June 2022 arXiv papers — page 5

Akihiko Sekine, Manabu Ohtomo, Kenichi Kawaguchi, Mari Ohfuchi

We study theoretically the electronic structure of three-dimensional (3D) higher-order topological insulators in the presence of step edges. We numerically find that a 1D conducting state with a helical spin structure, which also has a linear dispersion near the zero energy, emerges at a step edge and on the opposite surface of the step edge. We also find th

Xiaofeng Cao, Yaming Guo, Ivor W. Tsang, James T. Kwok

Hypothesis-pruning maximizes the hypothesis updates for active learning to find those desired unlabeled data. An inherent assumption is that this learning manner can derive those updates into the optimal hypothesis. However, its convergence may not be guaranteed well if those incremental updates are negative and disordered. In this paper, we introduce a blac

Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang

Deep learning on large-scale data is dominant nowadays. The unprecedented scale of data has been arguably one of the most important driving forces for the success of deep learning. However, there still exist scenarios where collecting data or labels could be extremely expensive, e.g., medical imaging and robotics. To fill up this gap, this paper considers th

Hasan Basri Celebi, Mikael Skoglund

In this paper, we study a quantized feedback scheme to maximize the goodput of a finite blocklength communication scenario over a quasi-static fading channel. It is assumed that the receiver has perfect channel state information (CSI) and sends back the CSI to the transmitter over a resolution-limited error-free feedback channel. With this partial CSI, the t

Cynthia Kop, Deivid Vale

Tuple interpretations are a class of algebraic interpretation that subsumes both polynomial and matrix interpretations as it does not impose simple termination and allows non-linear interpretations. It was developed in the context of higher-order rewriting to study derivational complexity of algebraic functional systems. In this short paper, we continue our

Chengjie Ma, Junping Du, Yingxia Shao, Ang Li

We provide a simple and general solution for the discovery of scarce topics in unbalanced short-text datasets, namely, a word co-occurrence network-based model CWIBTD, which can simultaneously address the sparsity and unbalance of short-text topics and attenuate the effect of occasional pairwise occurrences of words, allowing the model to focus more on the d

Thomas Erlebach, Murilo Santos de Lima, Nicole Megow, Jens Schlöter

We study how to utilize (possibly erroneous) predictions in a model for computing under uncertainty in which an algorithm can query unknown data. Our aim is to minimize the number of queries needed to solve the minimum spanning tree problem, a fundamental combinatorial optimization problem that has been central also to the research area of explorable uncerta

M. Winkler, L. Prodan, V. Tsurkan, P. Lunkenheimer

We present dielectric, polarization, resistivity, specific heat, and magnetic susceptibility data on single crystals of the lacunar spinels GaNb4Se8 and GaTa4Se8, tetrahedral cluster-based materials with substantial spin-orbit coupling. We concentrate on the possible occurrence of antipolar order in these compounds, as previously reported for the isoelectron

Soumya Sur, M. S. Laad, Arya Subramonian, S. R. Hassan

We investigate two variants of quantum compass models (QCMs). The first, an orbital-only honeycomb QCM, is shown to exhibit a quantum phase transition (QPT) from a $XX$- to $ZZ$-ordered phase in the $3d$-Ising universality class, in accord with earlier studies. In a fractionalized parton construction, this describes a ``superfluid-Mott insulator'' transition

Lakshya Kumar, Sagnik Sarkar

Efficient search is a critical component for an e-commerce platform with an innumerable number of products. Every day millions of users search for products pertaining to their needs. Thus, showing the relevant products on the top will enhance the user experience. In this work, we propose a novel approach of fusing a transformer-based model with various listw

Asghar Qadir, Noraiz Tahir, Francesco De Paolis, Achille A. Nucita

The rotation of the galactic objects has been seen by asymmetric Doppler shift in the CMB data. Molecular hydrogen clouds at virial temperature may contribute to the galactic halo dark matter and they might be the reason for the observed rotational asymmetry in the galactic halos. We present a method to constrain the parameters of these virial clouds given t

Pritam Chatterjee, Saurabh Pradhan, Ashis K. Nandy, Arijit Saha

We theoretically investigate the phase transition from a non-trivial topological $p$-wave superconductor to a trivial $s$-wave like superconducting phase through a gapless phase, driven by different magnetic textures as an one-dimensional spin-chain impurity, e.g. Bloch-type, in-plane and out-of-plane N\'eel-type spin-chains etc. In our proposal, the chain o

