December 2023 arXiv papers — page 121

Marvin Fritz, Luca Scarpa

In this work, we present and analyze a system of PDEs, which models tumor growth by considering chemotaxis, active transport, and random effects. The stochasticity of the system is modelled by random initial data and Wiener noises that appear in the tumor and nutrient equations. The volume fraction of the tumor is governed by a stochastic phase-field equatio

Mehdi Delrobaei

Cognitive distraction and measurement noise are two distinct factors that significantly impact the performance of humans and engineering systems. Cognitive distraction occurs when an individual's attention is diverted from a task, while measurement noise refers to the random variation that can occur in system measurements. Although humans and engineering sys

Hamid Hamidani, Shigeo S. Kimura, Masaomi Tanaka, Kunihito Ioka

Follow-up observations of short gamma-ray bursts (sGRBs) have continuously unveiled late extended/plateau emissions, attributed to jet launch due to late engine activity, the nature of which remains enigmatic. Observations of GW170817 confirmed that sGRBs are linked to neutron star (NS) mergers, and discovered a kilonova (KN) transient. Nevertheless, the ori

Hui Lu, Albert ali Salah, Ronald Poppe

Diffusion models achieve remarkable quality in image generation, but at a cost. Iterative denoising requires many time steps to produce high fidelity images. We argue that the denoising process is crucially limited by an accumulation of the reconstruction error due to an initial inaccurate reconstruction of the target data. This leads to lower quality output

Gaoting Lin, Jinlong Jiao, Xiyang Li, Mingfang Shu

Kitaev interactions, arising from the interplay of frustration and bond anisotropy, can lead to strong quantum fluctuations and, in an ideal case, to a quantum-spin-liquid state. However, in many nonideal materials, spurious non-Kitaev interactions typically promote a zigzag antiferromagnetic order in the d-orbital transition metal compounds. By combining ne

Daniel Köglmayr, Christoph Räth

Model-free and data-driven prediction of tipping point transitions in nonlinear dynamical systems is a challenging and outstanding task in complex systems science. We propose a novel, fully data-driven machine learning algorithm based on next-generation reservoir computing to extrapolate the bifurcation behavior of nonlinear dynamical systems using stationar

Anina Gruica, Altan B. Kilic, Alberto Ravagnani

We present the theory of linear rank-metric codes from the point of view of their fundamental parameters. These are: the minimum rank distance, the rank distribution, the maximum rank, the covering radius, and the field size. The focus of this chapter is on the interplay among these parameters and on their significance for the code's (combinatorial) structur

Samuel J. Paech

We introduce EQ-Bench, a novel benchmark designed to evaluate aspects of emotional intelligence in Large Language Models (LLMs). We assess the ability of LLMs to understand complex emotions and social interactions by asking them to predict the intensity of emotional states of characters in a dialogue. The benchmark is able to discriminate effectively between

Gabriela Sejnova, Michal Vavrecka, Karla Stepanova

Variational Autoencoders (VAEs) are powerful generative models that have been widely used in various fields, including image and text generation. However, one of the known challenges in using VAEs is the model's sensitivity to its hyperparameters, such as the latent space size. This paper presents a simple extension of VAEs for automatically determining the

JeongJun Park, Lusungu J. Mwasinga, Huigyu Yang, Syed M. Raza

Mobile traffic data in urban regions shows differentiated patterns during different hours of the day. The exploitation of these patterns enables highly accurate mobile traffic prediction for proactive network management. However, recent Deep Learning (DL) driven studies have only exploited spatiotemporal features and have ignored the geographical correlation

Gung-Min Gie, Youngjoon Hong, Chang-Yeol Jung, Tselmuun Munkhjin

Singularly perturbed boundary value problems pose a significant challenge for their numerical approximations because of the presence of sharp boundary layers. These sharp boundary layers are responsible for the stiffness of solutions, which leads to large computational errors, if not properly handled. It is well-known that the classical numerical methods as

Ahmet Utku Canbolat, Ozgur Cakir

We investigate the long-range behavior and size dependence of the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction in hexagonal and triangular graphene nanoflakes with zigzag and arm-chair edges. We employ the tight-binding model with exact diagonalization to calculate the RKKY interaction as a function of the distance between magnetic impurities, nanoflake

Stefanie A. Zimmermann, Stig Moberg

Nonparametric estimates of frequency response functions (FRFs) are often suitable for describing the dynamics of a mechanical system. If treating these estimates as measurement inputs, they can be used for parametric identification of, e.g., a gray-box model. Classical methods for nonparametric FRF estimation of MIMO systems require at least as many experime

Christian Weihsbach, Christian N. Kruse, Alexander Bigalke, Mattias P. Heinrich

Purpose: Applying pre-trained medical deep learning segmentation models on out-of-domain images often yields predictions of insufficient quality. In this study, we propose to use a powerful generalizing descriptor along with augmentation to enable domain-generalized pre-training and test-time adaptation, achieving high-quality segmentation in unseen domains.

