May 2024 arXiv papers — page 67

Marco Mascheroni, Antonio Perez-Calero Yzquierdo, Edita Kizinevic, Farrukh Aftab Khan

The former CMS Run 2 High Level Trigger (HLT) farm is one of the largest contributors to CMS compute resources, providing about 25k job slots for offline computing. This CPU farm was initially employed as an opportunistic resource, exploited during inter-fill periods, in the LHC Run 2. Since then, it has become a nearly transparent extension of the CMS capac

Hauke Fischer, Christian Käding, Mario Pitschmann

The last few decades have provided abundant evidence for physics beyond the two standard models of particle physics and cosmology. As is now known, the by far largest part of our universe's matter/energy content lies in the `dark' and consists of dark energy and dark matter. Despite intensive efforts on the experimental as well as the theoretical side, the o

H. Gfrerer, J. V. Outrata

For the numerical solution of nonsmooth problems, sometimes it is not necessary that an exact subgradient/generalized Jacobian is at our disposal, but it suffices that a semismooth derivative, i.e., a mapping satisfying a certain semismoothness property, is available. In this paper we consider not only semismooth derivatives of single-valued mappings, but al

Zheming Yang, Yuanhao Yang, Chang Zhao, Qi Guo

With the rapid growth in the number of large language model (LLM) users, it is difficult for bandwidth-constrained cloud servers to simultaneously process massive LLM services in real-time. Recently, edge-cloud infrastructures have been used to improve the processing efficiency of large-scale LLM services. However, the diversity of task requirements and the

Ryan H. Allaire, Linda J. Cummings, Lou Kondic

Metal films and other geometries of nanoscale thickness, when exposed to laser irradiation, melt and evolve as fluids as long as their temperature is sufficiently high. This evolution often leads to pattern formation, which may be influenced strongly by material parameters that are temperature dependent. In addition, the laser heat absorption itself depends

Rebecca E. Garcia, Pamela E. Harris, Alex Moon, Aaron Ortiz

A defective $(m,n)$-parking function with defect $d$ is a parking function with $m$ cars attempting to park on a street with $n$ parking spots in which exactly $d$ cars fail to park. We establish a way to compute the defect of a defective $(m,n)$-parking function and show that the defect of a parking function is invariant under the action of $\mathfrak{S}_m$

D. S. M. Alencar, J. F. S. Neto, T. F. A. Alves, F. W. S. Lima

We discuss the short-time behavior of the majority vote dynamics on scale-free networks at the critical threshold. We introduce a heterogeneous mean-field theory on the critical short-time behavior of the majority-vote model on scale-free networks. In addition, we also compare the heterogeneous mean-field predictions with extensive Monte Carlo simulations of

Qijian Zhang, Junhui Hou, Wenping Wang, Ying He

Surface parameterization plays an essential role in numerous computer graphics and geometry processing applications. Traditional parameterization approaches are designed for high-quality meshes laboriously created by specialized 3D modelers, thus unable to meet the processing demand for the current explosion of ordinary 3D data. Moreover, their working mecha

Jingyi Chen, Ju-Seung Byun, Micha Elsner, Andrew Perrault

Recent advancements in generative models have sparked a significant interest within the machine learning community. Particularly, diffusion models have demonstrated remarkable capabilities in synthesizing images and speech. Studies such as those by Lee et al. (2023), Black et al. (2023), Wang et al. (2023), and Fan et al. (2024) illustrate that Reinforcement

Antonio Perez-Calero Yzquierdo, Marco Mascheroni, Edita Kizinevic, Farrukh Aftab Khan

The computing resource needs of LHC experiments are expected to continue growing significantly during the Run 3 and into the HL-LHC era. The landscape of available resources will also evolve, as High Performance Computing (HPC) and Cloud resources will provide a comparable, or even dominant, fraction of the total compute capacity. The future years present a

Kedar Karhadkar, Michael Murray, Guido Montúfar

Bounds on the smallest eigenvalue of the neural tangent kernel (NTK) are a key ingredient in the analysis of neural network optimization and memorization. However, existing results require distributional assumptions on the data and are limited to a high-dimensional setting, where the input dimension $d_0$ scales at least logarithmically in the number of samp

Takuya Hiraoka, Guanquan Wang, Takashi Onishi, Yoshimasa Tsuruoka

In reinforcement learning (RL) with experience replay, experiences stored in a replay buffer influence the RL agent's performance. Information about how these experiences influence the agent's performance is valuable for various purposes, such as identifying experiences that negatively influence underperforming agents. One method for estimating the influence

