February 2024 arXiv papers — page 50

Jean-François Burnol

For $b>1$ and $\alpha\beta$ a string of two digits in base $b$, let $K_1$ be the subsum of the harmonic series with only those integers having exactly one occurrence of $\alpha\beta$. We obtain a theoretical representation of such $K_1$ series which, say for $b=10$, allows computing them all to thousands of digits. This is based on certain specific measures

Chen Jia

Preference learning (PL) with large language models (LLMs) aims to align the LLMs' generations with human preferences. Previous work on reinforcement learning from human feedback (RLHF) has demonstrated promising results in in-distribution PL. However, due to the difficulty of obtaining human feedback, discretely training reward models for every encountered

Fabio Cuzzolin

The purpose of this paper is to look into how central notions in statistical learning theory, such as realisability, generalise under the assumption that train and test distribution are issued from the same credal set, i.e., a convex set of probability distributions. This can be considered as a first step towards a more general treatment of statistical learn

Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian

Most leading implementations of black-box variational inference (BBVI) are based on optimizing a stochastic evidence lower bound (ELBO). But such approaches to BBVI often converge slowly due to the high variance of their gradient estimates and their sensitivity to hyperparameters. In this work, we propose batch and match (BaM), an alternative approach to BBV

Divija Swetha Gadiraju, Saeed Eftekhar Azam, Deepak Khazanchi

This work focuses on using advanced techniques for structural health monitoring (SHM) for bridges with Traffic. We propose an approach using deep reinforcement learning (DRL)-based control for Unmanned Aerial Vehicle (UAV). Our approach conducts a concrete bridge deck survey while traffic is ongoing and detects cracks. The UAV performs the crack detection, a

Ataleshvara Bhargava, Tiklung Chan, Zi Li Lim, Yixuan Pang

This article serves as a study guide for the $\ell^2$ decoupling theorem for the paraboloid originally proved by Bourgain and Demeter. Given its popularity and importance, many expositions about the $\ell^2$ decoupling theorem already exist. Our study guide is intended to complement and combine these existing resources in order to provide a more gentle intro

Dmitrii Pirozhkov

Consider a Grassmannian $\mathrm{Gr}(2, V)$ for an even-dimensional vector space $V$. Its derived category of coherent sheaves has a Lefschetz exceptional collection with respect to the Pl\"ucker embedding. We consider a variety $X_1$ of pairs consisting of a degenerate $2$-form on $V$ and a line in its kernel. Note that $X_1$ is generically a $\mathbb{P}^1$

Aaditya K. Singh, DJ Strouse

Tokenization, the division of input text into input tokens, is an often overlooked aspect of the large language model (LLM) pipeline and could be the source of useful or harmful inductive biases. Historically, LLMs have relied on byte pair encoding, without care to specific input domains. With the increased use of LLMs for reasoning, various number-specific

Aleksandar Petrov, Philip H. S. Torr, Adel Bibi

Despite the widespread adoption of prompting, prompt tuning and prefix-tuning of transformer models, our theoretical understanding of these fine-tuning methods remains limited. A key question is whether one can arbitrarily modify the behavior of pretrained model by prompting or prefix-tuning it. Formally, whether prompting and prefix-tuning a pretrained mode

M. B. Hastings

We answer two questions regarding the sum-of-squares for the SYK model left open in Ref. 1, both of which are related to graphs. First (a "limitation"), we show that a fragment of the sum-of-squares, in which one considers commutation relations of degree-$4$ Majorana operators but does not impose any other relations on them, does not give the correct order o

Divija Swetha Gadiraju, Ryan McMaster, Saeed Eftekhar Azam, Deepak Khazanchi

Bridge health monitoring becomes crucial with the deployment of IoT sensors. The challenge lies in securely storing vast amounts of data and extracting useful information to promptly identify unhealthy bridge conditions. To address this challenge, we propose BIONIB, wherein real-time IoT data is stored on the blockchain for monitoring bridges. One of the eme

Nikhil S. Mande, Manaswi Paraashar, Swagato Sanyal, Nitin Saurabh

A tournament is a complete directed graph. A king in a tournament is a vertex v such that every other vertex is reachable from v via a path of length at most 2. It is well known that every tournament has at least one king, one of which is a maximum out-degree vertex. The tasks of finding a king, a maximum out-degree vertex and a source in a tournament has be

Wendi Zhou, Tianyi Li, Pavlos Vougiouklis, Mark Steedman

Identifying and understanding user intents is a pivotal task for E-Commerce. Despite its essential role in product recommendation and business user profiling analysis, intent understanding has not been consistently defined or accurately benchmarked. In this paper, we focus on predicative user intents as "how a customer uses a product", and pose intent unders

