April 2024 arXiv papers — page 35

Hasan F. Ates, Suleyman Yildirim, Bahadir K. Gunturk

Blind single image super-resolution (SISR) is a challenging task in image processing due to the ill-posed nature of the inverse problem. Complex degradations present in real life images make it difficult to solve this problem using na\"ive deep learning approaches, where models are often trained on synthetically generated image pairs. Most of the effort so f

Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng

Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more. In this paper, we propose a framework for rigorously evaluating the capabilities of LLMs on time series understanding, encompassing both univariate and m

Salvatore Torquato, Jaeuk Kim

3D bicontinuous two-phase materials are increasingly gaining interest because of their unique multifunctional characteristics and advancements in techniques to fabricate them. Due to their complex topological and structural properties, it still has been nontrivial to develop explicit microstructure-dependent formulas to predict accurately their physical prop

Yiming Yao, Fei Liu, Ji Cheng, Qingfu Zhang

Many real-world optimization scenarios involve expensive evaluation with unknown and heterogeneous costs. Cost-aware Bayesian optimization stands out as a prominent solution in addressing these challenges. To approach the global optimum within a limited budget in a cost-efficient manner, the design of cost-aware acquisition functions (AFs) becomes a crucial

Ruiyang Wang, Haonan Wang, Junfeng Sun, Mingjia Zhao

In recent years, with the rapid development of computer information technology, the development of artificial intelligence has been accelerating. The traditional geometry recognition technology is relatively backward and the recognition rate is low. In the face of massive information database, the traditional algorithm model inevitably has the problems of lo

Benjamin Schwendinger, Florian Schwendinger, Laura Vana-Gür

In this paper, we show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models to fully automate the model selection process. Concretely, we directly optimize for the Akaike and Bayesian information criteria while imposing constraints designed to deal with multicollinearity in the feature s

Ivana Vasić, Jakša Vučičević

Quantum simulations are quickly becoming an indispensable tool for studying particle transport in correlated lattice models. One of the central topics in the study of transport is the bad-metal behavior, characterized by the direct current (dc) resistivity linear in temperature. In the fermionic Hubbard model, optical conductivity has been studied extensivel

Leandro Di Bella, Yangxintong Lyu, Adrian Munteanu

This paper presents DeepKalPose, a novel approach for enhancing temporal consistency in monocular vehicle pose estimation applied on video through a deep-learning-based Kalman Filter. By integrating a Bi-directional Kalman filter strategy utilizing forward and backward time-series processing, combined with a learnable motion model to represent complex motion

Kuofeng Gao, Jindong Gu, Yang Bai, Shu-Tao Xia

Despite the exceptional performance of multi-modal large language models (MLLMs), their deployment requires substantial computational resources. Once malicious users induce high energy consumption and latency time (energy-latency cost), it will exhaust computational resources and harm availability of service. In this paper, we investigate this vulnerability

Parul Gupta, Munawar Hayat, Abhinav Dhall, Thanh-Toan Do

Few-shot image synthesis entails generating diverse and realistic images of novel categories using only a few example images. While multiple recent efforts in this direction have achieved impressive results, the existing approaches are dependent only upon the few novel samples available at test time in order to generate new images, which restricts the divers

Maria Vasilyeva

In this paper, we consider a time-dependent discrete network model with highly varying connectivity. The approximation by time is performed using an implicit scheme. We propose the coarse scale approximation construction of network models based on the Generalized Multiscale Finite Element Method. An accurate coarse-scale approximation is generated by solving

Venkatesh C, Harshit Oberoi, Anil Goyal, Nikhil Sikka

We propose an end-to-end real-estate recommendation system, RE-RecSys, which has been productionized in real-world industry setting. We categorize any user into 4 categories based on available historical data: i) cold-start users; ii) short-term users; iii) long-term users; and iv) short-long term users. For cold-start users, we propose a novel rule-based en

Petr Hruby, Timothy Duff, Marc Pollefeys

We revisit certain problems of pose estimation based on 3D--2D correspondences between features which may be points or lines. Specifically, we address the two previously-studied minimal problems of estimating camera extrinsics from $p \in \{ 1, 2 \}$ point--point correspondences and $l=3-p$ line--line correspondences. To the best of our knowledge, all of the

Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová

Performance prediction has been a key part of the neural architecture search (NAS) process, allowing to speed up NAS algorithms by avoiding resource-consuming network training. Although many performance predictors correlate well with ground truth performance, they require training data in the form of trained networks. Recently, zero-cost proxies have been pr

Masahito Hayashi

This paper reviews Holevo's contributions to quantum information theory during the 20 century. At that time, he mainly studied three topics, classical-quantum channel coding, quantum estimation with Cramero-Rao approach, and quantum estimation with the group covariant approach. This paper addresses these three topics.

