March 2024 arXiv papers — page 70

Vaibhav Khanna, Bikash Kanungo, Vikram Gavini, Ambuj Tewari

This work introduces Extent of Violation Indices (EVIs), a novel metric for quantifying how well exchange-correlation functionals adhere to local conditions. Applying EVIs to a diverse set of molecules for GGA functionals reveals widespread violations, particularly for semi-empirical functionals. We leverage EVIs to explore potential connections between thes

Margaret Y. Li, Alisa Liu, Zhaofeng Wu, Noah A. Smith

Ambiguity is an critical component of language that allows for more effective communication between speakers, but is often ignored in NLP. Recent work suggests that NLP systems may struggle to grasp certain elements of human language understanding because they may not handle ambiguities at the level that humans naturally do in communication. Additionally, di

Peng Liu

The correlation-based financial networks are studied intensively. However, previous studies ignored the importance of the anti-correlation. This paper is the first to consider the anti-correlation and positive correlation separately, and accordingly construct the weighted temporal anti-correlation and positive correlation networks among stocks listed in the

Minju Park, Sojung Kim, Seunghyun Lee, Soonwoo Kwon

As the recent Large Language Models(LLM's) become increasingly competent in zero-shot and few-shot reasoning across various domains, educators are showing a growing interest in leveraging these LLM's in conversation-based tutoring systems. However, building a conversation-based personalized tutoring system poses considerable challenges in accurately assessin

Zhuang Xiong, Wei Jiang, Yang Gao, Feng Liu

Quantitative Susceptibility Mapping (QSM) dipole inversion is an ill-posed inverse problem for quantifying magnetic susceptibility distributions from MRI tissue phases. While supervised deep learning methods have shown success in specific QSM tasks, their generalizability across different acquisition scenarios remains constrained. Recent developments in diff

Guang-Yih Sheu, Nai-Ru Liu

Taiwan's auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify data using a Naive Bayes class

Nitya Mani, Dan Mikulincer

We investigate the distribution of monochromatic subgraph counts in random vertex $2$-colorings of large graphs. We give sufficient conditions for the asymptotic normality of these counts and demonstrate their essential necessity (particularly for monochromatic triangles). Our approach refines the fourth-moment theorem to establish new, local influence-based

Jose Blanchet, Jiajin Li, Markus Pelger, Greg Zanotti

In this paper, we propose a novel conceptual framework to detect outliers using optimal transport with a concave cost function. Conventional outlier detection approaches typically use a two-stage procedure: first, outliers are detected and removed, and then estimation is performed on the cleaned data. However, this approach does not inform outlier removal wi

Hantao Zhang, Yuhe Liu, Jiancheng Yang, Shouhong Wan

Patient data from real-world clinical practice often suffers from data scarcity and long-tail imbalances, leading to biased outcomes or algorithmic unfairness. This study addresses these challenges by generating lesion-containing image-segmentation pairs from lesion-free images. Previous efforts in medical imaging synthesis have struggled with separating les

Kiwamu Watanabe

Let $X$ be a complex smooth Fano variety of dimension $n$. In this paper, we give a classification of such $X$ when the pseudoindex is equal to $\dfrac{\dim X+1}{2}$ and the Picard number greater than one.

Mario Carneiro

In this paper we present a new "external checker" for the Lean theorem prover, written in Lean itself. This is the first complete typechecker for Lean 4 other than the reference implementation in C++ used by Lean itself, and our new checker is competitive with the original, running between 20% and 50% slower and usable to verify all of Lean's mathlib library

Divyanshu Daiya, Monika Yadav, Harshit Singh Rao

In this work, we propose an approach to generalize denoising diffusion probabilistic models for stock market predictions and portfolio management. Present works have demonstrated the efficacy of modeling interstock relations for market time-series forecasting and utilized Graph-based learning models for value prediction and portfolio management. Though convi

Jarah Evslin, Kehinde Ogundipe, Baiyang Zhang, Hengyuan Guo

We consider the domain wall in the (2+1)-dimensional $\phi^4$ double well model, created by extending the $\phi^4$ kink in an additional infinite direction. Classically, the tension is $m^3/3\lambda$ where $\lambda$ is the coupling and $m$ is the meson mass. At order $O(\lambda^0)$ all ultraviolet divergences can be removed by normal ordering, less trivial d

