April 2024 arXiv papers — page 146

Miguel Jiménez Arribas, Agustín Martínez Hellín, Manuel Prieto Mateo, Iván Gamino del Río

The ability to collect statistics about the execution of a program within a CPU is of the utmost importance across all fields of computing since it allows characterizing the timing performance of a program. This capability is even more relevant in safety-critical software systems, where it is mandatory to analyze software timing requirements to ensure the co

Boming Xia, Qinghua Lu, Liming Zhu, Zhenchang Xing

The advent of advanced AI underscores the urgent need for comprehensive safety evaluations, necessitating collaboration across communities (i.e., AI, software engineering, and governance). However, divergent practices and terminologies across these communities, combined with the complexity of AI systems-of which models are only a part-and environmental affor

Jiahuang Chen, Siqi He

In this article, we study the eigenvalues and eigenfunction problems for the Laplace operator on multivalued functions, defined on the complement of the 2n points on the round sphere. These eigenvalues and eigensections could also be viewed as functions on the configuration spaces of points, introduced and systematically studied by Taubes-Wu. Critical eigenf

Ming Li, Lin Li, Xiaohui Tao, Jimmy Xiangji Huang

Meal recommendation, as a typical health-related recommendation task, contains complex relationships between users, courses, and meals. Among them, meal-course affiliation associates user-meal and user-course interactions. However, an extensive literature review demonstrates that there is a lack of publicly available meal recommendation datasets including me

Rintaro Eto, Masahito Mochizuki

We study the effects of photoirradiation with circularly polarized light on the Dirac half-metal state induced by the ferrimagnetic order in a triangular Kondo-lattice model. Our analysis based on the Floquet theory reveals that two types of Floquet Chern insulator phases appear as photoinduced nonequilibrium steady states and that these two phases can be ex

Dazhong Shen, Guanglu Song, Zeyue Xue, Fu-Yun Wang

Classifier-Free Guidance (CFG) has been widely used in text-to-image diffusion models, where the CFG scale is introduced to control the strength of text guidance on the whole image space. However, we argue that a global CFG scale results in spatial inconsistency on varying semantic strengths and suboptimal image quality. To address this problem, we present a

EunKang Kim, Biswajit Jana, Aayush Arya, Michael Block

We report first results obtained during the commissioning of the Laser Resonance Chromatography (LRC) apparatus, which is conceived to enable atomic structure investigations in the region of the heaviest elements beyond nobelium. In our studies we first established optimum conditions for the operation of the different components of the setup, including the r

Max Karrer, Bernd Aichner, Katja Wurster, César Magén

Controlled engineering of vortex pinning sites in copper-oxide superconductors is a critical issue in manufacturing devices based on magnetic flux quanta. To address this, we employed a focused He-ion beam (He-FIB) to irradiate thin YBa$_2$Cu$_3$O$_{7-\delta}$ films and create ultradense hexagonal arrays of defects with lattice spacings as small as 20 nm. Cr

Martin Friesen

We introduce a local non-determinism condition for Volterra It\^{o} processes that captures smoothing properties of possibly degenerate noise. By combining the stochastic sewing lemma with one-step Euler approximations, we first prove the joint space-time regularity for their occupation measure, self-intersection measure, and time marginals for such Volterra

Sujiphat Janaun, Anajak Phonchantuek, Pichet Vanichchapongjaroen

The Sen formulation for chiral $(2p)$-form in $4p+2$ dimensions describes a system with two separate sectors, one is physical while the other is unphysical. Each contains a chiral form and a metric. In this paper, we focus on the cases where the self-duality condition for the unphysical sector is linear while for the physical sector can be nonlinear. We show

Akriti Srivastava, Mubasher Rashid

Cellular decision-making (CDM) is a dynamic phenomenon often controlled by regulatory networks defining interactions between genes and transcription factor proteins. Traditional studies have focussed on molecular switches such as positive feedback circuits that exhibit at most bistability. However, higher-order dynamics such as tristability is also prominent

Timofey Mukha, Philipp Schlatter

This article analyses the simulation methodology for wall-modeled large-eddy simulations using solvers based on the spectral-element method (SEM). To that end, algebraic wall modeling is implemented in the popular SEM solver Nek5000. It is combined with explicit subgrid-scale (SGS) modeling, which is shown to perform better than the high-frequency filtering

Kai Tu, Zhi Chen, Man-Chung Yue

Robust optimization (RO) is a powerful paradigm for decision making under uncertainty. Existing algorithms for solving RO, including the reformulation approach and the cutting-plane method, do not scale well, hindering the application of RO to large-scale decision problems. In this paper, we devise a first-order algorithm for solving RO based on a novel max-

