March 2024 arXiv papers — page 42

Jingyuan Zhu, Huimin Ma, Jiansheng Chen, Jian Yuan

Large-scale text-to-image diffusion models have achieved great success in synthesizing high-quality and diverse images given target text prompts. Despite the revolutionary image generation ability, current state-of-the-art models still struggle to deal with multi-concept generation accurately in many cases. This phenomenon is known as ``concept bleeding" and

Christian Dreher, Tamim Asfour

Learning task models of bimanual manipulation from human demonstration and their execution on a robot should take temporal constraints between actions into account. This includes constraints on (i) the symbolic level such as precedence relations or temporal overlap in the execution, and (ii) the subsymbolic level such as the duration of different actions, or

Jiasheng Ye, Peiju Liu, Tianxiang Sun, Jun Zhan

Pretraining data of large language models composes multiple domains (e.g., web texts, academic papers, codes), whose mixture proportions crucially impact the competence of outcome models. While existing endeavors rely on heuristics or qualitative strategies to tune the proportions, we discover the quantitative predictability of model performance regarding th

Reza Farahani

Multimedia applications, mainly video streaming services, are currently the dominant source of network load worldwide. In recent Video-on-Demand (VoD) and live video streaming services, traditional streaming delivery techniques have been replaced by adaptive solutions based on the HTTP protocol. Current trends toward high-resolution (e.g., 8K) and/or low-lat

Yinhong Liu, Han Zhou, Zhijiang Guo, Ehsan Shareghi

Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language. However, LLMs still exhibit biases in evaluation and often struggle to generate coherent evaluations that align with human assessments. In this work, we first conduct a systematic study of the misalignment betw

L. Thummel, B. Bose, A. Pourtsidou, L. Lombriser

We study the capacity of Bayesian Neural Networks (BNNs) to detect new physics in the dark matter power spectrum. As in previous studies, the Bayesian Cosmological Network (BaCoN) classifies spectra into one of 5 classes: $\Lambda$CDM, $f(R)$, $w$CDM, Dvali-Gabadaze-Porrati (DGP) gravity and a 'random' class, with this work extending it to include the effect

Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose

Reinforcement Learning (RL)-based recommender systems have demonstrated promising performance in meeting user expectations by learning to make accurate next-item recommendations from historical user-item interactions. However, existing offline RL-based sequential recommendation methods face the challenge of obtaining effective user feedback from the environm

Deepak K. D, Jaydeb Sarkar, Sreejith Siju

We investigate the structure of $p$-sets, $M$-ideals, and a newly introduced notion of analytic primes in $H^\infty(\mathbb{D})$, where $H^\infty(\mathbb{D})$ denotes the Banach algebra of all bounded analytic functions on the open unit disc $\mathbb{D}$ in $\mathbb{C}$. We prove that $M$-ideals in $H^\infty(\mathbb{D})$ are analytic primes and are dense in

N. Ö. Kaya, H. A. Dal

We present results regarding the longitudinal migrations of cool stellar spots that exhibit remarkable oscillations and explore their possible causes. We conducted analyses using high-quality data from nine target systems of various spectral types, spanning from F to M, which were observed by the Kepler Satellite. The systems in which the behaviour of the sp

John M. Campbell, M. Lawrence Glasser, Yajun Zhou

Through the application of an evaluation technique based on cyclotomic multiple zeta values recently due to Au, we solve open problems on inverse binomial series that were included in a 2010 analysis textbook by Chen.

Leisa K. Townsley, Patrick S. Broos, Matthew S. Povich

The Tarantula Nebula (30 Doradus) is the most important star-forming complex in the Local Group, offering a microscope on starburst astrophysics. At its heart lies the exceptionally rich young stellar cluster R136, containing the most massive stars known. Stellar winds and supernovae have carved 30 Dor into an amazing display of arcs, pillars, and bubbles. W

Yu. S. Ponosov, E. V. Komleva, E. A. Pankrushina, D. Mikhailova

Raman spectroscopy together with density functional calculations were used to study lattice dynamics in a layered honeycomb cobaltite Na$_3$Co$_2$SbO$_6$, which can host a field-induced phase related with the Kitaev physics. We show that there develops an additional mode well above Neel temperature (at $\approx 200$K) at 525 cm$^{-1}$, which origin can be re

