March 2024 arXiv papers — page 186

Yidong Zhao, Joao Tourais, Iain Pierce, Christian Nitsche

Deep learning (DL)-based methods have achieved state-of-the-art performance for many medical image segmentation tasks. Nevertheless, recent studies show that deep neural networks (DNNs) can be miscalibrated and overconfident, leading to "silent failures" that are risky for clinical applications. Bayesian DL provides an intuitive approach to DL failure detect

Amey Agrawal, Nitin Kedia, Ashish Panwar, Jayashree Mohan

Each LLM serving request goes through two phases. The first is prefill which processes the entire input prompt and produces the first output token and the second is decode which generates the rest of output tokens, one-at-a-time. Prefill iterations have high latency but saturate GPU compute due to parallel processing of the input prompt. In contrast, decode

Pedro Neto Mendes, Paulo André, Emmanuel Zambrini Cruzeiro

Quantum Key Distribution (QKD) has become an essential technology in the realm of secure communication, with applications ranging from secure data transmission to quantum networks. This paper presents a simple, compact, and cost-effective setup for undergraduate tutorial demonstrations of QKD. It relies on using weak coherent pulses, which can be readily pro

Yuchen Duan, Weiyun Wang, Zhe Chen, Xizhou Zhu

Transformers have revolutionized computer vision and natural language processing, but their high computational complexity limits their application in high-resolution image processing and long-context analysis. This paper introduces Vision-RWKV (VRWKV), a model adapted from the RWKV model used in the NLP field with necessary modifications for vision tasks. Si

P. Bilha Githinji, Xi Yuan, Zhenglin Chen, Ijaz Gul

Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models. Moreover, these models exhibit a bias for contrast-based images, with diminished performance on texture-based medical images. This study introduces the notion of a population-level context for pa

Johannes Jahan, Claudia Ratti, Maria Stefaniak, Klaus Werner

Proxies for cumulants of baryon number $B$, electric charge $Q$ and strangeness $S$ are usually measured in heavy-ion collisions via moments of net-number distribution of given hadronic species. Since these cumulants of conserved charges are expected to be sensitive to the existence of a critical point in the phase diagram of nuclear matter, it is crucial to

Aleksandr Artemev, Igor Chaban

In view of recent progress in studying matrix model-2D gravity duality, we reexamine some features of $(2,2p+1)$ minimal string. After reviewing both sides of the proposed correspondence in this case, a previously unnoted identification between correlation numbers of tachyon operators in certain domain of parameter space and "$p$-deformed volumes", which are

Bingjie Wang, Anna de Graaff, Rebecca L. Davies, Jenny E. Greene

The JWST discovery of ``little red dots'' (LRDs) is reshaping our picture of the early Universe, yet the physical mechanisms driving their compact size and UV-optical colors remain elusive. Here we report an unusually bright LRD ($z=3.1$) observed as part of the RUBIES program. This LRD exhibits broad emission lines (FWHM $\sim4000$km/s), a blue UV continuum

Nicola De Nitti, Federico Glaudo, Tobias König

The fractional Caffarelli-Kohn-Nirenberg inequality states that $$ \int_{\mathbb{R}^n}\int_{\mathbb{R}^n} \frac{(u(x)-u(y))^2}{|x|^\alpha |x-y|^{n+2s} |y|^\alpha} \mathrm{d} x \, \mathrm{d} y \geq \Lambda_{n, s, p, \alpha,\beta} \|u |x|^{-\beta}\|_{L^p}^2, $$ for $0<s<\min\{1, n/2\}$, $2<p<2^*_s$, and $\alpha,\beta\in\mathbb R$ so that $\beta-\alpha = s - n\

Maksim Kuprashevich, Grigorii Alekseenko, Irina Tolstykh

Multimodal Large Language Models (MLLMs) have recently gained immense popularity. Powerful commercial models like ChatGPT-4V and Gemini, as well as open-source ones such as LLaVA, are essentially general-purpose models and are applied to solve a wide variety of tasks, including those in computer vision. These neural networks possess such strong general knowl

Felipe Diaz, Carlo Iazeolla, Per Sundell

This paper completes the analysis initiated in the companion work arXiv:2403.02283 -- referred to as Paper I -- by showing how Vasiliev's 4D higher-spin gravity (HSG) and 3D coloured conformal matter fields coupled to conformal higher-spin gauge fields and colour gauge fields (coloured conformal HSG, or CCHSG) emerge as consistent reductions of a common pare

Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis

We study the problem of estimating the mean of an identity covariance Gaussian in the truncated setting, in the regime when the truncation set comes from a low-complexity family $\mathcal{C}$ of sets. Specifically, for a fixed but unknown truncation set $S \subseteq \mathbb{R}^d$, we are given access to samples from the distribution $\mathcal{N}(\boldsymbol{

Michael Gomez, Eric Lauga

We study slender, helical elastic rods subject to distributed forces and moments. Focussing on the case when the helix axis remains straight, we employ the method of multiple scales to systematically derive an 'equivalent-rod' theory from the Kirchhoff rod equations: the helical filament is described as a naturally-straight rod (aligned with the helix axis)

Pierre Aboulker, Frédéric Havet, François Pirot, Juliette Schabanel

Let $D$ be a digraph. Its acyclic number $\vec{\alpha}(D)$ is the maximum order of an acyclic induced subdigraph and its dichromatic number $\vec{\chi}(D)$ is the least integer $k$ such that $V(D)$ can be partitioned into $k$ subsets inducing acyclic subdigraphs. We study ${\vec a}(n)$ and $\vec t(n)$ which are the minimum of $\vec\alpha(D)$ and the maximum

Wenbo Shao, Jiahui Xu, Zhong Cao, Hong Wang

Autonomous driving systems face the formidable challenge of navigating intricate and dynamic environments with uncertainty. This study presents a unified prediction and planning framework that concurrently models short-term aleatoric uncertainty (SAU), long-term aleatoric uncertainty (LAU), and epistemic uncertainty (EU) to predict and establish a robust fou

Bjarno Oeyen, Joeri De Koster, Wolfgang De Meuter

Context: Reactive programming (RP) is a declarative programming paradigm suitable for expressing the handling of events. It enables programmers to create applications that react automatically to changes over time. Whenever a time-varying signal changes -- e.g. in response to values produced by event stream (e.g., sensor data, user input...) -- the program st

Yuan Fang, Lei Chen, Andrey Prokofiev, Iñigo Robredo

Weyl-Kondo semimetals are strongly correlated topological semimetals that develop through the cooperation of the Kondo effect with space group symmetries. The Kondo effect, capturing quantum fluctuations associated with strong correlations, is usually suppressed by magnetic order. Here we develop the theory of magnetic Weyl-Kondo semimetal. The key of the pr

Christopher Tong, Helena Zhang, Bibek Pokharel

Dynamical decoupling (DD) is a low-overhead method for quantum error suppression. Despite extensive work in DD design, finding pulse sequences that optimally decouple computational qubits on noisy quantum hardware is not well understood. In this work, we describe how learning algorithms can empirically tailor DD strategies for any quantum circuit and device.

Dharmaraj Deka, Shuvam Sen

In this work, a higher order compact (HOC) discretization is developed on the nonuniform polar grid. The discretization conceptualized using the unsteady convection-diffusion equation (CDE) is further extended to flow problems governed by the Navies-Stokes (N-S) equations as well as the Boussinesq equations. The scheme developed here combines the advantages

Alireza Pirhadi, Mohammad Hossein Moslemi, Alexander Cloninger, Mostafa Milani

Ensuring Conditional Independence (CI) constraints is pivotal for the development of fair and trustworthy machine learning models. In this paper, we introduce \sys, a framework that harnesses optimal transport theory for data repair under CI constraints. Optimal transport theory provides a rigorous framework for measuring the discrepancy between probability

Omer Akgul, Sai Teja Peddinti, Nina Taft, Michelle L. Mazurek

We present an analysis of 12 million instances of privacy-relevant reviews publicly visible on the Google Play Store that span a 10 year period. By leveraging state of the art NLP techniques, we examine what users have been writing about privacy along multiple dimensions: time, countries, app types, diverse privacy topics, and even across a spectrum of emoti

Łukasz Patryk Michalak

In this work we are focused on the existence of Morse functions on a closed manifold $M$ which are far from being ordered, i.e. whose Reeb graphs have positive first Betti number, especially the maximal possible, equals $\operatorname{corank}(\pi_1(M))$. In the case of $3$-manifolds we describe the minimal number of critical points needed to construct such f

Preston Rozwood, Edward Mehrez, Ludger Paehler, Wen Sun

The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman equation, are ubiquitous in reinforcement learning and control theory. However, these equations become intractable for high-dimensional or nonlinear systems. This paper develops two new reinforcement learning algorithms based on the data-driven Koopman operator, which lifts a nonlinear

Jonas Ekeland Kittelsen, Eric Aislan Antonelo, Eduardo Camponogara, Lars Struen Imsland

