March 2024 arXiv papers — page 30

Sage Anastasi, Giulio Dalla Riva

In this paper we present new methods of measuring polarisation in social networks. We use Random Dot Product Graphs to embed social networks in metric spaces. Singular Value Decomposition of this social network then provider an embedded dimensionality which corresponds to the number of uncorrelated dimensions in the network. A decrease in the optimal dimensi

Frank Lübeck

The values of the ordinary Green functions are known for almost all groups of Lie type, a long term achievement by various authors. In this note we solve the last open cases, which are for exceptional groups $E_8(q)$ where $q$ is a power of $2$, $3$ or $5$.

Yudi Dai, Junlin Xiong, Yanfeng Ge, Bin Cheng

The spin Hall effect (SHE) allows efficient generation of spin polarization or spin current through charge current and plays a crucial role in the development of spintronics. While SHE typically occurs in non-magnetic materials and is time-reversal even, exploring time-reversal-odd (T-odd) SHE, which couples SHE to magnetization in ferromagnetic materials, o

Changjie Chen, Yu Han, Andrea Galinski, Christian Calle

Coastal communities are confronted with a growing incidence of climate-induced flooding, necessitating adaptation measures for resilience. In this paper, we introduce a framework that integrates an urban digital twin with a geospatial dashboard to allow visualization of the vulnerabilities within critical infrastructure across a range of spatial and temporal

Megan A. Witherow, Norou Diawara, Janice Keener, John W. Harrington

Purpose: Facial expression production and perception in autism spectrum disorder (ASD) suggest potential presence of behavioral biomarkers that may stratify individuals on the spectrum into prognostic or treatment subgroups. Construct validity and group discriminability have been recommended as criteria for identification of candidate stratification biomarke

Julian Jorge Andrade Guerreiro, Naoto Inoue, Kento Masui, Mayu Otani

Finding a suitable layout represents a crucial task for diverse applications in graphic design. Motivated by simpler and smoother sampling trajectories, we explore the use of Flow Matching as an alternative to current diffusion-based layout generation models. Specifically, we propose LayoutFlow, an efficient flow-based model capable of generating high-qualit

Haiwei Chen, Yajie Zhao

We present a method for large-mask pluralistic image inpainting based on the generative framework of discrete latent codes. Our method learns latent priors, discretized as tokens, by only performing computations at the visible locations of the image. This is realized by a restrictive partial encoder that predicts the token label for each visible block, a bid

Cosmin Ilut, Rosen Valchev

We develop a novel framework of bounded rationality under cognitive frictions that studies learning over optimal behavior through both deliberative reasoning and accumulated experiences. Using both types of information, agents engage in Bayesian non-parametric estimation of the unknown action value function. Reasoning signals are produced internally through

Hanyu Li, Giovanna Bucci, Nicholas W. Brady, Nicholas R. Cross

In this paper, we introduce a density-based topology optimization framework to design porous electrodes for maximum energy storage. We simulate the full cell with a model that incorporates electronic potential, ionic potential, and electrolyte concentration. The system consists of three materials, namely pure liquid electrolyte and the porous solids of the a

Hsiu-Wei Yang, Abhinav Agrawal, Pavlos Fragkogiannis, Shubham Nitin Mulay

A well-designed document communicates not only through its words but also through its visual eloquence. Authors utilize aesthetic elements such as colors, fonts, graphics, and layouts to shape the perception of information. Thoughtful document design, informed by psychological insights, enhances both the visual appeal and the comprehension of the content. Wh

Injy Hamed, Fadhl Eryani, David Palfreyman, Nizar Habash

We present ZAEBUC-Spoken, a multilingual multidialectal Arabic-English speech corpus. The corpus comprises twelve hours of Zoom meetings involving multiple speakers role-playing a work situation where Students brainstorm ideas for a certain topic and then discuss it with an Interlocutor. The meetings cover different topics and are divided into phases with di

Tomoya Nishikata, Jun Ohkubo

Machine learning methods allow the prediction of nonlinear dynamical systems from data alone. The Koopman operator is one of them, which enables us to employ linear analysis for nonlinear dynamical systems. The linear characteristics of the Koopman operator are hopeful to understand the nonlinear dynamics and perform rapid predictions. The extended dynamic m

Krushi Patel, Fengjun Li, Guanghui Wang

Detecting and segmenting polyps is crucial for expediting the diagnosis of colon cancer. This is a challenging task due to the large variations of polyps in color, texture, and lighting conditions, along with subtle differences between the polyp and its surrounding area. Recently, vision Transformers have shown robust abilities in modeling global context for