Tahrima Hashem, Negin Yousefpour

Scour around bridge piers is a critical challenge for infrastructures around the world. In the absence of analytical models and due to the complexity of the scour process, it is difficult for current empirical methods to achieve accurate predictions. In this paper, we exploit the power of deep learning algorithms to forecast the scour depth variations around

Daniel Dworak, Mateusz Komorkiewicz, Paweł Skruch, Jerzy Baranowski

In this paper, we propose a novel approach to address the problem of camera and radar sensor fusion for 3D object detection in autonomous vehicle perception systems. Our approach builds on recent advances in deep learning and leverages the strengths of both sensors to improve object detection performance. Precisely, we extract 2D features from camera images

Giampiero Salvi

This paper is concerned with automatic continuous speech recognition using trainable systems. The aim of this work is to build acoustic models for spoken Swedish. This is done employing hidden Markov models and using the SpeechDat database to train their parameters. Acoustic modeling has been worked out at a phonetic level, allowing general speech recognitio

Federico Cipolletta, Nina Schwarz, Matthias Hoelzl, Salvatore Ventre

JOREK is an advanced non-linear simulation code for studying MHD instabilities in magnetically confined fusion plasmas and their control and/or mitigation. A free-boundary and resistive wall extension was introduced via coupling to the STARWALL and CARIDDI codes, both able to provide dense response matrices describing the electromagnetic interactions between

Yosef Nir, Purvaash Panduranghan Udhayashankar

There is now experimental evidence for Higgs boson decay into a pair of muons, and significant constraints on the Higgs boson decay into a charm quark-antiquark pair. The data on Higgs boson decays into second generation fermions probes various extensions of the Standard Model. We analyze the implications for the Standard Model effective field theory (SMEFT)

Subrata Mukherjee, Thibaud Coroller, Craig Wang, Ravi K. Samala

Patients diagnosed with metastatic breast cancer (mBC) typically undergo several radiographic assessments during their treatment. mBC often involves multiple metastatic lesions in different organs, it is imperative to accurately track and assess these lesions to gain a comprehensive understanding of the disease's response to treatment. Computerized analysis

Michael Reiter, Duong Ngoc Son

In this paper, we study CR maps between hyperquadrics and Winkelmann hypersurfaces. Based on a previous study on the CR Ahlfors derivative of Lamel-Son and a recent result of Huang-Lu-Tang-Xiao on CR maps between hyperquadrics, we prove that a transversal CR map from a hyperquadric into a hyperquadric or a Winkelmann hypersurface extends to a local holomorph

Alex Dornburg, Kristin Davin

The advent of generative AI models holds tremendous potential for aiding teachers in the generation of pedagogical materials. However, numerous knowledge gaps concerning the behavior of these models obfuscate the generation of research-informed guidance for their effective usage. Here we assess trends in prompt specificity, variability, and weaknesses in for

Manuel Hauke, Agamemnon Zafeiropoulos

We study the notion of inhomogeneous Poissonian pair correlations, proving several properties that show similarities and differences to its homogeneous counterpart. In particular, we show that sequences with inhomogeneous Poissonian pair correlations need not be uniformly distributed, contrary to what was till recently believed.

Vladlen Galetsky, Pol Julià Farré, Soham Ghosh, Christian Deppe

In this work, we present two new methods for Variational Quantum Circuit (VQC) Process Tomography onto $n$ qubits systems: PT_VQC and U-VQSVD. Compared to the state of the art, PT_VQC halves in each run the required amount of qubits for process tomography and decreases the required state initializations from $4^{n}$ to just $2^{n}$, all while ensuring high-f

Chen Wang, Chao Wang, Gregory Z. Gutin, Xiaoyan Zhang

Let $G=(V, E)$ be a graph and let each vertex of $G$ has a lamp and a button. Each button can be of $\sigma^+$-type or $\sigma$-type. Assume that initially some lamps are on and others are off. The button on vertex $x$ is of $\sigma^+$-type ($\sigma$-type, respectively) if pressing the button changes the lamp states on $x$ and on its neighbors in $G$ (the la

