April 2024 arXiv papers — page 104

Meas Len

In this paper, we establish the well-posedness in energy space for the quintic energy critical wave inside a cylindrical convex domain $\Omega\subset\mathbb{R}^3$ with smooth boundary $\partial\Omega\neq\emptyset$. The key tools to prove local well-posedness are the dispersive estimates obtained in \cite{L,L1,L3} and the Strichartz estimates in \cite{L2}. We

Yang Lin, Xinyu Ma, Xu Chu, Yujie Jin

Parameter-efficient fine-tuning methods, represented by LoRA, play an essential role in adapting large-scale pre-trained models to downstream tasks. However, fine-tuning LoRA-series models also faces the risk of overfitting on the training dataset, and yet there's still a lack of theoretical guidance and practical mechanism to control overfitting on LoRA-bas

Daniele Barducci, Alessandro Dondarini

We study the phenomenology of $d=6$ dipole portal operators connecting active and sterile neutrinos at a futuristic muon collider. These operators can be the dominant portal between the Standard Model and the New Physics sector in scenarios in which the active-sterile mixing is suppressed. We identify two production modes for sterile neutrinos: one proceedin

Natalia Gorobey, Alexander Lukyanenko, A. V. Goltsev

A generalized canonical form of action of dynamic theories with higher derivatives is proposed, which does not require the introduction of additional dynamic variables. This form is the initial point for the construction of quantum theory, in which the state of motion of the system is described by the wave functional on its trajectories in the configuration

Djamal Belazzougui, Gregory Kucherov, Stefan Walzer

We consider the problem of reconstructing the symmetric difference between similar sets from their representations (sketches) of size linear in the number of differences. Exact solutions to this problem are based on error-correcting coding techniques and suffer from a large decoding time. Existing probabilistic solutions based on Invertible Bloom Lookup Tabl

Pengfei Liu, Jun Tao, Zhixiang Ren

The task of chemical reaction predictions (CRPs) plays a pivotal role in advancing drug discovery and material science. However, its effectiveness is constrained by the vast and uncertain chemical reaction space and challenges in capturing reaction selectivity, particularly due to existing methods' limitations in exploiting the data's inherent knowledge. To

Valentinian Lungu, Ioannis Kontoyiannis

The problem of binary hypothesis testing between two probability measures is considered. New sharp bounds are derived for the best achievable error probability of such tests based on independent and identically distributed observations. Specifically, the asymmetric version of the problem is examined, where different requirements are placed on the two error p

Viny Saajan Victor, Andre Schmeißer, Heike Leitte, Simone Gramsch

In the last ten years, the average annual growth rate of nonwoven production was 4%. In 2020 and 2021, nonwoven production has increased even further due to the huge demand for nonwoven products needed for protective clothing such as FFP2 masks to combat the COVID19 pandemic. Optimizing the production process is still a challenge due to its high nonlinearity

Kihyun Kim, Taegyu Kim, Soonsik Kwon

We consider the Calogero--Moser derivative nonlinear Schr\"odinger equation (CM-DNLS), which is an $L^{2}$-critical nonlinear Schr\"odinger equation with explicit solitons, self-duality, and pseudo-conformal symmetry. More importantly, this equation is known to be completely integrable in the Hardy space $L_{+}^{2}$ and the solutions in this class are referr

Jiaqi Zhou, Samuel Poncé, Jean-Christophe Charlier

Emerging two-dimensional (2D) materials bring unprecedented opportunities for electronic applications. The design of high-performance devices requires an accurate prediction of carrier mobility in 2D materials, which can be obtained using state-of-the-art $ab~initio$ calculations. However, various factors impact the computational accuracy, leading to contrad

Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek

Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences. Various approaches to suppress model reliance on harmful features have been proposed that can be applied post-hoc without additional training. Whereas those methods can be applied with efficienc

Harry J. D. Miller

The space of quantum states can be endowed with a metric structure using the second order derivatives of the relative entropy, giving rise to the so-called Kubo-Mori-Bogoliubov inner product. We explore its geometric properties on the submanifold of faithful, zero-displacement Gaussian states parameterised by their covariance matrices, deriving expressions f