Ilan Perez, Raphael Reinauer

In recent years, the introduction of the Transformer models sparked a revolution in natural language processing (NLP). BERT was one of the first text encoders using only the attention mechanism without any recurrent parts to achieve state-of-the-art results on many NLP tasks. This paper introduces a text classifier using topological data analysis. We use BER

Olaf Kaczmarek

We discuss recent progress in lattice QCD studies on various aspects involving strange quarks. Appropriate combinations of conserved net strange and net charm fluctuations and their correlations with other conserved charges provide evidence that in the hadronic phase so far unobserved hadrons contribute to the thermodynamics and need to be included in hadron

Halo Anwar Abdulkhalaq, Nzar Rauf Abdullah, Vidar Gudmundsson

We study electron transport properties through a double quantum dot (DQD) system coupled to a single mode photon cavity, DQD-cavity. The DQD system has a complex multilevel energy spectrum, in which by tuning the photon energy several anti-crossings between the electron states of the DQD system and photon dressed states are produced, which have not been seen

Xinxin Zhou, Jingru Feng, Jian Wang, Jianhong Pan

Load forecasting is very essential in the analysis and grid planning of power systems. For this reason, we first propose a household load forecasting method based on federated deep learning and non-intrusive load monitoring (NILM). For all we know, this is the first research on federated learning (FL) in household load forecasting based on NILM. In this meth

Andreas Fring, Rebecca Tenney

We construct Lewis-Riesenfeld invariants from two dimensional point transformations for two oscillators that are coupled to each other in space in a PT-symmetrical and time-dependent fashion. The non-Hermitian Hamiltonian of the model is conveniently expressed in terms of generators of the symplectic $sp(4)$ Lie algebra. This allows for an alternative system

Lucas Guerreiro, Filipi N. Silva, Diego R. Amancio

Sequences arise in many real-world scenarios; thus, identifying the mechanisms behind symbol generation is essential to understanding many complex systems. This paper analyzes sequences generated by agents walking on a networked topology. Given that in many real scenarios, the underlying processes generating the sequence is hidden, we investigate whether the

Huitong Chen, Yu Wang, Qinghua Hu

Deep learning models suffer from catastrophic forgetting when learning new tasks incrementally. Incremental learning has been proposed to retain the knowledge of old classes while learning to identify new classes. A typical approach is to use a few exemplars to avoid forgetting old knowledge. In such a scenario, data imbalance between old and new classes is

Kazufumi Hosoda, Shigeto Seno, Tsutomu Murata

The human brain can recognize objects hidden in even severely degraded images after observing them for a while, which is known as a type of Eureka effect, possibly associated with human creativity. A previous psychological study suggests that the basis of this "Eureka recognition" is neural processes of coincidence of multiple stochastic activities. Here we

Nick Brettell, James Oxley, Charles Semple, Geoff Whittle

The class of 2-regular matroids is a natural generalisation of regular and near-regular matroids. We prove an excluded-minor characterisation for the class of 2-regular matroids. The class of 3-regular matroids coincides with the class of matroids representable over the Hydra-5 partial field, and the 3-connected matroids in the class with a $U_{2,5}$- or $U_

Lijing Shao

General relativity offers a classical description to gravitation and spacetime, and is a cornerstone for modern physics. It has passed a number of empirical tests with flying colours, mostly in the weak-gravity regimes, but nowadays also in the strong-gravity regimes. Radio pulsars provide one of the earliest extrasolar laboratories for gravity tests. They,

Deval Mehta, Yaniv Gal, Adrian Bowling, Paul Bonnington

Recent years have witnessed a rapid development of automated methods for skin lesion diagnosis and classification. Due to an increasing deployment of such systems in clinics, it has become important to develop a more robust system towards various Out-of-Distribution(OOD) samples (unknown skin lesions and conditions). However, the current deep learning models

Suzanne M Fielding

We introduce a model of friction between two contacting (stationary or co-sliding) rough surfaces, each comprising a random ensemble of polydisperse hemispherical bumps. In the simplest version of the model, the bumps experience on contact with each other only pairwise elastic repulsion and dissipative drag. These minimal ingredients are sufficient to captur

Kosuke Nomura

A model is presented that simultaneously describes shape coexistence and quadrupole and octupole collective excitations within a theoretical framework based on the nuclear density functional theory and the interacting boson model. An optimal interacting-boson Hamiltonian that incorporates the configuration mixing between normal and intruder states, as well a

