April 2023 arXiv papers — page 104

Xingwu Ji, Peilin Liu, Haochen Niu, Xiang Chen

Visual simultaneous localization and mapping (SLAM) systems face challenges in detecting loop closure under the circumstance of large viewpoint changes. In this paper, we present an object-based loop closure detection method based on the spatial layout and semanic consistency of the 3D scene graph. Firstly, we propose an object-level data association approac

Oriol Serra, Lluís Vena

Given a family $S$ of $k$--subsets of $[n]$, its lower shadow $\Delta(S)$ is the family of $(k-1)$--subsets which are contained in at least one set in $S$. The celebrated Kruskal--Katona theorem gives the minimum cardinality of $\Delta(S)$ in terms of the cardinality of $S$. F\"uredi and Griggs (and M\"ors) showed that the extremal families for this shadow m

Erik G. Larsson, Joao Vieira

Antenna arrays can be either reciprocity calibrated (R-calibrated), which facilitates reciprocity-based beamforming, or fully calibrated (F-calibrated), which additionally facilitates transmission and reception in specific physical directions. We first expose, to provide context, the fundamental principles of over-the-air R- and F-calibration of distributed

Christian Kosel, Angelina Mooseder, Tina Seidel, Juergen Pfeffer

This article adds to the understanding of teachers' visual expertise by measuring visual information processing in real-world classrooms (mobile eye-tracking) with the newly introduced Gaze Relational Index (GRI) metric, which is defined as the ratio of mean fixation duration to mean fixation number. In addition, the aim was to provide a methodological contr

Evan Piermont

I examine how a decision maker can incentivize an expert to reveal novel actions, expanding the set from which he can choose, without making ex-ante commitments regarding as-of-yet unrevealed actions. The outcomes achievable by any (incentive compatible) mechanism are characterized by the iterated revelation protocol: a simple dynamic interaction where, each

Ya-Nan Dai, Jing-Jing Jiang, Yu-Hang Jiang, Rashid Shaisultanov

We investigated the effects of the momentum spread of photon around incoming electrons during nonlinear Compton scattering of an elliptically polarized laser off an ultrarelativistic electron beam. It has been assumed to be a good approximation to neglect the angular spread in the strong-field QED codes considering its smallness in relativistic regime. Here,

Wenbin Hu, Bidan Huang, Wang Wei Lee, Sicheng Yang

Continuous in-hand manipulation is an important physical interaction skill, where tactile sensing provides indispensable contact information to enable dexterous manipulation of small objects. This work proposed a framework for end-to-end policy learning with tactile feedback and sim-to-real transfer, which achieved fine in-hand manipulation that controls the

Sourav Biswas, Hemanta Kumar Kundu, Vladimir Umansky, Moty Heiblum

The remarkable Cooper-like pairing phenomenon in the Aharonov-Bohm interference of a Fabry-Perot interferometer (FPI)$\rm{-}$operating in the integer quantum Hall regime$\rm{-}$remains baffling. Here, we report the interference of paired electrons employing 'interface edge modes'. These modes are born at the interface between the bulk of the FPI and an outer

Dan Ruta, Andrew Gilbert, John Collomosse, Eli Shechtman

Style transfer is the task of reproducing the semantic contents of a source image in the artistic style of a second target image. In this paper, we present NeAT, a new state-of-the art feed-forward style transfer method. We re-formulate feed-forward style transfer as image editing, rather than image generation, resulting in a model which improves over the st

Xiaobing Dai, Zewen Yang, Sihua Zhang, Di-Hua Zhai

In the realm of the cooperative control of multi-agent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theoretical prediction error bound. Online learning, which involves incorporating newly acquired training data int

Wei Lu, Nic A. Lee, Markus J. Buehler

Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structure

V. I. Yukalov

When interactions between particles are strong, at low temperature, these particles can form self-organized quantum crystals, and when the particles interact weakly, periodic structures can be imposed by external fields, e.g. by optical lattices. These opposite cases usually are treated separately, dealing either with quantum crystals or with optical lattice

Yue Cui, Syed Irfan Ali Meerza, Zhuohang Li, Luyang Liu

Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies have shown that private information can still be leaked through shared gradients. To further minimize the risk of privacy leakage, existing defenses usually require clients to locall