Guanghui Cheng, Wenjuan Hu, Ruitao Lin, Chen Wang

We propose a novel and robust online function-on-scalar regression technique via geometric median to learn associations between functional responses and scalar covariates based on massive or streaming datasets. The online estimation procedure, developed using the average stochastic gradient descent algorithm, offers an efficient and cost-effective method for

Junichi Haruna, Keito Shimizu, Masatoshi Yamada

It is known that the $U(2)$ Wess-Zumino-Witten model is dual to the free fermion theory in two dimensions via non-Abelian bosonization. While it is decomposed into the $SU(2)$ Wess-Zumino-Witten model and a free compact boson, the former is believed to be equivalent to the $O(3)$ nonlinear sigma model with the theta term at $\theta=\pi$. In this work, we ree

Laura Duarte, Pedro Neto

The featured dataset, the Event-based Dataset of Assembly Tasks (EDAT24), showcases a selection of manufacturing primitive tasks (idle, pick, place, and screw), which are basic actions performed by human operators in any manufacturing assembly. The data were captured using a DAVIS240C event camera, an asynchronous vision sensor that registers events when cha

Belle II Collaboration, I. Adachi, K. Adamczyk, L. Aggarwal

We present a measurement of the ratio $R_\mu = \mathcal{B}(\tau^-\to \mu^-\bar\nu_\mu\nu_\tau) / \mathcal{B}(\tau^-\to e^-\bar\nu_e\nu_\tau)$ of branching fractions $\mathcal{B}$ of the $\tau$ lepton decaying to muons or electrons using data collected with the Belle II detector at the SuperKEKB $e^+e^-$ collider. The sample has an integrated luminosity of $3

Ke Sun, Mingyu Kang, Hanggai Nuomin, George Schwartz

The spin-boson model, involving spins interacting with a bath of quantum harmonic oscillators, is a widely used representation of open quantum systems. Trapped ions present a natural platform for simulating the quantum dynamics of such models, thanks to the presence of both high quality internal qubit states and the motional modes of the ions that can simula

Indu Solomon, Aye Phyu Phyu Aung, Uttam Kumar, Senthilnath Jayavelu

Continual learning (CL) models are designed to learn new tasks arriving sequentially without re-training the network. However, real-world ML applications have very limited label information and these models suffer from catastrophic forgetting. To address these issues, we propose an unsupervised CL model with task experts called Unsupervised Task Expert Lifel

Yiyang Zhou, Zhiyuan Fan, Dongjie Cheng, Sihan Yang

Large Vision-Language Models (LVLMs) have made substantial progress by integrating pre-trained large language models (LLMs) and vision models through instruction tuning. Despite these advancements, LVLMs often exhibit the hallucination phenomenon, where generated text responses appear linguistically plausible but contradict the input image, indicating a misa

Xin Guan, Xiao Liu, Yan-Qing Ma, Wen-Hao Wu

In this article, we present the package {\tt Blade} as the first implementation of the block-triangular form improved Feynman integral reduction method. The block-triangular form has orders of magnitude fewer equations compared to the plain integration-by-parts system, allowing for strictly block-by-block solutions. This results in faster evaluations and red

Shu Wei, Yanjie Li, Lina Yu, Weijun Li

The quest for analytical solutions to differential equations has traditionally been constrained by the need for extensive mathematical expertise. Machine learning methods like genetic algorithms have shown promise in this domain, but are hindered by significant computational time and the complexity of their derived solutions. This paper introduces SSDE (Symb

Jiyang Zhang, Yu Liu, Pengyu Nie, Junyi Jessy Li

Many popular programming languages, including C#, Java, and Python, support exceptions. Exceptions are thrown during program execution if an unwanted event happens, e.g., a method is invoked with an illegal argument value. Software developers write exceptional behavior tests (EBTs) to check that their code detects unwanted events and throws appropriate excep

Hiroo Totsuji

Phase separations in strongly coupled fine particles in plasmas are discussed and two-component mixtures are simulated by molecular dynamics with the background plasma being treated as continuum. The system size of laboratory experiments is assumed and separations into phases with a common electron density of the background plasma are analyzed. Since the cha

Nicolas Oderbolz, Beatrix Marosvölgyi, Matthias Hafner

This paper examines the economic and security implications of Proof-of-Stake (POS) designs, providing a survey of POS design choices and their underlying economic principles in prominent POS-blockchains. The paper argues that POS-blockchains are essentially platforms that connect three groups of agents: users, validators, and investors. To meet the needs of

Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu

Time series forecasting is widely used in extensive applications, such as traffic planning and weather forecasting. However, real-world time series usually present intricate temporal variations, making forecasting extremely challenging. Going beyond the mainstream paradigms of plain decomposition and multiperiodicity analysis, we analyze temporal variations

Ryan M. L. McFadden, Tobias Junginger

Empirical surface treatments, such as low-temperature baking (LTB) in a gaseous atmosphere or in vacuum, are important for the surface preparation of Nb superconducting radio frequency (SRF) cavities. These treatments inhomogeneously dope the first $\sim$50 nm of Nb's subsurface and are expected to impart depth-dependent characteristics to its Meissner respo

Zachary Wojtowicz, Shrey Jain, Nicholas Vincent

We propose a framework for measuring attentional agency, which we define as a user's ability to allocate attention according to their own desires, goals, and intentions on digital platforms that use statistical learning to prioritize informational content. Such platforms extend people's limited powers of attention by extrapolating their preferences to large

Keke Li, Xinmin Yang

This paper discusses the convergence of the modified predictive method (MPM) proposed by Liang and stokes corresponding to high-resolution differential equations (HRDE) in bilinear games. First, we present the high-resolution differential equations (MPM-HRDE) corresponding to the MPM. Then, we discuss the uniqueness of the solution for MPM-HRDE in bilinear g

Loris Giulivi, Giacomo Boracchi

Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in understanding and generating content across various modalities, such as images and text. However, their interpretability remains a challenge, hindering their adoption in critical applications. This research proposes a novel approach to enhance the interpretability of MLLMs

Oliver Linton, Raghavendra Rau, Patrick Baert, Peter Bossaerts

This paper critically evaluates the HESA (Higher Education Statistics Agency) Data Report for the Employer Justified Retirement Age (EJRA) Review Group at the University of Cambridge (\cite{CambridgeHESA2024}), identifying significant methodological flaws and misinterpretations. Our analysis reveals issues such as unclear application of data filters, inconsi

Jan Heisig

We present the phenomenology of a simplified dark matter model within the dark minimal flavor violation framework, extending the standard model with a Majorana fermion flavor triplet and colored scalar mediator. We explore the allowed parameter space considering constraints from flavor observables, direct and indirect dark matter detection, and the relic den

Evgueni Doubtsov

We estimate the energy and Hausdorff dimensions of the Riesz products on the unit sphere of $\mathbb{C}^n$, $n\ge 2$. Also, we obtain similar results for the pluriharmonic measures on the torus.

Xuan-May Le, Ling Luo, Uwe Aickelin, Minh-Tuan Tran

Multivariate time series classification (MTSC) has attracted significant research attention due to its diverse real-world applications. Recently, exploiting transformers for MTSC has achieved state-of-the-art performance. However, existing methods focus on generic features, providing a comprehensive understanding of data, but they ignore class-specific featu

Diego Pazó

We study a paradigmatic random recurrent neural network introduced by Sompolinsky, Crisanti, and Sommers (SCS). In the infinite size limit, this system exhibits a direct transition from a homogeneous rest state to chaotic behavior, with the Lyapunov exponent gradually increasing from zero. We generalize the SCS model considering odd saturating nonlinear tran

Veeti Ahvonen, Damian Heiman, Antti Kuusisto, Carsten Lutz

In pioneering work from 2019, Barcel\'o and coauthors identified logics that precisely match the expressive power of constant iteration-depth graph neural networks (GNNs) relative to properties definable in first-order logic. In this article, we give exact logical characterizations of recurrent GNNs in two scenarios: (1) in the setting with floating-point nu

Luca Bergamaschi, Angeles Martinez, John Pearson, Andreas Potschka

In this paper, we describe and analyze the spectral properties of a symmetric positive definite inexact block preconditioner for a class of symmetric, double saddle-point linear systems. We develop a spectral analysis of the preconditioned matrix, showing that its eigenvalues can be described in terms of the roots of a cubic polynomial with real coefficients

Minjia Mao, Dongjun Wei, Zeyu Chen, Xiao Fang

Recent advancements in large language models (LLMs) have highlighted the risk of misusing them, raising the need for accurate detection of LLM-generated content. In response, a viable solution is to inject imperceptible identifiers into LLMs, known as watermarks. Our research extends the existing watermarking methods by proposing the novel Sampling One Then

Alban Joseph, Jayakrishnan M. P. Nair, Mawgan A. Smith, Rory Holland

Recently the field of cavity magnonics, a field focused on controlling the interaction between magnons and confined microwave photons within microwave resonators, has drawn significant attention as it offers a platform for enabling advancements in quantum- and spin-based technologies. Here, we introduce excitation vector fields, whose polarisation and profil