Jian Huang, Cheng Liu, Xun-Wei Xu, Jie-Qiao Liao

We propose and prove two theorems for determining the number of dark modes in linear two-component quantum networks composed of two types of bosonic modes. This is achieved by diagonalizing the two sub-networks of the same type of modes, mapping the networks to either a standard or a thick arrowhead matrix, and analyzing the linear dependence and independenc

Fengpeng Li, Kemou Li, Jinyu Tian, Jiantao Zhou

The deep model training procedure requires large-scale datasets of annotated data. Due to the difficulty of annotating a large number of samples, label noise caused by incorrect annotations is inevitable, resulting in low model performance and poor model generalization. To combat label noise, current methods usually select clean samples based on the small-lo

Seul-Ki Yeom, Julian von Klitzing

Semantic segmentation has witnessed remarkable advancements with the adaptation of the Transformer architecture. Parallel to the strides made by the Transformer, CNN-based U-Net has seen significant progress, especially in high-resolution medical imaging and remote sensing. This dual success inspired us to merge the strengths of both, leading to the inceptio

I. A. Gudim, N. V. Mikhashenok

The phase formation of terbium chromium borate in melt solutions based on bismuth trimolybdate and lithium tungstate was studied. It was shown that there is no trigonal phase of terbium chromium borate in a system based on bismuth trimolybdate at all component ratios. The ratio of components of a system based on lithium tungstate has been found, at which tri

Jiaping Wu, Xin Wang, Zheng Wang, Yuhua Yin

Self-assembly of sphere-forming solution-state amphiphilic diblock copolymers under spherical nanopore confinement is investigated using a simulated annealing technique. For two types of cases of different pore-surface/copolymer interactions, sequences of self-assembled patchy nanospheres are obtained, and phase diagrams are constructed. Self-assembled patch

Anna Derington, Hagen Wierstorf, Ali Özkil, Florian Eyben

Machine learning models for speech emotion recognition (SER) can be trained for different tasks and are usually evaluated based on a few available datasets per task. Tasks could include arousal, valence, dominance, emotional categories, or tone of voice. Those models are mainly evaluated in terms of correlation or recall, and always show some errors in their

Franziska Greinert, Malte S. Ubben

Quantum physics modeling is technically complex and often non-descriptive. This article presents some approaches how quantum physical ideas can be represented by haptic models. For this purpose, models made from 3D printers, models made from paper strips, and models made from textiles are compared. A novelty is the use of zippers instead of paper strips, whi

Amalia Artemis Koufopoulou, Athanasios Papadimitriou, Aggelos Pikrakis, Mihalis Psarakis

High-level synthesis (HLS) tools have provided significant productivity enhancements to the design flow of digital systems in recent years, resulting in highly-optimized circuits, in terms of area and latency. Given the evolution of hardware attacks, which can render them vulnerable, it is essential to consider security as a significant aspect of the HLS des

Amalia Artemis Koufopoulou, Kalliopi Xevgeni, Athanasios Papadimitriou, Mihalis Psarakis

As the complexity of digital circuits increases, High-Level Synthesis (HLS) is becoming a valuable tool to increase productivity and design reuse by utilizing relevant Electronic Design Automation (EDA) flows, either for Application-Specific Integrated Circuits (ASIC) or for Field Programmable Gate Arrays (FPGA). Side Channel Analysis (SCA) and Fault Injecti

Malte Neul, Isabelle V. Sprave, Laura K. Diebel, Lukas G. Zinkl

Si/SiGe heterostructures are of high interest for high mobility transistor and qubit applications, specifically for operations below 4.2 K. In order to optimize parameters such as charge mobility, built-in strain, electrostatic disorder, charge noise and valley splitting, these heterostructures require Ge concentration profiles close to mono-layer precision.