Matti Siekkinen, Teemu Kämäräinen

Depth maps are needed by various graphics rendering and processing operations. Depth map streaming is often necessary when such operations are performed in a distributed system and it requires in most cases fast performing compression, which is why video codecs are often used. Hardware implementations of standard video codecs enable relatively high resolutio

Andrey Grabovsky, Vitaly Vanchurin

We analyze algorithmic and computational aspects of biological phenomena, such as replication and programmed death, in the context of machine learning. We use two different measures of neuron efficiency to develop machine learning algorithms for adding neurons to the system (i.e. replication algorithm) and removing neurons from the system (i.e. programmed de

Agnieszka Mikołajczyk, Sylwia Majchrowska, Sandra Carrasco Limeros

New medical datasets are now more open to the public, allowing for better and more extensive research. Although prepared with the utmost care, new datasets might still be a source of spurious correlations that affect the learning process. Moreover, data collections are usually not large enough and are often unbalanced. One approach to alleviate the data imba

Bidisha Roy, Masha Vlasenko

The paper deals with a class of periods, Frobenius constants, which describe monodromy of Frobenius solutions of differential equations arising in algebraic geometry. We represent Frobenius constants related to families of elliptic curves as iterated integrals of modular forms. Using the theory of periods of modular forms, we then witness some of these const

Marko Filipovic, Alexander Pukhov

New laser facilities will reach intensities of $10^{23} \textrm{W cm}^{-2}$. This advance enables novel experimental setups in the study of laser-plasma interaction. In these setups with extreme fields quantum electrodynamic (QED) effects like photon emission via non-linear Compton scattering and Breit-Wheeler pair production become important. We study high-

Xu Song, Tianyu Shen, Hui Li, Xiao-Jun Wu

In image fusion tasks, an ideal image decomposition method can bring better performance. MDLatLRR has done a great job in this aspect, but there is still exist some space for improvement. Considering that MDLatLRR focuses solely on the detailed parts (salient features) extracted from input images via latent low-rank representation (LatLRR), the basic parts (

Alessandra R. Brazzale, Valentina Mameli

This paper reviews the most common situations where one or more regularity conditions which underlie classical likelihood-based parametric inference fail. We identify three main classes of problems: boundary problems, indeterminate parameter problems -- which include non-identifiable parameters and singular information matrices -- and change-point problems.

Viktor Nilsson, Pierre Nyquist

Finding equilibrium points in continuous minmax games has become a key problem within machine learning, in part due to its connection to the training of generative adversarial networks and reinforcement learning. Because of existence and robustness issues, recent developments have shifted from pure equilibria to focusing on mixed equilibrium points. In this

Akash Puliyadi Jegannathan, Rounak Saha, Sourav Kanti Addya

Serverless computing is a buzzword that is being used commonly in the world of technology and among developers and businesses. Using the Function-as-a-Service (FaaS) model of serverless, one can easily deploy their applications to the cloud and go live in a matter of days, it facilitates the developers to focus on their core business logic and the backend pr

I. Baliukin, J. L. Bertaux, M. Bzowski, V. Izmodenov

This review summarizes our current understanding of the outer heliosphere and local interstellar medium (LISM) inferred from observations and modeling of interplanetary Lyman-$\alpha$ emission. The emission is produced by solar Lyman-alpha photons (121.567 nm) backscattered by interstellar H atoms inflowing to the heliosphere from the LISM. Studies of Lyman-

Mohammad Sabbaqi, Elvin Isufi

Devising and analyzing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an established approach to learn from time-invariant network data. The graph convolution operation offers a principled approach

Amin Ghodousian, Sara Zal

In this paper, we introduce a type of latticized optimization problem whose objective function is the maximum component function and the feasible region is defined as a system of fuzzy relational equalities (FRE) defined by the Dombi t-norm. Dombi family of t-norms includes a parametric family of continuous strict t-norms, whose members are increasing functi

Durakhshan Ashraf Qadri, Abdul W. Khanday, Prince A. Ganai

We developed the functional form of the two-point correlation function under the approximation of fixed particle number density n(bar). We solved the quasi-linear partial differential equation (PDE) through the method of characteristics to obtain the parametric solution for the canonical ensemble. We attempted many functional forms and concluded that the fun