Ziqi Shi, Fan Lyu, Ye Liu, Fanhua Shang

Continual Test-Time Adaptation (CTTA) is an emerging and challenging task where a model trained in a source domain must adapt to continuously changing conditions during testing, without access to the original source data. CTTA is prone to error accumulation due to uncontrollable domain shifts, leading to blurred decision boundaries between categories. Existi

Mahsa Shamsabadi, Jennifer D'Souza

This demo will present the Research Assistant (RA) tool developed to assist with six main types of research tasks defined as standardized instruction templates, instantiated with user input, applied finally as prompts to well-known--for their sophisticated natural language processing abilities--AI tools, such as ChatGPT (https://chat.openai.com/) and Gemini

Adrian F. Amil, Ismael T. Freire, Paul F. M. J. Verschure

The hippocampus has been associated with both spatial cognition and episodic memory formation, but integrating these functions into a unified framework remains challenging. Here, we demonstrate that forming discrete memories of visual events in sparse autoencoder neurons can produce spatial tuning similar to hippocampal place cells. We then show that the res

Tom Fischer, Pascal Peter, Joachim Weickert, Eddy Ilg

Deep learning has revolutionized the field of computer vision by introducing large scale neural networks with millions of parameters. Training these networks requires massive datasets and leads to intransparent models that can fail to generalize. At the other extreme, models designed from partial differential equations (PDEs) embed specialized domain knowled

Shiqi Yang, Zhi Zhong, Mengjie Zhao, Shusuke Takahashi

In recent years, with the realistic generation results and a wide range of personalized applications, diffusion-based generative models gain huge attention in both visual and audio generation areas. Compared to the considerable advancements of text2image or text2audio generation, research in audio2visual or visual2audio generation has been relatively slow. T

Qingyuan Li, Ran Meng, Yiduo Li, Bo Zhang

We introduce Integer Scale, a novel post-training quantization scheme for large language models that effectively resolves the inference bottleneck in current fine-grained quantization approaches while maintaining similar accuracies. Integer Scale is a free lunch as it requires no extra calibration or fine-tuning which will otherwise incur additional costs. I

Ryuichi Kanoh, Mahito Sugiyama

Linear Mode Connectivity (LMC) refers to the phenomenon that performance remains consistent for linearly interpolated models in the parameter space. For independently optimized model pairs from different random initializations, achieving LMC is considered crucial for understanding the stable success of the non-convex optimization in modern machine learning m

Siyuan Shen, Tianjia Shao, Kun Zhou, Chenfanfu Jiang

We present a framework of elastic locomotion, which allows users to enliven an elastic body to produce interesting locomotion by prescribing its high-level kinematics. We formulate this problem as an inverse simulation problem and seek the optimal muscle activations to drive the body to complete the desired actions. We employ the interior-point method to mod

Yuni Susanti

We present data augmentation techniques for process extraction tasks in scientific publications. We cast the process extraction task as a sequence labeling task where we identify all the entities in a sentence and label them according to their process-specific roles. The proposed method attempts to create meaningful augmented sentences by utilizing (1) proce

Elisa Maria Alessi, Inmaculada Baldomá, Mar Giralt, Marcel Guardia

Motivated by the practical interest in the third-body perturbation as a natural cleaning mechanism for high-altitude Earth orbits, we investigate the dynamics stemming from the secular Hamiltonian associated with the lunar perturbation, assuming that the Moon lies on the ecliptic plane. The secular Hamiltonian defined in that way is characterized by two time

Michael Li

In this paper, we investigate the eigenvalues of the Laplacian matrix of the "graph of graphs", in which cubic graphs of order n are joined together using Whitehead moves. Our work follows recent results from arXiv:2303.13923 , which discovered a significant "bottleneck" in the graph of graphs. We found that their bottleneck implies an eigenvalue of order at

Xin Men, Mingyu Xu, Bingning Wang, Qingyu Zhang

Position embedding is a core component of current Large Language Models (LLMs). Rotary position embedding (RoPE), a technique that encodes the position information with a rotation matrix, has been the de facto choice for position embedding in many LLMs, such as the Llama series. RoPE has been further utilized to extend long context capability, which is rough

Muning Wen, Ziyu Wan, Weinan Zhang, Jun Wang

Language models as intelligent agents push the boundaries of sequential decision-making agents but struggle with limited knowledge of environmental dynamics and exponentially huge action space. Recent efforts like GLAM and TWOSOME manually constrain the action space to a restricted subset and employ reinforcement learning to align agents' knowledge with spec

