April 2024 arXiv papers — page 128

Saeid Tizpaz-Niari, Sriram Sankaranarayanan

This paper leverages the statistics of extreme values to predict the worst-case convergence times of machine learning algorithms. Timing is a critical non-functional property of ML systems, and providing the worst-case converge times is essential to guarantee the availability of ML and its services. However, timing properties such as worst-case convergence t

Silvia Almada Monter, Max Gronke

The diverse Lyman-alpha (Ly$\alpha$) line profiles are essential probes of gas in and around galaxies. While isotropic models can successfully reproduce a range of Ly$\alpha$ observables, the correspondence between the model and actual physical parameters remains uncertain. We investigate the effect of anisotropies of Ly$\alpha$ escape using a simplified set

Yuan Wang, Max McCandless, Abdulhamit Donder, Giovanni Pittiglio

The ability to accurately model mechanical hysteretic behavior in tendon-actuated continuum robots using deep learning approaches is a growing area of interest. In this paper, we investigate the hysteretic response of two types of tendon-actuated continuum robots and, ultimately, compare three types of neural network modeling approaches with both forward and

Manuel Längle, Barbara Maria Mayer, Jacob Madsen, Diana Propst

Monolayer hexagonal boron nitride (hBN) has recently become the focus of intense research as a material to host quantum emitters. Although it is well known that such emission is associated with point defects, so far no conclusive correlation between the spectra and specific defects has been demonstrated. Here, we prepare atomically clean suspended hBN sample

Marta Brzezińska, Sergii Grytsiuk, Malte Rösner, Marco Gibertini

Recent experiments in twisted bilayer transition-metal dichalcogenides have revealed a variety of strongly correlated phenomena. To theoretically explore their origin, we combine here ab initio calculations with correlated model approaches to describe and study many-body effects in twisted bilayer WSe$_2$ under pressure. We find that the interlayer distance

Steve Rhyner, Haocong Luo, Juan Gómez-Luna, Mohammad Sadrosadati

Modern Machine Learning (ML) training on large-scale datasets is a very time-consuming workload. It relies on the optimization algorithm Stochastic Gradient Descent (SGD) due to its effectiveness, simplicity, and generalization performance. Processor-centric architectures (e.g., CPUs, GPUs) commonly used for modern ML training workloads based on SGD are bott

James Trussler, Christopher Conselice, Nathan Adams, Duncan Austin

We develop a photometric search method for identifying smouldering galaxies at $5< z < 8$, which are defined to have weak emission lines and thus generally have low specific star formation rates and may even be in a state of (temporary) quiescence. The deep NIRCam imaging (${\sim}29.5$ AB mag, 5$\sigma$) from the JADES second data release is essential for fi

Francesco Deangelis

In this paper we consider minimizers of the Mumford-Shah functional with Dirichlet boundary conditions. We study blow-ups at the boundary and prove an epsilon-regularity theorem.

Sungwon In, Erick Krokos, Kirsten Whitley, Chris North

The computational notebook serves as a versatile tool for data analysis. However, its conventional user interface falls short of keeping pace with the ever-growing data-related tasks, signaling the need for novel approaches. With the rapid development of interaction techniques and computing environments, there is a growing interest in integrating emerging te

Dang Pham, Hanno Rein

Observations point to old white dwarfs (WDs) accreting metals at a relatively constant rate over 8~Gyrs. Exo-Oort clouds around WDs have been proposed as potential reservoirs of materials, with galactic tide as a mechanism to deliver distant comets to the WD's Roche limit. In this work, we characterise the dynamics of comets around a WD with a companion havi

Gianpaolo Alvari, Ersilia Vallefuoco, Melanie Cristofolini, Elio Salvadori

Virtual Reality (VR) has emerged as a promising tool for enhancing social skills and emotional well-being in individuals with Autism Spectrum Disorder (ASD). Through a technical exploration, this study employs a multiplayer serious gaming environment within VR, engaging 34 individuals diagnosed with ASD and employing high-precision biosensors for a comprehen

Mitchell Black, Georgios Fainekos, Bardh Hoxha, Hideki Okamoto

This paper introduces CBFKit, a Python/ROS toolbox for safe robotics planning and control under uncertainty. The toolbox provides a general framework for designing control barrier functions for mobility systems within both deterministic and stochastic environments. It can be connected to the ROS open-source robotics middleware, allowing for the setup of mult

