May 2024 arXiv papers — page 27

C. Roca, N. S. Bowden, L. Carman, S. A. Dazeley

A $^6$Li-doped plastic scintillator with pulse-shape discrimination capabilities, commercially identified as EJ-299-50, has been developed and produced at the kilogram-scale. A total of 44 kg-scale bars of dimensions 5.5 cm $\times$ 5.5 cm $\times$ 50 cm of this material have been characterized. Optical properties like light output and effective attenuation

Marcos López-De-Castro, Alberto García-Galindo, Rubén Armañanzas

Unlike traditional statistical methods, Conformal Prediction (CP) allows for the determination of valid and accurate confidence levels associated with individual predictions based only on exchangeability of the data. We here introduce a new feature selection method that takes advantage of the CP framework. Our proposal, named Conformal Recursive Feature Elim

Nastaran Einabadi

We prove that a generic element of the Anosov-Katok class of the torus, $\overline{\mathcal{O}}^{\infty}(\mathbb{T}^2)$, acts parabolically and non-properly on the fine curve graph $C^{\dagger}(\mathbb{T}^2)$. Additionally, we show that a generic element of $\overline{\mathcal{O}}^{\infty}(\mathbb{T}^2)$ admits generalized rotation sets of any point-symmetri

Alice Barthe, Michele Grossi, Sofia Vallecorsa, Jordi Tura

Parameterized quantum circuits have been extensively used as the basis for machine learning models in regression, classification, and generative tasks. For supervised learning, their expressivity has been thoroughly investigated and several universality properties have been proven. However, in the case of quantum generative modelling, much less is known, esp

Lianlei Shan, Wenzhang Zhou, Wei Li, Xingyu Ding

Lifelong learning aims to train a model with good performance for new tasks while retaining the capacity of previous tasks. However, some practical scenarios require the system to forget undesirable knowledge due to privacy issues, which is called selective forgetting. The joint task of the two is dubbed Learning with Selective Forgetting (LSF). In this pape

Mina Arzaghi, Florian Carichon, Golnoosh Farnadi

Large Language Models (LLMs) are increasingly integrated into critical decision-making processes, such as loan approvals and visa applications, where inherent biases can lead to discriminatory outcomes. In this paper, we examine the nuanced relationship between demographic attributes and socioeconomic biases in LLMs, a crucial yet understudied area of fairne

Yanxiao Liu, Wei-Ning Chen, Ayfer Özgür, Cheuk Ting Li

To reduce the communication cost of differential privacy mechanisms, we introduce a novel construction, called Poisson private representation (PPR), designed to compress and simulate any local randomizer while ensuring local differential privacy. Unlike previous simulation-based local differential privacy mechanisms, PPR exactly preserves the joint distribut

CMS Collaboration

The Large Hadron Collider at CERN, delivering proton-proton collisions at much higher energies and far higher luminosities than previous machines, has enabled a comprehensive programme of measurements of the standard model (SM) processes by the CMS experiment. These unprecedented capabilities facilitate precise measurements of the properties of a wide array

Ashot Matevosyan

Angular momentum conservation influences equilibrium statistical mechanics, leading to a generalized microcanonical density for an isolated system and a generalized Gibbs density for a weakly coupled system. We study the stochastic decay of angular momentum due to weakly imperfect rotational symmetry of the external potential that confines the isolated many-

Andreas Erlebach, Katrin Thieme, Marek Sierka, Christian Rüssel

Solid solutions of SiO2 and B2O3 in Li2O 2SiO2 are synthesized and characterized for the first time. Their structure and crystallization mechanisms are investigated employing a combination of simulations at the density functional theory level and experiments on the crystallization of SiO2 and B2O3 doped lithium disilicate glasses. The remarkable agreement of

Haoyu Hu, Hongrun Zhang, Chao Li

Brain network is an important tool for understanding the brain, offering insights for scientific research and clinical diagnosis. Existing models for brain networks typically primarily focus on brain regions or overlook the complexity of brain connectivities. MRI-derived brain network data is commonly susceptible to connectivity noise, underscoring the neces

Haarika Manda, Varshika Srinivasavaradhan, Laasya Koduru, Kevin Zhang

Residential fixed broadband internet access in the United States (US) has long been distributed inequitably, drawing significant attention from researchers and policymakers. This paper evaluates the efficacy of the Connect America Fund (CAF), a key policy intervention aimed at addressing disparities in US internet access. CAF subsidizes the creation of new r

