February 2024 arXiv papers — page 141

Harikrishnan K., J. Harshan, Anwitaman Datta

Coded blockchains have acquired prominence as a promising solution to reduce storage costs and facilitate scalability. Within this class, Luby Transform (LT) coded blockchains are an appealing choice for scalability owing to the availability of a wide range of low-complexity decoders. In the first part of this work, we identify that traditional LT decoders l

Haruyuki Nakagawa, Yoshitaka Miyatani, Asako Kanezaki

Various robots, rovers, drones, and other agents of mass-produced products are expected to encounter scenes where they intersect and collaborate in the near future. In such multi-agent systems, individual identification and communication play crucial roles. In this paper, we explore camera-based visible light communication using event cameras to tackle this

Marta Fuente, Jamie Goggins, Daniel Rabago, Ismael Fuente

A one-year monitoring study was conducted in a pilot house with high radon levels to investigate the ability and efficiency of radon mitigation by soil depressurisation (SD) both active and passive. The study included monitoring of radon concentration, pressure field extension (pfe) under the slab and some atmospheric parameters for different testing phases.

Tomoki Takahashi, Keiichi Watanabe

Consider the three-dimensional Navier--Stokes flow past a moving rigid body $\mathscr{O} \subset \mathbb{R}^3$ with prescribed translational and angular velocities, where $\mathscr{O}$ stands for a bounded Lipschitz domain. We prove that the solution to the linearized problem is governed by a $C_0$-semigroup on solenoidal $L^q$-vector spaces with the $L^q$-$

Elena Senger, Mike Zhang, Rob van der Goot, Barbara Plank

Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extraction and classification from job postings. Because of its quick growth and its interdisciplinary nature, there is no exhaustive assessment of

Ben Fauber

We propose that small pretrained foundational generative language models with millions of parameters can be utilized as a general learning framework for sequence-based tasks. Our proposal overcomes the computational resource, skill set, and timeline challenges associated with training neural networks and language models from scratch. Further, our approach fo

Maik Riedl, Norbert Marwan, Jürgen Kurths

The generalized recurrence plot is a modern tool for quantification of complex spatial patterns. Its application spans the analysis of trabecular bone structures, Turing patterns, turbulent spatial plankton patterns, and fractals. Determinism is a central measure in this framework quantifying the level of regularity of spatial structures. We show by basic ex

Daniel Batrakhanov, Tuomas Eerola, Kaisa Kraft, Lumi Haraguchi

Plankton recognition provides novel possibilities to study various environmental aspects and an interesting real-world context to develop domain adaptation (DA) methods. Different imaging instruments cause domain shift between datasets hampering the development of general plankton recognition methods. A promising remedy for this is DA allowing to adapt a mod

Benoît Gay, Sébastien Galtier

Wave turbulence is by nature a multiple time scale problem for which there is a natural asymptotic closure. The main result of this analytical theory is the kinetic equation that describes the long-time statistical behaviour of such turbulence composed of a set of weakly nonlinear interacting waves. In the case of gravitational waves, it involves four-wave i

Feng Xue, Jiaheng Li, Yizhou Liu, Ruqian Wu

Topological and valleytronic materials are promising for spintronic and quantum applications due to their unique properties. Using first principles calculations, we demonstrate that germanene (Ge)-based ferromagnetic heterostructures can exhibit multiple quantum states such as quantum anomalous Hall effect (QAHE) with Chern numbers of C=-1 or C=-2, quantum v

Linxiao Chen, Alice Contat

Consider a supercritical Bienaym\'e--Galton--Watson tree $ \mathcal{T}$ with geometric offspring distribution. Each vertex of this tree represents a parking spot which can accommodate at most one car. On the top of this tree, we add $(A_u : u \in \mathcal{T})$ i.i.d.\ non negative integers sampled according to a given law $ \mu$, which are the car arrivals o

Carmen Delgado, Sergio Batista, María Canales, José Ramón Gállego

We present a system architecture implementation to perform dynamic application allocation in shared sensor networks, where highly integrated wireless sensor systems are used to support multiple applications. The architecture is based on a central controller that collects the received data from the sensor nodes, dynamically decides which applications must be

Thomas Pöllabauer, Jan Emrich, Volker Knauthe, Arjan Kuijper

Estimating the 6D pose of objects accurately, quickly, and robustly remains a difficult task. However, recent methods for directly regressing poses from RGB images using dense features have achieved state-of-the-art results. Stereo vision, which provides an additional perspective on the object, can help reduce pose ambiguity and occlusion. Moreover, stereo c