Lennart Alexander Van der Goten, Jingyu Guo, Kevin Smith

The presence of motion artifacts in magnetic resonance imaging (MRI) scans poses a significant challenge, where even minor patient movements can lead to artifacts that may compromise the scan's utility.This paper introduces MAsked MOtion Correction (MAMOC), a novel method designed to address the issue of Retrospective Artifact Correction (RAC) in motion-affe

Andrew Parry, Sean MacAvaney, Debasis Ganguly

Large Language Models (LLMs) have significantly impacted many facets of natural language processing and information retrieval. Unlike previous encoder-based approaches, the enlarged context window of these generative models allows for ranking multiple documents at once, commonly called list-wise ranking. However, there are still limits to the number of docum

Hui-Ying Deng, Zhao-Yang Peng, Jia-Ming Chen, Yue Yin

There is no consensus yet on whether the precursor and the main burst of gamma-ray bursts (GRBs) have the same origin, and their jet composition is still unclear. In order to further investigate this issue, we systematically search 21 Fermi GRBs with both precursor and main burst for spectral analysis. We first perform Bayesian time-resolved spectral analysi

Tommaso Flaminio, Lluis Godo, Paula Menchón, Ricardo O. Rodriguez

The present paper is devoted to study the effect of connected and disconnected rotations of G\"odel algebras with operators grounded on directly indecomposable structures. The structures resulting from this construction we will present are nilpotent minimum (with or without negation fixpoint, depending on whether the rotation is connected or disconnected) wi

Patrick Emonts, Mengyao Hu, Albert Aloy, Jordi Tura

Bell nonlocality is the resource that enables device-independent quantum information processing tasks. It is revealed through the violation of so-called Bell inequalities, indicating that the observed correlations cannot be reproduced by any local hidden variable model. While well explored in few-body settings, the question of which Bell inequalities are bes

M. P. Adams, R. Bastardis, A. Michels, H. Kachkachi

In this work, we show that surface anisotropy in nanomagnets induces a nutational motion of their magnetization at various frequencies, the lowest of which can be described by the macrospin model whose dynamics is governed by an effective energy potential. We derive analytical expressions for the precession and nutation frequencies and amplitudes as function

E. Gabrielli, L. Marzola, K. Müürsepp

We propose a novel test to assess the unitarity of the Cabibbo-Kobayashi-Maskawa matrix, $V_{\rm CKM}$, at present and future collider experiments. Our strategy makes use of the $W^+W^-$ production cross section to directly probe the $V_{\rm CKM}^\dagger V_{\rm CKM}$ product, which regulates the high-energy behavior of the observable. The violation of unitar

Mariusz Wiśniewski, Loris Giulivi, Giacomo Boracchi

For more than a decade, deep learning models have been dominating in various 2D imaging tasks. Their application is now extending to 3D imaging, with 3D Convolutional Neural Networks (3D CNNs) being able to process LIDAR, MRI, and CT scans, with significant implications for fields such as autonomous driving and medical imaging. In these critical settings, ex

Jean-Michel Bismut, Shu Shen

The purpose of this paper is to prove that if $Y$ is a compact manifold, if $Z$ is an Anosov vector field on $Y$, and if $F$ is a flat vector bundle, there is a corresponding canonical nonzero section $\tau_{\nu}\left(i_{Z}\right)$ of the determinant line $\nu=\det H\left(Y,F\right)$. In families, this section is $C^{1}$ with respect to the canonical smooth

Yong Zhong, Min Zhao, Zebin You, Xiaofeng Yu

In this paper, we introduce PoseCrafter, a one-shot method for personalized video generation following the control of flexible poses. Built upon Stable Diffusion and ControlNet, we carefully design an inference process to produce high-quality videos without the corresponding ground-truth frames. First, we select an appropriate reference frame from the traini

Tomasz Kowalski, Katarzyna Słomczyńska

We give a new construction of free distributive p-algebras. Our construction relies on a detailed description of completely meet-irreducible congruences, so it is purely universal algebraic. It yields a normal form theorem for p-algebra terms, simpler proofs of several existing results, as well as a complete characterisation of structurally complete varietie

Rengan Xie, Wenting Zheng, Kai Huang, Yizheng Chen

Previous efforts have managed to generate production-ready 3D assets from text or images. However, these methods primarily employ NeRF or 3D Gaussian representations, which are not adept at producing smooth, high-quality geometries required by modern rendering pipelines. In this paper, we propose LDM, a novel feed-forward framework capable of generating high