Adrián Andrada, María Laura Barberis

We apply the quaternionic Jordan form to classify the hypercomplex nilpotent almost abelian Lie algebras in all dimensions and to carry out the complete classification of 12-dimensional hypercomplex almost abelian Lie algebras. Moreover, we determine which 12-dimensional simply connected hypercomplex almost abelian Lie groups admit lattices. Finally, for eac

Ping-Han Hsieh, Ru-Xiu Hsiao, Katalin Ferenc, Anthony Mathelier

Paired single-cell sequencing technologies enable the simultaneous measurement of complementary modalities of molecular data at single-cell resolution. Along with the advances in these technologies, many methods based on variational autoencoders have been developed to integrate these data. However, these methods do not explicitly incorporate prior biological

Pritam Sarkar, Sayna Ebrahimi, Ali Etemad, Ahmad Beirami

Despite their significant advancements, Multimodal Large Language Models (MLLMs) often generate factually inaccurate information, referred to as hallucination. In this work, we address object hallucinations in MLLMs, where information is generated about an object not present in the input image. We introduce Data-augmented Phrase-level Alignment (DPA), a nove

Yutao Yang, Jie Zhou, Xuanwen Ding, Tianyu Huai

Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV). Unlike traditional neural network models, foundation LMs obtain a great ability for transfer learning by acquiring rich commonsense knowledge through pre-training on extensive unsupervised datasets with

Beril Bulat, Martin Hilbert

Bots have become increasingly prevalent in the digital sphere and have taken up a proactive role in shaping democratic processes. While previous studies have focused on their influence at the individual level, their potential macro-level impact on communication dynamics is still little understood. This study adopts an information theoretic approach from dyna

Suraj Anand, David Getzen

Numerous algorithms have been proposed to $\textit{align}$ language models to remove undesirable behaviors. However, the challenges associated with a very large state space and creating a proper reward function often result in various jailbreaks. Our paper aims to examine this effect of reward in the controlled setting of positive sentiment language generati

Wen-Han Zhou, Patrick Michel

Context. The YORP effect is the thermal torque generated by radiation from the surface of an asteroid. The effect is sensitive to surface topology, including small-scale roughness, boulders, and craters. Aims: The aim of this paper is to develop a computationally efficient semi-analytical model for the crater-induced YORP (CYORP) effect that can be used to i

Yinxu Tang, Stylianos Loukas Vasileiou, William Yeoh

Explainable AI Planning (XAIP) aims to develop AI agents that can effectively explain their decisions and actions to human users, fostering trust and facilitating human-AI collaboration. A key challenge in XAIP is model reconciliation, which seeks to align the mental models of AI agents and humans. While existing approaches often assume a known and determini

Nan Jiang, Xiaopeng Li, Shiqi Wang, Qiang Zhou

In the domain of code generation, self-debugging is crucial. It allows LLMs to refine their generated code based on execution feedback. This is particularly important because generating correct solutions in one attempt proves challenging for complex tasks. Prior works on self-debugging mostly focus on prompting methods by providing LLMs with few-shot example

Dylan G. Stone, Benjamin Whitefield, Mehran Kianinia, Carlo Bradac

Optically detected magnetic resonance (ODMR) has become a well-established and powerful technique for measuring the spin state of solid-state quantum emitters, at room temperature. Relying on spin-dependent recombination processes involving the emitters ground, excited and metastable states, ODMR is enabling spin-based quantum sensing of nanoscale electric a

Theodore Maranets, Yan Wang

In both particle and wave descriptions of phonons, the dense, aperiodically arranged interfaces in aperiodic superlattices are expected to strongly attenuate thermal transport due to phonon-interface scattering or broken long-range coherence. However, considerable thermal conductivity is still observed in these structures. In this study, we reveal that incoh

Andreas Erlebach, Timm Ott, Christoph Otzen, Stephanie Schubert

Achieving optimal solubility of active substances in polymeric carriers is of fundamental importance for a number of industrial applications, including targeted drug delivery within the growing field of nanomedicine. However, its experimental optimization using a trial-and-error approach is cumbersome and time-consuming. Here, an approach based on molecular

James Borger, Jaiung Jun

We set up some basic module theory over semirings, with particular attention to what is needed in scheme theory over semirings. We show that while not all the usual definitions of vector bundle agree over semirings, all the usual definitions of line bundle do agree. We also show that the narrow class group of a number field can be recovered as a reflexive Pi