Pasquale Tucci

In the Occhialini-Dilworth Archives of the University of Milan are preserved four typescripts by E. Amaldi, dealing with F.G. Houtermans, German physicist, who fled to the USSR to escape the Nazis. During a Stalinist purge he was arrested. The typescripts were sent to the two Milanese physicists so that they might give a judgement. G.S. Occhialini and C. Dil

Yu-Ming Yang, Xiao-Jun Bi, Peng-Fei Yin

In recent astronomical observations, an almost dark galaxy, designated as Nube, has unveiled an intriguing anomaly in its stellar distribution. Specifically, Nube exhibits an exceptionally low central brightness, with the 2D half-light radius of its stars far exceeding the typical values found in dwarf galaxies, and even surpassing those observed in ultra-di

Taixia Shi, Yang Chen, Jianping Yao

Integration of radar, wireless communications, and spectrum sensing is being investigated for 6G with an increased spectral efficiency. Microwave photonics (MWP), a technique that combines microwave engineering and photonic technology to take advantage of the wide bandwidth offered by photonics for microwave signal generation and processing is considered an

Rabia Bashir, Ramoona Shahzadi, M. U. Ashraf

This study presents a comprehensive analysis of particle production in proton-proton ($pp$) collisions at $\sqrt{s}$ = 7 TeV using Pythia~8 event generator. We investigate the transverse momentum $p_T$ spectra of light charged hadrons ($\pi^\pm$, $K^\pm$ and $p(\bar p)$), their yield ratios ($\pi^-/\pi^+$, $K^-/K^+$ and $\bar{p}/p$), and $p_T$-differential r

Andrea Pinto, Antonio Scala

Securitization is a financial process where the cash flows of income-generating assets are sold to institutional investors as securities, liquidating illiquid assets. This practice presents persistent challenges due to the absence of a comprehensive mathematical framework for structuring asset-backed securities. While existing literature provides technical a

Alessio Marino, Clara Dehman, Konstantinos Kovlakas, Nanda Rea

Neutron stars are the dense and highly magnetic relics of supernova explosions of massive stars. The quest to constrain the Equation of State (EoS) of ultra-dense matter and thereby probe the behavior of matter inside neutron stars, is one of the core goals of modern physics and astrophysics. A promising method involves investigating the long-term cooling of

Kenji Sakugawa

We study the action of the infinite Frobenius on the de Rham fundamental groups of affine curves defined over $\bfR$. As an application, we compute extension classes of real mixed Hodge structures associated with the motivic fundamental groups of affine curves. In the case of modular curves, we relate our computation to special values of Rankin-Selberg L-fun

Dennis Kudlacik, Nataliia E. Kopteva, Mladen Kotur, Dmitri R. Yakovlev

Optical orientation of carrier spins by circularly polarized light is the basic concept and tool of spin physics in semiconductors. We study the optical orientation of electrons and holes in a crystal of the FA$_{0.9}$Cs$_{0.1}$PbI$_{2.8}$Br$_{0.2}$ lead halide perovskite by means of polarized photoluminescence, time-resolved differential reflectivity, and t

Jan Klhufek, Miroslav Safar, Vojtech Mrazek, Zdenek Vasicek

Energy efficiency and memory footprint of a convolutional neural network (CNN) implemented on a CNN inference accelerator depend on many factors, including a weight quantization strategy (i.e., data types and bit-widths) and mapping (i.e., placement and scheduling of DNN elementary operations on hardware units of the accelerator). We show that enabling rich

Anton Pozharskiy, Armin Nurkanović, Moritz Diehl

The Finite Elements with Switch Detection (FESD) method is a highly accurate direct transcription method for optimal control of several classes of nonsmooth dynamical systems. This paper extends the FESD method to Projected Dynamical Systems (PDS) and first-order sweeping processes with time-independent sets. This method discretizes an equivalent dynamic com

Sai Bhargav Rongali, Sarthak Mehrotra, Ankit Jha, Mohamad Hassan N C

In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes. A salient challenge arises due to domain shifts between these datasets. To address this, we present a novel setting: Across Domain Generalized Category Discovery (AD-GCD) and bring forth CDAD-NET (Class Discoverer

Atnafu Lambebo Tonja, Fazlourrahman Balouchzahi, Sabur Butt, Olga Kolesnikova

The paper focuses on the marginalization of indigenous language communities in the face of rapid technological advancements. We highlight the cultural richness of these languages and the risk they face of being overlooked in the realm of Natural Language Processing (NLP). We aim to bridge the gap between these communities and researchers, emphasizing the nee