Bradley Greig, David Prelogović, Jordan Mirocha, Yuxiang Qin

The detection of the 21-cm signal at $z\gtrsim6$ will reveal insights into the properties of the first galaxies responsible for driving reionisation. To extract this information, we perform parameter inference which requires embedding 3D simulations of the 21-cm signal within a Bayesian inference pipeline. Presently, when performing inference we must choose

Bradley Greig, David Prelogović, Yuxiang Qin, Yuan-Sen Ting

Enlightening our understanding of the first galaxies responsible for driving reionisation requires detecting the 21-cm signal from neutral hydrogen. Interpreting the wealth of information embedded in this signal requires Bayesian inference. Parameter inference from the 21-cm signal is primarily restricted to the spherically averaged power spectrum (1D PS) ow

Fanfan Lin, Junhua Liu, Xinze Li, Shuai Zhao

This paper proposes PE-GPT, a custom-tailored large language model uniquely adapted for power converter modulation design. By harnessing in-context learning and specialized tiered physics-informed neural networks, PE-GPT guides users through text-based dialogues, recommending actionable modulation parameters. The effectiveness of PE-GPT is validated through

Yasith Jayawardana, Dineth Jayakody, Sampath Jayarathna, Dushan N. Wadduwage

Deep neural networks (DNNs) have the potential to power many biomedical workflows, but training them on truly representative, IID datasets is often infeasible. Most models instead rely on biased or incomplete data, making them prone to out-of-distribution (OoD) inputs that closely resemble in-distribution samples. Such near-OoD cases are harder to detect tha

Bo Chen, Hao Liu, Qi-Yi Wu, Chen Zhang

In this study, we investigate the electronic structure of a spin-triplet superconductor candidate CeRh$_2$As$_2$ using high-resolution angle-resolved photoemission spectroscopy and density functional theory calculations. Notably, Fermi surface nesting hints at connections to magnetic excitation or quadrupole density wave phenomena, elucidating the supercondu

Connor Lee, Saraswati Soedarmadji, Matthew Anderson, Anthony J. Clark

We present a new method to automatically generate semantic segmentation annotations for thermal imagery captured from an aerial vehicle by utilizing satellite-derived data products alongside onboard global positioning and attitude estimates. This new capability overcomes the challenge of developing thermal semantic perception algorithms for field robots due

Alexandru B. Georgescu, Peiwen Ren, Harshul Bhatt, Christopher Karpovich

Metal-insulator transition (MIT) materials are a useful platform for emerging microelectronic, optoelectronic, and neuromorphic devices, but their discovery is hindered by the high computational cost of electronic structure modeling, the complexity of underlying mechanisms, and the challenges of experimental validation. Here, we present a physics-informed ma

Simin Rashidi, Bahram Golestani Eimani

Due to the concerns of the society about the increase of antibiotic resistant infections, many studies and research have been done on nanoparticles and applications of nano-biotechnology. Zirconium Oxide ($\text{ZrO}_{2}$) in which called zirconia, is a white oxide of zirconium metal that its diameter is 20 nm. The colloidal size of these particles is often

Paul R. Kaneelil, J. Pedro de Souza, Günther Turk, Amir A. Pahlavan

The fluid-fluid interface is a complex environment for a floating object where the statics and dynamics may be governed by capillarity, gravity, inertia, and other external body forces. Yet, the alignment of these forces in intricate ways might result in beautiful pattern formation and self-assembly of these objects, as in the case of bubble rafts or colloid

Santiago Badia, Wei Li, Alberto F. Martín

The use of neural networks to approximate partial differential equations (PDEs) has gained significant attention in recent years. However, the approximation of PDEs with localised phenomena, e.g., sharp gradients and singularities, remains a challenge, due to ill-defined cost functions in terms of pointwise residual sampling or poor numerical integration. In

Jiacong Xu, Mingqian Liao, K Ram Prabhakar, Vishal M. Patel

Neural Radiance Fields (NeRF) accomplishes photo-realistic novel view synthesis by learning the implicit volumetric representation of a scene from multi-view images, which faithfully convey the colorimetric information. However, sensor noises will contaminate low-value pixel signals, and the lossy camera image signal processor will further remove near-zero i

Tetsutaro Shibata

We study the one-dimensional nonlocal elliptic equation of Kirchhoff type with convolutional Kirchhoff functions. We establish the exact solutions $u_\lambda$ and bifurcation curves $\lambda(\alpha)$, where $\alpha:= \Vert u_\lambda\Vert_\infty$.