Kaye Jiale Li, Kinwah Wu, Ziri Younsi, Joana Teixeira

General relativity predicts that two counter-orbiting clocks around a spinning mass differ in the time required to complete the same orbit. The difference in these two values for the orbital period is generally referred to as the gravito-magnetic (GM) clock effect. It has been proposed to measure the GM clock effect using atomic clocks carried by satellites

Andrés García-Silva, Cristian Berrío, José Manuel Gómez-Pérez

Detecting salient parts in text using natural language processing has been widely used to mitigate the effects of information overflow. Nevertheless, most of the datasets available for this task are derived mainly from academic publications. We introduce SPACE-IDEAS, a dataset for salient information detection from innovation ideas related to the Space domai

Buddhika Nettasinghe, Allon G. Percus, Kristina Lerman

Politically divided societies are also often divided emotionally: people like and trust those with similar political views (in-group favoritism) while disliking and distrusting those with different views (out-group animosity). This phenomenon, called affective polarization, influences individual decisions, including seemingly apolitical choices such as wheth

Sulakashna, Rupam Barman

For an odd prime $p$ and a positive integer $n$, let ${_n}G_n[\cdots]_p$ denote McCarthy's $p$-adic hypergeometric function. In this article, we prove $p$-adic analogue of certain classical hypergeometric identities and using these identities we express the $p$-th Fourier coefficient of certain weight three newforms in terms of special values of ${_3}G_3[\cd

Gaëtan Borot, Nitin Kumar Chidambaram, Giacomo Umer

We upgrade the results of Borot--Bouchard--Chidambaram--Creutzig to show that the Gaiotto vector in $4d$ $\mathcal{N} = 2$ pure supersymmetric gauge theory admits an analytic continuation with respect to the energy scale (which can therefore be taken to be finite, instead of infinitesimal), and is computed by topological recursion on the (ramified) half Seib

Mohammad Saeed Ebrahimi Saadabadi, Ali Dabouei, Sahar Rahimi Malakshan, Nasser M. Nasrabad

Aiming to enhance the utilization of metric space by the parametric softmax classifier, recent studies suggest replacing it with a non-parametric alternative. Although a non-parametric classifier may provide better metric space utilization, it introduces the challenge of capturing inter-class relationships. A shared characteristic among prior non-parametric

Dani Dorfman, Haim Kaplan, Robert E. Tarjan, Mikkel Thorup

An electric car equipped with a battery of a finite capacity travels on a road network with an infrastructure of charging stations. Each charging station has a possibly different cost per unit of energy. Traversing a given road segment requires a specified amount of energy that may be positive, zero or negative. The car can only traverse a road segment if it

Hang Dong, Pengfei Zhang, Ceren B. Dag, Yu Gao

The spectral form factor (SFF) captures universal spectral fluctuations as signatures of quantum chaos, and has been instrumental in advancing multiple frontiers of physics including the studies of black holes and quantum many-body systems. However, the measurement of SFF in many-body systems is challenging due to the difficulty in resolving level spacings t

Gerald Schweiger, Adrian Barnett, Peter van den Besselaar, Lutz Bornmann

Research funding systems are not isolated systems - they are embedded in a larger scientific system with an enormous influence on the system. This paper aims to analyze the allocation of competitive research funding from different perspectives: How reliable are decision processes for funding? What are the economic costs of competitive funding? How does compe

Benjamin Ellenberger, Paul Haider, Jakob Jordan, Kevin Max

How physical networks of neurons, bound by spatio-temporal locality constraints, can perform efficient credit assignment, remains, to a large extent, an open question. In machine learning, the answer is almost universally given by the error backpropagation algorithm, through both space and time. However, this algorithm is well-known to rely on biologically i

Maiara F. Bollauf, Hsuan-Yin Lin

The theta series of a lattice is a power series that characterizes the number of lattice vectors at certain norms. It is closely related to a critical quantity widely used in physical layer security and cryptography, known as the flatness factor, or equivalently, the smoothing parameter of a lattice. Both fields raise the fundamental question of determining

Luyan Yang, Andrii S. Savchenko, Fengshan Zheng, Nikolai S. Kiselev

Magnetic skyrmions are topologically nontrivial spin configurations that possess particle-like properties. Earlier research was mainly focused on a specific type of skyrmion with topological charge Q = -1. However, theoretical analyses of two-dimensional chiral magnets have predicted the existence of skyrmion bags -- solitons with arbitrary positive or negat

Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic

Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e.g., mobile devices, IoT edge nodes). It enables Artificial Intelligence (AI) at the edge by creating models without sharing actual data across the network. Existing research typically focuses on generic aspects of non-II

Jake J. Harmon, Svetlana Tokareva, Anatoly Zlotnik

We propose a predictor-corrector adaptive method for the simulation of hyperbolic partial differential equations (PDEs) on networks under general uncertainty in parameters, initial conditions, or boundary conditions. The approach is based on the stochastic finite volume (SFV) framework that circumvents sampling schemes or simulation ensembles while also pres

Jaime Mora-Paz

We build a transient multidimensional multiphysical model based on continuum theories, involving the coupled mechanical, thermal and electrochemical phenomena occurring simultaneously in the discharge or charge of lithium-ion batteries. The process delivers a system of coupled nonlinear partial differential equations. Besides initial and boundary conditions,

Manas Vijay Upadhyay, Jorge Viñals

This work addresses differences in predicted elastic fields created by dislocations either by the Phase Field Crystal (PFC) model, or by static Field Dislocation Mechanics (FDM). The PFC order parameter describes the topological content of the lattice, but it fails to correctly capture the elastic distortion. In contrast, static FDM correctly captures the la

John Leland, Ming Fang, Satwik Pani, Yuri Venturini

Fast electronic readout for high-channel density scintillator-based systems is needed for radiation tracking and imaging in a wide range of applications, including nuclear physics, nuclear security and nonproliferation. Programmable electronics, like FPGAs and ASICs, provide a fast way of conditioning and processing the signal in real time. In this paper, we

CMS Collaboration

The discovery of the Higgs boson has led to new possible signatures for heavy resonance searches at the LHC. Since then, search channels including at least one Higgs boson plus another particle have formed an important part of the program of new physics searches. In this report, the status of these searches by the CMS Collaboration is reviewed. Searches are

Gaël Rouillé, Johannes Schmitt, Cornelia Jäger, Thomas Henning

Reports on the detection of carbonates in planetary nebulae (PNe) and protostars suggested the existence of a mechanism that produce these compounds in stellar winds and outflows. A consecutive laboratory study reported a possible mechanism by observing the non-thermodynamic equilibrium (TE), gas-phase condensation of amorphous silicate grains with amorphous

F. F. Karpeshin

The possibility of refining the energy of the 8.36-eV 229mTh nuclear isomer -- the most likely candidate for the role of a nuclear frequency standard -- by means of resonant optical pumping is discussed. Attention is focused on considering the broadening of the resonance in order to reduce scanning time. The two-photon method proposed exploits the radical br

Pablo Capuzzi, Luca Tessieri, Zehra Akdeniz, Anna Minguzzi

We study the localization dynamics of a SU(2) fermionic wavepacket launched in a (pseudo)random potential. We show that in the limit of strong inter-component repulsions, the total wavepacket exhibits a boomerang-like dynamics, returning near its initial position as expected for non-interacting particles, while separately each spin-component does not. This s

R. P. Dufresne, G. Del Zanna, P. R. Young, K. P. Dere

Version 11 of the CHIANTI database and software package is presented. Advanced ionization equilibrium models have been added for low charge states of seven elements (C, N, O, Ne, Mg, Si and S), and represent a significant improvement especially when modelling the solar transition region. The models include the effects of higher electron density and charge tr

Jaywon Koo, Ziyan Yang, Paola Cascante-Bonilla, Baishakhi Ray

Visual Programming has recently emerged as an alternative to end-to-end black-box visual reasoning models. This type of method leverages Large Language Models (LLMs) to generate the source code for an executable computer program that solves a given problem. This strategy has the advantage of offering an interpretable reasoning path and does not require finet

Julian Hofstadler

We study the Markov chain Monte Carlo (MCMC) estimator for numerical integration for functions that do not need to be square integrable w.r.t. the invariant distribution. For chains with a spectral gap we show that the absolute mean error for $L^p$ functions, with $p \in (1,2)$, decreases like $n^{1/p -1}$, which is known to be the optimal rate. This improve

Margaret Hawton

A conserved photon current is derived from the commutation relations satisfied by the electromagnetic four-potential and field tensor operators. The density is found to be a sum over positive and negative frequency terms, both of which contribute a positive number density and propagate in a common direction. Discrete positive and negative frequency excitatio

Andrea Conti

We give a notion of $p$-adic families of Hecke eigenforms that allows for the slope of the forms be infinite at $p$. We prove that, contrary to the case of finite slope when every eigenform lives in a Hida or Coleman family, the only families of infinite slope are either twists of Hida or Coleman families with Dirichlet characters of $p$-power conductor, or

T. T. Dang, G. Orlandi

This paper introduces the concept of renormalized solution for a general class of non-coercive nonlinear parabolic problems, including both singularities and unbounded lower order terms. We prove existence and uniqueness of renormalized solutions for such class of problems.