Jose D. Mella, L. E. F. Foa Torres, Roberto E. Troncoso

The interplay of spin and lattice fluctuations in two-dimensional magnets without inversion symmetry is investigated. We find a general form for the magnetoelastic coupling between magnons and existing chiral phonons based on the symmetries of the crystalline lattice. We show that in hexagonal lattices, the coupling of magnons and chiral phonons derives from

Mike Pols, Geert Brocks, Sofía Calero, Shuxia Tao

With the use of chiral organic cations in two-dimensional metal halide perovskites, chirality can be induced in the metal halide layers, which results in semiconductors with intriguing chiral optical and spin-selective transport properties. The chiral properties strongly depend upon the temperature, despite the basic crystal symmetry not changing fundamental

Xiaobo Guo, Jay Desai, Srinivasan H. Sengamedu

To generate summaries that include multiple aspects or topics for text documents, most approaches use clustering or topic modeling to group relevant sentences and then generate a summary for each group. These approaches struggle to optimize the summarization and clustering algorithms jointly. On the other hand, aspect-based summarization requires known aspec

Tiansheng Huang, Sihao Hu, Fatih Ilhan, Selim Furkan Tekin

Recent studies show that Large Language Models (LLMs) with safety alignment can be jail-broken by fine-tuning on a dataset mixed with harmful data. First time in the literature, we show that the jail-broken effect can be mitigated by separating states in the finetuning stage to optimize the alignment and user datasets. Unfortunately, our subsequent study sho

Lorenzo Gavassino

We study the internal dynamics of a hypothetical spaceship traveling on a close timelike curve in an axially symmetric universe. We choose the curve so that the generator of evolution in proper time is the angular momentum. Using Wigner's theorem, we prove that the energy levels internal to the spaceship must undergo spontaneous discretization. The level sep

Neil Chulpongsatorn, Thien-Kim Nguyen, Nicolai Marquardt, Ryo Suzuki

This paper introduces holographic cross-device interaction, a new class of remote cross-device interactions between local physical devices and holographically rendered remote devices. Cross-device interactions have enabled a rich set of interactions with device ecologies. Most existing research focuses on co-located settings (meaning when users and devices a

Brian A. Yuan, Joseph G. Makin

Recent work on intracranial brain-machine interfaces has demonstrated that spoken speech can be decoded with high accuracy, essentially by treating the problem as an instance of supervised learning and training deep neural networks to map from neural activity to text. However, such networks pay for their expressiveness with very large numbers of labeled data

Aparna Elangovan, Ling Liu, Lei Xu, Sravan Bodapati

In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon insights from disciplines such as user experience research and human behavioral psychology to ensure that the experimental design and results are reliable. The conclusions from these evaluations, thus, mu

Andreas Erlebach, Heinz-Dieter Kurland, Janet Grabow, Frank A. Müller

The atomic structure and properties of nanoparticulate Fe2O3 are characterized starting from its smallest Fe2O3 building unit through (Fe2O3)n clusters to nanometer-sized Fe2O3 particles. This is achieved by combining global structure optimizations at the density functional theory level, molecular dynamics simulations by employing tailored, ab initio paramet

Jiawei Zhang

As one of the most enduring metaphors within legal discourse, the marketplace of ideas has wielded considerable influence over the jurisprudential landscape for decades. A century after the inception of this theory, ChatGPT emerged as a revolutionary technological advancement in the twenty-first century. This research finds that ChatGPT effectively manifests

Xuefeng Du, Yiyou Sun, Yixuan Li

Detecting data points deviating from the training distribution is pivotal for ensuring reliable machine learning. Extensive research has been dedicated to the challenge, spanning classical anomaly detection techniques to contemporary out-of-distribution (OOD) detection approaches. While OOD detection commonly relies on supervised learning from a labeled in-d

Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka

Going beyond mimicking limited human experiences, recent studies show initial evidence that, like humans, large language models (LLMs) are capable of improving their abilities purely by self-correction, i.e., correcting previous responses through self-examination, in certain circumstances. Nevertheless, little is known about how such capabilities arise. In t

Satish Vedula, Ayobami Olajube, Koto Omiloli, Olugbenga Moses Anubi

The presence of Pulse Power Loads (PPLs) in the Notional Shipboard Power System (SPS) presents a challenge in the form of meeting their high ramp rate requirements. Considering the ramp rate limitations on the generators, this might hinder the power flow in the grid. Failure to meet the ramp rate requirements might cause instability. Aggregating generators w

