March 2024 arXiv papers — page 141

Suqi Li, Yihan Wang, Bailu Wang, Giorgio Battistelli

Long Time Coherent Integration (LTCI) aims to accumulate target energy through long time integration, which is an effective method for the detection of a weak target. However, for a moving target, defocusing can occur due to range migration (RM) and Doppler frequency migration (DFM). To address this issue, RM and DFM corrections are required in order to achi

Yizhuang Liu

In this work, we investigate a coordinate space structure function ${\cal E}(z^2m^2,\lambda)$ in the 2D $U(N)$ Gross-Neveu model to the next-to-leading order in the large-$N$ expansion. We analytically perform the twist expansion in the Bjorken limit through double Mellin representations. Hard and non-perturbative scaling functions are naturally generated in

Bram Vanherle, Nick Michiels, Frank Van Reeth

Data augmentations are useful in closing the sim-to-real domain gap when training on synthetic data. This is because they widen the training data distribution, thus encouraging the model to generalize better to other domains. Many image augmentation techniques exist, parametrized by different settings, such as strength and probability. This leads to a large

Martin Heida, Benedikt Jahnel, Anh Duc Vu

We exhibit a percolating ergodic and isotropic lattice model in all but at least two dimensions that has zero effective conductivity in all spatial directions and for all non-trivial choices of the connectivity parameter. The model is based on the so-called randomly stretched lattice where we additionally elongate layers containing few open edges.

Haiyun Deng, Jingwen Ji, Feida Jiang, Jiabin Yin

In this paper, we provide an affirmative answer to the {\it conjecture A} for bounded simple rotationally symmetric domains $\Omega\subset \mathbb{R}^n(n\geq 3)$ along $x_n$ axis. Precisely, we use a new simple argument to study the symmetry of positive stable solutions for two kinds of semilinear elliptic equations. To do this, when $f(\cdot,s)$ is convex w

Ruohui Chen, Tuo Lin, Lin Liu, Jinyuan Liu

The Mann-Whitney-Wilcoxon rank sum test (MWWRST) is a widely used method for comparing two treatment groups in randomized control trials, particularly when dealing with highly skewed data. However, when applied to observational study data, the MWWRST often yields invalid results for causal inference. To address this limitation, Wu et al. (2014) introduced an

Alexandre de Sousa, Frederico Girão

We express the $q$-th Gauss-Bonnet-Chern mass of an immersed submanifold of Euclidean space as a linear combination of two terms: the total $(2q)$-th mean curvature and the integral, over the entire manifold, of the inner product between the $(2q+1)$-th mean curvature vector and the position vector of the immersion. As a consequence, we obtain, for each $q$,

Simone Costa

A subset $S$ of a group $(G,+)$ is $t$-weakly sequenceable if there is an ordering $(y_1, \ldots, y_k)$ of its elements such that the partial sums~$s_0, s_1, \ldots, s_k$, given by $s_0 = 0$ and $s_i = \sum_{j=1}^i y_j$ for $1 \leq i \leq k$, satisfy $s_i \neq s_j$ whenever and $1 \leq |i-j|\leq t$. In this paper, we consider the weak sequenceability problem

Jiachen Zhang, J. Christopher Beck

We propose domain-independent dynamic programming (DIDP) and constraint programming (CP) models to exactly solve type-1 and type-2 assembly line balancing problem with sequence-dependent setup times (SUALBP). The goal is to assign tasks to assembly stations and to sequence these tasks within each station, while satisfying precedence relations specified betwe

Junyi Ye, Bhaskar Goswami, Jingyi Gu, Ajim Uddin

This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It explores how 1) ML models, including supervised, unsupervised, semi-su

Yuki Amari, Minoru Eto, Muneto Nitta

We study topological lumps supported by the second homotopy group $\pi_2(S^2) \simeq {\mathbb Z}$ in a gauged $O(3)$ model without any potential term coupled with a (non)dynamical $U(1)$ gauge field. It is known that gauged-lumps are stable with an easy-plane potential term but are unstable to expand if the model has no potential term. In this paper, we find

Matthew Sutcliffe, Aleks Kissinger

The ZX-calculus is an algebraic formalism that allows quantum computations to be simplified via a small number of simple graphical rewrite rules. Recently, it was shown that, when combined with a family of "sum-over-Cliffords" techniques, the ZX-calculus provides a powerful tool for classical simulation of quantum circuits. However, for several important cla