Roger Fan, Nitya Mani

In this paper, we present a unified study of the limiting density in one-dimensional random sequential adsorption (RSA) processes where segment lengths are drawn from a given distribution. In addition to generic bounds, we are also able to characterize specific cases, including multidisperse RSA, in which we draw from a finite set of lengths, and power-law R

Hadas Kotek, David Q. Sun, Zidi Xiu, Margit Bowler

As modern Large Language Models (LLMs) shatter many state-of-the-art benchmarks in a variety of domains, this paper investigates their behavior in the domains of ethics and fairness, focusing on protected group bias. We conduct a two-part study: first, we solicit sentence continuations describing the occupations of individuals from different protected groups

Mahdi Rezapour

Sentiment analysis is a natural language processing task that aims to identify and extract the emotional aspects of a text. However, many existing sentiment analysis methods primarily classify the overall polarity of a text, overlooking the specific phrases that convey sentiment. In this paper, we applied an approach to sentiment analysis based on a question

Yihao Liu, Mehran Armand

The rapid development of generative technology opens up possibility for higher level of automation, and artificial intelligence (AI) embodiment in robotic systems is imminent. However, due to the blackbox nature of the generative technology, the generation of the knowledge and workflow scheme is uncontrolled, especially in a dynamic environment and a complex

Alice Baird, Rachel Manzelli, Panagiotis Tzirakis, Chris Gagne

The NeurIPS 2023 Machine Learning for Audio Workshop brings together machine learning (ML) experts from various audio domains. There are several valuable audio-driven ML tasks, from speech emotion recognition to audio event detection, but the community is sparse compared to other ML areas, e.g., computer vision or natural language processing. A major limitat

Dhruv Parikh, Shouyi Li, Bingyi Zhang, Rajgopal Kannan

Vision Transformers (ViTs) have achieved state-of-the-art accuracy on various computer vision tasks. However, their high computational complexity prevents them from being applied to many real-world applications. Weight and token pruning are two well-known methods for reducing complexity: weight pruning reduces the model size and associated computational dema

Stephan Pohl, Edgar Y. Walker, David L. Barack, Jennifer Lee

Despite the centrality of the notion of representation in neuroscience, the field lacks a unified framework for the concepts used to characterize representation, leading to disparate use of both terminology and measures associated with it. To offer clarification, we propose a core set of conceptual dimensions that characterize representations in neuroscience

Laura Lechler, Kamil Wojcicki

With the advent of generative audio features, there is an increasing need for rapid evaluation of their impact on speech intelligibility. Beyond the existing laboratory measures, which are expensive and do not scale well, there has been comparatively little work on crowdsourced assessment of intelligibility. Standards and recommendations are yet to be define

Raffaele Di Vora, Caterina Braggio, Giovanni Carugno, Antonios Gardikiotis

In the search for axionic Dark Matter, the high frequency part of the QCD axion parameter space is favored, as indicated by both cosmological and astrophysical arguments and recent indications from lattice QCD calculations. To extend the probing range of cavity haloscopes, solutions addressing the unfavorable scaling of cavity volume with frequency must be d

Rostislav Akhmechet, Peter K. Johnson, Sunghyuk Park

We introduce an invariant of negative definite plumbed knot complements unifying knot lattice homology, due to Ozsváth, Stipsicz, and Szabó, and the BPS $q$-series of Gukov and Manolescu. This invariant is a natural extension of weighted graded roots of negative definite plumbed 3-manifolds introduced earlier by the first two authors and Krushkal. We prove a

Hong-Li Zeng, Cheng-Long Yang, Bo Jing, John Barton

Throughout the course of the SARS-CoV-2 pandemic, genetic variation has contributed to the spread and persistence of the virus. For example, various mutations have allowed SARS-CoV-2 to escape antibody neutralization or to bind more strongly to the receptors that it uses to enter human cells. Here, we compared two methods that estimate the fitness effects of

Kohei Shimamura, Shinnosuke Hattori, Ken-ichi Nomura, Akihide Koura

In this study, we derive the heat flux formula for the Allegro model, one of machine-learning interatomic potentials using the equivariant deep neural network, to calculate lattice thermal conductivity using the homogeneous non-equilibrium molecular dynamics (HNEMD) method based on the Green-Kubo formula. Allegro can construct more advanced atomic descriptor