Jan Dereziński, Christian Gaß, Joonas Mikael Vättö

We review properties of confluent functions and the closely related Laguerre polynomials, and determine their bilinear integrals. As is well-known, these integrals are convergent only for a limited range of parameters. However, when one uses the generalized integral they can be computed essentially without restricting the parameters. This gives the (generali

Ye Mao, Junpeng Jing, Krystian Mikolajczyk

Recent open-world 3D representation learning methods using Vision-Language Models (VLMs) to align 3D point cloud with image-text information have shown superior 3D zero-shot performance. However, CAD-rendered images for this alignment often lack realism and texture variation, compromising alignment robustness. Moreover, the volume discrepancy between 3D and

Shen Zhang, Haojie Zhang, Jing Zhang, Xudong Zhang

In human-computer interaction, it is crucial for agents to respond to human by understanding their emotions. Unraveling the causes of emotions is more challenging. A new task named Multimodal Emotion-Cause Pair Extraction in Conversations is responsible for recognizing emotion and identifying causal expressions. In this study, we propose a multi-stage framew

Tim Keil, Mario Ohlberger, Felix Schindler, Julia Schleuß

To efficiently tackle parametrized multi and/or large scale problems, we propose an adaptive localized model order reduction framework combining both local offline training and local online enrichment with localized error control. For the latter, we adapt the residual localization strategy introduced in [Buhr, Engwer, Ohlberger, Rave, SIAM J. Sci. Comput., 2

Guohao Li, Hongyu Yang, Di Huang, Yunhong Wang

Generative 3D face models featuring disentangled controlling factors hold immense potential for diverse applications in computer vision and computer graphics. However, previous 3D face modeling methods face a challenge as they demand specific labels to effectively disentangle these factors. This becomes particularly problematic when integrating multiple 3D f

András Bazsó

We prove effective finiteness results concerning polynomial values of the sums $$ b^k +\left(a+b\right)^k + \cdots + \left(a\left(x-1\right) + b\right)^k $$ and $$ b^k - \left(a+b\right)^k + \left(2a+b\right)^k - \ldots + (-1)^{x-1} \left(a\left(x-1\right) + b\right)^k , $$ where $a \neq 0,b, k$ are given integers with $\gcd(a,b)=1$ and $k \geq 2$.

Emily Sullivan

Explainable AI (xAI) methods are important for establishing trust in using black-box models. However, recent criticism has mounted against current xAI methods that they disagree, are necessarily false, and can be manipulated, which has started to undermine the deployment of black-box models. Rudin (2019) goes so far as to say that we should stop using black-

Lukas Hecht, Kay-Robert Dormann, Kai Luca Spanheimer, Mahdieh Ebrahimi

The Active Matter Evaluation Package (AMEP) is a Python library for analyzing simulation data of particle-based and continuum simulations. It provides a powerful and simple interface for handling large data sets and for calculating and visualizing a broad variety of observables that are relevant to active matter systems. Examples range from the mean-square d

Jonas Teufel, Pascal Friederich

Beyond improving trust and validating model fairness, xAI practices also have the potential to recover valuable scientific insights in application domains where little to no prior human intuition exists. To that end, we propose a method to extract global concept explanations from the predictions of graph neural networks to develop a deeper understanding of t

Filip Novkoski, Jules Fillette, Chi-Tuong Pham, Eric Falcon

We show that the natural resonant frequency of a suspended flexible string is significantly modified (by one order of magnitude) by adding a freely pivoting attached mass at its lower end. This articulated system then exhibits complex nonlinear dynamics such as bending oscillations, similar to those of a swing becoming slack, thereby strongly modifying the s

Deepak P, James Steinhoff, Stanley Simoes

Web search engines arguably form the most popular data-driven systems in contemporary society. They wield a considerable power by functioning as gatekeepers of the Web, with most user journeys on the Web beginning with them. Starting from the late 1990s, search engines have been dominated by the paradigm of link-based web search. In this paper, we critically

Daniel Schober, Ronja Güldenring, James Love, Lazaros Nalpantidis

Laboratory processes involving small volumes of solutions and active ingredients are often performed manually due to challenges in automation, such as high initial costs, semi-structured environments and protocol variability. In this work, we develop a flexible and cost-effective approach to address this gap by introducing a vision-based system for liquid vo

