March 2024 arXiv papers — page 58

Nicholas Hartman

Period-prevalent cohorts are often used for their cost-saving potential in epidemiological studies of survival outcomes. Under this design, prevalent patients allow for evaluations of long-term survival outcomes without the need for long follow-up, whereas incident patients allow for evaluations of short-term survival outcomes without the issue of left-trunc

Guillermo Infante, David Kuric, Anders Jonsson, Vicenç Gómez

Conventional reinforcement learning (RL) methods can successfully solve a wide range of sequential decision problems. However, learning policies that can generalize predictably across multiple tasks in a setting with non-Markovian reward specifications is a challenging problem. We propose to use successor features to learn a policy basis so that each (sub)po

Michał Szachniewicz, Jinhe Ye

We study model-complete fields that avoid a given quasi-project variety $V$. There is a close connection between hyperbolicity of $V$ and the existence of the model companion for the theory of characteristic-zero fields avoiding rational points on $V$. This gives a model theoretic notion of hyperbolicity that we call excludability. In particular, we show tha

Kévin Le Balc'h, Jérémy Martin

In this paper, we investigate quantitative propagation of smallness properties for the Schr\"odinger operator on a bounded domain in $\mathbb R^d$. We extend Logunov, Malinnikova's results concerning propagation of smallness for $A$-harmonic functions to solutions of divergence elliptic equations perturbed by a bounded zero order term. We also prove similar

Raju Kumar Gupta, Sourav Sarkar, Sagar S. Sawant, Samir Shukla

The matching complex $\mathsf{M}(G)$ of a graph $G$ is a simplicial complex whose simplices are matchings in $G$. These complexes appear in various places and found applications in many areas of mathematics including computational geometry, representation theory, combinatorics, etc. In this article, we consider the matching complexes of the categorical produ

Tiansi Dong, Mateja Jamnik, Pietro Liò

The success of Large Language Models (LLMs), e.g., ChatGPT, is witnessed by their planetary popularity, their capability of human-like communication, and also by their steadily improved reasoning performance. However, it remains unclear whether LLMs reason. It is an open problem how traditional neural networks can be qualitatively extended to go beyond the s

ATLAS Collaboration

Measurements of integrated and differential cross-sections for electroweak $W^{\pm}Z$ production in association with two jets ($W^{\pm}Zjj$) in proton$-$proton collisions are presented. The data collected by the ATLAS detector at the Large Hadron Collider from $2015$ to $2018$ at a centre-of-mass energy of $\sqrt{s} = 13$ TeV are used, corresponding to an in

Jun-Yong Yan, Liang Zhai, Hans-Georg Babin, Yuanzhen Li

Complete quantum control of a stationary quantum bit embedded in a quantum emitter is crucial for photonic quantum information technologies. Recently, the orbital degree of freedom in optically active quantum dots has emerged as a promising candidate. However, the essential ability to perform arbitrary rotations on orbital qubits remains elusive. Here, we de

Jinaykumar Patel, Kamesh Subbarao

This paper investigates the application of reachability analysis to the re-entry problem faced by vehicles entering Earth's atmosphere. The study delves into the time evolution of reachable sets for the system, particularly when subject to nonlinear implicit controls, given the potential damage from the intense heat generated during hypersonic re-entry. Our

Valerio Capraro

Over the last two decades, a growing body of experimental research has provided evidence that linguistic frames influence human behaviour in economic games, beyond the economic consequences of the available actions. This article proposes a novel framework that transcends the traditional confines of outcome-based preference models. According to the LENS model

Tristan van Leeuwen, Yunan Yang

Inverse problems are ubiquitous in science and engineering. Many of these are naturally formulated as a PDE-constrained optimization problem. These non-linear, large-scale, constrained optimization problems know many challenges, of which the inherent non-linearity of the problem is an important one. In this paper, we focus on a relaxed formulation of the PDE

Matteo Gasparin, Aaditya Ramdas

Conformal prediction equips machine learning models with a reasonable notion of uncertainty quantification without making strong distributional assumptions. It wraps around any prediction model and converts point predictions into set predictions with a predefined marginal coverage guarantee. However, conformal prediction only works if we fix the underlying m

