May 2024 arXiv papers — page 119

Bo Yuan, Beom Hyun Kim, Qiang Chen, Daniel Dobrowolski

Molecular orbital (MO) systems with clusters of heavy transition metal (TM) ions are one of the most important classes of model materials for studying the interplay between local physics and effects of itinerancy. Despite a large number of candidates identified in the family of 4d TM materials, an understanding of their physics from competing \textit{microsc

Ran Ding, Jingshu Li, Meng Lu, Zhengyun You

With the improvement of muon acceleration technology, it has received great interest to exploit high-energy muon beams for collision or target experiments. We investigate possible charged Lepton Flavor Violation (cLFV) processes mediated by an extra massive neutral gauge boson Zprime in electron muon interactions, either at a proposed electron muon collider

M. Atzori Corona, M. Cadeddu, N. Cargioli, F. Dordei

The weak mixing angle is a fundamental parameter of the electroweak theory of the standard model whose measurement in the low-energy regime is still not precisely determined. Different probes are sensitive to its value, among which atomic parity violation, coherent elastic neutrino-nucleus scattering and parity-violating electron scattering on different nucl

Anna Rapberger, Markus Ulbricht, Francesca Toni

The relation between (a fragment of) assumption-based argumentation (ABA) and logic programs (LPs) under stable model semantics is well-studied. However, for obtaining this relation, the ABA framework needs to be restricted to being flat, i.e., a fragment where the (defeasible) assumptions can never be entailed, only assumed to be true or false. Here, we rem

Wei-Yu Chen

This study explores the integration of the ChatGPT API with GPT-4 model and Microsoft Copilot Studio on the Microsoft Teams platform to develop an intelligent tutoring system. Designed to provide instant support to students, the system dynamically adjusts educational content in response to the learners' progress and feedback. Utilizing advancements in natura

Nira Dyn, Nir Sharon

This work presents several new results concerning the analysis of the convergence of binary, univariate, and linear subdivision schemes, all related to the {\it contractivity factor} of a convergent scheme. First, we prove that a convergent scheme cannot have a contractivity factor lower than half. Since the lower this factor is, the faster is the convergenc

Pavan Uttej Ravva, Pinar Kullu, Mohammad Fahim Abrar, Roghayeh Leila Barmaki

Over the last decade, there has been significant progress in the field of interactive virtual rehabilitation. Physical therapy (PT) stands as a highly effective approach for enhancing physical impairments. However, patient motivation and progress tracking in rehabilitation outcomes remain a challenge. This work addresses the gap through a machine learning-ba

Robert Worden

The paper describes a program which computes the best possible Bayesian model of 3D space from vision (in bees) or echo location (in bats), at Marrs [1982] Level 2. The model exploits the strong Bayesian prior probability that most other things do not move, as the animal moves. 3D locations of things are computed from successive sightings or echoes, computin

Pitpimon Choorod, Tobias J. Bauer, Andreas Aßmuth

For journalists reporting from a totalitarian regime, whistleblowers and resistance fighters, the anonymous use of cloud services on the Internet can be vital for survival. The Tor network provides a free and widely used anonymization service for everyone. However, there are different approaches to distinguishing Tor from non-Tor encrypted network traffic, m

Eren Gulmez, Bayram Tekin

We simulated spin-spin interactions of $N$-bodies in linearized General Relativity (GR) and linearized Massive Gravity of the Fierz-Pauli type (mGR). It was noted earlier that there is a discrete difference between the spin-spin interaction potential in GR and mGR for a $2$-body system, akin to the van Dam-Veltman-Zakharov discontinuity in the static Newton'

Paolo Sentinelli

We present a formula relating the set of left descents of an element of a Coxeter group with the sets of left descents of its projections on maximal quotients indexed by simple right descents. This formula is an instance of a general result involving quotients and descents.

Igor Shevchenko

Insufficient reference data is ubiquitous in data-driven computational fluid dynamics, as it is usually too expensive to compute or impossible to observe over long enough times needed for data-driven methods. The lack of data can significantly compromise the fidelity of results computed with data-driven methods or render them inapplicable. To challenge this

Markus R. Bujotzek, Ünal Akünal, Stefan Denner, Peter Neher

Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles, lea

Ezra Rozier, Jörn Behrens

In convection-dominated flows, robustness of the spatial discretisation is a key property. While Interior Penalty Galerkin (IPG) methods already proved efficient in the situation of large mesh Peclet numbers, Arbitrary Lagrangian-Eulerian (ALE) methods are able to reduce the convection-dominance by moving the mesh. In this paper, we introduce and analyse a v