Muhammad Umar Farooq, Zahid Ullah, Jeonghwan Gwak

To improve the recognition ability of computer-aided breast mass classification among mammographic images, in this work we explore the state-of-the-art classification networks to develop an ensemble mechanism. First, the regions of interest (ROIs) are obtained from the original dataset, and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, ar

Peter Verwayen, Constantinos Skordis, Céline Bœhm

There have been many efforts in the last three decades to embed the empirical MOND program into a robust theoretical framework. While many such theories can explain the profile of galactic rotation curves, they usually cannot explain the evolution 15 the primordial fluctuations and the formation of large-scale-structures in the Universe. The Aether Scalar Te

Evelyn Herberg

These lecture notes provide an overview of Neural Network architectures from a mathematical point of view. Especially, Machine Learning with Neural Networks is seen as an optimization problem. Covered are an introduction to Neural Networks and the following architectures: Feedforward Neural Network, Convolutional Neural Network, ResNet, and Recurrent Neural

Anand Agrawal, Praneeta Maganti, Rajib Ranjan Maiti

Aquaponics system promises a sustainable urban development and food production by combining vegetable and fish farming in a single water loop. However, traditional aquaponics suffers from a significant amount of manual intervention with regard to decision-making in the water circulation and water quality control. In this work, we design, build and deploy a l

Xiaobing Dai, Huanzhuo Wu, Siyi Wang, Junjie Jiao

Many applications involve humans in the loop, where continuous and accurate human motion monitoring provides valuable information for safe and intuitive human-machine interaction. Portable devices such as inertial measurement units (IMUs) are applicable to monitor human motions, while in practice often limited computational power is available locally. The hu

G. Lusztig

We define precuspidal families in proper parabolic subgroups of a Weyl group and we show how to use them to index the irreducible representations of that Weyl group in terms of certain pairs of finite groups. Some additional material is added.

Anastasia Kireeva, Jean-Christophe Mourrat

Let $S$ and $\tilde S$ be two independent and identically distributed random variables, which we interpret as the signal, and let $P_1$ and $P_2$ be two communication channels. We can choose between two measurement scenarios: either we observe $S$ through $P_1$ and $P_2$, and also $\tilde S$ through $P_1$ and $P_2$; or we observe $S$ twice through $P_1$, and

Xinyun Chen, Maxwell Lin, Nathanael Schärli, Denny Zhou

Large language models (LLMs) have achieved impressive performance on code generation. However, for complex programming tasks, generating the correct solution in one go becomes challenging, thus some prior works have designed program repair approaches to improve code generation performance. In this work, we propose Self-Debugging, which teaches a large langua

Xiangjian Hou, Sarit Khirirat, Mohammad Yaqub, Samuel Horvath

Federated learning (FL) is a distributed machine learning (ML) framework where multiple clients collaborate to train a model without exposing their private data. FL involves cycles of local computations and bi-directional communications between the clients and server. To bolster data security during this process, FL algorithms frequently employ a differentia

Nick S. Blunt, Laura Caune, Róbert Izsák, Earl T. Campbell

Quantum phase estimation (QPE) is a key quantum algorithm, which has been widely studied as a method to perform chemistry and solid-state calculations on future fault-tolerant quantum computers. Recently, several authors have proposed statistical alternatives to QPE that have benefits on early fault-tolerant devices, including shorter circuits and better sui

Seunghoan Song, Francois Le Gall, Masahito Hayashi

Quantum private information retrieval (QPIR) for quantum messages is a quantum communication task, in which a user retrieves one of the multiple quantum states from the server without revealing which state is retrieved. In the one-server setting, we find an exponential gap in the communication complexities between the presence and absence of prior entangleme

Thomas Ortner, Lorenzo Pes, Joris Gentinetta, Charlotte Frenkel

Recurrent neural networks trained with the backpropagation through time (BPTT) algorithm have led to astounding successes in various temporal tasks. However, BPTT introduces severe limitations, such as the requirement to propagate information backwards through time, the weight symmetry requirement, as well as update-locking in space and time. These problems

