March 2024 arXiv papers — page 18

Joshua Green, Ivan D. Haigh, Niall Quinn, Jeff Neal

Compound flooding, where the combination or successive occurrence of two or more flood drivers leads to an extreme impact, can greatly exacerbate the adverse consequences associated with flooding in coastal regions. This paper reviews the practices and trends in coastal compound flood research methodologies and applications, as well as synthesizes key findin

Valentin Delabrosse, Catherine Dougados, Sylvie Cabrit, Benoit Tabone

The origin and impact of outflows on proto-planetary disks and planet formation are key open questions. DG Tau B, a Class I protostar with a structured disk and a striking rotating conical CO outflow, recently identified with ALMA as one of the best MHD disk wind candidate, is an ideal target for studying these phenomena. Our aim is to analyse the outflow co

Rustem Yeshpanov, Alina Polonskaya, Huseyin Atakan Varol

We introduce KazParC, a parallel corpus designed for machine translation across Kazakh, English, Russian, and Turkish. The first and largest publicly available corpus of its kind, KazParC contains a collection of 371,902 parallel sentences covering different domains and developed with the assistance of human translators. Our research efforts also extend to t

Elizaveta Artser, Anastasiia Birillo, Yaroslav Golubev, Maria Tigina

In many MOOCs, whenever a student completes a programming task, they can see previous solutions of other students to find potentially different ways of solving the problem and to learn new coding constructs. However, a lot of MOOCs simply show the most recent solutions, disregarding their diversity or quality, and thus hindering the students' opportunity to

Marta Pieropan, Damaris Schindler

We combine the split torsor method and the hyperbola method for toric varieties to count rational points and Campana points of bounded height on certain subvarieties of toric varieties.

Matthias Aschenbrenner, Lou van den Dries, Joris van der Hoeven

We define the universal exponential extension of an algebraically closed differential field and investigate its properties in the presence of a nice valuation and in connection with linear differential equations. Next we prove normalization theorems for algebraic differential equations over $H$-fields, as a tool in solving such equations in suitable extensio

Hugo Henneuse

To our knowledge, the analysis of convergence rates for persistence diagrams estimation from noisy signals has predominantly relied on lifting signal estimation results through sup-norm (or other functional norm) stability theorems. We believe that moving forward from this approach can lead to considerable gains. We illustrate it in the setting of nonparamet

Yudan Xiong, Fangjun Xu

Let $X=\{X_n: n\in \mathbb{N}\}$ be a linear process with bounded probability density function $f(x)$. Under certain conditions, we use the kernel estimator \[ \frac{2}{n(n-1)h_n} \sum_{1\le i<j\le n}K\Big(\frac{X_i-X_j}{h_n}\Big) \] to estimate the quadratic functional of $\int_{\mathbb{R}}f^2(x)dx$ of the linear process $X=\{X_n: n\in \mathbb{N}\}$ and imp

Britta U. Westner, Daniel R. McCloy, Eric Larson, Alexandre Gramfort

Most scientists need software to perform their research (Barker et al., 2020; Carver et al., 2022; Hettrick, 2014; Hettrick et al., 2014; Switters and Osimo, 2019), and neuroscientists are no exception. Whether we work with reaction times, electrophysiological signals, or magnetic resonance imaging data, we rely on software to acquire, analyze, and statistic

Katherine Daftari, Katherine Newhall

Emergent behavior in active systems is a complex byproduct of local, often pairwise, interactions. One such interaction is self-avoidance, which experimentally can arise as a response to self-generated environmental signals; such experiments have inspired non-Markovian mathematical models. In previous work, we set out to find ``hallmarks of self-avoidant mem

Hengyu Xu, Yizhi Zhan, Shao-Jun Zhang

We study the phenomenon of spontaneous scalarization in parameterized Schwarzschild-like black holes. Two metrics are considered, the Konoplya-Zhidenko metric and the Johannsen-Psaltis metric. While these metrics can mimic the Schwarzschild black hole well in the weak-field regime, they have deformed geometries in the near-horizon strong-field region. Such d

Robert de Mello Koch, Pratik Roy, Hendrik J. R. Van Zyl

In this paper we consider the collective field theory description of a single free massless scalar matrix theory in 2+1 dimensions. The collective fields are given by $k$-local operators obtained by tracing a product of $k$-matrices. For $k=2$ and $k=3$ we argue that the collective field packages the fields associated to a single and two Regge trajectories r