Neural networks, while powerful, often lack interpretability. Physics-Informed Neural Networks (PINNs) address this limitation by incorporating physics laws into the loss function, making them applicable to solving Ordinary Differential Equations (ODEs) and Partial Differential Equations (PDEs). The recently introduced PINC framework extends PINNs to control

Joonas Kalda, Clément Pagés, Ricard Marxer, Tanel Alumäe

A major drawback of supervised speech separation (SSep) systems is their reliance on synthetic data, leading to poor real-world generalization. Mixture invariant training (MixIT) was proposed as an unsupervised alternative that uses real recordings, yet struggles with overseparation and adapting to long-form audio. We introduce PixIT, a joint approach that c

Joshua Cooper, Zhibin Du

The Steiner distance of a set of vertices in a graph is the fewest number of edges in any connected subgraph containing those vertices. The order-$k$ Steiner distance hypermatrix of an $n$-vertex graph is the $n \times \cdots \times n$ ($k$ terms) array indexed by vertices, whose entries are the Steiner distances of their corresponding indices. In the case o

Ziniu Wu, Ryan Marcus, Zhengchun Liu, Parimarjan Negi

Query performance (e.g., execution time) prediction is a critical component of modern DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate execution time prediction for many downstream tasks, ranging from high-level optimizations, such as automatically creating materialized views, to low-level tasks on the critical path of quer

Jonathan Lautenschlager, Emma Sköldberg, Simon Hengchen, Dominik Schlechtweg

This study addresses the task of Unknown Sense Detection in English and Swedish. The primary objective of this task is to determine whether the meaning of a particular word usage is documented in a dictionary or not. For this purpose, sense entries are compared with word usages from modern and historical corpora using a pre-trained Word-in-Context embedder t

Dario Stein, Fabio Zanasi, Robin Piedeleu, Richard Samuelson

Convex analysis and Gaussian probability are tightly connected, as mostly evident in the theory of linear regression. Our work introduces an algebraic perspective on such relationship, in the form of a diagrammatic calculus of string diagrams, called Graphical Quadratic Algebra (GQA). We show that GQA is a complete axiomatisation for the category of quadrati

Felipe Diaz, Carlo Iazeolla, Per Sundell

In this work and in the companion paper arXiv:2403.02301, we initiate an approach to holography based on the AKSZ formalism. As the first step, we refine Vasiliev's holography proposal in arXiv:1203.5554 by obtaining 4D higher-spin gravity (HSG) and 3D coloured conformal higher-spin gravity (CCHSG) -- i.e., coloured conformal matter fields coupled to conform

Luuk Stehouwer

We establish the spin-statistics theorem for topological quantum field theories (TQFTs) in the framework of Atiyah. We incorporate spin via spin structures on bordisms, and represent statistics using super vector spaces. Unitarity is implemented using dagger categories, in a manner that is equivalent to the approach of Freed-Hopkins, who employed $\mathbb{Z}

Krishnapriya Vishnubhotla, Daniela Teodorescu, Mallory J. Feldman, Kristen A. Lindquist

We are united in how emotions are central to shaping our experiences; and yet, individuals differ greatly in how we each identify, categorize, and express emotions. In psychology, variation in the ability of individuals to differentiate between emotion concepts is called emotion granularity (determined through self-reports of one's emotions). High emotion gr

Simon Boche, Sebastián Barbas Laina, Stefan Leutenegger

Autonomous navigation is one of the key requirements for every potential application of mobile robots in the real-world. Besides high-accuracy state estimation, a suitable and globally consistent representation of the 3D environment is indispensable. We present a fully tightly-coupled LiDAR-Visual-Inertial SLAM system and 3D mapping framework applying local

Pablo Rodriguez-Lopez, Jian-Sheng Wang, Mauro Antezza

We compare three models of graphene electric conductivity: a non-local Kubo model, a local model derived by Falkovsky, and finally, a non-local quantum field theory (QFT) polarization-based model. These models are supposed to provide consistent results since they are derived from the same Hamiltonian. While we confirm that the local model is a proper $\textb

Iivari Pietikäinen, Ondřej Černotík, Alec Eickbusch, Aniket Maiti

Three-dimensional microwave cavity resonators have been shown to reach lifetimes of the order of a second by maximizing the cavity volume relative to its surface, using better materials, and improving surface treatments. Such cavities represent an ideal platform for quantum computing with bosonic qubits, but their efficient control remains an outstanding pro

