March 2024 arXiv papers — page 82

Iason Papaioannou, Daniel Straub

Global variance-based reliability sensitivity indices arise from a variance decomposition of the indicator function describing the failure event. The first-order indices reflect the main effect of each variable on the variance of the failure event and can be used for variable prioritization; the total-effect indices represent the total effect of each variabl

Dongyeong Hwang, Hyunju Kim, Sunwoo Kim, Kijung Shin

The success of a specific neural network architecture is closely tied to the dataset and task it tackles; there is no one-size-fits-all solution. Thus, considerable efforts have been made to quickly and accurately estimate the performances of neural architectures, without full training or evaluation, for given tasks and datasets. Neural architecture encoding

Zhiwei Zhao

Delicate cloth simulations have long been desired in computer graphics. Various methods were proposed to improve engaged force interactions, collision handling, and numerical integrations. Deep learning has the potential to achieve fast and real-time simulation, but common neural network structures often demand many parameters to capture cloth dynamics. This

Daniel Greenhouse, Chris Bowman, Bruce Lipschultz, Kevin Verhaegh

An integrated data analysis system based on Bayesian inference has been developed for application to data from multiple diagnostics over the two-dimensional cross-section of tokamak divertors. Tests of the divertor multi-instrument Bayesian analysis system (D-MIBAS) on a synthetic data set (including realistic experimental uncertainties) generated from SOLPS

Hugo Yèche, Manuel Burger, Dinara Veshchezerova, Gunnar Rätsch

This study advances Early Event Prediction (EEP) in healthcare through Dynamic Survival Analysis (DSA), offering a novel approach by integrating risk localization into alarm policies to enhance clinical event metrics. By adapting and evaluating DSA models against traditional EEP benchmarks, our research demonstrates their ability to match EEP models on a tim

Jian Wang

Searches for lepton flavor violation (LFV) stand at the forefront of experimental particle physics research, offering a sensitive probe to many scenarios of physics beyond the Standard Model. The high proton-proton collision energy and luminosity provided by the CERN Large Hadron Collider (LHC) and the excellent CMS detector performance allow for an extensiv

Jonathan Ganz, Jonas Ammeling, Samir Jabari, Katharina Breininger

In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primary origin of metastases. These models require large datasets for training, which must be anonymized to prevent possible patient identity leaks. This study demonstrates that even rel

Weixiang Sun, Han Shen, Biwei Jiang, Xiaowei Liu

The Milky Way is widely considered to exhibit features of a rotational bar or quadrupole bar. In either case, the feature of the resonance of the Galactic bar should be present in the properties of the chemistry and kinematics, over a large area of the disk. With a sample of over 170,000 red clump (RC) stars from LAMOST-APOGEE data, we attempt to detect the

Kuiyu Wang, Zhen Gao, Sheng Chen, Boyu Ning

Acquiring accurate channel state information (CSI) at an access point (AP) is challenging for wideband millimeter wave (mmWave) ultra-massive multiple-input and multiple-output (UMMIMO) systems, due to the high-dimensional channel matrices, hybrid near- and far- field channel feature, beam squint effects, and imperfect hardware constraints, such as low-resol

Raghul Saravanan, Sai Manoj Pudukotai Dinakarrao

In recent years, there has been a notable surge in attention towards hardware security, driven by the increasing complexity and integration of processors, SoCs, and third-party IPs aimed at delivering advanced solutions. However, this complexity also introduces vulnerabilities and bugs into hardware systems, necessitating early detection during the IC design

Angelo Caravano, Keisuke Inomata, Sébastien Renaux-Petel

For the first time, we investigate the non-perturbative dynamics of single field inflation with a departure from slow-roll. Using simulations, we find that oscillatory features in the potential can drastically alter the course of inflation, with major phenomenological implications. In certain cases, the entire Universe gets trapped in a forever inflating de

Dmitry A. Zezyulin

We introduce a class of one-dimensional complex optical potentials that feature a nonlinearity-induced stability restoration, i.e., the existence of stable nonlinear modes propagating in a waveguide whose linear eigenmodes are unstable. The optical potential is an even function of the transverse coordinate, i.e., the system is parity symmetric but not parity

Zhixue Zhao, Nikolaos Aletras

In many real natural language processing application scenarios, practitioners not only aim to maximize predictive performance but also seek faithful explanations for the model predictions. Rationales and importance distribution given by feature attribution methods (FAs) provide insights into how different parts of the input contribute to a prediction. Previo

