February 2024 arXiv papers — page 153

Erin Lipman, Abel Rodriguez

The most common approach to implementing data analysis pipelines involves obtaining point estimates from the upstream modules and then treating these as known quantities when working with the downstream ones. This approach is straightforward, but it is likely to underestimate the overall uncertainty associated with any final estimates. An alternative approac

Changjun Gao

K-essence theories are usually studied in the framework of one scalar field $\phi$. Namely, the Lagrangian of K-essence is the function of scalar field $\phi$ and its covariant derivative. However, in this paper, we explore a two-field pure K-essence, i.e. the corresponding Lagrangian is the function of covariant derivatives of two scalar fields without the

D. M. Dantchev, N. S. Tonchev, J. Rudnick

Attention in the literature has increasingly turned to the issue of the dependence on ensemble and boundary conditions of fluctuation-induced forces. We have recently investigated this problem in the one-dimensional Ising model with periodic and antiperiodic boundary conditions (Annals of Physics {\bf 459}, 169533 (2023)). Significant variations of the behav

Alma L. Albujer, Jónatan Herrera, Rafael M. Rubio

The aim of this manuscript is to obtain rigidity and non-existence results for parabolic spacelike submanifolds with causal mean curvature vector field in orthogonally splitted spacetimes, and in particular, in globally hyperbolic spacetimes. We also obtain results regarding the geometry of submanifolds by ensuring, under some mild hypothesis, the non-existe

Jesús Bobadilla, Abraham Gutierrez, Fernando Ortega, Bo Zhu

Users want to know the reliability of the recommendations; they do not accept high predictions if there is no reliability evidence. Recommender systems should provide reliability values associated with the predictions. Research into reliability measures requires the existence of simple, plausible and universal reliability quality measures. Research into reco

Shai Haran

The usual dictionary between geometry and commutative algebra is not appropriate for Arithmetic geometry because addition is a singular operation at the "Real prime". We replace Rings, with addition and multiplication, by Props (=strict symmetric monoidal category generated by one object), or by Bioperad (=two closed symmetric operads acting on each other):

Alma L. Albujer, Jónatan Herrera, Rafael M. Rubio

In this work we deal with an elliptic non-linear problem, which arises naturally from Riemannian geometry. This problem has clasically been studied in the the Euclidean $n$-dimensional space and it is known as the Moser-Bernstein problem. Nevertheless we solve this type of problems in a wide family of Riemannian manifolds, constructed as Riemannian warped pr

Haoran Wan, Kyle Jamieson

Mobile cloud gaming places the simultaneous demands of high capacity and low latency on the wireless network, demands that Private and Metropolitan-Area Standalone 5G networks are poised to meet. However, lacking introspection into the 5G Radio Access Network (RAN), cloud gaming servers are ill-poised to cope with the vagaries of the wireless last hop to a m

Tobias Vente, Joeran Beel

Automated Machine Learning (AutoML) has greatly advanced applications of Machine Learning (ML) including model compression, machine translation, and computer vision. Recommender Systems (RecSys) can be seen as an application of ML. Yet, AutoML has found little attention in the RecSys community; nor has RecSys found notable attention in the AutoML community.

Moritz Graf, Thorsten Engesser, Bernhard Nebel

The sequential equilibrium is a standard solution concept for extensive-form games with imperfect information that includes an explicit representation of the players' beliefs. An assessment consisting of a strategy and a belief is a sequential equilibrium if it satisfies the properties of sequential rationality and consistency. Our main result is that both p

Spencer Barclay, Kshitij Jerath

Prior works in human-swarm interaction (HSI) have sought to guide swarm behavior towards established objectives, but may be unable to handle specific scenarios that require finer human supervision, variable autonomy, or application to large-scale swarms. In this paper, we present an approach that enables human supervisors to tune the level of swarm control,

Josué Manik Nava-Sedeño, Haralampos Hatzikirou, Anja Voß-Böhme, Lutz Brusch

We introduce a novel lattice-gas cellular automaton (LGCA) for compressible vectorial active matter with polar and nematic velocity alignment. Interactions are, by construction, zero-range. For polar alignment, we show the system undergoes a phase transition that promotes aggregation with strong resemblance to the classic zero-range process. We find that abo

Tey Berendschot, Soham Chakraborty, Milan Donvil, Se-Jin Kim

We give a characterisation of factoriality of the groupoid von Neumann algebra $L(\mathcal{G})$ associated to a discrete measured groupoid $(\mathcal{G},\mu)$. We introduce the notion of groupoids with `infinite conjugacy classes' and show that this property together with ergodicity of the groupoid is equivalent to factoriality of $L(\mathcal{G})$.