Daniel Dörfler, Andreas Löhne

The intersection of an affine subspace with the cone of positive semidefinite matrices is called a spectrahedron. An orthogonal projection thereof is called a spectrahedral shadow or projected spectrahedron. Spectrahedra and their projections can be seen as a generalization of polyhedra. This article is concerned with the problem of approximating the recessi

Benard Mulilo, Mounia Laassiri, Diallo Boye

Higher education and advanced scientific research lead to social, economic, and political development of any country. All developed societies like the current 2022 G7 countries: Canada, France, Germany, Italy, Japan, the UK, and the US have all not only heavily invested in higher education but also in advanced scientific research in their respective countrie

Amin Ghodousian, Sara Zal

In this paper, an optimization model with a linear objective function subjected to a system of fuzzy relation equations (FRE) is studied where the feasible region is defined by the Dombi t-norm. Dombi family of t-norms includes a parametric family of continuous strict t-norms, whose members are increasing functions of the parameter. This family of t-norms co

Ardi Tampuu, Romet Aidla, Jan Are van Gent, Tambet Matiisen

The core task of any autonomous driving system is to transform sensory inputs into driving commands. In end-to-end driving, this is achieved via a neural network, with one or multiple cameras as the most commonly used input and low-level driving command, e.g. steering angle, as output. However, depth-sensing has been shown in simulation to make the end-to-en

Georgios Argyris, Alberto Lluch Lafuente, Alexander Leguizamon Robayo, Mirco Tribastone

Quantitative notions of bisimulation are well-known tools for the minimization of dynamical models such as Markov chains and ordinary differential equations (ODEs). In \emph{forward bisimulations}, each state in the quotient model represents an equivalence class and the dynamical evolution gives the overall sum of its members in the original model. Here we i

Kuan Li, Yang Liu, Xiang Ao, Jianfeng Chi

Benefiting from the message passing mechanism, Graph Neural Networks (GNNs) have been successful on flourish tasks over graph data. However, recent studies have shown that attackers can catastrophically degrade the performance of GNNs by maliciously modifying the graph structure. A straightforward solution to remedy this issue is to model the edge weights by

Shahram Janbaz, Daniel Fan, Mahya Ganjian, Teunis van Manen

Machine-matter, of which mechanical metamaterials and meta-devices are important sub-categories, is emerging as a major paradigm for designing advanced functional materials. Various exciting applications of these concepts have been recently demonstrated, ranging from exotic mechanical properties to device-like and adaptive functionalities. The vast majority

Wei-Hung Liao, Tsung-Ming Huang, Wen-Wei Lin, Mei-Heng Yueh

In this paper, we first derive a theoretical basis for spherical conformal parameterizations between a simply connected closed surface $\mathcal{S}$ and a unit sphere $\mathbb{S}^2$ by minimizing the Dirichlet energy on $\overline{\mathbb{C}}$ by stereographic projection. The Dirichlet energy can be rewritten as the sum of the energies associated with the so

Renan Hirayama, Jan Staudenmaier, Hannah Elfner

Effective spectral functions of the $\rho$ meson are reconstructed by considering the lifetimes inside different media using the hadronic transport SMASH (Simulating Many Accelerated Strongly-interacting Hadrons). Due to inelastic scatterings, resonance lifetimes are dynamically shortened (collisional broadening), even though the employed approach assumes va

Orian Leitersdorf, Ronny Ronen, Shahar Kvatinsky

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM) systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper vas

Hiraku Toida, Koji Sakai, Tetsuhiko F. Teshima, Masahiro Hori

We demonstrate magnetometry of cultured neurons on a polymeric film using a superconducting flux qubit that works as a sensitive magnetometer in a microscale area. The neurons are cultured in Fe$^{3+}$ rich medium to increase magnetization signal generated by the electron spins originating from the ions. The magnetometry is performed by insulating the qubit

Alberto García-Durán, Akhil Arora, Robert West

Candidate generation is a crucial module in entity linking. It also plays a key role in multiple NLP tasks that have been proven to beneficially leverage knowledge bases. Nevertheless, it has often been overlooked in the monolingual English entity linking literature, as naive approaches obtain very good performance. Unfortunately, the existing approaches for

Ahmet Tuğrul Bayrak

Today, machine learning is applied in almost any field. In machine learning, where there are numerous methods, classification is one of the most basic and crucial ones. Various problems can be solved by classification. The feature selection for model setup is extremely important, and producing new features via feature engineering also has a vital place in th