Sukran Karaosmanoglu, Sebastian Cmentowski, Lennart E. Nacke, Frank Steinicke

Many people struggle to exercise regularly, raising the risk of serious health-related issues. Extended reality (XR) exergames address these hurdles by combining physical exercises with enjoyable, immersive gameplay. While a growing body of research explores XR exergames, no previous review has structured this rapidly expanding research landscape. We conduct

Cornelius Wittig, Michael Wagner, Romain Vallon, Thomas Crouzier

Bacillus subtilis biofilms were grown in laminar channel flow at wall shear stress spanning one order of magnitude ($\tau_w = 0.068$ Pa to $\tau_w = 0.67$ Pa). We monitor, non-invasively, the evolution of the three-dimensional distribution of biofilm over seven days using optical coherence tomography (OCT). The obtained biofilms consist of many microcolonies

Pengchong Qiao, Lei Shang, Chang Liu, Baigui Sun

Subject-driven generation has garnered significant interest recently due to its ability to personalize text-to-image generation. Typical works focus on learning the new subject's private attributes. However, an important fact has not been taken seriously that a subject is not an isolated new concept but should be a specialization of a certain category in the

Philipp Schmitt, Matthias Schötz

We prove a noncommutative real Nullstellensatz for 2-step nilpotent Lie algebras that extends the classical, commutative real Nullstellensatz as follows: Instead of the real polynomial algebra $\mathbb R[x_1, \dots, x_d]$ we consider the universal enveloping *-algebra of a 2-step nilpotent real Lie algebra (i.e. the universal enveloping algebra of its comple

Philippe Balbiani, Han Gao, Çiğdem Gencer, Nicola Olivetti

We investigate intuitionistic modal logics with locally interpreted $\square$ and $\lozenge$. The basic logic LIK is stronger than constructive modal logic WK and incomparable with intuitionistic modal logic IK. We propose an axiomatization of LIK and some of its extensions. We propose bi-nested calculi for LIK and these extensions, thus providing both a dec

Andrea Failla, Rémy Cazabet, Giulio Rossetti, Salvatore Citraro

Groups -- such as clusters of points or communities of nodes -- are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of ``events". However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbi

Rui Wen, Shi Yin, Wei-jie Fu

Convergence of three different expansion schemes at finite baryon chemical potentials, including the conventional Taylor expansion, the Pad\'e approximants, and the $T'$ expansion proposed recently in lattice QCD simulations, have been investigated in a low energy effective theory within the fRG approach. It is found that the $T'$ expansion or the Pad\'e app

Tong Zhang, Chen Huang, Yang Deng, Hongru Liang

We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system's objectives. This poses two main challenges for existing dialogue agents: 1) The inability to integrate user-specific characteristics into the strategic planning

Jae-Jun Lee, Sung Whan Yoon

Meta-learning, which pursues an effective initialization model, has emerged as a promising approach to handling unseen tasks. However, a limitation remains to be evident when a meta-learner tries to encompass a wide range of task distribution, e.g., learning across distinctive datasets or domains. Recently, a group of works has attempted to employ multiple m

Alexandru Chirvasitu, Benjamin Passer

We study the local-triviality dimensions of actions on $C^*$-algebras, which are invariants developed for noncommutative Borsuk-Ulam theory. While finiteness of the local-triviality dimensions is known to guarantee freeness of an action, we show that free actions need not have finite weak local-triviality dimension. Moreover, the local-triviality dimensions

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The number of $\psi(3686)$ events collected by the BESIII detector during the 2021 run period is determined to be $(2259.3\pm 11.1)\times 10^6$ by counting inclusive $\psi(3686)$ hadronic events. The uncertainty is systematic and the statistical uncertainty is negligible. Meanwhile, the numbers of $\psi(3686)$ events collected during the 2009 and 2012 run pe

Zhiwei Liu, Boyang Liu, Paul Thompson, Kailai Yang

The internet has brought both benefits and harms to society. A prime example of the latter is misinformation, including conspiracy theories, which flood the web. Recent advances in natural language processing, particularly the emergence of large language models (LLMs), have improved the prospects of accurate misinformation detection. However, most LLM-based

Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai

In this study, we identify the inefficient attention phenomena in Large Vision-Language Models (LVLMs), notably within prominent models like LLaVA-1.5, QwenVL-Chat and Video-LLaVA. We find out that the attention computation over visual tokens is of extreme inefficiency in the deep layers of popular LVLMs, suggesting a need for a sparser approach compared to

Alex Guinard, Agnes Fienga, Anthony Memin, Clement Ganino

Computing the tidal deformations of Mars, we explored various Mars internal structures by examining profiles that include or exclude a basal molten layer within the mantle and a solid inner core. By assessing their compatibility with a diverse set of geophysical observations we show that despite the very short periods of excitation, tidal deformation is very

Fabian Schütze, Leonard Doyle, Jörg Schreiber, Matt Zepf

We put forward a concrete experimental setup allowing to measure light-by-light scattering in the collision of two optical high-intensity laser beams at state-of-the-art high-field facilities operating petawatt class laser systems. Our setup uses the same focusing optics for both laser beams to be collided and employs a dark-field approach for the detection

Johanna Bimmermann, Levin Maier

We compute the Hofer-Zehnder capacity of disk tangent bundles of certain lens spaces with respect to the round metric. Interestingly we find that the Hofer-Zehnder capacity does not see the covering, i.e. the capacity of the disk tangent bundle of the lens space coincides with the capacity of the disk tangent bundle of the 3-sphere covering it. In particular

Y. Kashiwagi, K. Abe, C. Bronner, Y. Hayato

Among multi-messenger observations of the next galactic core-collapse supernova, Super-Kamiokande (SK) plays a critical role in detecting the emitted supernova neutrinos, determining the direction to the supernova (SN), and notifying the astronomical community of these observations in advance of the optical signal. On 2022, SK has increased the gadolinium di

Theodore Barfoot, Luis Garcia-Peraza-Herrera, Ben Glocker, Tom Vercauteren

Deep neural networks for medical image segmentation often produce overconfident results misaligned with empirical observations. Such miscalibration, challenges their clinical translation. We propose to use marginal L1 average calibration error (mL1-ACE) as a novel auxiliary loss function to improve pixel-wise calibration without compromising segmentation qua

Gabriele Berton, Alex Stoken, Barbara Caputo, Carlo Masone

Astronaut photography, spanning six decades of human spaceflight, presents a unique Earth observations dataset with immense value for both scientific research and disaster response. Despite its significance, accurately localizing the geographical extent of these images, crucial for effective utilization, poses substantial challenges. Current manual localizat

Sebastian Lehuede

Research and activism have increasingly denounced the problematic environmental record of the infrastructure and value chain underpinning Artificial Intelligence (AI). Water-intensive data centres, polluting mineral extraction and e-waste dumping are incontrovertibly part of AI's footprint. In this article, I turn to areas affected by AI-fuelled environmenta

Anthony Frion, Lucas Drumetz, Guillaume Tochon, Mauro Dalla Mura

In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed. However, the vast majority of those works are limited to deterministic predictions, while the knowledge of uncertainty is critical in fields like meteorology and clim

Yu-Hang Xiao, David Ramírez, Lei Huang, Xiao Peng Li

One-bit sampling has emerged as a promising technique in multiple-input multiple-output (MIMO) radar systems due to its ability to significantly reduce data volume and processing requirements. Nevertheless, current detection methods have not adequately addressed the impact of colored noise, which is frequently encountered in real scenarios. In this paper, we

T. A. Kuchar, G. C. Sloan, D. R. Mizuno, Kathleen E. Kraemer

We present point-source photometry from the Spitzer Space Telescope's final survey of the Small Magellanic Cloud (SMC). We mapped 30 square degrees in two epochs in 2017, with the second extending to early 2018 at 3.6 and 4.5 microns using the Infrared Array Camera. This survey duplicates the footprint from the SAGE-SMC program in 2008. Together, these surve

Yuhang Lai, Siyuan Wang, Shujun Liu, Xuanjing Huang

We introduce ALaRM, the first framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF), which is designed to enhance the alignment of large language models (LLMs) with human preferences. The framework addresses the limitations of current alignment approaches, which often struggle with the inconsistency and sparsity of huma

Ricardo Omar Ramirez Flores, Philipp Sven Lars Schäfer, Leonie Küchenhoff, Julio Saez-Rodriguez

The application of single-cell molecular profiling coupled with spatial technologies has enabled charting cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relations

