April 2023 arXiv papers — page 136

Mehrasa Ahmadipour, Michèle Wigger

This paper considers information-theoretic models for integrated sensing and communication (ISAC) over multi-access channels (MAC) and device-to-device (D2D) communication. The models are general and include as special cases scenarios with and without perfect or imperfect state-information at the MAC receiver as well as causal state-information at the D2D te

Sebastian Blacker, Andreas Bauswein, Stefan Typel

We study the importance of the thermal behavior of the hadron-quark phase transition in neutron star (NS) mergers. To this end, we devise a new scheme approximating thermal effects to supplement any cold, barotropic hybrid equation of state (EoS) model, i.e. two-phase EoS constructions with a hadronic regime and a phase of deconfined quark matter. The consid

M. A. Gómez-Muñoz, L. Bianchi, A. Manchado

Planetary nebulae (PNe) consist of an ionized envelope surrounding a hot central star (CSPN) that emits mostly at ultraviolet (UV) wavelengths. Ultraviolet observations, therefore, provide important information on both the CSPN and the nebula. We have matched the PNe in The Hong Kong/AAO/Strasbourg H$\alpha$ (HASH) catalog with the Galaxy Evolution Explorer

Priyanka Kargupta, Tanay Komarlu, Susik Yoon, Xuan Wang

Text classification is essential for organizing unstructured text. Traditional methods rely on human annotations or, more recently, a set of class seed words for supervision, which can be costly, particularly for specialized or emerging domains. To address this, using class surface names alone as extremely weak supervision has been proposed. However, existin

Lina Alasfar, Luca Cadamuro, Christina Dimitriadi, Arnaud Ferrari

Higgs boson pair production is traditionally considered to be of particular interest for a measurement of the trilinear Higgs self-coupling. Yet it can offer insights into other couplings as well, since - in an effective field theory (EFT) parameterisation of potential new physics - both the production cross section and kinematical properties of the Higgs bo

B. N. Khabibullin, E. G. Kudasheva, R. R. Muryasov

We give a new scale of completeness conditions for exponential systems in two types of functional spaces on subsets of the complex plane. The first is the Banach spaces of functions that are continuous on a compact and simultaneously holomorphic in the interior of this compact, if this interior is nonempty, with a uniform norm. The second is the spaces of ho

Roberto Gualdi, Martín Sombra

We study the distribution of the height of the intersection between the projective line defined by the linear polynomial $x_{0}+x_{1}+x_{2}$ and its translate by a torsion point. We show that for a strict sequence of torsion points, the corresponding heights converge to a real number that is a rational multiple of a quotient of special values of the Riemann

Jiajun Chen, Xiaolong Du, Mingzhen Zhou, Andrew Benson

We study the gravitational Bose-Einstein condensation of a massive vector field in the kinetic regime and the non-relativistic limit using non-linear dynamical numerical methods. Gravitational condensation leads to the spontaneous formation of solitons. We measure the condensation time and growth rate, and compare to analytical models in analogy to the scala

Aditi Mishra, Utkarsh Soni, Anjana Arunkumar, Jinbin Huang

Large Language Models (LLMs) have gained widespread popularity due to their ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple natural language prompt. Part of the appeal for LLMs is their approachability to the general public, including individuals with no prior technical experience in NLP techniques. However, natural language p

Andreas Hauptmann, Jenni Poimala

Learned iterative reconstructions hold great promise to accelerate tomographic imaging with empirical robustness to model perturbations. Nevertheless, an adoption for photoacoustic tomography is hindered by the need to repeatedly evaluate the computational expensive forward model. Computational feasibility can be obtained by the use of fast approximate model

Xuanchao Ma, Yuchen Liu

Ethylene leakage detection has become one of the most important research directions in the field of target detection due to the fact that ethylene leakage in the petrochemical industry is closely related to production safety and environmental pollution. Under infrared conditions, there are many factors that affect the texture characteristics of ethylene, suc

Jheng-Hong Yang, Carlos Lassance, Rafael Sampaio de Rezende, Krishna Srinivasan

This paper presents the AToMiC (Authoring Tools for Multimedia Content) dataset, designed to advance research in image/text cross-modal retrieval. While vision-language pretrained transformers have led to significant improvements in retrieval effectiveness, existing research has relied on image-caption datasets that feature only simplistic image-text relatio