J. E. G. Silva, Ö. Yeşiltaş, J. Furtado, A. A. Araújo Filho

In this work, we explore the strain and curvature effects on the electronic properties of a curved graphene structure, called the graphene wormhole. The electron dynamics is described by a massless Dirac fermion containing position--dependent Fermi velocity. In addition, the strain produces a pseudo--magnetic vector potential to the geometric coupling. For a

Sougata Bose, Rasmus Ibsen-Jensen, Patrick Totzke

We study the computational complexity of solving stochastic games with mean-payoff objectives. Instead of identifying special classes in which simple strategies are sufficient to play $\epsilon$-optimally, or form $\epsilon$-Nash equilibria, we consider general partial-information multiplayer games and ask what can be achieved with (and against) finite-memor

Roland Schwan, Nicolaj Schmid, Etienne Chassaing, Karim Samaha

We present the identification of the non-linear dynamics of a novel hovercraft design, employing end-to-end deep learning techniques. Our experimental setup consists of a hovercraft propelled by racing drone propellers mounted on a lightweight foam base, allowing it to float and be controlled freely on an air hockey table. We learn parametrized physics-inspi

Taha Emre, Arunava Chakravarty, Dmitrii Lachinov, Antoine Rivail

Contrastive pretraining provides robust representations by ensuring their invariance to different image transformations while simultaneously preventing representational collapse. Equivariant contrastive learning, on the other hand, provides representations sensitive to specific image transformations while remaining invariant to others. By introducing equivar

Haiyu Wu, Sicong Tian, Jacob Gutierrez, Aman Bhatta

A fundamental tenet of pattern recognition is that overlap between training and testing sets causes an optimistic accuracy estimate. Deep CNNs for face recognition are trained for N-way classification of the identities in the training set. Accuracy is commonly estimated as average 10-fold classification accuracy on image pairs from test sets such as LFW, CAL

Asaf Shapira

A linear equation $E$ is said to be sparse if there is $c>0$ so that every subset of $[n]$ of size $n^{1-c}$ contains a solution of $E$ in distinct integers. The problem of characterizing the sparse equations, first raised by Ruzsa in the 90's, is one of the most important open problems in additive combinatorics. We say that $E$ in $k$ variables is abundant

Shaohua Pi, Razieh Ganjee, Lingyun Wang, Riley K. Arbuckle

This study introduces a groundbreaking optical coherence tomography (OCT) imaging system dedicated for high-throughput screening applications using ex vivo tissue culture. Leveraging OCT's non-invasive, high-resolution capabilities, the system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging

Guram Bezhanishvili, Luca Carai

The Blok-Esakia Theorem establishes that the lattice of superintuitionistic logics is isomorphic to the lattice of extensions of Grzegorczyk's logic. We prove that the Blok-Esakia isomorphism $\sigma$ does not extend to the fragments of the corresponding predicate logics of already one fixed variable. In other words, we prove that $\sigma$ is no longer an is

Ismael Medina, Bernhard Schmitzer

Domain decomposition has been shown to be a computationally efficient distributed method for solving large scale entropic optimal transport problems. However, a naive implementation of the algorithm can freeze in the limit of very fine partition cells (i.e. it asymptotically becomes stationary and does not find the global minimizer), since information can on

A. Mustill

In seven billion years, the Sun will be dead. As stars like the Sun pass from their present state to that of a dead white dwarf star, they undergo two phases of extremely high luminosity and radius -- the red giant branch and the asymptotic giant branch -- during which they will lose half or more of their mass. These changes to the star have a significant im

Tobias J. Bauer, Andreas Aßmuth

Modern software engineering trends towards Cloud-native software development by international teams of developers. Cloud-based version management services, such as GitHub, are used for the source code and other artifacts created during the development process. However, using such a service usually means that every developer has access to all data stored on t

Luigi Ambrosio, Federico Vitillaro, Dario Trevisan

We investigate the random bipartite optimal matching problem on a flat torus in two-dimensions, considering general strictly convex power costs of the distance. We extend the successful ansatz first introduced by Caracciolo et al. for the quadratic case, involving a linear Poisson equation, to a non-linear equation of $q$-Poisson type, allowing for a more co

Marco B. Caminati

Classifying formal languages according to the expressiveness of grammars able to generate them is a fundamental problem in computational linguistics and, therefore, in the theory of computation. Furthermore, such kind of analysis can give insight into the classification of abstract algebraic structure such as groups, for example through the correspondence gi