Zhurun Ji, Heonjoon Park, Mark E. Barber, Chaowei Hu

Fractional quantum Hall effect (FQHE) is a prime example of topological quantum many-body phenomena, arising from the interplay between strong electron correlation, topological order, and time reversal symmetry breaking. Recently, a lattice analog of FQHE at zero magnetic field has been observed, confirming the existence of a zero-field fractional Chern insu

Jaehyeok Jin, Gregory A. Voth

Applying an excess entropy scaling formalism to the coarse-grained (CG) dynamics of liquids, we discovered that missing rotational motions during the CG process are responsible for artificially accelerated CG dynamics. In the context of the dynamic representability between the fine-grained (FG) and CG dynamics, this work introduces the well-known Stokes-Eins

Senqiao Yang, Zhuotao Tian, Li Jiang, Jiaya Jia

This paper introduces Unified Language-driven Zero-shot Domain Adaptation (ULDA), a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge. We identify the constraints in the existing language-driven zero-shot domain adaptation task, particularly the requirement for domain IDs and domain-specifi

Jay A. Wood

This paper examines the $w$-weight enumerators of weights $w$ with maximal symmetry over finite chain rings and matrix rings over finite fields. In many cases, including the homogeneous weight, the MacWilliams identities for $w$-weight enumerators fail because there exist two linear codes with the same $w$-weight enumerator whose dual codes have different $w

Ofir Shifman, Yair Weiss

Deep neural networks that achieve remarkable performance in image classification have previously been shown to be easily fooled by tiny transformations such as a one pixel translation of the input image. In order to address this problem, two approaches have been proposed in recent years. The first approach suggests using huge datasets together with data augm

Howard E. Bond, Gregory R. Zeimann

We present optical spectroscopy of the 12th-mag central star of the planetary nebula (PN) Patchick 27 (Pa 27), obtained during a survey of faint PN nuclei (PNNi) with the Low-Resolution Spectrograph (LRS2) of the Hobby-Eberly Telescope. The optical spectrum of Pa 27 is that of a K0 III red giant with rotationally broadened lines. However, the star is detecte

Soorya Rethinasamy, Margarite L. LaBorde, Mark M. Wilde

A pure state of fixed Hamming weight is a superposition of computational basis states such that each bitstring in the superposition has the same number of ones. Given a Hilbert space of the form $\mathcal{H} = (\mathbb{C}_2)^{\otimes n}$, or an $n$-qubit system, the identity operator can be decomposed as a sum of projectors onto subspaces of fixed Hamming we

Cristiano Capone, Luca Falorsi

Behavioral changes in animals and humans, as a consequence of an error or a verbal instruction, can be extremely rapid. Improvement in behavioral performances are usually associated in machine learning and reinforcement learning to synaptic plasticity, and, in general, to changes and optimization of network parameters. However, such rapid changes are not coh

Fabo Feng, Yicheng Rui, Zhimao Du, Qing Lin

Giant planets like Jupiter and Saturn, play important roles in the formation and habitability of Earth-like planets. The detection of solar system analogs that have multiple cold giant planets is essential for our understanding of planet habitability and planet formation. Although transit surveys such as Kepler and TESS have discovered thousands of exoplanet

Ahmed Agiza, Mohamed Mostagir, Sherief Reda

In an era where language models are increasingly integrated into decision-making and communication, understanding the biases within Large Language Models (LLMs) becomes imperative, especially when these models are applied in the economic and political domains. This work investigates the impact of fine-tuning and data selection on economic and political biase

Unnseo Park, Venkatesh Sivaraman, Adam Perer

Reinforcement learning (RL) is a promising approach to generate treatment policies for sepsis patients in intensive care. While retrospective evaluation metrics show decreased mortality when these policies are followed, studies with clinicians suggest their recommendations are often spurious. We propose that these shortcomings may be due to lack of diversity

George B. Mertzios, Sotiris Nikoletseas, Christoforos Raptopoulos, Paul G. Spirakis

We consider random simple temporal graphs in which every edge of the complete graph $K_n$ appears once within the time interval [0,1] independently and uniformly at random. Our main result is a sharp threshold on the size of any maximum $\delta$-clique (namely a clique with edges appearing at most $\delta$ apart within [0,1]) in random instances of this mode

