February 2024 arXiv papers — page 5

Jianfei Yang, Shijie Tang, Yuecong Xu, Yunjiao Zhou

Human activity recognition (HAR) has been playing an increasingly important role in various domains such as healthcare, security monitoring, and metaverse gaming. Though numerous HAR methods based on computer vision have been developed to show prominent performance, they still suffer from poor robustness in adverse visual conditions in particular low illumin

Siavash Askari, Manouchehr Zaker

Many phenomena in real world social networks are interpreted as spread of influence between activated and non-activated network elements. These phenomena are formulated by combinatorial graphs, where vertices represent the elements and edges represent social ties between elements. A main problem is to study important subsets of elements (target sets or dynam

Magnus Aspenberg, Mats Bylund, Weiwei Cui

In this paper we study perturbations of complex unicritical polynomials satisfying the Collet-Eckmann condition. We show that Collet-Eckmann parameters are Lebesgue density points of the complement of the Mandelbrot set (i.e. the connectedness locus).

Qintong Li, Leyang Cui, Xueliang Zhao, Lingpeng Kong

Large language models (LLMs) have achieved impressive performance across various mathematical reasoning benchmarks. However, there are increasing debates regarding whether these models truly understand and apply mathematical knowledge or merely rely on shortcuts for mathematical reasoning. One essential and frequently occurring evidence is that when the math

Kristin Lauter, Cathy Yuanchen Li, Krystal Maughan, Rachel Newton

Motivated by cryptographic applications, we investigate two machine learning approaches to modular multiplication: namely circular regression and a sequence-to-sequence transformer model. The limited success of both methods demonstrated in our results gives evidence for the hardness of tasks involving modular multiplication upon which cryptosystems are based

Gen Yue, Longye Wang, Tian Lan

We review the condensation completion of a modular tensor category $\mathcal{C}$, which yields a fusion 2-category $\Sigma\mathcal{C}$ of separable algebras, bimodules over algebras and bimodule maps in $\mathcal{C}$. Physically, $\Sigma\mathcal{C}$ is the fusion 2-category of codimension-1 defects, codimension-2 defects and instantons in the $2+1$D topologi

Aditya V. Jonnalagadda, Hashim A. Hashim

This research addresses the pressing challenge of enhancing processing times and detection capabilities in Unmanned Aerial Vehicle (UAV)/drone imagery for global wildfire detection, despite limited datasets. Proposing a Segmented Neural Network (SegNet) selection approach, we focus on reducing feature maps to boost both time resolution and accuracy significa

Zewei Xiong, Meng-Ru Wu, Manu George, Chun-Yu Lin

Fast flavor conversions (FFCs) of neutrinos, which can occur in core-collapse supernovae (CCSNe), are multiangle effects. They depend on the angular distribution of the neutrino's electron lepton number (ELN). In this work, we present a comprehensive study of the FFCs by solving the multienergy and multiangle quantum kinetic equations with an extended set of

Haicheng Liao, Yongkang Li, Zhenning Li, Chengyue Wang

In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency. Incorporating human decision-making insights enables AVs to more effectively anticipate the potential actions of other vehicles, significantly improving prediction accuracy and responsiven

Vivek Singh, Shailza Sharma, Fabio Cuzzolin

The complexity of scene parsing grows with the number of object and scene classes, which is higher in unrestricted open scenes. The biggest challenge is to model the spatial relation between scene elements while succeeding in identifying objects at smaller scales. This paper presents a novel feature-boosting network that gathers spatial context from multiple

Lawrence Yunliang Chen, Kush Hari, Karthik Dharmarajan, Chenfeng Xu

The ability to reuse collected data and transfer trained policies between robots could alleviate the burden of additional data collection and training. While existing approaches such as pretraining plus finetuning and co-training show promise, they do not generalize to robots unseen in training. Focusing on common robot arms with similar workspaces and 2-jaw

Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang

How to better evaluate the capabilities of Large Language Models (LLMs) is the focal point and hot topic in current LLMs research. Previous work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer the latest dynamic questions well. To promote the improvement of Chinese LLMs' ability to answer dynamic qu

Lewis Wright, Conor Mc Keever, Jeremy T. First, Rory Johnston

We design and implement quantum circuits for the simulation of the one-dimensional wave equation on the Quantinuum H1-1 quantum computer. The circuit depth of our approach scales as $O(n^{2})$ for $n$ qubits representing the solution on $2^{n}$ grid points, and leads to infidelities of $O(2^{-4n} t^{2})$ for simulation time $t$ assuming smooth initial condit