Naotaka Kubo, Tomoki Nosaka

We construct the Fermi gas formalism for the partition function of supersymmetric Chern-Simons theories with affine $D$-type quiver diagrams with non-uniform ranks of the gauge groups and Fayet-Illiopoulos parameters by two different approaches: the open string formalism and the closed string formalism. In the closed string formalism approach, we find a nove

Jinbo Wen, Jiawen Kang, Zehui Xiong, Hongyang Du

Web 3.0 is regarded as a revolutionary paradigm that enables users to securely manage data without a centralized authority. Blockchains, which enable data to be managed in a decentralized and transparent manner, are key technologies for achieving Web 3.0 goals. However, Web 3.0 based on blockchains is still in its infancy, such as ensuring block freshness an

Zhipeng Huang, Zhizheng Zhang, Yiting Lu, Zheng-Jun Zha

At present, large multimodal models (LMMs) have exhibited impressive generalization capabilities in understanding and generating visual signals. However, they currently still lack sufficient capability to perceive low-level visual quality akin to human perception. Can LMMs achieve this and show the same degree of generalization in this regard? If so, not onl

Pierre Dognin, Jesus Rios, Ronny Luss, Inkit Padhi

Developing value-aligned AI agents is a complex undertaking and an ongoing challenge in the field of AI. Specifically within the domain of Large Language Models (LLMs), the capability to consolidate multiple independently trained dialogue agents, each aligned with a distinct moral value, into a unified system that can adapt to and be aligned with multiple mo

Jiasheng Lin

We construct a $P(\phi)_2$ Gibbs state on infinite volume periodic surfaces (namely, with discrete ``time translations'') by analogy with 1-dimensional spin chains and establish the mass gap for our Gibbs state, there are no phase transitions. We also derive asymptotic properties of the $P(\phi)_2$ partition function on certain towers of cyclic covers of lar

Yunxiang Fu, Chaoqi Chen, Yu Qiao, Yizhou Yu

The acquisition of large-scale, high-quality data is a resource-intensive and time-consuming endeavor. Compared to conventional Data Augmentation (DA) techniques (e.g. cropping and rotation), exploiting prevailing diffusion models for data generation has received scant attention in classification tasks. Existing generative DA methods either inadequately brid

Monika Blanke

These proceedings provide a brief overview of the status of $B$ meson physics, putting particular emphasis on precision tests of the Standard Model with meson mixing data, and on the anomalies in charged- and neutral-current semileptonic $B$ decays. In addition to summarising the current status, some promising directions to be pursued at future collider expe

Zhipeng Huang, Zhizheng Zhang, Zheng-Jun Zha, Yan Lu

The development of Large Vision-Language Models (LVLMs) is striving to catch up with the success of Large Language Models (LLMs), yet it faces more challenges to be resolved. Very recent works enable LVLMs to localize object-level visual contents and ground text to them. Nonetheless, current LVLMs still struggle to precisely understand visual relations due t

Jingyu Lin, Jiaqi Gu, Bojian Wu, Lubin Fan

We introduce a novel neural volumetric pose feature, termed PoseMap, designed to enhance camera localization by encapsulating the information between images and the associated camera poses. Our framework leverages an Absolute Pose Regression (APR) architecture, together with an augmented NeRF module. This integration not only facilitates the generation of no

Mirza Alim Mutasodirin, Radityo Eko Prasojo

The parallelism of Transformer-based models comes at the cost of their input max-length. Some studies proposed methods to overcome this limitation, but none of them reported the effectiveness of summarization as an alternative. In this study, we investigate the performance of document truncation and summarization in text classification tasks. Each of the two

Lin Xie, Sonja Otten

In the era of digital commerce, the surge in online shopping and the expectation for rapid delivery have placed unprecedented demands on warehouse operations. The traditional method of order fulfilment, where human order pickers traverse large storage areas to pick items, has become a bottleneck, consuming valuable time and resources. Robotic Mobile Fulfilme

Davide Carminati

This paper introduces a parallel implementation in CUDA/C++ of the Gaussian process with a decomposed kernel. This recent formulation, introduced by Joukov and Kuli\'c (2022), is characterized by an approximated -- but much smaller -- matrix to be inverted compared to plain Gaussian process. However, it exhibits a limitation when dealing with higher-dimensio

Ke Yang, Tianbo Liu, Peng Sun, Yuxiang Zhao

We present a global analysis of the trans-helicity worm-gear distribution function, $g_{1T}^\perp$, by fitting the longitudinal-transverse double spin asymmetry data of the semi-inclusive deep inelastic scattering. The analysis is performed within the framework of transverse momentum dependent factorization and evolution. It is found that the $u$-quark favor