Vojtech Franc, Jakub Paplham, Daniel Prusa

This paper addresses the problem of designing reliable prediction models that abstain from predictions when faced with uncertain or out-of-distribution samples - a recently proposed problem known as Selective Classification in the presence of Out-of-Distribution data (SCOD). We make three key contributions to SCOD. Firstly, we demonstrate that the optimal SC

Atsushi Keyaki, Ribeka Keyaki

Fine-tuning in information retrieval systems using pre-trained language models (PLM-based IR) requires learning query representations and query-document relations, in addition to downstream task-specific learning. This study introduces coarse-tuning as an intermediate learning stage that bridges pre-training and fine-tuning. By learning query representations

Erik Burman, Mingfei Lu, Lauri Oksanen

In this paper, we consider the unique continuation problem for the Schr\"odinger equations. We prove a H\"older type conditional stability estimate and build up a parameterized stabilized finite element scheme adaptive to the \textit{a priori} knowledge of the solution, achieving error estimates in interior domains with convergence up to continuous stability

Shun Zhang, Jian Yang, Jiaqi Bai, Chaoran Yan

New Intent Discovery (NID) aims to recognize known and infer new intent categories with the help of limited labeled and large-scale unlabeled data. The task is addressed as a feature-clustering problem and recent studies augment instance representation. However, existing methods fail to capture cluster-friendly representations, since they show less capabilit

Roberto Artuso, Dario Javier Zamora

Motion in bounded domains represents a paradigm in several settings: from billiard dynamics, to random walks in a finite lattice, with applications to relevant physical, ecological and biological problems. A remarkable universal property, involving the average of return times to the boundary, has been theoretically proposed, and experimentally verified in qu

NaYeoun Kim, Ji-Hyeok Huh, YongDeok Cho, Sung Hun Park

This study demonstrates the developments of self-assembled optical metasurfaces to overcome inherent limitations in polarization density (P) within natural materials, which hinder achieving high refractive indices (n) at optical frequencies. The Maxwellian macroscopic description establishes a link between P and n, revealing a static limit in natural materia

Emilia Kilpua, Simon Good, Matti Ala-Lahti, Adnane Osmane

In this work, we perform a statistical study of magnetic field fluctuations in the solar wind at 1 au using permutation entropy and complexity analysis, and the investigation of the temporal variations of the Hurst exponents. Slow and fast wind, magnetic clouds, interplanetary coronal mass ejection (ICME)-driven sheath regions and slow-fast stream interactio

Junjie Zhang, Zheming Zhang, Huachen Xiang, Yangquan Tan

Physical function monitoring (PFM) plays a crucial role in healthcare especially for the elderly. Traditional assessment methods such as the Short Physical Performance Battery (SPPB) have failed to capture the full dynamic characteristics of physical function. Wearable sensors such as smart wristbands offer a promising solution to this issue. However, challe

Shinka Mori, Oana Ignat, Andrew Lee, Rada Mihalcea

Synthetic data generation has the potential to impact applications and domains with scarce data. However, before such data is used for sensitive tasks such as mental health, we need an understanding of how different demographics are represented in it. In our paper, we analyze the potential of producing synthetic data using GPT-3 by exploring the various stre

Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker

Understanding driving scenes and communicating automated vehicle decisions are key requirements for trustworthy automated driving. In this article, we introduce the Qualitative Explainable Graph (QXG), which is a unified symbolic and qualitative representation for scene understanding in urban mobility. The QXG enables interpreting an automated vehicle's envi

Aziz Kolkiran

We propose a novel, to the best of our knowledge, approach to superresolution optical imaging by combining quantum optics and near-field optics. Our concept involves the utilization of single-photon quantum emitters to generate a stand-alone evanescent wave. We demonstrate that the quantum interference effects of single-photon emitters, in conjunction with t

Huw Llewelyn

If the prior probability distributions of all possible hypothetical true means and all possible observed means of a continuous variable are conditional on the universal set of all numbers (i.e., before the nature of a study is known and a Bayesian prior distribution can be estimated), that prior probability distribution will be uniform. It would follow that