Arturo de la Barcena, Collin Rhodes, John McCarroll, Marzia Cescon

As the space domain becomes increasingly congested, autonomy is proposed as one approach to enable small numbers of human ground operators to manage large constellations of satellites and tackle more complex missions such as on-orbit or in-space servicing, assembly, and manufacturing. One of the biggest challenges in developing novel spacecraft autonomy is m

Leandro Cieri, Prasanna K. Dhani, Germán Rodrigo

We consider the most general form of soft and collinear factorization for hard-scattering amplitudes to all orders in perturbative Quantum Chromodynamics. Specifically, we present the generalization of collinear factorization to configurations with several collinear directions, where the most singular behaviour is encoded by generalized collinear splitting a

S. Hauksson, E. Iancu, A. H. Mueller, D. N. Triantafyllopoulos

Using the colour dipole picture and the colour glass condensate effective theory, we study the diffractive production of two or three jets via coherent photon-nucleus interactions at high energy. We consider the hard regime where the photon virtuality and/or the transverse momenta of the produced jets are much larger than the saturation momentum $Q_s$ of the

Tarun Ram Kanuri, Subhadeep Roy, Soumyajyoti Biswas

We have numerically studied a mean-field fiber bundle model of fracture at a non-zero temperature and acted by a constant external tensile stress. The individual fibers fail (local damage) due to creep-like dynamics that lead up to a catastrophic breakdown (global failure). We quantify the variations in sizes of the resulting avalanches by calculating the Lo

B. N. Kausik

Recent LLMs have hundreds of billions of parameters consuming vast resources. Furthermore, the so called "AI scaling law" for transformers suggests that the number of parameters must scale linearly with the size of the data. In response, we inquire into efficient LLMs, i.e. those with the fewest parameters that achieve the desired accuracy on a training corp

Luca Carai, Miriam Kurtzhals, Tommaso Moraschini

A quasivariety has the weak ES property when the epimorphisms between its finitely generated members are surjective. A characterization of quasivarieties with the weak ES property is obtained and a method for detecting failures of this property in quasivarieties with a near unanimity term and in congruence permutable varieties is given. It is also shown that

Jiawei Wang, Renhe Jiang, Chuang Yang, Zengqing Wu

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: alig

Şaziye Betül Özateş, Tarık Emre Tıraş, Efe Eren Genç, Esma Fatıma Bilgin Taşdemir

This study introduces a pretrained large language model-based annotation methodology for the first de dency treebank in Ottoman Turkish. Our experimental results show that, iteratively, i) pseudo-annotating data using a multilingual BERT-based parsing model, ii) manually correcting the pseudo-annotations, and iii) fine-tuning the parsing model with the corre

Valentino Smaldore, Corrado Zanella, Ferdinando Zullo

Let $1<t<n$ be integers, where $t$ is a divisor of $n$. An R-$q^t$-partially scattered polynomial is a $\mathbb F_q$-linearized polynomial $f$ in $\mathbb F_{q^n}[X]$ that satisfies the condition that for all $x,y\in\mathbb F_{q^n}^*$ such that $x/y\in\mathbb F_{q^t}$, if $f(x)/x=f(y)/y$, then $x/y\in\mathbb F_q$; $f$ is called scattered if this implication

Daniel Capellán-Martín, Abhijeet Parida, Juan J. Gómez-Valverde, Ramon Sanchez-Jacob

Tuberculosis (TB) remains a significant global health challenge, with pediatric cases posing a major concern. The World Health Organization (WHO) advocates for chest X-rays (CXRs) for TB screening. However, visual interpretation by radiologists can be subjective, time-consuming and prone to error, especially in pediatric TB. Artificial intelligence (AI)-driv

Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee

AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is increasingly treated as a crucial ingredient for high performance large language models. Proximal Policy Optimization (PPO) has been positioned by recent literature as the canonical method for the RL part of RLHF. However, it involves both high computational cost and sensitive

Chinmay Vilas Samak, Tanmay Vilas Samak

Modeling and simulation of autonomous vehicles plays a crucial role in achieving enterprise-scale realization that aligns with technical, business and regulatory requirements. Contemporary trends in digital lifecycle treatment have proven beneficial to support SBD as well as V&V of these complex systems. Although, the development of appropriate fidelity simu