Deyuan Liu, Zecheng Wang, Bingning Wang, Weipeng Chen

The rapid proliferation of large language models (LLMs) such as GPT-4 and Gemini underscores the intense demand for resources during their training processes, posing significant challenges due to substantial computational and environmental costs. To alleviate this issue, we propose checkpoint merging in pretraining LLM. This method utilizes LLM checkpoints w

Forrest Laine

Mathematical Program Networks (MPNs) are introduced in this work. An MPN is a collection of interdependent Mathematical Programs (MPs) which are to be solved simultaneously, while respecting the connectivity pattern of the network defining their relationships. The network structure of an MPN impacts which decision variables each constituent mathematical prog

Pasquale Bosso, Fabrizio Illuminati, Luciano Petruzziello, Fabian Wagner

Modified dispersion relations (MDRs) and noncommutative geometries are phenomenological models of Planck-scale corrections to relativistic kinematics, motivated by several approaches to quantum gravity. High-energy astrophysical observations, while commonly used to test such effects, are limited by significant systematic uncertainties. In contrast, low-energ

Uriya A. First, Tali Kaufman

We study sheaves on posets, showing that cosystolic expansion of such sheaves can be derived from local expansion conditions of the sheaf and the poset (typically a high dimensional expander). When the poset at hand is a cell complex, a sheaf on it may be thought of as generalizing coefficient groups used for defining homology and cohomology, by letting the

Jianguo Pan, Zhengxin Hu, Lingdun Zhang, Xia Cai

Sensor-based human activity recognition is important in daily scenarios such as smart healthcare and homes due to its non-intrusive privacy and low cost advantages, but the problem of out-of-domain generalization caused by differences in focusing individuals and operating environments can lead to significant accuracy degradation on cross-person behavior reco

Felipe Lopes Oliveira, Binquan Luan, Pierre Mothé Esteves, Mathias Steiner

Automated molecular simulations are used extensively for predicting material properties. Typically, these simulations exhibit two regimes: a dynamic equilibration part, followed by a steady state. For extracting observable properties, the simulations must first reach a steady state so that thermodynamic averages can be taken. However, as equilibration depend

Yanglin Feng, Yang Qin, Dezhong Peng, Hongyuan Zhu

In this paper, we present and study a new instance-level retrieval task: PointCloud-Text Matching (PTM), which aims to identify the exact cross-modal instance that matches a given point-cloud query or text query. PTM has potential applications in various scenarios, such as indoor/urban-canyon localization and scene retrieval. However, there is a lack of suit

Pavel Tonkaev, Fangxing Lai, Sergey Kruk, Qinghai Song

Generation of even-order optical harmonics requires noncentrosymmetric structures being conventionally observed in crystals lacking the center of inversion. In centrosymmetric systems, even-order harmonics may arise, e.g., at surfaces but such effects are usually very weak. Here we observe optical harmonics up to 4-th order generated under the normal inciden

The MMGPDs Collaboration, Muhammad Goharipour, Hadi Hashamipour, Fatemeh Irani

It is well established now that the generalized parton distributions (GPDs) at zero skewness are playing important roles in some physical process such as elastic electron-nucleon scattering, elastic (anti)neutrino-nucleon scattering, and wide-angle Compton scattering (WACS) via various types of form factors (FFs). In this study, we are going to utilize the r

Jerzy Lewandowski, Maciej Ossowski

We consider isolated horizons (Killing horizons up to the second order) whose null flow has the structure of a U(1) principal fiber bundle over a compact Riemann surface. We impose the vacuum Einstein equations (with the cosmological constant) and the condition that the spacetime Weyl tensor is of Petrov D type on the geometry of the horizons. We derive all

R. Mincigrucci, E. Paltanin, J. -S. Pelli-Cresi, F. Gala

The relentless pursuit of understanding matter at ever-finer scales has pushed optical microscopy to surpass the diffraction limit and produced the super-resolution microscopy which enables visualizing structures shorter than the wavelength of light. In the present work, we harnessed extreme ultraviolet beams to create a sub-{\mu}m grating structure, which w

Ziyu Wang, Chris Holmes

Bayesian modelling allows for the quantification of predictive uncertainty which is crucial in safety-critical applications. Yet for many machine learning (ML) algorithms, it is difficult to construct or implement their Bayesian counterpart. In this work we present a promising approach to address this challenge, based on the hypothesis that commonly used ML

Alon Nishry, Elliot Paquette

We consider the range of random analytic functions with finite radius of convergence. We show that any unbounded random Taylor series with rotationally invariant coefficients has dense image in the plane. We moreover show that if in addition the coefficients are complex Gaussian with sufficiently regular variances, then the image is the whole complex plane.