Ashish Moharana, Yael Kapon, Fabian Kammerbauer, David Anthofer

The observation of spin-dependent transmission of electrons through chiral molecules has led to the discovery of chiral-induced spin selectivity (CISS). The remarkably high efficiency of the spin polarizing effect has recently gained significant interest due to the high potential for novel sustainable hybrid chiral molecule magnetic applications. However, th

Jialiang Gao, Fons van der Laan, Joanna A. Zielinska, Andrei Militaru

Macroscopic rotors are interesting model systems to test quantum theory and for quantum sensing. A promising approach for bringing these systems to the quantum regime is to combine sensitive detection with feedback cooling to reduce the thermal occupation of the mechanics. Here, we implement a backward-scattering scheme to efficiently detect all three librat

Sikun Yang, Heinz Koeppl

The edge partition model (EPM) is a generative model for extracting an overlapping community structure from static graph-structured data. In the EPM, the gamma process (GaP) prior is adopted to infer the appropriate number of latent communities, and each vertex is endowed with a gamma distributed positive memberships vector. Despite having many attractive pr

Danh-Phuong Nguyen, Geva Arwas, Cristiano Ciuti

We investigate many-body topological and transport properties of a one-dimensional Su-Schrieffer-Heeger (SSH) topological chain coupled to the quantum field of a cavity mode. The quantum conductance is determined via Green's function formalism in terms of the light-matter eigenstates calculated via exact diagonalization for a finite number of electrons. We s

Yuan-Lin Lyu, Qu-Zhi Li, Zhiguang Xiao, Han-Qing Zheng

Roy-equation analyses on lattice data of $\pi\pi$ scattering phase shifts at $m_\pi=391$MeV reveals that the lowest $f_0$ meson becomes a bound state under this condition. In addition, there is a pair of complex poles below threshold generated by crossing symmetry [X.-H. Cao et al., Phys. Rev. D 108, 034009 (2023)]. We use the $N/D$ method to partially recov

Yuan Qiu, Nolan Bridges, Peng Chen

The deep operator networks (DeepONet), a class of neural operators that learn mappings between function spaces, have recently been developed as surrogate models for parametric partial differential equations (PDEs). In this work we propose a derivative-enhanced deep operator network (DE-DeepONet), which leverages derivative information to enhance the solution

Hamid Reza Naeij

Random and uncontrollable noises from the environment during the design and measurement of superconducting qubits lead to limitations in qubit coherence time and gate fidelity, which is a major challenge in the current state of the art for superconducting quantum computing. To advance superconducting qubits technologies it is essential to understand and miti

Ali Arslan, Giovanni Fantuzzi, John Craske, Andrew Wynn

We consider an internally heated fluid between parallel plates with fixed thermal fluxes. For a large class of heat sources that vary in the direction of gravity, we prove that $\langle\delta T \rangle_h \geq \sigma R^{-1/3} - \mu$, where $\langle\delta T \rangle_h$ is the average temperature difference between the bottom and top plates, $R$ is a `flux' Rayl

Niklas Rasch, Aleksandr N. Mikheev, Thomas Gasenzer

Wilson's renormalization-group approach to the weakly-interacting single-component Bose gas is discussed within the symmetry-broken, condensate phase. Extending upon the work by Bijlsma and Stoof [Phys. Rev. A 54, 5085 (1996), see http://doi.org/10.1103/PhysRevA.54.5085 ], wave-function renormalization of the temporal derivative contributions to the effectiv

Zakary Buras-Stubbs, Ilídio Lopes

This research studies the intricate interplay between dark and baryonic matter within hybrid neutron stars enriched by anisotropic bosonic dark matter halos. Our modelling, guided by the equation of state with a free parameter, reveals diverse mass-radius correlations for these astronomical objects. A pivotal result is the influence of dark matter characteri

A. de la Torre, D. M. Kennes, E. Malic, S. Kar

In this short review, we provide an overview of recent progress in deploying advanced characterization techniques to understand the effects of local inhomogeneities in moir\'e heterostructures over multiple length scales. Particular emphasis is placed on correlating the impact of twist angle misalignment, nano-scale disorder, and atomic relaxation on the moi