Chen Chen, Yunfan Wang, Gursharn Kaur, Aniruddha Adiga

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surv

Daniel R. DeSena, Brian C. Tiburzi

Low-energy scattering is well described by the effective-range expansion. In quantum mechanics, a tower of contact interactions can generate terms in this expansion after renormalization. Scattering parameters are also encoded in the self-adjoint extension of the Hamiltonian. We briefly review this well-known result for two particles with s-wave interactions

Wei Liu

This paper considers the state estimation problem for discrete-time linear systems under event-triggered scheme. In order to improve performance, a novel event-triggered scheme based on confidence level is proposed using the chi-square distribution and mild regularity assumption. In terms of the novel event-triggered scheme, a minimum mean squared error (MMS

Jerry Jun-Yan Zhang, Nicolas Lodieu, Eduardo Martín

Context. Euclid will carry out a deep survey benefiting the discovery and characterisation of ultracool dwarfs (UCDs), especially in the Euclid Deep Fields (EDFs), which the telescope will scan repeatedly throughout its mission. The photometric and spectroscopic standards in the EDFs are important benchmarks, crucial for the classification and characterisati

Susanne Pumpluen

Witt rings for nondegenerate forms $\varphi$ of degree $d$ over a field of characteristic 0 or greater than $d$ were defined by Harrison and Pareigis. We revisit and discuss their definition as well as some special cases, classify the $H$-forms employed in their definition, and define Witt rings of diagonal forms of degree $d$. We also define two new Witt ri

Jenny Schubert, Marc C. Steinbach, Christian Hente, David Märtins

We consider the aeroelastic simulation of flexible mechanical structures submerged in subsonic fluid flows at low Mach numbers. The nonlinear kinematics of flexible bodies are described in the total Lagrangian formulation and discretized by finite elements. The aerodynamic loads are computed using the unsteady vortex-lattice method wherein a free wake is tra

Jiawen Kang, Xiaofeng Luo, Jiangtian Nie, Tianhao Wu

Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving

Sara Amato, Andrea Arnold

Neuroinflammation immediately follows the onset of ischemic stroke in the middle cerebral artery. During this process, microglial cells are activated in and recruited to the penumbra. Microglial cells can be activated into two different phenotypes: M1, which can worsen brain injury; or M2, which can aid in long-term recovery. In this study, we contribute a s

Xiangru Tao, Aiqin Yang, Yundi Quan, Biao Wan

Advances in theoretical calculations boosted the searches for high temperature superconductors, such as sulfur hydrides and rare-earth polyhydrides. However, the required extremely high pressures for stabilizing these superconductors handicapped further implementations. Based upon thorough structural searches, we identified series of unprecedented supercondu

Giulia Belluccini, Qianying Lin, Bevelynn Williams, Yijun Lou

With a single circulating vector-borne virus, the basic reproduction number incorporates contributions from tick-to-tick (co-feeding), tick-to-host and host-to-tick transmission routes. With two different circulating vector-borne viral strains, resident and invasive, and under the assumption that co-feeding is the only transmission route in a tick population

Jiafu An, Difang Huang, Chen Lin, Mingzhu Tai

In traditional decision making processes, social biases of human decision makers can lead to unequal economic outcomes for underrepresented social groups, such as women, racial or ethnic minorities. Recently, the increasing popularity of Large language model based artificial intelligence suggests a potential transition from human to AI based decision making.

Lijun Wang, Zhuo Cao, Xiaodan Fan, Hua-bai Li

The formation of protostellar disks is still a mystery, largely due to the difficulties in observations that can constrain theories. For example, the 3D alignment between the rotation of the disk and the magnetic fields (B-fields) in the formation environment is critical in some models, but so far impossible to observe. Here, we study the possibility of prob

Max Dallabetta, Conrad Dobberstein, Adrian Breiding, Alan Akbik

This paper introduces Fundus, a user-friendly news scraper that enables users to obtain millions of high-quality news articles with just a few lines of code. Unlike existing news scrapers, we use manually crafted, bespoke content extractors that are specifically tailored to the formatting guidelines of each supported online newspaper. This allows us to optim

Claudia Collacciani, Andrea Amelio Ravelli, Marianna Marcella Bolognesi

This paper introduces a novel annotation framework for the fine-grained modeling of Noun Phrases' (NPs) genericity in natural language. The framework is designed to be simple and intuitive, making it accessible to non-expert annotators and suitable for crowd-sourced tasks. Drawing from theoretical and cognitive literature on genericity, this framework is gro