Evangelos Varvelis, David P. DiVincenzo

Recently it has been shown that transmon qubit architectures experience a transition between a many-body localized and a quantum chaotic phase. While it is crucial for quantum computation that the system remains in the localized regime, the most common way to achieve this has relied on disorder in Josephson junction parameters. Here we propose a quasi-period

Ignacio Vergara

We define the notion of almost invariant conditionally negative definite kernel and use it to give a characterisation of groups admitting a proper uniformly Lipschitz affine action on a subspace of an $L^1$ space. We show that this condition is satisfied by groups acting properly on products of quasi-trees, weakly amenable groups with Cowling-Haagerup consta

Benjamin Grimmer, Kevin Shu, Alex L. Wang

This work considers gradient descent for L-smooth convex optimization with stepsizes larger than the classic regime where descent can be ensured. The stepsize schedules considered are similar to but differ slightly from the recent silver stepsizes of Altschuler and Parrilo. For one of our stepsize sequences, we prove a $O\left(N^{- 1.2716\dots}\right)$ conve

Rikuta Hamaya, Peilu Wang, Lin Ge, Edward L. Giovannucci

With advancement of medicine, alternative exposures or interventions are emerging with respect to a common outcome, and there are needs to formally test the difference in the associations of multiple exposures. We propose a duplication method-based multivariate Wald test in the Cox proportional hazard regression analyses to test the difference in the associa

Wesley H. Holliday

Non-classical generalizations of classical modal logic have been developed in the contexts of constructive mathematics and natural language semantics. In this paper, we discuss a general approach to the semantics of non-classical modal logics via algebraic representation theorems. We begin with complete lattices $L$ equipped with an antitone operation $\neg$

Yousef Qaddura

Given an inner product space $V$ and a group $G$ of linear isometries, max filtering offers a rich class of convex $G$-invariant maps. In this paper, we identify sufficient conditions under which these maps are locally bilipschitz on $R(G)$, the set of orbits with maximal dimension, with respect to the quotient metric on the orbit space $V/G$. Central to our

Hui-Ru Ho, Edward Hubbard, Bilge Mutlu

Learning companion robots for young children are increasingly adopted in informal learning environments. Although parents play a pivotal role in their children's learning, very little is known about how parents prefer to incorporate robots into their children's learning activities. We developed prototype capabilities for a learning companion robot to deliver

Nripsuta Ani Saxena, Abigail L. Horn, Wenbin Zhang, Cyrus Shahabi

Despite location being increasingly used in decision-making systems deployed in sensitive domains such as mortgages and insurance, little attention has been paid to the unfairness that may seep in due to the correlation of location with characteristics considered protected under anti-discrimination law, such as race or national origin. This position paper ar

Rajeev Singh Rathour, Gergely Hajdu, Radosław Smolec, Paulina Karczmarek

Period change studies give a window to probe into the evolution and dynamics of Cepheids. While evolutionary period changes have been well studied both observationally and theoretically, non-evolutionary period changes lack a systematic and quantitative description. The overall objective is to have a quantitative understanding of the full picture of non-evol

Omar Hammad, Md Rezwanur Rahman, Gopala Krishna Vasanth Kanugo, Nicholas Clements

Frequent disruptions like highway constructions are common now-a-days, often impacting environmental justice communities (communities with low socio-economic status with disproportionately high and adverse human health and environmental effects) that live nearby. Based on our interactions via focus groups with the members of four environmental justice commun

Christian Mancas

Existence constraints were defined in the Relational Data Model, but, unfortunately, are not provided by any Relational Database Management System, except for their NOT NULL particular case. Our (Elementary) Mathematical Data Model extended them to function products and introduced their dual non-existence constraints. MatBase, an intelligent data and knowled

Sheetal Harris, Jinshuo Liu, Hassan Jalil Hadi, Yue Cao

Misinformation can seriously impact society, affecting anything from public opinion to institutional confidence and the political horizon of a state. Fake News (FN) proliferation on online websites and Online Social Networks (OSNs) has increased profusely. Various fact-checking websites include news in English and barely provide information about FN in regio