Kenneth Goodenough, Tim Coopmans, Don Towsley

Losses are one of the main bottlenecks for the distribution of entanglement in quantum networks, which can be overcome by the implementation of quantum repeaters. The most basic form of a quantum repeater chain is the swap ASAP repeater chain. In such a repeater chain, elementary links are probabilistically generated and deterministically swapped as soon as

Michael Juhos, Zakhar Kabluchko, Joscha Prochno

The study of Schatten classes has a long tradition in geometric functional analysis and related fields. In this paper we study a variety of geometric and probabilistic aspects of finite-dimensional Schatten classes of not necessarily square matrices. Among the main results are the exact and asymptotic volume of the Schatten-$\infty$ unit ball, the boundednes

Clément Carlé, Andrei Mursa, Petri Karvinen, Shervin Keshavarzi

Stability and precision of atomic devices are closely tied to the quality and stability of the internal atmosphere of the atomic vapor cells on which they rely. Such atmosphere can be stabilized by building the cell with low permeation materials such as sapphire, or aluminosilicate glass in microfabricated devices. Recently, we showed that permeation barrier

Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal

This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in

Bela Bajnok, Peter E. Francis

A close look at double-quantified statements in a playful setting.

Sonja M. Hyrynsalmi, Sebastian Baltes, Chris Brown, Rafael Prikladnicki

Introduction: Digital products increasingly reshape industries, influencing human behavior and decision-making. However, the software development teams developing these systems often lack diversity, which may lead to designs that overlook the needs, equal treatment or safety of diverse user groups. These risks highlight the need for fostering diversity and i

Steven De Keyser, Irene Gijbels

We generalize 2-Wasserstein dependence coefficients to measure dependence between a finite number of random vectors. This generalization includes theoretical properties, and in particular focuses on an interpretation of maximal dependence and an asymptotic normality result for a proposed semi-parametric estimator under a Gaussian copula assumption. In additi

Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar

Systems of interest for theoretical or experimental work often exhibit high-order interactions, corresponding to statistical interdependencies in groups of variables that cannot be reduced to dependencies in subsets of them. While still under active development, the framework of partial information decomposition (PID) has emerged as the dominant approach to

Kavita Kumari, Murtuza Jadliwala, Sumit Kumar Jha, Anindya Maiti

Model explanations improve the transparency of black-box machine learning (ML) models and their decisions; however, they can also be exploited to carry out privacy threats such as membership inference attacks (MIA). Existing works have only analyzed MIA in a single "what if" interaction scenario between an adversary and the target ML model; thus, it does not

Alberto Enciso, Angela Pistoia, Luigi Provenzano

Given a compact manifold $\mathcal M$ with boundary of dimension $n\geq 3$ and any integers $K$ and $N$, we show that there exists a metric on $\mathcal M$ for which the first $K$ nonconstant eigenfunctions of the Dirichlet-to-Neumann map on $\partial\mathcal M$ have at least $N$ nodal components. This provides a negative answer to the question of whether th

Giulia Ceriotti, Alice Bosco-Santos, Sergey M. Borisov, Jasmine S. Berg

Iron (Fe) reduction is one of Earth's most ancient microbial metabolisms, but after atmosphere-ocean oxygenation, this anaerobic process was relegated to niche anoxic environments below the water and soil surface. However, new technologies to monitor redox processes at the microscale relevant to microbial cells have recently revealed that the oxygen (O2) con

Danijel Aleksić, Bojana Milošević

In this paper, we focus on testing multivariate normality using the BHEP test with data that are missing completely at random. Our objective is twofold: first, to gain insight into the asymptotic behavior of BHEP test statistics under two widely used approaches for handling missing data, namely complete-case analysis and imputation, and second, to compare th

Jinwei Lu, Yuanfeng Song, Haodi Zhang, Chen Zhang

Text-to-Vis is an emerging task in the natural language processing (NLP) area that aims to automatically generate data visualizations from natural language questions (NLQs). Despite their progress, existing text-to-vis models often heavily rely on lexical matching between words in the questions and tokens in data schemas. This overreliance on lexical matchin

Jie Ou, Yueming Chen, Wenhong Tian

While Large Language Models (LLMs) have shown remarkable abilities, they are hindered by significant resource consumption and considerable latency due to autoregressive processing. In this study, we introduce Adaptive N-gram Parallel Decoding (ANPD), an innovative and lossless approach that accelerates inference by allowing the simultaneous generation of mul