Christian Carrick, Michael A. Hill, Douglas C. Ravenel

For a motivic spectrum $E\in \mathcal{SH}(k)$, let $\Gamma(E)$ denote the global sections spectrum, where $E$ is viewed as a sheaf of spectra on $\mathrm{Sm}_k$. Voevodsky's slice filtration determines a spectral sequence converging to the homotopy groups of $\Gamma(E)$. In this paper, we introduce a spectral sequence converging instead to the mod 2 homology

Taehoon Kim, Bohyung Han

We propose a novel domain generalization technique, referred to as Randomized Adversarial Style Perturbation (RASP), which is motivated by the observation that the characteristics of each domain are captured by the feature statistics corresponding to style. The proposed algorithm perturbs the style of a feature in an adversarial direction towards a randomly

Shuchi Chawla, Dimitris Christou

In the Time-Windows TSP (TW-TSP) we are given requests at different locations on a network; each request is endowed with a reward and an interval of time; the goal is to find a tour that visits as much reward as possible during the corresponding time window. For the online version of this problem, where each request is revealed at the start of its time windo

Navid Afrasiabian, Venkat Balasubramanian, Colin Denniston

Introducing nanorods into a polymeric matrix can enhance the physical and mechanical properties of the resulting material. In this paper, we focus on understanding the dispersion and orientation patterns of nanorods in an unentangled polymer melt, particularly as a function of nanorod concentration, using Molecular Dynamics (MD) simulations. The system is co

Criston Hyett, Laurent Pagnier, Jean Alisse, Lilach Sabban

We build and experiment with a realistic but reduced natural gas model of Israel. The system is unusual because (a) it is controlled from a limited number of points which are at, or close to, the gas extraction sites offshore of Israel's Mediterranean coast; (b) control specifies average flux at inlet, not pressure; (c) there are no inland compressors to reg

Zongchen Chen, Kuikui Liu, Nitya Mani, Ankur Moitra

Strong spatial mixing (SSM) is an important quantitative notion of correlation decay for Gibbs distributions arising in statistical physics, probability theory, and theoretical computer science. A longstanding conjecture is that the uniform distribution on proper $q$-colorings on a $\Delta$-regular tree exhibits SSM whenever $q \ge \Delta+1$. Moreover, it is

Ranjani Srinivasan, Rohit Bhattacharya, Razieh Nabi, Elizabeth L. Ogburn

Despite the growing interest in causal and statistical inference for settings with data dependence, few methods currently exist to account for missing data in dependent data settings; most classical missing data methods in statistics and causal inference treat data units as independent and identically distributed (i.i.d.). We develop a graphical modeling bas

Claude Bardos, Daniel W. Boutros, Edriss S. Titi

We consider the three-dimensional incompressible Euler equations on a bounded domain $\Omega$ with $C^4$ boundary. We prove that if the velocity field $u \in C^{0,\alpha} (\Omega)$ with $\alpha > 0$ (where we are omitting the time dependence), it follows that the corresponding pressure $p$ of a weak solution to the Euler equations belongs to the H\"older spa

José Niño-Mora

This paper develops a polyhedral approach to the design, analysis, and computation of dynamic allocation indices for scheduling binary-action (engage/rest) Markovian stochastic projects which can change state when rested (restless bandits (RBs)), based on partial conservation laws (PCLs). This extends previous work by the author [J. Ni\~no-Mora. Restless ban

Jingqi Li, Chih-Yuan Chiu, Lasse Peters, Fernando Palafox

Decision-making in multi-player games can be extremely challenging, particularly under uncertainty. In this work, we propose a new sample-based approximation to a class of stochastic, general-sum, pure Nash games, where each player has an expected-value objective and a set of chance constraints. This new approximation scheme inherits the accuracy of objectiv

Linsheng Wang, Shengxuan Zhou

We introduce the fiberwise Bergman kernel for a flat family of polarized varieties over a Riemann surface, which extends the classical Bergman kernel defined on the reduced fibers. We establish the continuity of the fiberwise Bergman kernel and provide a result on uniform convergence for the Fubini-Study currents. As a consequence, we show that the fiberwise

Junqiao Lin

We prove that any two-party correlation in the commuting operator model can be approximated using a tracially embeddable strategy, a class of strategies defined on a finite tracial von Neumann algebra, which we define in this paper. Using this characterization, we show that any approximately synchronous correlation can be approximated to the average of a col

Jiayun Liang, Ke Ma, Xiao Zhao, Guanyu Lu

Intercalation is a process of inserting chemical species into the heterointerfaces of two-dimensional (2D) layered materials. While much research has focused on intercalating metals and small gas molecules into graphene, the intercalation of larger molecules through the basal plane of graphene remains highly unexplored. In this work, we present a new mechani

