March 2024 arXiv papers — page 33

Michael Poli, Armin W Thomas, Eric Nguyen, Pragaash Ponnusamy

The development of deep learning architectures is a resource-demanding process, due to a vast design space, long prototyping times, and high compute costs associated with at-scale model training and evaluation. We set out to simplify this process by grounding it in an end-to-end mechanistic architecture design (MAD) pipeline, encompassing small-scale capabil

Danielly de Paula, Daniel Juehling, Falk Uebernickel

Stigma, a recognized global barrier to effective disease management, impacts social interactions, resource access, and psychological well-being. In this study, we developed a patient-centered framework for deriving design requirements and interventions for health conditions subject to social stigma. This study introduces a patient-centered framework, grounde

Guanghui He, Bingtian Ye, Ruotian Gong, Changyu Yao

Floquet (periodically driven) systems can give rise to unique non-equilibrium phases of matter without equilibrium analogs. The most prominent example is the realization of discrete time crystals. An intriguing question emerges: what other novel phases can manifest when the constraint of time periodicity is relaxed? In this study, we explore quantum systems

M. Boushabi, S. Eddargani, M. J. Ibáñez, A. Lamnii

This paper deals with Hermite osculatory interpolating splines. For a partition of a real interval endowed with a refinement consisting in dividing each subinterval into two small subintervals, we consider a space of smooth splines with additional smoothness at the vertices of the initial partition, and of the lowest possible degree. A normalized B-spline-li

Robin J. Dolleman, Alexander Rothstein, Ammon Fischer, Lennart Klebl

We report on the observation of negative electronic compressibility in twisted bilayer graphene for Fermi energies close to insulating states. To observe this negative compressibility, we take advantage of naturally occurring twist angle domains that emerge during the fabrication of the samples, leading to the formation of charge islands. We accurately measu

Yuhuan Yang, Chaofan Ma, Jiangchao Yao, Zhun Zhong

Referring Image Segmentation~(RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks. However, the quadratic computation cost makes it resource-consuming in capturing long-range visual-language dependencies. Fortunately, Mamba addresses this with efficient linear complexity in processing. However, direc

Jeffrey Mohan, Philipp Fabritius, Mohsen Talebi, Simon Wili

The transport properties of strongly interacting fermionic systems can reveal exotic states of matter, but experiments and theory have predominantly focused on bulk systems in the hydrodynamic limit describable with linear response coefficients such as electrical and thermal conductivity. In a ballistic channel connecting two superfluid reservoirs, recent ex

Hrishav Bakul Barua, Kalin Stefanov, KokSheik Wong, Abhinav Dhall

High Dynamic Range (HDR) content (i.e., images and videos) has a broad range of applications. However, capturing HDR content from real-world scenes is expensive and time-consuming. Therefore, the challenging task of reconstructing visually accurate HDR images from their Low Dynamic Range (LDR) counterparts is gaining attention in the vision research communit

Jonas Greiner, Ivan Gianni, Tommaso Nottoli, Filippo Lipparini

We present a novel implementation of the complete active space self-consistent field (CASSCF) method that makes use of the many-body expanded full configuration interaction (MBE-FCI) method to incrementally approximate electronic structures within large active spaces. On the basis of a hybrid first-order algorithm employing both Super-CI and quasi-Newton str

Aleksandar Radic

Scanning Helium Microscopy (SHeM) offers a combination of spatial and angular resolution via a pinhole-collimated beam of thermal energy, neutral helium-4 atoms for non-destructive imaging. This thesis introduces a novel 3D imaging mode, "heliometric stereo", enabling true-to-size 3D surface reconstruction using an adapted photometric stereo algorithm. Stere

Ibrahim Ethem Hamamci, Sezgin Er, Chenyu Wang, Furkan Almas

Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3D medical images with corresponding textual reports. CT-RATE comprises 25,692 non-contrast 3D chest CT scans from 21,304 unique patients. Each scan is accompanied by its correspond

Pierre-Emmanuel Novac, Laurent Rodriguez, Stéphane Barland

Self-mixing interferometry is a measurement approach in which a laser beam is re-injected into the emitting laser itself after reflection on a target. Information about the position of the target can be obtained from monitoring the voltage across the laser. However, analyzing this signal is difficult. In previous works, neural networks have been used with gr