Szymon Cygan, Anna Marciniak-Czochra, Grzegorz Karch

Systems consisting of a single ordinary differential equation coupled with one reaction-diffusion equation in a bounded domain and with the Neumann boundary conditions are studied in the case of particular nonlinearities from the Brusselator model, the Gray-Scott model, the Oregonator model and a certain predator-prey model. It is shown that the considered s

Yangrui Chen, Jie Zhang

We experimentally investigate the velocity distributions of quasi two-dimensional granular materials, which are homogeneously driven, i.e. uniformly heated, by an electromagnetic vibrator, where the translational velocity and the rotation of a single particle are Gaussian and independent. We observe the non-Gaussian distributions of particle velocity, with t

Kelin Li, Nicholas Baron, Xian Zhang, Nicolas Rojas

Autonomous grasping of novel objects that are previously unseen to a robot is an ongoing challenge in robotic manipulation. In the last decades, many approaches have been presented to address this problem for specific robot hands. The UniGrasp framework, introduced recently, has the ability to generalize to different types of robotic grippers; however, this

Shuokai Li, Yongchun Zhu, Ruobing Xie, Zhenwei Tang

Conversational recommender systems (CRS) aim to capture user's current intentions and provide recommendations through real-time multi-turn conversational interactions. As a human-machine interactive system, it is essential for CRS to improve the user experience. However, most CRS methods neglect the importance of user experience. In this paper, we propose tw

Gerald Cooray, Richard Rosch, Karl Friston

Neural mass models are used to simulate cortical dynamics and to explain the electrical and magnetic fields measured using electro- and magnetoencephalography. Simulations evince a complex phase-space structure for these kinds of models; including stationary points and limit cycles and the possibility for bifurcations and transitions among different modes of

Tim Broedermann, Christos Sakaridis, Dengxin Dai, Luc Van Gool

Besides standard cameras, autonomous vehicles typically include multiple additional sensors, such as lidars and radars, which help acquire richer information for perceiving the content of the driving scene. While several recent works focus on fusing certain pairs of sensors - such as camera with lidar or radar - by using architectural components specific to

Shubham Dutta, Sayan Ghosh, Satyaki Bhattacharya, Satyajit Saha

An essential metric for the quality of a particle-identification experiment is its statistical power to discriminate between signal and background. Pulse shape discrimination (PSD) is a basic method for this purpose in many nuclear, high-energy and rare-event search experiments where scintillation detectors are used. Conventional techniques exploit the diffe

Yeonjong Choi, Chao Xie, Tomoki Toda

This paper presents a new voice conversion (VC) framework capable of dealing with both additive noise and reverberation, and its performance evaluation. There have been studied some VC researches focusing on real-world circumstances where speech data are interfered with background noise and reverberation. To deal with more practical conditions where no clean

Georgi Pramatarov, Daniele De Martini, Matthew Gadd, Paul Newman

This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified components where each vertex corresponds to an object instance and encodes its shape. Optimal vertex association across graphs allows for fu

Can Xiang, Chunming Tang

Linear codes and $t$-designs are interactive with each other. It is well known that some $t$-designs have been constructed by using certain linear codes in recent years. However, only a small number of infinite families of the extended codes of linear codes holding an infinite family of $t$-designs with $t\geq 3$ are reported in the literature. In this paper

Hui Liu, Lei Liu

In this paper, we establish the existence of an equidistributed sequence of nondegenerate closed geodesics for generic Finsler, symmetric Finsler and Riemannian metrics on every closed surface. The proof relies on the volume property of embedded contact homology, established by Cristofaro-Gardiner, Hutchings and Ramos, along with specific local variational c

D J Goldie, S. Withington, C. N. Thomas, P. A. R. Ade

Far-infrared detectors for future cooled space telescopes require ultra-sensitive detectors with optical noise equivalent powers of order 0.2 aW/\sqrt Hz. This performance has already been demonstrated in arrays of transition edge sensors. A critical step is demonstrating a method of fabrication and assembly that maintains the performance but that is extenda

M. D'Andrea, K. Ravensberg, A. Argan, D. Brienza

The X-IFU is the cryogenic spectrometer onboard the future ATHENA X-ray observatory. It is based on a large array of TES microcalorimeters, which works in combination with a Cryogenic AntiCoincidence detector (CryoAC). This is necessary to reduce the particle background level thus enabling part of the mission science goals. Here we present the first joint te