Théophile Dolmaire, Juan J. L. Velázquez

In this article we continue the study of the collapse of three inelastic particles in dimension $d \geq 2$, complementing the results we obtained in its companion paper. We focus on the particular case of the nearly-linear inelastic collapse, when the order of collisions becomes eventually the infinite repetition of the period $0-1$, $0-2$, under the assumpt

Önder Gürcan, Nataliya Yakymets, Sara Tucci-Piergiovanni, Ansgar Radermacher

Failure Mode, Effects and Criticality Analysis (FMECA) is one of the safety analysis methods recommended by most of the international standards. The classical FMECA is made in a form of a table filled in either manually or by using safety analysis tools. In both cases, the design engineers have to choose the trade-offs between safety and other development co

Zaynah Dargaye, Önder Gürcan, Florent Kirchner, Sara Tucci-Piergiovanni

Distributed immutable ledgers, or blockchains, allow the secure digitization of evidential transactions without relying on a trusted third-party. Evidential transactions involve the exchange of any form of physical evidence, such as money, birth certificate, visas, tickets, etc. Most of the time, evidential transactions occur in the context of complex proced

Wei Mao, Weiyang Zhang, Chen Huang, Liqi Zhou

Advances in bioimaging methods and hardware facilities have revolutionised the determination of numerous biological structures at atomic or near-atomic resolution. Among these developments, electron ptychography has recently attracted considerable attention because of its superior resolution, remarkable sensitivity to light elements, and high electron dose e

Calum Williams, Richard Cousins, Christopher J. Mellor, Sarah E. Bohndiek

We introduce the concept of `hyperpixels' in which each element of a pixel filter array (suitable for CMOS image sensor integration) has a spectral transmission tailored to a target spectral component expected in application-specific scenes. These are analogous to arrays of multivariate optical elements that could be used for sensing specific analytes. Spect

Akua Dickson, Christos G. Cassandras, Roberto Tron

We propose an output feedback control-based motion planning technique for agents to enable them to converge to a specified polynomial trajectory while imposing a set of safety constraints on our controller to avoid collisions within the free configuration space (polygonal environment). To achieve this, we 1) decompose our polygonal environment into different

Carmen Amo Alonso, Jerome Sieber, Melanie N. Zeilinger

In recent years, there has been a growing interest in integrating linear state-space models (SSM) in deep neural network architectures of foundation models. This is exemplified by the recent success of Mamba, showing better performance than the state-of-the-art Transformer architectures in language tasks. Foundation models, like e.g. GPT-4, aim to encode seq

Jialun Cao, Wuqi Zhang, Shing-Chi Cheung

Various techniques have been proposed to leverage the capabilities of code language models (CLMs) for SE tasks. While these techniques typically evaluate their effectiveness using publicly available datasets, the evaluation can be subject to data contamination threats where the evaluation datasets have already been used to train the concerned CLMs. This can

Junshu Tang, Yanhong Zeng, Ke Fan, Xuheng Wang

Creating and animating 3D biped cartoon characters is crucial and valuable in various applications. Compared with geometry, the diverse texture design plays an important role in making 3D biped cartoon characters vivid and charming. Therefore, we focus on automatic texture design for cartoon characters based on input instructions. This is challenging for dom

Sofia Eriksson, Jonas Nordqvist

Square matrices of the form $\widetilde{\mathbf{A}} =\mathbf{A} + \mathbf{e}D \mathbf{f}^*$ are considered. An explicit expression for the inverse is given, provided $\widetilde{\mathbf{A}}$ and $D$ are invertible with $\text{rank}(\widetilde{\mathbf{A}}) =\text{rank}(\mathbf{A})+\text{rank}(\mathbf{e}D \mathbf{f}^*)$. The inverse is presented in two ways, o

Auste Simkute, Ewa Luger, Michael Evans, Rhianne Jones

Artificial Intelligence (AI) is becoming ubiquitous in domains such as medicine and natural science research. However, when AI systems are implemented in practice, domain experts often refuse them. Low acceptance hinders effective human-AI collaboration, even when it is essential for progress. In natural science research, scientists' ineffective use of AI-en

Céline Degrande, Matteo Maltoni

We illustrate the importance of interference revival, when higher-order corrections are included, by presenting LO and NLO differential cross sections and $K$-factors for three processes that are sensitive to the dimension-6 SMEFT operator $O_W$ : $Z$-plus-two-jets ($Zjj$) through VBF, leptonic diboson $WZ$ and $W\gamma$ production. We show how lifting the i