Dina Barak-Pelleg, Daniel Berend, Thomas J. Robinson, Itamar Zimmerman

DoS and DDoS attacks are widely used and pose a constant threat. Here we explore Probability Packet Marking (PPM), one of the important methods for reconstructing the attack-graph and detect the attackers. We present two algorithms. Differently from others, their stopping time is not fixed a priori. It rather depends on the actual distance of the attacker fr

G. Karapetyan, W. de Paula, R. da Rocha

Strange axial-vector kaons, $K_1$, and $f_1$ meson resonances are investigated in the 4-flavor AdS/QCD model. Their underlying differential configurational entropy is computed and the mass spectra of higher-excited resonances, in both these mesonic families, are achieved and discussed. This technique merges the 4-flavor AdS/QCD and experimental data regardin

Ruichen Li, Yuyang Wang, Handi Yin, Jean-Rémy Chardonnet

Users would experience individually different sickness symptoms during or after navigating through an immersive virtual environment, generally known as cybersickness. Previous studies have predicted the severity of cybersickness based on physiological and/or kinematic data. However, compared with kinematic data, physiological data rely heavily on biosensors

Kisub Kim, Ferdian Thung, Ting Zhang, Ivana Clairine Irsan

Utilizing pre-existing software artifacts, such as libraries and Application Programming Interfaces (APIs), is crucial for software development efficiency. However, the abundance of artifacts that provide similar functionality can lead to confusion among developers, resulting in a challenge for proper selection and implementation. Through our preliminary inv

Gerasimos Arvanitis, Nikos Piperigkos, Christos Anagnostopoulos, Aris S. Lalos

In collaborative tasks where humans work alongside machines, the robot's movements and behaviour can have a significant impact on the operator's safety, health, and comfort. To address this issue, we present a multi-stereo camera system that continuously monitors the operator's posture while they work with the robot. This system uses a novel distributed fusi

Zhaorui Wang, Ya-Feng Liu, Ziyue Wang, Liang Liu

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for s

Ewelina Rupnik, Marc Pierrot-Deseilligny

Bundle adjustment (BA) is the standard way to optimise camera poses and to produce sparse representations of a scene. However, as the number of camera poses and features grows, refinement through bundle adjustment becomes inefficient. Inspired by global motion averaging methods, we propose a new bundle adjustment objective which does not rely on image featur

J. Cernicharo, C. Cabezas, J. R. Pardo, M. Agúndez

We found four series of harmonically related lines in IRC\,+10216 with the Yebes\,40m and IRAM\,30m telescopes. The first series corresponds to a molecule with a rotational constant, $B$, of 1448.5994$\pm$0.0013 MHz and a distortion constant, $D$, of 63.45$\pm$1.15 Hz and covers upper quantum numbers from $J_u$=11 up to 33 (B1449). The second series is fitte

Theodor Westny, Joel Oskarsson, Björn Olofsson, Erik Frisk

Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physicall

Jianfei Zhang, Mathieu Rosenbaum

News can convey bearish or bullish views on financial assets. Institutional investors need to evaluate automatically the implied news sentiment based on textual data. Given the huge amount of news articles published each day, most of which are neutral, we present a systematic news screening method to identify the ``true'' impactful ones, aiming for more effe

Petr Liška, Tomáš Musálek, Tomáš Šamořil, Matouš Kratochvíl

We report on the optical properties of CsPbBr3 polycrystalline thin film on a single grain level. A sample comprised of isolated nanocrystals (NCs) mimicking the properties of the polycrystalline thin film grains that can be individually probed by photoluminescence spectroscopy was prepared. These NCs were analyzed using correlative microscopy allowing the e

Arnab Bera, Samir Rom, Suman Kalyan Pradhan, Satyabrata Bera

(TaSe$_4$)$_3$I, a compound belonging to the family of quasi-one-dimensional transition-metal tetrachalcogenides, has drawn significant attention due to a recent report on possible coexistence of two antagonistic phenomena, superconductivity and magnetism below 2.5~K (Bera et. al, arXiv:2111.14525). Here, we report a structural phase transition of the trimer

Zhiwei Yang, Jing Liu, Zhaoyang Wu, Peng Wu

Video anomaly detection (VAD) is a significant computer vision problem. Existing deep neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or frame prediction. However, the lack of mining and learning of higher-level visual features and temporal context relationships in videos limits the further performance of these two appr