Shangqing Liu, Wei Ma, Jian Wang, Xiaofei Xie

Source code vulnerability detection aims to identify inherent vulnerabilities to safeguard software systems from potential attacks. Many prior studies overlook diverse vulnerability characteristics, simplifying the problem into a binary (0-1) classification task for example determining whether it is vulnerable or not. This poses a challenge for a single deep

Mikolaj Czerkawski, Fraser Stewart, Christos Ilioudis, Craig Michie

The monitoring of diver health during emergency events is crucial to ensuring the safety of personnel. A non-invasive system continuously providing a measure of the respiration rate of individual divers is exceedingly beneficial in this context. The paper reports on the application of short-range radar to record the respiration rate of divers within hyperbar

Xu Xian, Wang Taotao

In this paper, we are concerned with the sign-changing solutions of variational inequality problems. In order to give the existence results of the sign-changing solutions for variational inequality problems, we first construct a suitable penalty problem related to the variational inequality, and prove the existence of a sign-changing solution for this penalt

Lucía Montesinos, Halfdan Hauch Jensen, Anders Sundnes Løvlie

This paper presents a Research through Design exploration of the potential for using tangible interactions to enable active music experiences - musicking - for non-musicians. We present the Tubularium prototype, which aims to help non-musicians play music without requiring any initial skill. We present the initial design of the prototype and the features imp

Dušan Popov

We examine some properties of the non-normalized (or canonical) density matrix in the coherent states representation, by two equivalent ways. On the one hand by its definition, and on the other hand as a solution to Bloch's canonical equation. It is concluded that, since in many cases Bloch's differential equation is difficult to solve, in applications it is

Lisang Zhou, Meng Wang, Ning Zhou

Distributed training can facilitate the processing of large medical image datasets, and improve the accuracy and efficiency of disease diagnosis while protecting patient privacy, which is crucial for achieving efficient medical image analysis and accelerating medical research progress. This paper presents an innovative approach to medical image classificatio

Yukun Cheng, Wei Chen, Bo Ai

The concept of semantic communication provides a novel approach for applications in scenarios with limited communication resources. In this paper, we propose an end-to-end (E2E) semantic molecular communication system, aiming to enhance the efficiency of molecular communication systems by reducing the transmitted information. Specifically, following the join

Yves Aubry, Fabien Herbaut, Ali Issa

Comparisons of arithmetic and geometric monodromy groups coupled with the Chebotarev density theorem enable to obtain families of trinomials defined over finite fields of even characteristic with high differential uniformity when the base field is large enough.

Zepeng Ding, Wenhao Huang, Jiaqing Liang, Deqing Yang

Relation triple extraction, which outputs a set of triples from long sentences, plays a vital role in knowledge acquisition. Large language models can accurately extract triples from simple sentences through few-shot learning or fine-tuning when given appropriate instructions. However, they often miss out when extracting from complex sentences. In this paper

Gordian Edenhofer, João Alves, Catherine Zucker, Laura Posch

Recent advancements in 3D dust mapping have transformed our understanding of the Milky Way's local interstellar medium, enabling us to explore its structure in three spatial dimensions for the first time. In this Letter, we use the most recent 3D dust map by Edenhofer et al. to study the well-known Chameleon, Musca, and Coalsack cloud complexes, located abou

Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun

While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which can lead to poor-quality renderings, and reliance on a good initialization. In this work, we rethink the set of 3D Gaussians as a random sample drawn from an underlying probab

Mikolaj Czerkawski, Christos Ilioudis, Carmine Clemente, Craig Michie

The paper centres on an assessment of the modelling approaches for the processing of signals in CW and FMCW radar-based systems for the detection of vital signs. It is shown that the use of the widely adopted phase extraction method, which relies on the approximation of the target as a single point scatterer, has limitations in respect of the simultaneous es

Julien Verges

We consider the standard first passage percolation model on $\mathbb Z^d$ with bounded and bounded away from zero weights. We show that the rescaled passage time $\widetilde{\mathbf T}_{n,X}$ restricted to a compact set $X$ satisfies a large deviation principle (LDP) at speed $n^d$ in a space of geodesic metrics, i.e. an estimation of the form $\mathbb P\lef

Gastón Vergara-Hermosilla

In this work we address some questions concerning the Cauchy problem for a generalized nonlinear heat equations considering as functional framework the variable Lebesgue spaces $L^{p(\cdot)}(\mathbb{R}^n)$. More precisely, by mixing some structural properties of these spaces with decay estimates of the fractional heat kernel, we were able to prove two well-p