Tibor Szendrei, Arnab Bhattacharjee, Mark E. Schaffer

Growth-at-Risk is vital for empirical macroeconomics but is often suspect to quantile crossing due to data limitations. While existing literature addresses this through post-processing of the fitted quantiles, these methods do not correct the estimated coefficients. We advocate for imposing non-crossing constraints during estimation and demonstrate their equ

Arash Atibi, Abdulaziz Alqahtani, Mohammed Younis, Chrysanthe Preza

We present a study that evaluates the impact of regularization on the achieved resolution in restorations from a novel three-dimensional (3D) Structured Illumination Microscopy (3D-SIM) system with desirable tunability properties. This contribution is the first performance evaluation of the Tunable SIM (TSIM) system through the restoration process. The study

Holger Bech Nielsen

We fit the three finestructure constants of the Standard Model with three, in first approximation theoretically estimable parameters, 1) a "unifiedscale",turning out not equal to the Planck scale and thus only estimable by a very speculative story, 2) a "number of layers" being a priori the number of families, and 3) a unified coupling related to a critical

José-Miguel Díaz-Báñez, Ruy Fabila-Monroy

The use of drones or Unmanned Aerial Vehicles (UAVs) for aerial photography and cinematography is becoming widespread. The following optimization problem has been recently considered. Let us imagine a sporting event where a group of runners are competing and a team of drones with cameras are used to cover the event. The media \emph{director} selects a set of

Junxi Zhang, Shui Feng, Yaozhong Hu

Homogeneous normalized random measures with independent increments (hNRMIs) represent a broad class of Bayesian nonparametric priors and thus are widely used. In this paper, we obtain the strong law of large numbers, the central limit theorem and the functional central limit theorem of hNRMIs when the concentration parameter $a$ approaches infinity. To quant

Victor Berezin, Inna Ivanova

The action of an ideal fluid in Euler variables with a variable number of particles is used for the phenomenological description of the processes of particle creation in strong external fields. It has been demonstrated that the conformal invariance of the creation law imposes quite strict restrictions on the possible types of sources. It is shown that combin

Genival da Silva

The existence of radially symmetric solutions is discussed for a Lane-Emden type system. This answer a question posed by da Silva and do O (2024). We also comment on the inhomogeneous version of the same system and discuss some open questions.

Mohammad Ghufran, Sourish Tetakayala, Jack Hughes, Aron Wilson

The paper focuses on modeling and experimental evaluation of a quadcopter team configurable coordination guided by a single quadruped robot. We consider the quadcopter team as particles of a two-dimensional deformable body and propose a two-dimensional affine transformation model for safe and collision-free configurable coordination of this heterogeneous rob

Shirantha Welikala, Christos G. Cassandras

We consider a class of multi-agent optimal coverage problems in which the goal is to determine the optimal placement of a group of agents in a given mission space so that they maximize a coverage objective that represents a blend of individual and collaborative event detection capabilities. This class of problems is extremely challenging due to the non-conve

Wenjun Huang, Hanning Chen, Yang Ni, Arghavan Rezvani

Detecting marine objects inshore presents challenges owing to algorithmic intricacies and complexities in system deployment. We propose a difficulty-aware edge-cloud collaborative sensing system that splits the task into object localization and fine-grained classification. Objects are classified either at the edge or within the cloud, based on their estimate

Willem Conradie, Krishna Manoorkar, Alessandra Palmigiano, Mattia Panettiere

Graph-based frames have been introduced as a logical framework which internalizes an inherent boundary to knowability. They also support the interpretation of lattice-based (modal) logics as hyper-constructive logics of evidential reasoning. Conceptually, the present paper proposes graph-based frames as a formal framework suitable for generalizing Pawlak's r

Jason R. Wilson, Emily Jensen

In this paper, we discuss the opportunities and challenges of teaching a human-robot interaction course at an undergraduate liberal arts college. We provide a sample syllabus adapted from a previous version of a course.