G. Lu, Y. Liu, Y. Jin, Q. Liu

Addressing stability in functional equations is a critical task with broad implications across mathematics and its applications. In this paper, we present a novel direct method for proving the stability of the following equation, \begin{eqnarray*} f(x,y)=\alpha f(f_1(x,y))+\beta f(f_2(x,y)) \end{eqnarray*} subjecting to certain constraints on the constants $

Mohammed A. Alshahrani, Ahmad Adnan Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani

In recent years, there has been a significant expansion in the Internet of Things (IoT), with a growing number of devices being connected to the internet. This has led to an increase in data collection and analysis as well as the development of new technologies and applications. The rise of IoT has also brought about new challenges, such as security concerns

Ivan Hristov, Radoslava Hristova

A new efficient approach for searching three-body periodic equal-mass collisionless orbits passing through Eulerian configuration is presented. The approach is based on a symmetry property of the solutions at the half period. Depending on two previously established symmetry types on the shape sphere, each solution is presented by one or two distinct initial

Tongtong Ren, Yirong Wang, Xiaoyu Dai, Xiaoxu Gao

Ultracold quantum gases are usually prepared in conservative traps for quantum simulation experiments. The atomic density inhomogeneity, together with the consequent position-dependent energy and time scales of cold atoms in traditional harmonic traps, makes it difficult to manipulate and detect the sample at a better level. These problems are partially solv

Jiwei Yu, Zhangwei Wang, Aparna Saksena, Shaolou Wei

Quantitative analysis of microstructural features on the nanoscale, including precipitates, local chemical orderings (LCOs) or structural defects (e.g. stacking faults) plays a pivotal role in understanding the mechanical and physical responses of engineering materials. Atom probe tomography (APT), known for its exceptional combination of chemical sensitivit

Daniel Brady, Simon Ohler, Johannes Otterbach, Michael Fleischhauer

The facilitation of Rydberg excitations in a gas of atoms provides an ideal model system to study epidemic evolution on (dynamic) networks and self organization of complex systems to the critical point of a non-equilibrium phase transition. Using Monte-Carlo simulations and a machine learning algorithm we show that the universality class of this phase transi

Bo Peng, Xiaofeng Li, Xinyu Li, Zhenghan Wang

Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart conditions that can progress to heart failure if untreated. They exhibit similar echocardiographic characteristics, often leading to diagnostic challenges. This paper introduces a novel multi-view deep learning approach that utilizes 2D echocardiography for differentiating between

Jianguo Zhou, Yu-Zhe Liu, Chao Zhang

The present paper mainly considers the representation type of the enveloping algebra of monomial algebra. Let $A$ be a monomial algebra and $A^e= A\otimes_{\mathrm{l}\!\mathrm{k}} A^{\mathrm{op}}$ its enveloping algebra. It is shown that $A^e$ is representation-finite if and only if $A \cong \pmb{A}_n/\mathrm{rad}^2 \pmb{A}_n$, where $\pmb{A}_n$ is the path

Grant Wilkins, Srinivasan Keshav, Richard Mortier

Both the training and use of Large Language Models (LLMs) require large amounts of energy. Their increasing popularity, therefore, raises critical concerns regarding the energy efficiency and sustainability of data centers that host them. This paper addresses the challenge of reducing energy consumption in data centers running LLMs. We propose a hybrid data

Julian Legendre, Eduárd Zsurka, Daniele Di Miceli, Llorenç Serra

Heterostructures of magnetic topological insulators (MTIs) and superconductors (SCs) in two-dimensional (2D) slab and one-dimensional (1D) nanoribbon geometries have been predicted to host, respectively, chiral Majorana edge states (CMESs) and Majorana bound states (MBSs). We study the topological properties of such MTI/SC heterostructures upon variation of

Masahiro Kobayashi, Kazuho Watanabe

This paper focuses on the Bregman divergence defined by the reciprocal function, called the inverse divergence. For the loss function defined by the monotonically increasing function $f$ and inverse divergence, the conditions for the statistical model and function $f$ under which the estimating equation is unbiased are clarified. Specifically, we characteriz

C. Aiswarya, Amaldev Manuel, Saina Sunny

We lift metrics over words to metrics over word-to-word transductions, by defining the distance between two transductions as the supremum of the distances of their respective outputs over all inputs. This allows to compare transducers beyond equivalence. Two transducers are close (resp. $k$-close) with respect to a metric if their distance is finite (resp. a