Benjamin Kenwright

This paper presents a novel approach for exploring diverse and expressive motions that are physically correct and interactive. The approach combining user participation in with the animation development process using crowdsourcing to remove the need for data-driven libraries while address aesthetic limitations. A core challenge for character animation soluti

Marta Kwiatkowska, Gethin Norman, David Parker, Gabriel Santos

Game-theoretic concepts have been extensively studied in economics to provide insight into competitive behaviour and strategic decision making. As computing systems increasingly involve concurrently acting autonomous agents, game-theoretic approaches are becoming widespread in computer science as a faithful modelling abstraction. These techniques can be used

Asier Gutiérrez-Fandiño, David Pérez-Fernández, Jordi Armengol-Estapé, David Griol

In the recent years, transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web cr

Alessandro Fasano, Alexandre Beelen, Alain Benoit, Andreas Lundgren

CarbON CII line in post-rEionization and ReionizaTiOn (CONCERTO) is a low-resolution spectrometer with an instantaneous field-of-view of 18.6 arcmin, operating in the 130-310 GHz transparent atmospheric window. It is installed on the 12-meter Atacama Pathfinder Experiment (APEX) telescope at 5100 m above sea level. The Fourier transform spectrometer (FTS) co

Yi Zhong, Ke Yang, Yu-Xiao Liu

In this work, thick branes in Rastall gravity are investigated. Three types of maximally symmetric thick brane models are constructed and the linear tensor perturbation is analyzed. In the flat brane model, the tensor modes of the perturbation are either unstable or nonlocalizable for a nonvanishing Rastall parameter. In the de Sitter brane model, only the g

Alex Damian, Jason D. Lee, Mahdi Soltanolkotabi

Significant theoretical work has established that in specific regimes, neural networks trained by gradient descent behave like kernel methods. However, in practice, it is known that neural networks strongly outperform their associated kernels. In this work, we explain this gap by demonstrating that there is a large class of functions which cannot be efficien

Lin Zhang, Shaohuai Shi, Wei Wang, Bo Li

The second-order optimization methods, notably the D-KFAC (Distributed Kronecker Factored Approximate Curvature) algorithms, have gained traction on accelerating deep neural network (DNN) training on GPU clusters. However, existing D-KFAC algorithms require to compute and communicate a large volume of second-order information, i.e., Kronecker factors (KFs),

Yuan Miao, Vladimir Gritsev, Denis V. Kurlov

Quantum integrability has proven to be a useful tool to study quantum many-body systems out of equilibrium. In this paper we construct a generic framework for integrable quantum circuits through the procedure of Floquet Baxterisation. The integrability is guaranteed by establishing a connection between Floquet evolution operators and inhomogeneous transfer m

Dinh Van Le, Hop D. Nguyen

Ideals in infinite-dimensional polynomial rings that are invariant under the action of the monoid of increasing functions have been extensively studied recently. Of particular interest is the asymptotic behavior of truncations of such an ideal in finite-dimensional polynomial subrings. It has been conjectured that the Castelnuovo--Mumford regularity and proj

N. Michel, J. G. Li, L. H. Ru, W. Zuo

The $^{16}$F nucleus is situated at the proton drip-line and is unbound by proton emission by only about 500 keV. Continuum coupling is then prominent in this nucleus. Added to that, its low-lying spectrum consists of narrow proton resonances as well. It is then a very good candidate to study nuclear structure and reactions at proton drip-line. The low-lying

Kyriakos D. Kantarakias, George Papadakis

We present an enriched formulation of the Least Squares (LSQ) regression method for Uncertainty Quantification (UQ) using generalised polynomial chaos (gPC). More specifically, we enrich the linear system with additional equations for the gradient (or sensitivity) of the Quantity of Interest with respect to the stochastic variables. This sensitivity is compu

Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, Luigi Lo Iacono

Risk-based authentication (RBA) aims to protect users against attacks involving stolen passwords. RBA monitors features during login, and requests re-authentication when feature values widely differ from previously observed ones. It is recommended by various national security organizations, and users perceive it more usable and equally secure than equivalent