Xiao Zhao, Haojie Zheng, Hengzhe Li

In this paper, we mainly investigate $K_{1,2}$-structure-connectivity for any connected graph. Let $G$ be a connected graph with $n$ vertices, we show that $\kappa(G; K_{1,2})$ is well-defined if $diam(G)\geq 4$, or $n\equiv 1\pmod 3$, or $G\notin \{C_{5},K_{n}\}$ when $n\equiv 2\pmod 3$, or there exist three vertices $u,v,w$ such that $N_{G}(u)\cap (N_{G}(v

Irina Holmes Fay, Guillermo Rey, Kristina Ana Škreb

We find the exact Bellman function associated to the level-sets of sparse operators acting on characteristic functions.

Dulhan Jayalath, Steven Morad, Amanda Prorok

Existing communication methods for multi-agent reinforcement learning (MARL) in cooperative multi-robot problems are almost exclusively task-specific, training new communication strategies for each unique task. We address this inefficiency by introducing a communication strategy applicable to any task within a given environment. We pre-train the communicatio

Alessandro Berti, Humam Kourani, Hannes Hafke, Chiao-Yun Li

Using Large Language Models (LLMs) for Process Mining (PM) tasks is becoming increasingly essential, and initial approaches yield promising results. However, little attention has been given to developing strategies for evaluating and benchmarking the utility of incorporating LLMs into PM tasks. This paper reviews the current implementations of LLMs in PM and

Manxi Lin, Nina Weng, Kamil Mikolaj, Zahra Bashir

Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training set. While existing research primarily investigates this in the realm of image classification, this study extends the exploration of shortcut learning into medical image segmentation

Wenhao Wu, Jialiang Zhou, Ailong He, Shuguang Han

Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the c

Zhenming Yu, Stephan Menzel, John Paul Strachan, Emre Neftci

Simulation frameworks such MemTorch, DNN+NeuroSim, and aihwkit are commonly used to facilitate the end-to-end co-design of memristive machine learning (ML) accelerators. These simulators can take device nonidealities into account and are integrated with modern ML frameworks. However, memristors in these simulators are modeled with either lookup tables or sim

Shaojie Dai, Xin Liu, Ping Luo, Yue Yu

Large language model (LLM) has achieved promising performance in multilingual machine translation tasks through zero/few-shot prompts or prompt-tuning. However, due to the mixture of multilingual data during the pre-training of LLM, the LLM-based translation models face the off-target issue in both prompt-based methods, including a series of phenomena, namel

Love Panta

Trajectory tracking for an Omni-drive robot presents a challenging task that demands an efficient controller design. This paper introduces a self-optimizing controller, Type-1 fuzzyPID, which leverages dynamic and static system response analysis to overcome the limitations of manual tuning. To account for system uncertainties, an Interval Type-2 fuzzyPID con

Carmelo Cisto, Rizwan Jahangir, Francesco Navarra

In this paper we provide a description of the package \textit{PolyominoIdeals} for \textit{Macaulay2} that allows to deal with collections of cells, polyominoes and related binomial ideals.

ATLAS Collaboration

A search for charged-lepton-flavour violating $\mu\tau qt$ ($q=u,c$) interactions is presented, considering both top-quark production and decay. The data analysed correspond to 140 $\textrm{fb}^{-1}$ of proton-proton collisions at a centre-of-mass energy of $\sqrt{s}= $13 TeV recorded with the ATLAS detector at the Large Hadron Collider. The analysis targets

Haowei Zhu, Ling Yang, Jun-Hai Yong, Hongzhi Yin

The scale and quality of a dataset significantly impact the performance of deep models. However, acquiring large-scale annotated datasets is both a costly and time-consuming endeavor. To address this challenge, dataset expansion technologies aim to automatically augment datasets, unlocking the full potential of deep models. Current data expansion techniques

Xin Lin, Lijun Zhu

The efficiencies of the spin-orbit torques (SOTs) play a key role in the determination of the power consumption, integration density, and endurance of SOT-driven devices. Accurate and time-efficient determination of the SOT efficiencies is of great importance not only for evaluating the practical potential of SOT devices but also for developing new mechanism

Yun Guo, Luhua Qiu, Ruizhe Zhao, Michael Strickland

We compute the energy loss of heavy fermions moving in a plasma, taking into account the modification of the photon collective modes induced by collisions using a Bhatnagar-Gross-Krook collisional kernel. We include contributions from both hard and soft scatterings of the heavy fermion using a collisionally modified hard-thermal-loop resummed propagator. Usi