Tiago Fonseca, Luis Ferreira, Bernardo Cabral, Ricardo Severino

Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) emerge as a potential solution to the Electric Vehicles' (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced prospective, the adoption o

Sangin Kim, C. Y. Hui, Jianqi Yan, Alex P. Leung

Because of the small strain amplitudes of gravitational-wave (GW) signals, unveiling them in the presence of detector/environmental noise is challenging. For visualizing the signals and extracting its waveform for a comparison with theoretical prediction, a frequency-domain whitening process is commonly adopted for filtering the data. In this work, we propos

Qi Li, Xianjun Zeng, Shuliang Wang, Wenhao Zhu

Missing datasets, in which some objects have missing values in certain dimensions, are prevalent in the Real-world. Existing clustering algorithms for missing datasets first impute the missing values and then perform clustering. However, both the imputation and clustering processes require input parameters. Too many input parameters inevitably increase the d

Michal Pinos, Lukas Sekanina, Vojtech Mrazek

Integrating the principles of approximate computing into the design of hardware-aware deep neural networks (DNN) has led to DNNs implementations showing good output quality and highly optimized hardware parameters such as low latency or inference energy. In this work, we present ApproxDARTS, a neural architecture search (NAS) method enabling the popular diff

Hassan Keshvarikhojasteh, Josien Pluim, Mitko Veta

This paper introduces MAD-MIL, a Multi-head Attention-based Deep Multiple Instance Learning model, designed for weakly supervised Whole Slide Images (WSIs) classification in digital pathology. Inspired by the multi-head attention mechanism of the Transformer, MAD-MIL simplifies model complexity while achieving competitive results against advanced models like

Mischa Huisman, Carlos Murguia, Erjen Lefeber, Nathan van de Wouw

To enhance the robustness of cooperative driving to cyberattacks, we study a controller-oriented approach to mitigate the effect of a class of False-Data Injection (FDI) attacks. By reformulating a given dynamic Cooperative Adaptive Cruise Control scheme (the base controller), we show that a class of new but equivalent controllers (base controller realizatio

Marta Leocata, Julien Vovelle

We prove the existence of global-in-time regular solutions to a system of stochastic quadratic reaction-diffusion equations. Global-in-time existence is based on a $L^\infty$-estimate obtained by an approach {\`a} la De Giorgi, as in [GoudonVasseur10]. The adaptation of this technique to the stochastic case requires in its final step an $L^2\ln(L^2)$-bound,

Quentin Renau, Emma Hart

The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selection that use short trajectories obtained from running a solver as input have shown promise. However, it is unclear to what extent these trajectories reliably discriminate between sol

Sarah-Alexa Hauschild, Nicole Marheineke

The high volatility of renewable energies calls for more energy efficiency. Thus, different physical systems need to be coupled efficiently although they run on various time scales. Here, the port-Hamiltonian (pH) modeling framework comes into play as it has several advantages, e.g., physical properties are encoded in the system structure and systems running

David Hagens, Jan M. Knaup, Elke Hergenröther, Andreas Weinmann

The automation of games using Deep Reinforcement Learning Strategies (DRL) is a well-known challenge in AI research. While for feature extraction in a video game typically the whole image is used, this is hardly practical for many real world games. Instead, using a smaller game state reducing the dimension of the parameter space to include essential paramete

Jennifer J. Abreu, Alyxander R. Anchordoqui, Nyamekye J. Fosu, Michael G. Kwakye

Millimeter-waveband spectra of Venus from both the James Clerk Maxwell Telescope (JCMT) and the Atacama Large Millimeter/submillimeter Array (ALMA) seem to indicate there may be evidence (signal-to-noise ratio of about $15\sigma$) of a phosphine absorption-line profile against the thermal background from deeper, hotter layers of the atmosphere. Phosphine is

George Haller, Roshan S. Kaundinya

We extend the theory of spectral submanifolds (SSMs) to general non-autonomous dynamical systems that are either weakly forced or slowly varying. Examples of such systems arise in structural dynamics, fluid-structure interactions and control problems. The time-dependent SSMs we construct under these assumptions are normally hyperbolic and hence will persist

S. Aiello, A. Albert, M. Alshamsi, S. Alves Garre

Gamma-ray bursts are promising candidate sources of high-energy astrophysical neutrinos. The recent GRB 221009A event, identified as the brightest gamma-ray burst ever detected, provides a unique opportunity to investigate hadronic emissions involving neutrinos. The KM3NeT undersea neutrino detectors participated in the worldwide follow-up effort triggered b