Subir Ghosh

Generalizations of coordinate $x$-momentum $p_x$ Uncertainty Principle, with $\Delta x$ and $\Delta p_x$ dependent terms ($\Delta$ denoting standard deviation), $$\Delta x \Delta p_x\geq i\hbar (1+\alpha\Delta p_x^2 +\beta \Delta x^2)$$ have provided rich dividends as a poor person's approach towards Quantum Gravity, because these can introduce coordinate an

T. -H. Hsieh, J. E. Pineda, D. M. Segura-Cox, P. Caselli

Complex Organic Molecules (COMs) have been found toward low-mass protostars but the origins of the COM emission are still unclear. It can be associated with, for example, hot corinos, outflows, and/or accretion shock/disk atmosphere. We have conducted NOEMA observations toward SVS13A from the PROtostars & DIsks: Global Evolution (PRODIGE) program. Our previo

Xingyu Xu, Yuejie Chi

In a great number of tasks in science and engineering, the goal is to infer an unknown image from a small number of measurements collected from a known forward model describing certain sensing or imaging modality. Due to resource constraints, this task is often extremely ill-posed, which necessitates the adoption of expressive prior information to regularize

Antônio Tadeu Azevedo Gomes, Weslley da Silva Pereira, Frédéric Valentin

The multiscale hybrid-mixed (MHM) method consists of a multi-level strategy to approximate the solution of boundary value problems with heterogeneous coefficients. In this context, we propose a family of low-order finite elements for the linear elasticity problem which are free from Poisson locking. The finite elements rely on face degrees of freedom associa

M. R. Magee, L. Siebenaler, K. Maguire, K. Ackley

Multiple explosion mechanisms have been proposed to explain type Ia supernovae (SNe Ia). Empirical modelling tools have also been developed that allow for fast, customised modelling of individual SNe and direct comparisons between observations and explosion model predictions. Such tools have provided useful insights, but the subjective nature with which empi

Laiyan Ding, Panwen Hu, Jie Li, Rui Huang

Semantic Scene Completion (SSC) aims to jointly infer semantics and occupancies of 3D scenes. Truncated Signed Distance Function (TSDF), a 3D encoding of depth, has been a common input for SSC. Furthermore, RGB-TSDF fusion, seems promising since these two modalities provide color and geometry information, respectively. Nevertheless, RGB-TSDF fusion has been

Andrew Gray

The use of ChatGPT and similar Large Language Model (LLM) tools in scholarly communication and academic publishing has been widely discussed since they became easily accessible to a general audience in late 2022. This study uses keywords known to be disproportionately present in LLM-generated text to provide an overall estimate for the prevalence of LLM-assi

Weidong Mei, Xin Wei, Boyu Ning, Zhi Chen

Fluid antennas (FAs) and movable antennas (MAs) have emerged as promising technologies in wireless communications, which offer the flexibility to improve channel conditions by adjusting transmit/receive antenna positions within a spatial region. In this letter, we focus on an MA-enhanced multiple-input single-output (MISO) communication system, aiming to opt

Yingji Zhong, Lanqing Hong, Zhenguo Li, Dan Xu

Neural Radiance Fields (NeRF) have shown impressive capabilities for photorealistic novel view synthesis when trained on dense inputs. However, when trained on sparse inputs, NeRF typically encounters issues of incorrect density or color predictions, mainly due to insufficient coverage of the scene causing partial and sparse supervision, thus leading to sign

Ulrich Ellwanger, Cyril Hugonie

We investigate whether it is possible within the NMSSM to describe simultaneously a correct dark matter relic density complying with the latest null results from the LZ experiment, an extra Higgs boson with a mass of ~95 GeV visible in the bb channel at LEP and the diphoton channel at the LHC, and the deviation of the anomalous magnetic moment of the muon fr

Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda, Alexandros Kalousis

Learning graph generative models over latent spaces has received less attention compared to models that operate on the original data space and has so far demonstrated lacklustre performance. We present GLAD a latent space graph generative model. Unlike most previous latent space graph generative models, GLAD operates on a discrete latent space that preserves

Georgii Oblapenko, Manuel Torrilhon

A framework for numerical evaluation of entropy-conservative volume fluxes in gas flows with internal energies is developed, for use with high-order discretization methods. The novelty of the approach lies in the ability to use arbitrary expressions for the internal degrees of freedom of the constituent gas species. The developed approach is implemented in a