Shijie Na, Yuzhi Liang, Siu-Ming Yiu

Federated learning client selection is crucial for determining participant clients while balancing model accuracy and communication efficiency. Existing methods have limitations in handling data heterogeneity, computational burdens, and independent client treatment. To address these challenges, we propose GPFL, which measures client value by comparing local

S. Thévenin, B. -J. Gréa, G. Kluth, B. Nadiga

In this work, we consider the problem of inferring the initial conditions of a Rayleigh-Taylor mixing zone by measuring the 0D turbulent quantities at an unspecified time. To this aim, we have generated a comprehensive dataset through direct numerical simulations (DNS), focusing on miscible fluids with slight density contrasts. The initial interface deformat

Thomas Wolgast, Astrid Nieße

To solve the optimal power flow (OPF) problem, reinforcement learning (RL) emerges as a promising new approach. However, the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment. In this work, we collect and implement diverse environment design decisions from the literature regarding training data, obs

Jose L. Goity, Christian Weiss

The target normal single-spin asymmetry in electron nucleon scattering is studied in the framework of the 1/Nc expansion of QCD, which allows for a rigorous description in the energy range that includes the Delta resonance and below the second baryon resonance region. The asymmetry is driven by the absorptive part of the two-photon exchange component of the

Zhelun Shi, Zhipin Wang, Hongxing Fan, Zaibin Zhang

Large Language Models (LLMs) aim to serve as versatile assistants aligned with human values, as defined by the principles of being helpful, honest, and harmless (hhh). However, in terms of Multimodal Large Language Models (MLLMs), despite their commendable performance in perception and reasoning tasks, their alignment with human values remains largely unexpl

Olivia Beckwith, Andreas Mono

We discover a non-trivial relation between the mock modular generating functions of the level $1$ and level $N$ Hurwitz class numbers. This relation yields a holomorphic modular form of weight $\frac{3}{2}$ and level $4N$, where $N > 1$ is stipulated to be odd and square-free. We extend this observation to a non-holomorphic framework and obtain a higher leve

Lingqi Meng, Weiwei Zhu, Michael Kramer, Xueli Miao

We observe systematic profile changes in the visible pulsar of the compact double neutron star system PSR~J1946+2052 using observations with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The interpulse of PSR~J1946+2052 changed from single-peak to double-peak shape from 2018 to 2021. We attribute this evolution as the result of the relati

Sammy Christen, Shreyas Hampali, Fadime Sener, Edoardo Remelli

Generating natural hand-object interactions in 3D is challenging as the resulting hand and object motions are expected to be physically plausible and semantically meaningful. Furthermore, generalization to unseen objects is hindered by the limited scale of available hand-object interaction datasets. In this paper, we propose a novel method, dubbed DiffH2O, w

Gregor Behnke, Marcel Steinmetz

Stackelberg planning is a recently introduced single-turn two-player adversarial planning model, where two players are acting in a joint classical planning task, the objective of the first player being hampering the second player from achieving its goal. This places the Stackelberg planning problem somewhere between classical planning and general combinatori

L. Barbieri-Viale

Making a survey of recent constructions of universal cohomologies we suggest a new framework for a theory of motives in algebraic geometry.

Evan O'Leary, Lin-Lin Wang, Yevhen Kushnirenko, Ben Schrunk

We use high-resolution angle resolved photoemission spectroscopy (ARPES) and density functional theory (DFT) to investigate the electronic structure of the charge density wave (CDW) system LaSb$_2$. This compound is among an interesting group of materials that manifests both a CDW transition and lower temperature superconductivity. We find the DFT calculatio

Alexandre Eymaël, Renaud Vandeghen, Anthony Cioppa, Silvio Giancola

Self-supervised pre-training of image encoders is omnipresent in the literature, particularly following the introduction of Masked autoencoders (MAE). Current efforts attempt to learn object-centric representations from motion in videos. In particular, SiamMAE recently introduced a Siamese network, training a shared-weight encoder from two frames of a video

Matias Turkulainen, Xuqian Ren, Iaroslav Melekhov, Otto Seiskari

High-fidelity 3D reconstruction of common indoor scenes is crucial for VR and AR applications. 3D Gaussian splatting, a novel differentiable rendering technique, has achieved state-of-the-art novel view synthesis results with high rendering speeds and relatively low training times. However, its performance on scenes commonly seen in indoor datasets is poor d

Jesse Atuhurra, Hidetaka Kamigaito, Taro Watanabe, Eric Nichols

Dialogue agents, which perform specific tasks, are part of the long-term goal of NLP researchers to build intelligent agents that communicate with humans in natural language. Such systems should adapt easily from one domain to another to assist users in completing tasks. Researchers have developed a broad range of techniques, objectives, and datasets for int

Federica Mennuni, Addolorata Salvatore

We prove the existence of multiple signed bounded solutions for a quasilinear elliptic equation with concave and convex nonlinearities. For this, we use a variational approach in an intersection Banach space indroduced by Candela and Palmieri, and a truncation technique given by Garcia Azorero and Peral.