Moayad Elamin, Muhammad Omer, Yonas Chanie, Henslaac Ndlovu

Automatic Speech Recognition (ASR) systems are a crucial technology that is used today to design a wide variety of applications, most notably, smart assistants, such as Alexa. ASR systems are essentially dialogue systems that employ Spoken Language Understanding (SLU) to extract meaningful information from speech. The main challenge with designing such syste

Mark Rubin

The inflation of Type I error rates is thought to be one of the causes of the replication crisis. Questionable research practices such as p-hacking are thought to inflate Type I error rates above their nominal level, leading to unexpectedly high levels of false positives in the literature and, consequently, unexpectedly low replication rates. In this article

Katarzyna Sadecka, Yasser Saleem, Daniel Miravet, Matthew Albert

We predict here the fine structure of an electrically tunable negatively charged exciton (trion) composed of two electrons and a hole confined in a gated bilayer graphene quantum dot (QD). We start with an atomistic approach, allowing us to compute confined electron and confined hole QD states for a structure containing over one million atoms. Using atomisti

A. N. Grekov, N. A. Grekov, K. A. Kuzmin, S. S. Peliushenko

The paper presents the results of a study of the impact of acoustic and vibration signals on Black Sea mussels, and determines the necessary technical characteristics of vibration sensors. A method has been developed based on the analysis of the time interval recorded by a valve motion sensor in the form of a monotonically decreasing function after the respo

Ningyi Li, Junhong Li, Lijingting Qing, Shicheng Ma

At low temperatures, colloidal particles with short-range attractive and long-range repulsive interactions can form various periodic microphases in bulk.In this paper, we investigate the self-assembly behaviour of colloids with competing interactions under spherical confinement by conducting molecular dynamics simulations. We find that the cluster, mixture,

Ruonan Liu, Quanhu Zhang, Te Han

Industrial Cyber-Physical Systems (ICPS) integrate the disciplines of computer science, communication technology, and engineering, and have emerged as integral components of contemporary manufacturing and industries. However, ICPS encounters various challenges in long-term operation, including equipment failures, performance degradation, and security threats

Arnaud Casteigts, Timothée Corsini, Nils Morawietz

A temporal graph is a graph whose edges appear at certain points in time. These graphs are temporally connected (in class TC) if all vertices can reach each other by temporal paths (traversing the edges in chronological order). Reachability based on temporal paths is not transitive, with important consequences. For instance, TC graphs do not always admit TC

Zhiyi Pan, Nan Zhang, Wei Gao, Shan Liu

Utilizing uniformly distributed sparse annotations, weakly supervised learning alleviates the heavy reliance on fine-grained annotations in point cloud semantic segmentation tasks. However, few works discuss the inhomogeneity of sparse annotations, albeit it is common in real-world scenarios. Therefore, this work introduces the probability density function i

Dianyu Zhong, Yiqin Yang, Qianchuan Zhao

The large action space is one fundamental obstacle to deploying Reinforcement Learning methods in the real world. The numerous redundant actions will cause the agents to make repeated or invalid attempts, even leading to task failure. Although current algorithms conduct some initial explorations for this issue, they either suffer from rule-based systems or d

Jiwoo Chung, Sangeek Hyun, Jae-Pil Heo

Despite the impressive generative capabilities of diffusion models, existing diffusion model-based style transfer methods require inference-stage optimization (e.g. fine-tuning or textual inversion of style) which is time-consuming, or fails to leverage the generative ability of large-scale diffusion models. To address these issues, we introduce a novel arti

Peter Danchev, Arash Javan, Ahmad Moussavi

Some variations of $\pi$-regular and nil clean rings were recently introduced in \cite{5,8,7}, respectively. In this paper, we examine the structure and relationships between these classes of rings. Specifically, we prove that $(m, n)$-regularly nil clean rings are left-right symmetric and also show that the inclusions ($D$-regularly nil clean) $\subseteq$ (

Marco Lepri, Davide Bacciu, Cosimo Della Santina

This work concerns control-oriented and structure-preserving learning of low-dimensional approximations of high-dimensional physical systems, with a focus on mechanical systems. We investigate the integration of neural autoencoders in model order reduction, while at the same time preserving Hamiltonian or Lagrangian structures. We focus on extensively evalua

Chao Min, Guoyong Liao, Guoquan Wen, Yingjun Li

To address the issues of stability and fidelity in interpretable learning, a novel interpretable methodology, ensemble interpretation, is presented in this paper which integrates multi-perspective explanation of various interpretation methods. On one hand, we define a unified paradigm to describe the common mechanism of different interpretation methods, and