Bo Peng, Wei Xiang, Yue Jiang, Wei Wang

This paper presents the summary report on our DFGC 2022 competition. The DeepFake is rapidly evolving, and realistic face-swaps are becoming more deceptive and difficult to detect. On the contrary, methods for detecting DeepFakes are also improving. There is a two-party game between DeepFake creators and defenders. This competition provides a common platform

Genki Shibukawa, Satoshi Tsuchimi

We introduce a one parameter deformation of the Zwegers' $\mu$-function as the image of $q$-Borel and $q$-Laplace transformations of a fundamental solution for the $q$-Hermite-Weber equation. We further give some formulas for our generalized $\mu$-function, for example, forward and backward shift, translation, symmetry, a difference equation for the new para

Simon C. Stähler, Anna Mittelholz, Clément Perrin, Taichi Kawamura

The InSight mission has measured Mars' seismicity since February 2018 and has allowed to investigate tectonics on another planet. Seismic data shows that most of the widely distributed surface faults are not seismically active, and that seismicity is mostly originating from a single graben structure, the Cerberus Fossae. We show that both major families of m

Johanna Marie Gegenfurtner

The study of minimal surfaces has a long history, due to the important applications. Given a fixed boundary, one wants to minimise the surface area: this can be used, for example, to minimise the area of the roof of a building. Similarly, looking for constant mean curvature (CMC) provides us with many interesting applications in physics: one of the easiest e

Absos Ali Shaikh, Prosenjit Mandal, Chandan Kumar Mondal

This paper aims to study the $(m,\rho)$-quasi Einstein manifold. This article shows that a complete and connected Riemannian manifold under certain conditions becomes compact. Also, we have determined an upper bound of the diameter for such a manifold. It is also exhibited that the potential function acquiesces to the Hodge-de Rham potential up to a real con

Yi Lin, Zeyu Wang, Kwang-Ting Cheng, Hao Chen

Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis. However, deep-learning-based nuclei segmentation methods often suffer from limited annotations. This paper proposes a realistic data augmentation method for nuclei segmentation, named InsMix, that follows a Copy-Paste-Smooth principle and performs morphology-constr

Junwen Tang, Mingyao Cui, Shenheng Xu, Linglong Dai

Reconfigurable intelligent surface (RIS) has been widely considered as a key technique to improve spectral efficiency for 6G communications. Compared with most existing research that only focuses on the reflective RIS, the design and prototyping of a novel transmissive RIS are presented in this paper, and its enhancement to the RIS-aided communication system

Jiajia Guo, Chao-Kai Wen, Shi Jin, Xiao Li

The 3rd Generation Partnership Project started the study of Release 18 in 2021. Artificial intelligence (AI)-native air interface is one of the key features of Release 18, where AI for channel state information (CSI) feedback enhancement is selected as the representative use case. This article provides an overview of AI for CSI feedback enhancement in 5G-Adv

Janis Kummer, Lennart Rustige, Florian Griese, Kerstin Borras

Novel techniques are indispensable to process the flood of data from the new generation of radio telescopes. In particular, the classification of astronomical sources in images is challenging. Morphological classification of radio galaxies could be automated with deep learning models that require large sets of labelled training data. Here, we demonstrate the

Anna Abbatiello, Eduard Feireisl

We consider the Oberbeck--Boussinesq system with non--local boundary conditions arising as a singular limit of the full Navier--Stokes--Fourier system in the regime of low Mach and low Froude number. The existence of strong solutions is shown on a maximal time interval $[0, T_{\rm max})$. Moreover, $T_{\rm max} = \infty$ in the two dimensional setting.

Atanu R Sinha, Navita Goyal, Sunny Dhamnani, Tanay Asija

Cognitive biases are mental shortcuts humans use in dealing with information and the environment, and which result in biased actions and behaviors (or, actions), unbeknownst to themselves. Biases take many forms, with cognitive biases occupying a central role that inflicts fairness, accountability, transparency, ethics, law, medicine, and discrimination. Det

Mingyu Dong, Jiahao Chen, Diqun Yan, Jingxing Gao

Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs) which are maliciously designed to fool target models. The normal examples (NEs) added with imperceptible adversarial perturbation, can be a security threat to DNNs. Although the existing AEs detection methods have achieved a high accuracy, they failed to exploit t

Zidong Lin, Lin Zhang, Xinyue Long, Yu-ang Fan

Noise is ubiquitous in real quantum systems, leading to non-Hermitian quantum dynamics, and may affect the fundamental states of matter. Here we report in experiment a quantum simulation of the two-dimensional non-Hermitian quantum anomalous Hall (QAH) model using the nuclear magnetic resonance processor. Unlike the usual experiments using auxiliary qubits,