Lawrence A. Bull, Chiho Jeon, Mark Girolami, Andrew Duncan

We suggest a multilevel model, to represent aggregate train-passing events from the Staffordshire bridge monitoring system. We formulate a combined model from simple units, representing strain envelopes (of each train passing) for two types of commuter train. The measurements are treated as a longitudinal dataset and represented with a (low-rank approximatio

Amir Ghasemi, Paul Guinand

Wireless spectrum regulation is a complex and demanding process due to the rapid pace of technological progress, increasing demand for spectrum, and a multitude of stakeholders with potentially conflicting interests, alongside significant economic implications. To navigate this, regulators must engage effectively with all parties, keep pace with global techn

Hünkar Can Tunç, Ameya Prashant Deshmukh, Berk Çirisci, Constantin Enea

Dynamic analyses are a standard approach to analyzing and testing concurrent programs. Such techniques observe program traces and analyze them to infer the presence or absence of bugs. At its core, each analysis maintains a partial order $P$ that represents order dependencies between events of the analyzed trace $\sigma$. Naturally, the scalability of the an

Bithika Karmakar, Dusan Zigic, Pasi Huovinen, Marko Djordjevic

This study investigates Quark-Gluon Plasma (QGP) in heavy-ion collisions through two avenues: high-$p_{\perp}$ frameworks and hydrodynamic modeling. Using the T$_{\text{R}}$ENTo model, we find that IP-Glasma mimicking $p=0$ value aligns well with high-$p_{\perp}$ data, in agreement with Bayesian analysis of the low-$p_{\perp}$ regime. While adjusting $p$ val

Wallyson Lemes de Oliveira, Vahid Shamsaddini, Ali Ghofrani, Rahul Singh Inda

This scientific report presents a novel methodology for the early prediction of important political events using News datasets. The methodology leverages natural language processing, graph theory, clique analysis, and semantic relationships to uncover hidden predictive signals within the data. Initially, we designed a preliminary version of the method and te

Zhandos Moldabekov, Thomas D. Gawne, Sebastian Schwalbe, Thomas R. Preston

The combination of isochoric heating of solids by free electron lasers (FEL) and in situ diagnostics by X-ray Thomson scattering (XRTS) allows for measurements of material properties at warm dense matter (WDM) conditions relevant for astrophysics, inertial confinement fusion, and material science. In the case of metals, the FEL beam pumps energy directly int

Xiaobing Yuan, Ling Chen

In time series forecasting, effectively disentangling intricate temporal patterns is crucial. While recent works endeavor to combine decomposition techniques with deep learning, multiple frequencies may still be mixed in the decomposed components, e.g., trend and seasonal. Furthermore, frequency domain analysis methods, e.g., Fourier and wavelet transforms,

Pham Ngoc Ánh, Francesca Mantese

A very first step to develop non-commutative algebraic geometry is the arithmetic of polynomials in non-commuting variables over a commutative field, that is, the study of elements in free associative algebras. This investigation is presented as a natural extension of the classical theory in one variable by using Leavitt algebras, which are localizations of

Luiz Paulo de Oliveira

In line with the tradition of employing nuclear reactors for neutrino physics research, we present potential experiments that could be undertaken using the new Brazilian neutron source, the Brazilian Multipurpose Reactor (RMB). This upcoming facility has clearly defined objectives, including the production of radioisotopes for the healthcare system, material

Leonidas Gee, Andrea Zugarini, Novi Quadrianto

To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to minority subgroups defined by the labels and attributes of a dataset. In this paper, we investigate the effects of 18 different compression methods and settings on the subgroup rob

Bo Lin, Chuanbin Zhao, Feifei Gao, Geoffrey Ye Li

Integrated sensing and communications (ISAC) has been deemed as a key technology for the sixth generation (6G) wireless communications systems. In this paper, we explore the inherent clustered nature of wireless users and design a multi-user based environment reconstruction scheme. Specifically, we first select users based on the estimation precision of chan

