesh Raja, A. Govindarajan, M. Lakshmanan

In this work, we predict that periodic structures without gain and loss do not exhibit an S-shaped hysteresis curve in the presence of saturable nonlinearity (SNL). Instead, the input-output characteristics of the system admit ramp-like optical bistability (OB) and multistability (OM) curves that are unprecedented in the context of conventional periodic stru

Andrzej Komisarski, Teresa Rajba

We establish an integral representation for Popoviciu's convex functions of $d$ variables. This representation serves as a~foundation for deriving several functional inequalities, analogous to those well-known for usual convex functions. Our results generalize and extend the results obtained by S.~Gal, C.~Niculescu, B.~Gavrea, T.~Popoviciu, and others, who c

Victor Dudarev, Lars Banko, Alfred Ludwig

Information technology and data science development stimulate transformation in many fields of scientific knowledge. In recent years, a large number of specialized systems for information and knowledge management have been created in materials science. However, the development and deployment of open adaptive systems for research support in materials science

Arnulf Hagen

The thesis develops a view of design in a concept formation framework and outlines a language to describe both the object of the design and the process of designing. The unknown object at the outset of the design work may be seen as an unknown concept that the designer is to define. Throughout the process, she develops a description of this object by relatin

Zhimin Zhu, Siqi Xu, Jiatong Wu, Hongyao Yu

Within the Basis Light-Front Quantization framework, we systematically investigate the subleading twist (twist-3) transverse-momentum-dependent parton distribution functions (TMDs) of the proton beyond the Wandzura-Wilczek (WW) approximation. The subleading twist TMDs are not independent and can be decomposed into twist-2 and genuine twist-3 terms from the e

Lukas D. Pöhler, Klaus Diepold, Wendell Wallach

Autonomous and intelligent systems (AIS) facilitate a wide range of beneficial applications across a variety of different domains. However, technical characteristics such as unpredictability and lack of transparency, as well as potential unintended consequences, pose considerable challenges to the current governance infrastructure. Furthermore, the speed of

Nobuaki Obata, Aurel I. Stan, Hiroaki Yoshida

We find the position-momentum decomposition of the quantum operators of the classic Meixner random variables. The position-momentum decomposition involves translation operators, which are used to give a new characterization of the Meixner random variables.

Pablo S. Casas, Fátima Drubi, Santiago Ibáñez

We study a one-parameter family of time-reversible Hamiltonian vector fields in $\mathbb{R}^4$, which has received great attention in the literature. On the one hand, it is due to the role it plays in the context of certain applications in the field of Physics or Engineering and, on the other hand, we especially highlight its relevance within the framework o

Jens Lang, Bernhard A. Schmitt

This paper is concerned with the theory, construction and application of implicit Peer two-step methods that are super-convergent for variable stepsizes, i.e., preserve their classical order achieved for uniform stepsizes when applied to ODE constrained optimal control problems in a first-discretize-then-optimize setting. We upgrade our former implicit two-s

Aimilios Leftheriotis, Achilleas Tzenetopoulos, George Lentaris, Dimitrios Soudris

The B5G/6G evolution relies on connect-compute technologies and highly heterogeneous clusters with HW accelerators, which require specialized coding to be efficiently utilized. The current paper proposes a custom tool for generating multiple SW versions of a certain AI function input in high-level language, e.g., Python TensorFlow, while targeting multiple d

Zhuwu Shao, Yujuan Wang, Huanyu Yang, Yongduan Song

Among the existing works on enhancing system performance via prescribed performance functions (PPFs), the decay rates of PPFs need to be predetermined by the designer, directly affecting the convergence time of the closed-loop system. However, if only considering accelerating the system convergence by selecting a big decay rate of the performance function, i

Susana Furtado, Charles Johnson

In prioritization schemes, based on pairwise comparisons, such as the Analytical Hierarchy Process, it is necessary to extract a cardinal ranking vector from a reciprocal matrix that is unlikely to be consistent. It is natural to choose such a vector only from efficient ones. One of the most used ranking methods employs the (right) Perron eigenvector of the