Vincent Bosboom, Herbert Egger, Matthias Schlottbom

The propagation of charged particles through a scattering medium in the presence of a magnetic field can be described by a Fokker-Planck equation with Lorentz force. This model is studied both, from a theoretical and a numerical point of view. A particular trace estimate is derived for the relevant function spaces to clarify the meaning of boundary values. E

Shunichiro Kinoshita, Keiju Murata, Daichi Takeda

We find, in the AdS/CFT, a source on the boundary which generates one wave packet drawing a null geodesic inside the bulk. Once such a wave packet dives into the bulk, it comes back to the boundary after a specific time, at which the expectation value of the corresponding boundary operator finally stands up. Since this behavior strongly reflects the existenc

Guang-Liang Li, Junpeng Cao, Yi Qiao, Wen-Li Yang

In this paper, we study the exact solution of the $q$-deformed $D^{(1)}_3$ quantum lattice model with non-diagonal open boundary condition. We demonstrate the crossing symmetry of the transfer matrix and obtain the quantum determinant. We construct the independent transfer matrix fusion identities and show that the fusion processes can be closed. Based on th

Alex Taekyung Lee, Hyowon Park, Anh T. Ngo

We study the effect of the off-diagonal elements of the Wannier Hamiltonian on the electronic structure of low-symmetry material Li$_2$MnO$_3$ ($C2/m$), using dynamical mean field theory calculations with continuous-time Quantum Monte Carlo impurity solver. Presence of significant off-diagonal elements leads to a pronounced suppression of the energy gap. The

Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu

The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e.g., ChatDoctor) or instruction data (e.g., Alpaca). Among the various fine-tuning methods, adapter-based parameter-efficient fine-tuni

Emanuele Paolini, Eugene Stepanov

We construct an example of an infinite planar embedded self-similar binary tree $\Sigma$ which is the essentially unique solution to the Steiner problem of finding the shortest connection of a given planar self-similar fractal set $C$ of positive Hausdorff dimension. The set $C$ can be considered the set of leaves, or the ``boundary``, of the tree $\Sigma$,

J. E. Nunn, A. McEllistrim, A. Weston, A. Garcia-Ruiz

Diverse emergent correlated electron phenomena have been observed in twisted graphene layers due to electronic interactions with the moir\'e superlattice potential. Many electronic structure predictions have been reported exploring this new field, but with few momentum-resolved electronic structure measurements to test them. Here we use angle-resolved photoe

TianYi Zhang, XiuFeng Han

Magnon, as elementary excitation in magnetic systems, can carry and transfer angular momentum. Due to the absence of Joule heat during magnon transport, researches on magnon transport have gained considerable interests over the past decade. Recently, a full quantum theory has been employed to investigate magnon transport in ferromagnetic insulators (FMIs). H

Erick Lavoie

***** This design is a duplicate of a Causal Length Set (see notes in the comments). We leave nonetheless the original paper here because the proofs are referred to in another submission.***** The 2P-Set Conflict-Free Replicated Data Type (CRDT) supports two phases for each possible element: in the first phase an element can be added to the set and the subse

Mouaad Boughellaba, Abdelhamid Tayebi

We consider the problem of distributed attitude estimation of multi-agent systems, evolving on $SO(3)$, relying on individual angular velocity and relative attitude measurements. The interaction graph topology is assumed to be an undirected tree. First, we propose a continuous nonlinear distributed attitude estimation scheme with almost global asymptotic sta

K. Tamilselvan, A. Govindarajan, I. Inbavalli, T. Alagesan

We investigate the nontrivial characteristics of modulational instability (MI) in a system of Bragg gratings with saturable nonlinearity. We also introduce an equal amount of gain and loss into the existing system which gives rise to an additional degree of freedom, thanks to the concept of $\cal PT$- symmetry. We obtain the nonlinear dispersion relation of

Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Ali Mousavi

There is an increasing adoption of machine learning for encoding data into vectors to serve online recommendation and search use cases. As a result, recent data management systems propose augmenting query processing with online vector similarity search. In this work, we explore vector similarity search in the context of Knowledge Graphs (KGs). Motivated by t