Taylor Brysiewicz

We introduce the concept of monodromy coordinates for representing solutions to large polynomial systems. Representing solutions this way provides a time-memory trade-off in a monodromy solving algorithm. We describe an algorithm, which interpolates the usual monodromy solving algorithm, for computing such a representation and analyze its space and time comp

Carsten Baum, Jens Berlips, Walther Chen, Helena Cozzarini

Printing custom DNA sequences is essential to scientific and biomedical research, but the technology can be used to manufacture plagues as well as cures. Just as ink printers recognize and reject attempts to counterfeit money, DNA synthesizers and assemblers should deny unauthorized requests to make viral DNA that could be misused. There are three complicati

Guillermo Marshall, Alejandro Soba

Electroporation (EP), the temporary or permanent permeabilization of the cell membrane induced by an electric field, is the basis of various applications in medicine and food processing. In EP-based protocol optimization in terms of pulse number, such as in electrochemotherapy (ECT), irreversible electroporation (IRE), and gene electrotransfer (GET), it is e

Stephan Schiller, Jean-Philippe Karr

The proton, deuteron and triton masses can be determined relative to the electron mass via rovibrational spectroscopy of molecular hydrogen ions. This has to occur via comparison of the experimentally measured transition frequencies and the ab initio calculated frequencies, whose dependence on the mass ratios can be calculated precisely. In precision experim

Ye Tao, Hongyi Wu, Ehsan Javanmardi, Manabu Tsukada

Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Disti

Tarek Kunze, Paul Templier, Dennis G Wilson

In evolutionary policy search, neural networks are usually represented using a direct mapping: each gene encodes one network weight. Indirect encoding methods, where each gene can encode for multiple weights, shorten the genome to reduce the dimensions of the search space and better exploit permutations and symmetries. The Geometric Encoding for Neural netwo

Isaac Ahlgren, Jack West, Kyuin Lee, George Thiruvathukal

Zero Involvement Pairing and Authentication (ZIPA) is a promising technique for autoprovisioning large networks of Internet-of-Things (IoT) devices. In this work, we present the first successful signal injection attack on a ZIPA system. Most existing ZIPA systems assume there is a negligible amount of influence from the unsecured outside space on the secured

Garry Goldstein

In this work we analyze two axis twisting in the presence of depolarizing channel dissipation. We find that spin squeezing is only possible if the dissipation is parametrically weaker than the squeezing coupling. Squeezing may be used for meteorologically useful decrease of spin noise but only in the case where the squeezing occurs before measurement, in the

Fabio Ragosta, Tomas Ahumada, Silvia Piranomonte, Igor Andreoni

The up-coming Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) opens a new opportunity to rapidly survey the southern Sky at optical wavelenghts (\ie ugrizy bands). In this study, we aim to test the possibility of using LSST observations to constrain the mass and velocity of different KN ejecta components from the observation of a combined set

Yifeng Tian, Soumendu Bagchi, Liam Myhill, Giacomo Po

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of

David Porfirio, Allison Sauppé, Maya Cakmak, Aws Albarghouthi

Demonstration is an effective end-user development paradigm for teaching robots how to perform new tasks. In this paper, we posit that demonstration is useful not only as a teaching tool, but also as a way to understand and assist end-user developers in thinking about a task at hand. As a first step toward gaining this understanding, we constructed a lightwe

Abdelhakim Abdellaoui, Loubna Benabbou, Issmail El Hallaoui

Efficiently solving a vehicle routing problem (VRP) in a practical runtime is a critical challenge for delivery management companies. This paper explores both a theoretical and experimental connection between the Capacitated Vehicle Routing Problem (CVRP) and the Constrained Centroid-Based Clustering (CCBC). Reducing a CVRP to a CCBC is a synonym for a trans

Edine Silva, R. C. Bento Ribeiro, Heron Caldas, Mucio A. Continentino

The Kitaev superconducting chain is a model of spinless fermions with triplet-like superconductivity. It has raised interest since for some values of its parameters it presents a non-trivial topological phase that host Majorana fermions. The physical realization of a Kitaev chain is complicated by the scarcity of triplet superconductivity in real physical sy

Ruolin Li, Philip N. Brown, Roberto Horowitz

In this study, we introduce a toll lane framework that optimizes the mixed flow of autonomous and high-occupancy vehicles on freeways, where human-driven and autonomous vehicles of varying commuter occupancy share a segment. Autonomous vehicles, with their ability to maintain shorter headways, boost traffic throughput. Our framework designates a toll lane fo

Feras Al Taha, Eilyan Bitar

We examine a class of optimization problems involving the optimal operation of a single lossy energy storage system, where energy losses occur during charging and discharging. These inefficiencies typically lead to a nonconvex set of feasible charging and discharging power profiles. In this paper, we derive an equivalent reformulation of this class of optimi