H. Ness, F. Corsetti, D. Pashov, B. Verstichel

The electronic structure of surfaces and interfaces plays a key role in the properties of quantum devices. Here, we study the electronic structure of realistic Al/InAs/Al heterojunctions using a combination of density functional theory (DFT) with hybrid functionals and state-of-the-art quasi-particle $GW$ (QS$GW$) calculations. We find a good agreement betwe

Rüveyda Yilmaz, Dennis Eschweiler, Johannes Stegmaier

The segmentation and tracking of living cells play a vital role within the biomedical domain, particularly in cancer research, drug development, and developmental biology. These are usually tedious and time-consuming tasks that are traditionally done by biomedical experts. Recently, to automatize these processes, deep learning based segmentation and tracking

Si Chen, Haocong Cheng, Jason Situ, Desirée Kirst

Previous research underscored the potential of danmaku--a text-based commenting feature on videos--in engaging hearing audiences. Yet, for many Deaf and hard-of-hearing (DHH) individuals, American Sign Language (ASL) takes precedence over English. To improve inclusivity, we introduce "Signmaku," a new commenting mechanism that uses ASL, serving as a sign lan

Michael Hanna, Sandro Pezzelle, Yonatan Belinkov

Many recent language model (LM) interpretability studies have adopted the circuits framework, which aims to find the minimal computational subgraph, or circuit, that explains LM behavior on a given task. Most studies determine which edges belong in a LM's circuit by performing causal interventions on each edge independently, but this scales poorly with model

Axel Brunnbauer, Luigi Berducci, Peter Priller, Dejan Nickovic

The automated generation of diverse and complex training scenarios has been an important ingredient in many complex learning tasks. Especially in real-world application domains, such as autonomous driving, auto-curriculum generation is considered vital for obtaining robust and general policies. However, crafting traffic scenarios with multiple, heterogeneous

Oscar Mañas, Pietro Astolfi, Melissa Hall, Candace Ross

Impressive advances in text-to-image (T2I) generative models have yielded a plethora of high performing models which are able to generate aesthetically appealing, photorealistic images. Despite the progress, these models still struggle to produce images that are consistent with the input prompt, oftentimes failing to capture object quantities, relations and

Emanuel Carneiro, Micah B. Milinovich

Inspired by a result of Soundararajan, assuming the Riemann hypothesis (RH), we prove a new inequality for the logarithm of the modulus of the Riemann zeta-function on the critical line in terms of a Dirichlet polynomial over primes and prime powers. Our proof uses the Guinand-Weil explicit formula in conjunction with extremal one-sided bandlimited approxima

Genni Fragnelli, Dimitri Mugnai, Amine Sbai

We consider a degenerate/singular wave equation in one dimension, with drift and in presence of a leading operator which is not in divergence form. We impose a homogeneous Dirichlet boundary condition where the degeneracy occurs and a boundary damping at the other endpoint. We provide some conditions for the uniform exponential decay of solutions for the ass

Fangzhou Mu, Carter Sifferman, Sacha Jungerman, Yiquan Li

We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. These cameras, operating as time resolved image sensors, illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a hi

Maximilian Böhle, Bernhard Schick, Steffen Müller

The validity of the subjective evaluation of steering feedback in driving simulators is crucial for modern vehicle development. Although there are established objective steering characteristics for the assessment of both stationary and dynamic feedback behaviour, factors such as steering wheel vibrations and vehicle body motion, particularly in high-frequenc

P. V. Padmanabh, S. M. Ransom, P. C. C. Freire, A. Ridolfi

We report the discovery of ten new pulsars in the globular cluster Terzan 5 as part of the Transients and Pulsars with MeerKAT (TRAPUM) Large Survey Project. We observed Terzan 5 at L-band (856--1712 MHz) with the MeerKAT radio telescope for four hours on two epochs, and performed acceleration searches of 45 out of 288 tied-array beams covering the core of t

Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon

Reinforcement learning has recently gained unprecedented popularity, yet it still grapples with sample inefficiency. Addressing this challenge, federated reinforcement learning (FedRL) has emerged, wherein agents collaboratively learn a single policy by aggregating local estimations. However, this aggregation step incurs significant communication costs. In t

Jesse Atuhurra

The emergence of large language models (LLM) and, consequently, vision language models (VLM) has ignited new imaginations among robotics researchers. At this point, the range of applications to which LLM and VLM can be applied in human-robot interaction (HRI), particularly socially assistive robots (SARs), is unchartered territory. However, LLM and VLM prese

Nursel Erey, Antonino Ficarra

In this note, we classify all the weighted oriented forests whose edge ideals have the property that one of their matching powers has linear resolution.