Ids van der Werf, Richard Heusdens, Richard C. Hendriks, Geert Leus

This paper investigates the positioning of the pilot symbols, as well as the power distribution between the pilot and the communication symbols in the orthogonal time frequency space (OTFS) modulation scheme. We analyze the pilot placements that minimize the mean squared error (MSE) in estimating the channel taps. This allows us to identify two new pilot all

Toon Boeckling, Antoon Bronselaer

The repair problem for functional dependencies is the problem where an input database needs to be modified such that all functional dependencies are satisfied and the difference with the original database is minimal. The output database is then called an optimal repair. If the allowed modifications are value updates, finding an optimal repair is NP-hard. A w

Hamidul Ahmed, B. Krishna Das, Samir Panja

We consider de Branges-Rovnyak spaces of a considerably large class of reproducing kernel Hilbert spaces and find a characterization for them to be complete Nevanlinna-Pick spaces. This extends as well as recovers earlier characterizations obtained for the Hardy space over the unit disc (\cite{Chu}) as well as for the Drury-Arveson space over the unit ball (

Matteo Caligiuri, Adriano Simonetto, Pietro Zanuttigh

The acquisition of objects outside the Line-of-Sight of cameras is a very intriguing but also extremely challenging research topic. Recent works showed the feasibility of this idea exploiting transient imaging data produced by custom direct Time of Flight sensors. In this paper, for the first time, we tackle this problem using only data from an off-the-shelf

Andrew Dudash, Scott James, Ryan Rubel

Robotic access monitoring of multiple target areas has applications including checkpoint enforcement, surveillance and containment of fire and flood hazards. Monitoring access for a single target region has been successfully modeled as a minimum-cut problem. We generalize this model to support multiple target areas using two approaches: iterating on individu

Yu Gu, Puyang Huang, Tianhao Chen, Chenyi Fu

We report a spin-orbit torque(SOT) magnetoresistive random-access memory(MRAM)-based probabilistic binary neural network(PBNN) for resource-saving and hardware noise-tolerant computing applications. With the presence of thermal fluctuation, the non-destructive SOT-driven magnetization switching characteristics lead to a random weight matrix with controllable

L. L. Lage, O. Arroyo-Gascón, Leonor Chico, A. Latgé

The electronic properties of one- and two-dimensional biphenylene-based systems, such as nanoribbons and bilayers, are studied within a unified approach. Besides the bilayer with direct (AA) stacking, we present two additional symmetric stackings for bilayer biphenylene that we denote by AB, by analogy with bilayer graphene, and AX, which can be derived by a

Jacob Moran, Lucas C. Graham, Mikhail Tikhonov

Microbial ecosystems exhibit a surprising amount of functionally relevant diversity at all levels of taxonomic resolution, presenting a significant challenge for most modeling frameworks. A long-standing hope of theoretical ecology is that some patterns might persist despite community complexity -- or perhaps even emerge because of it. A deeper understanding

Isabel A. Martínez Ávila, Carlos Jerez-Hanckes, Irina Pettersson

We simulate the electrical response of multiple disjoint biological 3D cells undergoing an electropermeabilization process. Instead of solving the boundary value problem in the unbounded volume, we reduce it to a system of boundary integrals equations--the local Multiple Traces Formulation--coupled with nonlinear dynamics on the cell membranes. Though in tim

Krishnendu Patra, Viktor Christiansson, Ferdi Aryasetiawan, Priya Mahadevan

It is known from density functional theory (DFT) calculations that RhSi has a multifold degenerate Dirac point at the Fermi energy, with the dominant states in the low-energy region displaying mostly Rh $d$ character. Using DFT+U, we calculate the band structure by considering an effective local interaction on the Rh $d$ states, with a realistic effective Hu

Ceng Zhang, Xin Meng, Dongchen Qi, Gregory S. Chirikjian

This paper introduces an automatic affordance reasoning paradigm tailored to minimal semantic inputs, addressing the critical challenges of classifying and manipulating unseen classes of objects in household settings. Inspired by human cognitive processes, our method integrates generative language models and physics-based simulators to foster analytical thin

David Candal-Ventureira, Pablo Fondo-Ferreiro, Felipe Gil-Castiñeira, Francisco Javier González-Castaño

The unstoppable adoption of the Internet of Things (IoT) is driven by the deployment of new services that require continuous capture of information from huge populations of sensors, or actuating over a myriad of "smart" objects. Accordingly, next generation networks are being designed to support such massive numbers of devices and connections. For example, t