Edwin A. Bedolla-Montiel, Jochem T. Lange, Alberto Pérez de Alba Ortíz, Marjolein Dijkstra

The development of new materials typically involves a process of trial and error, guided by insights from past experimental and theoretical findings. The inverse design approach for soft-matter systems has the potential to optimize specific physical parameters such as particle interactions, particle shape, or composition and packing fraction. This optimizati

M. S. Guimaraes, I. Roditi, S. P. Sorella

Unitary operators are employed to investigate the violation of the Bell-CHSH inequality. The ensuing modifications affecting both classical and quantum bounds are elucidated. The relevance of a particular class of unitary operators whose expectation values are real is pointed out. For these operators, the classical and quantum bounds remain unaltered, being

Eleanor Archer, Ariane Carrance, Laurent Ménard

We prove some technical results relating to the Brownian snake on a stable L\'evy tree. This includes some estimates on the range of the snake, estimates on its occupation measure around its minimum and also a proof of the fact that the snake and the height function of the associated tree have no common increase points.

Jinge Wang, Zien Cheng, Qiuming Yao, Li Liu

The year 2023 marked a significant surge in the exploration of applying large language model (LLM) chatbots, notably ChatGPT, across various disciplines. We surveyed the applications of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bi

Xiaobin Zhang, Liangjun Zang, Qianwen Liu, Shuchong Wei

Event temporal relation (TempRel) is a primary subject of the event relation extraction task. However, the inherent ambiguity of TempRel increases the difficulty of the task. With the rise of prompt engineering, it is important to design effective prompt templates and verbalizers to extract relevant knowledge. The traditional manually designed templates stru

Jialu Wang, Kaichen Zhou, Andrew Markham, Niki Trigoni

Despite the advancements in deep learning for camera relocalization tasks, obtaining ground truth pose labels required for the training process remains a costly endeavor. While current weakly supervised methods excel in lightweight label generation, their performance notably declines in scenarios with sparse views. In response to this challenge, we introduce

Yue Xiao, Yi He, Xiaoli Zhang, Qian Wang

The proliferation of consumer IoT products in our daily lives has raised the need for secure device authentication and access control. Unfortunately, these resource-constrained devices typically use token-based authentication, which is vulnerable to token compromise attacks that allow attackers to impersonate the devices and perform malicious operations by s

J. Classen-Howes, P. Fendley, A. Pandey, S. A. Parameswaran

We introduce an SU(M)-symmetric disordered bipartite spin model with unusual characteristics. Although superficially similar to the Sachdev-Ye model, it has several markedly different properties for M>2. In particular, it has a large non-trivial nullspace whose dimension grows exponentially with system size. The states in this nullspace are frustration-free,

Ki-Young Choi, Erdenebulgan Lkhagvadorj, Satyabrata Mahapatra

In this article, we investigate the stochastic gravitational waves (GWs) spectrum, resulting from the emission of gravitons through bremsstrahlung, in the decay of particles produced by Hawking radiation. Although particle decays inevitably entail the emission of graviton due to bremsstrahlung, the associated decay width is notably suppressed due to the Plan

Huanxuan Liao, Shizhu He, Yao Xu, Yuanzhe Zhang

Retrieval-Augmented-Generation and Generation-Augmented-Generation have been proposed to enhance the knowledge required for question answering with Large Language Models (LLMs) by leveraging richer context. However, the former relies on external resources, and both require incorporating explicit documents into the context, which increases execution costs and

Nicolò Botteghi, Urban Fasel

Optimal control of parametric partial differential equations (PDEs) is crucial in many applications in engineering and science. In recent years, the progress in scientific machine learning has opened up new frontiers for the control of parametric PDEs. In particular, deep reinforcement learning (DRL) has the potential to solve high-dimensional and complex co

Brian H. Lee, James P. Larentzos, John K. Brennan, Alejandro Strachan

Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the re

Bahareh Mastiani, Daniël W. S. Cox, Ivo M. Vellekoop

Wavefront shaping is a technique for directing light through turbid media. The theoretical aspects of wavefront shaping are well understood, and under near-ideal experimental conditions, accurate predictions for the expected signal enhancement can be given. In practice, however, there are many experimental factors that negatively affect the outcome of the ex