Umutcan Serles, Elias Kärle, Richard Hunkel, Dieter Fensel

Tourism is one of the most critical sectors of the global economy. Due to its heterogeneous and fragmented nature, it provides one of the most suitable use cases for knowledge graphs. In this poster, we introduce the German Tourism Knowledge Graph that integrates tourism-related data from 16 federal states of Germany and various other sources to provide a cu

Song Xia, Yi Yu, Xudong Jiang, Henghui Ding

Randomized Smoothing (RS) has been proven a promising method for endowing an arbitrary image classifier with certified robustness. However, the substantial uncertainty inherent in the high-dimensional isotropic Gaussian noise imposes the curse of dimensionality on RS. Specifically, the upper bound of ${\ell_2}$ certified robustness radius provided by RS exhi

Xian Xu, Baoxia Qin

In this paper, some existence results for sign-changing critical points of locally Lipschitz functionals in real Banach space are obtained by the method combining the invariant sets of descending ow method with a quantitative deformation. First we assume the locally Lipschitz functionals to be outwardly directed on the the boundary of some closed convex sets

Masahito Toba, Seiichi Uchida, Hideaki Hayashi

In pseudo-labeling (PL), which is a type of semi-supervised learning, pseudo-labels are assigned based on the confidence scores provided by the classifier; therefore, accurate confidence is important for successful PL. In this study, we propose a PL algorithm based on an energy-based model (EBM), which is referred to as the energy-based PL (EBPL). In EBPL, a

Shalutha Rajapakshe, Atharva Dastenavar, Michael Hagenow, Jean-Marc Odobez

We introduce GeoSACS, a geometric framework for shared autonomy (SA). In variable environments, SA methods can be used to combine robotic capabilities with real-time human input in a way that offloads the physical task from the human. To remain intuitive, it can be helpful to simplify requirements for human input (i.e., reduce the dimensionality), which crea

Cunyuan Jiang, Zihan Zheng, Yangrui Chen, Matteo Baggioli

In recent years, active Brownian particles have emerged as a prominent model system for comprehending the behaviors of active matter, wherein particles demonstrate self-propelled motion by harnessing energy from the surrounding environment. A fundamental objective of studying active matter is to elucidate the physical mechanisms underlying its collective beh

Andreas Hohl, Konstantin Jakob

We define categories of Stokes filtered and Stokes graded $G$-local systems for reductive groups $G$ and use the formalism of Tannakian categories to show that they are equivalent to the category of $G$-connections. We then use the interpretation of moduli spaces of Stokes filtered $G$-local systems as braid varieties to prove physical rigidity of two well-k

Sherzod M. Mirakhmedov

Let n points be taken at random on a circle of unit circumference and clockwise ordered. Uniform spacings are defined as the clockwise arc-lengths between the successive points from this sample. We are interested in the asymptotic behavior of the sum of functions of the m-tuples of successive spacings under the assumption that m can grow together with n. Asy

Gianmarco Brocchi, Andreas Rosén

We prove a Kato square root estimate with anisotropically degenerate matrix coefficients. We do so by doing the harmonic analysis using an auxiliary Riemannian metric adapted to the operator. We also derive $L^2$-solvability estimates for boundary value problems for divergence form elliptic equations with matrix degenerate coefficients. Main tools are chain

Xian Xu, Baoxia Qin

In this paper, we extend the method of invariant sets of descending flow that proposed by Sun Jingxian for smooth functionals to the locally Lipschitz functionals. By this way, we obtain the existence results for the positive, negative and sign-changing critical points of the locally Lipschitz functionals, and apply these theoretical results to the study of

Jumbly Grindrod

This paper argues that large language models have a valuable scientific role to play in serving as scientific models of public languages. Linguistic study should not only be concerned with the cognitive processes behind linguistic competence, but also with language understood as an external, social entity. Once this is recognized, the value of large language

Junjie Huang, Guohao Cai, Jieming Zhu, Zhenhua Dong

Click-through rate (CTR) prediction plays an indispensable role in online platforms. Numerous models have been proposed to capture users' shifting preferences by leveraging user behavior sequences. However, these historical sequences often suffer from severe homogeneity and scarcity compared to the extensive item pool. Relying solely on such sequences for us