Omead Pooladzandi, Jeffrey Jiang, Sunay Bhat, Gregory Pottie

Data poisoning attacks pose a significant threat to the integrity of machine learning models by leading to misclassification of target distribution data by injecting adversarial examples during training. Existing state-of-the-art (SoTA) defense methods suffer from limitations, such as significantly reduced generalization performance and significant overhead

Toru Ishida, Tongxi Liu, Hailong Wang, William K. Cheung

As the importance of comprehensive evaluation in workshop courses increases, there is a growing demand for efficient and fair assessment methods that reduce the workload for faculty members. This paper presents an evaluation conducted with Large Language Models (LLMs) using actual student essays in three scenarios: 1) without providing guidance such as rubri

Fil Simovic, Daniel R. Terno

We compute quasinormal mode frequencies for static limits of physical black holes - semi-classical black hole solutions to Einstein-Hilbert gravity characterized by the finite formation time of an apparent horizon and its weak regularity. These assumptions lead to a highly constrained yet non-trivial form of the metric and components of the energy-momentum t

Sergiu Ivanov, Damien Regnault

The abstract tile assembly model (aTam) is a model of DNA self-assembly. Most of the studies focus on cooperative aTAM where a form of synchronization between the tiles is possible. Simulating Turing machines is achievable in this context. Few results and constructions are known for the non-cooperative case (a variant of Wang tilings where assemblies do not

Félix Marcoccia, Cédric Adjih, Paul Mühlethaler

This work introduces Cross-Attentive Modulation (CAM) tokens, which are tokens whose initial value is learned, gather information through cross-attention, and modulate the nodes and edges accordingly. These tokens are meant to improve the global awareness of link predictions models which, based on graph neural networks, can struggle to capture graph-level fe

Andreas Erlebach, Carolin Hühn, Richard Jana, Marek Sierka

Global minimum structures of neutral (Fe2O3)n clusters with n = 1-5 were determined employing the genetic algorithm in combination with ab initio parameterized interatomic potentials and subsequent refinement at the density functional theory level. Systematic investigations of magnetic configurations of the clusters using a broken symmetry approach reveal an

Hao Mark Chen, Wayne Luk, Ka Fai Cedric Yiu, Rui Li

The auto-regressive decoding of Large Language Models (LLMs) results in significant overheads in their hardware performance. While recent research has investigated various speculative decoding techniques for multi-token generation, these efforts have primarily focused on improving processing speed such as throughput. Crucially, they often neglect other metri

Sunay Bhat, Jeffrey Jiang, Omead Pooladzandi, Alexander Branch

Train-time data poisoning attacks threaten machine learning models by introducing adversarial examples during training, leading to misclassification. Current defense methods often reduce generalization performance, are attack-specific, and impose significant training overhead. To address this, we introduce a set of universal data purification methods using a

Rahul Madhavan, Aurghya Maiti, Gaurav Sinha, Siddharth Barman

We study a variant of causal contextual bandits where the context is chosen based on an initial intervention chosen by the learner. At the beginning of each round, the learner selects an initial action, depending on which a stochastic context is revealed by the environment. Following this, the learner then selects a final action and receives a reward. Given

Chaoyi Pan, Zeji Yi, Guanya Shi, Guannan Qu

Recent advances in diffusion models have demonstrated their strong capabilities in generating high-fidelity samples from complex distributions through an iterative refinement process. Despite the empirical success of diffusion models in motion planning and control, the model-free nature of these methods does not leverage readily available model information a

Hank Chen, Joaquin Liniado

In recent years, significant progress has been made in the study of integrable systems from a gauge theoretic perspective. This development originated with the introduction of $4$d Chern-Simons theory with defects, which provided a systematic framework for constructing two-dimensional integrable systems. In this article, we propose a novel approach to studyi

Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari

Protecting Internet of things (IoT) devices against cyber attacks is imperative owing to inherent security vulnerabilities. These vulnerabilities can include a spectrum of sophisticated attacks that pose significant damage to both individuals and organizations. Employing robust security measures like intrusion detection systems (IDSs) is essential to solve t

Unggi Lee, Yeil Jeong, Junbo Koh, Gyuri Byun

This preliminary study explores the integration of GPT-4 Vision (GPT-4V) technology into teacher analytics, focusing on its applicability in observational assessment to enhance reflective teaching practice. This research is grounded in developing a Video-based Automatic Assessment System (VidAAS) empowered by GPT-4V. Our approach aims to revolutionize teache

