May 2024 arXiv papers — page 50

Stefan Steinerberger

Let $\Omega \subset \mathbb{R}^d$ be a bounded domain and let $\lambda_1, \lambda_2, \dots$ denote the sequence of eigenvalues of the Laplacian subject to Dirichlet boundary conditions. We consider inequalities for $\lambda_n$ that are independent of the domain $\Omega$. A well--known such inequality follows from the Berezin--Li--Yau approach. The purpose of

Samuel Epstein

Using Kolmogorov Game Derandomization, upper bounds of the Kolmogorov complexity of deterministic winning players against deterministic environments can be proved. This paper gives improved upper bounds of the Kolmogorov complexity of such players. This paper also generalizes this result to probabilistic games. This applies to computable, lower computable, a

Christian Weidemann

Studying and understanding social networks is crucial for accurately defining ideological polarization, since they enable precise modeling of social structures. One of the limitations of many methods for quantifying polarization on networks is the assumption of a two-dimensional opinion space. This prevents accurate study of multipolar systems like multi-par

Zachariah Malik, Yu-Jui Huang

This paper proposes a new theoretical lens to view Wasserstein generative adversarial networks (WGANs). To minimize the Wasserstein-1 distance between the true data distribution and our estimate of it, we derive a distribution-dependent ordinary differential equation (ODE) which represents the gradient flow of the Wasserstein-1 loss, and show that a forward

Angelo Porrello, Lorenzo Bonicelli, Pietro Buzzega, Monica Millunzi

The fine-tuning of deep pre-trained models has revealed compositional properties, with multiple specialized modules that can be arbitrarily composed into a single, multi-task model. However, identifying the conditions that promote compositionality remains an open issue, with recent efforts concentrating mainly on linearized networks. We conduct a theoretical

Ken Ono, Sudhir Pujahari, Hasan Saad, Neelam Saikia

We consider a special family of Gaussian hypergeometric functions whose entries are cubic and trivial characters over finite fields. The special values of these functions are known to give the Frobenius traces of families of Hessian elliptic curves. Using the theory of harmonic Maass forms and mock modular forms, we prove that the limiting distribution of th

Claudio Muena, Mario Riquelme, Andreas Reisenegger, Astor Sandoval

Context. The diffuse TeV gamma-ray emission detected in the inner $\sim$ 100 pc of the Galactic Center suggests the existence of a central cosmic-ray accelerator reaching $\sim$ PeV energies. It is interesting to associate this so-called PeVatron with the point source HESS J1745$-$290, whose position is consistent with that of the central supermassive black

Hans Georg Schaathun, Ben David Normann, Kenny Solevåg-Hoti

Gravitational lensing occurs as the path of light from distant celestial bodies is distorted due to gravitational attraction by other celestial bodies, whose mass is partly invisible, being so-called dark matter. When observed through a gravitational lens, distant galaxies appear distorted. A lot of research activity goes into mapping the dark matter in the

Taoufik Sakhraoui, František Karlický

We employed the recently developed density functional tight binding (DFTB) method's Hamiltonian, GFN1-xTB, for modeling the mixed termination in Ti$_{{\rm2}}$C MXene, namely three types of termination by combining -O and -OH, -O and -F, as well as -F and -OH. We demonstrated that the approach yields reliable predictions for electronic and magnetic properties

Nyckollas Brandão, André Jesus, André Páscoa, Alexandre P. Francisco

Phylogenetic analysis, which allow to understand the evolution of bacterial and viral epidemics, requires large quantities of data to be analysed and processed for knowledge extraction. One of the major challenges consists on the integration of the results from typing and phylogenetic inference methods with epidemiological data, namely in what concerns their

Junxiang Zhu, Nicholas Nisbet, Mengtian Yin, Ran Wei

Graph is considered a promising way for managing building information. A new graphic form of IFC (Industry Foundation Classes) data has just been developed, referred to as IFC-Graph. However, understanding of IFC-Graph is insufficient, especially for information query. This study aims to explore graphic building information query and develop a graph query la

Emily Jensen, Sriram Sankaranarayanan, Bradley Hayes

We claim that LLMs can be paired with formal analysis methods to provide accessible, relevant feedback for HRI tasks. While logic specifications are useful for defining and assessing a task, these representations are not easily interpreted by non-experts. Luckily, LLMs are adept at generating easy-to-understand text that explains difficult concepts. By integ