Florian Kurpicz, Pascal Mehnert, Peter Sanders, Matthias Schimek

String sorting is an important part of tasks such as building index data structures. Unfortunately, current string sorting algorithms do not scale to massively parallel distributed-memory machines since they either have latency (at least) proportional to the number of processors $p$ or communicate the data a large number of times (at least logarithmic). We p

Ruben Dario Gonzalez Betancourt, Jan Zubáč, Kevin Geishendorf, Philipp Ritzinger

Recently, MnTe was established as an altermagnetic material that hosts spin-polarized electronic bands as well as anomalous transport effects like the anomalous Hall effect. In addition to these effects arising from altermagnetism, MnTe also hosts other magnetoresistance effects. Here, we study the manipulation of the magnetic order by an applied magnetic fi

Naveen Kumar, Arpita Chatterjee

We investigate nonclassical properties of a state generated by the interaction of a three-level atom with a quantized cavity field and an external classical driving field. In this study, the fields being degenerate in frequency, are highly detuned from the atom. The atom interacts with the quantized field in a dispersive manner. The experimental set-up invol

Ahmed Aboudonia, John Lygeros

We propose a novel adaptive learning-based model predictive control (MPC) scheme for interconnected systems which can be decomposed into several smaller dynamically coupled subsystems with uncertain coupling. The proposed scheme is mainly divided into two main online phases; a learning phase and an adaptation phase. Set membership identification is used in t

Lisa M. Rütten, Harald Schmid, Eva Liebhaber, Giada Franceschi

Magnetic adatoms on superconductors give rise to Yu-Shiba-Rusinov (YSR) states that hold considerable interest for the design of topological superconductivity. Here, we show that YSR states are also an ideal platform to engineer structures with intricate wave-function symmetries. We assemble structures of iron atoms on the quasi-two-dimensional superconducto

Sophie Tsiatsiou, Yves Sibony, Devesh Nandal, Luca Sciarini

The first stars might have been fast rotators. This would have important consequences for their radiative, mechanical and chemical feedback. We discuss the impact of fast initial rotation on the evolution of massive Population III models and on their nitrogen and oxygen stellar yields. We explore the evolution of Population III stars with initial masses in t

Hon-Ka Hui, Lap-Ming Lin

Apparent horizon plays an important role in numerical relativity as it provides a tool to characterize the existence and properties of black holes on three-dimensional spatial slices in 3+1 numerical spacetimes. Apparent horizon finders based on different techniques have been developed. In this paper, we revisit the apparent horizon finding problem in numeri

Shaocong Dong, Lihe Ding, Zhanpeng Huang, Zibin Wang

3D object generation has undergone significant advancements, yielding high-quality results. However, fall short of achieving precise user control, often yielding results that do not align with user expectations, thus limiting their applicability. User-envisioning 3D object generation faces significant challenges in realizing its concepts using current genera

Siyu Jiang, Fa Peng Huang, Pyungwon Ko

As a new type of dynamical dark matter mechanism, we discuss the stability of the gauged Q-ball dark matter and its production mechanism through a cosmological first-order phase transition. This work delves into the study of gauged Q-ball dark matter generated during the cosmic phase transition. We demonstrate detailed discussions on the stability of gauged

Nikita Smirnov, Sven Tomforde

This conceptual analysis examines the dynamics of data transmission in 5G networks. It addresses various aspects of sending data from cameras and LiDARs installed on a remote-controlled ferry to a land-based control center. The range of topics includes all stages of video and LiDAR data processing from acquisition and encoding to final decoding, all aspects

Xiaotong Yu, Chang-Wen Chen

Efficient visual perception using mobile systems is crucial, particularly in unknown environments such as search and rescue operations, where swift and comprehensive perception of objects of interest is essential. In such real-world applications, objects of interest are often situated in complex environments, making the selection of the 'Next Best' view base

Youmi Ma, An Wang, Naoaki Okazaki

Document-level Relation Extraction (DocRE) is the task of extracting all semantic relationships from a document. While studies have been conducted on English DocRE, limited attention has been given to DocRE in non-English languages. This work delves into effectively utilizing existing English resources to promote DocRE studies in non-English languages, with

Nathanaël Perraudin, Adrien Teutrie, Cécile Hébert, Guillaume Obozinski

We consider the problem of regularized Poisson Non-negative Matrix Factorization (NMF) problem, encompassing various regularization terms such as Lipschitz and relatively smooth functions, alongside linear constraints. This problem holds significant relevance in numerous Machine Learning applications, particularly within the domain of physical linear unmixin