William Loh, David Reens, Dave Kharas, Alkesh Sumant

Optical atomic clocks have demonstrated revolutionary advances in precision timekeeping, but their applicability to the real world is critically dependent on whether such clocks can operate outside a laboratory setting. The challenge to clock portability stems from the many obstacles not only in miniaturizing the underlying components of the clock $-$ namely

Nadhir Ben Rached, Erik von Schwerin, Gaukhar Shaimerdenova, Raul Tempone

In this work we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differential equation (SDE) within the framework of ensemble Kalman filtering (EnKF). Between two observation times of the EnKF, we propose to use IS with respect to the initial condition of

Yohai Reani, Omer Bobrowski

We study the $k$-th nearest neighbor distance function from a finite point-set in $\mathbb{R}^d$. We provide a Morse theoretic framework to analyze the sub-level set topology. In particular, we present a simple combinatorial-geometric characterization for critical points and their indices, along with detailed information about the possible changes in homolog

Lubomíra Dvořáková, Zuzana Masáková, Edita Pelantová

We define a new class of ternary sequences that are 2-balanced. These sequences are obtained by colouring of Sturmian sequences. We show that the class contains sequences of any given letter frequencies. We provide an upper bound on factor and abelian complexity of these sequences. Using the interpretation by rectangle exchange transformation, we prove that

Sravan K. R. Vuyyuru, Le Hao, Markus Rupp, Sergei A. Tretyakov

This work aims to build connections between the electromagnetic and communication aspects of Reconfigurable Intelligent Surfaces (RIS) by proposing a methodology to combine outputs from electromagnetic RIS design into an RIS-tailored system-level simulator and a ray tracer. In this first part of the contribution, a periodic anomalous reflector is designed us

Ruyi Pan, Luis E. Nieto-Barajas, Radu Craiu

Parametric copula families have been known to flexibly capture various dependence patterns, e.g., either positive or negative dependence in either the lower or upper tails of bivariate distributions. In this paper, our objective is to construct a model that is adaptable enough to capture several of these features simultaneously in $m$ dimensions. We propose

Kenda Alhadid, Robert W. Regenhardt, Natalia S. Rost, Markus D. Schirmer

Brain parenchymal fraction (BPF) has been used as a surrogate measure of global brain atrophy, and as a biomarker of brain reserve in studies evaluating clinical outcomes after brain injury. Total brain volume at the time of injury has recently been shown to influence functional outcomes, where larger brain volumes are associated with better outcomes. Here,

Zhenyu Bu, Yang Liu, Jiayu Huo, Jingjing Peng

Accurate identification of End-Diastolic (ED) and End-Systolic (ES) frames is key for cardiac function assessment through echocardiography. However, traditional methods face several limitations: they require extensive amounts of data, extensive annotations by medical experts, significant training resources, and often lack robustness. Addressing these challen

Maia A Nelsen, Darin Ragozzine, Benjamin C. N. Proudfoot, William G. Giforos

Dynamically studying Trans-Neptunian Object (TNO) binaries allows us to measure masses and orbits. Most of the known objects appear to have only two components, except (47171) Lempo which is the single known hierarchical triple system with three similar-mass components. Though hundreds of TNOs have been imaged with high-resolution telescopes, no other hierar

Darin Ragozzine, Seth Pincock, Benjamin C. N. Proudfoot, Dallin Spencer

Many details of the formation and evolution of the solar system are best inferred by understanding the orbital and physical properties of small bodies in the solar system. For example, small body binaries are particularly valuable for measuring masses. By extending the models of small body binaries beyond point masses, new information about the shape and spi

Daichi Haraguchi, Wataru Shimoda, Kota Yamaguchi, Seiichi Uchida

In this paper, we demonstrate a total disentanglement of font images. Total disentanglement is a neural network-based method for decomposing each font image nonlinearly and completely into its style and content (i.e., character class) features. It uses a simple but careful training procedure to extract the common style feature from all `A'-`Z' images in the

Benjamin C. N. Proudfoot, Darin A. Ragozzine, Meagan L. Thatcher, Will Grundy

About 40 transneptunian binaries (TNBs) have fully determined orbits with about 10 others being solved except for breaking the mirror ambiguity. Despite decades of study almost all TNBs have only ever been analyzed with a model that assumes perfect Keplerian motion (e.g., two point masses). In reality, all TNB systems are non-Keplerian due to non-spherical s