Maximilian Böther, Ties Robroek, Viktor Gsteiger, Robin Holzinger

In real-world machine learning (ML) pipelines, datasets are continuously growing. Models must incorporate this new training data to improve generalization and adapt to potential distribution shifts. The cost of model retraining is proportional to how frequently the model is retrained and how much data it is trained on, which makes the naive approach of retra

Lahiru Samarakoon, Samuel J. Broughton, Marc Härkönen, Ivan Fung

End-to-end neural diarization with encoder-decoder based attractors (EEND-EDA) is a method to perform diarization in a single neural network. EDA handles the diarization of a flexible number of speakers by using an LSTM-based encoder-decoder that generates a set of speaker-wise attractors in an autoregressive manner. In this paper, we propose to replace EDA

Nicola Gigli, Fabio Marconi

In this paper we develop a general `analytic' splitting principle for RCD spaces: we show that if there is a function with suitable Laplacian and Hessian, then the space is (isomorphic to) a warped product. Our result covers most of the splitting-like results currently available in the literature about RCD spaces. We then apply it to extend to the non-smooth

John Fernley, Peter Mörters, Marcel Ortgiese

We show existence of a non-trivial phase transition for the contact process, a simple model for infection without immunity, on a network which reacts dynamically to the infection trying to prevent an epidemic. This network initially has the distribution of an Erd\H{o}s-R\'enyi graph, but is made adaptive via updating in only the infected neighbourhoods, at c

Xueyuan Wang, M. Cenk Gursoy

Unmanned aerial vehicle (UAV)-based networks and Internet of Things (IoT) are being considered as integral components of current and next-generation wireless networks. In particular, UAVs can provide IoT devices with seamless connectivity and high coverage and this can be accomplished with effective UAV path planning. In this article, we study robust and dec

Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou

Multimodal Large Language Models (MLLMs) demonstrate exceptional problem-solving capabilities, but few research studies aim to gauge the ability to generate visual instruction tuning data. This paper proposes to explore the potential of empowering MLLMs to generate data independently without relying on GPT-4. We introduce Genixer, a comprehensive data genera

Alejo García-Sassi, Pierre-Antoine Guihéneuf, Pablo Lessa

We prove a structure theorem for ergodic homological rotation sets of homeomorphisms isotopic to the identity on a closed orientable hyperbolic surface: this set is made of a finite number of pieces that are either one-dimensional or almost convex. The latter ones give birth to horseshoes; in the case of a zero-entropy homeomorphism we show that there exists

P. Vijayakumar, C. Manikandan, R. M. Sarguna, Edward Prabu Amaladass

A novel bottom-cooling high-temperature solution growth technique is developed for growing large-sized relaxor ferroelectric 0.91Pb(Zn1/3Nb2/3O3)-0.09PbTiO3 (PZN-PT) single crystals. During the growth, an inverse temperature gradient is maintained in the crucible base by flowing air at a controlled rate. This method restricts the number of spontaneously nucl

Laurent Fallot

This paper is a study of the set of rational numbers of the form 1 < a^q /b^p < a with a and b co-prime integers. The set F (a,b) of these numbers, with an appropriate binary law, is a monoid isomorphic to (N, +, 0). We identify the sequences of minimum and maximum record holders in F (a,b) and prove that the first one converges to 1 while the second one con

M. Bondi, R. Scaramella, G. Zamorani, P. Ciliegi

We present the first deep (72 hours of observations) radio image of the Euclid Deep Field North (EDFN) obtained with the LOw-Frequency ARray (LOFAR) High Band Antenna (HBA) at 144 MHz. The EDFN is the latest addition to the LOFAR Two-Metre Sky Survey (LoTSS) Deep Fields and these observations represent the first data release for this field. The observations

Giorgio Nicoletti, Daniel Maria Busiello

Complex systems are characterized by multiple spatial and temporal scales. A natural framework to capture their multiscale nature is that of multilayer networks, where different layers represent distinct physical processes that often regulate each other indirectly. We model these regulatory mechanisms through triadic higher-order interactions between nodes a

Greta Brianti, Roberto Iuppa, Marco Cristoforetti

Machine Learning is a rapidly expanding field with a wide range of applications in science. In the field of physics, the Large Hadron Collider, the world's largest particle accelerator, utilizes Neural Networks for various tasks, including flavour tagging. Flavour tagging is the process of identifying the flavour of the hadron that initiates a jet in a colli