J. van Bon

Let $q$ be a power of the prime 3. A locally 5-arc transitive $G$-graph of pushing up type is constructed for each value of $q$. For $q=3$, the $G$-graph constructed provides an example of a graph with a vertex stabilizer amalgam of shape ${\cal E}_1$ in the sense of [1]. Whereas, for the other values of $q$, the vertex stabilizer amalgam of the $G$-graph is

J. Clarke, C. Karoumpis, D. Riechers, B. Magnelli

We have implemented a novel method to create simulated [CII] emission line intensity mapping (LIM) data cubes using COSMOS 2020 galaxy catalogue data. It allows us to provide solid lower limits for previous simulation-based model predictions and the expected signal strength of upcoming surveys. We applied [CII]158$\mu$m luminosity models to COSMOS 2020 to cr

Umberto Albertin, Alessandro Navone, Mauro Martini, Marcello Chiaberge

Ultra-Wideband (UWB) technology is an emerging low-cost solution for localization in a generic environment. However, UWB signal can be affected by signal reflections and non-line-of-sight (NLoS) conditions between anchors; hence, in a broader sense, the specific geometry of the environment and the disposition of obstructing elements in the map may drasticall

Chengyan Fu, Wenjie Wang

Randomized smoothing is the primary certified robustness method for accessing the robustness of deep learning models to adversarial perturbations in the l2-norm, by adding isotropic Gaussian noise to the input image and returning the majority votes over the base classifier. Theoretically, it provides a certified norm bound, ensuring predictions of adversaria

James A. Duffy, Sophocles Mavroeidis

While it is widely recognised that linear (structural) VARs may fail to capture important aspects of economic time series, the use of nonlinear SVARs has to date been almost entirely confined to the modelling of stationary time series, because of a lack of understanding as to how common stochastic trends may be accommodated within nonlinear models. This has

Yu-Hsi Chen

Automated data labeling techniques are crucial for accelerating the development of deep learning models, particularly in complex medical imaging applications. However, ensuring accuracy and efficiency remains challenging. This paper presents iterative refinement strategies for automated data labeling in facial landmark diagnosis to enhance accuracy and effic

Lorenzo Oghittu, Juliette Simonet, Philipp Wessels-Staarmann, Markus Drescher

We investigate the dynamics of an ion moving through a homogeneous Bose-Einstein condensate (BEC) after an initial momentum is imparted. For this, we derive a master equation in the weak-coupling limit and Lamb-Dicke approximation for the reduced density matrix of the ion. We study the time evolution of the ion's kinetic energy and observe that its expectati

Wei Gao, Pengyu Shi, Matteo Parsani, Pedro Costa

In particle-laden turbulent wall flows, lift forces can influence the near-wall turbulence. This has been recently observed in particle-resolved simulations, which, however, are too expensive to be used in upscaled models. Instead, point-particle simulations have been the method of choice to simulate the dynamics of these flows during the last decades. While

Giovanni Abramo, Ciriaco Andrea D'Angelo, Leonardo Grilli

In the evaluation of scientific publications' impact, the interplay between intrinsic quality and non-scientific factors remains a subject of debate. While peer review traditionally assesses quality, bibliometric techniques gauge scholarly impact. This study investigates the role of non-scientific attributes alongside quality scores from peer review in deter

Elisa Conti, Armando Vannucci, Amina Piemontese, Giulio Colavolpe

In the context of signal detection in the presence of an unknown time-varying channel parameter, receivers based on the Expectation Propagation (EP) framework appear to be very promising. EP is a message-passing algorithm based on factor graphs with an inherent ability to combine prior knowledge of system variables with channel observations. This suggests th

Matteo Sperti, Marco Ambrosio, Mauro Martini, Alessandro Navone

Autonomous navigation is the foundation of agricultural robots. This paper focuses on developing an advanced autonomous navigation system for a rover operating within row-based crops. A position-agnostic system is proposed to address the challenging situation when standard localization methods, like GPS, fail due to unfavorable weather or obstructed signals.