Siguo Bi, Xin Yuan, Shuyan Hu, Kai Li

The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability. This survey enriches the discourse on failures, failure analysis, and countermeasures in the context of th

Ta Seen Reaz Niloy, Zoheb Hasan, Rob Smith, Vikram R. Anapana

Spectrum sharing between terrestrial 5G and incumbent networks in the satellite bands presents a promising avenue to satisfy the ever-increasing bandwidth demand of the next-generation wireless networks. However, protecting incumbent operations from harmful interference poses a fundamental challenge in accommodating terrestrial broadband cellular networks in

Kimberley M. Bird, Xujiong Ye, Alan M. Race, James M. Brown

Imaging mass cytometry (IMC) is a relatively new technique for imaging biological tissue at subcellular resolution. In recent years, learning-based segmentation methods have enabled precise quantification of cell type and morphology, but typically rely on large datasets with fully annotated ground truth (GT) labels. This paper explores the effects of imperfe

Ryan J. French, Sijie Yu, Bin Chen, Chengcai Shen

We report striking Doppler velocity gradients observed during the well-observed September 10th 2017 solar flare, and argue that they are consistent with the presence of an above-the-looptop termination shock beneath the flare current sheet. Observations from the Hinode Extreme-ultraviolet Imaging Spectrometer (EIS) measure plasma sheet Doppler shifts up to 3

Yuqi Zhou, Ahmed Zamzam, Andrey Bernstein

The increasing number of wildfires in recent years consistently challenges the safe and reliable operations of power systems. To prevent power lines and other electrical components from causing wildfires under extreme conditions, electric utilities often deploy public safety power shutoffs (PSPS) to mitigate the wildfire risks therein. Although PSPS are effe

Alberto Rebassa-Mansergas, Mark Hollands, Steven G. Parsons, Leandro G. Althaus

Ultra-short period compact binaries are important sources of gravitational waves, which include e.g. the progenitors of type Ia supernovae or the progenitors of merger episodes that may lead to massive and magnetic single white dwarfs. J0526+5934 is an unresolved compact binary star with an orbital period of 20.5 minutes that belongs to this category. The vi

Olumide Ebenezer Ojo, Olaronke Oluwayemisi Adebanji, Alexander Gelbukh, Hiram Calvo

Effective communication between healthcare providers and patients is crucial to providing high-quality patient care. In this work, we investigate how Doctor-written and AI-generated texts in healthcare consultations can be classified using state-of-the-art embeddings and one-shot classification systems. By analyzing embeddings such as bag-of-words, character

Brendan Creutz, Jesse Pajwani, Jose Felipe Voloch

For a variety over a global field, one can consider subsets of the set of adelic points of the variety cut out by finite abelian descent or Brauer-Manin obstructions. Given a Galois extension of the ground field one can consider similar sets over the extension and take Galois invariants. In this paper, we study under which circumstances the Galois invariants

Thomas Kerby, Teresa White, Kevin Moon

In domains such as ecological systems, collaborations, and the human brain the variables interact in complex ways. Yet accurately characterizing higher-order variable interactions (HOIs) is a difficult problem that is further exacerbated when the HOIs change across the data. To solve this problem we propose a new method called Local Correlation Explanation (

L. Zhou, K. Wirth, M. N. Bui, R. Rani

Surface phonon-polaritons propagating along interfaces of polar dielectrics coexist with excitons in many van der Waals heterostructures, so understanding their mutual interactions is of great interest. Here, we investigate the type I surface phonon polariton of hBN via low-temperature resonant-Raman spectroscopy in hBN/WSe2 heterostructures. The resonantly

Nathalie Aubrun, Manon Blanc, Olivier Bournez

One of the most fundamental problems in tiling theory is the domino problem: given a set of tiles and tiling rules, decide if there exists a way to tile the plane using copies of tiles and following their rules. The problem is known to be undecidable in general and even for sets of Wang tiles, which are unit square tiles wearing colours on their edges which

Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans

Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured information from unstructured text. Prior works typically represent information extraction as triplet-centric and use classical metrics such as precision and recall for evaluation. We