Diego H. Correa, Victor I. Giraldo-Rivera, Martín Lagares

We study the integrability properties of Wilson loops in the ${\cal N}=6$ three-dimensional Chern-Simons-matter (ABJM) theory. We begin with the construction of an open spin chain that describes the anomalous dimensions of operators inserted along the contour of a 1/2 BPS Wilson loop. Moreover, we compute the all-loop reflection matrices that govern the inte

Nelson Callegari, Adrián Rodríguez

Aegaeon (S/2008 S 1) is the last satellite discovered by the Cassini spacecraft at the end of the 2000s. Like the satellites Methone and Anthe, it is involved in mean motion resonance with the mid-sized Mimas. In this work, we give a detailed analysis of the current orbit of Aegaeon identifying the resonant, secular and long-term perturbations due to Mimas a

Michal Štefánik, Marek Kadlčík, Piotr Gramacki, Petr Sojka

Despite the rapid recent progress in creating accurate and compact in-context learners, most recent work focuses on in-context learning (ICL) for tasks in English. However, the ability to interact with users of languages outside English presents a great potential for broadening the applicability of language technologies to non-English speakers. In this work,

Junlong Feng, Sokbae Lee

We introduce a novel framework for individual-level welfare analysis. It builds on a parametric model for continuous demand with a quasilinear utility function, allowing for heterogeneous coefficients and unobserved individual-good-level preference shocks. We obtain bounds on the individual-level consumer welfare loss at any confidence level due to a hypothe

Kristo Ment, David Charbonneau, Jonathan Irwin, Jennifer G. Winters

Based on photometric observations by TESS, we present the discovery of a Venus-sized planet transiting LHS 475, an M3 dwarf located 12.5 pc from the Sun. The mass of the star is $0.274 \pm 0.015~\rm{M_{Sun}}$. The planet, originally reported as TOI 910.01, has an orbital period of $2.0291025 \pm 0.0000020$ days and an estimated radius of $0.955 \pm 0.053~\rm

Jiaqi Wu, John Joon Young Chung, Eytan Adar

Creating stylized visualization requires going beyond the limited, abstract, geometric marks produced by most tools. Rather, the designer builds stylized idioms where the marks are both transformed (e.g., photographs of candles instead of bars) and also synthesized into a 'scene' that pushes the boundaries of traditional visualizations. To support this, we i

Samyadeep Basu, Daniela Massiceti, Shell Xu Hu, Soheil Feizi

Few-shot classification (FSC) entails learning novel classes given only a few examples per class after a pre-training (or meta-training) phase on a set of base classes. Recent works have shown that simply fine-tuning a pre-trained Vision Transformer (ViT) on new test classes is a strong approach for FSC. Fine-tuning ViTs, however, is expensive in time, compu

Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, Swati Biswas

Multi-gene panel testing allows many cancer susceptibility genes to be tested quickly at a lower cost making such testing accessible to a broader population. Thus, more patients carrying pathogenic germline mutations in various cancer-susceptibility genes are being identified. This creates a great opportunity, as well as an urgent need, to counsel these pati

Keller Jordan

Typical neural network trainings have substantial variance in test-set performance between repeated runs, impeding hyperparameter comparison and training reproducibility. In this work we present the following results towards understanding this variation. (1) Despite having significant variance on their test-sets, we demonstrate that standard CIFAR-10 and Ima

Aniruddha Tiwari, Rushit Dave, Mounika Vanamala

Conspicuous progression in the field of machine learning and deep learning have led the jump of highly realistic fake media, these media oftentimes referred as deepfakes. Deepfakes are fabricated media which are generated by sophisticated AI that are at times very difficult to set apart from the real media. So far, this media can be uploaded to the various s

Roberto Mota Navarro, Francois Leyvraz, Hernán Larralde

The study of order volumes in financial markets has shown that these display several non-trivial statistical properties. Most studies have been focused on the bulk properties of volume of incoming orders or of realized transactions rather than the dynamical aspects. The present work is a study of the dynamical properties of volume. Unlike previous works, we

Tianyu Du, Ayush Kanodia, Susan Athey

The $\texttt{torch-choice}$ is an open-source library for flexible, fast choice modeling with Python and PyTorch. $\texttt{torch-choice}$ provides a $\texttt{ChoiceDataset}$ data structure to manage databases flexibly and memory-efficiently. The paper demonstrates constructing a $\texttt{ChoiceDataset}$ from databases of various formats and functionalities o

Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges

Language models (LMs) have recently shown remarkable performance on reasoning tasks by explicitly generating intermediate inferences, e.g., chain-of-thought prompting. However, these intermediate inference steps may be inappropriate deductions from the initial context and lead to incorrect final predictions. Here we introduce REFINER, a framework for finetun