Jarrod Lim, Tom Manuel Opalla Piccio, Chua Min Jie Michelle, Maoyang Xiang

The objective of this project is to utilize an FPGA board which is the CMOD A7 35t to obtain a pseudo random number which can be used for encryption. We aim to achieve this by leveraging the inherent randomness present in environmental data captured by sensors. This data will be used as a seed to initialize an algorithm implemented on the CMOD A7 35t FPGA bo

A. A. Molavi Choobini, S. S. Ghaffari-Oskooei, F. Farahi, F. M. Aghamir

Both theoretical and numerical analyses are conducted to investigate terahertz (THz) radiation emission from free-electron lasers. The angular and spectral characteristics of radiation are analytically evaluated leveraging Linenard Wiechert field formalism, The analysis spanned across varying beam profiles and undulator parameters, including harmonic order,

Richard Schubert, Cedrik Kaufmann, Marcus Nolte, Markus Maurer

Supervising the safe operation of automated vehicles is a key requirement in order to unleash their full potential in future transportation systems. In particular, previous publications have argued that SAE Level 4 vehicles should be aware of their capabilities at runtime to make appropriate behavioral decisions. In this paper, we present a framework that en

Xu Zheng, Pengyuan Zhou, Athanasios V. Vasilakos, Lin Wang

In this paper, we address the challenging source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation, given only a pinhole image pre-trained model (i.e., source) and unlabeled panoramic images (i.e., target). Tackling this problem is non-trivial due to three critical challenges: 1) semantic mismatches from the distinct

Richard Schubert, Marvin Loba, Jasper Sünnemann, Torben Stolte

Automated driving systems require monitoring mechanisms to ensure safe operation, especially if system components degrade or fail. Their runtime self-representation plays a key role as it provides a-priori knowledge about the system's capabilities and limitations. In this paper, we propose a data-driven approach for deriving such a self-representation model

Bihan Banerjee, Mayank Narang, P. Manoj, Thomas Henning

Giant planets exhibit diverse orbital properties, hinting at their distinct formation and dynamic histories. In this paper, using $\textit{Gaia}$ DR3, we investigate if and how the orbital properties of Jupiters are linked to their host star properties, particularly their metallicity and age. We obtain metallicities for main sequence stars of spectral type F

Geeta Vichare, Ankush Bhaskar, Rahul Rawat, Virendra Yadav

In April 2023, low-latitude aurora observation by the all-sky camera at Hanle, Ladakh, India ($33^{\circ} {} N $ geographic latitude (GGLat)) was reported, which stimulated a lot of discussion among scientists as well as masses across the globe. The reported observation was intriguing as the solar storm that triggered this aurora was moderate and the first s

Juan Bisquert, Baurzhan Ilyassov, Nir Tessler

Hysteresis in organic electrochemical transistors (OECT) is a basic effect in which the measured current depends on the voltage sweep direction and velocity. This phenomenon has an important impact on different aspects of the application of OECT, such as the switching time and the synaptic properties for neuromorphic applications. Here we address the combine

Giorgio Ciliberto, Stephanie Emig, Nicolas Pavloff, Mathieu Isoard

Signals of entanglement and nonlocality are quantitatively evaluated at zero and finite temperature in an analogue black hole realized in the flow of a quasi one-dimensional Bose-Einstein condensate. The violation of Lorentz invariance inherent to this analog system opens the prospect to observe 3-mode quantum correlations and we study the corresponding viol

Filippo Fiocchi, Domna Ladopoulou, Petros Dellaportas

We provide a condition monitoring system for wind farms, based on normal behaviour modelling using a probabilistic multi-layer perceptron with transfer learning via fine-tuning. The model predicts the output power of the wind turbine under normal behaviour based on features retrieved from supervisory control and data acquisition (SCADA) systems. Its advantag

Evandro S. Ortigossa, Fábio F. Dias, Brian Barr, Claudio T. Silva

The development of machine learning applications has increased significantly in recent years, motivated by the remarkable ability of learning-powered systems to discover and generalize intricate patterns hidden in massive datasets. Modern learning models, while powerful, often exhibit a complexity level that renders them opaque black boxes, lacking transpare

Sanchi Monga, Manjari Jain, Claudia Draxl, Saswata Bhattacharya

Antiperovskite nitrides are potential candidates for applications harvesting solar light. With a comprehensive state-of-the-art approach combining hybrid density-functional theory, many-body perturbation theory, the Wannier-Mott model, density-functional perturbation theory, and the Feynman polaron model, we explore excitonic and polaronic effects in X$_3$NA