Ryotaku Suzuki, Shinya Tomizawa

Utilizing the electric Harrison transformation developed in five-dimensional minimal supergravity, we construct an exact solution characterizing non-BPS charged rotating black holes with a horizon cross-section of a lens space L(n;1). Among these solutions, only the ones corresponding to n=0 and n=1 do not have any curvature singularities, conical singularit

F. Ya. Khalili

In detuned optical cavities, the radiation pressure force acting on the mirrors depends on their displacements. This is equivalent to the rigidity (the optical spring), inserted between the mirrors. This effect can be used for optimization of the mechanical susceptibility of probe mirrors in high-precision force sensors. However, in some cases, the use of de

Ke Yang, Rongen Dong, Wei Gao, Feng Shu

Active intelligent reflecting surface (IRS) is a revolutionary technique for the future 6G networks. The conventional far-field single-IRS-aided directional modulation(DM) networks have only one (no direct path) or two (existing direct path) degrees of freedom (DoFs). This means that there are only one or two streams transmitted simultaneously from base stat

Yuhao Liu, Shize Che, Junyu Zhou, Yunong Shi

This paper introduces Fermihedral, a compiler framework focusing on discovering the optimal Fermion-to-qubit encoding for targeted Fermionic Hamiltonians. Fermion-to-qubit encoding is a crucial step in harnessing quantum computing for efficient simulation of Fermionic quantum systems. Utilizing Pauli algebra, Fermihedral redefines complex constraints and obj

Muhammad Zakwan, Liang Xu, Giancarlo Ferrari-Trecate

This paper proposes a novel learning-based approach for achieving exponential stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics (CCMs) framework to co-synthesize Neural Contraction Metrics (NCMs) and Neural Network (NN) controllers. First, we transform the infinite-dimensional semi-definite program (SDP) for CCM c

Elio Faddoul, Yuan Guo, Christodoulos Skouroumounis, Ioannis Krikidis

In this letter, a novel communication paradigm for simultaneous wireless information and power transfer (SWIPT) is proposed, which leverages the thermal characteristics of electromagnetic signals. In particular, the proposed scheme exploits the inherent thermal dynamics of electromagnetic signals, enabling the seamless integration of information decoding and

Benjamin Call, David Constantine, Alena Erchenko, Noelle Sawyer

Let $S$ be a compact surface of genus $\geq 2$ equipped with a metric that is flat everywhere except at finitely many cone points with angles greater than $2\pi$. We examine the geodesic flow on $S$ and prove local product structure for a wide class of equilibrium states. Using this, we establish the Bernoulli property for these systems. We also establish lo

Mahrokh Ghoddousi Boroujeni, Clara Lucía Galimberti, Andreas Krause, Giancarlo Ferrari-Trecate

Stochastic Nonlinear Optimal Control (SNOC) involves minimizing a cost function that averages out the random uncertainties affecting the dynamics of nonlinear systems. For tractability reasons, this problem is typically addressed by minimizing an empirical cost, which represents the average cost across a finite dataset of sampled disturbances. However, this

Noga Entin, Mor M. Roses, Reuven Cohen, Nadav Katz

Quantum computing is currently hindered by hardware noise. We present a freestyle superconducting pulse optimization method, incorporating two-qubit channels, which enhances flexibility, execution speed, and noise resilience. A minimal 0.22 ns pulse is shown to determine the H2 groundstate to within chemical accuracy upon real-hardware, approaching the quant

Felix Esser, Gereon Tombrink, Andre Cornelißen, Lasse Klingbeil

The creation of precise and high-resolution crop point clouds in agricultural fields has become a key challenge for high-throughput phenotyping applications. This work implements a novel calibration method to calibrate the laser scanning system of an agricultural field robot consisting of two industrial-grade laser scanners used for high-precise 3D crop poin

Fouad Trad, Ali Chehab

The success of Large Language Models (LLMs) has led to a parallel rise in the development of Large Multimodal Models (LMMs), which have begun to transform a variety of applications. These sophisticated multimodal models are designed to interpret and analyze complex data by integrating multiple modalities such as text and images, thereby opening new avenues f