Jumbly Grindrod

The transformer architecture, introduced by Vaswani et al. (2017), is at the heart of the remarkable recent progress in the development of language models, including widely-used chatbots such as Chat-GPT and Claude. In this paper, I argue that we can extract from the way the transformer architecture works a theory of the relationship between context and mean

Jumbly Grindrod

Do large language models like Chat-GPT or LLaMa meaningfully use the words they produce? Or are they merely clever prediction machines, simulating language use by producing statistically plausible text? There have already been some initial attempts to answer this question by showing that these models meet the criteria for entering meaningful states according

Étienne Fouvry, Peter Koymans

Given a binary quadratic form $F \in \mathbb{Z}[X, Y]$, we define its value set $F(\mathbb{Z}^2)$ to be $\{F(x, y) : (x, y) \in \mathbb{Z}^2\}$. If $F$ and $G$ are two binary quadratic forms with integer coefficients, we give necessary and sufficient conditions on $F$ and $G$ for $F(\mathbb{Z}^2) = G(\mathbb{Z}^2)$.

Chi Zhang, Janis Sprenger, Zhongjun Ni, Christian Berger

Understanding and predicting pedestrian crossing behavior is essential for enhancing automated driving and improving driving safety. Predicting gap selection behavior and the use of zebra crossing enables driving systems to proactively respond and prevent potential conflicts. This task is particularly challenging at unsignalized crossings due to the ambiguou

Thomas Richard

Green's inequality shows that a compact Riemannian manifold with scalar curvature at least $n(n-1)$ has injectivity radius at most $\pi$, and that equality is achieved only for the radius 1 sphere. In this work we show how extra topological assumptions can lead to stronger upper bounds. The topologies we consider are $\mathbb{S}^2\times\mathbb{T}^{n-k-2}\tim

Abhijit Chakraborty, Yuichi Ikeda

The increasing use of cryptoassets for international remittances has proven to be faster and more cost-effective, particularly for migrants without access to traditional banking. However, the inherent volatility of cryptoasset prices, independent of blockchain-based remittance mechanisms, introduces potential risks during periods of high volatility. This stu

Laurent Miclo, Nhat-Thang Le

Consider the global optimisation of a function $U$ defined on a finite set $V$ endowed with an irreducible and reversible Markov generator.By integration, we extend $U$ to the set $\mathcal{P}(V)$ of probability distributions on $V$ and we penalise it with a time-dependent generalised entropy functional.Endowing $\mathcal{P}(V)$ with a Maas' Wasserstein-type

Yuxuan Jiang, Chen Feng, Fan Zhang, David Bull

Knowledge distillation (KD) has emerged as a promising technique in deep learning, typically employed to enhance a compact student network through learning from their high-performance but more complex teacher variant. When applied in the context of image super-resolution, most KD approaches are modified versions of methods developed for other computer vision

Gabriele Rosi, Claudia Cuttano, Niccolò Cavagnero, Giuseppe Averta

Recent advancements in image segmentation have focused on enhancing the efficiency of the models to meet the demands of real-time applications, especially on edge devices. However, existing research has primarily concentrated on single-task settings, especially on semantic segmentation, leading to redundant efforts and specialized architectures for different

Qiye Liu, Wenjie Su, Yue Gu, Xi Zhang

Interlayer magnetic interactions play a pivotal role in determining the magnetic arrangement within van der Waals (vdW) magnets, and the remarkable tunability of these interactions through applied pressure further enhances their significance. Here, we investigate NiI2 flakes, a representative vdW magnet, under hydrostatic pressures up to 11 GPa. We reveal a

Pierre Collet, Sylvie Méléard, Jaime San MARTIN

In this article we study the long time behavior of linear functionals of branching diffusion processesas well as the time reversal of the spinal process by means of spectral properties of the Feynman-Kacsemigroup. We generalize for this non Markovian semigroup the theory of quasi-stationary distribution(q.s.d.) and Q-process. The most amazing result is the i

Tai-shan Lou, Guang-sheng Guan, Zhe-peng Yue, Yu Wang

To solve the Unmanned Aerial Vehicle (UAV) path planning problem, a meta-heuristic optimization algorithm called competitive game optimizer (CGO) is proposed. In the CGO model, three phases of exploration and exploitation, and candidate replacement, are established, corresponding to the player's search for supplies and combat, and the movement toward a safe

