May 2022 arXiv papers — page 86

Liang Huang, Senjie Liang, Feiyang Ye, Nan Gao

Intent detection and slot filling are two main tasks in natural language understanding and play an essential role in task-oriented dialogue systems. The joint learning of both tasks can improve inference accuracy and is popular in recent works. However, most joint models ignore the inference latency and cannot meet the need to deploy dialogue systems at the

Palle E. T. Jorgensen, James Tian

We show that a Krein-Feller operator is naturally associated to a fixed measure $\mu$, assumed positive, $\sigma$-finite, and non-atomic. Dual pairs of operators are introduced, carried by the two Hilbert spaces, $L^{2}\left(\mu\right)$ and $L^{2}\left(\lambda\right)$, where $\lambda$ denotes Lebesgue measure. An associated operator pair consists of two spec

Jian He, Jing He, Panyue Zhou

Nakaoka-Ogawa-Sakai considered the localization of an extriangulated category. This construction unified the Serre quotient of abelian categories and the Verdier quotient of triangulated categories. Recently, Herschend-Liu-Nakaoka defined $n$-exangulated categories as a higher dimensional analogue of extriangulated categories. Let $\mathcal C$ be an $n$-exan

Xuwen Zhang

In this note, we establish a boundary maximum principle for a class of stationary pairs of varifolds satisfying a fixed contact angle condition in any compact Riemannian manifold with smooth boundary.

Marcony S. Cunha, G. Alencar, Celio R. Muniz, Valdir B. Bezerra

In this paper, we obtain two different static black string solutions by considering as sources axisymmetric dark matter distributions in 3+1 dimensions. These solutions tend asymptotically to the usual static and uncharged black string vacuum solution predicted by General Relativity (GR). We show that both the solutions present an event horizon each, like th

Asher Klug, Cade Peters, Andrew Forbes

Structured light is routinely used in free space optical communication channels, both classical and quantum, where information is encoded in the spatial structure of the mode for increased bandwidth. Unlike polarisation, the spatial structure of light is perturbed through such channels by atmospheric turbulence, and consequently, much attention has focused o

Mirrelijn M. van Nee, Lodewyk F. A. Wessels, Mark A. van de Wiel

High-dimensional prediction considers data with more variables than samples. Generic research goals are to find the best predictor or to select variables. Results may be improved by exploiting prior information in the form of co-data, providing complementary data not on the samples, but on the variables. We consider adaptive ridge penalised generalised linea

Hannaneh Akrami, Noga Alon, Bhaskar Ray Chaudhury, Jugal Garg

The existence of EFX allocations is a fundamental open problem in discrete fair division. Given a set of agents and indivisible goods, the goal is to determine the existence of an allocation where no agent envies another following the removal of any single good from the other agent's bundle. Since the general problem has been illusive, progress is made on tw

Csaba Veres

Natural Language Processing is one of the leading application areas in the current resurgence of Artificial Intelligence, spearheaded by Artificial Neural Networks. We show that despite their many successes at performing linguistic tasks, Large Neural Language Models are ill-suited as comprehensive models of natural language. The wider implication is that, i

Marin Vlastelica, Patrick Ernst, György Szarvas

Utilizing amortized variational inference for latent-action reinforcement learning (RL) has been shown to be an effective approach in Task-oriented Dialogue (ToD) systems for optimizing dialogue success. Until now, categorical posteriors have been argued to be one of the main drivers of performance. In this work we revisit Gaussian variational posteriors for

Miceline Mésidor, Caroline Sirois, Marc Simard, Denis Talbot

The use of longitudinal finite mixture models such as group-based trajectory modeling has seen a sharp increase during the last decades in the medical literature. However, these methods have been criticized especially because of the data-driven modelling process which involves statistical decision-making. In this paper, we propose an approach that uses boots

Ämin Baumeler, Carla Rieger, Stefan Wolf

We discuss a simple toy model which allows, in a natural way, for deriving central facts from thermodynamics such as its fundamental laws, including Carnot's version of the second principle. Our viewpoint represents thermodynamic systems as binary strings, and it links their temperature to their Hamming weight. From this, we can reproduce the possibility of

Chao Wang

Recent studies have shown that deep reinforcement learning (DRL) policies are vulnerable to adversarial attacks, which raise concerns about applications of DRL to safety-critical systems. In this work, we adopt a principled way and study the robustness of DRL policies to adversarial attacks from the perspective of robust optimization. Within the framework of

Hung Viet Chu

Recently, a relation between Schreier-type sets and Tur\'{a}n graphs was discovered. In this note, we give a combinatorial proof and obtain a generalization of the relation. Specifically, for $p, q\ge 1$, let $$\mathcal{A}_q := \{F\subset\mathbb{N}: |F| = 1 \mbox{ or }F\mbox{ is an arithmetic progression with difference } q\}$$ and $$Sr(n, p, q)\ :=\ \#\{F\s