Jens Frieß, Tobias Gattermayer, Nethanel Gelernter, Haya Schulmann

Recent works showed that it is feasible to hijack resources on cloud platforms. In such hijacks, attackers can take over released resources that belong to legitimate organizations. It was proposed that adversaries could abuse these resources to carry out attacks against customers of the hijacked services, e.g., through malware distribution. However, to date,

Pablo Fondo-Ferreiro, David Candal-Ventureira, Francisco Javier González-Castaño, Felipe Gil-Castiñeira

Vehicle automation is driving the integration of advanced sensors and new applications that demand high-quality information, such as collaborative sensing for enhanced situational awareness. In this work, we considered a vehicular sensing scenario supported by 5G communications, in which vehicle sensor data need to be sent to edge computing resources with st

Mark Roantree, Niamh Murphi, Dinh Viet Cuong, Vuong Minh Ngo

Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide and play an important role in many urban transportation systems. BSSs alleviate congestion, reduce pollution and promote physical exercise. It is essential to explore the spatiotemporal patterns of bike-sharing demand, as well as the factors that influence these patterns, in order t

Bartosz Wcisło

Answering a question of Kaye, we show that the compositional truth theory with a full collection scheme is conservative over Peano Arithmetic. We demonstrate it by showing that countable models of compositional truth which satisfy the internal induction or collection axioms can be end-extended to models of the respective theory.

Qiankun Liu, Rui Liu, Bolun Zheng, Hongkui Wang

Recently, infrared small target detection (IRSTD) has been dominated by deep-learning-based methods. However, these methods mainly focus on the design of complex model structures to extract discriminative features, leaving the loss functions for IRSTD under-explored. For example, the widely used Intersection over Union (IoU) and Dice losses lack sensitivity

Atnafu Lambebo Tonja, Olga Kolesnikova, Alexander Gelbukh, Jugal Kalita

Recent research in natural language processing (NLP) has achieved impressive performance in tasks such as machine translation (MT), news classification, and question-answering in high-resource languages. However, the performance of MT leaves much to be desired for low-resource languages. This is due to the smaller size of available parallel corpora in these

David Candal-Ventureira, Francisco Javier González-Castaño, Felipe Gil-Castiñeira, Pablo Fondo-Ferreiro

In this article, we evaluate the first experience of computation offloading from drones to real fifth-generation (5G) operator systems, including commercial and private carrier-grade 5G networks. A follow-me drone service was implemented as a representative testbed of remote video analytics. In this application, an image of a person from a drone camera is pr

Roopayan Ghosh, Bin Yi, Sougato Bose

Investigating causation in the quantum domain is crucial. Despite numerous studies of correlations in quantum many-body systems, causation, which is very distinct from correlations, has hardly been studied. We address this by demonstrating the efficacy of the newly established causation measure, quantum Liang information flow, in quantifying causality across

Muzi Chen, Nan Li, Lifen Zheng, Difang Huang

The connectivity of stock markets reflects the information efficiency of capital markets and contributes to interior risk contagion and spillover effects. We compare Shanghai Stock Exchange A-shares (SSE A-shares) during tranquil periods, with high leverage periods associated with the 2015 subprime mortgage crisis. We use Pearson correlations of returns, the

Ting-Ting Ge, Xiao-Na Sun, Rui-Zhi Yang, Pak-Hin Thomas Tam

We report the detection of gamma-ray emission by the Fermi Large Area Telescope (Fermi-LAT) towards the young massive star cluster RCW 38 in the 1-500 GeV photon energy range. We found spatially extended GeV emission towards the direction of RCW 38, which is best modelled by a Gaussian disc of 0.23$\deg$ radius with a significance of the extension is $\sim 1

Steven Duplij

We generalize $\sigma$-matrices to higher arities using the polyadization procedure proposed by the author. We build the nonderived $n$-ary version of $SU\left( 2\right) $ using cyclic shift block matrices. We define a new function, the polyadic trace, which has an additivity property analogous to the ordinary trace for block diagonal matrices and which can

Vito F. Pecile, Michael Leskowschek, Norbert Modsching, Valentin J. Wittwer

Within the domain of optical frequency comb systems operating in the mid-infrared, extensive exploration has been undertaken regarding critical parameters such as stabilization, coherence, or spectral tunability. Despite this, certain essential parameters remain inadequately addressed, particularly concerning the light source prerequisites for advanced spect

David Candal-Ventureira, Francisco Javier González-Castaño, Felipe Gil-Castiñeira, Pablo Fondo-Ferreiro