Julian D. Schiller, Matthias A. Müller

We propose a moving horizon estimation scheme for estimating the states and time-varying parameters of nonlinear systems. We consider the case where observability of the parameters depends on the excitation of the system and may be absent during operation, with the parameter dynamics fulfilling a weak incremental bounded-energy bounded-state property to ensu

Sergio Burdisso, Dairazalia Sánchez-Cortés, Esaú Villatoro-Tello, Petr Motlicek

Evaluating the reliability of news sources is a routine task for journalists and organizations committed to acquiring and disseminating accurate information. Recent research has shown that predicting sources' reliability represents an important first-prior step in addressing additional challenges such as fake news detection and fact-checking. In this paper,

Andrew Mummery, Samuel G. D. Turner

We use numerical stochastic-viscous hydrodynamic simulations and new analytical results from thin disc theory to probe the turbulent variability of accretion flows, as observed at high energies. We show that the act of observing accretion discs in the Wien tail exponentially enhances small-scale temperature variability in the flow, which in a real disc will

Shao-Peng Tang, Yong-Jia Huang, Ming-Zhe Han, Yi-Zhong Fan

Very recently, it has been shown that there is an upper bound on the squared sound speed of nuclear matter from the transport, which reads $c_{\rm s}^2 \leq 0.781$. In this work, we demonstrate that this upper bound is corroborated by the reconstructed equation of state (EOS; modeled with a nonparametric method) for ultradense matter. The reconstruction inte

Arnaud Pannatier, Evann Courdier, François Fleuret

Autoregressive models, such as the GPT family, use a fixed order, usually left-to-right, to generate sequences. However, this is not a necessity. In this paper, we challenge this assumption and show that by simply adding a positional encoding for the output, this order can be modulated on-the-fly per-sample which offers key advantageous properties. It allows

Biplab Chatterjee, Ratnesh Kumar Mishra

In this article, We introduce a condition that is both necessary and sufficient for a linear code to achieve minimality when analyzed over the rings $\mathbb{Z}_{n}$.The fundamental inquiry in minimal linear codes is the existence of a $[m,k]$ minimal linear code where $k$ is less than or equal to $m$. W. Lu et al. ( see \cite{nine}) showed that there exists

Christina Jörg, Marius Jürgensen, Sebabrata Mukherjee, Mikael C. Rechtsman

Solitons are self-consistent solutions of the nonlinear Schr\"odinger equation that maintain their shape during propagation. Here we show, using a pump-probe technique, that soliton formation can be used to optically induce and control a linear topological end state in the bulk of a Su-Schrieffer-Heeger lattice, using evanescently-coupled waveguide arrays. S

Qile Liu, Zhihao Zhou, Jiyuan Wang, Zhen Liang

The integration of human emotions into multimedia applications shows great potential for enriching user experiences and enhancing engagement across various digital platforms. Unlike traditional methods such as questionnaires, facial expressions, and voice analysis, brain signals offer a more direct and objective understanding of emotional states. However, in

Tian Shuai Shang, Hui Hui Xie, Jian Li, Haozhao Liang

A deep neural network (DNN) has been developed to generate the distributions of nuclear charge density, utilizing the training data from the relativistic density functional theory and incorporating available experimental charge radii of 1014 nuclei into the loss function. The DNN achieved a root-mean-square (rms) deviation of 0.0193 fm for charge radii on it

Yuting Fu, Jochen Seemann, Caspar Hanselaar, Tim Beurskens

Automated Driving (AD) systems have the potential to increase safety, comfort and energy efficiency. Recently, major automotive companies have started testing and validating AD systems (ADS) on public roads. Nevertheless, the commercial deployment and wide adoption of ADS have been moderate, partially due to system functional insufficiencies (FI) that underm

Fabian Isensee, Tassilo Wald, Constantin Ulrich, Michael Baumgartner

The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results. Despite this, the pursuit of novel architectures, and the respective claims of superior performance over the U-Net baseline, continued. In this study, we demonstrate that ma

Žiga Babnik, Fadi Boutros, Naser Damer, Peter Peer

Face Image Quality Assessment (FIQA) techniques have seen steady improvements over recent years, but their performance still deteriorates if the input face samples are not properly aligned. This alignment sensitivity comes from the fact that most FIQA techniques are trained or designed using a specific face alignment procedure. If the alignment technique cha