Eduardo Witter, Ingrid Nunes, Dietmar Jannach

Modern Code Review (MCR) is an informal tool-assisted quality assurance practice. It relies on the asynchronous communication among the authors of code changes and reviewers, who are developers that provide feedback. However, from candidate developers, some are able to provide better feedback than others given a particular context. The selection of reviewers

V. A. Sautenkov, S. A. Saakyan, A. A. Bobrov, B. B. Zelener

Nonlinear selective reflection from the interface YAG window-high density rubidium vapor in the high-temperature cell is studied at the transition 5S$_{1/2}$-5P$_{3/2}$. In the experiment tunable pump and probe lasers are used. The selective reflection spectra for the laser probe beam are investigated at four different rubidium atomic densities and five diff

M. A. Bezuglov, A. I. Onishchenko

Hypergeometric functions of one and many variables play an important role in various branches of modern physics and mathematics. Often we have hypergeometric functions with indices linear dependent on a small parameter with respect to which one needs to perform Laurent expansions. Moreover such expansions are desirable to be expressed in terms of well known

Shuo Jiang, Daniel Evans-Yamamoto, Dennis Bersenev, Sucheendra K. Palaniappan

Protocol standardization and sharing are crucial for reproducibility in life sciences. In spite of numerous efforts for standardized protocol description, adherence to these standards in literature remains largely inconsistent. Curation of protocols are especially challenging due to the labor intensive process, requiring expert domain knowledge of each exper

Xueyuan Wang, M. Cenk Gursoy

In this paper, we investigate jamming-resilient UAV path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically, the typical UAV is required to collect data from multiple distributed IoT nodes under collision avoidance, mission complet

Michael Baudoin, Virginie Daru

Gigahertz acoustic streaming microjets, with the capability of achieving fluid speeds up to meters per second, open new avenues for precision fluid and particle manipulation at microscales. However, theoretical and numerical investigations of acoustic streaming at these frequencies remain relatively scarce due to significant challenges including: (i) The ina

Dazhao Du, Enhan Li, Lingyu Si, Fanjiang Xu

Underwater image enhancement (UIE) aims to generate clear images from low-quality underwater images. Due to the unavailability of clear reference images, researchers often synthesize them to construct paired datasets for training deep models. However, these synthesized images may sometimes lack quality, adversely affecting training outcomes. To address this

Xiaoyue Cao, Ran Li, Nan Li, Rui Li

Galaxy-galaxy strong gravitational lensing (GGSL) is a powerful probe for the formation and evolution of galaxies and cosmology, while the sample size of GGSLs leads to considerable uncertainties and potential bias. The China Space Station Telescope (CSST, to be launched in late 2026) will conduct observations across 17,500 square degrees of the sky, capturi

Rym Smaï

In [6], Geroch, Kronheimer and Penrose introduced a way to attach ideal points to a spacetime M , defining the causal completion of M. They established that this is a topological space which is Hausdorff when M is globally hyperbolic. In this paper, we prove that if, in addition, M is simply-connected and conformally flat, its causal completion is a topologi

Gabriele Lobbia

It is known that monoidal categories have a finite definition, whereas multicategories have an infinite (albeit finitary) definition. Since monoidal categories correspond to representable multicategories, it goes without saying that representable multicategories should also admit a finite description. With this in mind, we give a new finite definition of a s

Emily Gavrilenko, Foaad Khosmood, Mahdi Rastad, Sadra Amiri Moghaddam

Investors are interested in predicting future success of startup companies, preferably using publicly available data which can be gathered using free online sources. Using public-only data has been shown to work, but there is still much room for improvement. Two of the best performing prediction experiments use 17 and 49 features respectively, mostly numeric

Moritz Peters, Nicolas Gaudin, Jan Philipp Thoma, Vianney Lapôtre

Randomizing the mapping of addresses to cache entries has proven to be an effective technique for hardening caches against contention-based attacks like Prime+Prome. While attacks and defenses are still evolving, it is clear that randomized caches significantly increase the security against such attacks. However, one aspect that is missing from most analyses

Meng Zhao, Fei Yu, Dakun Wu, Xinyue Zhu

In this paper we explore the application of low-loss multimode anti-resonant hollow-core fiber (MM-AR-HCF) in the delivery of nanosecond laser pulses at 1 um wavelength. MM-AR-HCF of large core offers a rich content of low-loss higher-order modes which plays a key role in the efficient coupling and transmission of high-power laser of degraded beam quality. I