Michael W. Trosset, Carey E. Priebe

Multidimensional scaling (MDS) is the act of embedding proximity information about a set of $n$ objects in $d$-dimensional Euclidean space. As originally conceived by the psychometric community, MDS was concerned with embedding a fixed set of proximities associated with a fixed set of objects. Modern concerns, e.g., that arise in developing asymptotic theori

Enyan Dai, Minhua Lin, Suhang Wang

Pretraining on Graph Neural Networks (GNNs) has shown great power in facilitating various downstream tasks. As pretraining generally requires huge amount of data and computational resources, the pretrained GNNs are high-value Intellectual Properties (IP) of the legitimate owner. However, adversaries may illegally copy and deploy the pretrained GNN models for

Xinwei Wang, Seán R. Kavanagh, David O. Scanlon, Aron Walsh

Antimony selenide (Sb2Se3) is at the forefront of an emerging class of sustainable photovoltaic materials. Despite notable developments over the past decade, the light-to-electricity conversion efficiency of Sb2Se3 has reached a plateau of ~10%. Is this an intrinsic limitation of the material or is there scope to rival the success of metal halide perovskite

Fabrizio Ghezzi, Eduardo Rossi, Lorenzo Trapani

We study online changepoint detection in the context of a linear regression model. We propose a class of heavily weighted statistics based on the CUSUM process of the regression residuals, which are specifically designed to ensure timely detection of breaks occurring early on during the monitoring horizon. We subsequently propose a class of composite statist

Zahra Moslemi, Logan Clark, Sarah Kernal, Samantha Rehome

California's significant role as the second-largest consumer of energy in the United States underscores the importance of accurate energy consumption predictions. With a thriving industrial sector, a burgeoning population, and ambitious environmental goals, the state's energy landscape is dynamic and complex. This paper presents a comprehensive analysis of C

Ali Sedaghatgoo, Amir M. Hajisadeghi, Mahmoud Momtazpour, Nader Bagherzadeh

The Convolutional Neural Network (CNN) has emerged as a powerful and versatile tool for artificial intelligence (AI) applications. Conventional computing architectures face challenges in meeting the demanding processing requirements of compute-intensive CNN applications, as they suffer from limited throughput and low utilization. To this end, specialized acc

Chiaki Moriguchi, Yusuke Narita, Mari Tanaka

What happens if selective colleges change their admission policies? We study this question by analyzing the world's first implementation of nationally centralized meritocratic admissions in the early twentieth century. We find a persistent meritocracy-equity tradeoff. Compared to the decentralized system, the centralized system admitted more high-achievers a

Robert Styer

We consider the number of solutions in positive integers $(x,y,z)$ for the purely exponential Diophantine equation $a^x+b^y =c^z$ (with $\gcd(a,b)=1$). Apart from a list of known exceptions, a conjecture published in 2016 claims that this equation has at most one solution in positive integers $x$, $y$, and $z$. We show that this is true for some ranges of $a

Jonathon C. Davidson, Nicholas R. Anderson, Karen L. Livesey

Many estimates for the magnetic relaxation time of magnetic nanoparticle systems neglect the effect of the applied field strength. This is despite many applications of magnetic nanoparticles involving relaxation dynamics under the influence of applied fields. Here, an analytic approximation for the field-dependent Brownian relaxation time of single-domain, s

Abhijeet Parida, Zhifan Jiang, Roger J. Packer, Robert A. Avery

Image harmonization is an important preprocessing strategy to address domain shifts arising from data acquired using different machines and scanning protocols in medical imaging. However, benchmarking the effectiveness of harmonization techniques has been a challenge due to the lack of widely available standardized datasets with ground truths. In this contex

Juan E. Ruiz-Castro, Christian Acal, Ana M. Aguilera, M. Carmen Aguilera-Morillo

Functional principal component analysis based on Karhunen Loeve expansion allows to describe the stochastic evolution of the main characteristics associated to multiple systems and devices. Identifying the probability distribution of the principal component scores is fundamental to characterize the whole process. The aim of this work is to consider a family

Luca Sardellitti, Glen Takahara, Fady Alajaji

We consider a two sensor distributed detection system transmitting a binary non-uniform source over a Gaussian multiple access channel (MAC). We model the network via binary sensors whose outputs are generated by binary symmetric channels of different noise levels. We prove an optimal one dimensional constellation design under individual sensor power constra