Santiago Celaya, Ismael Fuente, Luis Quindos, Carlos Sainz

The new EU Council Directive 2013/51/Euratom of 22 October 2013 introduced limits for the content of 222Rn in drinking water. Radon analysis in water requires a lengthy task of collection, storage, transport and subsequent measurement in a laboratory. A portable liquid scintillation counting device allows rapid sampling with significant savings of time, spac

Zhengcong Fei, Mingyuan Fan, Changqian Yu, Junshi Huang

This paper presents a new exploration into a category of diffusion models built upon state space architecture. We endeavor to train diffusion models for image data, wherein the traditional U-Net backbone is supplanted by a state space backbone, functioning on raw patches or latent space. Given its notable efficacy in accommodating long-range dependencies, Di

Jing Xie, Fabio Bonassi, Riccardo Scattolini

This paper explores the use of Control Affine Neural Nonlinear AutoRegressive eXogenous (CA-NNARX) models for nonlinear system identification and model-based control design. The idea behind this architecture is to match the known control-affine structure of the system to achieve improved performance. Coherently with recent literature of neural networks for d

Jing Xie, Léo Simpson, Jonas Asprion, Riccardo Scattolini

Temperature control is a complex task due to its often unknown dynamics and disturbances. This paper explores the use of Neural Nonlinear AutoRegressive eXogenous (NNARX) models for nonlinear system identification and model predictive control of a temperature control unit. First, the NNARX model is identified from input-output data collected from the real pl

Andrew Fuchs, Andrea Passarella, Marco Conti

When humans and autonomous systems operate together as what we refer to as a hybrid team, we of course wish to ensure the team operates successfully and effectively. We refer to team members as agents. In our proposed framework, we address the case of hybrid teams in which, at any time, only one team member (the control agent) is authorized to act as control

Jia-Xiu Han, Jiang Zhang, Guang-Ming Xue, Haifeng Yu

Demonstrating that logical qubits outperform their physical counterparts is a milestone for achieving reliable quantum computation. Here, we propose to protect logical qubits with a novel dynamical decoupling scheme that implements iSWAP gates on nearest-neighbor physical qubits, and experimentally demonstrate the scheme on superconducting transmon qubits. I

Sergey N. Artekha, Natalya S. Artekha

Exactly solvable models are interesting for science and education, since they help in scientific search and in understanding of phenomena. Some exact solutions for simple quantum-mechanical models are considered. The models include two barriers, combinations of barrier pairs, three barriers, three wells etc. The model of two barriers can predict some interes

Reduan Achtibat, Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Aakriti Jain

Large Language Models are prone to biased predictions and hallucinations, underlining the paramount importance of understanding their model-internal reasoning process. However, achieving faithful attributions for the entirety of a black-box transformer model and maintaining computational efficiency is an unsolved challenge. By extending the Layer-wise Releva

Barbora Benešová, Daniel Campbell, Stanislav Hencl, Martin Kružík

Ensuring non-interpenetration of matter is a fundamental prerequisite when modeling the deformation response of solid materials. In this contribution, we thoroughly examine how this requirement, equivalent to the injectivity of deformations within bulk structures, manifests itself in dimensional-reduction problems. Specifically, we focus on the case of rods

Keiichi Shigechi

We study the set of networks, which consist of sources, sinks and neutral points, bijective to the permutations. The set of directed edges, which characterizes a network, is constructed from a polyomino or a Rothe diagram of a permutation through a Dyck tiling on a ribbon. We introduce a new combinatorial object similar to a tree-like tableau, which we call

Masahito Hayashi, Kazuyasu Shigemoto, Takuya Tsukioka

For quadratic curves over $F_p$, the number of solutions, which is governed by an analogue of the Mordell-Weil group, is expressed with the Legendre symbol of a coefficient of quadratic curves. Focusing on the number of solutions, a quadratic curve analogue of the modular form in the Taniyama-Shimura conjecture is proposed. This modular form yields the Gauss

Alexander Rudikov, Vladimir Fanaskov, Ekaterina Muravleva, Yuri M. Laevsky

Deep learning solvers for partial differential equations typically have limited accuracy. We propose to overcome this problem by using them as preconditioners. More specifically, we apply discretization-invariant neural operators to learn preconditioners for the flexible conjugate gradient method (FCG). Architecture paired with novel loss function and traini