P. Vijayakumar, Edward Prabu Amaladass K. Ganesan R. M. Sarguna, Varsha Roy, S. Ganesamoorthy

We report on the indigenous design and development of laboratory scale travelling heater method (THM) system to grow detector grade Cd0.9Zn0.1Te (CdZnTe) single crystals. THM system mainly consists of two-zone furnace with a tuneable temperature gradient (30 - 80 C/cm), high precision translation (1 - 25 mm per day) and rotation (1 - 50 rpm) assemblies to me

Van Duong Dinh, Nicolas Rougerie, Leonardo Tolomeo, Yuzhao Wang

In this paper, we investigate the Gibbs measures associated with the focusing nonlinear Schr\"odinger equation with an anharmonic potential. We establish a dichotomy for normalizability and non-normalizability of the Gibbs measures in one dimension and higher dimensions with radial data. This extends a recent result of the third and fourth authors with Rober

Elodie Germani, Elisa Fromont, Camille Maumet

Analytical workflows in functional magnetic resonance imaging are highly flexible with limited best practices as to how to choose a pipeline. While it has been shown that the use of different pipelines might lead to different results, there is still a lack of understanding of the factors that drive these differences and of the stability of these differences

Danni Yuan, Shaokui Wei, Mingda Zhang, Li Liu

This work studies the task of poisoned sample detection for defending against data poisoning based backdoor attacks. Its core challenge is finding a generalizable and discriminative metric to distinguish between clean and various types of poisoned samples (e.g., various triggers, various poisoning ratios). Inspired by a common phenomenon in backdoor attacks

Sergio Bernardez Molina, Pantaleone Nespoli, Félix Gómez Mármol

There is no denying that the use of Information Technology (IT) is undergoing exponential growth in today's world. This digital transformation has also given rise to a multitude of security challenges, notably in the realm of cybercrime. In response to these growing threats, public and private sectors have prioritized the strengthening of IT security measure

Camille Stephanus, Josiane Vero

Employees in low-skilled jobs have limited agency when it comes to professional retraining. Career transitions for low-skilled white-collar workers, despite their frequent desire to change professions, are often hindered; they prove to be more common but constrained and externally driven for low-skilled blue-collar workers.

Sanghak Oh, Kiho Lee, Seonhye Park, Doowon Kim

AI-powered coding assistant tools have revolutionized the software engineering ecosystem. However, prior work has demonstrated that these tools are vulnerable to poisoning attacks. In a poisoning attack, an attacker intentionally injects maliciously crafted insecure code snippets into training datasets to manipulate these tools. The poisoned tools can sugges

Ruimeng Li, Yuanhao Pu, Zhaoyi Li, Hong Xie

This paper considers the out-of-distribution (OOD) generalization problem under the setting that both style distribution shift and spurious features exist and domain labels are missing. This setting frequently arises in real-world applications and is underlooked because previous approaches mainly handle either of these two factors. The critical challenge is

Claire Guille-Biel Winder

We are interested in the learning of 6 to 7 years old children in implementations of a situation of reproduction of figure by folding presented in the first part of this article. In the second part we expose our problem as well as our hypothesis and our methodology. The third part presents the mathematical and didactical potentialities of the PLIOX situation

Xueyuan Wang, M. Cenk Gursoy, Tugba Erpek, Yalin E. Sagduyu

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV non-cooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes is a challenging task. In this paper, we consider a path planning optimization problem to maximize the collected data f

Prashant Shrestha, Sanskar Amgain, Bidur Khanal, Cristian A. Linte

Medical Vision Language Pretraining (VLP) has recently emerged as a promising solution to the scarcity of labeled data in the medical domain. By leveraging paired/unpaired vision and text datasets through self-supervised learning, models can be trained to acquire vast knowledge and learn robust feature representations. Such pretrained models have the potenti

Xinyang Liu, Regina Gumenyuk

Chirped pulse amplification (CPA) has been adopted as a commonly used methodology to obtain powerful ultrashort laser pulses since its first demonstration. However, wavelength-tunable CPA systems are rarely reported. Wavelength-tunable ultrashort and intense laser pulses are desired in various fields like nonlinear spectroscopy and optical parametric amplifi

V Konakov, S Menozzi

We prove central and local limit theorems for random walks on the Poincar{\'e} hyperbolic space of dimension n {\v e} 2. To this end we use the ball model and describe the walk therein through the M{\"o}bius addition and multiplication. This also allows to derive a corresponding law of large numbers.