Yagor Romano Carvalho, Leonardo P. C. Da Cruz, Luiz F. S. Gouveia

Our main goal in this paper is to study the number of small-amplitude isolated periodic orbits, so-called limit cycles, surrounding only one equilibrium point a class of polynomial Kolmogorov systems. We denote by $\mathcal M_{K}(n)$ the maximum number of limit cycles bifurcating from the equilibrium point via a degenerate Hopf bifurcation for a polynomial K

Juhi Oudichhya, Keval Gandhi, Ajay kumar Rai

Triply heavy baryons with quark content $ccb$ and $cbb$ are investigated within the framework of Regge phenomenology. With the assumption of linear Regge trajectories, we have extracted the relations between Regge parameters and baryon masses. Using these relations, we compute the ground state masses of $\Omega_{ccb}$ and $\Omega_{cbb}$ baryons. Further, the

Attila Szabo, Georg Haaser, Harald Steinlechner, Andreas Walch

Reconstructing geometric shapes from point clouds is a common task that is often accomplished by experts manually modeling geometries in CAD-capable software. State-of-the-art workflows based on fully automatic geometry extraction are limited by point cloud density and memory constraints, and require pre- and post-processing by the user. In this work, we pre

A. Zabrodin

This is a short review of the construction of quasi-periodic (algebraic-geometrical) solutions to hierarchies of nonlinear integrable equations. As is well known, the solutions are expressed through Riemann's theta-functions associated with algebraic curves. It is explained how solutions from this class can be treated within the framework of the approach to

Phuoc Nguyen Thuan, Jorge Peña Queralta, Tomi Westerlund

Aerial scans with unmanned aerial vehicles (UAVs) are becoming more widely adopted across industries, from smart farming to urban mapping. An application area that can leverage the strength of such systems is search and rescue (SAR) operations. However, with a vast variability in strategies and topology of application scenarios, as well as the difficulties i

Beihao Xia, Conghao Wong, Duanquan Xu, Qinmu Peng

With the fast development of AI-related techniques, the applications of trajectory prediction are no longer limited to easier scenes and trajectories. More and more trajectories with different forms, such as coordinates, bounding boxes, and even high-dimensional human skeletons, need to be analyzed and forecasted. Among these heterogeneous trajectories, inte

Yulong Gao, Shuhao Yan, Jian Zhou, Mark Cannon

This paper is concerned with model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and mixed constraints on the state and input, whereas the true disturbance set is unknown. Unlike most existing work on robust MPC, we propose an algorithm incorporating online learning that builds on prior knowledge of the dist

Pedro Conde, Tiago Barros, Rui L. Lopes, Cristiano Premebida

With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. This requires a thorough look not only at the accuracy of such systems, but also at their predictive uncertainty. Hence, we propose a novel technique (with two different variations, n

F. Taccogna, F. Cichocki, D. Eremin, G. Fubiani

This perspective paper deals with an overview of particle-in-cell / Monte Carlo collision models applied to different plasma-propulsion configurations and scenarios, from electrostatic (E x B and pulsed arc) devices to electromagnetic (RF inductive, helicon, electron cyclotron resonance) thrusters, with an emphasis on plasma plumes and their interaction with

M. Juvela

When dust far-infrared spectral energy distributions (SEDs) are fitted with a single modified black body (MBB), the optical depths tend to be underestimated. This is caused by temperature variations, and fits with several temperature components could lead to smaller errors. We want to quantify the performance of the standard model of a single MBB in comparis

Michel Vaquié

For any object x in a category C it is possible to define the category of Beck modules over x as the category Ab(C/x) of abelian group objects in the category C/x. We can deduce from this construction, at least for any locally presentable category, the notion of cotangent module or module of differentials $\Omega$x of x in Ab(C/x).In the case of the category

Pinku Routaray, Sailesh Ranjan Mohanty, H. C. Das, Sayantan Ghosh

The fastest and heaviest pulsar, PSR J0952-0607, with a mass of $M=2.35\pm0.17 \ M_\odot$, has recently been discovered in the disk of the Milky Way Galaxy. In response to this discovery, a new RMF model, `NITR' has been developed. The NITR model's naturalness has been confirmed by assessing its validity for various finite nuclei and nuclear matter propertie