Nathaniel Smith, Anvaya Shiney-Ajay, Emmanuel Fleurantin, Ivo Pasmans

We investigate ocean circulation changes through the lens of data assimilation using a reduced-order model. Our primary interest lies in the Stommel box model which reveals itself to be one of the most practicable models that has the ability of reproducing the meridional overturning circulation. The Stommel box model has at most two regimes: TH (temperature

Ramachandran Anantharaman, Alexandre Mauroy

Dynamic Mode Decomposition (DMD) and its extensions (EDMD) have been at the forefront of data-based approaches to Koopman operators. Most (E)DMD algorithms assume that the entire state is sampled at a uniform sampling rate. In this paper, we provide an algorithm where the entire state is not uniformly sampled, with individual components of the states measure

Jason R. Bailey, W. Brent Lindquist, Svetlozar T. Rachev

Using data from 2000 through 2022, we analyze the predictive capability of the annual numbers of new home constructions and four available environmental, social, and governance factors on the average annual price of homes sold in eight major U.S. cities. We contrast the predictive capability of a P-spline generalized additive model (GAM) against a strictly l

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A search for hidden-charm pentaquark states decaying to a range of $\Sigma_{c}\bar{D}$ and $\Lambda_{c}\bar{D}$ final states, as well as doubly-charmed pentaquark states to $\Sigma_{c}D$ and $\Lambda_{c}^{+}D$, is made using samples of proton-proton collision data corresponding to an integrated luminosity of $5.7fb^{-1}$ recorded by the LHCb detector at $\sq

Maxim Olshanskii, Henry von Wahl

The paper introduces a finite element method for an Eulerian formulation of partial differential equations governing the transport and diffusion of a scalar quantity in a time-dependent domain. The method follows the idea from Lehrenfeld & Olshanskii [ESAIM: M2AN, 53(2): 585-614, 2019] of a solution extension to realise the Eulerian time-stepping scheme. How

Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan

In-context learning is a powerful emergent ability in transformer models. Prior work in mechanistic interpretability has identified a circuit element that may be critical for in-context learning -- the induction head (IH), which performs a match-and-copy operation. During training of large transformers on natural language data, IHs emerge around the same tim

Michael Kohler, Adam Krzyzak, Alisha Sänger

Image classification from independent and identically distributed random variables is considered. Image classifiers are defined which are based on a linear combination of deep convolutional networks with max-pooling layer. Here all the weights are learned by stochastic gradient descent. A general result is presented which shows that the image classifiers are

Hongrui Gu, Haibo Yuan, Subo Dong, Chenfa Zheng

Utilizing high-cadence and continuous g- and r-band data over three nights acquired from the 3.6-meter Canada France Hawaii Telescope (CFHT) aimed to find short-duration microlensing events, we conduct a systematic search for variables, transients, and asteroids across a $\sim1^\circ$ field of view of the Andromeda Galaxy (M 31). We present a catalog of 5859

Philipp Bringmann, Ani Miraçi, Dirk Praetorius

This chapter provides an overview of state-of-the-art adaptive finite element methods (AFEMs) for the numerical solution of second-order elliptic partial differential equations (PDEs), where the primary focus is on the optimal interplay of local mesh refinement and iterative solution of the arising discrete systems. Particular emphasis is placed on the thoro

Chiara Di Vece, Antonio Cirigliano, Meala Le Lous, Raffaele Napolitano

This paper presents a pipeline designed to bring ultrasound (US) plane pose estimation closer to clinical use, demonstrating the feasibility of continuous, real-time proximity feedback for navigation to the standard planes (SPs) in the fetal brain. We propose a semi-supervised segmentation model that uses labeled SPs and unlabeled slices from 3D US volumes (

Thomas F Burns

I introduce a novel associative memory model named Correlated Dense Associative Memory (CDAM), which integrates both auto- and hetero-association in a unified framework for continuous-valued memory patterns. Employing an arbitrary graph structure to semantically link memory patterns, CDAM is theoretically and numerically analysed, revealing four distinct dyn

Dat Viet Thanh Nguyen, Anh Tran, Hoai Nam Vu, Cuong Pham

We propose a novel method to estimate a driver's points-of-gaze using a pair of ordinary cameras mounted on the windshield and dashboard of a car. This is a challenging problem due to the dynamics of traffic environments with 3D scenes of unknown depths. This problem is further complicated by the volatile distance between the driver and the camera system. To