Felipe Dilho Alves

Measurements have historically presented a problem for the consistent description of quantum theories, be it in non-relativistic quantum mechanics or in quantum field theory. Drawing on a recent surge of interest in the description of measurements in Algebraic Quantum Field theory, it was decided that this dissertation would be focused on trying to close the

Shun-Cai Zhao

A theoretical investigation is carried out into the effect of spontaneously generated coherence(SGC) on the left-handedness in a four-level Y-type atomic system with two highest nearly degenerate lying levels. It is found, with the spontaneously generated coherence intensity enhancing, the atomic system gradually displays left-handedness with simultaneous ne

Sri Yash Tadimalla, Mary Lou Maher

AI-empowered technologies' impact on the world is undeniable, reshaping industries, revolutionizing how humans interact with technology, transforming educational paradigms, and redefining social codes. However, this rapid growth is accompanied by two notable challenges: a lack of diversity within the AI field and a widening AI divide. In this context, This p

Abdelamin Laouar, Isma Bouchemakh, Eric Sopena

In 2001, D. Erwin \cite{Erw01} introduced in his Ph.D. dissertation the notion of broadcast independence in unoriented graphs. Since then, some results but not many, are published on this notion, including research work on the broadcast independence number of unoriented circulant graphs \cite{LBS23}. In this paper, we are focused in the same parameter but of

Ainhoa Genua Cerviño, Naroa Coretti Sanchez, Elaine Liu Wang, Arnaud Grignard

In recent years, the rapid growth of on-demand deliveries, especially in food deliveries, has spurred the exploration of innovative mobility solutions. In this context, lightweight autonomous vehicles have emerged as a potential alternative. However, their fleet-level behavior remains largely unexplored. To address this gap, we have developed an agent-based

Julien Ferry, Ricardo Fukasawa, Timothée Pascal, Thibaut Vidal

We introduce an optimization-based reconstruction attack capable of completely or near-completely reconstructing a dataset utilized for training a random forest. Notably, our approach relies solely on information readily available in commonly used libraries such as scikit-learn. To achieve this, we formulate the reconstruction problem as a combinatorial prob

Feng Lu, Xiangyuan Lan, Lijun Zhang, Dongmei Jiang

Over the past decade, most methods in visual place recognition (VPR) have used neural networks to produce feature representations. These networks typically produce a global representation of a place image using only this image itself and neglect the cross-image variations (e.g. viewpoint and illumination), which limits their robustness in challenging scenes.

Joanna Majsak, Daniel McNulty, Michał Oszmaniec

We propose a simple scheme to estimate fermionic observables and Hamiltonians relevant in quantum chemistry and correlated fermionic systems. Our approach is based on implementing a measurement that jointly measures noisy versions of any product of two or four Majorana operators in an $N$ mode fermionic system. To realize our measurement we use: (i) a random

Shing Chan, Hang Yuan, Catherine Tong, Aidan Acquah

Existing activity tracker datasets for human activity recognition are typically obtained by having participants perform predefined activities in an enclosed environment under supervision. This results in small datasets with a limited number of activities and heterogeneity, lacking the mixed and nuanced movements normally found in free-living scenarios. As su

Jan-Paul Lerch

We discuss a class of linear representations of the product poset of totally ordered sets $P= T_1 \times \cdots \times T_n$ which decompose into interval representations for block intervals. These can be characterised in terms of a homological property which is called middle exactness.

Arindam Majee

In the ever evolving landscape of deep learning, unlocking the potential of cutting-edge models demands computational resources that surpass the capabilities of individual machines. Enter the NVIDIA DeepOps Slurm cluster, a meticulously orchestrated symphony of high-performance nodes, each equipped with powerful GPUs and meticulously managed by the efficient

Johannes Aberl, Enrique Prado Navarrete, Merve Karaman, Diego Haya Enriquez

Silicon-based color-centers (SiCCs) have recently emerged as quantum-light sources that can be combined with telecom-range Si Photonics platforms. Unfortunately, using current SiCC fabrication, deterministic control over the vertical emitter position is impossible due to ion-implantation's stochastic nature. To overcome this bottleneck towards high-yield int

Pratik Gajane, Sean Newman, Mykola Pechenizkiy, John D. Piette

Chronic pain significantly diminishes the quality of life for millions worldwide. While psychoeducation and therapy can improve pain outcomes, many individuals experiencing pain lack access to evidence-based treatments or fail to complete the necessary number of sessions to achieve benefit. Reinforcement learning (RL) shows potential in tailoring personalize