Angeliki Koutsimpela, Stefan Grosskinsky

We establish a connection between tagged particles and size-biased empirical processes in interacting particle systems, in analogy to classical results on the propagation of chaos. In a mean-field scaling limit, the evolution of the occupation number on the tagged particle site converges to a time-inhomogeneous Markov process with non-linear master equation

Shun Taguchi, Hideki Deguchi

This study introduces a novel approach to online embedding of multi-scale CLIP (Contrastive Language-Image Pre-Training) features into 3D maps. By harnessing CLIP, this methodology surpasses the constraints of conventional vocabulary-limited methods and enables the incorporation of semantic information into the resultant maps. While recent approaches have ex

Cheuk Yin Lee, Shen-Ning Tung, Tai-Ho Wang

We study how trading fees and continuous-time arbitrage affect the profitability of liquidity providers (LPs) in Geometric Mean Market Makers (G3Ms). We use stochastic reflected diffusion processes to analyze the dynamics of a G3M model under the arbitrage-driven market. Our research focuses on calculating LP wealth and extends the findings of Tassy and Whit

Lingqing Shen, Nam Ho-Nguyen, Khanh-Hung Giang-Tran, Fatma Kılınç-Karzan

We consider an online strategic classification problem where each arriving agent can manipulate their true feature vector to obtain a positive predicted label, while incurring a cost that depends on the amount of manipulation. The learner seeks to predict the agent's true label given access to only the manipulated features. After the learner releases their p

William I. Jay, Raynette van Tonder, Ryoutaro Watanabe

This work summarizes recent results, both theoretical and experimental, in $B$-meson leptonic and semileptonic decays and metrology of $V_{ub}$ and $V_{cb}$, which were presented at the CKM 2023 workshop. We place these results in context and discuss future prospects in the field.

Mete Şeref Ahunbay

We investigate first-order notions of correlated equilibria in smooth games, in which players do not incur any regret against small modifications of their actions prescribed by some vector field. We define two such notions, based on local deviations and on stationarity of the distribution, and identify the notion of coarseness as the setting where the strate

Neda Taghizadeh Serajeh, Iman Mohammadi, Vittorio Fuccella, Mattia De Rosa

Efficient and accurate information extraction from scientific papers is significant in the rapidly developing human-computer interaction research in the literature review process. Our paper introduces and analyses a new information retrieval system using state-of-the-art Large Language Models (LLMs) in combination with structured text analysis techniques to

Shuyuan Yang, My H. Le, Kyle R. Golobish, Juan C. Beaver

In minimally invasive telesurgery, obtaining accurate force information is difficult due to the complexities of in-vivo end effector force sensing. This constrains development and implementation of haptic feedback and force-based automated performance metrics, respectively. Vision-based force sensing approaches using deep learning are a promising alternative

Alaeddine Zahir, Khalide Jbilou, Ahmed Ratnani

This paper explores the extension of dimension reduction (DR) techniques to the multi-dimension case by using the Einstein product. Our focus lies on graph-based methods, encompassing both linear and nonlinear approaches, within both supervised and unsupervised learning paradigms. Additionally, we investigate variants such as repulsion graphs and kernel meth

Weiguo Lyu, Zihao Qi, Jian Yang, Guodong Zhou

Generalising a previous work of Jiang and Sheng, a cohomology theory for differential Lie algebras of arbitrary weight is introduced. The underlying $L_\infty[1]$-structure on the cochain complex is also determined via a generalised version of higher derived brackets. The equivalence between $L_\infty[1]$-structures for absolute and relative differential Lie

Vivek Pandey, Sudhir K. Pandey

This work verifies the existence of dispersive \textit{nodal-arcs} and their evolution into Weyl nodes under the effect of spin-orbit coupling (SOC) in NbAs & NbP. The obtained features mimic the observations as reported for TaAs & TaP in our previous work. In addition, this work reports that the number of nodes in TaAs class of Weyl semimetals (WSMs) can be

C. Huang, L. Luo, M. Mootz, J. Shang

Nonlinear interactions of spin-waves and their quanta, magnons, have emerged as prominent candidates for interference-based technology, ranging from quantum transduction to antiferromagnetic spintronics. Yet magnon multiplication in the terahertz (THz) spectral region represents a major challenge. Intense, resonant magnetic fields from THz pulse-pairs with c