Trishala Mitra, Gurpreet Singh, Ali Akbar Darki, Søren Peder Madsen

We report on the theoretical and experimental investigations of optical microcavities consisting in the plane-plane arrangement of a broadband high-reflectivity mirror and a suspended one-dimensional grating mirror possessing a high-quality factor Fano resonance. By varying the length of these cavities from the millimeter to the few-micron range, we observe

Yuhao Zhao, Xiande Zhang

Frameproof codes have been extensively studied for many years due to their application in copyright protection and their connection to extremal set theory. In this paper, we investigate upper bounds on the cardinality of wide-sense $t$-frameproof codes. For $t=2$, we apply results from Sperner theory to give a better upper bound, which significantly improves

Yue Wang, Qi Zhao

Quantum algorithms typically demand prohibitively complicated circuits to solve practical problems. Previous studies have shown that classical randomness can accelerate some specific quantum algorithms. In this work, we introduce the Randomized Truncated Series (RTS) which extends this acceleration to all quantum algorithms that rely on truncated series appr

I. Orlovskyi, F. Proske, O. Tymoshenko

Using key tools such as It\^o formula for general semi-martingales, moments estimates for L\'{e}vy-type stochastic integrals and properties of regular varying functions we find conditions under which solutions of stochastic differential equation with jumps are almost sure asymptotically equivalent nonrandom function with $t\to \infty$.

Thomas Pöllabauer, Julius Kühn

In medieval times, stuccoworkers used a red color, called sinopia, to first create a sketch of the to-be-made statue on the wall. Today, many of these statues are destroyed, but using the original drawings, deriving from the red color also called sinopia, we can reconstruct how the final statue might have looked.We propose a fully-automated approach to recon

Anisha Ghosh, Aditya Mitra, Anik Saha, Sibi Chakkaravarthy Sethuraman

A standardized control system called Metaverse Extended Reality Portal (MERP) is presented as a solution to the issues with conventional VR eyewear. The MERP system improves user awareness of the physical world while offering an immersive 3D view of the metaverse by using a shouldermounted projector to display a Heads-Up Display (HUD) in a designated Metaver

Juhwan Choi, Kyohoon Jin, Junho Lee, Sangmin Song

Rule-based text data augmentation is widely used for NLP tasks due to its simplicity. However, this method can potentially damage the original meaning of the text, ultimately hurting the performance of the model. To overcome this limitation, we propose a straightforward technique for applying soft labels to augmented data. We conducted experiments across sev

Yi Han

Given $A_n:=\frac{1}{\sqrt{n}}(a_{ij})$ an $n\times n$ symmetric random matrix, with elements above the diagonal given by i.i.d. random variables having mean zero and unit variance. It is known that when $\lim_{x\to\infty}x^4\mathbb{P}(|a_{ij}|>x)=0$, then fluctuation of the largest eigenvalue of $A_n$ follows a Tracy-Widom distribution. When the law of $a_{

Ying Zang, Chenglong Fu, Runlong Cao, Didi Zhu

Referring expression segmentation (RES), a task that involves localizing specific instance-level objects based on free-form linguistic descriptions, has emerged as a crucial frontier in human-AI interaction. It demands an intricate understanding of both visual and textual contexts and often requires extensive training data. This paper introduces RESMatch, th

Diogo Boito, Maarten Golterman, Kim Maltman, Santiago Peris

Non-perturbative effects have a small but non-trivial impact on the determination of the strong coupling from hadronic $\tau$ decay data. Several approaches have been proposed to take these into account, the two most important of which are the ``truncated OPE'' approach and ``DV-model'' approach. Recently, Pich and Rodr\'iguez-S\'anchez have raised a number

Lal Verda Cakir, Sarah Al-Shareeda, Sema F. Oktug, Mehmet Özdem

Synchronization is fundamental for mirroring real-world entities in real-time and supporting effective operations of Digital Twins (DTs). Such synchronization is enabled by the communication between the physical and virtual realms, and it is mostly assumed to occur in real-time. However, this is not the case, as real-life scenarios witness performance degrad

Francesco Guarneri, Luca Pasquini, Valentina D'Odorico, Stefano Cristiani

Primordial abundances of light elements are sensitive to the physics of the early Universe and can directly constrain cosmological quantities, such as the baryon-to-photon ratio $\eta_{10}$, the baryon density and the number of neutrino families. Deuterium is especially suited for these studies: its primordial abundance is sensitive and monotonically depende