Richard J. Cook

This paper is based on two insights: (1) that general relativity alone does not specify how much of the matter density contributes to the source term in Friedmann's equation, and how much contributes as the source of the gravitational potential of condensed objects; and (2) the source of the gravitational potential in the currently accepted cosmological mode

Zichen Tang, Hongyu Yang

Recent advances in generative visual models and neural radiance fields have greatly boosted 3D-aware image synthesis and stylization tasks. However, previous NeRF-based work is limited to single scene stylization, training a model to generate 3D-aware cartoon faces with arbitrary styles remains unsolved. We propose ArtNeRF, a novel face stylization framework

Kunato Nishina, Yusuke Matsui

Text-to-image models have shown progress in recent years. Along with this progress, generating vector graphics from text has also advanced. SVG is a popular format for vector graphics, and SVG represents a scene with XML text. Therefore, Large Language Models can directly process SVG code. Taking this into account, we focused on editing SVG with LLMs. For qu

Robert E. Gaunt

We obtain exact formulas for the absolute raw and central moments of the variance-gamma distribution, as infinite series involving the modified Bessel function of the second kind and the modified Lommel function of the first kind. When the skewness parameter is equal to zero (the symmetric variance-gamma distribution), the infinite series reduces to a single

Yu-Jung Wu, Martin Hack, Katja Wurster, Simon Koch

We present a way to We present a way to fabricate bicrystal Josephson junctions of high-Tc cuprate superconductors that does not require bulk bicrystalline substrates. Based on vector substrate technology, this novel approach makes use of a few tens-of-nanometers-thick bicrystalline membranes transferred onto conventional substrates.We demonstrate 24{\deg} Y

Wei Liang, Haicheng Huang, Hongsheng Dai, Yinghui Wei

Traditional meta-analysis assumes that the effect sizes estimated in individual studies follow a Gaussian distribution. However, this distributional assumption is not always satisfied in practice, leading to potentially biased results. In the situation when the number of studies, denoted as K, is large, the cumulative Gaussian approximation errors from each

Vitali Petsiuk, Kate Saenko

Motivated by ethical and legal concerns, the scientific community is actively developing methods to limit the misuse of Text-to-Image diffusion models for reproducing copyrighted, violent, explicit, or personal information in the generated images. Simultaneously, researchers put these newly developed safety measures to the test by assuming the role of an adv

Francesco Ancilotto, Luciano Reatto

Superfluids with strong spatial modulation can be experimentally produced in the area of cold atoms under the influence of optical lattices. Here we address $^{87}$Rb bosons at T=0 K in a flat geometry under the influence of a periodic potential with the Gross-Pitaevskii theory. The statics and dynamics of vortex excitations are studied in the case of one di

Nada Saadi, Numan Saeed, Mohammad Yaqub, Karthik Nandakumar

Imaging modalities such as Computed Tomography (CT) and Positron Emission Tomography (PET) are key in cancer detection, inspiring Deep Neural Networks (DNN) models that merge these scans for tumor segmentation. When both CT and PET scans are available, it is common to combine them as two channels of the input to the segmentation model. However, this method r

José Antonio Carrillo, Xu'an Dou, Pierre Roux, Zhennan Zhou

We introduce a novel reformulation of the mean-field system for pulse-coupled oscillators. It is based on writing a closed equation for the inverse distribution function associated to the probability density of oscillators with a given phase in a suitable time scale. This new framework allows to show a hidden contraction/expansion of certain distances leadin

Sneh Pandya, Yuanyuan Yang, Nicholas Van Alfen, Jonathan Blazek

The intrinsic alignments (IA) of galaxies, regarded as a contaminant in weak lensing analyses, represents the correlation of galaxy shapes due to gravitational tidal interactions and galaxy formation processes. As such, understanding IA is paramount for accurate cosmological inferences from weak lensing surveys; however, one limitation to our understanding a

Guanlong Jiao, Chenyangguang Zhang, Haonan Yin, Yu Mo

Domain generalized semantic segmentation is an essential computer vision task, for which models only leverage source data to learn the capability of generalized semantic segmentation towards the unseen target domains. Previous works typically address this challenge by global style randomization or feature regularization. In this paper, we argue that given th