Aleksey K. Fedorov, Evgeniy O. Kiktenko, Nikolay N. Kolachevsky

Significant advances in the development of computing devices based on quantum effects and the demonstration of their use to solve various problems have rekindled interest in the nature of the "quantum computational advantage." Although various attempts to quantify and characterize the nature of the quantum computational advantage have previously been made, t

Tianshuai Hu, Jianhao Jiao, Yucheng Xu, Hongji Liu

Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed and efficient decisions. To comprehensively model the environment and effectively manage the map data structur

Tommi Muller, Adriana L. Duncan, Eric J. Verbeke, Joe Kileel

We revisit the topic of common lines between projection images in single particle cryo-electron microscopy (cryo-EM). We derive a novel low-rank constraint on a certain $2n \times n$ matrix storing properly-scaled basis vectors for the common lines between $n$ projection images of one molecular conformation. Using this algebraic constraint and others, we giv

Bjoern Bringmann, Sky Cao

We prove the global well-posedness of the stochastic Abelian-Higgs equations in two dimensions. The proof is based on a new covariant approach, which consists of two parts: First, we introduce covariant stochastic objects. The covariant stochastic objects and their multi-linear interactions are controlled using covariant heat kernel estimates. Second, we con

Luis E. Alvarez, James Jones, Austin Bryan, Andrew Weinert

Advanced Air Mobility encompasses emerging aviation technologies that transport people and cargo between local, regional, or urban locations that are currently underserved by aviation and other transportation modalities. The disruptive nature of these technologies has pushed industry, academia, and governments to devote significant investments to understand

Damien LaRocque, William Guimont-Martin, David-Alexandre Duclos, Philippe Giguère

Recent works in field robotics highlighted the importance of resiliency against different types of terrains. Boreal forests, in particular, are home to many mobility-impeding terrains that should be considered for off-road autonomous navigation. Also, being one of the largest land biomes on Earth, boreal forests are an area where autonomous vehicles are expe

Jorge Fariña-Asategui, Mikel E. Garciarena

We compute the Hausdorff dimension of the closure of the generalized Brunner-Sidki-Vieira group acting on the $m$-adic tree for $m\ge 2$, providing the first examples of self-similar topologically finitely generated closed subgroups of transcendental Hausdorff dimension in the group of $m$-adic automorphisms.

Owen Oertell, Jonathan D. Chang, Yiyi Zhang, Kianté Brantley

Reinforcement learning (RL) has improved guided image generation with diffusion models by directly optimizing rewards that capture image quality, aesthetics, and instruction following capabilities. However, the resulting generative policies inherit the same iterative sampling process of diffusion models that causes slow generation. To overcome this limitatio

Chen Yao, Yangtao Ge, Guowei Shi, Zirui Wang

Terrain-aware perception holds the potential to improve the robustness and accuracy of autonomous robot navigation in the wilds, thereby facilitating effective off-road traversals. However, the lack of multi-modal perception across various motion patterns hinders the solutions of Simultaneous Localization And Mapping (SLAM), especially when confronting non-g

Henry Cohn, David de Laat, Nando Leijenhorst

We show that the spectral embeddings of all known triangle-free strongly regular graphs are optimal spherical codes (the new cases are $56$ points in $20$ dimensions, $50$ points in $21$ dimensions, and $77$ points in $21$ dimensions), as are certain mutually unbiased basis arrangements constructed using Kerdock codes in up to $1024$ dimensions (namely, $2^{

Yan Liu, Abhinav K. Jha

Objective evaluation of quantitative imaging (QI) methods with patient data, while important, is typically hindered by the lack of gold standards. To address this challenge, no-gold-standard evaluation (NGSE) techniques have been proposed. These techniques have demonstrated efficacy in accurately ranking QI methods without access to gold standards. The devel

G. Lucatelli, R. Beswick, J. Moldon, M. Á. Pérez-Torres

Disentangling the radio flux contribution from star formation (SF) and active-galactic-nuclei (AGN) activity is a long-standing problem in extragalactic astronomy, since at frequencies of $\lesssim$ 10 GHz, both processes emit synchrotron radiation. We present in this work the general objectives of the PARADIGM Project, a multi-instrument concept to explore