Dongjun Wu, Bowen Yi, Ian R. Manchester

In this paper, we extend the control contraction metrics (CCM) approach, which was originally proposed for the universal tracking control of nonlinear systems, to those that evolves on Lie groups. Our idea is to view the manifold as a constrained set that is embedded in Euclidean space, and then propose the sufficient conditions for the existence of a CCM an

John Fischer, Marko Orescanin, Justin Loomis, Patrick McClure

Federated learning (FL) is an approach to training machine learning models that takes advantage of multiple distributed datasets while maintaining data privacy and reducing communication costs associated with sharing local datasets. Aggregation strategies have been developed to pool or fuse the weights and biases of distributed deterministic models; however,

Shun Yiu, Rob Seamans, Manav Raj, Ted Liu

In this project, we examine how freelancers changed their strategic positioning on an online work platform following the launch of ChatGPT in November 2022 - a major advance in AI technologies. We document that post-ChatGPT, freelancers bid on fewer jobs and reposition themselves by differentiating their distribution of bids (i.e., job applications) relative

Alfredo Hubard, Hugo Parlier

We show a generalization of the crossing lemma for multi-graphs drawn on orientable surfaces in which pairs of edges are assumed to be drawn by non-homotopic simple arcs which pairwise cross at most $k$ times.

Alvaro Gonzalez-Jimenez, Simone Lionetti, Dena Bazazian, Philippe Gottfrois

Out-Of-Distribution (OOD) detection is critical to deploy deep learning models in safety-critical applications. However, the inherent hierarchical concept structure of visual data, which is instrumental to OOD detection, is often poorly captured by conventional methods based on Euclidean geometry. This work proposes a metric framework that leverages the stre

Sergey Foss, Michael Scheutzow

For a stochastically monotone Markov chain taking values in a Polish space, we present a number of conditions for existence and for uniqueness of its stationary regime, as well as for closeness of its transient trajectories. In particular, we generalise a basic result by Bhattacharya and Majumdar (2007) where a certain form of mixing, or swap condition was a

Amparo Baíllo, Javier Cárcamo, Carlos Mora-Corral

We introduce a 2-dimensional stochastic dominance (2DSD) index to characterize both strict and almost stochastic dominance. Based on this index, we derive an estimator for the minimum violation ratio (MVR), also known as the critical parameter, of the almost stochastic ordering condition between two variables. We determine the asymptotic properties of the em

Fanrui Zhang, Jiawei Liu, Qiang Zhang, Xiaoling Zhu

Understanding information cascades in networks is a fundamental issue in numerous applications. Current researches often sample cascade information into several independent paths or subgraphs to learn a simple cascade representation. However, these approaches fail to exploit the hierarchical semantic associations between different modalities, limiting their

Haopeng Wang, Roberto Martinez-Velazquez, Haiwei Dong, Abdulmotaleb El Saddik

Metaverse aims to construct a large, unified, immersive, and shared digital realm by combining various technologies, namely XR (extended reality), blockchain, and digital twin, among others. This article explores the Metaverse from the perspective of multimedia communication by conducting and analyzing real-world experiments on four different Metaverse platf

Anna Duwenig, Dana P. Williams, Joel Zimmerman

Well-known work of Renault shows that if $\mathcal{E}$ is a twist over a second countable, effective, \'etale groupoid $G$, then there is a naturally associated Cartan subalgebra of the reduced twisted groupoid C*-algebra $C^*_{r}(G; E)$, and that every Cartan subalgebra of a separable C*-algebra arises in this way. However twisted C*-algebras of non-effecti

V. F. Suleimanov, K. V. Belyakov, J. M. Perales, V. V. Neustroev

We present a comparative analysis of photometric observations of the cataclysmic variable TT Ari in its bright state, obtained by the TESS orbital observatory in 2021 and 2023 and by ground-based amateur telescopes in 2022. The light curves from 2021 and 2022 are dominated by modulations with a period slightly shorter than the orbital one (negative superhump

Serge Nicaise, Lassi Paunonen, David Seifert

We study stability of abstract differential equations coupled by means of a general algebraic condition. Our approach is based on techniques from operator theory and systems theory, and it allows us to study coupled systems by exploiting properties of the components, which are typically much simpler to analyse. As our main results we establish resolvent esti