Jeremy Guntoro, Thomas Ouldridge

Sequence-directed assembly processes - such as protein folding - allow the assembly of a large number of structures with high accuracy from only a small handful of fundamental building blocks. We aim to explore how efficiently sequence information can be used to direct assembly by studying variants of the temperature-1 abstract tile assembly model (aTAM). We

Goran Senjanović, Michael Zantedeschi

It is well known that the minimal renormalizable $SU(5)$ grand unified theory is ruled out: it predicts same masses of down quarks and charged leptons, the gauge couplings do not unify and neutrinos are massless. We show here that all this can be cured simultaneously by the addition of higher-dimensional effective operators. However, the theory lives on the

Yuto Nakashima, Dominik Köppl, Mitsuru Funakoshi, Shunsuke Inenaga

We investigate the compression sensitivity [Akagi et al., 2023] of lex-parse [Navarro et al., 2021] for two operations: (1) single character edit and (2) modification of the alphabet ordering, and give tight upper and lower bounds for both operations. For both lower bounds, we use the family of Fibonacci words. For the bounds on edit operations, our analysis

Fernando Guedan Pecker, Cristian Ramirez Atencia

This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously, a novel approach not previously explored. The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airpo

Ajjath A H, Hua-Sheng Shao, Lukas Simon

We extend the local infrared-divergence subtraction formalism, originally proposed by Frixione, Kunszt and Signer (FKS), to calculate short-distance (differential) cross section for any inclusive process involving a quarkonium particle in non-relativistic QCD (NRQCD) factorisation at next-to-leading order (NLO) accuracy in the strong coupling constant $\alph

Yateng Wang, Bianca Baldassarri, Jiahong Shen, Jiangang He

Perovskite oxides have been extensively studied for their wide range of compositions and structures, as well as their valuable properties for various applications. Expanding from single perovskite ABO$_3$ to double perovskite $A_2BB^{\prime}$O$_6$ significantly enhances the ability to tailor specific physical and chemical properties. However, the vast number

Rafael Josip Penić, Tin Vlašić, Roland G. Huber, Yue Wan

While RNA has recently been recognized as an interesting small-molecule drug target, many challenges remain to be addressed before we take full advantage of it. This emphasizes the necessity to improve our understanding of its structures and functions. Over the years, sequencing technologies have produced an enormous amount of unlabeled RNA data, which hides

Shun-Cai Zhao, Zheng-Dong Liu, Qi-Xuan Wu

This paper attempts a probe into negative refraction without absorption by means of an incoherent pump field and a strong coherent field coupling the dense four-level atomic system.With the application of the incoherent pump field to manipulate the populations in atomic levels and the variable strong coherent field to create quantum coherence, the constraint

Marouane Assal, Setsuro fujiie, Kenta Higuchi

We consider a 1D $2\times 2$ matrix-valued operator \eqref{System0} with two semiclassical Schr\"odinger operators on the diagonal entries and small interactions on the off-diagonal ones. When the two potentials cross at a turning point with contact order $n$, the corresponding two classical trajectories at the crossing level intersect at one point in the ph

Stephan Raaijmakers, Roos Bakker, Anita Cremers, Roy de Kleijn

Conversational AI systems that rely on Large Language Models, like Transformers, have difficulty interweaving external data (like facts) with the language they generate. Vanilla Transformer architectures are not designed for answering factual questions with high accuracy. This paper investigates a possible route for addressing this problem. We propose to ext

Donald C Boone

This computational research study will analyze the multi-physics of lithium ion insertion into a silicon nanowire in an attempt to explain the electrochemical kinetics at the nanoscale and quantum level. The electron coherent states and a quantum field version of photon density waves will be the joining theories that will explain the electron-photon interact

MicroBooNE collaboration, P. Abratenko, O. Alterkait, D. Andrade Aldana

A detailed understanding of inclusive muon neutrino charged-current interactions on argon is crucial to the study of neutrino oscillations in current and future experiments using liquid argon time projection chambers. To that end, we report a comprehensive set of differential cross section measurements for this channel that simultaneously probe the leptonic

Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan

Super-resolution (SR) is an ill-posed inverse problem, where the size of the set of feasible solutions that are consistent with a given low-resolution image is very large. Many algorithms have been proposed to find a "good" solution among the feasible solutions that strike a balance between fidelity and perceptual quality. Unfortunately, all known methods ge