Ajinkya Borle, Ameya Bhave

Biclustering is a problem in machine learning and data mining that seeks to group together rows and columns of a dataset according to certain criteria. In this work, we highlight the natural relation that quantum computing models like boson and Gaussian boson sampling (GBS) have to this problem. We first explore the use of boson sampling to identify bicluste

Abhineet Agarwal, Anish Agarwal, Lorenzo Masoero, Justin Whitehouse

Online experimentation with interference is a common challenge in modern applications such as e-commerce and adaptive clinical trials in medicine. For example, in online marketplaces, the revenue of a good depends on discounts applied to competing goods. Statistical inference with interference is widely studied in the offline setting, but far less is known a

Aditya Gunturu, Shivesh Jadon, Nandi Zhang, Morteza Faraji

Large Language Models (LLMs) are gaining popularity as reading and summarization aids. However, little is known about their potential benefits when integrated with mixed reality (MR) interfaces to support everyday reading. In this iterative investigation, we developed RealitySummary, an MR reading assistant that seamlessly integrates LLMs with always-on came

Kaïs Ammari, Vilmos Komornik, Mauricio Sepúlveda, Octavio Vera

This paper is devoted to the solution and stability of a one-dimensional model depicting Rao--Nakra sandwich beams, incorporating damping terms characterized by fractional derivative types within the domain, specifically a generalized Caputo derivative with exponential weight. To address existence, uniqueness, stability, and numerical results, fractional der

Prakhar Ganesh, Ihsan Ibrahim Daldaban, Ignacio Cofone, Golnoosh Farnadi

Model multiplicity, the phenomenon where multiple models achieve similar performance despite different underlying learned functions, introduces arbitrariness in model selection. While this arbitrariness may seem inconsequential in expectation, its impact on individuals can be severe. This paper explores various individual concerns stemming from multiplicity,

Ehsan Paki, Atila Poro

Reanalyzing contact binaries with space-based photometric data and investigating possible parameter changes can yield accurate samples for theoretical studies. We investigated light curve solutions and fundamental parameters for twenty contact binary systems. The most recent Transiting Exoplanet Survey Satellite (TESS) data is used to analyze. The target sys

Rafael O. Cunha, Yunier Garcia-Basabe, Dunieskys G. Larrude, Matheus Gamino

We report experimental investigations of spin-to-charge current conversion and charge transfer dynamics (CT) at the interface of graphene/WS$_2$ van der Waals heterostructure. Pure spin current was produced by the spin precession in the microwave-driven ferromagnetic resonance of a permalloy film (Py-Ni$_{81}$Fe$_{19}$) and injected into the graphene/WS$_2$

Zhenhai Zhu, Radu Soricut

Non-overlapping patch-wise convolution is the default image tokenizer for all state-of-the-art vision Transformer (ViT) models. Even though many ViT variants have been proposed to improve its efficiency and accuracy, little research on improving the image tokenizer itself has been reported in the literature. In this paper, we propose a new image tokenizer ba

Víctor Pacheco-Valencia, Nodari Vakhania

In the Bounded Multiple Traveling Salesman Problem (BMTSP), a tour for each salesman, that starts and ends at the depot and that respects the bounds on the number of cities that a feasible salesman tour should satisfy, is to be constructed. The objective is to minimize the total length of all tours. Already Euclidean traveling salesman problem is NP-hard. We

Aditya Gunturu, Yi Wen, Nandi Zhang, Jarin Thundathil

We introduce Augmented Physics, a machine learning-integrated authoring tool designed for creating embedded interactive physics simulations from static textbook diagrams. Leveraging recent advancements in computer vision, such as Segment Anything and Multi-modal LLMs, our web-based system enables users to semi-automatically extract diagrams from physics text

Ali Hürriyetoğlu, Osman Mutlu, Fırat Duruşan, Erdem Yörük

GLOCON is a database of contentious events automatically extracted from national news sources from various countries in multiple languages. National news sources are utilized, and complete news archives are processed to create an event list for each source. Automation is achieved using a gold standard corpus sampled randomly from complete news archives (Y\"o

Carmen Cortázar, Fernando Quirós, Noemí Wolanski

We study the fully nonlocal semilinear equation $\partial_t^\alpha u+(-\Delta)^\beta u=|u|^{p-1}u$, $p\ge1$, where $\partial_t^\alpha$ stands for the Caputo derivative of order $\alpha\in (0,1)$ and $(-\Delta)^\beta$, $\beta\in(0,1]$, is the usual $\beta$ power of the Laplacian. We prescribe an initial datum in $L^q(\mathbb{R}^N)$. We give conditions ensurin

Mohamed Ali Hamza, Hatem Zaag

We consider the semilinear wave equation in higher dimensions with superconformal power nonlinearity. The purpose of this paper is to give a new upper bound on the blow-up rate in some space-time integral, showing a $|\log(T-t)|^q$ improvement in comparison with previous results obtained in \cite{HZdcds13,KSVsurc12}.