S. Cabanes, T. Gastine, A. Fournier

In order to characterize the global circulation of the subsurface ocean of Jovian and Saturnian moons, we analyze the properties of 21 three-dimensional simulations of Boussinesq thermal convection in a rapidly rotating spherical shell. Flow is driven by an adverse temperature contrast imposed across the domain, and is subjected to no-slip boundary condition

Pat Devlin, Leo Douhovnikoff

We study the diametric problem (i.e., optimal anticodes) in the space of permutations under the Ulam distance. That is, let $S_n$ denote the set of permutations on $n$ symbols, and for each $\sigma, \tau \in S_n$, define their Ulam distance as the number of distinct symbols that must be deleted from each until they are equal. We obtain a near-optimal upper b

Svyatoslav Gryaznov, Navid Talebanfard

A major open problem in proof complexity is to demonstrate that random 3-CNFs with a linear number of clauses require super-polynomial size refutations in bounded-depth Frege systems. We take the first step towards addressing this question by establishing a super-linear lower bound: for every $k$, there exists $\epsilon_k > 0$ such that any depth-$k$ Frege r

Snehesh Shrestha, Yantian Zha, Saketh Banagiri, Ge Gao

Recent advancements in multimodal Human-Robot Interaction (HRI) datasets have highlighted the fusion of speech and gesture, expanding robots' capabilities to absorb explicit and implicit HRI insights. However, existing speech-gesture HRI datasets often focus on elementary tasks, like object pointing and pushing, revealing limitations in scaling to intricate

Jack Liell-Cock, Tom Schrijvers

Context: Edge graphs are graphs whose edges are labelled with identifiers, and nodes can have multiple edges between them. They are used to model a wide range of systems, including networks with distances or degrees of connection and complex relational data. Inquiry: Unfortunately, the homogeneity of this graph structure prevents an effective representation

G. De Gregorio, R. Mancino, L. Coraggio, N. Itaco

For the first time, half-lives and energy spectra of forbidden $\beta$ decays are calculated within the realistic shell model. Namely, we approach this issue starting from a realistic nucleon-nucleon potential and deriving effective Hamiltonians and decay operators. Our goal is to explore the sensitivity of the shape of calculated energy spectra to the renor

Saeed Najafi, Alona Fyshe

Pre-trained Language Models (PLMs) can be accurately fine-tuned for downstream text processing tasks. Recently, researchers have introduced several parameter-efficient fine-tuning methods that optimize input prompts or adjust a small number of model parameters (e.g LoRA). In this study, we explore the impact of altering the input text of the original task in

Alessandro Scirè, Karim Ghonim, Roberto Navigli

Recent advancements in text summarization, particularly with the advent of Large Language Models (LLMs), have shown remarkable performance. However, a notable challenge persists as a substantial number of automatically-generated summaries exhibit factual inconsistencies, such as hallucinations. In response to this issue, various approaches for the evaluation

Haokun Luo, Yunxuan Wei, Fan O. Wu, Georgios G. Pyrialakos

The guided transmission of optical waves is critical for light-based applications in modern communication, information processing, and energy generation systems. Traditionally, guiding light waves in structures like optical fibres is predominantly achieved through the use of total internal reflection. In periodic platforms, a variety of other physical mechan

Amanda Cercas Curry, Gavin Abercrombie, Zeerak Talat

Natural language processing research has begun to embrace the notion of annotator subjectivity, motivated by variations in labelling. This approach understands each annotator's view as valid, which can be highly suitable for tasks that embed subjectivity, e.g., sentiment analysis. However, this construction may be inappropriate for tasks such as hate speech

Alexander Lenz, Maria Laura Piscopo, Aleksey V. Rusov

We provide an overview of the current experimental and theoretical status of charm CP violation and discuss recent progress in obtaining a Standard Model prediction for $\Delta a_{\rm CP}^{\rm dir}$ using the framework of light-cone sum rules. Furthermore, we present new results for the ratios of the direct CP asymmetries and of the branching fractions for t

Abhiram Soori

Majorana fermions, exotic particles with potential applications in quantum computing, have garnered significant interest in condensed matter physics. The Kitaev model serves as a fundamental framework for investigating the emergence of Majorana fermions in one-dimensional systems. We explore the intriguing question of whether Majorana fermions can arise in a

Ange Lou, Benjamin Planche, Zhongpai Gao, Yamin Li

Addressing the intricate challenge of modeling and re-rendering dynamic scenes, most recent approaches have sought to simplify these complexities using plane-based explicit representations, overcoming the slow training time issues associated with methods like Neural Radiance Fields (NeRF) and implicit representations. However, the straightforward decompositi