Vladimir Fanaskov, Alexander Rudikov, Ivan Oseledets

We propose a new loss function for supervised and physics-informed training of neural networks and operators that incorporates a posteriori error estimate. More specifically, during the training stage, the neural network learns additional physical fields that lead to rigorous error majorants after a computationally cheap postprocessing stage. Theoretical res

Juhwan Choi, Kyohoon Jin, Junho Lee, Sangmin Song

Text data augmentation is a complex problem due to the discrete nature of sentences. Although rule-based augmentation methods are widely adopted in real-world applications because of their simplicity, they suffer from potential semantic damage. Previous researchers have suggested easy data augmentation with soft labels (softEDA), employing label smoothing to

Eduardo Noboro Tominaga, Onel Luis Alcaraz López, Tommy Svensson, Richard Demo Souza

Contemporary wireless communication systems rely on Multi-User Multiple-Input Multiple-Output (MU-MIMO) techniques. In such systems, each Access Point (AP) is equipped with multiple antenna elements and serves multiple devices simultaneously. Notably, traditional systems utilize fixed antennas, i.e., antennas without any movement capabilities, while the idea

Yuxin Xie, Li Yu, Farhad Pakdaman, Moncef Gabbouj

Noisy images are a challenge to image compression algorithms due to the inherent difficulty of compressing noise. As noise cannot easily be discerned from image details, such as high-frequency signals, its presence leads to extra bits needed for compression. Since the emerging learned image compression paradigm enables end-to-end optimization of codecs, rece

Maxime Fily, Guillaume Wisniewski, Severine Guillaume, Gilles Adda

In the highly constrained context of low-resource language studies, we explore vector representations of speech from a pretrained model to determine their level of abstraction with regard to the audio signal. We propose a new unsupervised method using ABX tests on audio recordings with carefully curated metadata to shed light on the type of information prese

Sascha Eichmann

To avoid possible singularities in the Willmore flow, one usually works under an energy threshold provided by the Li-Yau inequality. Here we improve this threshold by also considering parts outside of a possible singularity together with Dirichlet boundary data. We work in the class of surfaces of revolution.

Michael Mandlmayr, Ali Kemal Uncu

We present effective procedures to calculate regular normal cones and other related objects using quantifier elimination. This method of normal cone calculations is complementary to computing Lagrangians and it works best at points where the constraint qualifications fail and extra work for other methods becomes inevitable. This method also serves as a tool

Athanasios Milionis, Despina Fragouli, Fernando Brandi, Ioannis Liakos

We report the development of magnetic nanocomposite sheets with superhydrophobic and superoleophilic surfaces generated by laser ablation. Polydimethylsiloxane elastomer freestanding films, loaded homogeneously with 2% wt. carbon coated iron nanoparticles, were ablated by UV (248 nm), nanosecond laser pulses. The laser irradiation induces chemical and struct

Harukuni Ikeda

In a recent preprint, ``A new universality class describes Vicsek's flocking phase in physical dimensions'', Patrick Jentsch and Chiu Fan Lee have computed the critical exponents of the Vicsek model in the ordered phase by means of functional renormalization group methods. In this note, we compare their results with our previous theoretical predictions for t

Giorgia Ciavolella, Nathalie Ferrand, Michèle Sabbah, Benoît Perthame

One of the most crucial and lethal characteristics of solid tumors is represented by the increased ability of cancer cells to migrate and invade other organs during the so-called metastatic spread. This is allowed thanks to the production of matrix metalloproteinases (MMPs), enzymes capable of degrading a type of collagen abundant in the basal membrane separ

Subham Pokhriyal, Shweta Jain, Ganesh Ghalme, Swapnil Dhamal

Existing approaches to fairness in stochastic multi-armed bandits (MAB) primarily focus on exposure guarantee to individual arms. When arms are naturally grouped by certain attribute(s), we propose Bi-Level Fairness, which considers two levels of fairness. At the first level, Bi-Level Fairness guarantees a certain minimum exposure to each group. To address t

Francesca Cibrario, Or Samimi Golan, Giacomo Ranieri, Emanuele Dri

This work introduces a novel approach to price rainbow options, a type of path-independent multi-asset derivatives, with quantum computers. Leveraging the Iterative Quantum Amplitude Estimation method, we present an end-to-end quantum circuit implementation, emphasizing efficiency by delaying the transition to price space. Moreover, we analyze two different