Lei Yu, Meng Cao, Jackie Chi Kit Cheung, Yue Dong

State-of-the-art language models (LMs) sometimes generate non-factual hallucinations that misalign with world knowledge. To explore the mechanistic causes of these hallucinations, we create diagnostic datasets with subject-relation queries and adapt interpretability methods to trace hallucinations through internal model representations. We discover two gener

Pan-Yang Su, Chinmay Maheshwari, Victoria Tuck, Shankar Sastry

The rise of advanced air mobility (AAM) is expected to become a multibillion-dollar industry in the near future. Market-based mechanisms are touted to be an integral part of AAM operations, which comprise heterogeneous operators with private valuations. In this work, we study the problem of designing a mechanism to coordinate the movement of electric vertica

T. Valchev

We consider a matrix nonlinear partial differential equation that generalizes Heisenberg ferromagnet equation. This generalized Heisenberg ferromagnet equation is completely integrable with a linear bundle Lax pair related to the pseudo-unitary algebra. This allows us to explicitly derive particular solutions by using dressing technique. We shall discuss two

Jair Certório, Nuno C. Martins, Richard J. La, Murat Arcak

We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium. Our approach is to design a dynamic payoff mechanism capable of shaping the population's strategy profile, thus affecting the ES's state, by offering inc

Michael DeBuse, Sean Warnick

In this work we consider the impact of information spread in time-varying social networks, where agents request to follow other agents with aligned opinions while dropping ties to neighbors whose posts are too dissimilar to their own views. Opinion control and rhetorical influence has a very long history, employing various methods including education, persua

Huy Q. Ngo, Mingyu Guo, Hung Nguyen

Microsoft Active Directory (AD) is the default security management system for Window domain network. We study the problem of placing decoys in AD network to detect potential attacks. We model the problem as a Stackelberg game between an attacker and a defender on AD attack graphs where the defender employs a set of decoys to detect the attacker on their way

Grayson LaCombe, Jianan Wang, Jeremy R. Rouxel, Marien Simeni Simeni

We report on the experimental observation of non-resonant, second-order optical Sum-Frequency Generation (SFG) in five different atomic and molecular gases. The measured signal is attributed to a SFG process by characterizing its intensity scaling and its polarization behavior. We show that the electric quadrupole mechanism cannot explain the observed trends

Slobodan N. Simić

The goal of this expository paper is to present the basics of geometric control theory suitable for advanced undergraduate or beginning graduate students with a solid background in advanced calculus and ordinary differential equations.

Daniel Gonçalves, Danilo Royer

We introduce the concept of "irrational paths" for a given subshift and use it to characterize all minimal left ideals in the associated unital subshift algebra. Consequently, we characterize the socle as the sum of the ideals generated by irrational paths. Proceeding, we construct a graph such that the Leavitt path algebra of this graph is graded is

Ana Cipriš, Ivana Puljić, Damir Aumiler, Ticijana Ban

We report on the absolute frequency determination of the 5s5p $^1P_1$ - 5s5d $^1D_2$ transition in atomic strontium, achieved through frequency comb-referenced laser-induced-fluorescence (LIF) spectroscopy. We excite the 5s$^2$ $^1S_0$ - 5s5p $^1P_1$ transition using an on-resonance laser at $\approx$461 nm, and then measure the variation in the LIF signal w

Elisa Bäumer, Vinay Tripathi, Alireza Seif, Daniel Lidar

In dynamic quantum circuits, classical information from mid-circuit measurements is fed forward during circuit execution. This emerging capability of quantum computers confers numerous advantages that can enable more efficient and powerful protocols by drastically reducing the resource requirements for certain core algorithmic primitives. In particular, in t

Suifang Zhou, Latisha Besariani Hendra, Qinshi Zhang, Jussi Holopainen

Conventional methods of assessing attitudes towards climate change are limited in capturing authentic opinions, primarily stemming from a lack of context-specific assessment strategies and an overreliance on simplistic surveys. Game-based Assessments (GBA) have demonstrated the ability to overcome these issues by immersing participants in engaging gameplay w

Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi, Kyunggeun Lee

Large generative models such as large language models (LLMs) and diffusion models have revolutionized the fields of NLP and computer vision respectively. However, their slow inference, high computation and memory requirement makes it challenging to deploy them on edge devices. In this study, we propose a light-weight quantization aware fine tuning technique