Ona de Gibert, Graeme Nail, Nikolay Arefyev, Marta Bañón

We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open

Alessio Rocci, Thomas Van Riet

This paper analyzes the effective field theory perspective on modern physics through the lens of the quantum theory of gravitational interaction. The historical part argues that the search for a theory of quantum gravity stimulated the change in outlook that characterizes the modern approach to the Standard Model of particle physics and General Relativity. W

Alejandro García-Fernández, José Antonio Parejo, Antonio Ruiz-Cortés

As the Software as a Service (SaaS) paradigm continues to reshape the software industry, a nuanced understanding of its operational dynamics becomes increasingly crucial. This paper delves into the intricate relationship between pricing strategies and software development within the SaaS model. Using PetClinic as a case study, we explore the implications of

Mostafa M. Amin, Björn W. Schuller

Recent studies have demonstrated the emerging capabilities of foundation models like ChatGPT in several fields, including affective computing. However, accessing these emerging capabilities is facilitated through prompt engineering. Despite the existence of some prompting techniques, the field is still rapidly evolving and many prompting ideas still require

Sergey Grigorian

In this paper we explore algebraic and geometric structures that arise on parallelizable manifolds. Given a parallelizable manifold $\mathbb{L}$, there exists a global trivialization of the tangent bundle, which defines a map $\rho_p:\mathfrak{l} \longrightarrow T_p \mathbb{L}$ for each point $p \in \mathbb{L}$, where $\mathfrak{l}$ is some vector space. Thi

Alejandro García-Fernández, José Antonio Parejo, Pablo Trinidad, Antonio Ruiz-Cortés

As the digital marketplace evolves, the ability to dynamically adjust or disable features and services in response to market demands and pricing strategies becomes increasingly crucial for maintaining competitive advantage and enhancing user engagement. This paper introduces a novel suite of software libraries named Pricing4SaaS, designed to facilitate the i

Alessandro Favero, Luca Zancato, Matthew Trager, Siddharth Choudhary

Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers that, however, are not always grounded in the input image. We investigate this phenomenon, usually referred to as "hallucination" and show that it stems from an excessive reliance on the language prior. In particular, we show that as more tokens are generated, t

Luiza Ribeiro Marnet, Yury Brodskiy, Stella Grasshof, Andrzej Wasowski

Active learning aims to select the minimum amount of data to train a model that performs similarly to a model trained with the entire dataset. We study the potential of active learning for image segmentation in underwater infrastructure inspection tasks, where large amounts of data are typically collected. The pipeline inspection images are usually semantica

Gaifan Zhang, Yi Zhou, Danushka Bollegala

Sentence embeddings produced by Pretrained Language Models (PLMs) have received wide attention from the NLP community due to their superior performance when representing texts in numerous downstream applications. However, the high dimensionality of the sentence embeddings produced by PLMs is problematic when representing large numbers of sentences in memory-

Yichen Cai, Jianfeng Gao, Christoph Pohl, Tamim Asfour

Task-oriented object grasping and rearrangement are critical skills for robots to accomplish different real-world manipulation tasks. However, they remain challenging due to partial observations of the objects and shape variations in categorical objects. In this paper, we propose the Multi-feature Implicit Model (MIMO), a novel object representation that enc

Maxim Braverman, Ahmad Reza Haj Saeedi Sadegh

We investigate elliptic operators with a symmetry that forces their index to vanish. We study the secondary index, defined modulo 2. We examine Callias-type operators with this symmetry on non-compact manifolds and establish mod 2 versions of the Gromov-Lawson relative index theorem, the Callias index theorem, and the Boutet de Monvel's index theorem for Toe

Shriya V. Nagpal, Gokul G. Nair, Steven H. Strogatz, Francesca Parise

Networks of coupled nonlinear oscillators have been used to model circadian rhythms, flashing fireflies, Josephson junction arrays, high-voltage electric grids, and many other kinds of self-organizing systems. Recently, several authors have sought to understand how coupled oscillators behave when they interact according to a random graph. Here we consider in

Jingyi Chen, Micah Warren

In this paper, we introduce a geometric flow for Lagrangian submanifolds in a K\"ahler manifold that stays in its initial Hamiltonian isotopy class and is a gradient flow for volume. The stationary solutions are the Hamiltonian stationary Lagrangian submanifolds. The flow is not strictly parabolic but it corresponds to a fourth order strictly parabolic scala