Paula Fraga-Lamas, Diego Noceda-Davila, Tiago M. Fernández-Caramés, Manuel A. Díaz-Bouza

As a result of the progressive implantation of the Industry 4.0 paradigm, many industries are experimenting a revolution that shipyards cannot ignore. Therefore, the application of the principles of Industry 4.0 to shipyards are leading to the creation of Shipyards 4.0. Due to this, Navantia, one of the 10 largest shipbuilders in the world, is updating its w

Chuang Li, Yichen Wei, Chao Qin, Shifan Chen

The strategic induction of cell death serves as a crucial immune defense mechanism for the eradication of pathogenic infections within host cells. Investigating the molecular mechanisms underlying immunogenic cell pathways has significantly enhanced our understanding of the host's immunity. This review provides a comprehensive overview of the immunogenic cel

Xin Chen, Sukanya Kudva, Yongzheng Dai, Anil Aswani

The main challenge with the tensor completion problem is a fundamental tension between computation power and the information-theoretic sample complexity rate. Past approaches either achieve the information-theoretic rate but lack practical algorithms to compute the corresponding solution, or have polynomial-time algorithms that require an exponentially-large

Zixiao Zhao, Millon Madhur Das, Fatemeh H. Fard

Pre-trained Code Language Models (Code-PLMs) have shown many advancements and achieved state-of-the-art results for many software engineering tasks in the past few years. These models are mainly targeted for popular programming languages such as Java and Python, leaving out many other ones like R. Though R has a wide community of developers and users, there

Angelina Wang, Xuechunzi Bai, Solon Barocas, Su Lin Blodgett

As machine learning applications proliferate, we need an understanding of their potential for harm. However, current fairness metrics are rarely grounded in human psychological experiences of harm. Drawing on the social psychology of stereotypes, we use a case study of gender stereotypes in image search to examine how people react to machine learning errors.

Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin, Joseph Y. Lo

Weakly supervised learning with noisy data has drawn attention in the medical imaging community due to the sparsity of high-quality disease labels. However, little is known about the limitations of such weakly supervised learning and the effect of these constraints on disease classification performance. In this paper, we test the effects of such weak supervi

Serhat Sönmez, Matthew J. Rutherford, Kimon P. Valavanis

Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Navigation controllers endowed with different attributes and onboard sensor suites enable multirotor autonomous or semi-autonomous, safe flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain

Mengfan Xu, Diego Klabjan

We study a robust, i.e. in presence of malicious participants, multi-agent multi-armed bandit problem where multiple participants are distributed on a fully decentralized blockchain, with the possibility of some being malicious. The rewards of arms are homogeneous among the honest participants, following time-invariant stochastic distributions, which are rev

Christopher Liao, Christian So, Theodoros Tsiligkaridis, Brian Kulis

Domain generalization (DG) is an important problem that learns a model which generalizes to unseen test domains leveraging one or more source domains, under the assumption of shared label spaces. However, most DG methods assume access to abundant source data in the target label space, a requirement that proves overly stringent for numerous real-world applica

Zhanxiang Hua, Yutong He, Chengqian Ma, Alexandra Anderson-Frey

Weather forecasting remains a crucial yet challenging domain, where recently developed models based on deep learning (DL) have approached the performance of traditional numerical weather prediction (NWP) models. However, these DL models, often complex and resource-intensive, face limitations in flexibility post-training and in incorporating NWP predictions,

Katarzyna Siudzińska

We use symmetric measurement operators to construct quantum channels that provide a further generalization of generalized Pauli channels. The resulting maps are bistochastic but in general no longer mixed unitary. We analyze their important properties, such as complete positivity and the ability to break quantum entanglement. In the main part, we consider th

N. V. Larionov, Yu. L. Kolesnikov

In a two-dimensional approximation, the probability density and current for a photoelectron near the localization of a quantum vortex are theoretically investigated. The wave function in the momentum representation, which we found earlier, is simplified near zero, corresponding to the center of the vortex. This allows us to obtain a simple analytical express

Ignacio Ojeda, José Carlos Rosales

Given two numerical semigroups $S$ and $T$ we say that $T$ is a multiple of $S$ if there exists an integer $d \in \mathbb{N} \setminus \{0\}$ such that $S = \{x \in \mathbb{N} \mid d x \in T\}$. In this paper we study the family of multiples of a (fixed) numerical semigroup. We also address the open problem of finding numerical semigroups of embedding dimens