Rintaro Yanagi, Yamato Okamoto, Shuhei Yokoo, Shin'ichi Satoh

The short video-sharing services that allow users to post 10-30 second videos (e.g., YouTube Shorts and TikTok) have attracted a lot of attention in recent years. However, conventional video copy detection (VCD) methods mainly focus on general video-sharing services (e.g., YouTube and Bilibili), and the effects of short video-sharing services on video copy d

Elias Huseby, Josephine Gissinger, Fabien Candelier, Nimish Pujara

We investigate the sedimentation of chiral particles in viscous fluid flow. We identify helical ribbons as simple particles with strong translation-rotation coupling whose symmetry ensures that the centers of mass, buoyancy, resistance, and mobility coincide. Experimental measurements of both relevant mobility tensors show excellent agreement with simulation

Cristina Maria Lofaro, Giulia Rodighiero, Andrea Enia, Ariel Werle

The main sequence (MS) of star-forming galaxies (SFGs) is the tight relation between the galaxy stellar mass and its star formation rate (SFR) and was observed up to z ~ 6. The MS relation can be used as a reference for understanding the differences among galaxies, characterised by different rates of stellar production (starbursts, SFGs, and passive galaxies

Yubo Jin, Pan Yan

In this article, we give a cohomological interpretation of (a special case of) the integrals constructed by the second named author and Q. Zhang \cite{YanZhang2023} which represent the product of Rankin-Selberg $L$-functions of $\mathrm{GL}_n\times\mathrm{GL}_m$ and $\mathrm{GL}_n\times\mathrm{GL}_{n-m-1}$ for $m<n$. As an application, we prove an algebraici

David J. Aldous, Madelyn Cruz, Shi Feng

Consider a compact metric space $S$ and a pair $(j,k)$ with $k \ge 2$ and $1 \le j \le k$. For any probability distribution $\theta \in P(S)$, define a Markov chain on $S$ by: from state $s$, take $k$ i.i.d. ($\theta$) samples, and jump to the $j$'th closest. Such a chain converges in distribution to a unique stationary distribution, say $\pi_{j,k}(\theta)$.

Toyin Aguda, Suchetha Siddagangappa, Elena Kochkina, Simerjot Kaur

Collecting labeled datasets in finance is challenging due to scarcity of domain experts and higher cost of employing them. While Large Language Models (LLMs) have demonstrated remarkable performance in data annotation tasks on general domain datasets, their effectiveness on domain specific datasets remains underexplored. To address this gap, we investigate t

Atsushi Teramoto, Ayano Michiba, Yuka Kiriyama, Tetsuya Tsukamoto

Objective: Cytology plays a crucial role in lung cancer diagnosis. Pulmonary cytology involves cell morphological characterization in the specimen and reporting the corresponding findings, which are extremely burdensome tasks. In this study, we propose a technique to generate cytologic findings from for cytologic images to assist in the reporting of pulmonar

Vincent Jouanny, Simone Frasca, Vera Jo Weibel, Leo Peyruchat

Superconducting microwave metamaterials offer enormous potential for quantum optics and information science, enabling the development of advanced quantum technologies for sensing and amplification. In the context of circuit quantum electrodynamics, such metamaterials can be implemented as coupled cavity arrays (CCAs). In the continuous effort to miniaturize

Ishaan Mahajan, Khai Nguyen, Sam Schoedel, Elakhya Nedumaran

Model-predictive control (MPC) is a state-of-the-art control method for constrained robotic systems, yet deployment on resource-limited hardware remains difficult. This challenge is magnified by expressive conic constraints, which offer greater modeling power but require significantly more computation than linear alternatives. To address this challenge, we e

Yoon Kyung Lee, Jina Suh, Hongli Zhan, Junyi Jessy Li

Large Language Models (LLMs) have demonstrated surprising performance on many tasks, including writing supportive messages that display empathy. Here, we had these models generate empathic messages in response to posts describing common life experiences, such as workplace situations, parenting, relationships, and other anxiety- and anger-eliciting situations

Jodie A. Cochrane, Adrian Wills, Sarah J. Johnson

Decision trees are commonly used predictive models due to their flexibility and interpretability. This paper is directed at quantifying the uncertainty of decision tree predictions by employing a Bayesian inference approach. This is challenging because these approaches need to explore both the tree structure space and the space of decision parameters associa

Hsienchih Ting, Zhaolin Wang, Yuanwei Liu

True-time delayers (TTDs) are popular analog devices for facilitating near-field wideband beamforming subject to the spatial-wideband effect. In this paper, an adaptive TTD configuration is proposed for short-range TTDs. Compared to the existing TTD configurations, the proposed one can effectively combat the spatial-widebandd effect for arbitrary user locati