Zilong Chen, Yikai Wang, Feng Wang, Zhengyi Wang

Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video

Chaochao Chen, Yizhao Zhang, Yuyuan Li, Jun Wang

With the growing privacy concerns in recommender systems, recommendation unlearning is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as unlearning target. However, attackers can extract private information from the model even if it has not been explicitly encountered during training. We name this unseen i

Khandakar Md Asif Elahi, Somnath Bharadwaj, Srijita Pal, Abhik Ghosh

We present a Wideband Tapered Gridded Estimator (TGE), which incorporates baseline migration and variation of the primary beam pattern for neutral hydrogen (${\rm H\hspace{0.5mm}}{\scriptsize {\rm I}}$) 21-cm intensity mapping (IM) with large frequency bandwidth radio-interferometric observations. Here we have analysed $394-494 \, {\rm MHz}$ $(z = 1.9 - 2.6)

Adarsh N L, Arun P, Aravindh N L

Research on generative models to produce human-aligned / human-preferred outputs has seen significant recent contributions. Between text and image-generative models, we narrowed our focus to text-based generative models, particularly to produce captions for images that align with human preferences. In this research, we explored a potential method to amplify

Keshara Weerasinghe, Saahith Janapati, Xueren Ge, Sion Kim

Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thinking and rapid decision-making. This paper presents CognitiveEMS, an end-to-end wearable cognitive assistant system that can act as a collaborative virtual partner engaging in the

G. G. Amosov, A. S. Mokeev, A. N. Pechen

We consider error correction, based on the theory of non-commutative graphs, for a model of a qubit interacting with quantum oscillator. The dynamics of the composite system is governed by the Schr\"odinger equation which generates positive operator-valued measure (POVM) for the system dynamics. We construct a quantum channel generating the non-commutative g

Paul Schwerdtner, Benjamin Peherstorfer

Dimensionality reduction on quadratic manifolds augments linear approximations with quadratic correction terms. Previous works rely on linear approximations given by projections onto the first few leading principal components of the training data; however, linear approximations in subspaces spanned by the leading principal components alone can miss informati

Paul Dommel, Alois Pichler

Various methods in statistical learning build on kernels considered in reproducing kernel Hilbert spaces. In applications, the kernel is often selected based on characteristics of the problem and the data. This kernel is then employed to infer response variables at points, where no explanatory data were observed. The data considered here are located in compa

Giuseppe Cannizzaro, Harry Giles

We investigate the large-scale behaviour of the Self-Repelling Brownian Polymer (SRBP) in the critical dimension $d=2$. The SRBP is a model of self-repelling motion, which is formally given by the solution a stochastic differential equation driven by a standard Brownian motion and with a drift given by the negative gradient of its own local time. As with its

Takatoshi Shibuya, Yohito Ito, Kenta Asai, Takanobu Kirihara

We super-resolve the seeing-limited Subaru Hyper Suprime-Cam (HSC) images for 32,187 galaxies at z~2-5 in three techniques, namely, the classical Richardson-Lucy (RL) point spread function (PSF) deconvolution, sparse modeling, and generative adversarial networks to investigate the environmental dependence of galaxy mergers. These three techniques generate ov

Nikos Katzourakis, Roger Moser

We study vector-valued functions that minimise the $L^\infty$-norm of their derivatives for prescribed boundary data. We construct a vector-valued, mass minimising $1$-current (i.e., a generalised geodesic) in the domain such that all solutions of the problem coincide on its support. Furthermore, this current can be interpreted as a streamline of the solutio

Chaoqun Du, Yulin Wang, Shiji Song, Gao Huang

Long-tailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Such imbalance issue considerably impairs the performance of standard supervised learning algorithms, which are mainly designed for balanced training sets. Recent investigations have revealed that supervised contras

Hengyuan Zhang, Zitao Liu, Shuyan Huang, Chenming Shang

Knowledge tracing (KT) aims to estimate student's knowledge mastery based on their historical interactions. Recently, the deep learning based KT (DLKT) approaches have achieved impressive performance in the KT task. These DLKT models heavily rely on the large number of available student interactions. However, due to various reasons such as budget constraints