Benjamin C. N. Proudfoot, Darin A. Ragozzine, William Giforos, Will M. Grundy

The dwarf planet Haumea is one of the most compelling transneptunian objects (TNOs) to study, hosting two small, dynamically interacting satellites, a family of nearby spectrally unique objects, and a ring system. Haumea itself is extremely oblate due to its 3.9 hour rotation period. Understanding the orbits of Haumea's satellites, named Hi'iaka and Namaka,

Hao Jiang, Wangqi Shi, Zaichen Zhang, Cunhua Pan

Existing works mainly rely on the far-field planar-wave-based channel model to assess the performance of reconfigurable intelligent surface (RIS)-enabled wireless communication systems. However, when the transmitter and receiver are in near-field ranges, this will result in relatively low computing accuracy. To tackle this challenge, we initially develop an

Colin Guillarmou, Antti Kupiainen, Rémi Rhodes

In the paper, we review the recent construction of the Liouville conformal field theory (CFT) from probabilistic methods, and the formalization of the conformal bootstrap. This model has offered a fruitful playground to unify the probabilistic construction of the path integral, the geometric axiomatics of CFT by Segal and the representation theoretical conte

Yuehao Song, Xinggang Wang, Jingfeng Yao, Wenyu Liu

Gaze following aims to interpret human-scene interactions by predicting the person's focal point of gaze. Prevailing approaches often adopt a two-stage framework, whereby multi-modality information is extracted in the initial stage for gaze target prediction. Consequently, the efficacy of these methods highly depends on the precision of the preceding modalit

Zeliang Zhang, Mingqian Feng, Zhiheng Li, Chenliang Xu

Machine learning models can perform well on in-distribution data but often fail on biased subgroups that are underrepresented in the training data, hindering the robustness of models for reliable applications. Such subgroups are typically unknown due to the absence of subgroup labels. Discovering biased subgroups is the key to understanding models' failure m

Jiuhai Chen, Jonas Mueller

Large Language Models have become the de facto approach to sequence-to-sequence text generation tasks, but for specialized tasks/domains, a pretrained LLM lacks specific capabilities to produce accurate or well-formatted responses. Supervised fine-tuning specializes a LLM by training it on dataset of example prompts with target responses, but real-world data

Mihyun Kang, Christoph Koch, Tamás Makai

We investigate the behaviour of $r$-neighbourhood bootstrap percolation on the binomial $k$-uniform random hypergraph $H_k(n,p)$ for given integers $k\geq 2$ and $r\geq 2$. In $r$-neighbourhood bootstrap percolation, infection spreads through the hypergraph, starting from a set of initially infected vertices, and in each subsequent step of the process every

Robin Quillivic, Salma Mesmoudi

Language models (LM or LLM) are increasingly deployed in the field of artificial intelligence (AI) and its applications, but the question arises as to whether they can be a common resource managed and maintained by a community of users. Indeed, the dominance of private companies with exclusive access to massive data and language processing resources can crea

Claudia Silva, Erika Roldan, Rosemberg Toala-Enriquez

We study topological and geometric properties of a cell growth process in the Euclidean plane, where the cells are regular pentagons. To explore the aesthetic aspects of this model, we employ a laser cutter on various materials to create physical representations for some simulations of the model.

Jiao Li, Chao Liu, Changqing Luo, Bo Zhang

Hierarchical massive quadruple systems are ideal laboratories for examining the theories of star formation, dynamical evolution, and stellar evolution. The successive mergers of hierarchical quadruple systems might explain the mass gap between neutron stars and black holes. Looking for light curves of O-type binaries identified by LAMOST, we find a (2+2) qua

Zhantao Ma, Michael K. Ng

Multispectral images (MSI) contain light information in different wavelengths of objects, which convey spectral-spatial information and help improve the performance of various image processing tasks. Numerous techniques have been created to extend the application of total variation regularization in restoring multispectral images, for example, based on chann

Alexandre Prestes Uchoa, Daniel Schneider

This paper investigates the shift in crowdsourcing towards self-managed enterprises of crowdworkers (SMECs), diverging from traditional platform-controlled models. It reviews the literature to understand the foundational aspects of this shift, focusing on identifying key factors that may explain the rise of SMECs, particularly concerning power dynamics and t