Jiawei Liu, Yi Gong, Kaibin Huang

6G mobile networks aim to realize ubiquitous intelligence at the network edge via distributed learning, sensing, and data analytics. Their common operation is to aggregate high-dimensional data, which causes a communication bottleneck that cannot be resolved using traditional orthogonal multi-access schemes. A promising solution, called over-the-air computat

Jonathan Correa, Alexandr Ignatenko, David Pennicard, Sabine Lange

A readout system based on the Timepix4 ASIC, is being developed for photon science. The TEMPUS detector can be operated in two distinct modes: a photon counting mode, which allows for conventional full-frame readout at rates up to 40 kfps; and an event-driven time-stamping mode, which allows excellent time resolution in the nanosecond regime in measurements

Mauricio J. del Razo, Margarita Kostré

Reaction-diffusion processes are the foundational model for a diverse range of complex systems, ranging from biochemical reactions to social agent-based phenomena. The underlying dynamics of these systems occur at the individual particle/agent level, and in realistic applications, they often display interaction with their environment through energy or materi

Bela Bajnok

We provide an historical overview of how advances in technology influenced high school and university mathematical competitions in the United States and at the International Mathematical Olympiad. While students are not allowed the usage of technological aids during mathematical competitions, the developments in technology (especially graphing technology) th

Sara Kangaslahti, David Alvarez-Melis

As large language models (LLMs) have gained popularity for a variety of use cases, making them adaptable and controllable has become increasingly important, especially for user-facing applications. While the existing literature on LLM adaptation primarily focuses on finding a model (or models) that optimizes a single predefined objective, here we focus on th

Nicholas Cox, Joseph Murray, Joseph Hart, Brandon Redding

Reservoir computing (RC) is a machine learning paradigm that excels at dynamical systems analysis. Photonic RCs, which perform implicit computation through optical interactions, have attracted increasing attention due to their potential for low latency predictions. However, most existing photonic RCs rely on a nonlinear physical cavity to implement system me

Oliver Hahn, Ryuji Takagi, Giulia Ferrini, Hayata Yamasaki

We propose efficient algorithms for classically simulating Gaussian unitaries and measurements applied to non-Gaussian initial states. The constructions are based on decomposing the non-Gaussian states into linear combinations of Gaussian states. We use an extension of the covariance matrix formalism to efficiently track relative phases in the superpositions

Ian Drosos, Advait Sarkar, Andrew D. Gordon

Video tutorials are a popular medium for informal and formal learning. However, when learners attempt to view and follow along with these tutorials, they encounter what we call gaps, that is, issues that can prevent learning. We examine the gaps encountered by users of video tutorials for feature-rich software, such as spreadsheets. We develop a theory and t

Yang P. Liu, Mehtaab Sawhney

We prove that a subset $A\subseteq [1, N]$ with \[\sum_{n\in A}\frac{1}{n} \ge (\log N)^{4/5 + o(1)}\] contains a subset $B$ such that \[\sum_{n\in B} \frac{1}{n} = 1.\] Our techniques refine those of Croot and of Bloom. Using our refinements, we additionally consider a number of questions regarding unit fractions due to Erd\H{o}s and Graham.

Xianlu Li, Nicolas Nadisic, Shaoguang Huang, Aleksandra Pižurica

Deep subspace clustering methods are now prominent in clustering, typically using fully connected networks and a self-representation loss function. However, these methods often struggle with overfitting and lack interpretability. In this paper, we explore an alternative clustering approach based on deep unfolding. By unfolding iterative optimization methods

Chris Jantzen, Baiying Liu

In this paper, we give a uniform classification of the generic dual of quasi-split classical groups, their similitude counterparts, and general spin groups. As applications, for quasi-split classical groups, we show that the functorial lifting maps constructed by Cogdell, Kim, Piatetski-Shapiro and Shahidi are surjective. We also analyze structures of genera

Matías Mattamala, Jonas Frey, Piotr Libera, Nived Chebrolu

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we present Wild Visual Navigation (WVN), an online self-supervised learning system for visual traversability estimation. The system is able to continuously adapt from a

Maxime Labat, Emmanuel Giner, Guillaume Jeanmairet

This paper presents the first implementation of a coupling between advanced wave function theories and molecular density functional theory (MDFT). This method enables the modeling of solvent effect into quantum mechanical (QM) calculations by incorporating an electrostatic potential generated by solvent charges into the electronic Hamiltonian. Solvent charge