Hai Wu, Shijia Zhao, Xun Huang, Chenglu Wen

The prevalent approaches of unsupervised 3D object detection follow cluster-based pseudo-label generation and iterative self-training processes. However, the challenge arises due to the sparsity of LiDAR scans, which leads to pseudo-labels with erroneous size and position, resulting in subpar detection performance. To tackle this problem, this paper introduc

Catarina Faustino, Thomas Kahl, Rodrigo Lopes

Higher-dimensional automata, i.e., pointed labeled precubical sets, are a powerful combinatorial-topological model for concurrent systems. In this paper, we show that for every (nonempty) connected polyhedron there exists a shared-variable system such that the higher-dimensional automaton modeling the state space of the system has the homotopy type of the po

Gopal Datt, Daljeet Singh Bajaj

The aim of this article is to detect the ascent and descent of weighted composition operators on Lorentz spaces. We investigate the conditions on the measurable transformation $T$ and the complex-valued measurable function $u$ defined on measure space $(X, \mathcal{A}, \mu)$ that cause the weighted composition operators on Lorentz space $L(p, q)$, $1 < p \le

Simon N. Wood

Many varieties of cross validation would be statistically appealing for the estimation of smoothing and other penalized regression hyperparameters, were it not for the high cost of evaluating such criteria. Here it is shown how to efficiently and accurately compute and optimize a broad variety of cross validation criteria for a wide range of models estimated

Tianyu Zuo, Xueyan Tang, Bu Sung Lee

This paper studies an online replication problem for distributed data access. The goal is to dynamically create and delete data copies in a multi-server system as time passes to minimize the total storage and network cost of serving access requests. We study the problem in the emergent learning-augmented setting, assuming simple binary predictions about inte

D. N. Voskresensky

Possibilities for formation of the charged pion field vortices in a rotating empty vessel (in vacuum) and in the rotating pion gas with a dynamically fixed particle number at zero temperature are studied within the $\lambda|\phi|^4$ model. It is shown that in the former case at a rapid rotation a supervortex of a charged pion field can be formed. Important r

Mark Hammond, Taylor J. Bell, Ryan C. Challener, Neil T. Lewis

We present eclipse maps of the two-dimensional thermal emission from the dayside of the hot Jupiter WASP-43b, derived from an observation of a phase curve with the JWST MIRI/LRS instrument. The observed eclipse shapes deviate significantly from those expected for a planet emitting uniformly over its surface. We fit a map to this deviation, constructed from s

Maximilian Warsinke, Tanja Kojić, Maurizio Vergari, Robert Spang

This study investigates the feasibility of remote virtual reality (VR) studies conducted at home using VR headsets and video conferencing by deploying an experiment on emotion ratings. 20 participants used head-mounted displays to immerse themselves in 360° videos selected to evoke emotional responses. The research compares continuous ratings using a graphic

Ilaria Battiston, Kriti Kathuria, Peter Boncz

This demonstration presents a new Open Source SQL-to-SQL compiler for Incremental View Maintenance (IVM). While previous systems, such as DBToaster, implemented computational functionality for IVM in a separate system, the core principle of OpenIVM is to make use of existing SQL query processing engines and perform all IVM computations via SQL. This approach

Nils Berglund, Alexandra Blessing

The main goal of this work is to provide sample-path estimates for the solution of slowly time-dependent SPDEs perturbed by a cylindrical fractional Brownian motion. Our strategy is similar to the approach by Berglund and Nader for space-time white noise. However, the setting of fractional Brownian motion does not allow us to use any martingale methods. Usin

Parham Zolfaghari, Vitor Fortes Rey, Lala Ray, Hyun Kim

The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to the scarcity of labeled datasets. Despite the integration of advanced Inertial Measurement Units (IMUs) in ubiquitous wearable devices like smartwatches and fitness trackers, whic

Marcos V. Conde, Zhijun Lei, Wen Li, Cosmin Stejerean

This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a diverse test set containing a variety of 4K images ranging from digital art to gaming and photography. The images

Davide Liconti, Yasunori Toshimitsu, Robert Katzschmann

In the context of imitation learning applied to dexterous robotic hands, the high complexity of the systems makes learning complex manipulation tasks challenging. However, the numerous datasets depicting human hands in various different tasks could provide us with better knowledge regarding human hand motion. We propose a method to leverage multiple large-sc