Andrew A. Stirn, David A. Knowles

Widely used deep latent variable models (DLVMs), in particular Variational Autoencoders (VAEs), employ overly simplistic priors on the latent space. To achieve strong clustering performance, existing methods that replace the standard normal prior with a Gaussian mixture model (GMM) require defining the number of clusters to be close to the number of expected

Ashay A. Burungale, Haruzo Hida, Shilin Lai

Let $A$ be a non-CM simple abelian variety over a number field $K$. For a place $v$ of $K$ such that $A$ has good reduction at $v$, let $F(A,v)$ denote the Frobenius field generated by the corresponding Frobenius eigenvalues. Assuming $A$ has connected monodromy groups, we show that the set of places $v$ such that $F(A,v)$ is isomorphic to a fixed number fie

Yiyou Sun, Junjie Hu, Wei Cheng, Haifeng Chen

This paper introduces the retrieval-augmented large language model with Definite Finite Automaton (DFA-RAG), a novel framework designed to enhance the capabilities of conversational agents using large language models (LLMs). Traditional LLMs face challenges in generating regulated and compliant responses in special scenarios with predetermined response guide

Benjamin Shapiro

I construct infinite families of knots and links with totally geodesic spanning surfaces, which we call TGS knots and TGS links, in various 3-manifolds. These 3-manifolds include thickened orientable surfaces, the sphere cross the circle, lens spaces, and the solid torus. The totally geodesic spanning surfaces of knots embedded in thickened orientable surfac

Meiying Zhang, Huan Zhao, Sheldon Ebron, Kan Yang

The performance of clients in Federated Learning (FL) can vary due to various reasons. Assessing the contributions of each client is crucial for client selection and compensation. It is challenging because clients often have non-independent and identically distributed (non-iid) data, leading to potentially noisy or divergent updates. The risk of malicious cl

Hocine Kadi, Théo Sourget, Marzena Kawczynski, Sara Bendjama

In this work, we focused on deep learning image processing in the context of oral rare diseases, which pose challenges due to limited data availability. A crucial step involves teeth detection, segmentation and numbering in panoramic radiographs. To this end, we used a dataset consisting of 156 panoramic radiographs from individuals with rare oral diseases a

Jonathan W. Siegel

We consider the problem of determining the manifold $n$-widths of Sobolev and Besov spaces with error measured in the $L_p$-norm. The manifold widths control how efficiently these spaces can be approximated by general non-linear parametric methods with the restriction that the parameter selection and parameterization maps must be continuous. Existing upper a

Dahye Han, Nan Jiang, Santanu S. Dey, Weijun Xie

Incorporating energy storage systems (ESS) into power systems has been studied in many recent works, where binary variables are often introduced to model the complementary nature of battery charging and discharging. A conventional approach for these ESS optimization problems is to relax binary variables and convert the problem into a linear program. However,

Dian Wang, Zhigang Ren, Gen Kondo

This study introduces a novel machine learning framework, integrating domain knowledge, to accurately predict the bearing capacity of CFSTs, bridging the gap between traditional engineering and machine learning techniques. Utilizing a comprehensive database of 2621 experimental data points on CFSTs, we developed a Domain Knowledge Enhanced Neural Network (DK

Anaelle Hertz, Aaron Z. Goldberg, Khabat Heshami

The quadrature coherence scale (QCS) is a recently introduced measure that was shown to be an efficient witness of nonclassicality. It takes a simple form for pure and Gaussian states, but a general expression for mixed states tends to be prohibitively unwieldy. In this paper, we introduce a method for computing the quadrature coherence scale of quantum stat

Ariel Lubonja, Cencheng Shen, Carey Priebe, Randal Burns

New algorithms for embedding graphs have reduced the asymptotic complexity of finding low-dimensional representations. One-Hot Graph Encoder Embedding (GEE) uses a single, linear pass over edges and produces an embedding that converges asymptotically to the spectral embedding. The scaling and performance benefits of this approach have been limited by a seria

C. González-Galán, R. M. Madero-Castro, A. Luna-Triguero, J. M. Vicent-Luna

Separating C6 cyclic hydrocarbons, specifically benzene and cyclohexane, presents a significant industrial challenge due to their similar physicochemical properties. We conducted Monte Carlo simulations in the Grand-Canonical ensemble to acquire adsorption properties and separation performance data for benzene and cyclohexane in three metal-organic framework

Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu

Personalization in large language models (LLMs) is increasingly important, aiming to align the LLMs' interactions, content, and recommendations with individual user preferences. Recent advances have highlighted effective prompt design by enriching user queries with non-parametric knowledge through behavior history retrieval and textual profiles. However, the

Chao Pang, Xinzhuo Jiang, Nishanth Parameshwar Pavinkurve, Krishna S. Kalluri

Synthetic Electronic Health Records (EHR) have emerged as a pivotal tool in advancing healthcare applications and machine learning models, particularly for researchers without direct access to healthcare data. Although existing methods, like rule-based approaches and generative adversarial networks (GANs), generate synthetic data that resembles real-world EH

Ummy Habiba, Setareh Maghsudi, Ekram Hossain

Multi-access edge computing (MEC) is one of the enabling technologies for high-performance computing at the edge of the 6 G networks, supporting high data rates and ultra-low service latency. Although MEC is a remedy to meet the growing demand for computation-intensive applications, the scarcity of resources at the MEC servers degrades its performance. Hence

Sujay Nagaraj, Walter Gerych, Sana Tonekaboni, Anna Goldenberg

Many time series classification tasks, where labels vary over time, are affected by label noise that also varies over time. Such noise can cause label quality to improve, worsen, or periodically change over time. We first propose and formalize temporal label noise, an unstudied problem for sequential classification of time series. In this setting, multiple l

Subhajit Paul, Suman Majumder, Wolfhard Janke

Observing spontaneous velocity ordering or flocking during motility induced phase separation (MIPS) in a system of spherical active Brownian particles without alignment interaction is challenging. We take up this problem by performing simulations of spherical active inertial particles with purely repulsive potential in presence of thermal noise and absence o

Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov

Post-training quantization (PTQ) reduces the memory footprint of LLMs by quantizing their weights to low-precision. In this work, we introduce QuIP#, a weight-only PTQ method that achieves state-of-the-art results in extreme compression regimes ($\le$ 4 bits per weight) using three novel techniques. First, QuIP# improves QuIP's (Chee et al., 2023) incoherenc

Amir Omidi, Mai Banawan, Erwan Weckenmann, Benoit Paquin

We examine pulse amplitude modulation (PAM) for intensity modulation and direct detection systems. Using a straight-forward, mixed noise model, we optimize the constellations with an autoencoder-based neural network (NN), an improve required signal-to-noise ratio of 4 dB for amplified spontaneous emission (ASE)-limited PAM4 and PAM8, without increasing syste

Alma L. Albujer, Sylvia F. da Silva, Fábio R. dos Santos

The total mean curvature functional for submanifolds into the Riemannian product space $\mathbb{S}^n\times\mathbb{R}$ is considered and its first variational formula is presented. Later on, two second order differential operators are defined and a nice integral inequality relating both of them is proved. Finally we prove our main result: an integral inequali

D. Gomez Dumm, N. N. Scoccola

In our investigations on the effect of strong magnetic fields on the properties of elementary particles we have been faced with a definite integral of the form $$\int_0^{2\pi}d\theta\ L_{n}(s^2+t^2+2st\cos\theta)\ e^{-ik\theta}\, \exp{(-st\,e^{i\theta})}\ , $$ where $L_n(x)$ is a Laguerre polynomial, $s$ and $t$ are real numbers and $n$ and $k$ are integers,

Antonio Jiménez-Pastor, Ali Kemal Uncu

The Factorial Basis method, initially designed for quasi-triangular, shift-compatible factorial bases, provides solutions to linear recurrence equations in the form of definite-sums. This paper extends the Factorial Basis method to its q-analog, enabling its application in q-calculus. We demonstrate the adaptation of the method to q-sequences and its utility

Feilong Jiang, Xiaonan Hou, Min Xia

Although physics-informed neural networks (PINNs) have shown great potential in dealing with nonlinear partial differential equations (PDEs), it is common that PINNs will suffer from the problem of insufficient precision or obtaining incorrect outcomes. Unlike most of the existing solutions trying to enhance the ability of PINN by optimizing the training pro

Scott Greenhorn, Véronique Coizet, Victor Dupuit, Bruno Fernandez

Current techniques of neuroimaging, including electrical devices, are either of low spatiotemporal resolution or invasive, impeding multiscale monitoring of brain activity at both single cell and network levels. Overcoming this issue is of great importance to assess brain's computational ability and for neurorehabilitation projects that require real-time mon