Romario Gualdrón-Hurtado, Roman Jacome, Sergio Urrea, Henry Arguello

Deep-learning (DL)-based image deconvolution (ID) has exhibited remarkable recovery performance, surpassing traditional linear methods. However, unlike traditional ID approaches that rely on analytical properties of the point spread function (PSF) to achieve high recovery performance - such as specific spectrum properties or small conditional numbers in the

Jooyoung Shin

Let $R$ be a ring, $\sigma$ be an automorphism of $R$, and $D$ be a $\sigma$-derivation on $R$. We will show that if $R$ is an algebra over a field of characteristic $0$ and $D$ is $q$-skew, then $J(R[x;\sigma,D])=I\cap R+I_0$ where $I=\{r\in R : rx\in J(R[x;\sigma,D])\}$ and $I_0=\{\sum_{i\geq 1}r_ix^i: r_i\in I\}$. We will prove that $J(R[x;\sigma,D])\cap

Changhoon Kim, Kyle Min, Yezhou Yang

In the evolving landscape of text-to-image (T2I) diffusion models, the remarkable capability to generate high-quality images from textual descriptions faces challenges with the potential misuse of reproducing sensitive content. To address this critical issue, we introduce \textbf{R}obust \textbf{A}dversarial \textbf{C}oncept \textbf{E}rase (RACE), a novel ap

Guy Blanc, Caleb Koch, Carmen Strassle, Li-Yang Tan

Consider the expected query complexity of computing the $k$-fold direct product $f^{\otimes k}$ of a function $f$ to error $\varepsilon$ with respect to a distribution $\mu^k$. One strategy is to sequentially compute each of the $k$ copies to error $\varepsilon/k$ with respect to $\mu$ and apply the union bound. We prove a strong direct sum theorem showing t

Van Minh Nguyen, Cristian Ocampo, Aymen Askri, Louis Leconte

Deep learning is computationally intensive, with significant efforts focused on reducing arithmetic complexity, particularly regarding energy consumption dominated by data movement. While existing literature emphasizes inference, training is considerably more resource-intensive. This paper proposes a novel mathematical principle by introducing the notion of

Alex Fink, Jeffrey Giansiracusa, Noah Giansiracusa, Joshua Mundinger

A "tropical ideal" is an ideal in the idempotent semiring of tropical polynomials that is also, degree by degree, a tropical linear space. We introduce a construction based on transversal matroids that canonically extends any principal ideal to a tropical ideal. We call this the Macaulay tropical ideal. It has a universal property: any other extension of the

Nathaniel Weir, Muhammad Khalifa, Linlu Qiu, Orion Weller

Program synthesis with language models (LMs) has unlocked a large set of reasoning abilities; code-tuned LMs have proven adept at generating programs that solve a wide variety of algorithmic symbolic manipulation tasks (e.g. word concatenation). However, not all reasoning tasks are easily expressible as code, e.g. tasks involving commonsense reasoning, moral

Mauricio Elizalde, Stephan Sturm

We aim to provide an intertemporal, cost-efficient consumption model that extends the consumption optimization inspired by the Distribution Builder, a tool developed by Sharpe, Johnson, and Goldstein. The Distribution Builder enables the recovery of investors' risk preferences by allowing them to select a desired distribution of terminal wealth within their

Tom Jurgenson, Matan Sudry, Gal Avineri, Aviv Tamar

We present RoboArm-NMP, a learning and evaluation environment that allows simple and thorough evaluations of Neural Motion Planning (NMP) algorithms, focused on robotic manipulators. Our Python-based environment provides baseline implementations for learning control policies (either supervised or reinforcement learning based), a simulator based on PyBullet,

Haoyu Wang, Tao Li, Zhiwei Deng, Dan Roth

In this work, we introduce a novel approach that equips LLM agents with introspection, enhancing consistency and adaptability in solving complex tasks. Our approach prompts LLM agents to decompose a given task into manageable subtasks (i.e., to make a plan), and to continuously introspect upon the suitability and results of their actions. %; and when necessa

Yury Lebedev, Arunava Banerjee

Binomial trees are widely used in the financial sector for valuing securities with early exercise characteristics, such as American stock options. However, while effective in many scenarios, pricing options with CRR binomial trees are limited. Major limitations are volatility estimation, constant volatility assumption, subjectivity in parameter choices, and

Zeynep Yılmaz Uçar, Bayram Ali Ersoy, Ünsal Tekir, Ece Yetkin Çelikel

In this paper, all rings are commutative with nonzero identity. Let M be an R-module. We introduce the concept of phi classical 1-absorbing prime submodules. A proper submodule N of M is a phi classical 1-absorbing prime submodule if whenever non units a, b, c belongs to R and m belongs to M with abcm belongs to N and does not belong to phi(N), then abm belo