Yongqiang Ma, Lizhi Qing, Jiawei Liu, Yangyang Kang

Evaluating large language models (LLMs) is fundamental, particularly in the context of practical applications. Conventional evaluation methods, typically designed primarily for LLM development, yield numerical scores that ignore the user experience. Therefore, our study shifts the focus from model-centered to human-centered evaluation in the context of AI-po

Conall J. Campbell, Adam G. Hawkins, Giorgio Zicari, Mauro Paternostro

The recently proposed zero-added-loss multiplexing (ZALM) source of entangled photons enables higher efficiency in entanglement distribution than spontaneous parametric down-conversion sources and can be carried out using both space-to-ground and ground-to-ground links. We demonstrate the flexibility of ZALM architectures to be adapted to alternative entangl

Jheng-Wei Li, Xavier Waintal

Common wisdom says that the entanglement of fermionic systems can be low in the second quantization formalism but is extremely large in the first quantization. Hence Matrix Product State (MPS) methods based on moderate entanglement have been overwhelmingly formulated in second quantization. Here we introduce a first-quantized MPS approach to simulate quantum

Michael J. Probst, Arjun Khurana, Joel B. Slaby, Alec M. Hammond

Optimal multi-layer device design requires consideration of fabrication uncertainties associated with inter-layer alignment and conformal layering. We present layer-restricted topology optimization (TO), a novel technique which mitigates the effects of unwanted conformal layering for multi-layer structures and enables TO in multi-etch material platforms. We

Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy

Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora to alleviate the issue. However, in many domains, texts are interconnected (e.g., academic papers in a bibliogr

Xinyu Gu, Akashnathan Aranganathan, Pratyush Tiwary

Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direc

Dylan Fife, Dong-Chel Shin, Vivishek Sudhir

Temperature fluctuations over long time scales ($\gtrsim 1\,\mathrm{h}$) are an insidious problem for precision measurements. In optical laboratories, the primary effect of temperature fluctuations is drifts in optical circuits over spatial scales of a few meters and temporal scales extending beyond a few minutes. We present a lab-scale environment temperatu

Rémi Beisson, Pascal Vallet, Audrey Giremus, Guillaume Ginolhac

In this paper, we consider the problem of testing equality of the covariance matrices of L complex Gaussian multivariate time series of dimension $M$ . We study the special case where each of the L covariance matrices is modeled as a rank K perturbation of the identity matrix, corresponding to a signal plus noise model. A new test statistic based on the esti

Marie Michaelides, Hélène Cossette, Mathieu Pigeon

In this paper, we propose a novel approach for estimating Archimedean copula generators in a conditional setting, incorporating endogenous variables. Our method allows for the evaluation of the impact of the different levels of covariates on both the strength and shape of dependence by directly estimating the generator function rather than the copula itself.

Petar Orlić

Let $N$ be a positive integer. For every $d | N$ such that $(d, N/d) = 1$ there exists an Atkin-Lehner involution $w_d$ of the modular curve $X_0(N)$. In this paper we determine all quotient curves $X_0(N)/w_d$ whose $\mathbb{Q}$-gonality is equal to $4$ and all quotient curves $X_0(N)/w_d$ whose $\mathbb{C}$-gonality is equal to $4$.

Linas Nasvytis, Kai Sandbrink, Jakob Foerster, Tim Franzmeyer

While reinforcement learning (RL) algorithms have been successfully applied across numerous sequential decision-making problems, their generalization to unforeseen testing environments remains a significant concern. In this paper, we study the problem of out-of-distribution (OOD) detection in RL, which focuses on identifying situations at test time that RL a

Victor Churchill, H. Alice Li, Dongbin Xiu

This study utilizes an ensemble of feedforward neural network models to analyze large-volume and high-dimensional consumer touchpoints and their impact on purchase decisions. When applied to a proprietary dataset of consumer touchpoints and purchases from a global software service provider, the proposed approach demonstrates better predictive accuracy than b

Ahmad Sarlak, Hazim Alzorgan, Sayed Pedram Haeri Boroujeni, Abolfazl Razi

Sharing and joint processing of camera feeds and sensor measurements, known as Cooperative Perception (CP), has emerged as a new technique to achieve higher perception qualities. CP can enhance the safety of Autonomous Vehicles (AVs) where their individual visual perception quality is compromised by adverse weather conditions (haze as foggy weather), low ill