Sirui Wang, Peiguang Li, Yunsen Xian, Hongzhi Zhang

The sequential recommendation task aims to predict the item that user is interested in according to his/her historical action sequence. However, inevitable random action, i.e. user randomly accesses an item among multiple candidates or clicks several items at random order, cause the sequence fails to provide stable and high-quality signals. To alleviate the

Shoffan Saifullah, Andri Pranolo, Rafał Dreżewski

This study systematically investigates the impact of image enhancement techniques on Convolutional Neural Network (CNN)-based Brain Tumor Segmentation, focusing on Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and their hybrid variations. Employing the U-Net architecture on a dataset of 3064 Brain MRI images, the rese

Shaun Lung, Kai Wang, Nicolas R. H. Pedersen, Frank Setzpfandt

We formulate a new conceptual approach for one-shot complete polarization state measurement with nanostructured metasurfaces applicable to classical light and multi-photon quantum states, by drawing on the principles of generalized quantum measurements based on positive operator-valued measures (POVMs). Accurate polarization reconstruction from a combination

Raphael Schmetterling, Fulvio Forni, Alessio Franci, Rodolphe Sepulchre

We illustrate the potential of neuromorphic control on the simple mechanical model of a pendulum, with both event-based actuation and sensing. The controller and the pendulum are regarded as event-based systems that occasionally interact to coordinate their respective rhythms. Control occurs through a proper timing of the interacting events. We illustrate th

Alessandro Navone, Mauro Martini, Marco Ambrosio, Andrea Ostuni

Segmentation-based autonomous navigation has recently been presented as an appealing approach to guiding robotic platforms through crop rows without requiring perfect GPS localization. Nevertheless, current techniques are restricted to situations where the distinct separation between the plants and the sky allows for the identification of the row's center. H

Chenxu Wang, Bin Dai, Huaping Liu, Baoyuan Wang

Prominent large language models have exhibited human-level performance in many domains, even enabling the derived agents to simulate human and social interactions. While practical works have substantiated the practicability of grounding language agents in sandbox simulation or embodied simulators, current social intelligence benchmarks either stay at the lan

Tommaso Marcato, Jiwoo Oh, Zhan-Hong Lin, Sunil B. Shivarudraiah

Miniaturization of light-emitting diodes (LEDs) can enable high-resolution augmented and virtual reality displays and on-chip light sources for ultra-broadband chiplet communication. However, unlike silicon scaling in electronic integrated circuits, patterning of inorganic III-V semiconductors in LEDs considerably compromises device efficiencies at submicrom

Gian Marti, Flurin Arquint, Christoph Studer

Spatial filtering based on multiple-input multiple-output (MIMO) processing is a promising approach to jammer mitigation. Effective MIMO data detectors that mitigate smart jammers have recently been proposed, but they all assume perfect time synchronization between transmitter(s) and receiver. However, to the best of our knowledge, there are no methods for r

Haiying Ren, Yuanyuan Song, Rui Peng

A knowledge search is a key process for inventions. However, there is inadequate quantitative modeling of dynamic knowledge search processes and associated search costs. In this study, agent-based and complex network methodologies were proposed to quantitatively describe the dynamic process of knowledge search for actual inventions. Prior knowledge networks

Tomás Osório, Bernardo Leite, Henrique Lopes Cardoso, Luís Gomes

Leveraging research on the neural modelling of Portuguese, we contribute a collection of datasets for an array of language processing tasks and a corresponding collection of fine-tuned neural language models on these downstream tasks. To align with mainstream benchmarks in the literature, originally developed in English, and to kick start their Portuguese co

Borui Zhang, Zhao Zhang, Chengfeng Cai, Hong-Hao Zhang

In this paper, we conduct a comprehensive study of the Next-to-Minimal Composite Higgs Model (NMCHM) extended with a dilaton field $\chi$ (denoted as NMCHM$_\chi$). A pseudo-Nambu-Goldstone boson (pNGB) $\eta$, resulting from the SO(6)$\to$SO(5) breaking, serves as a dark matter (DM) candidate. The inclusion of the dilaton field is helpful for evading the st

Zhiqi Huang, Huixin Xiong, Haoyu Wang, Longguang Wang

Text-to-image generation has witnessed great progress, especially with the recent advancements in diffusion models. Since texts cannot provide detailed conditions like object appearance, reference images are usually leveraged for the control of objects in the generated images. However, existing methods still suffer limited accuracy when the relationship betw

Xiao-Qiang Shao, Shi-Lei Su, Lin Li, Rejish Nath

Dense atom ensembles with Rydberg excitations display intriguing collective effects mediated by their strong, long-range dipole-dipole interactions. These collective effects, often modeled using Rydberg superatoms, have gained significant attention across various fields due to their potential applications in quantum information processing and quantum optics.