Reid Dale

Null Hypothesis Statistical Testing is a dominant framework for conducting statistical analysis across the sciences. There remains considerable debate as to whether, and under what circumstances, evidence can be said to be confirmatory of a null hypothesis. This paper presents a modal logic of short-run frequentist confirmation developed by leveraging the du

Silky Singh, Surgan Jandial, Simra Shahid, Abhinav Java

Text-conditioned style transfer enables users to communicate their desired artistic styles through text descriptions, offering a new and expressive means of achieving stylization. In this work, we evaluate the text-conditioned image editing and style transfer techniques on their fine-grained understanding of user prompts for precise "local" style transfer. W

Qiubo Zhang, Yi Chen, Karen. C. Bustillo, Haimei Zheng

High-entropy alloys (HEAs) with various promising applications have attracted significant interest. However, alloying immiscible metal elements while controlling the morphology and crystallinity remains extremely challenging. We report a general route, by engineering liquid-liquid interfaces, for the synthesis of HEAs with controlled crystallinity (single cr

Xiaoyang Chen, Hao Zheng, Yifang Xie, Yuncong Ma

Current methods for developing foundation models in medical image segmentation rely on two primary assumptions: a fixed set of classes and the immediate availability of a substantial and diverse training dataset. However, this can be impractical due to the evolving nature of imaging technology and patient demographics, as well as labor-intensive data curatio

Fedor Kuyanov

In this paper, we prove two lower bounds for the maximum matching size in an arbitrary undirected graph. Despite their simplicity, these results are not widely known. This article aims to bring pleasure to the reader by giving short combinatorial proofs of these easy-to-state estimates.

Adolfo Anta, Diego Cifelli

The massive deployment of inverter-based generation poses several challenges to system operators but also offers new opportunities. In the context of grid recovery, inverter-based generation is expected to take over new responsibilities, as conventional generation that are blackstart capable are being decommissioned. Inverter-based facilities, being very fle

Samik Mitra, Santabrata Das

We investigate the global structure of general relativistic magneto-hydrodynamic (GRMHD) accretion flows around Kerr black holes containing shock waves, where the disk is threaded by radial and toroidal magnetic fields. We self-consistently solve the GRMHD equations that govern the flow motion inside the disk and for the first time to our knowledge, we obtai

Mohammad Mozaffari, Amir Yazdanbakhsh, Zhao Zhang, Maryam Mehri Dehnavi

We propose SLoPe, a Double-Pruned Sparse Plus Lazy Low-rank Adapter Pretraining method for LLMs that improves the accuracy of sparse LLMs while accelerating their pretraining and inference and reducing their memory footprint. Sparse pretraining of LLMs reduces the accuracy of the model, to overcome this, prior work uses dense models during fine-tuning. SLoPe

M. Saeid HaghighiFard, Sinem Coleri

Hierarchical Federated Learning (HFL) faces the significant challenge of adversarial or unreliable vehicles in vehicular networks, which can compromise the model's integrity through misleading updates. Addressing this, our study introduces a novel framework that integrates dynamic vehicle selection and robust anomaly detection mechanisms, aiming to optimize

Ting Yu Tsai, Li Lin, Shu Hu, Ming-Ching Chang

Biomedical image segmentation is critical for accurate identification and analysis of anatomical structures in medical imaging, particularly in cardiac MRI. Manual segmentation is labor-intensive, time-consuming, and prone to errors, highlighting the need for automated methods. However, current machine learning approaches face challenges like overfitting and

Xin-Nuo Zhang, Heng Guo, Yong-Tao Lu

Unlike the duality in four-dimensional spacetime, where the $q-$form fields corresponds to the scalar fields or the vector fields, in higher dimensional spacetime, they denote new types of particles. In this paper, we investigate the localization of the KK modes of the $q$-form fields in a five dimensional brane world. We introduce the coupling between the $

P. Gasik

This work reviews various methods used to minimize the probability of discharge occurrence in MPGDs or to mitigate the effect of spark discharges on the detector system. These include techniques that can reduce the probability that the fundamental discharge limits of MPGD structures are reached during detector operation, methods based on the HV scheme optimi