Zhiyao Luo, Mingcheng Zhu, Fenglin Liu, Jiali Li

Reinforcement learning (RL) has garnered increasing recognition for its potential to optimise dynamic treatment regimes (DTRs) in personalised medicine, particularly for drug dosage prescriptions and medication recommendations. However, a significant challenge persists: the absence of a unified framework for simulating diverse healthcare scenarios and a comp

Otman Benchekroun, Kaixiang Xie, Hsueh-Ti Derek Liu, Eitan Grinspun

Traditional character animation specializes in characters with a rigidly articulated skeleton and a bipedal/quadripedal morphology. This assumption simplifies many aspects for designing physically based animations, like locomotion, but comes with the price of excluding characters of arbitrary deformable geometries. To remedy this, our framework makes use of

Suraj Anand, Michael A. Lepori, Jack Merullo, Ellie Pavlick

Language models have the ability to perform in-context learning (ICL), allowing them to flexibly adapt their behavior based on context. This contrasts with in-weights learning (IWL), where memorized information is encoded in model parameters after iterated observations of data. An ideal model should be able to flexibly deploy both of these abilities. Despite

Joerg Bruedern, Trevor D. Wooley

We present estimates for smooth Weyl sums of use on sets of major arcs in applications of the Hardy-Littlewood method. In particular, we derive mean value estimates on major arcs for smooth Weyl sums of degree $k$ delivering essentially optimal bounds for moments of order $u$ whenever $u>2\lfloor k/2\rfloor +4$.

Patricia María Elena Vázquez, Germán Eduardo Caro

In previous works we presented a novel multiparametric sensor to simultaneously measure the refractive index and the speed of sound in a liquid by means of the acousto-optic effect. The sensor requires an illumination system that expands the laser beam so that it interacts effectively with the liquid under study. Also, a projection system is necessary in ord

Linh Van Ma, Tran Thien Dat Nguyen, Ba-Ngu Vo, Hyunsung Jang

We propose a 3D multi-object tracking (MOT) solution using only 2D detections from monocular cameras, which automatically initiates/terminates tracks as well as resolves track appearance-reappearance and occlusions. Moreover, this approach does not require detector retraining when cameras are reconfigured but only the camera matrices of reconfigured cameras

Mai H. Nguyen, Shibani Likhite, Jiawei Tang, Darshini Mahendran

The extraction of chemical-gene relations plays a pivotal role in understanding the intricate interactions between chemical compounds and genes, with significant implications for drug discovery, disease understanding, and biomedical research. This paper presents a data set created by merging the ChemProt and DrugProt datasets to augment sample counts and imp

Stefan Hollands, Vahid Toomani

Metric reconstruction is the general problem of parameterizing GR in terms of its two ``true degrees of freedom'', e.g., by a complex scalar ``potential'' -- in practice mostly with the aim of simplifying the Einstein equation (EE) within perturbative approaches. In this paper, we re-analyze the metric reconstruction procedure by Green, Hollands, and Zimmerm

W. Jacob Ogden, Yu Yuan

Constant rank theorems are obtained for saddle solutions to the special Lagrangian equation and the quadratic Hessian equation. The argument also leads to Liouville type results for the special Lagrangian equation with subcritical phase, matching the known rigidity results for semiconvex entire solutions to the quadratic Hessian equation.

Tae-wook Kim, Han-jin Lee, Hyeon-Jin Jung, Ji-Woong Yang

Traffic accidents are recognized as a major social issue worldwide, causing numerous injuries and significant costs annually. Consequently, methods for predicting and preventing traffic accidents have been researched for many years. With advancements in the field of artificial intelligence, various studies have applied Machine Learning and Deep Learning tech

Charles Guille-Escuret, Eugene Ndiaye

Conformal prediction methodologies have significantly advanced the quantification of uncertainties in predictive models. Yet, the construction of confidence regions for model parameters presents a notable challenge, often necessitating stringent assumptions regarding data distribution or merely providing asymptotic guarantees. We introduce a novel approach t