Ying Feng, Adittyo Paul, Zhe Chen, Jiaoyang Li

One area of research in multi-agent path finding is to determine how replanning can be efficiently achieved in the case of agents being delayed during execution. One option is to reschedule the passing order of agents, i.e., the sequence in which agents visit the same location. In response, we propose Switchable-Edge Search (SES), an A*-style algorithm desig

Joshua C. Zhao, Ahaan Dabholkar, Atul Sharma, Saurabh Bagchi

Federated learning is a decentralized learning paradigm introduced to preserve privacy of client data. Despite this, prior work has shown that an attacker at the server can still reconstruct the private training data using only the client updates. These attacks are known as data reconstruction attacks and fall into two major categories: gradient inversion (G

S. Cebrian

The investigation of rare phenomena requires an effective suppression of all the background components entangling the expected signal. This has compelled the development of a wide range of low radioactivity techniques and background mitigation strategies. Some examples of those applied to Large Time Projection Chambers (TPCs) will be discussed here, includin

Yongyi Yang, Jiaming Yang, Wei Hu, Michał Dereziński

As a variant of Graph Neural Networks (GNNs), Unfolded GNNs offer enhanced interpretability and flexibility over traditional designs. Nevertheless, they still suffer from scalability challenges when it comes to the training cost. Although many methods have been proposed to address the scalability issues, they mostly focus on per-iteration efficiency, without

Pierre Lazag

We prove Fredholm determinants build out from generalizations of Schur measures, or equivalently, arbitrary multiplicative statistics of the original Schur measures are tau-functions of the 2D Toda lattice hierarchy. Our result apply to finite temperature Schur measures, and extends both the result of Okounkov in \cite{okounkovschurmeasures} and of Cafasso-R

Roseval Malaquias Junior, Ramon Pires, Roseli Romero, Rodrigo Nogueira

The high compute cost associated with pretraining large language models limits their research. Two strategies have emerged to address this issue: domain specialization and pretraining with high-quality data. To explore these strategies, we specialized the Mistral-7B model with 1.9 billion unique tokens from reputable Brazilian legal sources and conducted few

Weijie Gan, Huidong Xie, Carl von Gall, Günther Platsch

Anatomically guided PET reconstruction using MRI information has been shown to have the potential to improve PET image quality. However, these improvements are limited to PET scans with paired MRI information. In this work we employed a diffusion probabilistic model (DPM) to infer T1-weighted-MRI (deep-MRI) images from FDG-PET brain images. We then use the D

Raju Krishnamoorthy, Yeuk Hay Joshua Lam

Let $U/K$ be a smooth affine curve over a number field and let $L$ be an irreducible rank 3 $\overline{\mathbb Q}_{\ell}$-local system on $U$ with trivial determinant and infinite geometric monodromy around a cusp. Suppose further that $L$ extends to an integral model such that the Frobenius traces are contained in a fixed totally real number field. Then, af

A. A. Radovskaya, A. G. Semenov

The problem of quantum scalar field evolution after an instantaneous local perturbation (quench) is considered. A new approach to descriptions of a quench from an arbitrary initial state is developed in the framework of the Keldysh technique. This approach does not require the procedure of the analytical continuation, which can be ambiguous in some cases. Th

Jane Downer, Ren Wang, Binghui Wang

Graph Neural Networks (GNNs) have gained popularity in numerous domains, yet they are vulnerable to backdoor attacks that can compromise their performance and ethical application. The detection of these attacks is crucial for maintaining the reliability and security of GNN classification tasks, but existing methods are often inflexible, relying on single met

Thomas D. Russell, Nathalie Degenaar, Jakob van den Eijnden, Thomas Maccarone

Relativistic jets are observed from accreting and cataclysmic transients throughout the Universe, and have a profound affect on their surroundings. Despite their importance, their launch mechanism is not known. For accreting neutron stars, the speed of their compact jets can reveal whether the jets are powered by magnetic fields anchored in the accretion flo

Zhan Shi, Jingwei Zhang, Jun Kong, Fusheng Wang

In digital pathology, the multiple instance learning (MIL) strategy is widely used in the weakly supervised histopathology whole slide image (WSI) classification task where giga-pixel WSIs are only labeled at the slide level. However, existing attention-based MIL approaches often overlook contextual information and intrinsic spatial relationships between nei