Allan Wing-Bocanegra, Carlos E. Quintero-Narvaez, Salvador E. Venegas-Andraca

We propose a modified version of the quantum walk-based search algorithm created by Shenvi, Kempe and Whaley, also known as the SKW algorithm. In our version of the algorithm, we modified the evolution operator of the system so that it is composed by the product of the shift operator associated to the $2^n$-complete graph with self-loops and a perturbed coin

Niels Grønbech-Jensen

We systematically develop beneficial and practical velocity measures for accurate and efficient statistical simulations of the Langevin equation with direct applications to computational statistical mechanics and molecular dynamics sampling. Recognizing that the existing velocity measures for the most statistically accurate discrete-time Verlet-type algorith

Fuad Kittaneh, Ali Zamani

We define a function on the $C^{\ast}$-algebra of all bounded linear Hilbert space operators, which generalizes the operator radii, and we present some basic properties of this function. Our results extend several results in the literature.

Scott McCullough, Georgios Tsikalas

Let $(\mathcal{H}_k, \mathcal{H}_{\ell})$ be a pair of Hilbert function spaces with kernels $k, \ell$. In a 2005 paper, Shimorin showed that a certain factorization condition on $(k, \ell)$ yields a commutant lifting theorem for multipliers $\mathcal{H}_k\to\mathcal{H}_{\ell}$, thus unifying and extending previous results due to Ball-Trent-Vinnikov and Volbe

Simon Liebl, Leah Lathrop, Ulrich Raithel, Andreas Aßmuth

The growing connectivity of industrial devices as a result of the Internet of Things is increasing the risks to Industrial Control Systems. Since attacks on such devices can also cause damage to people and machines, they must be properly secured. Therefore, a threat analysis is required in order to identify weaknesses and thus mitigate the risk. In this pape

Kim William Torre, Joost de Graaf

Colloidal gels have strong industrial relevance as they can behave liquid- and solid-like. The latter allows them to support the buoyant weight against gravity. However, the system is intrinsically out-of-equilibrium, which means that the colloids must eventually settle out of the suspension. The process of settling has been captured theoretically, but the p

Alexis A. Aguilar-Arevalo, Nicolas Avalos, Pablo Bellino, Xavier Bertou

Millicharged particles, proposed by various extensions of the standard model, can be created in pairs by high-energy photons within nuclear reactors and can interact electromagnetically with electrons in matter. Recently, the existence of a plasmon peak in the interaction cross-section with silicon in the eV range was highlighted as a promising approach to e

David A. Dahlbom, Jinu Thomas, Steven Johnston, Kipton Barros

We employ a classical limit grounded in SU(4) coherent states to investigate the temperature-dependent dynamical spin structure factor of the $S = 1/2$ ladder consisting of weakly coupled dimers. By comparing the outcomes of this classical approximation with density matrix renormalization group and exact diagonalization calculations in finite size ladders, w

Simon Liebl, Leah Lathrop, Ulrich Raithel, Matthias Söllner

As part of the Internet of Things, industrial devices are now also connected to cloud services. However, the connection to the Internet increases the risks for Industrial Control Systems. Therefore, a threat analysis is essential for these devices. In this paper, we examine Industrial Internet of Things devices, identify and rank different sources of threats

Rados Bakic

We discuss a form of a well-known problem of Kakeya for complex polynomials. Let p(z) be a complex polynomial. This problem requires to find disc that contains n zeros of some derivative of p(z), provided that location of several zeros of p(z) is known. We find a disc that contains a zero of (k-1)-th derivative, if we know disc that contains k zeros of p(z).

Jiaxi Hu, Disen Lan, Ziyu Zhou, Qingsong Wen

State Space Models (SSMs) have emerged as a potent tool in sequence modeling tasks in recent years. These models approximate continuous systems using a set of basis functions and discretize them to handle input data, making them well-suited for modeling time series data collected at specific frequencies from continuous systems. Despite its potential, the app

Agathe Balayn, Lorenzo Corti, Fanny Rancourt, Fabio Casati

Explainability and transparency of AI systems are undeniably important, leading to several research studies and tools addressing them. Existing works fall short of accounting for the diverse stakeholders of the AI supply chain who may differ in their needs and consideration of the facets of explainability and transparency. In this paper, we argue for the nee

Liam Jemison, Matthew Stahl, Ranjan K. Dash, Dexuan Xie

The Voltage-Dependent Anion Channel (VDAC) protein is the primary conduit for the regulated passage of ions and metabolites into and out of mitochondria. Calculating its solvation free energy is crucial for understanding its stability, function, and interactions within the cellular environment. In this paper, we introduce a total solvation free energy, $E$,