Andrzej A. Zdziarski, Sebastian Heinz

Accreting black-hole binaries change their properties during evolution, passing through two main luminous states, dominated by either hard or soft X-rays. In the hard state, steady compact jets emitting multiwavelength radiation are present. Those jets are usually observed in radio, and when resolved, their extent is $\lesssim\!10^{15}$ cm. Then, during hard

Austin Schmier

The study of jet production in small collision systems is essential for testing our understanding of perturbative and non perturbative QCD and cold nuclear matter (CNM) effects. In addition, studies at high multiplicity in small collision systems exhibit signatures of collectivity, which is still not fully understood within a unified picture across system si

Argaman Mordoch, Enrico Scala, Roni Stern, Brendan Juba

Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Manually designing such an action model is a notoriously challenging task. An alternative is to automatically learn action models from obs

Kun Sun, Rong Wang, Anders Søgaard

Amidst the rapid evolution of LLMs, the significance of evaluation in comprehending and propelling these models forward is increasingly paramount. Evaluations have revealed that factors such as scaling, training types, architectures and other factors profoundly impact the performance of LLMs. However, the extent and nature of these impacts continue to be sub

Geon Yeong Park, Hyeonho Jeong, Sang Wan Lee, Jong Chul Ye

The evolution of diffusion models has greatly impacted video generation and understanding. Particularly, text-to-video diffusion models (VDMs) have significantly facilitated the customization of input video with target appearance, motion, etc. Despite these advances, challenges persist in accurately distilling motion information from video frames. While exis

Sudhir Sornapudi, Rajhans Singh

Computer vision in agriculture is game-changing with its ability to transform farming into a data-driven, precise, and sustainable industry. Deep learning has empowered agriculture vision to analyze vast, complex visual data, but heavily rely on the availability of large annotated datasets. This remains a bottleneck as manual labeling is error-prone, time-co

Guido Bell, Kevin Brune, Goutam Das, Ding Yu Shao

We compute the gluon beam function for jet-veto resummation to next-to-next-to-leading order (NNLO) in the strong-coupling expansion. Our calculation is based on an automated framework that was previously used for the computation of the respective quark beam function, and which we significantly extended for the present calculation. In particular, the perturb

Orion Weller, Benjamin Chang, Sean MacAvaney, Kyle Lo

Modern Language Models (LMs) are capable of following long and complex instructions that enable a large and diverse set of user requests. While Information Retrieval (IR) models use these LMs as the backbone of their architectures, virtually none of them allow users to provide detailed instructions alongside queries, thus limiting their ability to satisfy co

Jian Li, Pu Ren, Yang Liu, Hao Sun

Object-centric learning aims to break down complex visual scenes into more manageable object representations, enhancing the understanding and reasoning abilities of machine learning systems toward the physical world. Recently, slot-based video models have demonstrated remarkable proficiency in segmenting and tracking objects, but they overlook the importance

Zhenyu Sun, Ermin Wei

Classical convergence analyses for optimization algorithms rely on the widely-adopted uniform smoothness assumption. However, recent experimental studies have demonstrated that many machine learning problems exhibit non-uniform smoothness, meaning the smoothness factor is a function of the model parameter instead of a universal constant. In particular, it ha

Chun Kit Wong, Mary Ngo, Manxi Lin, Zahra Bashir

Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic framework designed for deploying image-based AI models in such settings. Using this framework, we deployed a trained model for fetal ultrasound standard plane detection, and evaluated

Florian Krach, Josef Teichmann, Hanna Wutte

Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst-case outcome. In this work, we propose a generative adversarial network (GAN) approach to (approximately) solve robust utility optimization problems in general and realistic settings. In particular, we model both the inve

Javier D. Fuhr, J. Esteban Gayone, Hugo Ascolani

In this work, we report the growth of a single mixed Bi$_{1-x}$Sb$_x$ layer, with diverse stoichiometries, on a Ag(111) substrate. The atomic geometry has been thoroughly investigated by low energy electron diffraction, scanning tunneling microscopy, and X-ray photoelectron spectroscopy experiments, as well as calculations based on density functional theory

Junbo Yin, Jianbing Shen, Runnan Chen, Wei Li

Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D space in autonomous driving scenarios. However, objects in the BEV representation typically exhibit small sizes, and the associated point cloud context is inherently sparse, which leads to great challenges for reliable 3D perception. In this paper, we propose IS-Fusion,