Taeyoun Kim, Suhas Kotha, Aditi Raghunathan

The rise of "jailbreak" attacks on language models has led to a flurry of defenses aimed at preventing undesirable responses. We critically examine the two stages of the defense pipeline: (i) defining what constitutes unsafe outputs, and (ii) enforcing the definition via methods such as input processing or fine-tuning. To test the efficacy of existing enforc

Xiaoling Hu, Annabel Sorby-Adams, Frederik Barkhof, W Taylor Kimberly

White matter hyperintensities (WMH) are a hallmark of cerebrovascular disease and multiple sclerosis. Automated WMH segmentation methods enable quantitative analysis via estimation of total lesion load, spatial distribution of lesions, and number of lesions (i.e., number of connected components after thresholding), all of which are correlated with patient ou

Tsao-Hsien Chen, David Nadler

Let $G_\mathbb R$ be a connected real reductive group and let $X$ be the corresponding complex symmetric variety under the Cartan bijection. We construct a canonical equivalence between the relative Satake category of $G(\mathcal O)$-equivariant $\mathbb C$-constructible complexes on the loop space of $X$ and the real Satake category of $G_\mathbb R(\mathcal

Martin Nava-Callejas, Yuri Cavecchi, Dany Page

In this work we model stationary neutron star envelopes at high accretion rates and describe our new code for such studies. As a first step we put special emphasis on the rp-process which results in the synthesis of heavy elements and study in detail how this synthesis depends on the mass accretion rate and the chemical composition of the accreted matter. We

Sang-Hyun Chun, GyuChul Myeong, Jae-Joon Lee, Heeyoung Oh

Young massive star clusters, like the six red supergiant clusters in the Scutum complex, provide valuable insights into star-formation and galaxy structures. We investigated the high-resolution near-infrared spectra of 60 RSG candidates in these clusters using the Immersion Grating Infrared Spectrograph. Among the candidates in RSGC4, we found significant sc

Martin Willame, Laurent Storrer, Hasan Can Yildirim, François Horlin

Recent advancements in Wi-Fi sensing have sparked interest in exploiting OFDM modulated communication signals for target detection and tracking. In this study, we address the angle-based localization of multiple targets using a multistatic OFDM radar. While the maximum likelihood approach optimally merges data from each radar pair comprised by the system, it

Umer Zubair

We propose to study a smooth variant of the $\mu$-hybrid inflation model and a non-minimal Higgs model of inflation with quartic non-minimal coupling between the Higgs field and gravity within the context of a realistic GUT gauge group based on supersymmetric $SU(4)_{C}\times SU(2)_{L}\times SU(2)_{R}$. These models are incorporated with a realistic scenario

Chuliang Fu, Phum Siriviboon, Artittaya Boonkird, Michael Landry

The electron-phonon interaction plays a critical role in materials electrical, thermal, optical, and superconducting properties. However, measuring the phonon mode-resolved electron-phonon interaction has been challenging. Here we propose neutron-scattering-based Anomalous Neutron nUclear-Magnetic Interference Spectroscopy (ANUBIS), where the co-existence of

Keyur Joshi, Rahul Singh, Tommaso Bassetto, Sarita Adve

Instruction-level error injection analyses aim to find instructions where errors often lead to unacceptable outcomes like Silent Data Corruptions (SDCs). These analyses require significant time, which is especially problematic if developers wish to regularly analyze software that evolves over time. We present FastFlip, a combination of empirical error inject

David Porfirio, Mark Roberts, Laura M. Hiatt

End-user programming (EUP) tools must balance user control with the robot's ability to plan and act autonomously. Many existing task-oriented EUP tools enforce a specific level of control, e.g., by requiring that users hand-craft detailed sequences of actions, rather than offering users the flexibility to choose the level of task detail they wish to express.

Shah Mohammad Bahauddin, Catherine E. Fischer, Mark P. Rast, Ivan Milic

We present an interpretation of the recent Daniel K. Inouye Solar Telescope (DKIST) observations of propagating wavefronts in the lower solar atmosphere. Using MPS/University of Chicago MHD (MURaM) radiative magnetohydrodynamic simulations spanning solar photosphere, overshoot region, and lower chromosphere, we identify three acoustic-wave source mechanisms,