Karan Pattni, Wajid Ali, Mark Broom, Kieran J Sharkey

We consider the effect of network structure on the evolution of a population. Models of this kind typically consider a population of fixed size and distribution. Here we consider eco-evolutionary dynamics where population size and distribution can change through birth, death and migration, all of which are separate processes. This allows complex interaction

Micol Spitale, Hatice Gunes

Affective robotics research aims to better understand human social and emotional signals to improve human-robot interaction (HRI), and has been widely used during the last decade in multiple application fields. Past works have demonstrated, indeed, the potential of using affective robots (i.e., that can recognize, or interpret, or process, or simulate human

Max H. Cohen, Makai Mann, Kevin Leahy, Calin Belta

In this paper, we present a framework for online parameter estimation and uncertainty quantification in the context of adaptive safety-critical control. The key insight enabling our approach is that the parameter estimate generated by the continuous-time recursive least squares (RLS) algorithm at any point in time is an affine transformation of the initial p

Gwanghyun Kim, Ji Ha Jang, Se Young Chun

Recently, significant advancements have been made in 3D generative models, however training these models across diverse domains is challenging and requires an huge amount of training data and knowledge of pose distribution. Text-guided domain adaptation methods have allowed the generator to be adapted to the target domains using text prompts, thereby obviati

Taehoon Kim, Jaeyoo Park, Bohyung Han

We propose a novel class incremental learning approach by incorporating a feature augmentation technique motivated by adversarial attacks. We employ a classifier learned in the past to complement training examples rather than simply play a role as a teacher for knowledge distillation towards subsequent models. The proposed approach has a unique perspective t

C. H. Lenzi, G. Lugones, C. Vasquez

It has been shown recently that quark-hadron conversions at the interface of a hybrid star may have a key role on the dynamic stability of the compact object. In this work we perform a systematic study of hybrid stars with reactive interfaces using a model-agnostic piecewise-polytropic hadronic equation of state and the Nambu-Jona-Lasinio model for three-fla

Seungbae Kim, Jyun-Yu Jiang, Jinyoung Han, Wei Wang

As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propose InfluencerRank that ranks influencers

Faraz Zaidi

The discovery of small world and scale free properties of many real world networks has revolutionized the way we study, analyze, model and process networks. An important way to analyze these complex networks is to visualize them using graph layout algorithms. Due to their large size and complex connectivity, it is difficult to make deductions from the visual

Manuel Muñoz Sánchez, Emilia Silvas, Jos Elfring, René van de Molengraft

Accurate and robust trajectory predictions of road users are needed to enable safe automated driving. To do this, machine learning models are often used, which can show erratic behavior when presented with previously unseen inputs. In this work, two environment-aware models (MotionCNN and MultiPath++) and two common baselines (Constant Velocity and an LSTM)

Kartik Bhatnagar, Sampath Lonka, Jammi Kunal, Mahabala Rao M G

In this work, we develop language models for the Sanskrit language, namely Bidirectional Encoder Representations from Transformers (BERT) and its variants: A Lite BERT (ALBERT), and Robustly Optimized BERT (RoBERTa) using Devanagari Sanskrit text corpus. Then we extracted the features for the given text from these models. We applied the dimensional reduction

Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan

We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals. We draw on recent advances in guided diffusion modeling to achieve test-time controllability of trajectories, which is normally only associated with rule-based systems. Our guided diffusion model allows users to c

Sahil Bhola, Karthik Duraisamy

A novel information-theoretic approach is proposed to assess the global practical identifiability of Bayesian statistical models. Based on the concept of conditional mutual information, an estimate of information gained for each model parameter is used to quantify the identifiability with practical considerations. No assumptions are made about the structure

Gang Chen

Inspired by the progress on the spin liquid candidates Ce$_2$Sn$_2$O$_7$ and Ce$_2$Zr$_2$O$_7$ where the Ce ions carry the dipole-octupole doublets, we analyze the distinction between the thermodynamic and spectroscopic measurements for the octupolar U(1) spin liquid. Due to the peculiar properties of octupolar U(1) spin liquid and the selective Zeeman coupl

Yingzhe Li, Martin Campos Pinto, Florian Holderied, Stefan Possanner

We explore the possibilities of applying structure-preserving numerical methods to a plasma hybrid model with kinetic ions and mass-less fluid electrons satisfying the quasi-neutrality relation. The numerical schemes are derived by finite element methods in the framework of finite element exterior calculus (FEEC) for field variables, particle-in-cell (PIC) m

Antonis Maronikolakis, Abdullatif Köksal, Hinrich Schütze

We introduce HATELEXICON, a lexicon of slurs and targets of hate speech for the countries of Brazil, Germany, India and Kenya, to aid training and interpretability of models. We demonstrate how our lexicon can be used to interpret model predictions, showing that models developed to classify extreme speech rely heavily on target words when making predictions.