T. Rook, L. Cruz Rodriguez, C. Figueira de Morisson Faria

We discuss the effect of using potentials with a Coulomb tail and different degrees of softening in the photoelectron momentum distributions (PMDs) using the recently implemented hybrid forward-boundary CQSFA (H-CQSFA). We show that introducing a softening in the Coulomb interaction influences the ridges observed in the PMDs associated with backscattered ele

Rami Oueslati, Adel Trabelsi

Inspired by the picture portraying the KNO scaling violation as an extension of the geometrical scaling violation, the current study proposes a phenomenological model for multi-particle production in hadron collisions based on the geometrical approach and using the $U$-Matrix unitarization scheme of the scattering amplitude. The model has been fine-tuned and

Didier Pilod, Frédéric Valet

We study the dynamics of the collision of two solitary waves for the Zakharov-Kuznetsov equation in dimension $2$ and $3$. We describe the evolution of the solution behaving as a sum of $2$-solitary waves of nearly equal speeds at time $t=-\infty$ up to time $t=+\infty$. We show that this solution behaves as the sum of two modulated solitary waves and an err

Kristen M. Surrao, J. Colin Hill

The internal linear combination (ILC) method is a popular approach for constructing component-separated maps in cosmic microwave background (CMB) analyses. It optimally combines observed maps at different frequencies to produce an unbiased minimum-variance map of a component. When performed in harmonic space, it is straightforward to analytically compute the

Nguyen Fuda, Dániel Apai, Domenico Nardiello, Xianyu Tan

In this work, we present the longest photometric monitoring of up to 1200 hours of the strongly variable brown-dwarf binaries Luhman 16 AB and provide evidence of $\pm$5% variability on a timescale of several-to-hundreds of hours for this object. We show that short-period rotational modulation around 5 hours (k = 1 wavenumber) and 2.5 hours (k = 2 wavenumber

Cheng Ren, Zachary Pardos, Zhi Li

AI approaches are progressing besting humans at game-related tasks (e.g. chess). The next stage is expected to be Human-AI collaboration; however, the research on this subject has been mixed and is in need of additional data points. We add to this nascent literature by studying Human-AI collaboration on a common administrative educational task. Education is

Martin A. Cordiner, Alexander E. Thelen, Thibault Cavalié, Richard Cosentino

The study of planets and small bodies within our Solar System is fundamental for understanding the formation and evolution the Earth and other planets. Compositional and meteorological studies of the giant planets provide a foundation for understanding the nature of the most commonly observed exoplanets, while spectroscopic observations of the atmospheres of

Anik Kumar Saha, Md Abrar Jahin, Md. Rafiquzzaman, M. F. Mridha

Many studies have shown how ergonomically designed furniture improves productivity and well-being. As computers have become a part of students' academic lives, they will grow further in the future. We propose anthropometric-based furniture dimensions suitable for university students to improve computer laboratory ergonomics. We collected data from 380 partic

Prince Romeo Mensah

The system under study is a solute-solvent-structure (SSS) interaction problem for the interaction of a dilute three-dimensional Oldroyd-B polymeric fluid with a two-dimensional viscoelastic shell. We show that a unique global strong solution to this system exists under the condition that the classical Ladyzhenskaya--Prodi--Serrin criterion holds for the vel

Léo Vacher, Nils Schöneberg

Some attempts of easing the critical Hubble tension present in modern cosmology have resorted to using variations of fundamental constants, such as the fine-structure constant, at the time of recombination. In this article we demonstrate that there are critical hurdles to construct such viable models using scalar fields, due to the striking precision of loca

Sofía Guevara-Montoya, Felipe Ortiz-Ferreira, María Paula Silva-Arévalo, Paola A. Niño-Muñoz

In Colombia, astronomical research is experiencing accelerated growth. To better understand its evolution and current state, we conducted a bibliometric study using data from the Astrophysics Data System (ADS) and the Web of Science (WoS). In the ADS, we identified 422 peer-reviewed publications from 1980, the year of the first publication, until 2023, the c

Dimitrios Michael Manias, Joe Naoum-Sawaya, Abbas Javadtalab, Abdallah Shami

The development of next-generation networks is revolutionizing network operators' management and orchestration practices worldwide. The critical services supported by these networks require increasingly stringent performance requirements, especially when considering the aspect of network reliability. This increase in reliability, coupled with the mass genera