Erkan Karabulut, Victoria Degeler, Paul Groth

Association Rule Mining (ARM) is the task of learning associations among data features in the form of logical rules. Mining association rules from high-dimensional numerical data, for example, time series data from a large number of sensors in a smart environment, is a computationally intensive task. In this study, we propose an Autoencoder-based approach to

Eva Feillet, Adrian Popescu, Céline Hudelot

Class-incremental learning deals with sequential data streams composed of batches of classes. Various algorithms have been proposed to address the challenging case where samples from past classes cannot be stored. However, selecting an appropriate algorithm for a user-defined setting is an open problem, as the relative performance of these algorithms depends

Luke S. Baker, Andre Luiz P. de Lima, Anatoly Zlotnik, Jr-Shin Li

We present a computational method for open-loop minimum-norm control synthesis for fixed-endpoint transfer of bilinear ensemble systems that are indexed by two continuously varying parameters. We suppose that one ensemble parameter scales the homogeneous, linear part of the dynamics, and the second parameter scales the effect of the applied control inputs on

Yuichiro Aoyama, Evangelos A. Theodorou

We present a sampling-based trajectory optimization method derived from the maximum entropy formulation of Differential Dynamic Programming with Tsallis entropy. This method is a generalization of the legacy work with Shannon entropy, which leads to a Gaussian optimal control policy for exploration during optimization. With the Tsallis entropy, the policy ta

José A. Cortés, Alberto Pittolo, Irene Povedano, Francisco J. Cañete

This paper focuses on the parameterization of the multipath propagation model (MPM) for indoor broadband power line communications (PLC), which up to now has been established in an heuristic way. The MPM model was initially proposed in the PLC context for outdoor channels in the band up to 20 MHz, but its number of parameters becomes extremely large when use

Fahmida Liza Piya, Mehak Gupta, Rahmatollah Beheshti

While electronic health records (EHRs) are widely used across various applications in healthcare, most applications use the EHRs in their raw (tabular) format. Relying on raw or simple data pre-processing can greatly limit the performance or even applicability of downstream tasks using EHRs. To address this challenge, we present HealthGAT, a novel graph atte

Shi Feng, Nuoya Xiong, Zhijie Zhang, Wei Chen

Pseudo log-likelihood is a type of maximum likelihood estimation (MLE) method used in various fields including contextual bandits, influence maximization of social networks, and causal bandits. However, in previous literature \citep{li2017provably, zhang2022online, xiong2022combinatorial, feng2023combinatorial1, feng2023combinatorial2}, the log-likelihood fu

Victor Le Coz, Jean-Philippe Bouchaud

Twenty five years ago, several authors proposed to describe the forward interest rate curve (FRC) as an elastic string along which idiosyncratic shocks propagate, accounting for the peculiar structure of the return correlation across different maturities. In this paper, we revisit the specific "stiff'' elastic string field theory of Baaquie and Bouchaud (200

Evan Lucas, Kelly S. Steelman, Leo C. Ureel, Charles Wallace

While the rise of large language models (LLMs) has created rich new opportunities to learn about digital technology, many on the margins of this technology struggle to gain and maintain competency due to lexical or conceptual barriers that prevent them from asking appropriate questions. Although there have been many efforts to understand factuality of LLM-cr

Saif R. Kazi, Sidhant Misra, Svetlana Tokareva, Kaarthik Sundar

Natural gas consumption by users of pipeline networks is subject to increasing uncertainty that originates from the intermittent nature of electric power loads serviced by gas-fired generators. To enable computationally efficient optimization of gas network flows subject to uncertainty, we develop a finite volume representation of stochastic solutions of hyp

Jacques Delabrouille

Observations of cosmic microwave background polarisation, essential for probing a potential phase of inflation in the early universe, suffer from contamination by polarised emission from the Galactic interstellar medium. This work combines existing observations from the WMAP and Planck space missions to make a low-noise map of polarised synchrotron emission

Daniel Ocampo, Daniela Posso, Reza Namakian, Wei Gao

Training machine learning interatomic potentials often requires optimizing a loss function composed of three variables: potential energies, forces, and stress. The contribution of each variable to the total loss is typically weighted using fixed coefficients. Identifying these coefficients usually relies on iterative or heuristic methods, which may yield sub