Tommaso Marzi, Arshjot Khehra, Andrea Cini, Cesare Alippi

Graph-based representations and message-passing modular policies constitute prominent approaches to tackling composable control problems in reinforcement learning (RL). However, as shown by recent graph deep learning literature, such local message-passing operators can create information bottlenecks and hinder global coordination. The issue becomes more seri

Tianyuan Zhang, Yisong Xiao, Xiaoya Zhang, Hao Li

Adversarial attacks in the physical world can harm the robustness of detection models. Evaluating the robustness of detection models in the physical world can be challenging due to the time-consuming and labor-intensive nature of many experiments. Thus, virtual simulation experiments can provide a solution to this challenge. However, there is no unified dete

Weichuang Li, Longhao Zhang, Dong Wang, Bin Zhao

Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image. Most pioneering methods rely primarily on 2D representations and thus will inevitably suffer from face distortion when large head rotations are encountered. Recent works instead employ explicit 3D structural r

Jingyi Xu, Hieu Le, Dimitris Samaras

Two-stage object detectors generate object proposals and classify them to detect objects in images. These proposals often do not contain the objects perfectly but overlap with them in many possible ways, exhibiting great variability in the difficulty levels of the proposals. Training a robust classifier against this crop-related variability requires abundant

Kevin Brand, Trienko L. Grobler, Waldo Kleynhans, Mattia Vaccari

State-of-the-art radio observatories produce large amounts of data which can be used to study the properties of radio galaxies. However, with this rapid increase in data volume, it has become unrealistic to manually process all of the incoming data, which in turn led to the development of automated approaches for data processing tasks, such as morphological

Changlin Yang, Ying Liu, Kwan-Wu Chin, Jiguang Wang

Many applications, e.g., digital twins, rely on sensing data from Internet of Things (IoT) networks, which is used to infer event(s) and initiate actions to affect an environment. This gives rise to concerns relating to data integrity and provenance. One possible solution to address these concerns is to employ blockchain. However, blockchain has high resourc

Mohamed Hamdouche, Pierre Henry-Labordere, Huyên Pham

We propose a novel generative model for time series based on Schr{\"o}dinger bridge (SB) approach. This consists in the entropic interpolation via optimal transport between a reference probability measure on path space and a target measure consistent with the joint data distribution of the time series. The solution is characterized by a stochastic differenti

Hector Kohler, Riad Akrour, Philippe Preux

Interpretability of AI models allows for user safety checks to build trust in these models. In particular, decision trees (DTs) provide a global view on the learned model and clearly outlines the role of the features that are critical to classify a given data. However, interpretability is hindered if the DT is too large. To learn compact trees, a Reinforceme

Brian Moser, Federico Raue, Jörn Hees, Andreas Dengel

We present new Recurrent Neural Network (RNN) cells for image classification using a Neural Architecture Search (NAS) approach called DARTS. We are interested in the ReNet architecture, which is a RNN based approach presented as an alternative for convolutional and pooling steps. ReNet can be defined using any standard RNN cells, such as LSTM and GRU. One li

Rinaldo M. Colombo, Vincent Perrollaz, Abraham Sylla

Consider a Conservation Law and a Hamilton-Jacobi equation with a ux/Hamiltonian depending also on the space variable. We characterize rst the attainable set of the two equations and, second, the set of initial data evolving at a prescribed time into a prescribed prole. An explicit example then shows the deep dierences between the cases of x-independent and

Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk

Gaussian processes (GPs) are typically criticised for their unfavourable scaling in both computational and memory requirements. For large datasets, sparse GPs reduce these demands by conditioning on a small set of inducing variables designed to summarise the data. In practice however, for large datasets requiring many inducing variables, such as low-lengthsc

Paweł Foszner, Agnieszka Szczęsna, Luca Ciampi, Nicola Messina

Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in Computer Vision. Indeed, Deep Learning-based models systematically struggle when applied in new scenarios never seen during training and may not be adequately tested in non-ordinary yet crucial real-world situations. This paper presents and