Johannes Schneider

Generative AI (GenAI) marked a shift from AI being able to recognize to AI being able to generate solutions for a wide variety of tasks. As the generated solutions and applications become increasingly more complex and multi-faceted, novel needs, objectives, and possibilities have emerged for explainability (XAI). In this work, we elaborate on why XAI has gai

lunwu Liu, Shi Liu

This study adopts the fixed effects panel model and provincial panel data on anticorruption and the innovation efficiency of high-level technology and new technology enterprises in China from 2005 to 2014, to estimate the effects of the anticorruption movement on the innovation efficiency of enterprises at different corruption levels. The empirical results s

Patrick Cattiaux

In this paper we intend to present a unified treatment of a variety of singular interacting particle systems and their McKean-Vlasov limits. This unified approach is based on the use of the relative entropy on the path space in the spirit of our previous works together with C. L{\'e}onard. We show how it can be used to derive existence and uniqueness for som

Choon-Lin Ho

We consider Fokker-Planck equations that interpolate a pair of supersymmetrically related Fokker-Planck equations with constant coefficients. Based on the interesting property of shape-invariance, various one-parameter interpolations of the solutions of the supersymmetric pair of Fokker-Planck systems can be directly constructed.

A. V. Lipatov, G. I. Lykasov, M. A. Malyshev

We update the phenomenological parameters of the Transverse Momentum Dependent (TMD, or unintegrated) gluon density in a proton proposed in our previous studies. This analysis is based on the analytical expression for starting gluon distribution which provides a self-consistent simultaneous description of HERA data on proton structure function $F_2(x,Q^2)$,

Michael Heusener, Leila Ben Abdelghani

We study the local structure of the representation variety of a knot group into SL(n,C) at certain diagonal representations. In particular we determine the tangent cone of the representation variety at these diagonal representations, and show that the latter can be deformed into irreducible representations. Furthermore, we use Luna's slice theorem to analyze

Shuntaro Aoki, Lucas Pinol, Fumiya Sano, Masahide Yamaguchi

Using the recently developed cosmological bootstrap method, we compute the exact analytical solution for the seed integral appearing in cosmological correlators with double massive scalar exchanges. The result is explicit, valid in any kinematic configuration, and free from spurious divergences. It is applicable to any number of fields' species with any mass

Kewal Anand, Ranjeev Misra, J. S. Yadav, Pankaj Jain

We report simultaneous detection of twin kHz and $\sim 40$ Hz quasi-periodic oscillations (QPOs) in the time-resolved analysis of the AstroSat/LAXPC observation of the neutron star low mass X-ray binary, 4U 1728-34. The frequencies of the multiple sets of triplets are correlated with each other and are consistent with their identification as the orbital, per

Louis-Alexandre Couston

The meltwater mixing line (MML) model provides a theoretical prediction of near-ice water mass properties that is useful to compare with observations. If oceanographic measurements reported in a temperature-salinity diagram overlap with the MML prediction, then it is usually concluded that the local dynamics are dominated by the turbulent mixing of an ambien

Renate Krause, Stefan Reimann

What has an Artificial Neural Network (ANN) learned after being successfully trained to solve a task - the set of training items or the relations between them? This question is difficult to answer for modern applied ANNs because of their enormous size and complexity. Therefore, here we consider a low-dimensional network and a simple task, i.e., the network h

Tong Qiao, Jianlei Yang, Yingjie Qi, Ao Zhou

Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for various applications is non-trivial. Previous training methodologies suffer from inferior adaptability and lack a unified training optimization solution. To address the problem, this

Alexandre Llopez, Frédéric Leroy, Calvin Tagne-Kaegom, Boris Croes

Epitaxial growth of WTe$_2$ offers significant advantages, including the production of high-qualityfilms, possible long range in-plane ordering and precise control over layer thicknesses. However,the mean island size of WTe$_2$ grown by molecular beam epitaxy (MBE) in litterature is only a fewtens of nanometers, which is not suitable for an implementation of

Luca Beber, Edoardo Lamon, Giacomo Moretti, Daniele Fontanelli

This paper introduces a new method for estimating the penetration of the end effector and the parameters of a soft body using a collaborative robotic arm. This is possible using the dimensionality reduction method that simplifies the Hunt-Crossley model. The parameters can be found without a force sensor thanks to the information of the robotic arm controlle