R. K. P. Zia

The evolution of physical systems are often modeled by simple Markovian processes. When settled into stationary states, the probability distributions of such systems are time independent, by definition. However, they do not necessarily fall within the framework of equilibrium statistical mechanics. Instead, they may be non-equilibrium steady states (NESS). O

Francesco Malandrino, Alessandro Nordio, Carla Fabiana Chiasserini

Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communication security and privacy. In this work, we focus on the latter aspect and introduce a strategy to counteract the presence of passive eavesdroppers overhearing transmissions from a base station towards legitimate users that are facilit

Mahkam Khalilloev

In this work influence of gate extension, channel doping level, and channel thickness to short channel effects- DIBL effect and subthreshold swing, SS for the planar and vertical junctionless field effect transistors is compared. It is shown in the considered range of doping level and channel thickness the DIBL effect is less for junctionless vertical field

Alex A. Gorodetsky, John D. Jakeman, Michael S. Eldred

This paper analyzes the approximate control variate (ACV) approach to multifidelity uncertainty quantification in the case where weighted estimators are combined to form the components of the ACV. The weighted estimators enable one to precisely group models that share input samples to achieve improved variance reduction. We demonstrate that this viewpoint yi

Eshaan Nichani, Alex Damian, Jason D. Lee

The incredible success of transformers on sequence modeling tasks can be largely attributed to the self-attention mechanism, which allows information to be transferred between different parts of a sequence. Self-attention allows transformers to encode causal structure which makes them particularly suitable for sequence modeling. However, the process by which

Maria McEachern, Bretislav Friedrich

In 1896, Edward Charles Pickering (1846-1919), Director of the Harvard College Observatory (HCO), reported in a trio of publications the observation of "peculiar spectra" of the southern star $\zeta$ Puppis, which he attributed to an "element not yet found in other stars or on earth." Supported by laboratory spectra obtained by Alfred Fowler (1868-1940), Nie

Fabio Aratore, Oleg Yu. Tsupko, Volker Perlick

Gravitational lensing of luminous matter that surrounds a black hole or some other sufficiently compact object produces an infinite sequence of images. Besides the direct (or primary) image, it comprises demagnified and deformed replicas of the original known as photon rings which are progressively nearing the boundary of the socalled shadow. In the present

Pintu Debnath

In \cite[Proposition 8.21 Page-169]{F} Using the methods of topological dynamics, H. Furstenberg introduced the notion of central set and proved the famous Central Sets Theorem. Later, in \cite{DHS}, D. De, H. Hindman and D. Struss established a strong Central Sets Theorem, where they introduced the notion of $J$-set. Like $J$-set, in \cite{BG} V. Bergelson

Leo Brauner, Georg C. Hofstätter, Oscar Ortega-Moreno

We investigate the action of Alesker's Lefschetz operators on translation invariant valuations on convex bodies. For scalar valued valuations, we describe this action on the level of Klain-Schneider functions by a Radon type transform, generalizing a result by Schuster and Wannerer. In the case of rotationally equivariant Minkowski valuations, the Lefschetz

Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic

We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of $\mathrm{E}(p, q)$-equivariant CNNs. CS-CNNs process multivector fields on pseudo-Euclidean spaces $\mathbb{R}^{p,q}$. They cover, for instance, $\mathrm{E}(3)$-equivariance on $\mathbb{R}^3$ and Poincar\'e-equivariance on Minkowski spacetime $\mathbb{R}^{1,3}$. Our appro

Tiezhi Wang, Nils Strodthoff

This study aims to elucidate the significance of long-range correlations for deep-learning-based sleep staging. It is centered around S4Sleep(TS), a recently proposed model for automated sleep staging. This model utilizes electroencephalography (EEG) as raw time series input and relies on structured state space sequence (S4) models as essential model compone

Zefeng Wang, Zhen Han, Shuo Chen, Fan Xue

Multimodal LLMs (MLLMs) with a great ability of text and image understanding have received great attention. To achieve better reasoning with MLLMs, Chain-of-Thought (CoT) reasoning has been widely explored, which further promotes MLLMs' explainability by giving intermediate reasoning steps. Despite the strong power demonstrated by MLLMs in multimodal reasoni

Peter A. Monkewitz

The difficulty of determining the slope of the famed logarithmic law in the mean velocity profile in wall-bounded turbulent flows, the inverse of the Karman 'constant' $\kappa$, from direct numerical simulations (DNS) is discussed for channel flow. Unusual approaches, as well as the analysis of the standard log-indicator function are considered and analyzed,