Yufei Tao, Ameeta Agrawal, Judit Dombi, Tetyana Sydorenko

Recent advances in interactive large language models like ChatGPT have revolutionized various domains; however, their behavior in natural and role-play conversation settings remains underexplored. In our study, we address this gap by deeply investigating how ChatGPT behaves during conversations in different settings by analyzing its interactions in both a no

Jin Peng Zhou, Charles Staats, Wenda Li, Christian Szegedy

Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems. However, they still make unjustified logical and computational errors in their reasoning steps and answers. In this paper, we leverage the fact that if the training corpus of LLMs containe

Adam Wang

The following note proves that conditional entropy of a sequence is almost time-reversal invariant, specifically they only differ by a small constant factor dependent only upon the forward and backward models that the entropies are being calculated with respect to. This gives rise to a numerical value that quantifies learnability, as well as a methodology to

Alex Lovi, Baris Fidan, Christopher Nielsen

In this paper we develop a multiple model reference adaptive controller (MMRAC) with blending. The systems under consideration are non-square, i.e., the number of inputs is not equal to the number of states; multi-input, linear, time-invariant with uncertain parameters that lie inside of a known, compact, and convex set. Moreover, the full state of the plant

Qiao Gu, Zhaoyang Lv, Duncan Frost, Simon Green

In this paper we present EgoLifter, a novel system that can automatically segment scenes captured from egocentric sensors into a complete decomposition of individual 3D objects. The system is specifically designed for egocentric data where scenes contain hundreds of objects captured from natural (non-scanning) motion. EgoLifter adopts 3D Gaussians as the und

Javid Akhavan, Youmna Mahmoud, Ke Xu, Jiaqi Lyu

In the era of Industry 4.0, Additive Manufacturing (AM), particularly metal AM, has emerged as a significant contributor due to its innovative and cost-effective approach to fabricate highly intricate geometries. Despite its potential, this industry still lacks real-time capable process monitoring algorithms. Recent advancements in this field suggest that Me

Daniele Rege Cambrin, Paolo Garza

Earthquake monitoring is necessary to promptly identify the affected areas, the severity of the events, and, finally, to estimate damages and plan the actions needed for the restoration process. The use of seismic stations to monitor the strength and origin of earthquakes is limited when dealing with remote areas (we cannot have global capillary coverage). I

Justin B. DeMonte, Bonnie E. Shook-Sa, Michael G. Hudgens

Observational data are frequently used to evaluate real-world vaccine effectiveness (VE). For vaccines such as those developed against COVID-19, VE may vary over calendar time because of changes in circulating viral variants and over time since vaccination because of waning immunity. Nested trial emulation (NTE) provides a framework for estimating causal eff

Yihao Liu, Jiaming Zhang, Andres Diaz-Pinto, Haowei Li

The Segment Anything Model (SAM) has drawn significant attention from researchers who work on medical image segmentation because of its generalizability. However, researchers have found that SAM may have limited performance on medical images compared to state-of-the-art non-foundation models. Regardless, the community sees potential in extending, fine-tuning

C. S. Barroso

Let $K$ be a nonempty subset of a Banach space $X$. A mapping $T\colon K\to K$ is called $\mathfrak{cm}$-nonexpansive if for any sequence $(u_i)_{i=1}^\infty$ and $y$ in $K$, $\limsup_{i\to\infty} \sup_{A\subset\{1,\dots, n\}}\|\sum_{k\in A} \big(T u_{i+k} - Ty\big)\|\leq \limsup_{i\to\infty} \sup_{A\subset\{1,\dots, n\}}\|\sum_{k\in A} (u_{i+k} - y)\|$ for

Preshin Moodley

We calculate the next-to-leading order corrections to the $SU(2)\otimes SU(2)$ and $SU(3)\otimes SU(3)$ Gell-Mann-Oakes-Renner relations. We use a pseudoscalar correlator calculated from Perturbative QCD up to five loops and use the QCD Finite Energy Sum Rules with integration kernels tuned to suppress the importance of the hadronic resonances. This leads to

Z. H. Sun, A. Ekström, C. Forssén, G. Hagen

Atomic nuclei exhibit multiple energy scales ranging from hundreds of MeV in binding energies to fractions of an MeV for low-lying collective excitations. As the limits of nuclear binding is approached near the neutron- and proton driplines, traditional shell-structure starts to melt with an onset of deformation and an emergence of coexisting shapes. It is a

Duy K. Nguyen, Jenny Ma, Pedro Alejandro Perez, Lydia B. Chilton

Content retargeting is crucial for social media creators. Once great content is created, it is important to reach as broad an audience as possible. This is particularly important in journalism where younger audiences are shifting away from print and towards short-video platforms. Many newspapers already create rich graphics for the web that they want to be a