D Grenier, C Menini, P Sénéchaud, F Vandebrouck

We are convinced of the usefulness of sketches and diagrams during mathematical work but the observation is made in our practices that they are not spontaneously used by students. In order to study the understanding and use of sketches by mathematics students, we designed and then proposed a test at different university levels. The test consists of five exer

Javier Perera-Lago, Víctor Toscano-Durán, Eduardo Paluzo-Hidalgo, Sara Narteni

Machine learning algorithms are fundamental components of novel data-informed Artificial Intelligence architecture. In this domain, the imperative role of representative datasets is a cornerstone in shaping the trajectory of artificial intelligence (AI) development. Representative datasets are needed to train machine learning components properly. Proper trai

Chi Wang, Junming Huang, Rong Zhang, Qi Wang

Automatic 3D facial texture generation has gained significant interest recently. Existing approaches may not support the traditional physically based rendering pipeline or rely on 3D data captured by Light Stage. Our key contribution is a progressive latent space refinement approach that can bootstrap from 3D Morphable Models (3DMMs)-based texture maps gener

Jean Michel de Souza Sant Ana, Arliones Hoeller, Hirley Alves, Richard Demo Souza

This work presents the LR-FHSS-Sim, a free and open-source discrete-event simulator for LR-FHSS networks. We highlight the importance of network modeling for IoT coverage, especially when it is needed to capture dynamic network behaviors. Written in Python, we present the LR-FHSS-Sim main structure, procedures, and extensions. We discuss the importance of a

Wei-Han Tan, Niu Su, Hua-Xing Chen

We apply the QCD sum rule method to study the light single-gluon hybrid states with various (exotic) quantum numbers. We construct twenty-four single-gluon hybrid currents, and use eighteen of them to calculate the masses of forty-four single-gluon hybrid states with the quark-gluon contents $\bar q q g$ ($q=u/d$) and $\bar s s g$. We concentrate on the hybr

C. -J. Yang, K. M. Spohr, D. Doria

We investigate theoretically the possibility of achieving the stimulated amplification of $\gamma$-rays. Herein, our approach circumvents the so-called ``graser dilemma" through a non-linear, multi-photon mechanism. Our work foresees the combination of a high-intensity $\gamma-$flash generated by the interaction of a high-intensity laser pulse with plasma an

Talaya Farasat, Joachim Posegga

Software vulnerabilities are a fundamental reason for the prevalence of cyber attacks and their identification is a crucial yet challenging problem in cyber security. In this paper, we apply and compare different machine learning algorithms for source code vulnerability detection specifically for Python programming language. Our experimental evaluation demon

Yuchen Li, Ziqi Wang, Qingquan Zhang, Bo Yuan

This survey comprehensively reviews the multi-dimensionality of game scenario diversity, spotlighting the innovative use of procedural content generation and other fields as cornerstones for enriching player experiences through diverse game scenarios. By traversing a wide array of disciplines, from affective modeling and multi-agent systems to psychological

Sayan Biswas, Mathieu Even, Anne-Marie Kermarrec, Laurent Massoulie

Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used to infer training data. Conventional defenses such as differential privacy and secure aggregation fall short in effectively safeguarding user privacy in DL, either sacrificing mod

Ziyang Chen, Dongrui Yu, Ganbin Lu, Yufei Zhang

The large-scale clock network is the key ingredient to obtain high precision in many scenarios, from fundamental research to cutting-edge applications. The advantage of the time synchronization among microwave clocks is their cost, size, and accessibility. Here, we demonstrate a femtosecond-level time synchronization of microwave clocks through a commercial

L. Besombes, J. Kobak, W. Pacuski

We report on the optical spectroscopy of the spin of two Co atoms in a quantum dot and interacting with a single exciton. The spectrum of quantum dots containing two Co atoms are exchange interaction and by the strain at the location of the magnetic atoms. A wide range of spectrum can be obtained depending on the relative coupling of each atom to the confine

Bin Wang, Fei Deng, Peifan Jiang, Shuang Wang

Low-dose computed tomography (LDCT) has become the technology of choice for diagnostic medical imaging, given its lower radiation dose compared to standard CT, despite increasing image noise and potentially affecting diagnostic accuracy. To address this, advanced deep learning-based LDCT denoising algorithms have been developed, primarily using Convolutional

Haojun Sun, Chen Tang, Zhi Wang, Yuan Meng

Diffusion models have emerged as preeminent contenders in the realm of generative models. Distinguished by their distinctive sequential generative processes, characterized by hundreds or even thousands of timesteps, diffusion models progressively reconstruct images from pure Gaussian noise, with each timestep necessitating full inference of the entire model.