Agathe Balayn, Mireia Yurrita, Fanny Rancourt, Fabio Casati

With the widespread proliferation of AI systems, trust in AI is an important and timely topic to navigate. Researchers so far have largely employed a myopic view of this relationship. In particular, a limited number of relevant trustors (e.g., end-users) and trustees (i.e., AI systems) have been considered, and empirical explorations have remained in laborat

Liroy Lourenco, Aaron P Chippendale

This paper presents the effects of radio frequency interference (RFI) mitigation on a radio telescope's sensitivity and beam pattern. It specifically explores the impact of subspace-projection mitigation on the phased array feed (PAF) beams of the Australian SKA Pathfinder (ASKAP) telescope. The goal is to demonstrate ASKAP's ability to make science observat

L. Baratchart, H. Stahl, M. Yattselev

Let $ D $ be a bounded Jordan domain and $ A $ be its complement on the Riemann sphere. We investigate the $ n $-th root asymptotic behavior in $ D $ of best rational approximants, in the uniform norm on $ A $, to functions holomorphic on $ A $ having a multi-valued continuation to quasi every point of $ D $ with finitely many branches. More precisely, we st

Mathias Schubert, Rafal Korlacki, Vanya Darakchieva

In this work we derive the state of strain or stress under symmetry conserving conditions in pseudomorphic lattices with monoclinic symmetry. We compare surface vectors across the template epitaxial layer interface and impose conditions of a stress free epitaxial layer. As a result, we demonstrate the existence, in theory, of exactly three possible unit cell

Volodymyr Vovchenko

This work studies the influence of an external magnetic field on hadron yields and fluctuations in a hadron resonance gas by performing calculations within an updated version of the open-source Thermal-FIST package. The presence of the magnetic field has a sizable influence on certain hadron yield ratios. Most notably, it leads to enhanced $p/\pi$ and suppre

Siyuan Ma, Weidi Luo, Yu Wang, Xiaogeng Liu

With the advent and widespread deployment of Multimodal Large Language Models (MLLMs), ensuring their safety has become increasingly critical. To achieve this objective, it requires us to proactively discover the vulnerability of MLLMs by exploring the attack methods. Thus, structure-based jailbreak attacks, where harmful semantic content is embedded within

Anthony Gruber, Kookjin Lee, Haksoo Lim, Noseong Park

Metriplectic systems are learned from data in a way that scales quadratically in both the size of the state and the rank of the metriplectic data. Besides being provably energy conserving and entropy stable, the proposed approach comes with approximation results demonstrating its ability to accurately learn metriplectic dynamics from data as well as an error

Farhad Pourpanah, Mahdiyar Molahasani, Milad Soltany, Michael Greenspan

We address the problem of federated domain generalization in an unsupervised setting for the first time. We first theoretically establish a connection between domain shift and alignment of gradients in unsupervised federated learning and show that aligning the gradients at both client and server levels can facilitate the generalization of the model to new (t

Breno Costa, Abhik Banerjee, Prem Prakash Jayaraman, Leonardo R. Carvalho

Fog computing can provide computational resources and low-latency communication at the network edge. But with it comes uncertainties that must be managed in order to guarantee Service Level Agreements. Service observability can help the environment better deal with uncertainties, delivering relevant and up-to-date information in a timely manner to support de

Jonghoon Ahn, Christina Wicker, Nolan Bitner, Michael T. Solomon

Optically interfaced solid-state defects are promising candidates for quantum communication technologies. The ideal defect system would feature bright telecom emission, long-lived spin states, and a scalable material platform, simultaneously. Here, we employ one such system, vanadium (V4+) in silicon carbide (SiC), to establish a potential telecom spin-photo

Dae Ung Jo, Kyuewang Lee, JaeHo Chung, Jin Young Choi

Securing a sufficient amount of paired data is important to train an image-text retrieval (ITR) model, but collecting paired data is very expensive. To address this issue, in this paper, we propose an active learning algorithm for ITR that can collect paired data cost-efficiently. Previous studies assume that image-text pairs are given and their category lab

R. R. S. Oliveira

In this paper, we determine the bound-state solutions for Dirac fermions with electric dipole moment (EDM) and position-dependent mass (PDM) in the presence of a radial magnetic field generated by magnetic monopoles. To achieve this, we work with the $(2+1)$-dimensional (DE) Dirac equation with nonminimal coupling in polar coordinates. Posteriorly, we obtain