Felix S. Campbell, Alon Silberstein, Julia Stoyanovich, Yuval Moskovitch

Database queries are often used to select and rank items as decision support for many applications. As automated decision-making tools become more prevalent, there is a growing recognition of the need to diversify their outcomes. In this paper, we define and study the problem of modifying the selection conditions of an ORDER BY query so that the result of th

Muhammad Zakwan, Giancarlo Ferrari-Trecate

Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly simple scenarios. Parameterizing these policies using Neural Networks (NNs) can deliver good performance

Ting-Yao Hsu, Chieh-Yang Huang, Shih-Hong Huang, Ryan Rossi

Crafting effective captions for figures is important. Readers heavily depend on these captions to grasp the figure's message. However, despite a well-developed set of AI technologies for figures and captions, these have rarely been tested for usefulness in aiding caption writing. This paper introduces SciCapenter, an interactive system that puts together cut

Martin Ludvigsen, Markus Grasmair

The idea of adversarial learning of regularization functionals has recently been introduced in the wider context of inverse problems. The intuition behind this method is the realization that it is not only necessary to learn the basic features that make up a class of signals one wants to represent, but also, or even more so, which features to avoid in the re

CaiHeng Li, Venkata Raghu Tej Pantangi, Shujiao Song, Yilin Xie

Let $G\leqslant\mathrm{Sym}(\Omega)$ be transitive, and let $S$ be an intersecting subset, namely, the ratio $xy^{-1}$ of any elements $x,y\in S$ fixes some point. An EKR-type problem is to characterize transitive groups $G\leqslant\mathrm{Sym}(\Omega)$ such that any intersecting set is upper bounded by $|G_\omega|$, where $\omega\in\Omega$. A nice result of

Chenjian Gao, Boyan Jiang, Xinghui Li, Yingpeng Zhang

We present GenesisTex, a novel method for synthesizing textures for 3D geometries from text descriptions. GenesisTex adapts the pretrained image diffusion model to texture space by texture space sampling. Specifically, we maintain a latent texture map for each viewpoint, which is updated with predicted noise on the rendering of the corresponding viewpoint. T

Yuri Lubomirsky, Eran Bouchbinder

Cracks develop various surface patterns as they propagate in three-dimensional (3D) materials. Facet formation in nominally tensile (mode-I) fracture emerge in the slow, non-inertial regime and oftentimes takes the form of surface steps. We show that the same phase-field framework that recently shed basic light on dynamic (inertial) tensile fracture in 3D, a

Yanran Tang, Ruihong Qiu, Hongzhi Yin, Xue Li

In case law, the precedents are the relevant cases that are used to support the decisions made by the judges and the opinions of lawyers towards a given case. This relevance is referred to as the case-to-case reference relation. To efficiently find relevant cases from a large case pool, retrieval tools are widely used by legal practitioners. Existing legal c

MReza Alipour Sormoli, Mehrdad Dianati, Sajjad Mozaffari, Roger woodman

Accurate velocity estimation of surrounding moving objects and their trajectories are critical elements of perception systems in Automated/Autonomous Vehicles (AVs) with a direct impact on their safety. These are non-trivial problems due to the diverse types and sizes of such objects and their dynamic and random behaviour. Recent point cloud based solutions

Marco Reidelbach, Björn Schembera, Marcus Weber

Modeling-Simulation-Optimization workflows play a fundamental role in applied mathematics. The Mathematical Research Data Initiative, MaRDI, responded to this by developing a FAIR and machine-interpretable template for a comprehensive documentation of such workflows. MaRDMO, a Plugin for the Research Data Management Organiser, enables scientists from diverse

JoonHwan Cho, Yao Luo, Ruli Xiao

Economic data are often contaminated by measurement errors and truncated by ranking. This paper shows that the classical measurement error model with independent and additive measurement errors is identified nonparametrically using only two order statistics of repeated measurements. The identification result confirms a hypothesis by Athey and Haile (2002) fo

Pablo Sanchez-Martin, Sonja Utz, Isabel Valera

Ambient awareness refers to the ability of social media users to obtain knowledge about who knows what (i.e., users' expertise) in their network, by simply being exposed to other users' content (e.g, tweets on Twitter). Previous work, based on user surveys, reveals that individuals self-report ambient awareness only for parts of their networks. However, it i

Khac-Hoang Ngo, Johan Östman, Giuseppe Durisi, Alexandre Graell i Amat

Secure aggregation (SecAgg) is a commonly-used privacy-enhancing mechanism in federated learning, affording the server access only to the aggregate of model updates while safeguarding the confidentiality of individual updates. Despite widespread claims regarding SecAgg's privacy-preserving capabilities, a formal analysis of its privacy is lacking, making suc