Robert Burklund, Vignesh Subramanian

The Segal conjecture for $C_p$ (as proved by Lin and Gunawardena) asserts that the canonical map from the $p$-complete sphere spectrum to the Tate construction for the trivial action of $C_p$ on the $p$-complete sphere spectrum is an isomorphism. In this article we extend the collection of spectra for which the canonical map $X \to X^{tC_p}$ is known to be a

Lasse Beers, Hamied Nabizada, Maximilian Weigand, Felix Gehlhoff

A key aspect in creating models of production systems with the use of model-based systems engineering (MBSE) lies in the description of system functions. These functions shouldbe described in a clear and standardized manner.The VDI/VDE 3682 standard for Formalised Process De-scription (FPD) provides a simple and easily understandable representation of proces

Shuai-Xia Xu

In the present paper, we study the asymptotics of the Fredholm determinant $D(x,s)$ of the finite-temperature deformation of the sine kernel, which represents the probability that there is no particles on the interval $(-x/\pi,x/\pi)$ in the bulk scaling limit of the finite-temperature fermion system. The variable $s$ in $D(x,s)$ is related to the temperatur

Victoria Bencheva, Velichka Milousheva

In the present paper, we study timelike surfaces free of minimal points in the four-dimensional Minkowski space. For each such surface we introduce a geometrically determined pseudo-orthonormal frame field and writing the derivative formulas with respect to this moving frame field and using the integrability conditions, we obtain a system of six functions sa

Navneet Garg, Haifeng Luo, Tharmalingam Ratnarajah

In this paper, we consider a two-way wiretap Multi-Input Multi-Output Multi-antenna Eve (MIMOME) channel, where both nodes (Alice and Bob) transmit and receive in an in-band full-duplex (IBFD) manner. For this system with keyless security, we provide a novel artificial noise (AN) based signal design, where the AN is injected in both signal and null spaces. W

Xu Pan, Qiang Yuan

Precise measurements of energy spectra of different cosmic ray species were obtained in recent years, by particularly the AMS-02 experiment on the International Space Station. It has been shown that apparent differences exist in different groups of the primary cosmic rays. However, it is not straightforward to conclude that the source spectra of different pa

Akbar Asgharzadeh, Éric Marchand, Ali Saadati Nik

For exponentially distributed lifetimes, we consider the prediction of future order statistics based on having observed the first $m$ order statistics. We focus on the previously less explored aspects of predicting: (i) an arbitrary pair of future order statistics such as the next and last ones, as well as (ii) the next $N$ future order statistics. We provid

Norbert Ludant, Marinos Vomvas, Guevara Noubir

Over the years, several security vulnerabilities in the 3GPP cellular systems have been demonstrated in the literature. Most studies focus on higher layers of the cellular radio stack, such as the RRC and NAS, which are cryptographically protected. However, lower layers of the stack, such as PHY and MAC, are not as thoroughly studied, even though they are ne

Moritz Schneider, Lukas Halekotte, Tina Comes, Daniel Lichte

In emergencies, high stake decisions often have to be made under time pressure and strain. In order to support such decisions, information from various sources needs to be collected and processed rapidly. The information available tends to be temporally and spatially variable, uncertain, and sometimes conflicting, leading to potential biases in decisions. Cu

Ma. Elena Hernández-Hernández, Saul Jacka

Motivated in part by a problem in simulated tempering (a form of Markov chain Monte Carlo) we seek to minimise, in a suitable sense, the time it takes a (regular) diffusion with instantaneous reflection at 0 and 1 to travel to $1$ and then return to the origin (the so-called commute time from 0 to 1). Substantially extending results in a previous paper, we c

Pablo Dorta-González, Alejandro Rodríguez-Caro, María Isabel Dorta-González

This study investigates how scientific research influences policymaking by analyzing citations of research articles in policy documents (policy impact) for nearly 125,000 articles across 434 public policy journals. We reveal distinct citation patterns between policymakers and other stakeholders like researchers, journalists, and the public. News and blog men

Éva Tichy-Rács, Stepan Hurskyy, Uliana Yakhnevych, Piotr Gaczyński

The current work is focused on the impact of the lithium stoichiometry on electrical conductivity, acoustic properties and high-temperature stability of single crystalline Li(Nb,Ta)O$_{3}$ at high temperatures. The crystals grown from Li-deficient melts were treated by the vapor transport equilibration (VTE) method, achieving near stoichiometric Li-content.