Bo Shui, Chufan Shi, Yujiu Yang, Xiaomei Nie

ContextVis introduces a workflow by integrating generative models to create contextual learning materials. It aims to boost knowledge acquisition through the creation of resources with contextual cues. A case study on vocabulary learning demonstrates the effectiveness of generative models in developing educational resources that enrich language understanding

Qiangguo Jin, Hui Cui, Changming Sun, Yang Song

Acquiring pixel-level annotations is often limited in applications such as histology studies that require domain expertise. Various semi-supervised learning approaches have been developed to work with limited ground truth annotations, such as the popular teacher-student models. However, hierarchical prediction uncertainty within the student model (intra-unce

Rory D. Mills-Williams, Benjamin D. Goddard, Andrew J. Archer

We present dynamic density functional theory (DDFT) incorporating general inhomogeneous, incompressible, time dependent background flows and inertia, describing externally driven passive colloidal systems out of equilibrium. We start by considering the underlying nonequilibrium Langevin dynamics, including the effect of the local velocity of the surrounding

Konrad Mundinger, Max Zimmer, Sebastian Pokutta

We introduce Neural Parameter Regression (NPR), a novel framework specifically developed for learning solution operators in Partial Differential Equations (PDEs). Tailored for operator learning, this approach surpasses traditional DeepONets (Lu et al., 2021) by employing Physics-Informed Neural Network (PINN, Raissi et al., 2019) techniques to regress Neural

Paul Alphonse, Nikolay Tzvetkov

We show that, after a renormalisation, one can define the square of the modulus of the solution of the fractional Schr\"odinger equations on the circle with data in Sobolev spaces of arbitrary negative index. As an application, we obtain observability estimates with rough controls.

Yi Ke, Shangkun Weng

This paper concerns the structural stability of smooth cylindrical symmetric transonic flows in a concentric cylinder under helically symmetric perturbation of suitable boundary conditions. The deformation-curl decomposition developed by the second author and his collaborator is utilized to effectively decouple the elliptic-hyperbolic mixed structure in the

Riccardo Andrea Izzo, Gianluca Bardaro, Matteo Matteucci

This paper presents a novel approach to generating behavior trees for robots using lightweight large language models (LLMs) with a maximum of 7 billion parameters. The study demonstrates that it is possible to achieve satisfying results with compact LLMs when fine-tuned on a specific dataset. The key contributions of this research include the creation of a f

Yunqi Miao, Jiankang Deng, Jungong Han

Although diffusion models are rising as a powerful solution for blind face restoration, they are criticized for two problems: 1) slow training and inference speed, and 2) failure in preserving identity and recovering fine-grained facial details. In this work, we propose WaveFace to solve the problems in the frequency domain, where low- and high-frequency com

Peng Liu, William Q. Meeker

Many scientific and engineering applications require fitting regression models that are nonlinear in the parameters. Advances in computer hardware and software in recent decades have made it easier to fit such models. Relative to fitting regression models that are linear in the parameters, however, fitting nonlinear regression models is more complicated. In

Claudio Bonati, Andrea Pelissetto, Ettore Vicari

We consider a three-dimensional lattice Abelian Higgs gauge model for a charged $N$-component scalar field ${\phi}$, which is invariant under $SO(N)$ global transformations for generic values of the parameters. We focus on the strong-coupling regime, in which the kinetic Hamiltonian term for the gauge field is a small perturbation, which is irrelevant for th

Peng Liu, Yili Hong, Luis A. Escobar, William Q. Meeker

Fatigue data arise in many research and applied areas and there have been statistical methods developed to model and analyze such data. The distributions of fatigue life and fatigue strength are often of interest to engineers designing products that might fail due to fatigue from cyclic-stress loading. Based on a specified statistical model and the maximum l

Vassil Kanev

Given a smooth, projective curve $Y$, a point $y_0 \in Y$, a positive integer $n$, and a transitive subgroup $G$ of the symmetric group $S_{d}$ we study smooth, proper families, parameterized by algebraic varieties, of pointed degree $d$ covers of $(Y,y_0)$, $(X,x_{0})\to (Y,y_0)$, branched in $n$ points of $Y\setminus y_{0}$, whose monodromy group equals $G

Antoine Castagnède, Laura Filion, Frank Smallenburg

Conventional molecular dynamics (MD) simulations struggle when simulating particles with steeply varying interaction potentials, due to the need to use a very short time step. Here, we demonstrate that an event-driven Monte Carlo (EDMC) approach first introduced by Peters and de With [Phys. Rev. E 85, 026703 (2012)] represents an excellent substitute for MD