Xiaoliang Luo, Guangzhi Sun, Bradley C. Love

Recently, large language models (LLMs) have outperformed human experts in predicting the results of neuroscience experiments (Luo et al., 2024). What is the basis for this performance? One possibility is that statistical patterns in that specific scientific literature, as opposed to emergent reasoning abilities arising from broader training, underlie LLMs' p

Yuning Yang, Han Yu, Chuan Sun, Tianrun Gao

Federated Learning (FL) is a collaborative machine learning paradigm for training models on local sensitive data with privacy protection. Pre-trained transformer-based models have emerged as useful foundation models (FMs) to be fine-tuned for a wide range of downstream tasks. However, large-scale pre-trained models make it challenging for traditional FL due

Eric Rivals, Pengfei Wang

A correlation is a binary vector that encodes all possible positions of overlaps of two words, where an overlap for an ordered pair of words (u,v) occurs if a suffix of word u matches a prefix of word v. As multiple pairs can have the same correlation, it is relevant to count how many pairs of words share the same correlation depending on the alphabet size a

A. J. Clara Figueiredo, T. C. Jude, S. Alef, P. L. Cole

The coherent reaction, $\gamma d \rightarrow \pi^0\eta d$ was studied with the BGOOD experiment at ELSA from threshold to a centre-of-mass energy of 3200\,MeV. A full kinematic reconstruction was made, with final state deuterons identified in the forward spectrometer and $\pi^0$ and $\eta$ decays in the central BGO Rugby Ball. The strength of the differentia

Jack Liell-Cock, Sam Staton

Imprecise probability is concerned with uncertainty about which probability distributions to use. It has applications in robust statistics and machine learning. We look at programming language models for imprecise probability. Our desiderata are that we would like our model to support all kinds of composition, categorical and monoidal; in other words, guided

Gianluca Fontanesi, Francesc Wilhelmi, Lorenzo Galati-Giordano

This paper proposes a novel mechanism to enforce contention-free channel access in the unlicensed spectrum, as opposed to the traditional contention-based approach. To achieve this objective, we build on the Wi-Fi~7 multi-link operation (MLO) and define the means whereby independent channel access attempts are performed in all the addressable links to ensure

Emily Dolson, Santiago Rodriguez-Papa, Matthew Andres Moreno

In silico evolution instantiates the processes of heredity, variation, and differential reproductive success (the three "ingredients" for evolution by natural selection) within digital populations of computational agents. Consequently, these populations undergo evolution, and can be used as virtual model systems for studying evolutionary dynamics. This exper

Dimitrios Ampelogiannis, Benjamin Doyon

In the context of $C^*$ dynamical systems, we consider a locally compact group $G$ acting by $^*$-automorphisms on a C$^*$ algebra $\mathfrak{U}$ of observables, and assume a state of $\mathfrak{U}$ that satisfies the clustering property with respect to a net of group elements of $G$. That is, the two-point connected correlation function vanishes in the limi

Stefan Ivkovic, Bogdan D. Djordjevic, Giorgia Bellomonte

In this paper, we provide a representation of a certain class of C*-valued positive sesquilinear and linear maps on non-unital quasi *-algebras. Also, we illustrate our results on the concrete examples of non-unital Banach quasi *-algebras, such as the standard Hilbert module over a commutative C*-algebra, Schatten p-ideals, and noncommutative L2 spaces indu

Fulin Chen, Xin Huang, Fei Kong, Shaobin Tan

In this paper, we associate the quantum toroidal algebra $\mathcal{E}_N$ of type $\mathfrak{gl}_N$ with quantum vertex algebra through equivariant $\phi$-coordinated quasi modules. More precisely, for every $\ell\in \mathbb{C}$, by deforming the universal affine vertex algebra of $\mathfrak{sl}_\infty$, we construct an $\hbar$-adic quantum $\Z$-vertex algebr

E. G. S. Luna, M. G. Ryskin, V. A. Khoze

We perform the analysis of elastic scattering $pp$ and $\bar{p}p$ data at low momentum transfer $|t|<0.1$ GeV$^2$ within large collider energy interval $\sqrt{s}=50$ GeV - 13 TeV in order to evaluate quantitatively the possible Odderon contribution. We use the two-channel eikonal model, which naturally accounts for the screening of the Odderon amplitude by $

E. Osinga, R. J. van Weeren, G. Brunetti, R. Adam

Merging galaxy clusters often host spectacular diffuse radio synchrotron sources. These sources can be explained by a non-thermal pool of relativistic electrons accelerated by shocks and turbulence in the intracluster medium. The origin of the pool and details of the cosmic ray transport and acceleration mechanisms in clusters are still open questions. Due t