Sayan Bhattacharya, Niv Buchbinder, Roie Levin, Thatchaphol Saranurak

We study the problem of chasing positive bodies in $\ell_1$: given a sequence of bodies $K_{t}=\{x^{t}\in\mathbb{R}_{+}^{n}\mid C^{t}x^{t}\geq 1,P^{t}x^{t}\leq 1\}$ revealed online, where $C^{t}$ and $P^{t}$ are nonnegative matrices, the goal is to (approximately) maintain a point $x_t \in K_t$ such that $\sum_t \|x_t - x_{t-1}\|_1$ is minimized. This captur

M. Liu, N. Saadat, Y. Jeong, S. Roth

Intravoxel Incoherent Motion (IVIM) is a non-contrast magnetic resonance imaging diffusion-based scan that uses a multitude of b-values to measure various speeds of molecular perfusion and diffusion, sidestepping inaccuracy of arterial input functions or bolus kinetics in quantitative imaging. We test a new method of IVIM quantification and compare our value

R. Sammani, Y. Boujakhrout, E. H Saidi, R. Ahl Laamara

We give Swampland constraints on the three dimensional Landscape of Anti-de Sitter higher spin gravity in the Chern-Simons formulation with connection valued in various split real forms of Lie algebras. We derive the finiteness conjecture by computing the upper bound on the rank of possible gauge groups then we refine it using the AdS distance conjecture. We

Scott Macleod, Mauro Dragone

This work proposes and analyses the application of a robotic platform as an digital skills assistant. analysing the ethical issues relating to the decision making process in the use case of online food shopping in order to inform a co design session on what, and how, the digital skills assistant should make decisions.

Qilong Guo, Leyun Gao, Yajun Mao, Qiang Li

A feasibility study is performed for searching vector-like leptons at a muon collider, in the context of the "4321 model", an ultraviolet-complete model with rich collider phenomenology together with potential to explain recent existing some B physics measurements or anomalies. In this paper, we perform a Monte Carlo study with various machine learning techn

Mouaad Boughellaba, Abdelhamid Tayebi

In this paper, we address the distributed pose estimation problem for multi-agent systems, where the agents have unknown static positions and time-varying orientations. The interaction graph is assumed to be directed and acyclic with two leaders that have access to their position and orientation. We propose a nonlinear distributed pose estimation scheme rely

Djunes Janssens, Florian Brunbauer, Karl Jonathan Flöthner, Marta Lisowska

As demonstrated by the ATLAS New Small Wheel community with their MicroMegas (MM) design, resistive electrodes are now used in different detector types within the Micro Pattern Gaseous Detector family to improve their robustness or performance. The extended form of the Ramo-Shockley theorem for conductive media has been applied to a 1 M$\Omega$/$\Box$ 2D res

Hajin Lim, Lisa Kakonge, Yaxin Hu, Lyn S. Turkstra

Traumatic brain injury (TBI) can result in chronic sensorimotor, cognitive, psychosocial, and communication challenges that can limit social participation. Social media can be a useful outlet for social participation for individuals with TBI, but there are barriers to access. While research has drawn attention to the nature of access barriers, few studies ha

Mina Doosti, Lucas Hanouz, Anne Marin, Elham Kashefi

We show the security of multi-user key establishment on a single line of quantum communication. More precisely, we consider a quantum communication architecture where the qubit generation and measurement happen at the two ends of the line, whilst intermediate parties are limited to single-qubit unitary transforms. This network topology has been previously in

Vugar Ismailov, Ekrem Savas

In this paper, we study approximation properties of single hidden layer neural networks with weights varying on finitely many directions and thresholds from an open interval. We obtain a necessary and at the same time sufficient measure theoretic condition for density of such networks in the space of continuous functions. Further, we prove a density result f

Guoxing Chen, Wei-Chyung Wang, Shixi Yang, Yixin Wang

Cloud fraction significantly affects the short- and long-wave radiation. Its realistic representation in general circulation models (GCMs) still poses great challenges in modeling the atmosphere. Here, we present a neural network-based diagnostic scheme that uses the grid-mean temperature, pressure, liquid and ice water mixing ratios, and relative humidity t