Wanxing Chang, Ye Shi, Jingya Wang

Learning with noisy labels (LNL) poses a significant challenge in training a well-generalized model while avoiding overfitting to corrupted labels. Recent advances have achieved impressive performance by identifying clean labels and correcting corrupted labels for training. However, the current approaches rely heavily on the model's predictions and evaluate

Tanveer Hannan, Md Mohaiminul Islam, Thomas Seidl, Gedas Bertasius

Locating specific moments within long videos (20-120 minutes) presents a significant challenge, akin to finding a needle in a haystack. Adapting existing short video (5-30 seconds) grounding methods to this problem yields poor performance. Since most real life videos, such as those on YouTube and AR/VR, are lengthy, addressing this issue is crucial. Existing

Haoxin Wang, Yipeng Mo, Kunlan Xiang, Nan Yin

In the domain of multivariate time series analysis, the concept of channel independence has been increasingly adopted, demonstrating excellent performance due to its ability to eliminate noise and the influence of irrelevant variables. However, such a concept often simplifies the complex interactions among channels, potentially leading to information loss. T

Amina Ghoul, Itheri Yahiaoui, Fawzi Nashashibi

Predicting the future trajectories of dynamic agents in complex environments is crucial for a variety of applications, including autonomous driving, robotics, and human-computer interaction. It is a challenging task as the behavior of the agent is unknown and intrinsically multimodal. Our key insight is that the agents behaviors are influenced not only by th

Boris S. Maryshev, Lyudmila S. Klimenko

We have generalized the semi-analytic approach of special flow to the description of flows of passive particles taking into account internal noise. The model is represented by a series of recurrence relations. The recurrence relations are constructed by numerically solving the Langevin equations in the presence of a random force, for an ensemble of passive p

Patrick J. W. Koelewijn, Rajiv Sing, Peter Seiler, Roland Tóth

In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient

Xianglong Li, Zengxu Xu, Songbai Hu, Mingqiang Gu

Ferroelectric fluorite dioxides like hafnium (HfO2)-based materials are considered to be one of the most potential candidates for nowadays large-scale integrated-circuits (ICs). While zirconia (ZrO2)-based fluorites materials, which has the same structure as HfO2 and more abundant resources and lower cost of raw materials, is usually thought to be anti- or f

Valerie Urbach, Maëlle Briottet, Khadeeja Sy, Charlie London

In cystic fibrosis (CF), impaired mucociliary clearance leads to chronic infection and inflammation. However, cilia beating features in a CF altered environment, consisting of dehydrated airway surface liquid layer and abnormal mucus, has not been fully characterized. Furthermore, acute inflammation is normally followed by an active resolution phase requirin

Yu Xie, An Zhang, Bin Shu

As a sequel to [14], in this article we first introduce a so-called duplex Hecke algebras of type B which is a Q(q)-algebra associated with the Weyl group W (B) of type B, and symmetric groups S_l for l = 0, 1, . . . ,m, satisfying some Hecke relations. This notion originates from the degenerate duplex Hecke algebra arising from the course of study of a kind

Jean-Marie Malherbe

Imaging spectroscopy is intended to be coupled with adaptive optics (AO) on large solar telescopes, in order to produce high spatial and temporal resolution measurements of velocities and magnetic fields on a 2D target. We present the theoretical capabilities of a new generation 24-channel MSDP slicer for 8-meter class spectrographs which are common in solar

Chris A. J. Klaassen, Bert van Es

Kurtosis minus squared skewness is bounded from below by 1, but for unimodal distributions this parameter is bounded by 189/125. In some applications it is natural to compare distributions by comparing their kurtosis-minus-squared-skewness parameters. The asymptotic behavior of the empirical version of this parameter is studied here for i.i.d. random variabl

Fabio Bonassi, Carl Andersson, Per Mattsson, Thomas B. Schön

The goal of this paper is to provide a system identification-friendly introduction to the Structured State-space Models (SSMs). These models have become recently popular in the machine learning community since, owing to their parallelizability, they can be efficiently and scalably trained to tackle extremely-long sequence classification and regression proble

Saba Karimi, Junjie Yin, Thomas Salez, James A Forrest

We measure the isothermal rejuvenation of stable glass films of poly(styrene) and poly(methylmethacrylate). We demonstrate that the propagation of the front responsible for the transformation to a supercooled-liquid state can serve as a highly localized probe of the local supercooled dynamics. We use this connection to probe the depth-dependent relaxation ra