J. V. Sales-Silva, K. Cunha, V. V. Smith, S. Daflon

This study probes the chemical abundances of the neutron-capture elements cerium and neodymium in the inner Milky Way from an analysis of a sample of $\sim$2000 stars in the Galactic Bulge/bar spatially contained within $|X_{Gal}|<$5 kpc, $|Y_{Gal}|<$3.5 kpc, and $|Z_{Gal}|<$1 kpc, and spanning metallicities between $-$2.0$\lesssim$[Fe/H]$\lesssim$+0.5. We c

André Calero Valdez, Moreen Heine, Thomas Franke, Nicole Jochems

The evolution of AI is set to profoundly reshape the future. The European Union, recognizing this impending prominence, has enacted the AI Act, regulating market access for AI-based systems. A salient feature of the Act is to guard democratic and humanistic values by focusing regulation on transparency, explainability, and the human ability to understand and

Rafael López, Marian Ioan Munteanu

A soliton of the mean curvature flow in the product space $\mathbb{s}^2\times\mathbb{R}$ as a surface whose mean curvature $H$ satisfies the equation $H=\langle N,X\rangle$, where $N$ is the unit normal of the surface and $X$ is a Killing vector field. In this paper we consider the vector field tangent to the fibers and the vector field associated to a rotat

Andrei V. Konstantinov, Lev V. Utkin

A problem of incorporating the expert rules into machine learning models for extending the concept-based learning is formulated in the paper. It is proposed how to combine logical rules and neural networks predicting the concept probabilities. The first idea behind the combination is to form constraints for a joint probability distribution over all combinati

J. A. Aguilar-Saavedra

Quantum tomography in high-energy physics processes has usually been restricted to the spin degrees of freedom. We address the case of top quark decays $t \to W b$, in which the orbital angular momentum ($L$) and the spins of $W$ and $b$ are intertwined into a 54-dimensional $LWb$ density operator. The entanglement between $L$ and the $W$ or $b$ spin is larg

Roland Welter

Motivated by the need for energetically consistent climate models, the Boussinessq-Coriolis (BC) equations are studied with a focus on the averaged vertical heat transport, ie the Nusselt number. A set of formulae are derived by which arbitrary Fourier truncations of the BC model can be explicitly generated, and Criteria are given which precisely guarantee t

Cassie-Kay McQuinn, Kyle Dunlap, Nathaniel Hamilton, Jabari Wilson

A fundamental capability for On-orbit Servicing, Assembly, and Manufacturing (OSAM) is inspection of the vehicle to be serviced, or the structure being assembled. This research assumes autonomous slewing to maintain situational awareness of multiple vehicles operating in close proximity where several safety constraints must be satisfied. A variety of techniq

Drazen Adamovic, Ozren Perse, Ivana Vukorepa

The problem of determining maximal ideals in universal affine vertex algebras is difficult for levels beyond admissible, since there are no simple character formulas which can be applied. Here we investigate when certain quotient $\mathcal V$ of universal affine vertex algebra $V^k(\mathfrak{g})$ is simple. We present a new method for proving simplicity of q

Razieh Rastgoo, Kourosh Kiani, Sergio Escalera

Sign Language Recognition (SLR) has garnered significant attention from researchers in recent years, particularly the intricate domain of Continuous Sign Language Recognition (CSLR), which presents heightened complexity compared to Isolated Sign Language Recognition (ISLR). One of the prominent challenges in CSLR pertains to accurately detecting the boundari

Oliver Bentham, Nathan Stringham, Ana Marasović

Understanding the extent to which Chain-of-Thought (CoT) generations align with a large language model's (LLM) internal computations is critical for deciding whether to trust an LLM's output. As a proxy for CoT faithfulness, Lanham et al. (2023) propose a metric that measures a model's dependence on its CoT for producing an answer. Within a single family of

Steven D. Bass

Motivated by the stability of the electroweak Higgs vacuum we consider the possibility that the Standard Model might work up to large scales between about $10^{10}$ GeV and close to the Planck scale. A plausible scenario is an emergent Standard Model with gauge symmetries originating in some topological like phase transition deep in the ultraviolet. In this

Hideki Okawa, Qing-Guo Zeng, Xian-Zhe Tao, Man-Hong Yung

Charged particle reconstruction or track reconstruction is one of the most crucial components of pattern recognition in high-energy collider physics. It is known to entail enormous consumption of computing resources, especially when the particle multiplicity is high, which will be the conditions at future colliders, such as the High Luminosity Large Hadron C

Jennifer Przybilla, Igor Pontes Duff, Pawan Goyal, Peter Benner

This work discusses model reduction for differential-algebraic systems with quadratic output equations. Under mild conditions, these systems can be transformed into a Weierstra{\ss} canonical form and, thus, be decoupled into differential equations and algebraic equations. The corresponding decoupled states are referred to as proper and improper states. Due