Matteo Giordano

We consider the statistical linear inverse problem of making inference on an unknown source function in an elliptic partial differential equation from noisy observations of its solution. We employ nonparametric Bayesian procedures based on Gaussian priors, leading to convenient conjugate formulae for posterior inference. We review recent results providing th

Lei Niu, Xizhuang Xie

In this paper, we are concerned with the permanence of a Lotka-Volterra model of three competing species with seasonal succession. Based on the existence of a carrying simplex, that is a globally attracting hypersurface of codimension one, we provide a complete classification of the permanence and impermanence in terms of inequalities on the parameters of th

Ather Gattami

In this paper, we consider reinforcement learning of nonlinear systems with continuous state and action spaces. We present an episodic learning algorithm, where we for each episode use convex optimization to find a two-layer neural network approximation of the optimal $Q$-function. The convex optimization approach guarantees that the weights calculated at ea

Giusy Monzillo, Tim Penttila, Alessandro Siciliano

We provide classification results for translation generalized quadrangles of order less or equal to $64$, and hence, for all incidence geometries related to them. The results consist of the classification of all pseudo-ovals in $PG(3n-1,2)$, for $n=3,4$, and that of the pseudo-ovals in $PG(3n-1,q)$, for $n=5,6$, such that one of the associated projective pla

Myeong-Hwan Mun, Eunja Ha, H. Sagawa, Gianluca Colò

We investigate the symmetry energy in relation with the two-proton and two-neutron separation energies using different nuclear mass data. For this aim, we exploit the deformed relativistic Hartree-Bogoliubov theory in the continuum (DRHBc), FRDM2012 and AME2020 data. First, we study the two-proton and two-neutron separation energies in Pb and Ca isotopes by

Ger Koole, Siqiao Li, Sihan Ding

We analyze call center data on properties such as agent heterogeneity, customer patience and breaks. Then we compare simulation models that are different in the ways these properties are modeled. We classify them according to the extend in which they approach the actual service level and average waiting times. We obtain a theoretical understanding on how to

Biao-Feng Hou, Xin-Qiang Li, Meng Shen, Ya-Dong Yang

Recently, the Belle II collaboration announced the first measurement of $\mathcal B(B^+\to K^+\nu\bar\nu)$, which is found to be about $2.7\sigma$ higher than the SM prediction. We decipher the data with two new physics scenarios: the underlying $b\to s \nu\bar\nu$ transition is, besides the SM contribution, further affected by heavy new mediators that are m

Riccardo Tancredi, Antonio Feltrin, Giosuè Sardo Infirri, Simone Toso

Time series of conformational dynamics in proteins are usually evaluated with hidden Markov models (HMMs). This approach works well if the number of states and their connectivity is known. However, for the multi-domain protein Hsp90, a standard HMM analysis with optimization of the BIC (Bayesian information criterion) cannot explain long-lived states well. T

Tian Yang, Xi Chen, Yan-Kun Zhang, Xu-Jia Ouyang

An updated catalog consisting of 1092 6.7-GHz methanol maser sources was reported in this work. Additionally, the NH3 (1, 1), NH3 (2, 2), and NH3 (3, 3) transitions were observed towards 214 star forming regions using the Shanghai Tianma radio telescope (TMRT) in order to examine the differences in physical environments, such as excitation temperature and co

Haoyang Pei, Timothy M. Shepherd, Yao Wang, Fang Liu

Purpose: Echo modulation curve (EMC) modeling can provide accurate and reproducible quantification of T2 relaxation times. The standard EMC-T2 mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T2 mapping, called DeepEMC-T2 mapping, to efficiently estim

Guilherme Lamartine de Mello, Marcelo Finger, and Felipe Serras, Miguel de Mello Carpi

In this paper we present PeLLE, a family of large language models based on the RoBERTa architecture, for Brazilian Portuguese, trained on curated, open data from the Carolina corpus. Aiming at reproducible results, we describe details of the pretraining of the models. We also evaluate PeLLE models against a set of existing multilingual and PT-BR refined pret

Aurélien Alfonsi, Guillaume Szulda

We consider one-dimensional stochastic Volterra equations with jumps for which we establish conditions upon the convolution kernel and coefficients for the strong existence and pathwise uniqueness of a non-negative c\`adl\`ag solution. By using the approach recently developed in arXiv:2302.07758, we show the strong existence by using a nonnegative approximat