Jing Ye, Minzhi Fan, Xiaoyu Zhang, Shasha Lu

Graphene based nanomaterials have attracted significant attention for their potentials in biomedical and biotechnology applications in recent years, owing to the outstanding physical and chemical properties. However, the interaction mechanism and impact on biological activity of macro and micro biomolecules still require more concerns and further research in

Zhi-Gang Wang

We suppose that there exist three vector hidden-charm tetraquark states with the $J^{PC}=1^{--}$ at the energy about $4.5\,\rm{GeV}$, and investigate the two-body strong decays systematically. We obtain thirty QCD sum rules for the hadronic coupling constants based on rigorous quark-hadron duality, then obtain the partial decay widths, therefore the total wi

Vikrant V. Jadhav, Pavel Kroupa, Wenjie Wu, Jan Pflamm-Altenburg

Empirical constraints on the internal dynamics of open clusters are important for understanding their evolution and evaporation. High precision astrometry from Gaia DR3 are thus useful to observe aspects of the cluster dynamics. This work aims to identify dynamically peculiar clusters such as spinning and expanding clusters. We also quantify the spin frequen

Joint Physics Analysis Center Collaboration, D. Winney, A. Pilloni, R. J. Perry

Inclusive production processes will be important for the first observations of $XYZ$ states at new generation electron-hadron colliders, as they generally benefit from larger cross sections than their exclusive counterparts. We make predictions of semi-inclusive photoproduction of the $\chi_{c1}(1P)$ and $X(3872)$, whose peripheral production is assumed to b

Lisa V. Winkler, Kirsten Gerritsma, Albert van Rees, Philip P. J. Schrinner

We present hybrid-integrated extended cavity diode lasers tunable around 637 nm, with a gain-wide spectral coverage of 8 nm. This tuning range allows addressing the zero-phonon line of nitrogen vacancy centers and includes the wavelength of HeNe lasers (633 nm). The lasers provide wide mode-hop free tuning up to 97 GHz and a narrow intrinsic linewidth down t

Antonio Giganti, Sara Mandelli, Paolo Bestagini, Umberto Giuriato

Due to the latest environmental concerns in keeping at bay contaminants emissions in urban areas, air pollution forecasting has been rising the forefront of all researchers around the world. When predicting pollutant concentrations, it is common to include the effects of environmental factors that influence these concentrations within an extended period, lik

Hossein Fatheddin, Sedighe Sajadian

Transit photometry is currently the most efficient and sensitive method for detecting extrasolar planets (exoplanets) and a large majority of confirmed exoplanets have been detected with this method. The substantial success of space-based missions such as NASA's Kepler/K2 and Transiting Exoplanet Survey Satellite (TESS) has generated a large and diverse samp

Marcel Balle, Wenxiu Xu, Kevin FA Darras, Thomas Cherico Wanger

Recent advances in Internet of Things (IoT) and Artificial Intelligence (AI) technologies help ecosystem monitoring to shift towards automated monitoring with low power sensors and embedded vision on powerful processing units. Vision-based monitoring devices need an effective power management and control system (PMCS) with system-adapted power input and outp

Vibhoothi Vibhoothi, Julien Zouein, François Pitié, Anil Kokaram

Modern video encoders have evolved into sophisticated pieces of software in which various coding tools interact with each other. In the past, singlepass encoding was not considered for Video-On-Demand (VOD) use cases. In this work, we evaluate production-ready encoders for H.264 (x264), H.265 (HEVC), AV1 (SVT-AV1) along with direct comparisons to the latest

Sangyi Wu, Jialong Xue, Shaoxuan Zhou, Xianghang Mi

As an emerging black hat search engine optimization (SEO) technique, reflected search poisoning (RSP) allows a miscreant to free-ride the reputation of high-ranking websites, poisoning search engines with illicit promotion texts (IPTs) in an efficient and stealthy manner, while avoiding the burden of continuous website compromise as required by traditional p

Matthias Salzger, V. Vilasini

Formalisms for higher order quantum processes provide a theoretical formalisation of quantum processes where the order of agents' operations need not be definite and acyclic, but may be subject to quantum superpositions. This has led to the concept of indefinite causal structures (ICS) which have garnered much interest. However, the interface between these i

Hao Ma, Melanie Zeilinger, Michael Muehlebach

We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic systems. Our problem formulation accommodates constraints that model the evolution of a cyber-physical system, which has, in general, a continuous state and action space, is nonlinear,

Giuseppe Macario

This work proposes a WebXR-based cross-platform conceptual architecture, leveraging the A-Frame and Networked-Aframe frameworks, in order to facilitate the development of an open, accessible, and interoperable metaverse. By introducing the concept of spatial web app, this research contributes to the discourse on the metaverse, offering an architecture that d