Sara Collins, Alexey Nefediev, M. Padmanath, Sasa Prelovsek

The $DD^*$ scattering phase shifts in the $T_{cc}^+=cc\bar{u}\bar{d}$ channel are extracted from lattice QCD for five different charm quark masses and a fixed light-quark mass corresponding to $m_\pi\simeq 280$~MeV. The phase shifts are analysed employing two approaches: effective range expansion and Lippmann--Schwinger equation derived in the effective fiel

Seungduk Kim, Seungtaek Choi, Myeongho Jeong

This report introduces \texttt{EEVE-Korean-v1.0}, a Korean adaptation of large language models that exhibit remarkable capabilities across English and Korean text understanding. Building on recent highly capable but English-centric LLMs, such as SOLAR-10.7B and Phi-2, where non-English texts are inefficiently processed with English-centric tokenizers, we pre

Andrea Faúndez Quezada, Salvatore La Cavera, Sidahmed A Abayzeed

This paper presents a comparison of several Convolutional Neural Network (CNN) models for extracting target signals in highly noisy measurement conditions. Four CNN architectures were investigated. The first comprises six consecutive convolutional blocks while the second employs a U-Net structure. The third architecture introduces a new model inspired by the

Keshav Goyal, Duc Tu Dao, Mladen Kovačević, Han Mao Kiah

Analytic combinatorics in several variables refers to a suite of tools that provide sharp asymptotic estimates for certain combinatorial quantities. In this paper, we apply these tools to determine the Gilbert--Varshamov lower bound on the rate of optimal codes in $L_1$ metric. Several different code spaces are analyzed, including the simplex and the hypercu

Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang

Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE). Note that high-quality instruction data is the vital key for enhancing the specific capabilities of LLMs, while current IE datasets tend to be small in scale, fragmented, and lack standardiz

M. P. Roriz, N. Holanda, L. V. da Conceição, S. Junqueira

A classical Local Thermodynamic Equilibrium analysis, based on high-resolution spectroscopic data, is performed for a sample of three potential barium dwarf candidates and one star already recognized as such. We derived their atmospheric parameters, estimated their masses and luminosities, and determined chemical abundances for a set of 21 elements, includin

Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng

Credit card fraud poses a significant threat to the economy. While Graph Neural Network (GNN)-based fraud detection methods perform well, they often overlook the causal effect of a node's local structure on predictions. This paper introduces a novel method for credit card fraud detection, the \textbf{\underline{Ca}}usal \textbf{\underline{T}}emporal \textbf{

Zhenrong Shen, Manman Fei, Xin Wang, Jiangdong Cai

Automatic thin-prep cytologic test (TCT) screening can assist pathologists in finding cervical abnormality towards accurate and efficient cervical cancer diagnosis. Current automatic TCT screening systems mostly involve abnormal cervical cell detection, which generally requires large-scale and diverse training data with high-quality annotations to achieve pr

Kohei Inayoshi, Kohei Ichikawa

The James Webb Space Telescope (JWST) has unveiled numerous massive black holes (BHs) in faint, broad-line active galactic nuclei (AGNs). The discovery highlights the presence of dust-reddened AGN populations, referred to as "little red dots (LRDs)", more abundant than X-ray selected AGNs, which are less influenced by obscuration. This finding indicates that

Gui-Sheng Xu, Mudit Jain, Xiang-Fa Zhou, Guang-Can Guo

Artificial monopoles have been engineered in various systems, yet there has been no systematic study of the singular vector potentials associated with the monopole field. We show that the Dirac string, the line singularity of the vector potential, can be engineered, manipulated, and made manifest in a spinor atomic condensate. We elucidate the connection amo

Keren Tan, Kangyang Luo, Yunshi Lan, Zheng Yuan

Lexical Simplification (LS) aims to simplify text at the lexical level. Existing methods rely heavily on annotated data, making it challenging to apply in low-resource scenarios. In this paper, we propose a novel LS method without parallel corpora. This method employs an Adversarial Editing System with guidance from a confusion loss and an invariance loss to

Aram Akram Mohammed, Fakhraddin Mustafa Hama Salih

Budding and grafting are the strategies employed to combat unfavorable environmental conditions and improve some physiological defects in the Pistacia vera tree. Drought and salinity stresses are the most prominent adverse conditions encountered in pistachio production. It has been observed in different studies that various pistachio rootstocks can be used t