Zhen Zhang

The technical evolution of domestic multi-functional electricity meter is deeply discussed. With the rapid development of the domestic power market and the continuous innovation of technology, the domestic multi-functional electricity meters have experienced the transformation from simple billing to complex multi-functional, from a single application to a wi

Sonja Wieland, Abdurrahman Ali El Yumin, Simon Settele, Jana Zaumseil

Oxygen defects in semiconducting single-walled carbon nanotubes (SWCNTs) are localized disruptions in the carbon lattice caused by the formation of epoxy or ether groups, commonly through wet-chemical reactions. The associated modifications of the electronic structure can result in luminescent states with emission energies below those of pristine SWCNTs in t

José Alberto Benítez-Andrades, José-Manuel Alija-Pérez, Maria-Esther Vidal, Rafael Pastor-Vargas

Background: Eating disorders are increasingly prevalent, and social networks offer valuable information. Objective: Our goal was to identify efficient machine learning models for categorizing tweets related to eating disorders. Methods: Over three months, we collected tweets about eating disorders. A 2,000-tweet subset was labeled for: (1) being written by i

Jianan Zhang, Rujing Xiong, Junshuo Liu, Tiebin Mi

Reconfigurable Intelligent Surfaces (RISs) exhibit promising enhancements in coverage and data rates for wireless communication systems, particularly in the context of 5G and beyond. This paper introduces a novel approach by focusing on the design and prototyping of a transmissive RIS, contrasting with existing research predominantly centered on reflective R

Bohan Tang, Zexi Liu, Keyue Jiang, Siheng Chen

Hypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to generate informative node features for various downstream tasks like node classification. However, the message passing module in existing HNNs typically requires a computationally int

Xia Li, Fabian Zhang, Muheng Li, Damien Weber

Intra-fraction motion in radiotherapy is commonly modeled using deformable image registration (DIR). However, existing methods often struggle to balance speed and accuracy, limiting their applicability in clinical scenarios. This study introduces a novel approach that harnesses Neural Graphics Primitives (NGP) to optimize the displacement vector field (DVF).

Davide Salvi, Temesgen Semu Balcha, Paolo Bestagini, Stefano Tubaro

Recent advancements in synthetic speech generation have led to the creation of forged audio data that are almost indistinguishable from real speech. This phenomenon poses a new challenge for the multimedia forensics community, as the misuse of synthetic media can potentially cause adverse consequences. Several methods have been proposed in the literature to

Paloma Cantero-Arjona, Alfonso Sánchez-Macián

The rapid development of technologies and artificial intelligence makes deepfakes an increasingly sophisticated and challenging-to-identify technique. To ensure the accuracy of information and control misinformation and mass manipulation, it is of paramount importance to discover and develop artificial intelligence models that enable the generic detection of

Sascha Xu, Joscha Cüppers, Jilles Vreeken

SHAP is a popular approach to explain black-box models by revealing the importance of individual features. As it ignores feature interactions, SHAP explanations can be confusing up to misleading. NSHAP, on the other hand, reports the additive importance for all subsets of features. While this does include all interacting sets of features, it also leads to an

Marco Artusa

We use the theory of Condensed Mathematics to build a condensed cohomology theory for the Weil group of a $p$-adic field. The cohomology groups are proved to be locally compact abelian groups of finite ranks in some special cases. This allows us to enlarge the local Tate Duality to a more general category of non-necessarily discrete coefficients, where it ta

Peng Lv, Shaoyuan Li, Xiang Yin

In this paper, we investigate the problem of optimal supervisory control for the discrete event systems under energy constraints. We consider that the execution of events consumes energy and the energy can be replenished at specific reload states. When the energy level drops below zero, the system will be crashed. To capture the above scenario, we introduce

Vladimir Fanaskov

We propose a convenient matrix-free neural architecture for the multigrid method. The architecture is simple enough to be implemented in less than fifty lines of code, yet it encompasses a large number of distinct multigrid solvers. We argue that a fixed neural network without dense layers can not realize an efficient iterative method. Because of that, stand

Vladimir Fanaskov

Classical Krylov subspace projection methods for the solution of linear problem $Ax = b$ output an approximate solution $\widetilde{x}\simeq x$. Recently, it has been recognized that projection methods can be understood from a statistical perspective. These probabilistic projection methods return a distribution $p(\widetilde{x})$ in place of a point estimate