James P. Pidgeon, George A. Sutherland, Matthew S. Proctor, Shuangqing Wang

The orange carotenoid protein (OCP) is the water-soluble mediator of non-photochemical quenching in cyanobacteria, a crucial photoprotective mechanism in response to excess illumination. OCP converts from a dark-adapted inactive state (OCPo) to an active quenching conformation (OCPr) under high-light conditions, resulting in a concomitant redshift in the abs

Shuaipeng Li, Penghao Zhao, Hailin Zhang, Xingwu Sun

In current deep learning tasks, Adam style optimizers such as Adam, Adagrad, RMSProp, Adafactor, and Lion have been widely used as alternatives to SGD style optimizers. These optimizers typically update model parameters using the sign of gradients, resulting in more stable convergence curves. The learning rate and the batch size are the most critical hyperpa

Domenic Rosati, Jan Wehner, Kai Williams, Łukasz Bartoszcze

Releasing open-source large language models (LLMs) presents a dual-use risk since bad actors can easily fine-tune these models for harmful purposes. Even without the open release of weights, weight stealing and fine-tuning APIs make closed models vulnerable to harmful fine-tuning attacks (HFAs). While safety measures like preventing jailbreaks and improving

Nicola Vassena, Florin Avram, Rim Adenane

Mathematical Epidemiology (ME) shares with Chemical Reaction Network Theory (CRNT) the basic mathematical structure of its dynamical systems. Despite this central similarity, methods from CRNT have been seldom applied to solving problems in ME. We explore here the applicability of CRNT methods to find bifurcations at endemic equilibria of ME models. We adapt

Moshe Babaioff, Uriel Feige

We consider the problem of fair allocation of indivisible items to agents that have arbitrary entitlements to the items. Every agent $i$ has a valuation function $v_i$ and an entitlement $b_i$, where entitlements sum up to~1. Which allocation should one choose in situations in which agents fail to agree on one acceptable fairness notion? We study this proble

Seta Rakotomandimby, Jean-Philippe Chancelier, Michel de Lara, Mathieu Blondel

Fenchel-Young losses are a family of convex loss functions, encompassing the squared, logistic and sparsemax losses, among others. Each Fenchel-Young loss is implicitly associated with a link function, for mapping model outputs to predictions. For instance, the logistic loss is associated with the soft argmax link function. Can we build new loss functions as

Christopher Rawles, Sarah Clinckemaillie, Yifan Chang, Jonathan Waltz

Autonomous agents that execute human tasks by controlling computers can enhance human productivity and application accessibility. However, progress in this field will be driven by realistic and reproducible benchmarks. We present AndroidWorld, a fully functional Android environment that provides reward signals for 116 programmatic tasks across 20 real-world

Dmitry Ammosov, W. T. Leung, Buzheng Shan, Jian Huang

In this paper, we present the derivation of a multicontinuum model for the coupled flow and transport equations by applying multicontinuum homogenization. We perform the multicontinuum expansion for both flow and transport solutions and formulate novel coupled constraint cell problems to capture the multiscale property, where oversampled regions are utilized

ALICE Collaboration

The total charm-quark production cross section per unit of rapidity $\mathrm{d}\sigma({\rm c\overline{c}})/\mathrm{d}y$, and the fragmentation fractions of charm quarks to different charm-hadron species $f(\mathrm{c}\rightarrow {\rm h_{c}})$, are measured for the first time in p$-$Pb collisions at $\sqrt{s_\mathrm{NN}} = 5.02$ TeV at midrapidity ($-0.96<y<0.

Boris Ryabko

Nowadays there are several classes of constrained codes intended for different applications. The following two large classes can be distinguished. The first class contains codes with local constraints; for example, the source data must be encoded by binary sequences containing no sub-words 00 and 111. The second class contains codes with global constraints;

Tianshi Xu, Lemeng Wu, Runsheng Wang, Meng Li

Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead. We observe transforming the DNN weights into circulant matrices converts general matrix-vector multiplications into HE-friendly 1-dimensional convolutions, drastically reducing the HE computation cost. Hence

Tanguy Vernet

In this thesis, we study counts of quiver representations over finite rings of truncated power series. We prove a plethystic formula relating counts of quiver representations over these rings and counts of jets on fibres of quiver moment maps. This solves a conjecture of Wyss and allows us to compute both counts on additional examples, using local zeta funct

Jeongmin Han

In this paper, we are concerned with game-theoretic interpretations to the following oblique derivative boundary value problem \begin{align*} \left\{ \begin{array}{ll} \Delta_{p}^{N}u=0 & \textrm{in $ \Omega$,}\\ \langle \beta , Du \rangle + \gamma u = \gamma G & \textrm{on $ \partial \Omega$,}\\ \end{array} \right. \end{align*} where $\Delta_{p}^{N}$ is the