Zebin Qiu, Muneto Nitta

We study the ground state of the low energy dense QCD with the assumption of chiral condensates of quarks. Under an external magnetic field, mesons could form soliton lattices via the chiral anomaly. For such scenarios, we present a unified description of pions and $\eta$ meson with a $U(2)$ field in the framework of the chiral perturbation theory. Our resul

Antoine Gonon, Léon Zheng, Clément Lalanne, Quoc-Tung Le

Sparse neural networks are mainly motivated by ressource efficiency since they use fewer parameters than their dense counterparts but still reach comparable accuracies. This article empirically investigates whether sparsity could also improve the privacy of the data used to train the networks. The experiments show positive correlations between the sparsity o

Marius Bock, Hilde Kuehne, Kristof Van Laerhoven, Michael Moeller

Research has shown the complementarity of camera- and inertial-based data for modeling human activities, yet datasets with both egocentric video and inertial-based sensor data remain scarce. In this paper, we introduce WEAR, an outdoor sports dataset for both vision- and inertial-based human activity recognition (HAR). Data from 22 participants performing a

Deepak, Arpita Chatterjee

We investigate ideal and non-ideal continuous-variable quantum teleportation protocols realized by using an entangled displaced Fock state resource. The characteristic function formulation is applied to measure the relative performance of displaced Fock state for teleporting squeezed and coherent states. It is found that for such single-mode input fields, th

Wenhuan Kuang, Jie Zhang, Wei Zhang

Migration-based earthquake location methods may encounter the polarity reversal issue due to the non-explosive components of seismic source, leading to an unfocused migration image. Various methods have been proposed, yet producing an ideally focused migration source image is still a challenge. In this study, by taking advantage of the general pattern recogn

Maria Spethmann, Stefano Bosco, Andrea Hofmann, Jelena Klinovaja

Hybrid systems comprising superconducting and semiconducting materials are promising architectures for quantum computing. Superconductors induce long-range interactions between the spin degrees of freedom of semiconducting quantum dots. These interactions are widely anisotropic when the semiconductor material has strong spin-orbit interactions. We show that

Gilles Dowek, Ying Jiang

When a proposition has no proof in an inference system, it is sometimes useful to build a counter-proof explaining, step by step, the reason of this non-provability. In general, this counter-proof is a (possibly) infinite co-inductive proof in a different inference system. In this paper, we show that, for some decidable inference systems, this (possibly) inf

Xin Chen, Yuwen Qin, Weidong Zhao, Qiming Yang

Accurate state-of-health (SOH) estimation is critical to guarantee the safety, efficiency and reliability of battery-powered applications. Most SOH estimation methods focus on the 0-100\% full state-of-charge (SOC) range that has similar distributions. However, the batteries in real-world applications usually work in the partial SOC range under shallow-cycle

Laurent Chupin, Thierry Dubois

In this paper, we study non-isochoric models for mixtures of solid particles, at high volume concentration, and a gas. One of the motivations of this work concerns geophysics and more particularly the pyroclastic density currents which are precisely mixtures of pyroclast and lithic fragments and air. They are extremely destructive phenomena, capable of devas

Andrey Kupavskii, Elizaveta Popova

In this paper, we study tilings of $\mathbb Z$, that is, coverings of $\mathbb Z$ by disjoint sets (tiles). Let $T=\{d_1,\ldots, d_s\}$ be a given multiset of distances. Is it always possible to tile $\mathbb Z$ by tiles, for which the multiset of distances between consecutive points is equal to $T$? In this paper, we give a sufficient condition that such a

Jia-Ning Zhang, Jin-Xuan Han, Jin-Lei Wu, Jie Song

The Su-Schrieffer-Heeger (SSH) model, commonly used for robust state transfers through topologically protected edge pumping, has been generalized and exploited to engineer diverse functional quantum devices. Here, we propose to realize a fast topological beam splitter based on a generalized SSH model by accelerating the quantum state transfer (QST) process e

Sebastian Hafner, Yifang Ban, Andrea Nascetti

Accurate urban maps provide essential information to support sustainable urban development. Recent urban mapping methods use multi-modal deep neural networks to fuse Synthetic Aperture Radar (SAR) and optical data. However, multi-modal networks may rely on just one modality due to the greedy nature of learning. In turn, the imbalanced utilization of modaliti