Yuanqi Liu, Tao An, Shaoguang Guo, Yingkang Zhang

Aims. The X-ray luminous and radio-loud AGN SRGE J170245.3+130104 discovered at z $\sim$ 5.5 provides unique chances to probe the SMBH growth and evolution with powerful jets in the early Universe. Methods. We present 1.35 - 5.1 GHz Very Long Baseline Array (VLBA) results on the radio continuum emission and spectrum analysis for this quasar in a low flux den

Junaid Majeed Bhat, Jaš Bensa, Marko Žnidarič

We discuss dynamics obtained by increasing powers of non-normal matrices that are roots of the identity, and therefore have all eigenvalues on the unit circle. Naively, one would expect that the expectation value of such powers cannot grow as one increases the power. We demonstrate that, rather counterintuitively, a completely opposite behavior is possible.

Yong Yang, Changjiang Li, Qingming Li, Oubo Ma

Recently, large language models (LLMs) have garnered widespread attention for their exceptional capabilities. Prompts are central to the functionality and performance of LLMs, making them highly valuable assets. The increasing reliance on high-quality prompts has driven significant growth in prompt services. However, this growth also expands the potential fo

Márton Hajdu, Laura Kovács, Michael Rawson

Rewriting techniques based on reduction orderings generate "just enough" consequences to retain first-order completeness. This is ideal for superposition-based first-order theorem proving, but for at least one approach to inductive reasoning we show that we are missing crucial consequences. We therefore extend the superposition calculus with rewriting-based

Carlo Panu, Fabio Taddei, Marco Polini, Amir Yacoby

Separating heat from charge in a material is an extremely challenging task since they are transported by the very same carriers, i.e. electrons or holes. In this Letter we show that such separation can reach 100% efficiency in a hybrid superconducting quantum Hall setup, provided that the quantum Hall system is tuned to integer filling factor. We present mic

Kennard Yanting Chan, Fayao Liu, Guosheng Lin, Chuan Sheng Foo

Pixel-aligned implicit models, such as PIFu, PIFuHD, and ICON, are used for single-view clothed human reconstruction. These models need to be trained using a sampling training scheme. Existing sampling training schemes either fail to capture thin surfaces (e.g. ears, fingers) or cause noisy artefacts in reconstructed meshes. To address these problems, we int

Gerrit Felsch, Viacheslav Slesarenko

Generative machine learning models have shown notable success in identifying architectures for metamaterials - materials whose behavior is determined primarily by their internal organization - that match specific target properties. By examining kirigami metamaterials, in which dependencies between cuts yield complex design restrictions, we demonstrate that t

Tiroshan Madushanka, Ryutaro Ichise

Knowledge Graph Representation Learning (KGRL), or Knowledge Graph Embedding (KGE), is essential for AI applications such as knowledge construction and information retrieval. These models encode entities and relations into lower-dimensional vectors, supporting tasks like link prediction and recommendation systems. Training KGE models relies on both positive

Wenxin He, Manasvi Parikh, Westley Weimer, Madeline Endres

Anecdotal evidence of cannabis use by professional programmers abounds. Recent studies have found that some professionals regularly use cannabis while programming even for work-related tasks. However, accounts of the impacts of cannabis on programming vary widely and are often contradictory. For example, some programmers claim that it impairs their ability t

Jie Mei, Rui Wen, Shijun Mao, Mei Huang

In the framework of Nambu--Jona-Lasinio model beyond mean field approximation, the effects of pion fluctuations on (inverse) magnetic catalysis and magnetic susceptibility are studied. The negative magnetic susceptibility at low temperature is observed when contributions from both neutral and charged pions are taken into account. In weak field approximation,

Yuta Kuroda, Takeshi Kawasaki, Kunimasa Miyazaki

We numerically study two-dimensional athermal chiral active particles at high densities. The particles in this system perform the circular motion with frequency $\Omega$. We show that the system crystallizes at high densities even in two dimensions, accompanied by the true long-range translational order. This is due to the anomalous suppression of displaceme

Ruo Li, Weiming Li, Shengtong Liang, Yuehan Shao

We present an asymptotic-preserving (AP) numerical method for solving the three-temperature radiative transfer model, which holds significant importance in inertial confinement fusion. A carefully designedsplitting method is developed that can provide a general framework of extending AP schemes for the gray radiative transport equation to the more complex th