Yuheng Zhang, Nan Jiang

We study off-policy evaluation (OPE) in partially observable environments with complex observations, with the goal of developing estimators whose guarantee avoids exponential dependence on the horizon. While such estimators exist for MDPs and POMDPs can be converted to history-based MDPs, their estimation errors depend on the state-density ratio for MDPs whi

Somnath Banerjee, Maulindu Sarkar, Punyajoy Saha, Binny Mathew

Recently, influence functions present an apparatus for achieving explainability for deep neural models by quantifying the perturbation of individual train instances that might impact a test prediction. Our objectives in this paper are twofold. First we incorporate influence functions as a feedback into the model to improve its performance. Second, in a datas

Pietro Gravino, Giulio Prevedello, Emanuele Brugnoli

The digital age provides new challenges as information travels more quickly in a system of increasing complexity. But it also offers new opportunities, as we can track and study the system more efficiently. Several studies individually addressed different digital tracks, focusing on specific aspects like disinformation production or content-sharing dynamics.

Baihan Lin, Djallel Bouneffouf, Yulia Landa, Rachel Jespersen

The therapeutic working alliance is a critical predictor of psychotherapy success. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this paper, we present COMPASS, a novel framework to directly infer the therapeutic working alliance from the natural language used in psychotherapy sessions. Our

Zhihao Zhang, Jun Zhao, Qi Zhang, Tao Gui

Large Language Models (LLMs) have demonstrated considerable cross-lingual alignment and generalization ability. Current research primarily focuses on improving LLMs' cross-lingual generalization capabilities. However, there is still a lack of research on the intrinsic mechanisms of how LLMs achieve cross-lingual alignment. From the perspective of region part

Krzysztof J. Ciosmak

Let $X$ be a subset of a Hilbert space. We prove that if $v\colon X\to \mathbb{R}^m$ is such that \begin{equation*} \Big\lVert v(x)-\sum_{i=1}^m t_iv(x_i)\Big\rVert\leq \Big\lVert x-\sum_{i=1}^m t_ix_i\Big\rVert \end{equation*} for all $x,x_1,\dotsc,x_m\in\mathbb{R}^m$ and all non-negative $t_1,\dotsc,t_m$ that add up to one, then for any $1$-Lipschitz $u\co

Lei Yu, Ke Han

Earthwork-related locations (ERLs), such as construction sites, earth dumping ground, and concrete mixing stations, are major sources of urban dust pollution (particulate matters). The effective management of ERLs is crucial and requires timely and efficient tracking of these locations throughout the city. This work aims to identify and classify urban ERLs u

Ritabrata Sengupta, Ajit Iqbal Singh

Finite dimensional entanglement for pure states has been used extensively in quantum information theory. Depending on the tensor product structure, even set of separable states can show non-intuitive characters. Two situations are well studied in the literature, namely the unextendible product basis by Bennett et al. [Phys. Rev. Lett. 82, 5385, (1999)], and

Shi Jin, Nana Liu, Chuwen Ma

We analyze the Schr\"odingerization method for quantum simulation of a general class of non-unitary dynamics with inhomogeneous source terms. The Schr\"odingerization technique, introduced in [31], transforms any linear ordinary and partial differential equations with non-unitary dynamics into a system under unitary dynamics via a warped phase transition tha

Han Zhang, Daoping Zhang, Lok Ming Lui

Image segmentation plays a crucial role in extracting important objects of interest from images, enabling various applications. While existing methods have shown success in segmenting clean images, they often struggle to produce accurate segmentation results when dealing with degraded images, such as those containing noise or occlusions. To address this chal

Ethan N. Evans, Dominic Byrne, Matthew G. Cook

This paper provides an introduction to quantum machine learning, exploring the potential benefits of using quantum computing principles and algorithms that may improve upon classical machine learning approaches. Quantum computing utilizes particles governed by quantum mechanics for computational purposes, leveraging properties like superposition and entangle

Mohd Saif Ali Khan, Samar Agnihotri, Karthik R. M

The uplink sum-throughput of distributed massive multiple-input-multiple-output (mMIMO) networks depends majorly on Access point (AP)-User Equipment (UE) association and power control. The AP-UE association and power control both are important problems in their own right in distributed mMIMO networks to improve scalability and reduce front-haul load of the n

Yukiya Hono, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda

This paper presents a neural vocoder based on a denoising diffusion probabilistic model (DDPM) incorporating explicit periodic signals as auxiliary conditioning signals. Recently, DDPM-based neural vocoders have gained prominence as non-autoregressive models that can generate high-quality waveforms. The neural vocoders based on DDPM have the advantage of tra