Fatma Azmy Ebrahim, Alberto Facchini

In the study of pre-Lie algebras, the concept of pre-morphism arises naturally as a generalization of the standard notion of morphism. Pre-morphisms can be defined for arbitrary (not-necessarily associative) algebras over any commutative ring $k$ with identity, and can be dualized in various ways to generalized morphisms (related to pre-Jordan algebras) and

Huaiyuan Liu, Xianzhang Liu, Donghua Yang, Zhiyu Liang

Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology. Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different dimensions and also rarely consider the unique dynamic features of time series, which lack sufficient feature

Johannes Kleiner, Tim Ludwig

We demonstrate that if consciousness is relevant for the temporal evolution of a system's states--that is, if it is dynamically relevant--then AI systems cannot be conscious. That is because AI systems run on CPUs, GPUs, TPUs or other processors which have been designed and verified to adhere to computational dynamics that systematically preclude or suppress

M. V. Legnardi, A. P. Milone, G. Cordoni, E. P. Lagioia

The presence of differential reddening in the direction of Galactic globular clusters (GCs) has proven to be a serious limitation in the traditional colour-magnitude diagram (CMD) analysis. Here, we estimate local reddening variations in the direction of 56 Galactic GCs. To do that, we use the public catalogs derived as part of the Hubble Space Telescope UV

Rim Guizani, Hatem Mhiri, Philippe Bournot

The separation efficiency and pressure drop of the dynamic separator of cement particles can be affected by many factors, like structural type, geometric parameters, and operating characteristics. In this paper, CFD modeling is applied to investigate the fluid flow behavior and the efficiency of the industrial dynamic separator with different heights of the

Clement Papadacci, Ethan A Bunting, Elisa E Konofagou

In biological tissue, an increase in elasticity is often a marker of abnormalities. Techniques such as quasi-static ultrasound elastography have been developed to assess the strain distribution in soft tissues in two dimensions using a quasi-static compression. However, as abnormalities can exhibit very heterogeneous shapes, a three dimensional approach woul

Hugues Favre, Mathieu Pernot, Mickael Tanter, Clément Papadacci

Mapping blood microflows of the whole brain is crucial for early diagnosis of cerebral diseases. Ultrasound localization microscopy (ULM) was recently applied to map and quantify blood microflows in 2D in the brain of adult patients down to the micron scale. Whole brain 3D clinical ULM remains challenging due to the transcranial energy loss which significant

Alberto Maria Metelli, Mirco Mutti, Marcello Restelli

The most relevant problems in discounted reinforcement learning involve estimating the mean of a function under the stationary distribution of a Markov reward process, such as the expected return in policy evaluation, or the policy gradient in policy optimization. In practice, these estimates are produced through a finite-horizon episodic sampling, which neg

Shashikiran Venkatesha, Ranjani Parthasarathi

Rapid CMOS device size reduction resulted in billions of transistors on a chip have led to integration of many cores leading to many challenges such as increased power dissipation, thermal dissipation, occurrence of transient faults and permanent faults. The mitigation of transient faults and permanent faults at the core level has become an important design

Rui-Yang Ju, Weiming Cai

Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requir

Iovka Boneva, Benoit Groz, Jan Hidders, Filip Murlak

We investigate graph transformations, defined using Datalog-like rules based on acyclic conjunctive two-way regular path queries (acyclic C2RPQs), and we study two fundamental static analysis problems: type checking and equivalence of transformations in the presence of graph schemas. Additionally, we investigate the problem of target schema elicitation, whic

Andrea Natale

We study a class of discrete models in which a collection of particles evolves in time following the gradient flow of an energy depending on the cell areas of an associated Laguerre (i.e. a weighted Voronoi) tessellation. We consider the high number of cell limit of such systems and, using a modulated energy argument, we prove convergence towards smooth solu

Anatole Jimenez, Bruno Osmanski, Denis Vivien, Mickael Tanter

Imaging the brain vasculature can be critical for cerebral perfusion monitoring in the context of neurocritical care. Although ultrasensitive Doppler (UD) can provide good sensitivity to cerebral blood volume (CBV) in a large field of view, it remains difficult to perform through the skull. In this work, we investigate how a minimally invasive burr hole, per