Xiaoyi Zeng, Kaiwen Song, Leyuan Yang, Bailin Deng

Neural implicit fields have established a new paradigm for scene representation, with subsequent work achieving high-quality real-time rendering. However, reconstructing 3D scenes from oblique aerial photography presents unique challenges, such as varying spatial scale distributions and a constrained range of tilt angles, often resulting in high memory consu

Avinash Anand, Raj Jaiswal, Mohit Gupta, Siddhesh S Bangar

Large ground-truth datasets and recent advances in deep learning techniques have been useful for layout detection. However, because of the restricted layout diversity of these datasets, training on them requires a sizable number of annotated instances, which is both expensive and time-consuming. As a result, differences between the source and target domains

Siyan Zhao, Daniel Israel, Guy Van den Broeck, Aditya Grover

During inference for transformer-based large language models (LLM), prefilling is the computation of the key-value (KV) cache for input tokens in the prompt prior to autoregressive generation. For longer input prompt lengths, prefilling will incur a significant overhead on decoding time. In this work, we highlight the following pitfall of prefilling: for bat

Zhiqiang Liao, Sheng Dai, Eunji Lim, Timo Kuosmanen

Convex regression is a method for estimating the convex function from a data set. This method has played an important role in operations research, economics, machine learning, and many other areas. However, it has been empirically observed that convex regression produces inconsistent estimates of convex functions and extremely large subgradients near the bou

Johannes Selisko, Maximilian Amsler, Christopher Wever, Yukio Kawashima

Quantum computers (QC) could harbor the potential to significantly advance materials simulations, particularly at the atomistic scale involving strongly correlated fermionic systems where an accurate description of quantum many-body effects scales unfavorably with size. While a full-scale treatment of condensed matter systems with currently available noisy q

Bingyang Wu, Shengyu Liu, Yinmin Zhong, Peng Sun

The context window of large language models (LLMs) is rapidly increasing, leading to a huge variance in resource usage between different requests as well as between different phases of the same request. Restricted by static parallelism strategies, existing LLM serving systems cannot efficiently utilize the underlying resources to serve variable-length reques

Jesper Møller

A general setting for nested subdivisions of a bounded real set into intervals defining the digits $X_1,X_2,...$ of a random variable $X$ with a probability density function $f$ is considered. Under the weak condition that $f$ is almost everywhere lower semi-continuous, a coupling between $X$ and a non-negative integer-valued random variable $N$ is establish

Qi Zhang, Lei Xie, Weihua Xu, Hongye Su

Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement uncertainty. A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliabi

Gustaf Arrhenius, Klas Markström

In many circumstances there is a trade off between the number of voters and the time they can be given before having to make a decision since both aspects are costly. An example is the hiring of a committee with a fixed salary budget: more people but a shorter time for each to develop their competence about the issue at hand or less people with a longer time

Bärbel S. Koribalski, Ildar Khabibullin, Klaus Dolag, Eugene Churazov

We present the discovery of large radio shells around a massive pair of interacting galaxies and extended diffuse X-ray emission within the shells. The radio data were obtained with the Australian Square Kilometer Array Pathfinder (ASKAP) in two frequency bands centred at 944 MHz and 1.4 GHz, respectively, while the X-ray data are from the XMM-Newton observa

Tidiane Camaret Ndir, André Biedenkapp, Noor Awad

In this work, we address the challenge of zero-shot generalization (ZSG) in Reinforcement Learning (RL), where agents must adapt to entirely novel environments without additional training. We argue that understanding and utilizing contextual cues, such as the gravity level of the environment, is critical for robust generalization, and we propose to integrate

Teng Shi, Zihua Si, Jun Xu, Xiao Zhang

Nowadays, many platforms provide users with both search and recommendation services as important tools for accessing information. The phenomenon has led to a correlation between user search and recommendation behaviors, providing an opportunity to model user interests in a fine-grained way. Existing approaches either model user search and recommendation beha