Tom Kuipers, Renukanandan Tumu, Shuo Yang, Milad Kazemi

Off-Policy Prediction (OPP), i.e., predicting the outcomes of a target policy using only data collected under a nominal (behavioural) policy, is a paramount problem in data-driven analysis of safety-critical systems where the deployment of a new policy may be unsafe. To achieve dependable off-policy predictions, recent work on Conformal Off-Policy Prediction

Magdalena Lampa-Baczyńska, Daniel Wojcik

In the present note we focus on conic line arrangements in the plane with quasihomogeneous ordinary singularities from the perspective of weak Ziegler pairs. The foundations of this article come from an active area of research devoted to the freeness and nearly freeness of curve arrangements in the complex projective plane and the socalled Numerical Terao s

Tobias Pfandzelter, David Bermbach

Edge computing requires the complex software interaction of geo-distributed, heterogeneous components. The growing research and industry interest in edge computing software systems has necessitated exploring ways of testing and evaluating edge software at scale without relying on physical infrastructure. Beyond simulation, virtual testbeds that emulate edge

Chong Sun

We study electron localization in disordered quantum systems, focusing on both individual eigenstates and thermal states. We employ complex polarization as a numerical indicator to characterize the system's localization length. Furthermore, we assess the efficacy of mean-field approximation in providing a quantitative analysis of such systems. Through this s

Alexandre Toubiana, Nikolaos Karnesis, Astrid Lamberts, M. Coleman Miller

In this work, we investigate the impact of tidal torques and mass transfer on the population of double white dwarfs (DWDs) that will be observed with LISA. Starting from a distribution of DWDs at formation predicted by numerical simulations, we use a semi-analytical model to evolve DWDs under different hypotheses for the efficiency of tidal coupling and the

Silvia Frassu, Giuseppe Viglialoro

These notes aim to provide a deeper insight on the specifics of the paper "Refined criteria toward boundedness in an attraction-repulsion chemotaxis system with nonlinear productions" by A. Columbu, S. Frassu and G. Viglialoro [\textit{Appl. Anal.} 2024, 103:2, 415--431].

Gaofei Shen, Michaela Watkins, Afra Alishahi, Arianna Bisazza

Interpretability research has shown that self-supervised Spoken Language Models (SLMs) encode a wide variety of features in human speech from the acoustic, phonetic, phonological, syntactic and semantic levels, to speaker characteristics. The bulk of prior research on representations of phonology has focused on segmental features such as phonemes; the encodi

Teodor Rotaru, Panagiotis Patrinos, François Glineur

We consider a difference-of-convex formulation where one of the terms is allowed to be hypoconvex (or weakly convex). We first examine the precise behavior of a single iteration of the Difference-of-Convex algorithm (DCA), giving a tight characterization of the objective function decrease. This requires distinguishing between eight distinct parameter regimes

Guoliang He, Eiko Yoneki

Large language models (LLMs) have become a significant workload since their appearance. However, they are also computationally expensive as they have billions of parameters and are trained with massive amounts of data. Thus, recent works have developed dedicated CUDA kernels for LLM training and inference instead of relying on compilergenerated ones, so that

Florian Hahlbohm, Linus Franke, Moritz Kappel, Susana Castillo

We introduce a new approach for reconstruction and novel view synthesis of unbounded real-world scenes. In contrast to previous methods using either volumetric fields, grid-based models, or discrete point cloud proxies, we propose a hybrid scene representation, which implicitly encodes the geometry in a continuous octree-based probability field and view-depe

Gabriele Morello, Mojtaba Eshghie, Sofia Bobadilla, Martin Monperrus

The DISL dataset features a collection of $514,506$ unique Solidity files that have been deployed to Ethereum mainnet. It caters to the need for a large and diverse dataset of real-world smart contracts. DISL serves as a resource for developing machine learning systems and for benchmarking software engineering tools designed for smart contracts. By aggregati

Weize Wang, Yi Kuang

There is a growing trend to outsource the inference task of large transformer models to cloud servers. However, this poses a severe threat to users' private data as they are exposed to cloud servers after uploading. Although several works attempted to provide private inference for transformer models, their hundreds of communication rounds limit the applicati

Chufeng Li, Jianyong Chen

As a branch of time series forecasting, stock movement forecasting is one of the challenging problems for investors and researchers. Since Transformer was introduced to analyze financial data, many researchers have dedicated themselves to forecasting stock movement using Transformer or attention mechanisms. However, existing research mostly focuses on indivi