Tushar Sandhan, Sukanya Sonowal, Jin Young Choi

Automatic audio event recognition plays a pivotal role in making human robot interaction more closer and has a wide applicability in industrial automation, control and surveillance systems. Audio event is composed of intricate phonic patterns which are harmonically entangled. Audio recognition is dominated by low and mid-level features, which have demonstrat

Yi Ren, Hongyan Tang, Jiangpeng Rong, Siwen Zhu

Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback. Moreover, because of its general availability, we see wide adoption of implicit feedback data, such as click signal. There are mainly two challenges for the application of implicit feedback. First, implicit data ju

Muhammad Sohaib, Mary Adewunmi

In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. Based on the history of patients records, we reviewed a number of current research studies on lung cancer and its various stages. We identified that lung cancer i

Tomáš Fiedor, Lukáš Holík, Martin Hruška, Adam Rogalewicz

Several new algorithms for deciding emptiness of Boolean combinations of regular languages and of languages of alternating automata (AFA) have been proposed recently, especially in the context of analysing regular expressions and in string constraint solving. The new algorithms demonstrated a significant potential, but they have never been systematically com

Ivan Miro-Panades, Benoit Tain, Jean-Frederic Christmann, David Coriat

Increased capabilities such as recognition and self-adaptability are now required from IoT applications. While IoT node power consumption is a major concern for these applications, cloud-based processing is becoming unsustainable due to continuous sensor or image data transmission over the wireless network. Thus optimized ML capabilities and data transfers s

Daniel Dobrovodský, Carmelo Di Primo

Surface plasmon resonance (SPR)-based biosensors are widely used instruments for characterizing molecular interactions. In theory the SPR signal depends only on mass changes for interacting molecules of same chemical nature. Whether conformational changes of interacting molecules also contribute to the SPR signal is still a subject of lively debates. Works h

Arpita Chatterjee

An entangled quantum state is considered by applying a local photon excitation to each mode of an entangled coherent state. The entanglement property is investigated in terms of the entropy of entanglement. It is shown that applying a photon addition can improve the amount of entanglement. It is also examined that in a specific region of parameters, the stat

H. Moradpour, S. Jalalzadeh, Umesh Kumar Sharma

Is thermodynamics consistent with the quantum gravity reconciliation hypothesis [A. G. Cohen et al. Phys. Rev. Lett. 82, 4971 (1999)], which establishes holographic dark energy models? Here, we have attempted to address this issue in the affirmative by concentrating on the first law of thermodynamics.

Léo Bulckaen, Nilankur Dutta, Alexandre Nicolas

The parking issue is central in transport policies and drivers' concerns, but the determinants of the parking search time remain relatively poorly understood. The question is often handled in a fairly ad hoc way, or by resorting to crude approximations. Very recently, we proposed a more general agent-based approach, which notably takes due account of the rol

Alin Bostan, Xavier Caruso, Julien Roques

Given a linear differential equation with coefficients in $\mathbb{Q}(x)$, an important question is to know whether its full space of solutions consists of algebraic functions, or at least if one of its specific solutions is algebraic. After presenting motivating examples coming from various branches of mathematics, we advertise in an elementary way a beauti

Zhuo-Xu Cui, Chentao Cao, Yue Wang, Sen Jia

Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, existing reconstruction methods based on diffusion models are primarily formulated in the image domain, making the reconstruction quality susceptible to inaccuracies in coil sensitivi

Xingcheng Fu, Yuecen Wei, Qingyun Sun, Haonan Yuan

Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topological properties of the nodes (e.g., locations, roles) are unbalanced (topology-imbalance), other than the number of training labeled nodes (quantity-imbalance). Existing studies

Zhimin Zhu, Tiancai Peng, Zhi Hu, Siqi Xu

Under the basis light-front quantization framework, we investigate the leading-twist transverse-momentum-dependent parton distribution functions (TMDs) for $\Lambda$ and $\Lambda_c$ baryons, the spin-1/2 composite systems consisting of two light quarks ($u$ and $d$) and a $s/c$ quark. We evaluate the TMDs using the overlaps of the light-front wave functions