Yoni Kasten, Wuyue Lu, Haggai Maron

This paper addresses the long-standing challenge of reconstructing 3D structures from videos with dynamic content. Current approaches to this problem were not designed to operate on casual videos recorded by standard cameras or require a long optimization time. Aiming to significantly improve the efficiency of previous approaches, we present TracksTo4D, a le

Yiping Sun

The rapid growth of location acquisition technologies makes Point-of-Interest(POI) recommendation possible due to redundant user check-in records. In this paper, we focus on next POI recommendation in which next POI is based on previous POI. We observe that time plays an important role in next POI recommendation but is neglected in the recent proposed transl

Alberto Brollo, Herbert Spohn

The Ablowitz-Ladik chain is an integrable discretized version of the nonlinear Schr\"{o}dinger equation. We report on a novel underlying Hamiltonian particle system with properties similar to the ones known for the classical Toda chain and Calogero fluid with $1/\sinh^2$ pair interaction. Boundary conditions are imposed such that, both in the distant past an

Bedirhan Uguz, Ozhan Suat, Batuhan Karagoz, Emre Akbas

This paper presents Key2Mesh, a model that takes a set of 2D human pose keypoints as input and estimates the corresponding body mesh. Since this process does not involve any visual (i.e. RGB image) data, the model can be trained on large-scale motion capture (MoCap) datasets, thereby overcoming the scarcity of image datasets with 3D labels. To enable the mod

Hanzi Huang, Haoshuo Chen, Qian Hu, Di Che

We experimentally demonstrate the first carrier- and LO-free 800G/{\lambda} receiver enabling direct compatibility with standard coherent transmitters via phase retrieval, achieving net 835-Gb/s transmission over 100-km SMF and record 8.27-b/s/Hz net optical spectral efficiency.

Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li

This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE). We introduce a "time-aware head" in a framework trained through self-supervised learning (SSL) to leverage temporal information in latent space for data augmentation. This approach effectively integrates NODEs with SSL, offer

José Camacho-Mateu, Aniello Lampo, Saúl Ares, José A. Cuesta

We introduce a comprehensive analytical benchmark, relying on Fokker-Planck formalism, to study microbial dynamics in presence of both biotic and abiotic forces. In equilibrium, we observe a balance between the two kinds of forces, leading to no correlations between species abundances. This implies that real microbiomes, where correlations have been observed

Tommaso Aniello, Lucio Angelo Antonelli, Francesco Tombesi, Alessandra Lamastra

The search for periodicity in the multi-wavelength high variable emission of blazars is a key feature to understand dynamical processes at work in this class of active galactic nuclei. The blazar PG 1553+113 is an attractive target due to the evidence of periodic oscillations observed at different wavelengths, with a solid proof of a 2.2-year modulation dete

Volker Ziemann

Even though many of the experiments leading to the standard model of particle physics were done at large accelerator laboratories in the US and at CERN[1] many exciting developments happened in smaller national facilities all over the world. In this report we highlight the history of accelerator facilities in Sweden which was home to the highest-energy cyclo

Wen-Yu Su, Yu-Jing Liu, Nvsen Ma, Chen Cheng

In principle, the probability of configurations, determined by the system's partition function or wave function, encapsulates essential information about phases and phase transitions. Despite the exponentially large configuration space, we show that the generic correlation of distances between configurations, with a degree of freedom proportional to the latt

Gabriele Cugno, Polychronis Patapis, Andrea Banzatti, Michael Meyer

GQ Lup B is a forming brown dwarf companion ($M\sim10-30~M_J$) showing evidence for an infrared excess associated with a disk surronding the companion itself. Here we present mid-infrared (MIR) observations of GQ Lup B with the Medium Resolution Spectrograph (MRS) on JWST, spanning $4.8-11.7~\mu$m. We remove the stellar contamination using reference differen

Craig D. Duguid, Nils B. de Vries, Daniel Lecoanet, Adrian J. Barker

Recent work suggests that inwardly propagating internal gravity waves (IGWs) within a star can be fully converted to outward magnetic waves (MWs) if they encounter a sufficiently strong magnetic field. The resulting magnetic waves dissipate as they propagate outward to regions with lower Alfv\'{e}n velocity. While tidal forcing is known to excite IGWs, this