Filip Sabo, Martin Claverie, Michele Meroni, Arthur Hrast Essenfelder

This paper investigated the potential of a multivariate Transformer model to forecast the temporal trajectory of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for short (1 month) and long horizon (more than 1 month) periods at the regional level in Europe and North Africa. The input data covers the period from 2002 to 2022 and includes

Andrew Mummery, Francesco Mori, Steven Balbus

Accretion flows are fundamentally turbulent systems, yet are classically modelled with viscous theories only valid on length scales significantly greater than the typical size of turbulent eddies in the flow. We demonstrate that, while this will be a reasonable bulk description of the flow at large radii, this must break down as the flow approaches absorbing

Svein Høgemo

The graph invariant EPT-sum has cropped up in several unrelated fields in later years: As an objective function for hierarchical clustering, as a more fine-grained version of the classical edge ranking problem, and, specifically when the input is a vertex-weighted tree, as a measure of average/expected search length in a partially ordered set. The EPT-sum of

Rubén Saborido, Javier Ferrer, Francisco Chicano

Reducing the cognitive complexity of a piece of code to a given threshold is not trivial. Recently, we modeled software cognitive complexity reduction as an optimization problem and we proposed an approach to assist developers on this task. This approach enumerates sequences of code extraction refactoring operations until a stopping criterion is met. As a re

Mohammed Aljahdali, Ahmed M. Abdelmoniem, Marco Canini, Samuel Horváth

In Federated Learning (FL), forgetting, or the loss of knowledge across rounds, hampers algorithm convergence, particularly in the presence of severe data heterogeneity among clients. This study explores the nuances of this issue, emphasizing the critical role of forgetting in FL's inefficient learning within heterogeneous data contexts. Knowledge loss occur

Alvin Inderka, Florian Huber, Volker Steinhage

While a variety of methods offer good yield prediction on histogrammed remote sensing data, vision Transformers are only sparsely represented in the literature. The Convolution vision Transformer (CvT) is being tested to evaluate vision Transformers that are currently achieving state-of-the-art results in many other vision tasks. CvT combines some of the adv

Giulio Binosi

Holomorphic Cliffordian functions of order $k$ are functions in the kernel of the differential operator $\overline{\partial}\Delta^k$. When $\overline{\partial}\Delta^k$ is applied to functions defined on the paravector space of some Clifford Algebra $\mathbb{R}_m$ with an odd number of imaginary units, the Fueter-Sce construction establish a critical index

Antonino Ficarra

In the present paper, we study the Dao numbers $\mathfrak{d}_1(I),\mathfrak{d}_2(I)$ and $\mathfrak{d}_3(I)$ of an ideal $I$ of a Noetherian local ring $(R,\mathfrak{m},K)$ or a standard graded Noetherian $K$-algebra. They are defined as the smallest $\ell\ge0$ such that $I\mathfrak{m}^k$ is $\mathfrak{m}$-full, full, weakly $\mathfrak{m}$-full, respectively

Jiayu Peng, Jiajun Zeng, Manlin Lai, Ruobing Huang

Objective: Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS) while identifying the median nerve (MN) and diagnosing CTS depends heavily on the expertise of examiners. To alleviate this problem, we aimed to develop a one-stop automated CTS diagnosis system (OSA-CTSD) and evaluate its effectiveness as a computer-aided

Massimo Persic, Yoel Rephaeli, Riccardo Rando

Spiral galaxies M31 and M33 are Fermi/LAT-detected gamma-ray sources. We model the broadband non-thermal (NT) emission of the central region of M31 (R < 5.5 kpc) and of the disk of M33 (R ~ 9 kpc). For either galaxy, we self-consistently model the broadband SED of the diffuse NT emission based on published radio and gamma-ray data. All relevant radiative pro

Ales Wodecki, Jakub Marecek

We consider the problem of learning local quantum Hamiltonians given copies of their Gibbs state at a known inverse temperature, following Haah et al. [2108.04842] and Bakshi et al. [arXiv:2310.02243]. Our main technical contribution is a new flat polynomial approximation of the exponential function based on the Chebyshev expansion, which enables the formula

Federico Zocco, Denis Sleath, Shahin Rahimifard

The dependence on finite reserves of raw materials and the production of waste are two unsolved problems of the traditional linear economy. Healthcare, as a major sector of any nation, is currently facing them. Hence, in this paper, we report theoretical and practical advances of robotic reprocessing of small medical devices. Specifically, on the theory, we