Tim K. Smit, Hajo A. Reijers, Xixi Lu

Predictive Process Monitoring focuses on predicting future states of ongoing process executions, such as forecasting the remaining time. Recent developments in Object-Centric Process Mining have enriched event data with objects and their explicit relations between events. To leverage this enriched data, we propose the Heterogeneous Object Event Graph encodin

Pooja, Mohammad Yousuf Jamal, Partha Pratim Bhaduri, Marco Ruggieri

This study investigates the evolution and dissociation dynamics of $c\bar{c}$ and $b\bar{b}$ pairs within the pre-equilibrium, gluon-dominated stage of high energy nuclear collisions. An attractive potential made of a perturbative Coulomb-like term and of a confining term is used to simulate the attractive strong force in the pairs. Besides, we implement the

Edoardo Bocchi, Filippo Gazzola

We introduce a new measure for the stability of structures, such as the cross-section of the deck of a suspension bridge, subject to a 2D fluid force, such as the lift exerted by a laminar wind. We consider a wide class of possible flows, as well as a wide class of structural shapes. Within a suitable topological framework, we prove the existence of an optim

Youjun Wang

Let $j\geq 2$ be a given integer. Let $f$ be a normalized primitive holomorphic cusp form of even integral weight for the full modular group $\Gamma=SL(2,\mathbb{Z})$. Denote by $\lambda_{\text{sym}^{j}f}(n)$ the $n$th normalized coefficient of the Dirichlet expansion of the $j$th symmetric power $L$-function $L(s,\text{sym}^{j}f)$. In this paper, we are int

Sergei D. Odintsov, Tanmoy Paul, Soumitra SenGupta

We examine the second law of thermodynamics in the context of horizon cosmology, in particular, whether the change of total entropy (i.e. the sum of the entropy for the apparent horizon and the entropy for the matter fields) proves to be positive with the cosmic expansion of the universe. The matter fields inside the horizon obey the thermodynamics of an ope

Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe

We study the unique, less-well understood problem of generating sparse adversarial samples simply by observing the score-based replies to model queries. Sparse attacks aim to discover a minimum number-the l0 bounded-perturbations to model inputs to craft adversarial examples and misguide model decisions. But, in contrast to query-based dense attack counterpa

Santiago Hernández-Gómez, Francesco Poggiali, Paola Cappellaro, Francesco S. Cataliotti

Exchange energy statistics between two bodies at different thermal equilibrium obey the Jarzynski-W\'ojcik fluctuation theorem. The corresponding energy scale factor is the difference of the inverse temperatures associated to the bodies at equilibrium. In this work, we consider a dissipative quantum dynamics leading the quantum system towards a, possibly non

Philipp Rigoll, Laurenz Adolph, Lennart Ries, Eric Sax

Perception systems, especially cameras, are the eyes of automated driving systems. Ensuring that they function reliably and robustly is therefore an important building block in the automation of vehicles. There are various approaches to test the perception of automated driving systems. Ultimately, however, it always comes down to the investigation of the beh

Kenneth L. Baker, Kimihiko Motegi

Twisting a given knot $K$ about an unknotted circle $c$ a full $n \in \mathbb{N}$ times, we obtain a "twist family" of knots $\{ K_n \}$. Work of Kouno-Motegi-Shibuya implies that for a non-trivial twist family the crossing numbers $\{c(K_n)\}$ of the knots in a twist family grows unboundedly. However potentially this growth is rather slow and may never beco

Mathias Drton, Leonard Henckel, Benjamin Hollering, Pratik Misra

The implication problem for conditional independence (CI) asks whether the fact that a probability distribution obeys a given finite set of CI relations implies that a further CI statement also holds in this distribution. This problem has a long and fascinating history, cumulating in positive results about implications now known as the semigraphoid axioms as

Hou-I Liu, Marco Galindo, Hongxia Xie, Lai-Kuan Wong

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrolle

Sam Mattheus, Geertrui Van de Voorde

We use techniques from algebraic and extremal combinatorics to derive upper bounds on the number of independent sets in several (hyper)graphs arising from finite geometry. In this way, we obtain asymptotically sharp upper bounds for partial ovoids and EKR-sets of flags in polar spaces, line spreads in $\mathrm{PG}(2r-1,q)$ and plane spreads in $\mathrm{PG}(5

Ernst Seidel, Pejman Mowlaee, Tim Fingscheidt

In recent years, the introduction of neural networks (NNs) into the field of speech enhancement has brought significant improvements. However, many of the proposed methods are quite demanding in terms of computational complexity and memory footprint. For the application in dedicated communication devices, such as speakerphones, hands-free car systems, or sma