L. M. Spin, M. C. F. Donkers

In this paper, we will propose linear-matrix-inequality-based techniques for the design of sampled-data controllers that render the closed-loop system dissipative with respect to \textcolor{blue}{quadratic supply functions}, which includes passivity and an upper-bound on the system's $\mathcal{H}_\infty$-norm as a special case. To arrive at these results, we

Shaojie Yang, Jun Tao, Benrong Mu, Aoyun He

In this paper, we characterize the phase transitons of Born-Infeld AdS black holes in terms of Lyapunov exponents. We calculate the Lyapunov exponents for both null and timelike geodesics. It is found that black hole phase transitions can be described by multiple-valued Lyapunov exponents. And its phase diagram can be characterized by Lyapunov exponents and

Zehai Pang, Jinbing Hu, Yi Yang

Non-Abelian gauge fields are versatile tools for synthesizing topological phenomena but have so far been mostly studied in Hermitian systems, where gauge flux has to be defined from a closed loop in order for gauge fields, whether Abelian or non-Abelian, to become physically meaningful. We show that this condition can be relaxed in non-Hermitian systems by p

C. D. Rodríguez-Camargo, A. F. Urquijo-Rodríguez, E. A. Mojica-Nava

Multilayer networks provide a more comprehensive framework for exploring real-world and engineering systems than traditional single-layer networks, consisting of multiple interacting networks. However, despite significant research in distributed optimization for single-layer networks, similar progress for multilayer systems is lacking. This paper proposes tw

Shubham Ugare, Debangshu Banerjee, Sasa Misailovic, Gagandeep Singh

Complete verification of deep neural networks (DNNs) can exactly determine whether the DNN satisfies a desired trustworthy property (e.g., robustness, fairness) on an infinite set of inputs or not. Despite the tremendous progress to improve the scalability of complete verifiers over the years on individual DNNs, they are inherently inefficient when a deploye

N. Dupont, L. Gabardos, F. Arrouas, N. Ombredane

We report on the design of a Hamiltonian ratchet exploiting periodically at rest integrable trajectories in the phase space of a modulated periodic potential, leading to the linear non-diffusive transport of particles. Using Bose-Einstein condensates in a modulated one-dimensional optical lattice, we make the first observations of this new spatial ratchet tr

Matthias Scharitzer, Vivek Shende

For a certain class of Legendrian surfaces in the five-sphere, associated to cubic planar graphs, we show that the all-genus skein-valued holomorphic curve invariants of any filling are annihilated by certain explicit skein-valued operator equations.

José Niño-Mora

We address the intractable multi-armed bandit problem with switching costs, for which Asawa and Teneketzis introduced in [M. Asawa and D. Teneketzis. 1996. Multi-armed bandits with switching penalties. IEEE Trans. Automat. Control, 41 328-348] an index that partially characterizes optimal policies, attaching to each project state a ``continuation index'' (it

L. R. Dai, L. M. Abreu, A. Feijoo, E. Oset

We perform a fit to the LHCb data on the $T_{cc}(3875)$ state in order to determine its nature. We use a general framework that allows to have the $D^0 D^{*+}$, $D^+ D^{*0}$ components forming a molecular state, as well as a possible nonmolecular state or contributions from missing coupled channels. From the fits to the data we conclude that the state observ

Bas van Stein, Diederick Vermetten, Fabio Caraffini, Anna V. Kononova

Evaluating the performance of heuristic optimisation algorithms is essential to determine how well they perform under various conditions. Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms. The toolbox can be used to identify biases in existing algorithms, as well as to test for bias in newl

Przemysław Berk, Frank Trujillo

In this article, we consider skew product extensions over symmetric interval exchange transformations with respect to the cocycle $f(x)=\chi_{(0,1/2)}-\chi_{(1/2,1)}$. More precisely, we prove that for almost every interval exchange transformation $T$ with symmetric combinatorial data, the skew product $T_f: [0, 1) \times \mathbb Z \to [0, 1) \times \mathbb

Abba Ramadan, Atanas G. Stefanov

We consider the NLS system of the third-harmonic generation, which was introduced by Sammut. Our interest is in solitary wave solutions and their stability properties. The recent work of Oliveira and Pastor, discussed global well-posedness vs. finite time blow-up, as well as other aspects of the dynamics. These authors have also constructed solitary wave sol