Faustin Adiceam, Steven Robertson, Victor Shirandami, Ioannis Tsokanos

The Josephus problem is a well--studied elimination problem consisting in determining the position of the survivor after repeated applications of a deterministic rule removing one person at a time from a given group. A natural probabilistic variant of this process is introduced in this paper. More precisely, in this variant, the survivor is determined after

Malte Hassler, Dierk Schleicher

We prove that core entropy is H\"older continuous as a function of external angles for a large class of quadratic polynomials that are non-recurrent with respect to angle-doubling, in particular all of them that exhibit a finite Hubbard tree. The result follows from a symbolic analysis of the Mandelbrot set and the dynamics of Hubbard trees in terms of knead

G. Sabatini, L. Podio, C. Codella, Y. Watanabe

The origin of the chemical diversity observed around low-mass protostars probably resides in the earliest history of these systems. We aim to investigate the impact of protostellar feedback on the chemistry and grain growth in the circumstellar medium of multiple stellar systems. In the context of the ALMA Large Program FAUST, we present high-resolution (50

Stefaan Verhulst

Datafication -- the increase in data generation and advancements in data analysis -- offers new possibilities for governing and tackling worldwide challenges such as climate change. However, employing new data sources in policymaking carries various risks, such as exacerbating inequalities, introducing biases, and creating gaps in access. This paper articula

Ahmed Ammar Hussein Hammam

Subject headings are very important to machine catalogs, given the importance of thematic research. This study aims to measure the quality of a group of authorized subject headings with a sample of Arabic bibliographic records on the catalog of Egyptian university libraries by identifying the most important practices, policies, procedures followed, and tools

Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang

The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education. This survey paper summarizes the various technologies of LLMs in educational settings from multifaceted perspectives, encompassing student and teacher assistance, adaptive learning, and commercial tools. We systematically review the technological a

Huei-Chung Hu, Xuyang Wu, Yuan Wang, Yi Fang

Numerous works concerning head pose estimation (HPE) offer algorithms or proposed neural network-based approaches for extracting Euler angles from either facial key points or directly from images of the head region. However, many works failed to provide clear definitions of the coordinate systems and Euler or Tait-Bryan angles orders in use. It is a well-kno

Stanley H. Chan

The astonishing growth of generative tools in recent years has empowered many exciting applications in text-to-image generation and text-to-video generation. The underlying principle behind these generative tools is the concept of diffusion, a particular sampling mechanism that has overcome some shortcomings that were deemed difficult in the previous approac

Redi Haderi, Cihan Okay, Walker H. Stern

In this article, we characterize convexity in terms of algebras over a PROP, and establish a tensor-product-like symmetric monoidal structure on the category of convex sets. Using these two structures, and the theory of $\scr{O}$-monoidal categories, we state and prove a Grothendieck construction for lax $\scr{O}$-monoidal functors into convex sets. We apply

Mathieu Guilbert, Christel Vrain, Thi-Bich-Hanh Dao

The domain of explainable AI is of interest in all Machine Learning fields, and it is all the more important in clustering, an unsupervised task whose result must be validated by a domain expert. We aim at finding a clustering that has high quality in terms of classic clustering criteria and that is explainable, and we argue that these two dimensions must be

Hanzhe Li, Xiangxiang Wang, Yuan Feng, Yaqian Qi

This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the collected data is sent to the cloud for statistical analysis, prediction, and data analysis to achieve business objectives. Ho

Ugo Bruzzo, Valeriano Lanza, Pedro H. Dos Santos

For $n\ge 1$ we show that the length 1 nested Hilbert scheme of the total space $X_n$ of the line bundle $\mathcal O_{\mathbb P^1}(-n)$, parameterizing pairs of nested 0-cycles in $X_n$, is a quiver variety associated with a suitable quiver with relations. This generalizes previous work about nested Hilbert schemes on $\mathbb C^2$ in one direction, and abou

Ha-Thanh Nguyen, Hiroaki Yamada, Ken Satoh

In this paper, we explore the application of Generative Pre-trained Transformers (GPTs) in cross-lingual legal Question-Answering (QA) systems using the COLIEE Task 4 dataset. In the COLIEE Task 4, given a statement and a set of related legal articles that serve as context, the objective is to determine whether the statement is legally valid, i.e., if it can