James Davies, Robert Hickingbotham, Freddie Illingworth, Rose McCarty

We disprove the conjecture of Georgakopoulos and Papasoglu that a length space (or graph) with no $K$-fat $H$ minor is quasi-isometric to a graph with no $H$ minor. Our counterexample is furthermore not quasi-isometric to a graph with no 2-fat $H$ minor or a length space with no $H$ minor. On the other hand, we show that the following weakening holds: any gr

Chiara Guidolin, Emmanuelle Rio, Roberto Cerbino, Fabio Giavazzi

Pressure-driven coarsening triggers bubble rearrangements in liquid foams. Our experiments show that changing the continuous phase rheology can alter these internal bubble dynamics without influencing the coarsening kinetics. Through bubble tracking, we find that increasing the matrix yield stress permits bubble growth without stress relaxation via neighbor-

Camilla Brizzi, Gero Friesecke, Tobias Ried

We study barycenters of $N$ probability measures on $\mathbb{R}^d$ with respect to the $p$-Wasserstein metric ($1<p<\infty$). We prove that -- $p$-Wasserstein barycenters of absolutely continuous measures are unique, and again absolutely continuous -- $p$-Wasserstein barycenters admit a multi-marginal formulation -- the optimal multi-marginal plan is unique

Marguerite Epstein-Martin, Hiromichi Tagawa, Zoltan Haiman, Rosalba Perna

The brightest steady sources of radiation in the universe, active galactic nuclei (AGN), are powered by gas accretion onto a central supermassive black hole (SMBH). The large sizes and accretion rates implicated in AGN accretion disks are expected to lead to gravitational instability and fragmentation, effectively cutting off mass inflow to the SMBH. Radiati

Xian-Tao Huang, Shuai Liu

In this paper, we study 3-dimensional complete non-compact Riemannian manifolds with asymptotically nonnegative Ricci curvature and a uniformly positive scalar curvature lower bound. Our main result is that, if this manifold has $k$ ends and finite first Betti number, then it has at most linear volume growth, and furthermore, if the negative part of Ricci cu

Gianluca Giacchi

Housdorff-Young's inequality establishes the boundedness of the Fourier transform from $L^p$ to $L^q$ spaces for $1\leq p\leq2$ and $q=p'$, where $p'$ denotes the Lebesgue-conjugate exponent of $p$. This paper extends this classical result by characterizing the $L^p-L^q$ boundedness of all metaplectic operators, which play a significant role in harmonic anal

S. Aminpour, Y. Banad, S. Sharif

Quantum machine learning (QML) is a promising field that explores the applications of quantum computing to machine learning tasks. A significant hurdle in the advancement of quantum machine learning lies in the development of efficient and resilient quantum classifiers capable of accurately mapping input data to specific, discrete target outputs. In this pap

Björn Annby-Andersson, Debankur Bhattacharyya, Pharnam Bakhshinezhad, Daniel Holst

In scenarios coined Maxwell's demon, information on microscopic degrees of freedom is used to seemingly violate the second law of thermodynamics. This has been studied in the classical as well as the quantum domain. In this paper, we study an implementation of Maxwell's demon that can operate in both domains. In particular, we investigate information-to-work

Jingwei Song, Keke Yang, Han Chen, Jiayi Liu

This research reports VascularPilot3D, the first 3D fully autonomous endovascular robot navigation system. As an exploration toward autonomous guidewire navigation, VascularPilot3D is developed as a complete navigation system based on intra-operative imaging systems (fluoroscopic X-ray in this study) and typical endovascular robots. VascularPilot3D adopts pr

H. M. Nagesh

A numerical parameter, known as a topological index, is employed to represent the molecular structure of a compound by considering its graph-theoretical properties. In the study of quantitative structure-activity relationships (QSAR) and quantitative structure-property relationships (QSPR), topological indices are used to predict the physicochemical properti

Maria Lucia Fania, Flaminio Flamini

We prove that the modular component $\mathcal M(r)$, constructed in the Main Theorem of a former paper of us (published in Adv. Math on 2024), paramatrizing (isomorphism classes of) Ulrich vector bundles of rank $r$ and given Chern classes, on suitable $3$-fold scrolls $X_e$ over Hirzebruch surfaces $\mathbb{F}_{e\geq 0}$, which arise as tautological embeddi

Devansh Jain, Priyanshu Kumar, Samuel Gehman, Xuhui Zhou

Recent advances in large language models (LLMs) have led to their extensive global deployment, and ensuring their safety calls for comprehensive and multilingual toxicity evaluations. However, existing toxicity benchmarks are overwhelmingly focused on English, posing serious risks to deploying LLMs in other languages. We address this by introducing PolygloTo