Zhenming Yu, Ming-Jay Yang, Jan Finkbeiner, Sebastian Siegel

Memristive devices hold promise to improve the scale and efficiency of machine learning and neuromorphic hardware, thanks to their compact size, low power consumption, and the ability to perform matrix multiplications in constant time. However, on-chip training with memristor arrays still faces challenges, including device-to-device and cycle-to-cycle variat

David M. Long, Deborah Baker, Andy S. H. To, Lidia van Driel-Gesztelyi

The composition of the solar corona differs from that of the photosphere, with the plasma thought to fractionate in the solar chromosphere according to the First Ionisation Potential (FIP) of the different elements. This produces a FIP bias, wherein elements with a low FIP are preferentially enhanced in the corona compared to their photospheric abundance, bu

Florian Leiser, Sven Eckhardt, Valentin Leuthe, Merlin Knaeble

Large language models (LLMs) are prone to hallucinations, i.e., nonsensical, unfaithful, and undesirable text. Users tend to overrely on LLMs and corresponding hallucinations which can lead to misinterpretations and errors. To tackle the problem of overreliance, we propose HILL, the "Hallucination Identifier for Large Language Models". First, we identified d

Maryam Azizi, Jan Wilhelm, Dorothea Golze, Francisco A. Delesma

Electronic structure calculations based on many-body perturbation theory (e.g. GW or the random-phase approximation (RPA)) require function evaluations in the complex time and frequency domain, for example inhomogeneous Fourier transforms or analytic continuation from the imaginary axis to the real axis. For inhomogeneous Fourier transforms, the time-frequen

Rodrigo Maulen-Soto, Jalal Fadili, Hedy Attouch

To solve convex optimization problems with a noisy gradient input, we analyze the global behavior of subgradient-like flows under stochastic errors. The objective function is composite, being equal to the sum of two convex functions, one being differentiable and the other potentially non-smooth. We then use stochastic differential inclusions where the drift

David Binder, Ingo Skupin, Tim Süberkrüb, Klaus Ostermann

The expression problem describes how most types can easily be extended with new ways to produce the type or new ways to consume the type, but not both. When abstract syntax trees are defined as an algebraic data type, for example, they can easily be extended with new consumers, such as print or eval, but adding a new constructor requires the modification of

Houeibib Ahmed, Pantellini Filippo, Griton Lea

We model the transport of solar energetic particles (SEPs) in the solar wind. We propagated relativistic test particles in the field of a steady three-dimensional magnetohydrodynamic simulation of the solar wind. We used the code MPI-AMRVAC for the wind simulations and integrated the relativistic guiding center equations using a new third-order-accurate pred

Keshara Weerasinghe, Seyed Hamid Reza Roodabeh, Kay Hutchinson, Homa Alemzadeh

Real-time recognition and prediction of surgical activities are fundamental to advancing safety and autonomy in robot-assisted surgery. This paper presents a multimodal transformer architecture for real-time recognition and prediction of surgical gestures and trajectories based on short segments of kinematic and video data. We conduct an ablation study to ev

Azza Gaysin

The constraint satisfaction problem (CSP) can be formulated as a homomorphism problem between relational structures: given a structure $\mathcal{A}$, for any structure $\mathcal{X}$, whether there exists a homomorphism from $\mathcal{X}$ to $\mathcal{A}$. For years, it has been conjectured that all problems of this type are divided into polynomial-time and N

Juan Casado-Díaz

We carry out the homogenization of a fluid-structure interaction problem consisting in the periodic inclusions of a viscous fluid in an elastic body. We get a macrostructure model where the body behaves as a viscoelastic material with a long-range memory term. Our aim is not only to get this limit problem but also to study its main properties. Using the micr

Jinlu Zhang, Yiyi Zhou, Qiancheng Zheng, Xiaoxiong Du

Text-to-3D-aware face (T3D Face) generation and manipulation is an emerging research hot spot in machine learning, which still suffers from low efficiency and poor quality. In this paper, we propose an End-to-End Efficient and Effective network for fast and accurate T3D face generation and manipulation, termed $E^3$-FaceNet. Different from existing complex g

Kazuhiro Ichihara, Toshio Saito

A pair of Dehn surgeries on a knot is called chirally cosmetic if they yield orientation-reversingly homeomorphic 3-manifolds. In this paper, we consider exceptional or half-integral chirally cosmetic surgeries, and obtain several restrictions.