Alex Jäger, Gerhard Kramer

Joint detection and decoding (JDD) achieves rates based on information theory but is too complex to implement for many channels with memory or nonlinearities. Successive interference cancellation (SIC) at the receiver, combined with multistage encoding at the transmitter, is a method that lets one use coded modulation for memoryless channels to approach JDD

Nutan Chen, Botond Cseke, Elie Aljalbout, Alexandros Paraschos

We present a novel motion generation approach for robot arms, with high degrees of freedom, in complex settings that can adapt online to obstacles or new via points. Learning from Demonstration facilitates rapid adaptation to new tasks and optimizes the utilization of accumulated expertise by allowing robots to learn and generalize from demonstrated trajecto

Abhinav Sharma, Bojing Liu, Mattias Rantalainen

Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or histological grading). In this context, there is a need for improved spatial interpretability of predictions

Daniel Goncalves, Lisa Bombieri, Giovanni Ferioli, Sara Pancaldi

The driven Dicke model, wherein an ensemble of atoms is driven by an external field and undergoes collective spontaneous emission due to coupling to a leaky cavity mode, is a paradigmatic example of a system exhibiting a driven-dissipative phase transition as a function of driving strength. Recently, a similar phenomenon was experimentally observed, not in a

Qiang Zhang, Jiawei Liu, Fanrui Zhang, Xiaoling Zhu

Identifying key nodes in social networks plays a crucial role in timely blocking false information. Existing key node identification methods usually consider node influence only from the propagation structure perspective and have insufficient generalization ability to unknown scenarios. In this paper, we propose a novel Multi-perspective Memory Enhanced Netw

Qingyang Liu, Junqi You, Jianting Wang, Xinhao Tao

In the realm of image composition, generating realistic shadow for the inserted foreground remains a formidable challenge. Previous works have developed image-to-image translation models which are trained on paired training data. However, they are struggling to generate shadows with accurate shapes and intensities, hindered by data scarcity and inherent task

Olivia Gruza, Elias Heftrig, Oliver Jacobsen, Haya Schulmann

NSEC3 is a proof of non-existence in DNSSEC, which provides an authenticated assertion that a queried resource does not exist in the target domain. NSEC3 consists of alphabetically sorted hashed names before and after the queried hostname. To make dictionary attacks harder, the hash function can be applied in multiple iterations, which however also increases

Jorge J. Betancor, Juan C. Fariña, Lourdes Rodríguez-Mesa

We consider the Laplacian with drift in $\mathbb R^n$ defined by $\Delta_\nu = \sum_{i=1}^n(\frac{\partial^2}{\partial x_i^2} + 2 \nu_i\frac{\partial }{\partial{x_i}})$ where $\nu=(\nu_1,\ldots,\nu_n)\in \mathbb R^n\setminus\{0\}$. The operator $\Delta_\nu$ is selfadjoint with respect to the measure $d\mu_\nu(x)=e^{2\langle\nu,x\rangle}dx$. This measure is n

Indranil Biswas, Manish Kumar, A. J. Parameswaran

Let $f:X\rightarrow Y$ be a generically smooth morphism between irreducible smooth projective curves over an algebraically closed field of arbitrary characteristic. We prove that the vector bundle $((f_*{\mathcal O}_X)/{\mathcal O}_Y)^*$ is virtually globally generated. Moreover, $((f_*{\mathcal O}_X)/{\mathcal O}_Y)^*$ is ample if and only if $f$ is genuine

Jonathan Katzy, Răzvan-Mihai Popescu, Arie van Deursen, Maliheh Izadi

Does the training of large language models potentially infringe upon code licenses? Furthermore, are there any datasets available that can be safely used for training these models without violating such licenses? In our study, we assess the current trends in the field and the importance of incorporating code into the training of large language models. Additi

David T. S. Perkins, Alessandro Veneri, Aires Ferreira

Atomically-thin materials based on transition metal dichalcogenides and graphene offer a promising avenue for unlocking the mechanisms underlying the spin Hall effect (SHE) in heterointerfaces. Here, we develop a microscopic theory of the SHE for twisted van der Waals heterostructures that fully incorporates twisting and disorder effects, and illustrate the