Hui Kong, Ashley J. Ross, Klaus Honscheid, Dustin Lang

We use the forward modeling pipeline, Obiwan, to study the imaging systematics of the Luminous Red Galaxies (LRGs) targeted by the Dark Energy Spectroscopic Instrument (DESI). We update the Obiwan pipeline, which had previously been developed to simulate the optical images used to target DESI data, to further simulate WISE images in the infrared. This additi

Grant Hutchings, Derek Bingham, Kellin Rumsey, Earl Lawrence

Scalable surrogate models enable efficient emulation of computer models (or simulators), particularly when dealing with large ensembles of runs. While Gaussian process (GP) models are commonly employed for emulation, they face limitations in scaling to large datasets. Furthermore, when dealing with dense functional output, such as spatial or time-series data

Haiwen Guan, Troy Arcomano, Ashesh Chattopadhyay, Romit Maulik

We present a lightweight, easy-to-train, low-resolution, fully data-driven climate emulator, LUCIE, that can be trained on as low as $2$ years of $6$-hourly ERA5 data. Unlike most state-of-the-art AI weather models, LUCIE remains stable and physically consistent for $100$ years of autoregressive simulation with $100$ ensemble members. Long-term mean climatol

Jhen Hsieh

In this paper, we present a neural network-based approach for tracking and reconstructing the trajectories of baseball pitches from 2D video footage to 3D coordinates. We utilize OpenCV's CSRT algorithm to accurately track the baseball and fixed reference points in 2D video frames. These tracked pixel coordinates are then used as input features for our neura

Yuhao Chen, Zhimu Wang, Bo Wen, Farhana Zulkernine

Unstructured text in medical notes and dialogues contains rich information. Recent advancements in Large Language Models (LLMs) have demonstrated superior performance in question answering and summarization tasks on unstructured text data, outperforming traditional text analysis approaches. However, there is a lack of scientific studies in the literature tha

Ankur Agarwal, Shashi Prabha, Raghav Yadav

This paper explores the application of Exploratory Data Analytics (EDA) in the banking and finance domain, focusing on credit card usage and customer churning. It presents a step-by-step analysis using EDA techniques such as descriptive statistics, data visualization, and correlation analysis. The study examines transaction patterns, credit limits, and usage

Asad Ali, Saif Al-Kuwari, M. T. Rahim, Mehrdad Ghominejad

We investigate the behavior of various measures of quantum coherence and quantum correlation in the spin-1/2 Heisenberg XYZ model with added Dzyaloshinsky-Moriya (DM) and Kaplan--Shekhtman--Entin-Wohlman--Aharony (KSEA) interactions at a thermal regime described by a Gibbs density operator. We aim to understand the restricted hierarchical classification of d

Longke Li

We present recent results on charm physics at the Belle and Belle II experiments, covering measurements of charm lifetimes, branching fractions of the decays of charmed mesons and baryons and the decay asymmetry parameters of two-body decays of charmed baryons, searches for rare and forbidden decays, and measurements of $C\!P$ violating parameters in the fou

F. Bergamini

In the next generation network, the satellite network will play a fundamental role, in overcoming the limitation of the terrestrial network. Nonetheless, the satellite-terrestrial network integration presents a number of problems due to the time-variant topology of the first. One of the most important is the routing process of such networks. Many solutions h

Samardhi Yadav, Vishal Vaibhav

This paper addresses the numerical implementation of the transparent boundary condition (TBC) and its various approximations for the free Schr\"odinger equation on a rectangular computational domain. In particular, we consider the exact TBC and its spatially local approximation under high frequency assumption along with an appropriate corner condition. For t

Mang Ye, Wei Shen, Bo Du, Eduard Snezhko

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared samples, without leaking private data. Recent research has shown promising results addressing various challenges in VFL, highlighting its potential for practical applications in cros

Abdullah Guvendi, Omar Mustafa

Understanding the behavior of fermion-antifermion (\(f\overline{f}\)) pairs is crucial in modern physics. These systems, governed by fundamental forces, exhibit complex interactions essential for particle physics, high-energy physics, nuclear physics, and solid-state physics. This study introduces a novel theoretical model using the many-body Dirac equation

Zimo Zhao, Yifei Ma, Zipei Song, Jacopo Antonello

Adaptive optics (AO) is a powerful tool employed across various research fields, from aerospace to microscopy. Traditionally, AO has focused on correcting optical phase aberrations, with recent advances extending to polarisation compensation. However, intensity errors are also prevalent in optical systems, yet effective correction methods are still in their