Ruiji Liu, Francisco Yandun, George Kantor

Autonomous navigation is crucial for various robotics applications in agriculture. However, many existing methods depend on RTK-GPS devices, which can be susceptible to loss of radio signal or intermittent reception of corrections from the internet. Consequently, research has increasingly focused on using RGB cameras for crop-row detection, though challenges

Davide Basilico, Xavier Roca-Maza, Gianluca Colò

A theoretical understanding of a possible mechanism for synthesizing superheavy elements in the outer crust of magnetars is presented. We demonstrate that such a mechanism can be present whenever the baryon density in the outer crust of a neutron star reaches values around $10^{-2}$ fm$^{-3}$. This scenario could be realized in magnetars with hypothetical la

N. Lazarides, Ioannis Kourakis

The nonlinear dynamics of two co-propagating electrostatic wavepackets, characterized by different wavenumbers and amplitudes, in a 1D non-magnetized plasma fluid model is considered, from first principles. The original plasma model, consisting of \kappa-distributed electrons evolving against a cold ion background, is reduced, by means of a multiple-scale pe

Anuja Raorane, Ramon Brasser, Soko Matsumura, Tommy Chi Ho Lau

The formation history of Jupiter has been of interest due to its ability to shape the solar system's history. Yet little attention has been paid to the formation and growth of Saturn and the other giant planets. Here, we explore the implications of the simplest disc and pebble accretion model with steady-state accretion on the formation of giant planets in t

Yongrui Yu, Hanyu Chen, Zitian Zhang, Qiong Xiao

Despite the significant success achieved by deep learning methods in medical image segmentation, researchers still struggle in the computer-aided diagnosis of abdominal lymph nodes due to the complex abdominal environment, small and indistinguishable lesions, and limited annotated data. To address these problems, we present a pipeline that integrates the con

Matej Bajec, Sašo Grozdanov, Alexander Soloviev

The relaxation time approximation (RTA) of the kinetic Boltzmann equation is likely the simplest window into the microscopic properties of collective real-time transport. Within this framework, we analytically compute all retarded two-point Green's functions of the energy-momentum tensor and a conserved $U(1)$ current in thermal states with classical massles

Dongqi Liu, Yifan Wang, Jia Loy, Vera Demberg

Scientific news reports serve as a bridge, adeptly translating complex research articles into reports that resonate with the broader public. The automated generation of such narratives enhances the accessibility of scholarly insights. In this paper, we present a new corpus to facilitate this paradigm development. Our corpus comprises a parallel compilation o

Victor Leger, Romain Couillet

This article considers a semi-supervised classification setting on a Gaussian mixture model, where the data is not labeled strictly as usual, but instead with uncertain labels. Our main aim is to compute the Bayes risk for this model. We compare the behavior of the Bayes risk and the best known algorithm for this model. This comparison eventually gives new i

Xifan Yu, Ilias Zadik, Peiyuan Zhang

We study the computational limits of the following general hypothesis testing problem. Let H=H_n be an \emph{arbitrary} undirected graph on n vertices. We study the detection task between a ``null'' Erd\H{o}s-R\'{e}nyi random graph G(n,p) and a ``planted'' random graph which is the union of G(n,p) together with a random copy of H=H_n. Our notion of planted m

Yifan Yan, Ruomin He, Zhenghua Liu

We introduce MUTE-SLAM, a real-time neural RGB-D SLAM system employing multiple tri-plane hash-encodings for efficient scene representation. MUTE-SLAM effectively tracks camera positions and incrementally builds a scalable multi-map representation for both small and large indoor environments. As previous methods often require pre-defined scene boundaries, MU

Nu Ri Choi, Md Ashequr Rahman, Zitong Yu, Barry A. Siegel

Myocardial perfusion imaging using single-photon emission computed tomography (SPECT), or myocardial perfusion SPECT (MPS) is a widely used clinical imaging modality for the diagnosis of coronary artery disease. Current clinical protocols for acquiring and reconstructing MPS images are similar for most patients. However, for patients with outlier anatomical

Lilian Nowak, Chloe Malbrunot, Martin C. Simon, Claude Amsler

We present a Rabi-type measurement of two ground-state hydrogen hyperfine transitions performed in two opposite external magnetic field directions. This puts first constraints at the level of 2.3 10^-21 GeV on a set of coefficients of the Standard Model Extension, which were not measured by previous experiments. Moreover, we introduce a novel method, applica