Wireless connectivity is essential for industrial production processes and workflow management. Moreover, the connectivity requirements of industrial devices, which are usually long-term investments, are diverse and require different radio interfaces. In this regard, the 3GPP has studied how to support heterogeneous radio access technologies (RATs) such as W

Angelina Parfenova, Marianne Clausel

This paper addresses the problem of risk prediction on social media data, specifically focusing on the classification of Reddit users as having a pathological gambling disorder. To tackle this problem, this paper focuses on incorporating temporal and emotional features into the model. The preprocessing phase involves dealing with the time irregularity of pos

Neal Jackson, Shruti Badole, Thomas Dugdale, Hannah R. Stacey

We present 6-GHz Very Large Array radio images of 70 gravitational lens systems at 300-mas resolution, in which the source is an optically-selected quasar, and nearly all of which have two lensed images. We find that about in half of the systems (40/70, with 33/70 secure), one or more lensed images are detected down to our detection limit of 20microJy/beam,

Changqing Ye, Shubin Fu, Eric T. Chung, Jizu Huang

In this article, a two-level overlapping domain decomposition preconditioner is developed for solving linear algebraic systems obtained from simulating Darcy flow in high-contrast media. Our preconditioner starts at a mixed finite element method for discretizing the partial differential equation by Darcy's law with the no-flux boundary condition and is then

Mahbubunnabi Tamala, Mohammad Marufur Rahmanb, Maryam Alhasimc, Mobarak Al Mulhimd

For severely affected COVID-19 patients, it is crucial to identify high-risk patients and predict survival and need for intensive care (ICU). Most of the proposed models are not well reported making them less reproducible and prone to high risk of bias particularly in presence of imbalance data/class. In this study, the performances of nine machine and deep

Alexander Shirnin, Nikita Andreev, Vladislav Mikhailov, Ekaterina Artemova

This paper describes AIpom, a system designed to detect a boundary between human-written and machine-generated text (SemEval-2024 Task 8, Subtask C: Human-Machine Mixed Text Detection). We propose a two-stage pipeline combining predictions from an instruction-tuned decoder-only model and encoder-only sequence taggers. AIpom is ranked second on the leaderboar

Pablo Fondo-Ferreiro, Felipe Gil-Castiñeira, Francisco Javier González-Castaño, David Candal-Ventureira

Mobile core networks handle critical control functions for delivering services in modern cellular networks. Traditional point-to-point architectures, where network functions are directly connected through standardized interfaces, are being substituted by service-based architectures (SBAs), where core functionalities are finer-grained microservices decoupled

Felix Leeb, Bernhard Schölkopf

Babel Briefings is a novel dataset featuring 4.7 million news headlines from August 2020 to November 2021, across 30 languages and 54 locations worldwide with English translations of all articles included. Designed for natural language processing and media studies, it serves as a high-quality dataset for training or evaluating language models as well as offe

A. S. Inácio, W. Parker, B. Tam

SNO+ is a large multipurpose experiment with the ultimate goal of searching for the neutrinoless double beta decay in $^{130}\mathrm{Te}$. After a commissioning phase with water as the target medium, during which acquired data allowed for measurements of solar neutrinos and the detection of reactor antineutrinos, SNO+ is now filled with 780 tonnes of liquid

M. Kuźniak, S. Pawłowski, A. Abramowicz, A. F. V. Cortez

Polyethylene naphthalate (PEN) foils have been demonstrated as a wavelength shifter suitable for operation in liquid argon. At the same time, wavelength shifting efficiency of technical grades of PEN, commercially available on the market, is lower than that of tetraphenyl butadiene (TPB). This paper reports on an R&D program focused on exploring the intrinsi

Dongqin Ni, Florian Klämpfl, Michael Schmidt, Martin Hohmann

The previous research proves that the random laser emission reflects not only the scattering properties but also the absorption properties. The random laser is therefore considered a potential tool for optical properties sensing. Although the qualitative sensing using the random laser is extensively investigated, a quantitative measurement is still rare. In

Pablo Fondo-Ferreiro, Felipe Gil-Castiñeira, Francisco Javier González-Castaño, David Candal-Ventureira

Next-generation cellular networks will play a key role in the evolution of different vertical industries. Low latency will be a major requirement in many related uses cases. This requirement is specially challenging in scenarios with high mobility of end devices, such as vehicular communications. The Multi-Access Edge Computing (MEC) paradigm seeks to satisf

Binzong Geng, Zhaoxin Huan, Xiaolu Zhang, Yong He

With the rise of large language models (LLMs), recent works have leveraged LLMs to improve the performance of click-through rate (CTR) prediction. However, we argue that a critical obstacle remains in deploying LLMs for practical use: the efficiency of LLMs when processing long textual user behaviors. As user sequences grow longer, the current efficiency of