Adibvafa Fallahpour, Mahshid Alinoori, Wenqian Ye, Xu Cao

Transformers have significantly advanced the modeling of Electronic Health Records (EHR), yet their deployment in real-world healthcare is limited by several key challenges. Firstly, the quadratic computational cost and insufficient context length of these models hinder hospitals' ability in processing the extensive medical histories typical in EHR data. Add

Sharfin Islam, Zhanpeng He, Matei Ciocarlie

Underactuated manipulators reduce the number of bulky motors, thereby enabling compact and mechanically robust designs. However, fewer actuators than joints means that the manipulator can only access a specific manifold within the joint space, which is particular to a given hardware configuration and can be low-dimensional and/or discontinuous. Determining a

Paz Hashash

This paper is concerned with entropy solutions of scalar conservation laws of the form $\partial_{t}u+\diver f=0$ in $\mathbb{R}^d\times(0,\infty)$. The flux $f=f(x,u)$ depends explicitly on the spatial variable $x$. Using an extension of Kruzkov's method, we establish the $L^1$-contraction property of entropy solutions under minimal regularity assumptions o

Yasuyuki Hatsuda, Hai Lin, Tadashi Okazaki

We study the giant gravitons in the $AdS_4$ bagpipe geometries involving end-of-the-world (ETW) brane constructed by a single $5$-brane and either two stacks or one stack of D3-branes in Type IIB string theory. From the exact formulae and giant graviton expansions of the half-indices for the half-BPS boundary conditions and interfaces in $\mathcal{N}=4$ supe

Loris Giulivi, Giacomo Boracchi

Advances in multi-modal embeddings, and in particular CLIP, have recently driven several breakthroughs in Computer Vision (CV). CLIP has shown impressive performance on a variety of tasks, yet, its inherently opaque architecture may hinder the application of models employing CLIP as backbone, especially in fields where trust and model explainability are impe

Cosetta Baroni, Giacomo Lamporesi, Matteo Zaccanti

After decades of improvements in cooling techniques of several atomic species and in finding methods for the achievement of stable quantum mixtures, the field is now ready for an extensive use of such a versatile experimental platform for the investigation of a variety of physical problems. Among them, relevant examples are the dynamics of impurities in a qu

Pratibha Raghupati Hegde, Oleksandr Kyriienko, Hermanni Heimonen, Panagiotis Tolias

There is much debate on whether quantum computing on current NISQ devices, consisting of noisy hundred qubits and requiring a non-negligible usage of classical computing as part of the algorithms, has utility and will ever offer advantages for scientific and industrial applications with respect to traditional computing. In this position paper, we argue that

M. Farahani, T. Kozák, A. D. Pajdarová, J. Čapek

The reverse discharge (RD) phenomenon in bipolar HiPIMS has been observed when a sufficiently long positive pulse is applied to the magnetron. Due to the magnetic field, electrons accumulated behind the magnetic trap are prevented from reaching the positive target. Consequently, a space charge double layer (DL) is formed between the positive target and the p

Changwei Song, Qing Zhao, Jianqiang Li, Xin Yue

Acute intracerebral hemorrhage is a life-threatening condition that demands immediate medical intervention. Intraparenchymal hemorrhage (IPH) and intraventricular hemorrhage (IVH) are critical subtypes of this condition. Clinically, when such hemorrhages are suspected, immediate CT scanning is essential to assess the extent of the bleeding and to facilitate

Levi E. Lingsch, Dana Grund, Siddhartha Mishra, Georgios Kissas

The joint prediction of continuous fields and statistical estimation of the underlying discrete parameters is a common problem for many physical systems, governed by PDEs. Hitherto, it has been separately addressed by employing operator learning surrogates for field prediction while using simulation-based inference (and its variants) for statistical paramete

Tim Strobel, Stefan Kazmaier, Tobias Bauer, Marlon Schäfer

Fiber-based distribution of triggered, entangled, single-photon pairs is a key requirement for the future development of terrestrial quantum networks. In this context, semiconductor quantum dots (QDs) are promising candidates for deterministic sources of on-demand polarization-entangled photon pairs. So far, the best QD polarization-entangled-pair sources em

Nida Nasir, Mustafa Sameer, Feras Barneih, Omar Alshaltone

Continuous photoplethysmography (PPG)-based blood pressure monitoring is necessary for healthcare and fitness applications. In Artificial Intelligence (AI), signal classification levels with the machine and deep learning arrangements need to be explored further. Techniques based on time-frequency spectra, such as Short-time Fourier Transform (STFT), have bee