Dennis Gramlich, Sheng Gao, Hao Zhang, Carsten W. Scherer

We present a simple and effective way to account for non-convex costs and constraints~in~state feedback synthesis, and an interpretation for the variables in which state feedback synthesis is typically convex. We achieve this by deriving the controller design using moment matrices of state and input. It turns out that this approach allows the consideration o

Soyeon Yoon, Kwan Yun, Kwanggyoon Seo, Sihun Cha

Recent advances in 3D face stylization have made significant strides in few to zero-shot settings. However, the degree of stylization achieved by existing methods is often not sufficient for practical applications because they are mostly based on statistical 3D Morphable Models (3DMM) with limited variations. To this end, we propose a method that can produce

Qiong Wu, Weihao Ye, Yiyi Zhou, Xiaoshuai Sun

In this paper, we propose a novel parameter and computation efficient tuning method for Multi-modal Large Language Models (MLLMs), termed Efficient Attention Skipping (EAS). Concretely, we first reveal that multi-head attentions (MHAs), the main computational overhead of MLLMs, are often redundant to downstream tasks. Based on this observation, EAS evaluates

F. Chioma Onyeagusi, Jens Teiser, Tim Becker, Gerhard Wurm

Planetesimals or smaller bodies in protoplanetary disks are often considered to form as pebble piles in current planet formation models. They are supposed to be large but loose, weakly bound clusters of more robust dust aggregates. This makes them easy prey for destructive processes. In microgravity experiments, we apply strong electric fields on clusters of

John Delaney, Badih Ghazi, Charlie Harrison, Christina Ilvento

In this work, we study ad conversion measurement, a central functionality in digital advertising, where an advertiser seeks to estimate advertiser website (or mobile app) conversions attributed to ad impressions that users have interacted with on various publisher websites (or mobile apps). Using differential privacy (DP), a notion that has gained in popular

Ofer Lahav, Andrew R Liddle

This is a review article for The Review of Particle Physics 2024 (aka the Particle Data Book), appearing as Chapter 25. It forms a compact review of knowledge of the cosmological parameters near the end of 2023. Topics included are Parametrizing the Universe; Extensions to the standard model; Probes; Bringing observations together; Outlook for the future.

Andrea Menta, Alberto Archetti, Matteo Matteucci

Neural cellular automata represent an evolution of the traditional cellular automata model, enhanced by the integration of a deep learning-based transition function. This shift from a manual to a data-driven approach significantly increases the adaptability of these models, enabling their application in diverse domains, including content generation and artif

Lingfeng Zhang, Qiang Zhang, Hao Wang, Erjia Xiao

Navigating toward specific objects in unknown environments without additional training, known as Zero-Shot object navigation, poses a significant challenge in the field of robotics, which demands high levels of auxiliary information and strategic planning. Traditional works have focused on holistic solutions, overlooking the specific challenges agents encoun

John Mangeri, Martin Ovesen, Thomas Olsen

We report a comprehensive set of density functional theory calculations on the family of layered antiferromagnetic manganese pnictides (Ba, Ca, Sr)$\mathrm{Mn}_2\mathrm{(P, As, Sb)}_2$. We characterize all components to the linear magnetoelectric (ME) tensor $\alpha$ which are parsed into their contributions from spin and orbital moments for both lattice-med

Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li

In this paper, we present the Proportional Payoff Allocation Game (PPA-Game), which characterizes situations where agents compete for divisible resources. In the PPA-game, agents select from available resources, and their payoffs are proportionately determined based on heterogeneous weights attributed to them. Such dynamics simulate content creators on onlin

Maximilian Gehri, Nicolai Engelmann, Heinz Koeppl

The mutual information (MI) of Poisson-type channels has been linked to a filtering problem since the 70s, but its evaluation for specific continuous-time, discrete-state systems remains a demanding task. As an advantage, Markov renewal processes (MrP) retain their renewal property under state space filtering. This offers a way to solve the filtering problem

Felix Chan, Laszlo Matyas, Agoston Reguly

The paper deals with models in which the dependent variable, some explanatory variables, or both represent sensitive data. We introduce a novel discretization method that preserves data privacy when working with such variables. A multiple discretization method is proposed that utilizes information from the different discretization schemes. We show convergenc