Jingyuan Ma, Damai Dai, Zihang Yuan, Rui li

Large Language Models (LLMs) have shown remarkable success on a wide range of math and reasoning benchmarks. However, we observe that they often struggle when faced with unreasonable math problems. Instead of recognizing these issues, models frequently proceed as if the problem is well-posed, producing incorrect answers or falling into overthinking and verbo

Kangming Xu, Huiming Zhou, Haotian Zheng, Mingwei Zhu

With the rapid evolution of the Internet and the exponential proliferation of information, users encounter information overload and the conundrum of choice. Personalized recommendation systems play a pivotal role in alleviating this burden by aiding users in filtering and selecting information tailored to their preferences and requirements. Such systems not

Rafael Vazquez, Miroslav Krstic

In this work we advance the recently-introduced deep learning-powered approach to PDE backstepping control by proposing a method that approximates only the control gain function -- a function of one variable -- instead of the entire kernel function of the backstepping transformation, which depends on two variables. This idea is introduced using several bench

Bao-Yi Yang, Hui-Hua Zhong, Muyang Chen

We study the elastic electric and magnetic form factors of the proton, neutron and the charged roper resonance ($G_E^p$, $G_M^p$, $G_E^n$, $G_M^n$, $G_E^R$ and $G_M^R$) systematically in a constituent quark model. Three ingredients are crucial in this study: i) the mixing between the pure S-wave and other components which produces a nonzero neutron electric

Changqing Ye, Shubin Fu, Eric T. Chung, Jizu Huang

In this paper, we develop a multigrid preconditioner to solve Darcy flow in highly heterogeneous porous media. The key component of the preconditioner is to construct a sequence of nested subspaces $W_{\mathcal{L}}\subset W_{\mathcal{L}-1}\subset\cdots\subset W_1=W_h$. An appropriate spectral problem is defined in the space of $W_{i-1}$, then the eigenfuncti

Lorenzo Carletti

We show existence, uniqueness and positivity for the Green's function of the operator $(\Delta_g + \alpha)^k$ in a closed Riemannian manifold $(M,g)$, of dimension $n>2k$, $k\in \mathbb{N}$, $k\geq 1$, with Laplace-Beltrami operator $\Delta_g = -\operatorname{div}_g(\nabla \cdot)$, and where $\alpha >0$. We are interested in the case where $\alpha$ is large

Hyunbyung Park, Sukyung Lee, Gyoungjin Gim, Yungi Kim

To address the challenges associated with data processing at scale, we propose Dataverse, a unified open-source Extract-Transform-Load (ETL) pipeline for large language models (LLMs) with a user-friendly design at its core. Easy addition of custom processors with block-based interface in Dataverse allows users to readily and efficiently use Dataverse to buil

Jonathan Erskine, Matt Clifford, Alexander Hepburn, Raúl Santos-Rodríguez

Human-Computer Interaction has been shown to lead to improvements in machine learning systems by boosting model performance, accelerating learning and building user confidence. In this work, we aim to alleviate the expectation that human annotators adapt to the constraints imposed by traditional labels by allowing for extra flexibility in the form that super

A. Doff, C. A. de S. Pires

In this work we introduce scalar leptoquarks into the 3-3-1 model with right-handed neutrinos with the aim of solving the $(g-2)_{\mu}$ puzzle. We show that besides the model supports leptoquarks in the octet, sextet, triplet and singlet representations, we identified that only one specif leptoquark in the singlet representation leads to flip of chirality as

J. M. Coloma-Nadal, F. -S. Kitaura, J. E. García-Farieta, F. Sinigaglia

Accurate modeling of galaxy distributions is paramount for cosmological analysis using galaxy redshift surveys. However, this endeavor is often hindered by the computational complexity of resolving the dark matter halos that host these galaxies. To address this challenge, we propose the development of effective assembly bias models down to small scales, i.e.