Lucas S. Flores, The Anh Han

Studying social dilemmas prompts the question of how cooperation can emerge in situations where individuals are expected to act selfishly. Here, in the framework of the one-shot Public Goods Game (PGG), we introduce the concept that individuals can potentially adjust their behaviour based on the cooperative commitments made by other players in the group prio

J. A. Montanez-Barrera, Dennis Willsch, Kristel Michielsen

Solving combinatorial optimization problems (COPs) is a promising application of quantum computation, with the Quantum Approximate Optimization Algorithm (QAOA) being one of the most studied quantum algorithms for solving them. However, multiple factors make the parameter search of the QAOA a hard optimization problem. In this work, we study transfer learnin

Marcel Grimmer, Raymond N. J. Veldhuis, Christoph Busch

The recognition performance of biometric systems strongly depends on the quality of the compared biometric samples. Motivated by the goal of establishing a common understanding of face image quality and enabling system interoperability, the committee draft of ISO/IEC 29794-5 introduces expression neutrality as one of many component quality elements affecting

Hengguan Huang, Songtao Wang, Hongfu Liu, Hao Wang

Traditional applications of natural language processing (NLP) in healthcare have predominantly focused on patient-centered services, enhancing patient interactions and care delivery, such as through medical dialogue systems. However, the potential of NLP to benefit inexperienced doctors, particularly in areas such as communicative medical coaching, remains l

Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth

We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning. We find that offline actor-critic algorithms can outperform strong, supervised, behavioral cloning baselines for multi-task training on a large dataset containing both sub-optimal and expert beha

Bibiána Lajčinová, Patrik Valábek, Michal Spišiak

This paper introduces an approach for building a Named Entity Recognition (NER) model built upon a Bidirectional Encoder Representations from Transformers (BERT) architecture, specifically utilizing the SlovakBERT model. This NER model extracts address parts from data acquired from speech-to-text transcriptions. Due to scarcity of real data, a synthetic data

Ajay Chandra, Guilherme de Lima Feltes, Hendrik Weber

We show a priori bounds for solutions to $(\partial_t - \Delta) u = \sigma (u) \xi$ in finite volume in the framework of Hairer's Regularity Structures [Invent Math 198:269--504, 2014]. We assume $\sigma \in C_b^2 (\mathbb{R})$ and that $\xi$ is of negative H\"older regularity of order $- 1 - \kappa$ where $\kappa < \bar{\kappa}$ for an explicit $\bar{\kappa

Aida Calviño, Almudena Moreno-Ribera, Silvia Pineda

In this chapter we illustrate the use of some Machine Learning techniques in the context of omics data. More precisely, we review and evaluate the use of Random Forest and Penalized Multinomial Logistic Regression for integrative analysis of genomics and immunomics in pancreatic cancer. Furthermore, we propose the use of association rules with predictive pur

Aseem Rajan Kshirsagar, Sven Reichardt

BiFeO$_3$ is a technologically relevant multiferroic perovskite featuring ferroelectricity and antiferromagnetism. Its lattice, magnetic, and ferroelectric degrees of freedoms are coupled to its optically active excitations and thus hold the potential to be reversible probed and controlled by light. In this work, we combine ab initio density functional and m

Jialuo He, Wei Chen, Xiaojin Zhang

Federated Learning (FL) has emerged as a promising approach for privacy-preserving model training across decentralized devices. However, it faces challenges such as statistical heterogeneity and susceptibility to adversarial attacks, which can impact model robustness and fairness. Personalized FL attempts to provide some relief by customizing models for indi

Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno

This paper presents a range inertial localization algorithm for a 3D prior map. The proposed algorithm tightly couples scan-to-scan and scan-to-map point cloud registration factors along with IMU factors on a sliding window factor graph. The tight coupling of the scan-to-scan and scan-to-map registration factors enables a smooth fusion of sensor ego-motion e

Damien Calaque, Victor Roca i Lucio

This is a survey on Drinfeld associators and their generalizations, where we focus on operadic aspects.