Jianzhi Liu, Hexiang Gu, Tianyu Zheng, Liuyu Xiang

In the realm of mimicking human deliberation, large language models (LLMs) show promising performance, thereby amplifying the importance of this research area. Deliberation is influenced by both logic and personality. However, previous studies predominantly focused on the logic of LLMs, neglecting the exploration of personality aspects. In this work, we intr

Nathaniel Dean, Dilip Sarkar

Overparameterized deep neural networks (DNNs), if not sufficiently regularized, are susceptible to overfitting their training examples and not generalizing well to test data. To discourage overfitting, researchers have developed multicomponent loss functions that reduce intra-class feature correlation and maximize inter-class feature distance in one or more

Shulei Cao, Bharat Ratra

By using gamma-ray burst (GRB) data to simultaneously constrain Amati correlation parameters and cosmological parameters in six spatially-flat and nonflat dark energy cosmological models, we show that an updated 220 GRB version of the Jia et al. [Mon. Not. R. Astron. Soc. 516, 2575 (2022)] GRB data compilation are standardizable through the Amati correlation

Thomas Katsekpor, Latévi M. Lawson, Prince K. Osei, Ibrahim Nonkané

Position deformation of a Heisenberg algebra and Hilbert space representation of both maximal length and minimal momentum uncertainties may lead to loss of Hermiticity of some operators that generate this algebra. Consequently, the Hamiltonian operator constructed from these operators are also not Hermitian. In the present paper, with an appropriate positive

A. V. Ivanchik, O. A. Kurichin, V. Yu. Yurchenko

Nowadays, at least two relics of the Big Bang have survived - the cosmological microwave background (CMB) and the cosmological neutrino background (C$\nu$B). Being the second most abundant particle in the Universe, the neutrino has a significant impact on its evolution from the Big Bang to the present day. Neutrinos affect the following cosmological processe

Kirill V. Likhachev, Maxim V. Uchaev, Igor P. Veyshtort, Anastasia V. Batueva

The possibility of using axial spin color centers with $S=3/2$, oriented along the hexagonal $c$ axis in a silicon carbide (SiC) wafer, has been demonstrated for all-optical measurement of projection of the external magnetic field coinciding with the $c$ axis of the crystal, and the polar and azimuthal angles of the external measured magnetic field at room a

Estevão F. Borel, Aldo Procacci, Rémy Sanchis, Roger W. C. Silva

In this note, we consider the asymmetric nearest neighbor ferromagnetic Ising model on the $(d+s)$-dimensional unit cubic lattice $\Z^{d+s}$, at inverse temperature $\beta=1$ and with coupling constants $J_s>0$ and $J_d>0$ for edges of $\Z^s$ and $\Z^d$, respectively. We obtain a lower bound for the critical curve in the phase diagram of $(J_s,J_d)$. In part

Alexandros Xenos, Niki Maria Foteinopoulou, Ioanna Ntinou, Ioannis Patras

Recognising emotions in context involves identifying an individual's apparent emotions while considering contextual cues from the surrounding scene. Previous approaches to this task have typically designed explicit scene-encoding architectures or incorporated external scene-related information, such as captions. However, these methods often utilise limited c

Tariq Syed

We prove symplectic versions of Suslin's famous $n!$-theorem for algebras over quadratically closed perfect fields of characteristic $\neq 2$ and for algebras over finite fields of characteristic $\neq 2$.

Jan Andres, Pavel Ludvík

The Adler-Konheim-McAndrew type definitions and the Bowen-Dinaburg-Hood type definitions of parametric topological entropy will be considered on orbits and coincidence orbits of nonautonomous multivalued maps in compact Hausdorff spaces. Their mutual relationship and their link to various further types of definitions like those of (parametric) preimage entro

Zu-Cheng Chen, Lang Liu

The null energy condition (NEC) is a cornerstone of general relativity, and its violation could leave observable imprints in the cosmic gravitational wave spectrum. Theoretical models suggest that NEC violations during inflation can amplify the primordial tensor power spectrum, leading to distinct features in the stochastic gravitational wave background (SGW

Ganesh Narasimha, Dejia Kong, Paras Regmi, Rongying Jin

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. The local structures are conventionally probed using spatially resolved studies and the property correlations are usually deciphered by a researcher based on sequential explorations and auxiliary information, thus limiting the throughput efficiency. Here w