Daniel Pahr, Henry Ehlers, Hsiang-Yun Wu, Manuela Waldner

We conducted a study to systematically investigate the communication of complex dynamic processes along a two-dimensional design space, where the axes represent a representation's manifestation (physical or virtual) and operation (manual or automatic). We exemplify the design space on a model embodying cardiovascular pathologies, represented by a mechanism w

Kaushini S. Wickramasinghe, Candice Forrester, Martha R. McCartney, David J. Smith

Indium selenide, In2Se3, has recently attracted growing interest due to its novel properties, including room temperature ferroelectricity, outstanding photoresponsivity, and exotic in-plane ferroelectricity, which open up new regimes for next generation electronics. In2Se3 also provides the important advantage of tuning the electrical properties of ultra-thi

Karl-Henning Rehren

It is shown how string-localized QFT predicts interactions of the Standard Model. Detailed computations are presented for various non-abelian models: Yang-Mills, QCD, Higgs-Kibble. They serve as backup for [11,12] and as "proof of concept" for the treatment of the full Standard Model [4,5].

Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng

Contrastive learning has been effectively utilized to enhance the training of sequential recommendation models by leveraging informative self-supervised signals. Most existing approaches generate augmented views of the same user sequence through random augmentation and subsequently maximize their agreement in the representation space. However, these methods

Gabriele Spada, Sebastiano Pilati, Stefano Giorgini

We investigate the formation of quantum droplets at finite temperature in attractive Bose mixtures subject to a strong transverse harmonic confinement. By means of exact path-integral Monte Carlo methods we determine the equilibrium density of the gas and the liquid as well as the pressure vs. volume dependence along isothermal curves. Results for the equati

M. C. Martí, P. Mulet, D. F. Yáñez, D. Zorío

A set of arbitrarily high-order WENO schemes for reconstructions on nonuniform grids is presented. These non-linear interpolation methods use simple smoothness indicators with a linear cost with respect to the order, making them easy to implement and computationally efficient. The theoretical analysis to verify the accuracy and the essentially non-oscillator

Karl-Henning Rehren, Lucas T. Cardoso, Christian Gass, José M. Gracia-Bondía

Successful applications of a conceptually novel setup of Quantum Field Theory, that accounts for all subtheories of the Standard Model (QED, Electroweak Interaction and Higgs, Yang-Mills and QCD) and beyond (Helicity 2), call for a perspective view in a broader conceptual context. The setting is "autonomous" in the sense of being intrinsically quantum. Its p

Weijie Li, Wei Yang, Yuenan Hou, Li Liu

Despite the remarkable progress in synthetic aperture radar automatic target recognition (SAR ATR), recent efforts have concentrated on detecting and classifying a specific category, e.g., vehicles, ships, airplanes, or buildings. One of the fundamental limitations of the top-performing SAR ATR methods is that the learning paradigm is supervised, task-specif

Yu-Yang Zhang, Geng Li, Bo Wen

Space-based gravitational wave detection is extremely sensitive to disturbances. The Keplerian configuration cannot accurately reflect the variations in spacecraft configuration. Planetary gravitational disturbances are one of the main sources. Numerical simulation is an effective method to investigate the impact of perturbation on spacecraft orbits. This st

Zhi-Cheng Liu, Zhen-hua Zhao

In this paper, we have studied the consequences of some representative neutrino flavor-symmetry models for tri-resonant leptogenesis (which is realized by having three nearly degenerate right-handed neutrinos). To be specific, we have considered a neutrino flavor-symmetry model realizing the TM1 mixing and modular ${\rm A}^{}_4$, ${\rm S}^{}_4$ and ${\rm A}^

Yicheng Li, Haobo Zhang, Qian Lin

The saturation effect refers to the phenomenon that the kernel ridge regression (KRR) fails to achieve the information theoretical lower bound when the smoothness of the underground truth function exceeds certain level. The saturation effect has been widely observed in practices and a saturation lower bound of KRR has been conjectured for decades. In this pa

Vishwesh Sangarya, Richard Bradford, Jung-Eun Kim

In this paper, we propose a predictive quantifier to estimate the retraining cost of a trained model in distribution shifts. The proposed Aggregated Representation Measure (ARM) quantifies the change in the model's representation from the old to new data distribution. It provides, before actually retraining the model, a single concise index of resources - ep