Omran Ayoub, Davide Andreoletti, Aleksandra Knapińska, Róża Goścień

Adapting to concept drift is a challenging task in machine learning, which is usually tackled using incremental learning techniques that periodically re-fit a learning model leveraging newly available data. A primary limitation of these techniques is their reliance on substantial amounts of data for retraining. The necessity of acquiring fresh data introduce

Paul Scheffler, Luca Colagrande, Luca Benini

Stencil codes are performance-critical in many compute-intensive applications, but suffer from significant address calculation and irregular memory access overheads. This work presents SARIS, a general and highly flexible methodology for stencil acceleration using register-mapped indirect streams. We demonstrate SARIS for various stencil codes on an eight-co

L Horoto, F G Scholtz

By assuming that the geometry of spacetime is uniquely determined by the energy momentum tensor of matter alone, i.e. without any interactions, enables us to construct the Lagrangian from which the metric of higher dimensional spacetime follows. From the geodesic equations that follow it becomes clear that the incorrect mass of elementary particles predicted

Christian G. Boehmer, Antonio d'Alfonso del Sordo

Cosmological models can be studied effectively using dynamical systems techniques. Starting from Brown's formulation of the variational principle for relativistic fluids, we introduce new types of couplings involving a perfect fluid, a scalar field, and boundary terms. We describe three different coupling models, one of which turns out to be particularly rel

Su-Xi Yu, Jing-Yuan He, Yi Wang, Yu-Jiao Cai

Graves' disease is a common condition that is diagnosed clinically by determining the smoothness of the thyroid texture and its morphology in ultrasound images. Currently, the most widely used approach for the automated diagnosis of Graves' disease utilizes Convolutional Neural Networks (CNNs) for both feature extraction and classification. However, these me

Weslley G. D. P. Silva, Luis Bonah, Philipp C. Schmid, Stephan Schlemmer

The rotational spectrum of the molecular ion HCNH+ is revisited using double-resonance spectroscopy in an ion trap apparatus, with six transitions measured between 74 and 445 GHz. Due to the cryogenic temperature of the trap, the hyperfine splittings caused by the 14N quadrupolar nucleus were resolved for transitions up to J = 4-3, allowing for a refinement

Jihanne El Haouari, Jean-Michel Gaucel, Christelle Pittet, Jean-Yves Tourneret

Accurate estimates of Instrument Spectral Response Functions (ISRFs) are crucial in order to have a good characterization of high resolution spectrometers. Spectrometers are composed of different optical elements that can induce errors in the measurements and therefore need to be modeled as accurately as possible. Parametric models are currently used to esti

Sujin Han, Jinseo Kim, Sung-Ju Lee, Insu Yun

Decentralized Finance (DeFi) enables many novel applications that were impossible in traditional finances. However, it also introduces new types of vulnerabilities. An example of such vulnerabilities is a composability bug between token contracts and Decentralized Exchange (DEX) that follows the Constant Product Market Maker (CPMM) model. This type of bug, w

Wendlasida Ouedraogo, Andrea Araldo, Badii Jouaber, Hind Castel

Communication and computation services supporting Connected and Automated Vehicles (CAVs) are characterized by stringent requirements, in terms of response time and reliability. Fulfilling these requirements is crucial for ensuring road safety and traffic optimization. The conceptually simple solution of hosting these services in the vehicles increases their

Kento Kawaharazuka, Shogo Makino, Masaya Kawamura, Yuki Asano

The body structures of tendon-driven musculoskeletal humanoids are complex, and accurate modeling is difficult, because they are made by imitating the body structures of human beings. For this reason, we have not been able to move them accurately like ordinary humanoids driven by actuators in each axis, and large internal muscle tension and slack of tendon w

Shijun Mao

We investigate the chiral restoration and deconfinement phase transitions under external magnetic field in frame of a Pauli-Villars regularized PNJL model. A running Polyakov loop scale parameter $T_0(eB)$ is introduced to mimic the reaction of the gluon sector to the presence of magnetic fields. It is found that a decreasing $T_0(eB)$ with magnetic fields c

Kento Kawaharazuka, Kei Tsuzuki, Shogo Makino, Moritaka Onitsuka

The tendon-driven musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex muscle and bone structures is difficult and conventional model-based controls cannot realize intended movements. Therefore, a learning control mechanism that acquires nonlinear relationships between joint angles, muscle tensions, and muscle le