Dimitrios Bachtis

We review recent developments of machine learning algorithms pertinent to the inverse renormalization group, which was originally established as a generative numerical method by Ron-Swendsen-Brandt via the implementation of compatible Monte Carlo simulations. Inverse renormalization group methods enable the iterative generation of configurations for increasi

Xinyu Zhou, Boris Knyazev, Alexia Jolicoeur-Martineau, Jie Fu

A good initialization of deep learning models is essential since it can help them converge better and faster. However, pretraining large models is unaffordable for many researchers, which makes a desired prediction for initial parameters more necessary nowadays. Graph HyperNetworks (GHNs), one approach to predicting model parameters, have recently shown stro

Dilsat Berin Aytar, Semra Gunduc

Since technology is advancing so quickly in the modern era of information, data is becoming an essential resource in many fields. Correct data collection, organization, and analysis make it a potent tool for successful decision-making, process improvement, and success across a wide range of sectors. Synthetic data is required for a number of reasons, includi

Yifeng Gao, Yuhua Sun, Xingjun Ma, Zuxuan Wu

This paper presents a novel model protection paradigm ModelLock that locks (destroys) the performance of a model on normal clean data so as to make it unusable or unextractable without the right key. Specifically, we proposed a diffusion-based framework dubbed ModelLock that explores text-guided image editing to transform the training data into unique styles

Mateusz Woźny, Mateusz Lango

Clickbait posts are a widespread problem in the webspace. The generation of spoilers, i.e. short texts that neutralize clickbait by providing information that satisfies the curiosity induced by it, is one of the proposed solutions to the problem. Current state-of-the-art methods are based on passage retrieval or question answering approaches and are limited

Zhimin Ding, Jiawen Yao, Brianna Barrow, Tania Lorido Botran

An obvious way to alleviate memory difficulties in GPU-based AI computing is via CPU offload, where data are moved between GPU and CPU RAM, so inexpensive CPU RAM is used to increase the amount of storage available. While CPU offload is an obvious idea, it can greatly slow down a computation, due to the relatively slow transfer rate between CPU RAM and GPU R

Abhishek Kumar, Robert Morabito, Sanzhar Umbet, Jad Kabbara

As the use of Large Language Models (LLMs) becomes more widespread, understanding their self-evaluation of confidence in generated responses becomes increasingly important as it is integral to the reliability of the output of these models. We introduce the concept of Confidence-Probability Alignment, that connects an LLM's internal confidence, quantified by

Sara Kemper, Justin Cui, Kai Dicarlantonio, Kathy Lin

Conversational recommendation (ConvRec) systems must understand rich and diverse natural language (NL) expressions of user preferences and intents, often communicated in an indirect manner (e.g., "I'm watching my weight"). Such complex utterances make retrieving relevant items challenging, especially if only using often incomplete or out-of-date metadata. Fo

Jasper Dekoninck, Mark Niklas Müller, Martin Vechev

Public benchmarks play an essential role in the evaluation of large language models. However, data contamination can lead to inflated performance, rendering them unreliable for model comparison. It is therefore crucial to detect contamination and estimate its impact on measured performance. Unfortunately, existing detection methods can be easily evaded and f

Tom Delord, Richard Monge, Gabriel Lopez-Morales, Olaf Bach

The excited orbitals of color centers typically show stronger electric dipoles, which can serve as a resource for entanglement, emission tuning, or electric field sensing. Here, we use resonant laser excitation to expose strong transition dipoles in the excited state (ES) orbitals of the negatively charged nitrogen vacancy center in diamond. By applying micr

M. Diefenthaler, C. Fanelli, L. O. Gerlach, W. Guan

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like the ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics re

Rong Yang, Lian Hu, Songxiao Li

In this paper, the boundedness and compactness of generalized integration operators $T_g^{n,k}$ between different analytic tent spaces in the unit disc are completely characterized.