Kharisma Surya Putri, Tatsuki Mizuochi, Niklas Kolbe, Hirofumi Notsu

A new moving mesh scheme based on the Lagrange-Galerkin method for the approximation of the one-dimensional convection-diffusion equation is studied. The mesh movement, which is prescribed by a discretized dynamical system for the nodal points, follows the direction of convection. It is shown that under a restriction of the time increment the mesh movement c

Zhaoheng Huang, Zhicheng Dou, Yutao Zhu, Ji-rong Wen

Large language models (LLMs) may generate text that lacks consistency with human knowledge, leading to factual inaccuracies or \textit{hallucination}. Existing research for evaluating the factuality of LLMs involves extracting fact claims using an LLM and verifying them against a predefined fact source. However, these evaluation metrics are task-specific, an

Luca Dieci, Alessandro Pugliese

In this work we consider generic losses of rank for complex valued matrix functions depending on two parameters. We give theoretical results that characterize parameter regions where these losses of rank occur. Our main results consist in showing how following an appropriate smooth SVD along a closed loop it is possible to monitor the Berry phases accrued by

Mohamed Bouali

We investigate the convexity property on $(0,1)$ of the functions $\varphi_{a,b,c}$ and $1/\varphi_{a,b,c}$, where $$\varphi_{a,b,c}(x)= \frac{c-\log(1-x)}{\,_2F_1(a,b,a+b,x)},$$ whenever $a,b\geq 0$ and $a+b\leq 1$. We Show that $\varphi_{a,b,c}$ (respectively $1/\varphi_{a,b,c}$) is strictly convex on $(0,1)$ if and only if $c\leq -2\gamma-\psi(a)-\psi(b),

Kenneth Li, Samy Jelassi, Hugh Zhang, Sham Kakade

We present an approach called Q-probing to adapt a pre-trained language model to maximize a task-specific reward function. At a high level, Q-probing sits between heavier approaches such as finetuning and lighter approaches such as few shot prompting, but can also be combined with either. The idea is to learn a simple linear function on a model's embedding s

Frédéric Piedboeuf, Philippe Langlais

Textual data augmentation (DA) is a prolific field of study where novel techniques to create artificial data are regularly proposed, and that has demonstrated great efficiency on small data settings, at least for text classification tasks. In this paper, we challenge those results, showing that classical data augmentation (which modify sentences) is simply a

Harrison Gaebler, Pavlos Motakis, Bunyamin Sari

We construct a reflexive Banach space $X_\mathcal{D}$ with an unconditional basis such that all spreading models admitted by normalized block sequences in $X_\mathcal{D}$ are uniformly equivalent to the unit vector basis of $\ell_1$, yet every infinite-dimensional closed subspace of $X_\mathcal{D}$ fails the Lebesgue property. This is a new result in a progr

Benjamin Maaß, Norman Vincenz Ewald, Avijit Barua, Stephan Reitzenstein

On-demand storage and retrieval of quantum information in coherent light-matter interfaces is a key requirement for future quantum networking and quantum communication applications. Alkali vapor memories offer scalable and robust high-bandwidth storage at high repetition rates which makes them a natural fit to interface with solid-state single-photon sources

Jason R. Williams, Charles A. Sackett, Holger Ahlers, David C. Aveline

Ultracold atomic gases hold unique promise for space science by capitalizing on quantum advantages and extended freefall, afforded in a microgravity environment, to enable next-generation precision sensors. Atom interferometers are a class of quantum sensors which can use freely falling gases of atoms cooled to sub-photon-recoil temperatures to provide unpre

Baptiste Abélès, Joseph de Vilmarest, Olivier Wintemberger

Adaptive time series forecasting is essential for prediction under regime changes. Several classical methods assume linear Gaussian state space model (LGSSM) with variances constant in time. However, there are many real-world processes that cannot be captured by such models. We consider a state-space model with Markov switching variances. Such dynamical syst

Wen Huang, Hongbin Liu, Minxin Guo, Neil Zhenqiang Gong

Visual hallucination (VH) means that a multi-modal LLM (MLLM) imagines incorrect details about an image in visual question answering. Existing studies find VH instances only in existing image datasets, which results in biased understanding of MLLMs' performance under VH due to limited diversity of such VH instances. In this work, we propose a tool called VHT

G. M. Cappello, M. Temmer, A. Vourlidas, C. Braga

We track and investigate from white-light data taken with the Wide-field Instrument for Solar PRobe (WISPR) aboard Parker Solar Probe (PSP), localized density enhancements, reflecting small-scale magnetic structures belonging to a filament-related coronal mass ejection (CME). We aim to investigate the 3D location, morphology, and evolution of the internal ma