Gabriel Escrig, Roberto Campos, Hong Qi, M. A. Martin-Delgado

Advancements in gravitational-wave interferometers, particularly the next generation, are poised to profoundly impact gravitational wave astronomy and multimessenger astrophysics. A hybrid quantum algorithm is proposed to carry out quantum inference of parameters from compact binary coalescences detected in gravitational-wave interferometers. It performs qua

Fan Bai, Qianyu Chen, Yizhuo Xu

Heterogeneity of population is a key factor in modeling the transmission of disease among the population and has huge impact on the outcome of the transmission. In order to investigate the decision making process in the heterogeneous mixing population regarding whether to be vaccinated or not, we propose the modeling framework which includes the epidemic mod

Xiaolong Chen, Yifan Song, Jing Tang

Link recommendation systems in online social networks (OSNs), such as Facebook's ``People You May Know'', Twitter's ``Who to Follow'', and Instagram's ``Suggested Accounts'', facilitate the formation of new connections among users. This paper addresses the challenge of link recommendation for the purpose of social influence maximization. In particular, given

Yike Li, Lu Yua, Fuhui Zhou, Qihui Wu

Automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driven AMC schemes have achieved high accuracy. However, most of these schemes focus on generating additional prior knowledge or features of bli

Shuncai Zhao, Zhengdong Liu, Qixuan Wu

In this paper,we have investigated three external fields interacting with the four-level atomic system described by the density-matrix approach.The atomic system exhibits left-handedness with zero absorption as well as large negative refractive index.Varying the parameters of the three external fields,the properties of zero absorption,large negative refracti

Vrinda Garg, Rejoy Mathew, Riyan Ibrahim, Kulveer Singh

Polymer translocation in crowded environments is a ubiquitous phenomenon in biological systems. We studied polymer translocation through a pore in free, one-sided (asymmetric), and two-sided (symmetric) crowded environments. Extensive Langevin dynamics simulation is employed to model the dynamics of the flexible polymer and crowding particles. We studied how

Sarah Müller, Lisa M. Koch, Hendrik P. A. Lensch, Philipp Berens

Retinal fundus images play a crucial role in the early detection of eye diseases. However, the impact of technical factors on these images can pose challenges for reliable AI applications in ophthalmology. For example, large fundus cohorts are often confounded by factors like camera type, bearing the risk of learning shortcuts rather than the causal relation

Zhe Feng, Christopher Liaw, Zixin Zhou

In this work, we investigate the online learning problem of revenue maximization in ad auctions, where the seller needs to learn the click-through rates (CTRs) of each ad candidate and charge the price of the winner through a pay-per-click manner. We focus on two models of the advertisers' strategic behaviors. First, we assume that the advertiser is complete

A. F. Valente, R. Almeida, R. Dilão

We derive the main properties of adaptive Hagen-Poiseuille flows in elastic microchannel networks similar to biological veins found in organisms. We demonstrate that adaptive Hagen-Poiseuille flows effectively simulate key features of \textit{Physarum polycephalum} networks, replicating physiological out-of-equilibrium phenomena such as peristalsis and shutt

Seyed Parsa Neshaei, Richard Lee Davis, Adam Hazimeh, Bojan Lazarevski

Recent work exploring the capabilities of pre-trained large language models (LLMs) has demonstrated their ability to act as general pattern machines by completing complex token sequences representing a wide array of tasks, including time-series prediction and robot control. This paper investigates whether the pattern recognition and sequence modeling capabil

Jude Haris, Nicolas Bohm Agostini, Antonino Tumeo, David Kaeli

As custom hardware accelerators become more prevalent, it becomes increasingly important to automatically generate efficient host-driver code that can fully leverage the capabilities of these accelerators. This approach saves time and reduces the likelihood of errors that can occur during manual implementation. AXI4MLIR extends the MLIR compiler framework to

Alexander J. Barrios, Maila Brucal-Hallare, Alyson Deines, Piper Harris

Let $E_{1}$ and $E_{2}$ be elliptic curves defined over a number field $K$. We say that $E_{1}$ and $E_{2}$ are discriminant ideal twins if they are not $K$-isomorphic and have the same minimal discriminant ideal and conductor. Such curves are said to be discriminant twins if, for each prime $\mathfrak{p}$ of $K$, there are $\mathfrak{p}$-minimal models for

Antoine Gloria, Siguang Qi

The quantitative analysis of stochastic homogenization problems has been a very active field in the last fifteen years. Whereas the first results were motivated by applied questions (namely, the numerical approximation of homogenized coefficients), the more recent achievements in the field are much more analytically-driven and focus on the subtle interplay b