Yuexin Li, Chengyu Huang, Shumin Deng, Mei Lin Lock

Phishing attacks have inflicted substantial losses on individuals and businesses alike, necessitating the development of robust and efficient automated phishing detection approaches. Reference-based phishing detectors (RBPDs), which compare the logos on a target webpage to a known set of logos, have emerged as the state-of-the-art approach. However, a major

Anton A. Kutsenko

For branching processes, the generating functions for limit distributions of so-called ratios of probabilities of rare events satisfy the Schr\"oder-type integral-functional equations. Excepting limited special cases, the corresponding equations can not be solved analytically. I found a large class of Poisson-type offspring distributions, for which the Schr\

Filippo Bigi, Sanggyu Chong, Michele Ceriotti, Federico Grasselli

Regression methods are fundamental for scientific and technological applications. However, fitted models can be highly unreliable outside of their training domain, and hence the quantification of their uncertainty is crucial in many of their applications. Based on the solution of a constrained optimization problem, we propose "prediction rigidities" as a met

Pankaj Kumar, Valery M. Nakariakov, Judith T. Karpen, Kyung-Suk Cho

Mutual conversion of various kinds of magnetohydrodynamic (MHD) waves can have profound impacts on wave propagation, energy transfer, and heating of the solar chromosphere and corona. Mode conversion occurs when an MHD wave travels through a region where the Alfv\'en and sound speeds are equal (e.g., a 3D magnetic null point). Here we report the first EUV im

Ravi Chepuri, Dael Amzalag, Thomas Antonsen, Michelle Girvan

Reservoir computers (RCs) are powerful machine learning architectures for time series prediction. Recently, next generation reservoir computers (NGRCs) have been introduced, offering distinct advantages over RCs, such as reduced computational expense and lower training data requirements. However, NGRCs have their own practical difficulties, including sensiti

Kunyu Shi, Qi Dong, Luis Goncalves, Zhuowen Tu

Sequence-to-sequence vision-language models are showing promise, but their applicability is limited by their inference latency due to their autoregressive way of generating predictions. We propose a parallel decoding sequence-to-sequence vision-language model, trained with a Query-CTC loss, that marginalizes over multiple inference paths in the decoder. This

Hua Wang, Hong-Yi Xie, Kieran Mullen

We propose harnessing the tools of modern nano-fabrication to provide electrical control of exciton-polariton (EP) condensates. We develop the theory of a device based on the Josephson effect in which electric fields can be used to both switch between and monitor various dynamical modes. In particular, both the bias potential and the Josephson energy can be

Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh

LLMOps incur significant costs due to hardware requirements, hindering their widespread accessibility. Additionally, a lack of transparency in model training methods and data contributes to the majority of models being non-reproducible. To tackle these challenges, the LLM Efficiency Challenge was introduced at NeurIPS Workshop, aiming to adapt foundation mod

Fiona Anting Tan, Gerard Christopher Yeo, Kokil Jaidka, Fanyou Wu

The use of LLMs in natural language reasoning has shown mixed results, sometimes rivaling or even surpassing human performance in simpler classification tasks while struggling with social-cognitive reasoning, a domain where humans naturally excel. These differences have been attributed to many factors, such as variations in prompting and the specific LLMs us

Jeongmin Han, Juha Karvanen, Mikko Parviainen

We study sequential cost-efficient design in a situation where each update of covariates involves a fixed time cost typically considerable compared to a single measurement time. The problem arises from parameter estimation in switching measurements on superconducting Josephson junctions which are components needed in quantum computers and other superconducti

Quentin Changeat, Yuichi Ito, Ahmed F. Al-Refaie, Kai Hou Yip

Conventional atmospheric retrieval codes are designed to extract information, such as chemical abundances, thermal structures and cloud properties, from fully "reduced" spectra obtained during transit or eclipse. Reduced spectra, however, are assembled by fitting a series of simplified light-curves to time series observations, wavelength-by-wavelength. Thus,

Cameron R. Wolfe, Anastasios Kyrillidis

Low precision training can significantly reduce the computational overhead of training deep neural networks (DNNs). Though many such techniques exist, cyclic precision training (CPT), which dynamically adjusts precision throughout training according to a cyclic schedule, achieves particularly impressive improvements in training efficiency, while actually imp

Dylan Heuer

We investigate analogues of alternating sign matrices, called partial alternating sign matrices. We prove bijections between these matrices and several other combinatorial objects. We use an analogue of Wieland's gyration on fully-packed loops, which we relate to the study of toggles and order ideals. Finally, we show that rowmotion on order ideals of a cert