Brendan Park, Madeline Janecek, Naser Ezzati-Jivan, Yifeng Li

Large Language Models (LLMs) have demonstrated remarkable success in tasks like the Winograd Schema Challenge (WSC), showcasing advanced textual common-sense reasoning. However, applying this reasoning to multimodal domains, where understanding text and images together is essential, remains a substantial challenge. To address this, we introduce WinoVis, a no

Haoran Sun, Yurong Chen, Siwei Wang, Xu Chu

Fine-tuning large language models (LLMs) to aggregate multiple preferences has attracted considerable research attention. With aggregation algorithms advancing, a potential economic scenario arises where fine-tuning services are provided to agents with different preferences. In this context, agents may benefit from strategically misreporting their preference

Maurizio Palesi, Enrico Russo, Davide Patti, Giuseppe Ascia

Multi-core quantum architectures offer a solution to the scalability limitations of traditional monolithic designs. However, dividing the system into multiple chips introduces a critical bottleneck: communication between cores. This paper introduces qcomm, a simulation tool designed to assess the impact of communication on the performance of scalable multi-c

Maxine M. McCarthy, D. M. Whittaker

We present the theory and experimental demonstration of a topological classification of finite tight binding Hamiltonians with chiral symmetry. Using the graph-theoretic notion of complete matchings, we show that many chiral tight binding structures can be divided into a number of sections, each of which has independent topological phases. Hence the overall

Mingshuang Luo, Ruibing Hou, Zhuo Li, Hong Chang

This paper presents M$^3$GPT, an advanced $\textbf{M}$ultimodal, $\textbf{M}$ultitask framework for $\textbf{M}$otion comprehension and generation. M$^3$GPT operates on three fundamental principles. The first focuses on creating a unified representation space for various motion-relevant modalities. We employ discrete vector quantization for multimodal condit

Michael Dubem Igbomezie, Phuong T. Nguyen, Davide Di Ruscio

Code comments play a crucial role in software development, as they provide programmers with practical information, allowing them to understand better the intent and semantics of the underpinning code. Nevertheless, developers tend to leave comments unchanged after updating the code, resulting in a discrepancy between the two artifacts. Such a discrepancy may

Daniel Levin, Alexander Zuevsky

We consider infinite $\Z_\Z$-index complexes $\mathcal C$ of spaces with elements depending on a number of parameters, complete with respect to a linear associative regular inseparable multilinear product. The existence of nets of vanishing ideals of orders of and powers of differentials is assumed for subspaces of $\mathcal C$-spaces. In the polynomial case

Brian Liu, Nathan S. Sheffield, Alek Westover

We study the following generalization of the Hamiltonian cycle problem: Given integers $a,b$ and graph $G$, does there exist a closed walk in $G$ that visits every vertex at least $a$ times and at most $b$ times? Equivalently, does there exist a connected $[2a,2b]$ factor of $2b \cdot G$ with all degrees even? This problem is NP-hard for any constants $1 \le

Wei-Yang Liu, Edward Shuryak, Ismail Zahed

We revisit the hard QCD contributions to the pion gravitational form factors (GFFs), in terms of the twist-2,3 pion distribution amplitudes (DA), including novel semi-hard contributions from the instantons. The pion DAs are evaluated in the QCD instanton vacuum, and then properly evolved to higher resolution. The results are compared to our recent results fr

Fernando Gómez-Ortiz, Mauro Fava, Emma E. McCabe, Aldo H. Romero

We compare the various chirality measures most widely used in the literature to quantify chiral symmetry in extended solids, i.e., the continuous chirality measure, the Hausdorff distance, and the angular momentum. By studying these functions in an algebraically tractable case, we can evaluate their strengths and weaknesses when applied to more complex cryst

Alireza Olama, Andreas Lundell, Jan Kronqvist, Elham Ahmadi

This paper introduces the Bi-linear consensus Alternating Direction Method of Multipliers (Bi-cADMM), aimed at solving large-scale regularized Sparse Machine Learning (SML) problems defined over a network of computational nodes. Mathematically, these are stated as minimization problems with convex local loss functions over a global decision vector, subject t

Hamid Taheri, Seyed Rasoul Hosseini, Mohammad Ali Nekoui

Collision-free motion is essential for mobile robots. Most approaches to collision-free and efficient navigation with wheeled robots require parameter tuning by experts to obtain good navigation behavior. This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robo

Jikun Kang, Xin Zhe Li, Xi Chen, Amirreza Kazemi

Although Large Language Models (LLMs) achieve remarkable performance across various tasks, they often struggle with complex reasoning tasks, such as answering mathematical questions. Recent efforts to address this issue have primarily focused on leveraging mathematical datasets through supervised fine-tuning or self-improvement techniques. However, these met

Jindi Wang

The ongoing usage of artificial intelligence technologies in virtual reality has led to a large number of researchers exploring immersive virtual reality interaction. Gesture controllers and head-mounted displays are the primary pieces of hardware used in virtual reality applications. This article analyzes the advantages and disadvantages of various hardware