Maaz Khan, Syed Anausha Bin Zakir Khan, Arif Mohd

We study the Kramers-Wannier duality for the transverse-field Ising lattice on a ring. A careful consideration of the ring boundary conditions shows that the duality has to be implemented with a proper treatment of different charge sectors of both the twisted and untwisted Ising and the dual-Ising Hilbert spaces. We construct a superoperator that explicitly

Tolulope Fadina, Thorsten Schmidt

Fairness in decision-making processes is often quantified using probabilistic metrics. However, these metrics may not fully capture the real-world consequences of unfairness. In this article, we adopt a utility-based approach to more accurately measure the real-world impacts of decision-making process. In particular, we show that if the concept of $\varepsil

Chen Chen, Qikai Zou, Yuhang Song, Mingrui Yu

Current orthopedic robotic systems largely focus on navigation, aiding surgeons in positioning a guiding tube but still requiring manual drilling and screw placement. The automation of this task not only demands high precision and safety due to the intricate physical interactions between the surgical tool and bone but also poses significant risks when execut

Stefano Biagi, Marco Bramanti

We consider Kolmogorov-Fokker-Planck operators of the form $$ \mathcal{L}u=\sum_{i,j=1}^{q}a_{ij}(x,t)u_{x_{i}x_{j}}+\sum_{k,j=1}^{N} b_{jk}x_{k}u_{x_{j}}-\partial_{t}u, $$ with $\left( x,t\right) \in\mathbb{R}^{N+1},N\geq q\geq1$. We assume that $a_{ij}\in L^{\infty}\left( \mathbb{R}^{N+1}\right) $, the matrix $\left\{ a_{ij}\right\} $ is symmetric and unif

Wenfeng Shi, Tianlong Fan, Shuqi Xu, Rongmei Yang

Influence maximization aims to identify a set of influential individuals, referred to as influencers, as information sources to maximize the spread of information within networks, constituting a vital combinatorial optimization problem with extensive practical applications and sustained interdisciplinary interest. Diverse approaches have been devised to effi

Pamela Bustamante-Faúndez, Víctor Bucarey L., Martine Labbé, Vladimir Marianov

Anticipating the strategies of potential attackers is crucial for protecting critical infrastructure. We can represent the challenge of the defenders of such infrastructure as a Stackelberg security game. The defender must decide how to allocate limited resources to protect specific targets, aiming to maximize their expected utility (such as minimizing the e

Gary Sarwin, Alessandro Carretta, Victor Staartjes, Matteo Zoli

Localizing oneself during endoscopic procedures can be problematic due to the lack of distinguishable textures and landmarks, as well as difficulties due to the endoscopic device such as a limited field of view and challenging lighting conditions. Expert knowledge shaped by years of experience is required for localization within the human body during endosco

Yu Hamada, Wakutaka Nakano

We analytically calculate the spectrum of stochastic gravitational waves (GWs) emitted by expanding string loops on domain walls in the scenario where domain walls decay by nucleation of string loops. By introducing macroscopic parameters characterizing the nucleation of the loops, the stochastic GW spectrum is derived in a way that is independent of the det

Abhishek Shrestha, Jürgen Großmann

Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples. Further, these adversarial examples are found to be transferable from the source network in which they are crafted to a black-box target network. As the trend of using deep learning on embedded devices grows, it becomes relevant to study the transferability properties of adversar

Maike Clausen, Katherine E. Whitaker, Ivelina Momcheva, Sam E. Cutler

We present a study of the growth of the quiescent galaxy population between 0.5 < z < 3 by tracing the number density and structural evolution of a sample of 4518 old and 583 young quiescent galaxies with log($M_*$/$M_{\odot}$)>10.4, selected from the COSMOS2020 catalog with complementary HST/F160W imaging from the 3D-DASH survey. Among the quiescent populat

Fangwei Hao, Jiesheng Wu, Haotian Lu, Ji Du

The multi-scale receptive field and large kernel attention (LKA) module have been shown to significantly improve performance in the lightweight image super-resolution task. However, existing lightweight super-resolution (SR) methods seldom pay attention to designing efficient building block with multi-scale receptive field for local modeling, and their LKA m

Federica Fieramosca, Vittorio Rampa, Stefano Savazzi, Michele D'Amico

Electromagnetic (EM) body models based on the scalar diffraction theory allow to predict the impact of subject motions on the radio propagation channel without requiring a time-consuming full-wave approach. On the other hand, they are less effective in complex environments characterized by significant multipath effects. Recently, emerging radio sensing appli