Sebastian Eydam, Igor Franović, Louis Kang

We consider a heterogeneous, globally coupled population of excitatory quadratic integrate-and-fire neurons with excitability adaptation due to a metabolic feedback associated with ketogenic diet, a form of therapy for epilepsy. Bifurcation analysis of a three-dimensional mean-field system derived in the framework of next-generation neural mass models allows

Seungjun Lee, Hyeong-Ryul Kim, Wei Jiang, Young-Kyun Kwon

The piezoelectricity of group IV monochalcogenides (MXs, with M = Ge, Sn and X = S, Se) has attracted much attention due to their substantially higher piezoelectric coefficients compared to other 2D materials. However, with increasing layer number, their piezoelectricity rapidly disappears due to the antiferroelectric stacking order, severely limiting their

Maryam Salamatmoghadasi, Metin Ozturk, Halim Yanikomeroglu

In the evolving landscape of vertical heterogeneous networks, the practice of cell switching particularly for small base stations faces a significant challenge due to the lack of accurate data on the traffic load of sleeping SBSs. This information gap is crucial as it hinders the feasibility and applicability of existing power consumption optimization method

Daniel Alba-Cuellar

This paper describes a geometrical method for finding the roots $r_1$, $r_2$ of a quadratic equation in one complex variable of the form $x^2+c_1 x+c_2=0$, by means of a Line $L$ and a Circumference $C$ in the complex plane, constructed from known coefficients $c_1$, $c_2$. This Line-Circumference (LC) geometric structure contains the sought roots $r_1$, $r_

Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou, Aliaksandra Shysheya

Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including image and video generation, protein and material synthesis, weather forecasting, and neural surrogates of partial differential equations. Despite their ubiquity it is hard to find an i

O. Deniz Kose, Yanning Shen

Machine learning over graphs has recently attracted growing attention due to its ability to analyze and learn complex relations within critical interconnected systems. However, the disparate impact that is amplified by the use of biased graph structures in these algorithms has raised significant concerns for the deployment of them in real-world decision syst

Sopam Dasgupta, Farhad Shakerin, Joaquín Arias, Elmer Salazar

Machine learning models that automate decision-making are increasingly being used in consequential areas such as loan approvals, pretrial bail approval, hiring, and many more. Unfortunately, most of these models are black-boxes, i.e., they are unable to reveal how they reach these prediction decisions. A need for transparency demands justification for such p

Julien Hurtaud, Cécile Delacour, Carole Mathevon, Vincent Forge

Historically, amyloid fibers (AF) in research has always been linked to degenerative diseases. However, HET-s AF, by their morphology and function, have only little in common to pathogenic amyloid fibers such as {\alpha}-synuclein or a\b{eta} and they have appeared as promising candidate for biocoating since few years. Here we have shown than HET-s amyloid f

Nadia Alshahwan, Mark Harman, Inna Harper, Alexandru Marginean

In this paper we address the following question: How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engine

Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson

We propose fine-tuning large language models for generation of stable materials. While unorthodox, fine-tuning large language models on text-encoded atomistic data is simple to implement yet reliable, with around 90% of sampled structures obeying physical constraints on atom positions and charges. Using energy above hull calculations from both learned ML pot

Raza Ul Mustafa, Nathalie Japkowicz

Racism and intolerance on social media contribute to a toxic online environment which may spill offline to foster hatred, and eventually lead to physical violence. That is the case with online antisemitism, the specific category of hatred considered in this study. Tracking antisemitic themes and their associated terminology over time in online discussions co

Rohan Pandey, Harry Richman

This paper analyzes the M\"obius ($\mu(i)$) function defined on the partially ordered set of triangular numbers ($\mathcal T(i)$) under the divisibility relation. We make conjectures on the asymptotic behavior of the classical M\"obius and Mertens functions based on experimental data and other proven conjectures. We first introduce the growth of partial sums

Océane Terral, Guillaume Audic, Arnaud Claudel, Justine Magnat

Graphene field effect transistors (G-FETs) have appeared as suitable candidates for sensing charges and have thus attracted large interest for ion and chemical detections. In particular, their high sensitivity, chemical robustness, transparency and bendability offer a unique combination for interfacing living and soft matters. Here we have demonstrated their