Gavin M. Brown, Luke T. Peterson, Damennick B. Henry, Daniel J. Scheeres

The Hill Restricted 4-Body Problem (HR4BP) is a coherent time-periodic model that can be used to represent motion in the Sun-Earth-Moon (SEM) system. Periodic orbits were computed in this model to better understand the periodic orbit family structures that exist in these types of systems. First, periodic orbits in the Circular Restricted 3-Body Problem (CR3B

Hui-Chun Zhang, Xi-Ping Zhu

We establish the boundary regularity of harmonic maps from $RCD(K, N)$ metric measure spaces into $CAT(0)$ metric spaces.

Wentao Shi, Chenxu Wang, Fuli Feng, Yang Zhang

Optimization metrics are crucial for building recommendation systems at scale. However, an effective and efficient metric for practical use remains elusive. While Top-K ranking metrics are the gold standard for optimization, they suffer from significant computational overhead. Alternatively, the more efficient accuracy and AUC metrics often fall short of cap

Priya Mishra, Rudra Majhi, Sambit Kumar Pusty, Monojit Ghosh

In this paper we have studied the sensitivity of the future long-baseline neutrino experiments P2SO and T2HKK to the long-range force (LRF). In the context of these two experiments, our aim is to study: (i) the capability to put bounds on the LRF parameters, (ii) effect of LRF in the measurement of standard oscillation parameters and (iii) capability to cons

Ziyad Oulhaj, Yoshiyuki Ishii, Kento Ohga, Kimihiro Yamazaki

Acquiring plausible pathways on high-dimensional structural distributions is beneficial in several domains. For example, in the drug discovery field, a protein conformational pathway, i.e. a highly probable sequence of protein structural changes, is useful to analyze interactions between the protein and the ligands, helping to create new drugs. Recently, a s

Elena Hoster, Christian Stump

The paper concerns the coarse flag Hilbert-Poincar\'e series of Maglione-Voll in the case of the braid arrangement associated to the symmetric group. We explicitly construct a companion statistic $\operatorname{ino} : \mathfrak{S}_{n+1} \times \operatorname{Sym}(n) \rightarrow \mathbb{N}$ for the descent statistic on $\operatorname{Sym}(n)$ using reverse $(P

Tamar Friedmann, Phil Hanlon, Michelle L. Wachs

We continue our study, initiated in our prior work with Richard Stanley, of the representation of the symmetric group on the multilinear component of an $n$-ary generalization of the free Lie algebra known as the free Filippov $n$-algebra with $k$ brackets. Our ultimate aim is to determine the multiplicities of the irreducible representations in this represe

Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we

Barbara Pascal, Rémi Bardenet

A finite-energy signal is represented by a square-integrable, complex-valued function $t\mapsto s(t)$ of a real variable $t$, interpreted as time. Similarly, a noisy signal is represented by a random process. Time-frequency analysis, a subfield of signal processing, amounts to describing the temporal evolution of the frequency content of a signal. Loosely sp

Vittorio Cortellessa, J. Andres Diaz-Pace, Daniele Di Pompeo, Michele Tucci

Several approaches have recently used automated techniques to generate architecture design alternatives by means of optimization techniques. These approaches aim at improving an initial architecture with respect to quality aspects, such as performance, reliability, or maintainability. In this context, each optimization experiment usually produces a different

Mihai Masala, Traian Rebedea, Horia Velicu

In recent years,the entire field of Natural Language Processing (NLP) has enjoyed amazing novel results achieving almost human-like performance on a variety of tasks. Legal NLP domain has also been part of this process, as it has seen an impressive growth. However, general-purpose models are not readily applicable for legal domain. Due to the nature of the d

Adrian Beker

We construct skew corner-free subsets of $[n]^2$ of size $n^2\exp(-O(\sqrt{\log n}))$, thereby improving on recent bounds of the form $\Omega(n^{5/4})$ obtained by Pohoata and Zakharov. In the other direction, we prove that any such set has size at most $O(n^2(\log n)^{-c})$ for some absolute constant $c > 0$. This improves on the previously best known upper

Sayar Das, Deepak Patil

A disturbance decoupling problem for a $n$-link chain pendulum on a cart is considered. A model of the cart developed in a coordinate-free framework and the linearized equations of this system are considered from [1]. It is shown that it is possible to design a suitable state feedback such that the angular position or velocity of the $n^{th}$-link can always