Abhishek Kumar, Sarfaroz Yunusov, Ali Emami

Research on Large Language Models (LLMs) has often neglected subtle biases that, although less apparent, can significantly influence the models' outputs toward particular social narratives. This study addresses two such biases within LLMs: representative bias, which denotes a tendency of LLMs to generate outputs that mirror the experiences of certain identit

Chuanhao Li, Zhen Li, Chenchen Jing, Shuo Liu

Large vision-language models (LVLMs) are ignorant of the up-to-date knowledge, such as LLaVA series, because they cannot be updated frequently due to the large amount of resources required, and therefore fail in many cases. For example, if a LVLM was released on January 2024, and it wouldn't know the singer of the theme song for the new Detective Conan movie

Gilberto Bini, Maria Chiara Brambilla, Claudio Fontanari, Elisa Postinghel

Let $\overline{\mathrm{Mov}}^k(X)$ be the closure of the cone $\mathrm{Mov}^k(X)$ generated by classes of effective divisors on a projective variety $X$ with stable base locus of codimension at least $k+1$. We propose a generalized version of the Log Nonvanishing Conjecture and of the Log Abundance Conjecture for a klt pair $(X,\Delta)$, that is: if $K_X+\De

Henry Beuster, Thomas Doebbert, Christoph Cammin, Dmytro Krush

Modern manufacturing is characterized by a high degree of automation, with autonomous systems also frequently being used. In such environments human intervention in the event of malfunctions or maintenance becomes a rare but also necessary task. When human workers are no longer an integral part of the production process, but only intervene when necessary, e.

A. Tiribocchi, M. Durve, M. Lauricella, A. Montessori

Over the last decade, the Lattice Boltzmann method has found major scope for the simulation of a large spectrum of problems in soft matter, from multiphase and multi-component microfluidic flows, to foams, emulsions, colloidal flows, to name but a few. Crucial to many such applications is the role of supramolecular interactions which occur whenever mesoscale

Wenjing Lv, Jiaojiao Zhou, Y. Liu, Lin Xu

Either conformal transformation optics or geodesic mapping provides a design method to bend light rays in two-dimensional space with a nonuniform refractive index profile. In this paper, we combine both methods above to design a conformal invisible cloak based on bi-spheres with a refractive index profile varying from 0 to 10.7, smaller than 24.6 for the pre

Praveen Jayakumar, Priya J. Nadkarni, Shayan Srinivasa Garani

Transversal gates are logical gate operations on encoded quantum information that are efficient in gate count and depth, and are designed to minimize error propagation. Efficient encoding circuits for quantum codes that admit transversal gates are thus crucial to reduce noise and realize useful quantum computers. The class of punctured Quantum Reed-Muller co

Vinicius L S Silva, Geraldine Regnier, Pablo Salinas, Claire E Heaney

Reactive transport in porous media plays a pivotal role in subsurface reservoir processes, influencing fluid properties and geochemical characteristics. However, coupling fluid flow and transport with geochemical reactions is computationally intensive, requiring geochemical calculations at each grid cell and each time step within a discretized simulation dom

Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash, Matthias Grossglauser

The s-ID problem seeks to compute a causal effect in a specific sub-population from the observational data pertaining to the same sub population (Abouei et al., 2023). This problem has been addressed when all the variables in the system are observable. In this paper, we consider an extension of the s-ID problem that allows for the presence of latent variable

Yuma Fujimoto, Kaito Ariu, Kenshi Abe

This study examines the global behavior of dynamics in learning in games between two players, X and Y. We consider the simplest situation for memory asymmetry between two players: X memorizes the other Y's previous action and uses reactive strategies, while Y has no memory. Although this memory complicates their learning dynamics, we characterize the global

Hongzhi Zhang, Xiuwen Gong, Shirui Pan, Jia Wu

Drug-target interaction (DTI) prediction is a critical component of the drug discovery process. In the drug development engineering field, predicting novel drug-target interactions is extremely crucial.However, although existing methods have achieved high accuracy levels in predicting known drugs and drug targets, they fail to utilize global protein informat

Christopher Scarvelis, Justin Solomon

Penalizing the nuclear norm of a function's Jacobian encourages it to locally behave like a low-rank linear map. Such functions vary locally along only a handful of directions, making the Jacobian nuclear norm a natural regularizer for machine learning problems. However, this regularizer is intractable for high-dimensional problems, as it requires computing