Chen Wei, Jiachen Zou, Dietmar Heinke, Quanying Liu

A central question for cognitive science is to understand how humans process visual objects, i.e, to uncover human low-dimensional concept representation space from high-dimensional visual stimuli. Generating visual stimuli with controlling concepts is the key. However, there are currently no generative models in AI to solve this problem. Here, we present th

Amitha Mayya, Arsenia Chorti, Rafael F. Schaefer, Gerhard P. Fettweis

In this paper, the feasibility of implementing a lightweight key distribution scheme using physical layer security for secret key generation (SKG) is explored. Specifically, we focus on examining SKG with the received signal strength (RSS) serving as the primary source of shared randomness. Our investigation centers on a frequency-selective line-of-sight (Lo

Yaxin Mo, Tullio Traverso, Luca Magri

Turbulent flows are chaotic and multi-scale dynamical systems, which have large numbers of degrees of freedom. Turbulent flows, however, can be modelled with a smaller number of degrees of freedom when using the appropriate coordinate system, which is the goal of dimensionality reduction via nonlinear autoencoders. Autoencoders are expressive tools, but they

Duc-Anh Nguyen, Nhien-An Le-Khac

Human Activity Recognition (HAR) is a well-studied field with research dating back to the 1980s. Over time, HAR technologies have evolved significantly from manual feature extraction, rule-based algorithms, and simple machine learning models to powerful deep learning models, from one sensor type to a diverse array of sensing modalities. The scope has also ex

Przemek Mroz

The discovery of a terrestrial-mass free-floating planet candidate in the light curve of the star TIC 107150013 observed by the Transiting Exoplanet Survey Satellite (TESS) has recently been announced. A short-duration (~0.5 day), low-amplitude (~0.06 mag) brightening in the TESS light curve was interpreted as a short-timescale gravitational microlensing eve

Yash Saxena, Sarthak Chopra, Arunendra Mani Tripathi

Large Language Models (LLMs) are extensively used today across various sectors, including academia, research, business, and finance, for tasks such as text generation, summarization, and translation. Despite their widespread adoption, these models often produce incorrect and misleading information, exhibiting a tendency to hallucinate. This behavior can be a

Jonte R. Hance, Tomonori Matsushita, Holger F. Hofmann

The presence of an absorber in one of the paths of an interferometer changes the output statistics of that interferometer in a fundamental manner. Since the individual quantum particles detected at any of the outputs of the interferometer have not been absorbed, any non-trivial effect of the absorber on the distribution of these particles over these paths is

Xiaojing Yan, Saeed Razavikia, Carlo Fischione

In this paper, we consider the ChannelComp framework, which facilitates the computation of desired functions by multiple transmitters over a common receiver using digital modulations across a multiple access channel. While ChannelComp currently offers a broad framework for computation by designing digital constellations for over-the-air computation and emplo

Alessandro Chiarini, Marco Edoardo Rosti

We investigate by direct numerical simulations the fluid-solid interaction of non-dilute suspensions of spherical particles moving in triperiodic turbulence, at the relatively large Reynolds number of $Re_\lambda \approx 400$. The solid-to-fluid density ratio is varied between $1.3$ and $100$, the particle diameter $D$ ranges between $16 \le D/\eta \le 123$

Zhihao Shuai, Yinan Chen, Shunqiang Mao, Yihan Zho

Weakly supervised medical image segmentation (MIS) using generative models is crucial for clinical diagnosis. However, the accuracy of the segmentation results is often limited by insufficient supervision and the complex nature of medical imaging. Existing models also only provide a single outcome, which does not allow for the measurement of uncertainty. In

Tetsuyuki Ochiai

We present a detailed theoretical analysis of a peculiar generation of multiple bound states in the continuum (BICs) in two-dimensional periodic arrays of dielectric spheres. They emerge in high-symmetry lattices with the $C_{6v}$ and $C_{4v}$ point groups and involve doubly degenerate quasi-guided modes at the $\Gamma$ point that can couple to external radi

Panagiotis Sapoutzoglou, Georgios Giapitzakis, Georgios Floros, George Terzakis

We propose a generic procedure for assessing 6D object pose estimates. Our approach relies on the evaluation of discrepancies in the geometry of the observed object, in particular its respective estimated back-projection in 3D, against a putative functional shape representation comprising mixtures of Gaussian Processes, that act as a template. Each Gaussian