Meng Yang, Rui Xie, Yongjun Zhang, Yue Chen

With the rising adoption of distributed energy resources (DERs), microgrid dispatch is facing new challenges: DER owners are independent stakeholders seeking to maximize their individual profits rather than being controlled centrally; and the dispatch of renewable generators may affect the microgrid's exposure to uncertainty. To address these challenges, thi

Pranav Kulkarni, Adway Kanhere, Dharmam Savani, Andrew Chan

Curating annotations for medical image segmentation is a labor-intensive and time-consuming task that requires domain expertise, resulting in "narrowly" focused deep learning (DL) models with limited translational utility. Recently, foundation models like the Segment Anything Model (SAM) have revolutionized semantic segmentation with exceptional zero-shot ge

Samuel T. Elkin, Michael Haider, Thomas E. Roth

Josephson traveling-wave parametric amplifiers (JTWPAs) are wideband, ultralow-noise amplifiers used to enable the readout of superconducting qubits. While individual JTWPAs have achieved high performance, behavior between devices is inconsistent due to wide manufacturing tolerances. Amplifier designs could be modified to improve resilience towards variation

Maria Luce Lupetti, Dave Murray-Rust

This paper examines the role that enchantment plays in the design of AI things by constructing a taxonomy of design approaches that increase or decrease the perception of magic and enchantment. We start from the design discourse surrounding recent developments in AI technologies, highlighting specific interaction qualities such as algorithmic uncertainties a

Marieh Jahannia, Manon Stipulanti

We explore the Ziv-Lempel and Crochemore factorizations of some classical automatic sequences making an extensive use of the theorem prover Walnut.

Thales Bertaglia, Lily Heisig, Rishabh Kaushal, Adriana Iamnitchi

Large Language Models (LLMs) raise concerns about lowering the cost of generating texts that could be used for unethical or illegal purposes, especially on social media. This paper investigates the promise of such models to help enforce legal requirements related to the disclosure of sponsored content online. We investigate the use of LLMs for generating syn

ALICE Collaboration

Measurements of the $p_{\rm T}$-dependent flow vector fluctuations in Pb-Pb collisions at $\sqrt{s_{_{\rm NN}}} = 5.02~\mathrm{TeV}$ using azimuthal correlations with the ALICE experiment at the Large Hadron Collider are presented. A four-particle correlation approach [1] is used to quantify the effects of flow angle and magnitude fluctuations separately. Th

Abdelkader Dahmani, Benharrat Belaïdi

In this article, we study the growth of solutions of the homogeneous complex linear differential equation \begin{equation*} f^{(k)}+A_{k-1}(z)f^{(k-1)}+\cdots+A_{1}(z)f^{\prime}+ A_{0}(z)f=0, \end{equation*}% where the coefficients $A_{j}(z)$ $(j=0,1,\ldots ,k-1)$ are analytic or meromorphic functions in $\overline{\mathbb{C}}\setminus\{z_{0}\}$. Under the s

Chen Cecilia Liu, Iryna Gurevych

Prior research shows that differences in the early period of neural network training significantly impact the performance of in-distribution (ID) data of tasks. Yet, the implications of early learning dynamics on out-of-distribution (OOD) generalization remain poorly understood, primarily due to the complexities and limitations of existing analytical techniq

Taeheon Kim, Sangyun Chung, Damin Yeom, Youngjoon Yu

Multispectral pedestrian detection is attractive for around-the-clock applications due to the complementary information between RGB and thermal modalities. However, current models often fail to detect pedestrians in certain cases (e.g., thermal-obscured pedestrians), particularly due to the modality bias learned from statistically biased datasets. In this pa

Mohammed Alghazwi, Dewi Davies-Batista, Dimka Karastoyanova, Fatih Turkmen

Aggregate statistics play an important role in extracting meaningful insights from distributed data while preserving privacy. A growing number of application domains, such as healthcare, utilize these statistics in advancing research and improving patient care. In this work, we explore the challenge of input validation and public verifiability within privacy

André Bertolace, Konstatinos Gatsis, Kostas Margellos

Decision making and learning in the presence of uncertainty has attracted significant attention in view of the increasing need to achieve robust and reliable operations. In the case where uncertainty stems from the presence of adversarial attacks this need is becoming more prominent. In this paper we focus on linear and nonlinear classification problems and