E. Korec, M. Jirasek, H. S. Wong, E. Martínez-Pañeda

A model for corrosion-induced cracking of reinforced concrete subjected to non-uniform chloride-induced corrosion is presented. The gradual corrosion initiation of the steel surface is investigated by simulating chloride transport considering binding. The transport of iron from the steel surface, its subsequent precipitation into rust, and the associated pre

Hossein Bahak, Farzaneh Taheri, Zahra Zojaji, Arefeh Kazemi

In the current era, a multitude of language models has emerged to cater to user inquiries. Notably, the GPT-3.5 Turbo language model has gained substantial attention as the underlying technology for ChatGPT. Leveraging extensive parameters, this model adeptly responds to a wide range of questions. However, due to its reliance on internal knowledge, the accur

Peng Gao, Li-Zheng Lv, Xin Li

We study the excitations of dark solitons in a nonlinear optical fiber with the second- and fourth-order dispersion, and find the emergence of striped dark solitons (SDSs) and some multi-dark-soliton bound states. The SDSs can exhibit time-domain oscillating structures on a plane wave, and they have two types: the ones with or without the total phase step, w

Guanwen Zhong, Aditya Kolekar, Burin Amornpaisannon, Inho Choi

Today's data centers consist of thousands of network-connected hosts, each with CPUs and accelerators such as GPUs and FPGAs. These hosts also contain network interface cards (NICs), operating at speeds of 100Gb/s or higher, that are used to communicate with each other. We propose RecoNIC, an FPGA-based RDMA-enabled SmartNIC platform that is designed for com

Tao Sun, Hai-Wei Sun

In this paper, we investigate the numerical solution of the two-dimensional fractional Laplacian wave equations. After splitting out the Riesz fractional derivatives from the fractional Laplacian, we treat the Riesz fractional derivatives with an implicit scheme while solving the rest part explicitly. Thanks to the tensor structure of the Riesz fractional de

Kristian Georgiev, Joshua Vendrow, Hadi Salman, Sung Min Park

Diffusion models trained on large datasets can synthesize photo-realistic images of remarkable quality and diversity. However, attributing these images back to the training data-that is, identifying specific training examples which caused an image to be generated-remains a challenge. In this paper, we propose a framework that: (i) provides a formal notion of

Zhuoye Han, Tiandong Wang, Zhiliang Ying

In the analysis of complex networks, centrality measures and community structures play pivotal roles. For multilayer networks, a critical challenge lies in effectively integrating information across diverse layers while accounting for the dependence structures both within and between layers. We propose an innovative two-stage regression model for multilayer

Yitong Wang, Chang Liu, Jun Zhao

AI-Generated Content (AIGC), as a novel manner of providing Metaverse services in the forthcoming Internet paradigm, can resolve the obstacles of immersion requirements. Concurrently, edge computing, as an evolutionary paradigm of computing in communication systems, effectively augments real-time interactive services. In pursuit of enhancing the accessibilit

Yitong Wang, Chang Liu, Jun Zhao

Multiplicative Programming (MP) pertains to a spectrum of optimization problems that involve product term(s). As computational paradigms of communication systems continue to evolve, particularly concerning the offloading strategies of computationally intensive tasks simultaneously to centralized or decentralized servers, designing or optimizing effective com

Linlin Li, Kecheng Zhang, Wenyuan Cui, Jianrong Shi

Carbon stars are excellent kinematic tracers of galaxies and play important roles in understanding the evolution of the Galaxy. Therefore, it is worthwhile to search for them in a large amount of spectra. In this work, we build a new carbon star catalog based on the LAMOST DR7 spectra. The catalog contains 4542 spectra of 3546 carbon stars, identified throug

Shuai Yuan, Liuquan Yao, Yuan Li, Huazi Zhang

In this paper, we study the lossless analog compression for i.i.d. nonsingular signals via the polarization-based framework. We prove that for nonsingular source, the error probability of maximum a posteriori (MAP) estimation polarizes under the Hadamard transform, which extends the polarization phenomenon to analog domain. Building on this insight, we propo

Shangbo Wu, Yu-an Tan, Yajie Wang, Ruinan Ma

Adversarial transferability enables black-box attacks on unknown victim deep neural networks (DNNs), rendering attacks viable in real-world scenarios. Current transferable attacks create adversarial perturbation over the entire image, resulting in excessive noise that overfit the source model. Concentrating perturbation to dominant image regions that are mod