Riyaaz Uddien Shaik, Mohamad Alipour, Eric Rowell, Bharathan Balaji

Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources including Landsat-8 optical imagery, Sentinel-1 (C-band) Synthetic Aperture Radar (SAR) imagery, PALSAR (L-band) SAR imagery, and terrain features to capture comprehensive informati

Selin Tuquet, Nicole St. -Louis, Laurent Drissen, Sylvain Raaijmakers

We have conducted an analysis of nebulae around Wolf-Rayet (WR) stars in M33 using data collected by the imaging Fourier transform spectrometer SITELLE at the Canada-France-Hawaii telescope as part of the SIGNALS Large Program. Of the 211 known Wolf-Rayet stars in M33, 178 are located in the fields observed in this study. We present the results of this analy

Thiago de Souza Lamenza, Josef Kamysek, Bruno Jose Olivieri de Souza, Markus Endler

This work discusses the benefits of having multiple simulated environments with different degrees of realism for the development of algorithms in scenarios populated by autonomous nodes capable of communication and mobility. This approach aids the development experience and generates robust algorithms. It also proposes GrADyS-SIM NextGen as a solution that e

José L. Fernández, Pablo Fernández

Extending the classical Dirichlet's density theorem on coprime pairs, in this paper we describe completely the probability distribution of the number of coprime pairs in random squares of fixed side length in the lattice $\mathbb{N}^2$. The limit behaviour of this distribution as the side length of the random square tends to infinity is also considered.

Michael Greenblatt

We investigate estimating scalar oscillatory integrals by integrating by parts in directions based on $(x_1 \partial_{x_1} f(x) ,..., x_n \partial_{x_n}f(x))$, where $f(x)$ is the phase function. We prove a theorem which provides estimates that are uniform with respect to linear perturbations of the phase and investigate some consequences. When the phase fun

Nadja Egner

Given a finite category T, we consider the functor category [T,A], where A can in particular be any quasi-abelian category. Examples of quasi-abelian categories are given by any abelian category but also by non-exact additive categories as the categories of torsion(-free) abelian groups, topological abelian groups, locally compact abelian groups, Banach spac

Mario Zitelli, Louis Andreoli, Claire Autebert, Jean-Philippe Gauthier

We theoretically analyze and experimentally demonstrate the possibility of amplifying optical signals in an unrepeatered mode-division multiplexed tranmsmission system, through inter-modal stimulated Raman scattering process between signal and pump beams coupled onto distinct modes of a few-mode graded-index optical fiber.

Siyao Peng, Zihang Sun, Huangyan Shan, Marie Kolm

Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects. This paper introduces the first dialectal NER dataset for German, BarNER, with 161K tokens annotated on Bavarian Wikipedia articles (bar-wiki) and tweets (bar-tweet), using a schema adapted from German CoNLL 2006 and Ge

Matheus A. Cerqueira, Flávia Sprenger, Bernardo C. A. Teixeira, Alexandre X. Falcão

Brain tumor image segmentation is a challenging research topic in which deep-learning models have presented the best results. However, the traditional way of training those models from many pre-annotated images leaves several unanswered questions. Hence methodologies, such as Feature Learning from Image Markers (FLIM), have involved an expert in the learning

Rushi Qiang, Ruiyi Zhang, Pengtao Xie

Low-rank adaptation (LoRA) is a popular method for fine-tuning large-scale pre-trained models in downstream tasks by learning low-rank incremental matrices. Though LoRA and its variants effectively reduce the number of trainable parameters compared to full fine-tuning methods, they often overfit training data, resulting in sub-optimal generalization on test

Hiroki Matsumoto, Yasuhiro Todaka, Takuya Kawada, Masashi Kawaguchi

We have developed a simple detection scheme to study spin waves excited by surface acoustic wave (SAW) in ferromagnetic thin films. Metallic antennas made of Ta and a ferromagnetic element are placed along the SAW propagation path. The SAW excites spin waves in the ferromagnetic element and induces acoustoelectric current in the antennas, which are detected

E. M. C. Sijben, J. C. Jansen, M. de Ridder, P. A. N. Bosman

Volume measurement of a paraganglioma (a rare neuroendocrine tumor that typically forms along major blood vessels and nerve pathways in the head and neck region) is crucial for monitoring and modeling tumor growth in the long term. However, in clinical practice, using available tools to do these measurements is time-consuming and suffers from tumor-shape ass