Emanuel Indrei, Aram Karakhanyan

We prove the sharp quantitative stability in the radial isotropic Almgren problem. In addition, we develop a theory for estimating the sharp modulus in the context of minimal assumptions on the surface tension and the potential and obtain the sharp $\epsilon^2$ in any dimension. Inter-alia, we also solve the problem of calculating the critical mass which was

Christian Keilstrup Ingwersen, Christian Mikkelstrup, Janus Nørtoft Jensen, Morten Rieger Hannemose

Accurate 3D human pose estimation is essential for sports analytics, coaching, and injury prevention. However, existing datasets for monocular pose estimation do not adequately capture the challenging and dynamic nature of sports movements. In response, we introduce SportsPose, a large-scale 3D human pose dataset consisting of highly dynamic sports movements

Quan Huu Cap, Atsushi Fukuda, Hitoshi Iyatomi

Medical images are extremely valuable for supporting medical diagnoses. However, in practice, low-quality (LQ) medical images, such as images that are hazy/blurry, have uneven illumination, or are out of focus, among others, are often obtained during data acquisition. This leads to difficulties in the screening and diagnosis of medical diseases. Several gene

Andrei Tokovinin

Orbital motions in four hierarchical stellar systems discovered by speckle interferometry are studied. Their inner orbits are relatively well constrained, while the long outer orbits are less certain. The eccentric and misaligned inner orbits in the early-type hierarchies Epsilon Cha (B9V, central star of the 5 Myr old association, P=6.4 yr, e=0.73), and I~3

Adeline Fermanian, Jiawei Chang, Terry Lyons, Gérard Biau

The signature is a representation of a path as an infinite sequence of its iterated integrals. Under certain assumptions, the signature characterizes the path, up to translation and reparameterization. Therefore, a crucial question of interest is the development of efficient algorithms to invert the signature, i.e., to reconstruct the path from the informati

Andrei Tokovinin

New spectroscopic orbits of inner subsystems in 14 hierarchies are determined from long-term monitoring with the optical echelle spectrometer, CHIRON. Their main components are nearby solar-type stars belonging to nine triple systems (HIP 3645, 14307, 36165, 79980, 103735, 103814, 104440, 105879, 109443) and five quadruples of 2+2 hierarchy (HIP 41171, 49336

Jose Ma. Santiago, Richard Lance Parayno, Jordan Aiko Deja, Briane Paul V. Samson

AI Advancements have augmented casual writing and story generation, but their usage poses challenges in collaborative storytelling. In role-playing games such as Dungeons & Dragons (D&D), composing prompts using generative AI requires a technical understanding to generate ideal results, which is difficult for novices. Thus, emergent narratives organically de

L. M. Spin, C. Verhoek, W. P. M. H. Heemels, N. van de Wouw

In this paper, we present a novel approach to combine data-driven non-parametric representations with model-based representations of dynamical systems. Based on a data-driven form of linear fractional transformations, we introduce a data-driven form of generalized plants. This form can be leveraged to accomplish performance characterizations, e.g., in the fo

J. Lee, M. Acevedo, M. Sako, M. Vincenzi

Wavelength-dependent atmospheric effects impact photometric supernova flux measurements for ground-based observations. We present corrections on supernova flux measurements from the Dark Energy Survey Supernova Program's 5YR sample (DES-SN5YR) for differential chromatic refraction (DCR) and wavelength-dependent seeing, and we show their impact on the cosmolo

Peichun Li, Guoliang Cheng, Jiawen Kang, Rong Yu

In this work, we investigate the challenging problem of on-demand semantic communication over heterogeneous wireless networks. We propose a fidelity-adjustable semantic transmission framework (FAST) that empowers wireless devices to send data efficiently under different application scenarios and resource conditions. To this end, we first design a dynamic sub

Michele Rossi

The present paper is aimed to discussing three kinds of problems: (1) producing some ``mirror theorem'' for the recent mirror symmetric construction, called \emph{framed} duality ($f$-duality), described in \cite{R-fTV} and \cite{R-fpCI}: this is performed from the point of view proposed by Homological Mirror Symmetry (HMS), by studying \emph{derived equival

Julio Huato

This paper models a two-agent economy with production and appropriation as a noncooperative dynamic game, and determines its closed-form Markovian Nash equilibrium. The analysis highlights the para-metric conditions that tip the economy from a nonaggressive or "co-operative" equilibrium to outright distributional conflict. The model includes parameters that