Christian Kuehn, Sara-Viola Kuntz

Besides classical feed-forward neural networks such as multilayer perceptrons, also neural ordinary differential equations (neural ODEs) have gained particular interest in recent years. Neural ODEs can be interpreted as an infinite depth limit of feed-forward or residual neural networks. We study the input-output dynamics of finite and infinite depth neural

Yi-Ying Wang, Lei Lei, Shao-Peng Tang, Guan-Wen Yuan

Thanks to the successful performance of the James Webb Space Telescope, our understanding of the epoch of reionization of the Universe has been advanced. The ultraviolet luminosity functions (UV LFs) of galaxies span a wide range of redshift, not only revealing the connection between galaxies and dark matter (DM) halos but also providing the information duri

Makoto Ikoma, Soichiro Suzuki

Recently, Ikoma (2022) considered optimal constants and extremisers for the $2$-dimensional Dirac equation using the spherical harmonics decomposition. Though its argument is valid in any dimensions $d \geq 2$, the case $d \geq 3$ remains open since it leads us to too complicated calculation: determining all eigenvalues and eigenvectors of infinite dimension

Vladislav Kupriyanov, Maxim Kurkov, Alexey Sharapov

We consider a semi-classical approximation to the dynamics of a point particle in a noncommutative space. In this approximation, the noncommutativity of space coordinates is described by a Poisson bracket. For linear Poisson brackets, the corresponding phase space is given by the cotangent bundle of a Lie group, with the Lie group playing the role of a curve

Yabin Liu, Zhong-Nan Wang, Lei Tan, Paul Gary Tucker

Tip leakage flow in turbomachinery inherently generates intense unsteady features, named self-excited unsteadiness, which has not been well understood. A Zonalised LES (ZLES) is employed around a linear cascade, with wall-modelled Large Eddy Simulation (LES) forced to be active in the tip region. The simulation is well validated and demonstrated the advantag

Federica Fieramosca, Vittorio Rampa, Michele D'Amico, Stefano Savazzi

Recently, proposals of human-sensing-based services for cellular and local area networks have brought indoor localization to the attention of several research groups. In response to these stimuli, various Device-Free Localization (DFL) techniques, also known as Passive Localization methods, have emerged by exploiting ambient signals to locate and track indiv

Grégoire Magagnin, Jordan Bouaziz, Martine Le Berre, Sara Gonzalez

Over the last fifteen years, ferroelectric and antiferroelectric ultra thin films based on fluorite-structured materials have drawn significant attention for a wide variety of applications requiring high integration density. Antiferroelectric $ZrO_2$, in particular, holds significant promise for nanosupercapacitors, owing to its potential for high energy sto

Atitaya Phoemsuk, Vahid Abolghasemi

Coronary Artery Disease (CAD) diagnostic to be a major global cause of death, necessitating innovative solutions. Addressing the critical importance of early CAD detection and its impact on the mortality rate, we propose the potential of one-dimensional convolutional neural networks (1D-CNN) to enhance detection accuracy and reduce network complexity. This s

Christian Sorgatz, Christian Lüders, Michael Rademacher

This work reports on a measurement study to estimate the attenuation of 450 MHz LTE networks. The LTE band 72 is currently deployed in Germany, in particular for smart grid applications. Due to this use-case, we assume that a significant amount of future devices will be deployed stationary and indoor which motivated our campaign. We designed a custom measure

Xiaodan Xing, Fadong Shi, Jiahao Huang, Yinzhe Wu

Generative Artificial Intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music. Creating these advanced generative models requires significant resources, particularly large and high-quality datasets. To minimise training expenses, many algorithm developers use data created

Weberson S. Arcanjo, Alan S. Pereira, Diogo C. dos Santos, Roger W. C. Silva

We consider the constrained-degree percolation (CDP) model on the hypercubic lattice. This is a continuous-time percolation model defined by a sequence $(U_e)_{e\in\mathcal{E}^d}$ of i.i.d. uniform random variables and a positive integer $k$, referred to as the constraint. The model evolves as follows: each edge $e$ attempts to open at a random time $U_e$, i

Zhiwen Yang, Jiehua Zhang, Liang Li, Chenggang Yan

Image-guided depth completion aims at generating a dense depth map from sparse LiDAR data and RGB image. Recent methods have shown promising performance by reformulating it as a classification problem with two sub-tasks: depth discretization and probability prediction. They divide the depth range into several discrete depth values as depth categories, servin

Ruizhe Chen, Yichen Li, Zikai Xiao, Zuozhu Liu

Existing debiasing methods inevitably make unreasonable or undesired predictions as they are designated and evaluated to achieve parity across different social groups but leave aside individual facts, resulting in modified existing knowledge. In this paper, we first establish a new bias mitigation benchmark BiasKE leveraging existing and additional construct