Marc Cheneau

Judging by the enormous body of work that it has inspired, Elliott Lieb and Derek Robinson's 1972 article on the "Finite Group Velocity of Quantum Spin Systems" can be regarded as a \emph{high-impact paper}, as research accountants say. But for more than 30 years this major contribution to quantum physics has remained pretty much confidential. Lieb and Robin

Lou Barreau, Andrew D. Ross, Victor Kimberg, Pavel Krasnov

A vibrational wavepacket in SF$_6$, created by impulsive stimulated Raman scattering with a few-cycle infrared pulse, is mapped onto five sulfur core-excited states using table-top soft X-ray transient absorption spectroscopy between 170-200 eV. The amplitudes of the X-ray energy shifts of the femtosecond oscillations depend strongly on the nature of the sta

Yuting Wang, Hangning Zhou, Zhigang Zhang, Chen Feng

This technical report presents an effective method for motion prediction in autonomous driving. We develop a Transformer-based method for input encoding and trajectory prediction. Besides, we propose the Temporal Flow Header to enhance the trajectory encoding. In the end, an efficient K-means ensemble method is used. Using our Transformer network and ensembl

Andi Bodnariu, Sören Christensen, Kristoffer Lindensjö

We consider the game-theoretic approach to time-inconsistent stopping of a one-dimensional diffusion where the time-inconsistency is due to the presence of a non-exponential (weighted) discount function. In particular, we study (weak) equilibria for this problem in a novel class of mixed (i.e., randomized) stopping times based on a local time construction of

Erlend Grong

These notes give an introduction to the equivalence problem of sub-Riemannian manifolds. We first introduce preliminaries in terms of connections, frame bundles and sub-Riemannian geometry. Then we arrive to the main aim of these notes, which is to give the description of the canonical grading and connection existing on sub-Riemann manifolds with constant sy

Seiichiro Tani

Space-bounded computation has been a central topic in classical and quantum complexity theory. In the quantum case, every elementary gate must be unitary. This restriction makes it unclear whether the power of space-bounded computation changes by allowing intermediate measurement. In the bounded error case, Fefferman and Remscrim [STOC 2021, pp.1343--1356] a

Jonne Juusti

We study the existence of an extension operator $\Lambda \colon W^{1,\varphi}(\Omega)\to W^{1,\psi}(\mathbb{R}^n)$. We assume that $\varphi \in \Phi_\mathrm{w}(\Omega)$ has generalized Orlicz growth, $\psi \in \Phi_\mathrm{w}(\mathbb{R}^n)$ is an extension of $\varphi$, and that $\Omega\subset\mathbb{R}^n$ is an $(\epsilon,\delta)$-domain. Special cases incl

H. A. Gururaja, Niteesh Kumar

We classify complete orientable hypersurfaces of constant isotropic curvature in space forms. We show that such a hypersurface has constant mean curvature only if it is an isoparametric hypersurface, and that it is minimal if and only if it is totally geodesic or it is the Clifford minimal hypersurface ${\mathbb S}^{3}(\frac{4c}{3})\times {\mathbb S}^{1}(4c)

A. Bertipaglia, D. de Mol, M. Alirezaei, R. Happee

This paper provides a comprehensive comparison of model-based and data-driven approaches and analyses the benefits of using measured tyre forces for vehicle sideslip angle estimation. The model-based approaches are based on an extended Kalman filter and an unscented Kalman filter, in which the measured tyre forces are utilised in the observation model. An ad

Diego Delmastro, Jaume Gomis, Po-Shen Hsin, Zohar Komargodski

We study ordinary, zero-form symmetry $G$ and its anomalies in a system with a one-form symmetry $\Gamma$. In a theory with one-form symmetry, the action of $G$ on charged line operators is not completely determined, and additional data, a fractionalization class, needs to be specified. Distinct choices of a fractionalization class can result in different va

Chris J. Skipper, Anna M. M. Scaife, J. Patrick Leahy

The next generation of radio telescopes will strive for unprecedented dynamic range across wide fields of view, and direction-dependent gains such as the gain from the primary-beam pattern, or leakage of one Stokes product into another, must be removed from the cleaned images if dynamic range is to reach its full potential. Unfortunately, such processing is