F Adersh, M Muhsin, M Sahoo

We investigate the self-propulsion of an inertial active particle confined in a two-dimensional harmonic trap. The particle is suspended in a non-Newtonian or viscoelastic suspension with a friction kernel that decays exponentially with a time constant characterizing the memory timescale or transient elasticity of the medium. By solving the associated non-Ma

K. Yu. Golenitskii

Surface polaritons in an anisotropic media posses a strong dependence of the wavevector on the propagation direction, which is called the isofrequency contour. This can lead to the fact that polariton propagation is possible only in a limited range of angles in the boundary plane. Notable examples are Dyakonov surface waves at the boundary of two dielectrics

José Alberto Benítez-Andrades, María Teresa García-Ordás, Mayra Russo, Ahmad Sakor

Social networks are vital for information sharing, especially in the health sector for discussing diseases and treatments. These platforms, however, often feature posts as brief texts, posing challenges for Artificial Intelligence (AI) in understanding context. We introduce a novel hybrid approach combining community-maintained knowledge graphs (like Wikidat

Yaron Hay, Roy Friedman

Executing smart contracts is a compute and storage-intensive task, which currently dominates modern blockchain's performance. Given that computers are becoming increasingly multicore, concurrency is an attractive approach to improve programs' execution runtime. A unique challenge of blockchains is that all replicas (miners or validators) must execute all sma

Thomas Bläsius, Sarel Cohen, Philipp Fischbeck, Tobias Friedrich

Random graph models are widely used to understand network properties and graph algorithms. Key to such analyses are the different parameters of each model, which affect various network features, such as its size, clustering, or degree distribution. The exact effect of the parameters on these features is not well understood, mainly because we lack tools to th

Antonio Bueno, Rafael López

If $\xi$ is a Killing vector field of the hyperbolic space $\h^3$ whose flow are parabolic isometries, a surface $\Sigma\subset\h^3$ is a $\xi$-translator if its mean curvature $H$ satisfies $H=\langle N,\xi\rangle$, where $N$ is the unit normal of $\Sigma$. We classify all $\xi$-translators invariant by a one-parameter group of rotations of $\h^3$, exhibiti

Zhongqun Zhang, Jifei Song, Eduardo Pérez-Pellitero, Yiren Zhou

Modeling hand-object interactions is a fundamentally challenging task in 3D computer vision. Despite remarkable progress that has been achieved in this field, existing methods still fail to synthesize the hand-object interaction photo-realistically, suffering from degraded rendering quality caused by the heavy mutual occlusions between the hand and the objec

A2 Collaboration, F. Afzal, K. Spieker, P. Hurck

We report the measurement of the helicity asymmetry $E$ for the $p\pi^0$ and $n\pi^+$ final states using, for the first time, an elliptically polarized photon beam in combination with a longitudinally polarized target at the Crystal Ball experiment at MAMI. The results agree very well with data that were taken with a circularly polarized photon beam, showing

Mircea Cimpoeas, Alexandra Teodor

We prove new formulas for $\operatorname{DD}_k(n)$, the number of plane partition diamonds of length $k$ of $n$, and, also, for its polynomial part.

Elsa Rizk, Kun Yuan, Ali H. Sayed

In this work, we examine a network of agents operating asynchronously, aiming to discover an ideal global model that suits individual local datasets. Our assumption is that each agent independently chooses when to participate throughout the algorithm and the specific subset of its neighbourhood with which it will cooperate at any given moment. When an agent

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The ratio of branching fractions between $B^{0} \rightarrow J/\psi \pi^{0}$ and $B^{+} \rightarrow J/\psi K^{*+}$ decays is measured with proton-proton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9 fb$^{-1}$. The measured value is $\frac{\mathcal{B}_{B^{0} \rightarrow J/\psi \pi^{0}}}{\mathcal{B}_{B^{+} \righ

Antonio Bueno, Rafael López

A surface $\Sigma$ in the hyperbolic space $\h^3$ is called a horo-shrinker if its mean curvature $H$ satisfies $H=\langle N,\partial_z\rangle$, where $(x,y,z)$ are the coordinates of $\h^3$ in the upper half-space model and $N$ is the unit normal of $\Sigma$. In this paper we study horo-shrinkers invariant by one-parameter groups of isometries of $\h^3$ dep

Jamie Hayes, Ilia Shumailov, Itay Yona

Mixture of Experts (MoE) has become a key ingredient for scaling large foundation models while keeping inference costs steady. We show that expert routing strategies that have cross-batch dependencies are vulnerable to attacks. Malicious queries can be sent to a model and can affect a model's output on other benign queries if they are grouped in the same bat

Alexandre Rio, Merwan Barlier, Igor Colin, Albert Thomas

We address private deep offline reinforcement learning (RL), where the goal is to train a policy on standard control tasks that is differentially private (DP) with respect to individual trajectories in the dataset. To achieve this, we introduce PriMORL, a model-based RL algorithm with formal differential privacy guarantees. PriMORL first learns an ensemble o