Damien Teney, Armand Nicolicioiu, Valentin Hartmann, Ehsan Abbasnejad

Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free methods nor the simplicity bias recently observed in untrained networks. This paper seeks other sources of ge

Sukhpal Singh Gill, Oktay Cetinkaya, Stefano Marrone, Daniel Claudino

The recent development of quantum computing, which uses entanglement, superposition, and other quantum fundamental concepts, can provide substantial processing advantages over traditional computing. These quantum features help solve many complex problems that cannot be solved otherwise with conventional computing methods. These problems include modeling quan

Atila Poro, Mehmet Tanriver, Elham Sarvari, Shayan Zavvarei

The light curve analyses and orbital period variations for two contact binary stars, LS Del and V997 Cyg, were presented in this work which was conducted in the frame of the Binary Systems of South and North (BSN) Project. Ground-based photometric observations were performed at two observatories in France. We used the TESS (Transiting Exoplanet Survey Satell

Dimitrios Michael Manias, Ali Chouman, Abdallah Shami

The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices. This transition to ubiquitous intelligence demands high connectivity, synchronicity, and end-to-end communica

Souvik Bera, Tanay Pathak

We present our investigation of the study of two variable hypergeometric series, namely Appell $F_{1}$ and $F_{3}$ series, and obtain a comprehensive list of its analytic continuations enough to cover the whole real $(x,y)$ plane, except on their singular loci. We also derive analytic continuations of their 3-variable generalization, the Lauricella $F_{D}^{(

Darryl Hannan, Steven C. Nesbit, Ximing Wen, Glen Smith

Detecting elevated intracranial pressure (ICP) is crucial in diagnosing and managing various neurological conditions. These fluctuations in pressure are transmitted to the optic nerve sheath (ONS), resulting in changes to its diameter, which can then be detected using ultrasound imaging devices. However, interpreting sonographic images of the ONS can be chal

Junseo Kim, Jill Aghyourli Zalat, Yeganeh Bahoo, Sajad Saeedi

With the rising prominence of WiFi in common spaces, efforts have been made in the robotics community to take advantage of this fact by incorporating WiFi signal measurements in indoor SLAM (Simultaneous Localization and Mapping) systems. SLAM is essential in a wide range of applications, especially in the control of autonomous robots. This paper describes r

Fangzhou Hong, Jiaxiang Tang, Ziang Cao, Min Shi

We present a two-stage text-to-3D generation system, namely 3DTopia, which generates high-quality general 3D assets within 5 minutes using hybrid diffusion priors. The first stage samples from a 3D diffusion prior directly learned from 3D data. Specifically, it is powered by a text-conditioned tri-plane latent diffusion model, which quickly generates coarse

Yu Huang, Zixin Wen, Yuejie Chi, Yingbin Liang

Self-supervised learning has become a cornerstone in computer vision, primarily divided into reconstruction-based methods like masked autoencoders (MAE) and discriminative methods such as contrastive learning (CL). Recent empirical observations reveal that MAE and CL capture different types of representations: CL tends to focus on global patterns, while MAE

Zhenglin Li, Haibei Zhu, Houze Liu, Jintong Song

This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset. The aim is to advance cybersecurity capabilities by identifying and mitigating threats more effectively. Both ensemble and non-ensemble machine learning methods, such as

Hansi Hettiarachchi, Amna Dridi, Mohamed Medhat Gaber, Pouyan Parsafard

Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced M

Q. L. Zhao, P. M. Zhang, P. A. Horvathy

A special conformal transformation which carries a vacuum gravitational wave into another vacuum one is built by using M\"obius-redefined time. It can either transform a globally defined vacuum wave into a vacuum sandwich wave, or carry the gravitational wave into itself. The first type, illustrated by linearly and circularly polarized vacuum plane gravitati

Biswajit Paul, Tapan Mishra

Dynamical formation of doublons or onsite repulsively bound pairs of particles on a lattice is a highly non-trivial phenomenon. In this work, we show the signatures of doublon formation in a quantum computer by simulating the continuous time quantum walk in the framework of the one dimensional extended Fermi-Hubbard model. By considering two up-component and

Simon Vialaret

The systole of a contact form $\alpha$ is defined as the shortest period of closed Reeb orbits of $\alpha$. Given a non-trivial $\mathbb S^1$-principal bundle over $\mathbb S^2$ with total space $M$, we prove a sharp systolic inequality for the class of tight contact form on $M$ invariant under the $\mathbb S^1$-action. This inequality exhibits a behavior wh