Owen Burns, Hossein Maghsoumi, Yaser Fallah, Israel Charles

Cooperative driving, enabled by communication between automated vehicle systems, promises significant benefits to fuel efficiency, road capacity, and safety over single-vehicle driver assistance systems such as adaptive cruise control (ACC). However, the responsible development and implementation of these algorithms poses substantial challenges due to the ne

Rafael A. Costa-Silva, Henrique Boschi-Filho

In this work we consider anomalous and linear holographic hard wall (HW) models for light unflavored mesons inspired by the AdS/CFT correspondence. The anomalous dimensions depend on the logarithm of the spin S of the meson state and come from a semiclassical analysis of gauge/string duality. The anomalous HW model produces very good masses and good Regge tr

Gabriel Loewinger, Alexander W. Levis, Francisco Pereira

Optogenetics is a powerful neuroscience technique for studying how neural circuit manipulation affects behavior. Standard analysis conventions discard information and severely limit the scope of the causal questions that can be probed. To address this gap, we 1) draw connections to the causal inference literature on sequentially randomized experiments, 2) pr

Alex V Mbaziira, Maha F Sabir

Threat actors continue to exploit geopolitical and global public events launch aggressive campaigns propagating disinformation over the Internet. In this paper we extend our prior research in detecting disinformation using psycholinguistic and computational linguistic processes linked to deception and cybercrime to gain an understanding of the features impac

Katherine de Kleer, Ery C. Hughes, Francis Nimmo, John Eiler

Jupiter's moon Io hosts extensive volcanism driven by tidal heating. The isotopic composition of Io's inventory of volatile elements, including sulfur and chlorine, reflects its outgassing and mass loss history and provides an avenue for exploring its evolution. We used millimeter observations of Io's atmosphere to measure sulfur isotopes in gaseous SO2 and

Hamza Bodor, Laurent Carlier

In this article, we delve into the applications and extensions of the queue-reactive model for the simulation of limit order books. Our approach emphasizes the importance of order sizes, in conjunction with their type and arrival rate, by integrating the current state of the order book to determine, not only the intensity of order arrivals and their type, bu

Carlos Cotta, José E. Gallardo

Suicide bombing is an infamous form of terrorism that is becoming increasingly prevalent in the current era of global terror warfare. We consider the case of targeted attacks of this kind, and the use of detectors distributed over the area under threat as a protective countermeasure. Such detectors are non-fully reliable, and must be strategically placed in

Claus Michael Ringel, Markus Schmidmeier

We consider the category $\mathcal S(n)$ of all pairs $X = (U,V)$, where $V$ is a finite-dimensional vector space with a nilpotent operator $T$ with $T^n = 0$, and $U$ is a subspace of $V$ such that $T(U) \subseteq U$. Our main interest in an object $X=(U,V)$ are the three numbers $uX=\dim U$ (for the subspace), $wX=\dim V/U$ (for the factor) and $bX=\dim {\

Giuseppe Arrò, Francesco Califano, Francesco Pucci, Tomas Karlsson

Magnetic holes (MHs) are coherent magnetic field dips whose size ranges from fluid to kinetic scale, ubiquitously observed in the heliosphere and in planetary environments. Despite the longstanding effort in interpreting the abundance of observations, the origin and properties of MHs are still debated. In this letter, we investigate the interplay between pla

Michael Munn, Benoit Dherin, Javier Gonzalvo

There has been considerable effort to better understand the generalization capabilities of deep neural networks both as a means to unlock a theoretical understanding of their success as well as providing directions for further improvements. In this paper, we investigate margin-based multiclass generalization bounds for neural networks which rely on a recent

A. Sagués Carracedo, A. Goobar, E. Mörtsell, N. Arendse

The Zwicky Transient Facility (ZTF) was expected to detect more than one strong gravitationally-lensed supernova (glSN) per year, but only one event was identified in the first four years of the survey. This work investigates selection biases in the search strategy that could explain the discrepancy and revise discovery predictions. We present simulations of

A. Townsend, J. Nordin, A. Sagués Carracedo, M. Kowalski

Gravitationally lensed type Ia supernovae (glSNe Ia) are unique astronomical tools that can be used to study cosmological parameters, distributions of dark matter, the astrophysics of the supernovae, and the intervening lensing galaxies themselves. A small number of highly magnified glSNe Ia have been discovered by ground-based telescopes such as the Zwicky