Mikhail Chebunin, Günter Last

We consider a random connection model (RCM) on a general space driven by a Poisson process whose intensity measure is scaled by a parameter $t\ge 0$. We say that the infinite clusters are deletion stable if the removal of a Poisson point cannot split a cluster in two or more infinite clusters. We prove that this stability together with a natural irreducibili

Xingchao Yang, Takafumi Taketomi, Yuki Endo, Yoshihiro Kanamori

In this work, we introduce two types of makeup prior models to extend existing 3D face prior models: PCA-based and StyleGAN2-based priors. The PCA-based prior model is a linear model that is easy to construct and is computationally efficient. However, it retains only low-frequency information. Conversely, the StyleGAN2-based model can represent high-frequenc

Shijia Zhou, Leonie Weissweiler, Taiqi He, Hinrich Schütze

In this paper, we make a contribution that can be understood from two perspectives: from an NLP perspective, we introduce a small challenge dataset for NLI with large lexical overlap, which minimises the possibility of models discerning entailment solely based on token distinctions, and show that GPT-4 and Llama 2 fail it with strong bias. We then create fur

Davide Baldelli, Junfeng Jiang, Akiko Aizawa, Paolo Torroni

In this paper, we present TWOLAR: a two-stage pipeline for passage reranking based on the distillation of knowledge from Large Language Models (LLM). TWOLAR introduces a new scoring strategy and a distillation process consisting in the creation of a novel and diverse training dataset. The dataset consists of 20K queries, each associated with a set of documen

Fernando Chamizo, Francisco de la Hoz

In this paper, we give a rigorous proof for the expression of the angle between adjacent sides in the skew polygons appearing at rational times in the evolution of regular polygons of $M$ sides under the vortex filament equation. The proof depends on showing that some exponential sums with arithmetic content are purely imaginary.

Robert Platt, Rossella Arcucci, Cédric M. John

Hyperspectral data acquired by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) have allowed for unparalleled mapping of the surface mineralogy of Mars. Due to sensor degradation over time, a significant portion of the recently acquired data is considered unusable. Here a new data-driven model architecture, Noise2Noise4Mars (N2N4M), is introd

Ibrahim Bouzalmat, Benoîte de Saporta, Solym M. Manou-Abi

This study aims to estimate the parameters of a stochastic exposed-infected epidemiological model for the transmission dynamics of notifiable infectious diseases, based on observations related to isolated cases counts only. We use the setting of hidden multi-chain Markov models and adapt the Baum-Welch algorithm to the special structure of the multi-chain. F

Yang Yang

Image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features. Existing approaches are mostly supervised ones, i.e., each image has a corresponding sentence in the training set. However, considering that describing images always requires a huge

Jianwei Wang, Kai Wang, Xuemin Lin, Wenjie Zhang

Community search has been extensively studied in the past decades. In recent years, there is a growing interest in attributed community search that aims to identify a community based on both the query nodes and query attributes. A set of techniques have been investigated. Though the recent methods based on advanced learning models such as graph neural networ

Du Wang, Wen-Ling Wang, Fei Huang

In this work, we systematically calculate the spectrum of hidden-charm pentaquark states $qqqc\bar{c}$ $(q = u,d)$ in the chiral SU(3) quark model, which has been quite successful in reproducing consistently the energies of octet and decuplet baryon ground states, the binding energy of deuteron, and the nucleon-nucleon ($NN$) scattering phase shifts and mixi

Sihan Shang, Jiancheng Yang, Zhenglong Sun, Pascal Fua

In the realm of healthcare, the challenges of copyright protection and unauthorized third-party misuse are increasingly significant. Traditional methods for data copyright protection are applied prior to data distribution, implying that models trained on these data become uncontrollable. This paper introduces a novel approach, named DataCook, designed to saf

Hsien-Chih Chang, Jonathan Conroy, Hung Le, Lazar Milenkovic

A $(1+\varepsilon)\textit{-stretch tree cover}$ of a metric space is a collection of trees, where every pair of points has a $(1+\varepsilon)$-stretch path in one of the trees. The celebrated $\textit{Dumbbell Theorem}$ [Arya et~al. STOC'95] states that any set of $n$ points in $d$-dimensional Euclidean space admits a $(1+\varepsilon)$-stretch tree cover wit