Ariyan Bighashdel, Yongzhao Wang, Stephen McAleer, Rahul Savani

Game theory provides a mathematical way to study the interaction between multiple decision makers. However, classical game-theoretic analysis is limited in scalability due to the large number of strategies, precluding direct application to more complex scenarios. This survey provides a comprehensive overview of a framework for large games, known as Policy Sp

Matthew Route

Plasma flow-obstacle interactions, such as those between an exoplanet's magnetosphere and the host star's stellar wind, may lead to detectable radio emissions. Despite many attempts to detect magnetospheric (auroral) radio emissions from exoplanets, a reproducible, unambiguous detection remains elusive. This fourth paper of the ROME (Radio Observations of Ma

Davide Margaria, Alberto Carelli, Andrea Vesco

Nowadays, Internet of Things platforms are being deployed in a wide range of application domains. Some of these include use cases with security requirements, where the data generated by an IoT node is the basis for making safety-critical or liability-critical decisions at system level. The challenge is to develop a solution for data exchange while proving an

Ashish S. Nair, Shivam Barwey, Pinaki Pal, Jonathan F. MacArt

The neural ordinary differential equation (ODE) framework has emerged as a powerful tool for developing accelerated surrogate models of complex physical systems governed by partial differential equations (PDEs). A popular approach for PDE systems employs a two-step strategy: a nonlinear dimensionality reduction using an autoencoder, followed by time integrat

M. Kazemi, A. Kudlis, P. F. Bessarab, I. A. Shelykh

The potential for manipulating characteristics of skyrmions in a CrI$_3$ monolayer using circularly polarised light is explored. The effective skyrmion-light interaction is mediated by bright excitons whose magnetization is selectively influenced by the polarization of photons. The light-induced skyrmion dynamics is illustrated by the dependencies of the sky

H. C. Das, Jin-Biao Wei, G. F. Burgio, H. -J. Schulze

We study the cooling of isolated neutron stars, employing different nuclear equations of state with or without active direct Urca process, and investigate the interplay with the nuclear pairing gaps. We find that a consistent description of all current cooling data requires fast direct Urca cooling and reasonable proton 1S0 gaps, but no neutron 3P2 pairing.

Yilong Ren, Yue Chen, Shuai Liu, Boyue Wang

Traffic prediction constitutes a pivotal facet within the purview of Intelligent Transportation Systems (ITS), and the attainment of highly precise predictions holds profound significance for efficacious traffic management. The precision of prevailing deep learning-driven traffic prediction models typically sees an upward trend with a rise in the volume of t

Daniel Cirkovic, Tiandong Wang, Daren B. H. Cline

In this paper, we propose a multilayer inhomogeneous random graph model (MIRG), whose layers may consist of both single-edge and multi-edge graphs. In the single layer case, it has been shown that the regular variation of the weight distribution underlying the inhomogeneous random graph implies the regular variation of the typical degree distribution. We ext

Lázaro O. Rodríguez Díaz

Motivated by a valuation theorem, recently obtained by Rangachev, we study the \'etale extensions $A\subset B$ of polynomial rings over an algebraically closed field of characteristic zero, such that the integral closure $\overline{A}$ is a primary $\overline{A}$-submodule of $B$. We prove that in this case $\overline{A}$ has infinite cyclic divisor class gr

Billel Guelmame

In this paper, we propose a Hamiltonian regularization of scalar conservation laws, which is parametrized by $\ell > 0$ and conserves an $H^1$ energy. We prove the existence of global weak solutions for this regularization. Furthermore, we demonstrate that as $\ell$ approaches zero, the unique entropy solution of the original scalar conservation law is recov

Yudi Zhang, Qi Xu, Lei Zhang

Creating 3D textured meshes using generative artificial intelligence has garnered significant attention recently. While existing methods support text-based generative texture generation or editing on 3D meshes, they often struggle to precisely control pixels of texture images through more intuitive interaction. While 2D images can be edited generatively usin

Atul Tanaji Mohite

The phase transitions for many-body systems have been understood using field theories. A few canonical physical model classes encapsulate the underlying physical properties of a large number of systems. The finite-time driving of such systems and associated optimal energetic costs have not been investigated yet. We consider two universality classes Model A a

Yongquan Qu, Mohamed Aziz Bhouri, Pierre Gentine

Accurate representations of unknown and sub-grid physical processes through parameterizations (or closure) in numerical simulations with quantified uncertainty are critical for resolving the coarse-grained partial differential equations that govern many problems ranging from weather and climate prediction to turbulence simulations. Recent advances have seen