Jordan M. Adams, Daniel Heligman, Ryan O'Dell, Christine Y. Wang

We show that direct laser writing in aqueous silver nitrate with a $\lambda$ = 1030 nm femtosecond laser results in deposition of a mixture of silver oxide and silver, in contrast to the pure silver deposition previously reported with 780 nm femtosecond direct laser writing. However, adding photoinitiator prevents silver oxide formation in a concentration-de

Nicolas Drapier, Aladine Chetouani, Aurélien Chateigner

The prediction of ship trajectories is a growing field of study in artificial intelligence. Traditional methods rely on the use of LSTM, GRU networks, and even Transformer architectures for the prediction of spatio-temporal series. This study proposes a viable alternative for predicting these trajectories using only GNSS positions. It considers this spatio-t

Zongxia Liang, Qi Ye

This paper diverges from previous literature by considering the utility maximization problem in the context of investors having the freedom to actively acquire additional information to mitigate estimation risk. We derive closed-form value functions using CARA and CRRA utility functions and establish a criterion for valuing extra information through certaint

Cezar-Mihail Alexandru, Christian Konrad

We initiate the study of the Interval Selection problem in the (streaming) sliding window model of computation. In this problem, an algorithm receives a potentially infinite stream of intervals on the line, and the objective is to maintain at every moment an approximation to a largest possible subset of disjoint intervals among the $L$ most recent intervals,

Tim Evink

Caraiani and Newton have proven that if $F$ is an imaginary quadratic number field such that $X_0(15)$ has rank $0$ over $F$, then every elliptic curve over $F$ is modular. This paper is concerned with the quadratic fields $F=\mathbb{Q}(\sqrt{-p})$ for a prime number $p$. We give explicit conditions on $p$ under which the rank is $0$, and prove that these co

Tom Laclavère

The Q \& U Bolometric Interferometer for Cosmology (QUBIC) is a new kind of cosmological instrument that uses interferometry to observe the sky. The unique synthesized beam of QUBIC has an important frequency d\'ependance that we use to increase spectral resolution and perform new component separation methods, allowing us to mitigate foreground contamination

Santiago Fernández, J. Alfonso Bailón-Martínez, Juan E. Galeote-Cazorla, F. Javier López-Martínez

We introduce and characterize the operational diversity order (ODO) in fading channels, as a proxy to the classical notion of diversity order at any arbitrary operational signal-to-noise ratio (SNR). Thanks to this definition, relevant insights are brought up in a number of cases: (i) We quantify that in dominant line-of-sight scenarios an increased diversit

Maximilian Schmidt, Andrea Bartezzaghi, Ngoc Thang Vu

Although language models (LMs) have boosted the performance of Question Answering, they still need plenty of data. Data annotation, in contrast, is a time-consuming process. This especially applies to Question Answering, where possibly large documents have to be parsed and annotated with questions and their corresponding answers. Furthermore, Question Answer

Alexandre Piché, Aristides Milios, Dzmitry Bahdanau, Chris Pal

In order to be deployed safely, Large Language Models (LLMs) must be capable of dynamically adapting their behavior based on their level of knowledge and uncertainty associated with specific topics. This adaptive behavior, which we refer to as self-restraint, is non-trivial to teach since it depends on the internal knowledge of an LLM. By default, LLMs are t

Farnaz Khun Jush, Steffen Vogler, Tuan Truong, Matthias Lenga

While content-based image retrieval (CBIR) has been extensively studied in natural image retrieval, its application to medical images presents ongoing challenges, primarily due to the 3D nature of medical images. Recent studies have shown the potential use of pre-trained vision embeddings for CBIR in the context of radiology image retrieval. However, a bench

Yuedong Yuan, Linda-Sophie Schneider, Andreas Maier

Recent advances in computed tomography (CT) imaging, especially with dual-robot systems, have introduced new challenges for scan trajectory optimization. This paper presents a novel approach using Gated Recurrent Units (GRUs) to optimize CT scan trajectories. Our approach exploits the flexibility of robotic CT systems to select projections that enhance image

Abhishek Das, B. Theodore Zhang, Kohta Murase

The detection of high-energy neutrino signals from the nearby Seyfert galaxy NGC 1068 provides us with an opportunity to study nonthermal processes near the center of supermassive black holes. Using the IceCube and latest Fermi-LAT data, we present general multimessenger constraints on the energetics of cosmic rays and the size of neutrino emission regions.