Jiacui Huang, Hongtao Zhang, Mingbo Zhao, Zhou Wu

Vision-and-Language Navigation (VLN) is a challenging task that requires a robot to navigate in photo-realistic environments with human natural language promptings. Recent studies aim to handle this task by constructing the semantic spatial map representation of the environment, and then leveraging the strong ability of reasoning in large language models for

Rustem Yeshpanov, Huseyin Atakan Varol

This paper presents KazSAnDRA, a dataset developed for Kazakh sentiment analysis that is the first and largest publicly available dataset of its kind. KazSAnDRA comprises an extensive collection of 180,064 reviews obtained from various sources and includes numerical ratings ranging from 1 to 5, providing a quantitative representation of customer attitudes. T

Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu

Face Anti-Spoofing (FAS) is pivotal in safeguarding facial recognition systems against presentation attacks. While domain generalization (DG) methods have been developed to enhance FAS performance, they predominantly focus on learning domain-invariant features during training, which may not guarantee generalizability to unseen data that differs largely from

Luka Blagojević, Márton Pósfai

Data describing the three-dimensional structure of physical networks is increasingly available, leading to a surge of interest in network science to explore the relationship between the shape and connectivity of physical networks. We contribute to this effort by standardizing and analyzing 15 data sets from different domains. Each network is made of tube-lik

Manan Tayal, Hongchao Zhang, Pushpak Jagtap, Andrew Clark

Safety is a fundamental requirement of control systems. Control Barrier Functions (CBFs) are proposed to ensure the safety of the control system by constructing safety filters or synthesizing control inputs. However, the safety guarantee and performance of safe controllers rely on the construction of valid CBFs. Inspired by universal approximatability, CBFs

Gaoqing Cao

In this work, we extend the two-flavor Nambu--Jona-Lasinio model to one capable of exploring quark and nuclear matter consistently. With an extra term standing for quark-nucleon interactions, nucleons could automatically emerge as color-singlet three-quark entities by following a process similar to mesons. Besides the quark part in mean field approximation,

Marian Ivanov, Marian Ivanov, Giulio Eulise

The ALICE experiment at CERN LHC is specifically designed for investigating heavy ion collisions. The upgraded ALICE accommodates a tenfold increase in PbPb luminosity and a two-order of magnitude surge in minimum bias events. To address the challenges of high detector occupancy and event pile-ups, advanced multidimensional data analysis techniques, includin

Rumana Lakdawala, Joris Mulder, Roger Leenders

Many important social phenomena are characterized by repeated interactions among individuals over time such as email exchanges in an organization or face-to-face interactions in a classroom. To understand the underlying mechanisms of social interaction dynamics, statistical simulation techniques of longitudinal network data on a fine temporal granularity are

Antonio Avilés, Maciej Korpalski

Assume $\text{MA}(\kappa)$. We show that for every real chain of size $\kappa$ in the quotient Boolean algebra $P(\omega)/fin$ we can find an almost chain of representatives such that every $n\in\omega$ oscillates at most three times along the almost chain. This is used to show that for every countable discrete extension of a separable compact line $K$ of we

Hyejin Park, Jeongyeon Hwang, Sunung Mun, Sangdon Park

Test-time adaptation (TTA) has emerged as a promising solution to address performance decay due to unforeseen distribution shifts between training and test data. While recent TTA methods excel in adapting to test data variations, such adaptability exposes a model to vulnerability against malicious examples, an aspect that has received limited attention. Prev

Mikhail V. Beznogov, Adriana R. Raduta

The non-relativistic model of nuclear matter with Brussels extended Skyrme interactions is employed in order to build, within a Bayesian approach, models for the dense matter equation of state (EOS). In addition to a minimal set of constraints on nuclear empirical parameters; the density behavior of the energy per particle in pure neutron matter (PNM); a low

Ethan R. Burnett, Francesco Topputo

This paper addresses the challenge of accommodating nonlinear dynamics and constraints in rapid trajectory optimization, envisioned for use in the context of onboard guidance. We present a novel framework that uniquely employs overparameterized monomial coordinates and pre-computed fundamental solution expansions to facilitate rapid optimization while minimi

Michal Jablonowski

In this study of the Reidemeister moves within the classical knot theory, we focus on hard diagrams of knots and links, categorizing them as either rigid or shaky based on their adaptability to certain moves. We establish that every link possesses a diagram that is a rigid hard diagram and we provide an upper limit for the number of crossings in such diagram

Jiaxing Chen, Yuxuan Liu, Dehu Li, Xiang An

The rise of Multimodal Large Language Models (MLLMs), renowned for their advanced instruction-following and reasoning capabilities, has significantly propelled the field of visual reasoning. However, due to limitations in their image tokenization processes, most MLLMs struggle to capture fine details of text and objects in images, especially in high-resoluti

Gabriele Mainetti, Fabio Hernandez, Fabrice Jammes, Quentin Le Boulc'h

The Vera C. Rubin Observatory is preparing for the execution of the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently in its final phase of construction in the Andes mountains in Chile and due to start operations in 2025 for 10 years, its 8.4-meter telescope will nightly scan the southern sky and collect

Régis de la Bretèche, Gérald Tenenbaum

We provide new upper bounds for sums of certain arithmetic functions in many variables at polynomial arguments and, exploiting recent progress on the mean-value of the Erd\H os-Hooley $\Delta$-function, we derive lower bounds for the cardinality of those integers not exceeding a given limit that are expressible as some sums of powers.