Krystyna Mruczek-Nasieniewska, Mateusz Klonowski

The P{\l}onka sum is an algebra determined using a structure called a direct system. By a direct system, we mean an indexed family of algebras with disjoint universes whose indexes form a join-semilattice s.t. if two indexes are in a partial order relation, then there is a homomorphism from the algebra of the first index to the algebra of the second index. T

Soorya Rethinasamy, Ethan Guo, Alexander Wei, Mark M. Wilde

With a view toward addressing the explosive growth in the computational demands of nuclear structure and reactions modeling, we develop a novel quantum algorithm for neutron-nucleus simulations with general potentials, which provides acceptable bound-state energies even in the presence of noise, through the noise-resilient training method. In particular, the

Yiming Ai, Zhiwei He, Ziyin Zhang, Wenhong Zhu

In this study, we delve into the validity of conventional personality questionnaires in capturing the human-like personality traits of Large Language Models (LLMs). Our objective is to assess the congruence between the personality traits LLMs claim to possess and their demonstrated tendencies in real-world scenarios. By conducting an extensive examination of

Ian Bouche, Josh Javor, Abhishek Som, David K. Campbell

We present the coupled oscillator: a new mechanism for signal amplification with widespread application in metrology. We introduce the mechanical theory of this framework, and support it by way of simulations. We present a particular implementation of coupled oscillators: a microelectromechanical system (MEMS) that uses one large (~100mm) N52 magnet coupled

Guoling Yin, Chi-Kin Lai, Nana Chang, Yi Liang

Advancements in the experimental toolbox of cold atoms have enabled the meticulous control of atomic Bloch oscillation within optical lattices, thereby enhancing the capabilities of gravity interferometers. This work delves into the impact of thermal effects on Bloch oscillation in 1D accelerated optical lattices aligned with gravity by varying the system's

Svante Janson

Spiro, Surya and Zeng (Electron. J. Combin. 2023; arXiv:2207.11272) recently studied a semi-restricted variant of the well-known game Rock, Paper, Scissors; in this variant the game is played for $3n$ rounds, but one of the two players is restricted and has to use each of the three moves exactly $n$ times. They find the optimal strategy, and they show that i

Salomón Alarcón, Simón Masnú, Pedro Montero, Carolina Rey

Let $(M,g)$ be an analytic Riemannian manifold of dimension $n \geq 5$. In this paper, we consider the so-called constant $Q$-curvature equation \[ \varepsilon^4\Delta_{g}^2 u -\varepsilon^2 b \Delta_{g} u +a u = u^{p} , \qquad \text{in } M, \quad u>0, \quad u\in H^2_g(M) \] where $a,b$ are positive constants such that $b^2-4 a>0$, $p$ is a sub-critical expo

Joachim Meyer

Algorithmic decision support (ADS), using Machine-Learning-based AI, is becoming a major part of many processes. Organizations introduce ADS to improve decision-making and use available data, thereby possibly limiting deviations from the normative "homo economicus" and the biases that characterize human decision-making. However, a closer look at the developm

Mauro Caporuscio, Antoine Dupuis, Welf Löwe

The recent increase in renewable energy penetration at the distribution level introduces a multi-directional power flow that outdated traditional fault location techniques. To this extent, the development of new methods is needed to ensure fast and accurate fault localization and, hence, strengthen power system reliability. This paper proposes a data-driven

Giorgio De Simoni, Francesco Giazotto

We suggest using a device called the Bootstrap Superconducting Quantum Interference Device (BS-SQUID) to break the reciprocity in charge transport. This device uses magnetic flux back-action to create a nonreciprocal current-voltage characteristic, which results in a supercurrent rectification coefficient of up to approximately 95\%. The BS-SQUID works as a

Yu Gu, Yiheng Shu, Hao Yu, Xiao Liu

The applications of large language models (LLMs) have expanded well beyond the confines of text processing, signaling a new era where LLMs are envisioned as generalist agents capable of operating within complex environments. These environments are often highly expansive, making it impossible for the LLM to process them within its short-term memory. Motivated

I. Chalendar, L. Oger, J. R. Partington

A complete characterisation is given of all the linear isometries of the Fr\'echet space of all holomorphic functions on the unit disc, when it is given one of the two standard metrics: these turn out to be weighted composition operators of a particular form. Operators similar to an isometry are also classified. Further, the larger class of operators isometr