Rupini V. Kamat, Aaron L. Sharpe, Mihir Pendharkar, Jenny Hu

The electronic properties of moir\'e heterostructures depend sensitively on the relative orientation between layers of the stack. For example, near-magic-angle twisted bilayer graphene (TBG) commonly shows superconductivity, yet a TBG sample with one of the graphene layers rotationally aligned to a hexagonal Boron Nitride (hBN) cladding layer provided the fi

Raphaël Bergoin, Alessandro Torcini, Gustavo Deco, Mathias Quoy

The modular and hierarchical organization of the brain is believed to support the coexistence of segregated (specialization) and integrated (binding) information processes. A relevant question is yet to understand how such architecture naturally emerges and is sustained over time, given the plastic nature of the brain's wiring. Following evidences that the s

Francesca Stabile, Walter Lucia, Amr Youssef, Giuseppe Franze

The proliferation of cloud computing technologies has paved the way for deploying networked encrypted control systems, offering high performance, remote accessibility and privacy. However, in scenarios where the control algorithms run on third-party cloud service providers, the control logic might be changed by a malicious agent on the cloud. Consequently, i

Surya Dheeshjith, Adam Subel, Shubham Gupta, Alistair Adcroft

With the success of machine learning (ML) applied to climate reaching further every day, emulators have begun to show promise not only for weather but for multi-year time scales in the atmosphere. Similar work for the ocean remains nascent, with state-of-the-art limited to models running for shorter time scales or only for regions of the globe. In this work,

Lara Alegre, Philip Best, Jose Sabater, Huub Rottgering

Modern high-sensitivity radio telescopes are discovering an increased number of resolved sources with intricate radio structures and fainter radio emissions. These sources often present a challenge because source detectors might identify them as separate radio sources rather than components belonging to the same physically connected radio source. Currently,

Eric L. Flumerfelt

The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment that will probe the properties of these elusive particles with unparalleled precision. It will also act as an observatory for neutrino bursts caused by nearby supernovae, in the event that one occurs while the experiment is in operation. Given these goals, the DUNE trigg

Lowiek Van den Stockt, Remko Proesmans, Francis wyffels

Tactile sensing can enable robots to perform complex, contact-rich tasks. Magnetic sensors offer accurate three-axis force measurements while using affordable materials. Calibrating such a sensor involves either manual data collection, or automated procedures with precise mounting of the sensor relative to an actuator. We present an open-source magnetic tact

Hyunjin Seo, Taewon Kim, June Yong Yang, Eunho Yang

Recent advancements in text-attributed graphs (TAGs) have significantly improved the quality of node features by using the textual modeling capabilities of language models. Despite this success, utilizing text attributes to enhance the predefined graph structure remains largely unexplored. Our extensive analysis reveals that conventional edges on TAGs, treat

Alexander Windmann, Philipp Wittenberg, Marvin Schieseck, Oliver Niggemann

In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent lite

Seyun Kim, Bonnie Fan, Willa Yunqi Yang, Jessie Ramey

Technologies adopted by the public sector have transformed the work practices of employees in public agencies by creating different means of communication and decision-making. Although much of the recent research in the future of work domain has concentrated on the effects of technological advancements on public sector employees, the influence on work practi

Joseph Cummings, Jonathan D. Hauenstein, Hoon Hong, Clifford D. Smyth

A standard question in real algebraic geometry is to compute the number of connected components of a real algebraic variety in affine space. By adapting an approach for determining connectivity in complements of real hypersurfaces by Hong, Rohal, Safey El Din, and Schost, algorithms are presented for computing the number of connected components, the Euler ch

Quanqi Hu, Qi Qi, Zhaosong Lu, Tianbao Yang

In this paper, we study a class of non-smooth non-convex problems in the form of $\min_{x}[\max_{y\in Y}\phi(x, y) - \max_{z\in Z}\psi(x, z)]$, where both $\Phi(x) = \max_{y\in Y}\phi(x, y)$ and $\Psi(x)=\max_{z\in Z}\psi(x, z)$ are weakly convex functions, and $\phi(x, y), \psi(x, z)$ are strongly concave functions in terms of $y$ and $z$, respectively. It

Ali Alsetri, Xuancheng Shao

Let $\delta > 1/2$. We prove that if $A$ is a subset of the primes such that the relative density of $A$ in every reduced residue class is at least $\delta$, then almost all even integers can be written as the sum of two primes in $A$. The constant $1/2$ in the statement is best possible. Moreover we give an example to show that for any $\varepsilon > 0$ the