Parsa Moradi, Mohammad Ali Maddah-Ali

Resilience against stragglers is a critical element of prediction serving systems, tasked with executing inferences on input data for a pre-trained machine-learning model. In this paper, we propose NeRCC, as a general straggler-resistant framework for approximate coded computing. NeRCC includes three layers: (1) encoding regression and sampling, which genera

Ayush Jain, Andrea Montanari, Eren Sasoglu

Collecting large quantities of high-quality data can be prohibitively expensive or impractical, and a bottleneck in machine learning. One may instead augment a small set of $n$ data points from the target distribution with data from more accessible sources, e.g. data collected under different circumstances or synthesized by generative models. We refer to suc

Yvonne Zhou, Mingyu Liang, Ivan Brugere, Dana Dachman-Soled

The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset. To prevent leakage of sensitive data, we consider using differentially-private (DP), synthetic training data instead of real training data to train an ML model. A key desirable property

Adam Joshua Hung, Challen Enninful Adu, Talia Y. Moore

In both animals and robots, locomotion capabilities are determined by the physical structure of the system. The majority of legged animals and robots are bilaterally symmetric, which facilitates locomotion with consistent headings and obstacle traversal, but leads to constraints in their turning ability. On the other hand, radially symmetric animals have dem

Alakananda Mitra, Saraju P. Mohanty, Elias Kougianos

We live in the era of Generative Artificial Intelligence (GenAI). Deepfakes and Large Language Models (LLMs) are two examples of GenAI. Deepfakes, in particular, pose an alarming threat to society as they are capable of spreading misinformation and changing the truth. LLMs are powerful language models that generate general-purpose language. However due to it

Helmut Abels, Yadong Liu, Šárka Nečasová

In this paper, we consider a singular limit problem for a diffuse interface model for two immiscible compressible viscous fluids. Via a relative entropy method, we obtain a convergence result for the low Mach number limit to a corresponding system for incompressible fluids in the case of well-prepared initial data and same densities in the limit.

Gregory J. Loges

We present a new infinite class of non-abelian, 6d supergravities with eight supercharges. These theories not only satisfy all known low-energy consistency conditions, such as being free of anomalies, but also evade the constraints arising from the consistency of string probes, even after assuming BPS completeness. This demonstrates that some additional UV i

Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Jussi P. P. Jokinen, Antti Oulasvirta

This paper presents a model of pedestrian crossing decisions, based on the theory of computational rationality. It is assumed that crossing decisions are boundedly optimal, with bounds on optimality arising from human cognitive limitations. While previous models of pedestrian behaviour have been either 'black-box' machine learning models or mechanistic model

Alcides Buss, Siegfried Echterhoff

In this paper we study deformations of $C^*$-algebras that are given as cross-sectional $C^*$-algebras of Fell bundles over locally compact groups $G$. Our deformation comes from a direct deformation of the Fell bundles via certain parameters, like automorphisms of the Fell bundle, group cocycles, or central group extensions of $G$ by the circle group $\math

Dmytro Vovchuk, Mykola Khobzei, Vladyslav Tkach, Oleg Eliiashiv

The forthcoming era of massive drone delivery deployment in urban environments raises a need to develop reliable control and monitoring systems. While active solutions, i.e., wireless sharing of a real-time location between air traffic participants and control units, are of use, developing additional security layers is appealing. Among various surveillance s

Junhong Shen, Neil Tenenholtz, James Brian Hall, David Alvarez-Melis

Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding and generating natural language. However, their capabilities wane in highly specialized domains underrepresented in the pretraining corpus, such as physical and biomedical sciences. This work explores how to repurpose general LLMs into effective task solvers for specialize

Oleksandr Kuznetsov, Alex Rusnak, Anton Yezhov, Kateryna Kuznetsova

In the rapidly evolving landscape of blockchain technology, ensuring the integrity and security of data is paramount. This study delves into the security aspects of Merkle Trees, a fundamental component in blockchain architectures, such as Ethereum. We critically examine the susceptibility of Merkle Trees to hash collisions, a potential vulnerability that po

A. Andronic, P. B. Gossiaux, P. Petreczky, R. Rapp

This document summarizes the efforts of the EMMI Rapid Reaction Task Force on "Suppression and (re)generation of quarkonium in heavy-ion collisions at the LHC", centered around their 2019 and 2022 meetings. It provides a review of existing experimental results and theoretical approaches, including lattice QCD calculations and semiclassical and quantum approa