Zhi Cao, Youneng Bao, Fanyang Meng, Chao Li

Deep neural network-based image compression (NIC) has achieved excellent performance, but NIC method models have been shown to be susceptible to backdoor attacks. Adversarial training has been validated in image compression models as a common method to enhance model robustness. However, the improvement effect of adversarial training on model robustness is li

Lilia Toma, Markus Zajac, Uta Störl

Many modern manufacturing companies have evolved from a single production facility to a multi-factory production environment that must manage both regionally dispersed production orders and their multi-site production steps. The availability of a range of machines in different locations capable of performing the same operation and shipping times between fact

Mert Gulsen, Batuhan Cengiz, Yusuf H. Sahin, Gozde Unal

Point clouds are extensively employed in a variety of real-world applications such as robotics, autonomous driving and augmented reality. Despite the recent success of point cloud neural networks, especially for safety-critical tasks, it is essential to also ensure the robustness of the model. A typical way to assess a model's robustness is through adversari

Daniel Hug, Fabian Mussnig, Jacopo Ulivelli

We prove a functional version of the additive kinematic formula as an application of the Hadwiger theorem on convex functions together with a Kubota-type formula for mixed Monge-Amp\`ere measures. As an application, we give a new explanation for the equivalence of the representations of functional intrinsic volumes as singular Hessian valuations and as integ

Dung Le

It will be established that the mean oscillation of a function on a metric-measure space $X\times Y$ will be small if its mean oscillation on $X$ is small and some simple information on its (partial $Y$) upper-gradient is given. Applications to the regularity and global existence of bounded solutions to strongly coupled elliptic/parabolic systems on thin dom

Roman Korostinskiy, Eugene Darashkevich, Roman Rusyaev, Yegor Bugayenko

In C++, objects can be allocated in static memory, on the stack, or on the heap -- the latter being significantly more performance-costly than the former options. We hypothesized that programmers, particularly those involved in widely-used open-source projects, would be conscious of these performance costs and consequently avoid heap allocations. To test thi

Marta Piecyk

For a fixed graph $H$, in the graph homomorphism problem, denoted by $Hom(H)$, we are given a graph $G$ and we have to determine whether there exists an edge-preserving mapping $\varphi: V(G) \to V(H)$. Note that $Hom(C_3)$, where $C_3$ is the cycle of length $3$, is equivalent to $3$-Coloring. The question whether $3$-Coloring is polynomial-time solvable on

Omar Moured, Morris Baumgarten-Egemole, Alina Roitberg, Karin Muller

In a world driven by data visualization, ensuring the inclusive accessibility of charts for Blind and Visually Impaired (BVI) individuals remains a significant challenge. Charts are usually presented as raster graphics without textual and visual metadata needed for an equivalent exploration experience for BVI people. Additionally, converting these charts int

Tobias Mömke, Alexandru Popa, Aida Roshany-Tabrizi, Michael Ruderer

In a simple, undirected graph G, an edge 2-coloring is a coloring of the edges such that no vertex is incident to edges with more than 2 distinct colors. The problem maximum edge 2-coloring (ME2C) is to find an edge 2-coloring in a graph G with the goal to maximize the number of colors. For a relevant graph class, ME2C models anti-Ramsey numbers and it was c

Robert van Leeuwen

The early history of string theory is marked by a shift from strong interaction physics to quantum gravity. The first string models and associated theoretical framework were formulated in the late 1960s and early 1970s in the context of the S-matrix program for the strong interactions. In the mid-1970s, the models were reinterpreted as a potential theory uni

Guilherme S. Costa, Marcus A. M. de Aguiar

Synchronization is observed in many natural systems, with examples ranging from neuronal activation to walking pedestrians. The models proposed by Winfree and Kuramoto stand as the classic frameworks for investigating these phenomena. The Kuramoto model, in particular, has been extended in different ways since its original formulation to account for more gen

Shuyue Wang, Wuji Zhang, Chunfang Sun, Chunfeng Wu

In this study, we engineer the Dzyaloshinskii-Moriya interaction mediated by photons to emulate ground-state chiral current based on three-level atoms driven by quantum and classical fields. We employ adiabatic elimination techniques to derive an effective Dzyaloshinskii-Moriya interaction Hamiltonian of two-level systems, which can address the challenges ar