Zhenyu Wu, Chao Shen, Meng Jiang

Math word problem (MWP) solving requires generating a reasoning path based on a given problem description that often contains irrelevant conditions. Existing chain-of-thought (CoT) prompting methods elicited multi-step reasoning abilities of large language models (LLMs) to solve MWPs. However, they were seriously confused by the irrelevant conditions, result

Alex Ergasti, Claudio Ferrari, Tomaso Fontanini, Massimo Bertozzi

Semantic Image Synthesis (SIS) is among the most popular and effective techniques in the field of face generation and editing, thanks to its good generation quality and the versatility is brings along. Recent works attempted to go beyond the standard GAN-based framework, and started to explore Diffusion Models (DMs) for this task as these stand out with resp

Erlend Grong, Alexander Schmeding

In this article we consider diffeomorphism groups of manifolds with smooth boundary. We show that the diffeomorphism groups of the manifold and its boundary fit into a short exact sequence which admits local sections. In other words, they form an infinite-dimensional fibre bundle. Manifolds with boundary are of interest in numerical analysis and with a view

Richard P. Thomas

We compute all refined sheaf counting invariants -- Vafa-Witten, reduced DT, stable pairs and Gopakumar-Vafa -- for all classes on local $K3$ surfaces. Along the way we develop rank 0 Vafa-Witten theory on $K3$ surfaces. An important feature of the calculation is that the ``instanton contribution" -- of sheaves supported scheme theoretically on $S$ -- to any

Thibaud Maimbourg

Structural glasses display at low temperature a set of anomalies in thermodynamic observables. A prominent example is the linear-in-temperature scaling of the specific heat, at odds with the Debye cubic scaling found in crystals, due to acoustic phonons. Such an excess of specific heat in amorphous solids is thought of arising from phenomenological soft exci

Mohssen E. Elshaar, Mohammed R. Elbalshy, A. Hussien, Mohammed Abido

Efficiently planning an Unmanned Aerial Vehicle (UAV) path is crucial, especially in dynamic settings where potential threats are prevalent. A Dynamic Path Planner (DPP) for UAV using the Spherical Vector-based Particle Swarm Optimisation (SPSO) technique is proposed in this study. The UAV is supposed to go from a starting point to an end point through an op

V. Cerone, S. M. Fosson, S. Pirrera, D. Regruto

The continuous-time analysis of existing iterative algorithms for optimization has a long history. This work proposes a novel continuous-time control-theoretic framework for equality-constrained optimization. The key idea is to design a feedback control system where the Lagrange multipliers are the control input, and the output represents the constraints. Th

Maciej Pankiewicz, Ryan S. Baker

We examined the efficacy of AI-assisted learning in an introductory programming course at the university level by using a GPT-4 model to generate personalized hints for compiler errors within a platform for automated assessment of programming assignments. The control group had no access to GPT hints. In the experimental condition GPT hints were provided when

Sivan Doveh, Shaked Perek, M. Jehanzeb Mirza, Wei Lin

State-of-the-art Vision-Language Models (VLMs) ground the vision and the language modality primarily via projecting the vision tokens from the encoder to language-like tokens, which are directly fed to the Large Language Model (LLM) decoder. While these models have shown unprecedented performance in many downstream zero-shot tasks (eg image captioning, quest

José A. Carrillo, Ruiwen Shu, Li Wang, Wuzhe Xu

A simplified kinetic description of rapid granular media leads to a nonlocal Vlasov-type equation with a convolution integral operator that is of the same form as the continuity equations for aggregation-diffusion macroscopic dynamics. While the singular behavior of these nonlinear continuity equations is well studied in the literature, the extension to the

Vladimir Gladshtein, George Pîrlea, Ilya Sergey

We present the design and implementation of the Small Scale Reflection proof methodology and tactic language (a.k.a. SSR) for the Lean 4 proof assistant. Like its Coq predecessor SSReflect, our Lean 4 implementation, dubbed LeanSSR, provides powerful rewriting principles and means for effective management of hypotheses in the proof context. Unlike SSReflect

Hamish Flynn, David Reeb

Confidence bounds are an essential tool for rigorously quantifying the uncertainty of predictions. They are a core component in many sequential learning and decision-making algorithms, with tighter confidence bounds giving rise to algorithms with better empirical performance and better performance guarantees. In this work, we use martingale tail inequalities