Yujin Chen, Yinyu Nie, Benjamin Ummenhofer, Reiner Birkl

We present Mesh2NeRF, an approach to derive ground-truth radiance fields from textured meshes for 3D generation tasks. Many 3D generative approaches represent 3D scenes as radiance fields for training. Their ground-truth radiance fields are usually fitted from multi-view renderings from a large-scale synthetic 3D dataset, which often results in artifacts due

Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma

We introduce TableLLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios. We propose a distant supervision method for training, which comprises a reasoning process extension

T. Y. S. S Santosh, Vatsal Venkatkrishna, Saptarshi Ghosh, Matthias Grabmair

Legal professionals face the challenge of managing an overwhelming volume of lengthy judgments, making automated legal case summarization crucial. However, prior approaches mainly focused on training and evaluating these models within the same jurisdiction. In this study, we explore the cross-jurisdictional generalizability of legal case summarization models

Yue Gao, Jiaxuan Lu, Siqi Li, Yipeng Li

Action recognition from video data forms a cornerstone with wide-ranging applications. Single-view action recognition faces limitations due to its reliance on a single viewpoint. In contrast, multi-view approaches capture complementary information from various viewpoints for improved accuracy. Recently, event cameras have emerged as innovative bio-inspired s

Hyewon Han, Bogeun Gwak

We investigate the impact of oscillations of a black-hole mass around its average value on the three-dimensional black hole geometry. Drawing on a classical framework that conceptualizes fluctuations near an event horizon as mass variations, we introduce a model where the metric of a black hole, formed from the collapse of a massive null shell, exhibits osci

Xiaoyang Lyu, Chirui Chang, Peng Dai, Yang-Tian Sun

Scene reconstruction from multi-view images is a fundamental problem in computer vision and graphics. Recent neural implicit surface reconstruction methods have achieved high-quality results; however, editing and manipulating the 3D geometry of reconstructed scenes remains challenging due to the absence of naturally decomposed object entities and complex obj

Aleksander Sanjuan Ciepielewski, Jakub Tworzydło, Timo Hyart, Alexander Lau

Magic-angle twisted bilayer graphene is a tunable material with remarkably flat energy bands near the Fermi level, leading to fascinating transport properties and correlated states at low temperatures. However, grown pristine samples of this material tend to break up into landscapes of twist-angle domains, strongly influencing the physical properties of each

Bin Chen, Stefanie Gerke, Gregory Gutin, Hui Lei

An oriented graph is called $k$-anti-traceable if the subdigraph induced by every subset with $k$ vertices has a hamiltonian anti-directed path. In this paper, we consider an anti-traceability conjecture. In particular, we confirm this conjecture holds when $k\leq 4$. We also show that every sufficiently large $k$-anti-traceable oriented graph admits an anti

David Globosits, Jakob Hüpfl, Stefan Rotter

The physics of waves in time-varying media provides numerous opportunities for wave control that are unattainable with static media. In particular, Floquet systems with a periodic time modulation are currently of considerable interest. Here, we demonstrate how the scattering properties of a finite Floquet medium can be correctly described by a static Floquet

Patrik Pirkola, Marko Horbatsch

A model potential previously developed for the ammonia molecule is treated in a single-center partial-wave approximation in analogy with a self-consistent field method developed by Moccia. The latter was used in a number of collision studies. The model potential is used to calculate dc Stark resonance parameters, i.e., resonance positions and shifts within a

Eduardo Iglesius, Masato Kobayashi, Yuki Uranishi, Haruo Takemura

Recent advancements in robotics have led to the development of numerous interfaces to enhance the intuitiveness of robot navigation. However, the reliance on traditional 2D displays imposes limitations on the simultaneous visualization of information. Mixed Reality (MR) technology addresses this issue by enhancing the dimensionality of information visualizat

Yuqi Liu, Jose E. Roman, Meiyue Shao

In this work, the infinite GMRES algorithm, recently proposed by Correnty et al., is employed in contour integral-based nonlinear eigensolvers, avoiding the computation of costly factorizations at each quadrature node to solve the linear systems efficiently. Several techniques are applied to make the infinite GMRES memory-friendly, computationally efficient,