Shunsei Yamamura, Hirotaka Yuzurihara, Takahiro Yamamoto, Takashi Uchiyama

In gravitational wave astronomy, non-Gaussian noise, such as scattered light noise disturbs stable interferometer operation, limiting the interferometer's sensitivity, and reducing the reliability of the analyses. In scattered light noise, the non-Gaussian noise dominates the sensitivity in a low frequency range of less than a few hundred Hz, which is sensit

Kacper Sokol, Julia E. Vogt

Despite significant progress, evaluation of explainable artificial intelligence remains elusive and challenging. In this paper we propose a fine-grained validation framework that is not overly reliant on any one facet of these sociotechnical systems, and that recognises their inherent modular structure: technical building blocks, user-facing explanatory arte

Jingtao Sun, Yaonan Wang, Mingtao Feng, Chao Ding

Fully-supervised category-level pose estimation aims to determine the 6-DoF poses of unseen instances from known categories, requiring expensive mannual labeling costs. Recently, various self-supervised category-level pose estimation methods have been proposed to reduce the requirement of the annotated datasets. However, most methods rely on synthetic data o

Mattia Radice, Giampaolo Cristadoro

We consider a one-dimensional search process under stochastic resetting conditions. A target is located at $b\geq0$ and a searcher, starting from the origin, performs a discrete-time random walk with independent jumps drawn from a heavy-tailed distribution. Before each jump, there is a given probability $r$ of restarting the walk from the initial position. T

Hang Song, Hyun Joon Kim, Mingxia Wan, Bo Wei

Measuring the complex permittivity of material is essential in many scenarios such as quality check and component analysis. Generally, measurement methods for characterizing the material are based on the usage of vector network analyzer, which is large and not easy for on-site measurement, especially in high frequency range such as millimeter wave (mmWave).

Boris Goldfarb

Motion path planning is an intrinsically geometric problem which is central for design of robot systems. Since the early years of AI, robotics together with computer vision have been the areas of computer science that drove its development. Many questions that arise, such as existence, optimality, and diversity of motion paths in the configuration space that

Mohammad Nooraiepour, Mohammad Masoudi, Beyene Girma Haile, Helge Hellevang

Subsurface fluid flow and solute transport are pivotal in addressing pressing energy, environmental, and societal challenges, such as geological CO2 storage. Basaltic rocks have gained prominence as suitable geological substrates for injecting substantial CO2 volumes and carbon mineralization, driven by their widespread occurrence, high concentrations of cat

Ilya Yegorov, Eli Kobrin, Darya Parygina, Alexey Vishnyakov

Ensuring the security and reliability of machine learning frameworks is crucial for building trustworthy AI-based systems. Fuzzing, a popular technique in secure software development lifecycle (SSDLC), can be used to develop secure and robust software. Popular machine learning frameworks such as PyTorch and TensorFlow are complex and written in multiple prog

Hongyu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai

Holistic understanding of urban scenes based on RGB images is a challenging yet important problem. It encompasses understanding both the geometry and appearance to enable novel view synthesis, parsing semantic labels, and tracking moving objects. Despite considerable progress, existing approaches often focus on specific aspects of this task and require addit

Jianfeng Zhan, Lei Wang, Wanling Gao, Hongxiao Li

Evaluation is a crucial aspect of human existence and plays a vital role in various fields. However, it is often approached in an empirical and ad-hoc manner, lacking consensus on universal concepts, terminologies, theories, and methodologies. This lack of agreement has significant repercussions. This article aims to formally introduce the discipline of eval

Nikola Ljubešić, Taja Kuzman

This paper presents a collection of highly comparable web corpora of Slovenian, Croatian, Bosnian, Montenegrin, Serbian, Macedonian, and Bulgarian, covering thereby the whole spectrum of official languages in the South Slavic language space. The collection of these corpora comprises a total of 13 billion tokens of texts from 26 million documents. The compara

Shail Jadav, Johannes Heidersberger, Christian Ott, Dongheui Lee

This article introduces a framework for complex human-robot collaboration tasks, such as the co-manufacturing of furniture. For these tasks, it is essential to encode tasks from human demonstration and reproduce these skills in a compliant and safe manner. Therefore, two key components are addressed in this work: motion generation and shared autonomy. We pro

Angelica I. Aviles-Rivero, Chun-Wun Cheng, Zhongying Deng, Zoe Kourtzi

Early detection of Alzheimer's disease's precursor stages is imperative for significantly enhancing patient outcomes and quality of life. This challenge is tackled through a semi-supervised multi-modal diagnosis framework. In particular, we introduce a new hypergraph framework that enables higher-order relations between multi-modal data, while utilising mini