Anna Zafeiris

The emergence of opinion polarization within human communities -- the phenomenon that individuals within a society tend to develop conflicting attitudes related to the greatest diversity of topics -- has been a focus of interest for decades, both from theoretical and modelling points of view. Regarding modelling attempts, an entire scientific field -- opinio

André Artelt, Roel Visser, Barbara Hammer

The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring a sufficiently high certainty of predictions made by the system. While being able to reject uncertain samples is important, it is also of importance to be able to explain why a pa

Negin Amini, Josh Tuohey, John M. Long, Jun Zhang

While stress visualization within 3-dimensional particles would greatly advance our understanding of the behaviors of complex particles, traditional photoelastic methods suffer from a lack of available technology for producing suitable complex particles. Recently, 3D-printing has created new possibilities for enhancing the scope of stress analysis within phy

A. Möri, B. Lecampion

Hydraulic fractures propagating at depth are subjected to buoyant forces caused by the density contrast between fluid and solid. This paper is concerned with the analysis of the transition from an initially radial towards an elongated buoyant growth -- a critical topic for understanding the extent of vertical hydraulic fractures in the upper Earth crust. Usi

Zoltán Filus, Peng Ye, Tamás Csizmadia, Tímea Grósz

We present the design and implementation of a new, modular gas target suitable for high-order harmonic generation using high average power lasers. To ensure thermal stability in this high heat load environment, we implement an appropriate liquid cooling system. The system can be used in multiple-cell configurations allowing to control the cell length and ape

Philipp Sauerteig

We address the problem of load shaping within a network of coupled microgrids (MGs) in a bilevel optimisation framework. To this end, we consider the charging/discharging rates of residential energy storage devices within each MG on the lower level and the power exchange among neighbouring MGs on the upper level as optimisation variables. We improve a previo

Sai Peng, Peng Yu

Shear-thinning and viscoelasticity are two non-Newtonian fluid properties widely existing in biological fluids. In this study, we found that the translation motion of a rotating particle near a wall speed up firstly, and then slows down with enhancement of fluid viscoelasticity, which is different from the behavior reported in shear thinning fluid (Chen et a

Daniel Gomon, Hein Putter, Rob G. H. H. Nelissen, Stéphanie van der Pas

Rapidly detecting problems in the quality of care is of utmost importance for the well-being of patients. Without proper inspection schemes, such problems can go undetected for years. Cumulative sum (CUSUM) charts have proven to be useful for quality control, yet available methodology for survival outcomes is limited. The few available continuous time inspec

Lam Duc Nguyen, Arne Broering, Massimo Pizzol, Petar Popovski

In recent years, industrial manufacturing has undergone massive technological changes that embrace digitalization and automation towards the vision of intelligent manufacturing plants. With the aim of maximizing efficiency and profitability in production, an important goal is to enable flexible manufacturing, both, for the customer (desiring more individuali

Claire Hall, Liam P. Shaw

The Anthologies of the second-century astrologer Vettius Valens (120-c.175 CE) is the most extensive surviving practical astrological text from the period. Despite this, the theoretical underpinnings of the Anthologies have been understudied; in general, the work has been overshadowed by Ptolemy's contemporaneous Tetrabiblos. While the Tetrabiblos explicitly

Farzaneh Pourahmadi, Trine Krogh Boomsma

In this paper, we study the operational problem of connected hydro power reservoirs which involves sequential decision-making in an uncertain and dynamic environment. The problem is traditionally formulated as a stochastic dynamic program accounting for the uncertainty of electricity prices and reservoir inflows. This formulation suffers from the curse of di

Chao Wang, Chen Chen, Dong Li, Bin Wang

Recently, reinforcement learning has been used to address logic synthesis by formulating the operator sequence optimization problem as a Markov decision process. However, through extensive experiments, we find out that the learned policy makes decisions independent from the circuit features (i.e., states) and yields an operator sequence that is permutation i

Pirazh Khorramshahi, Vineet Shenoy, Rama Chellappa

As Computer Vision technologies become more mature for intelligent transportation applications, it is time to ask how efficient and scalable they are for large-scale and real-time deployment. Among these technologies is Vehicle Re-Identification which is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehi

Richard Ignace, Andrew Fullard, Manisha Shrestha, Yael Naze

Massive star binaries are critical laboratories for measuring masses and stellar wind mass-loss rates. A major challenge is inferring viewing inclination and extracting information about the colliding wind interaction (CWI) region. Polarimetric variability from electron scattering in the highly ionized winds provides important diagnostic information about sy

Haochen Han, Qinghua Zheng, Minnan Luo, Kaiyao Miao

Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely on high-quality manual annotations an

André Müller, Bertil Schmidt, Richard Membarth, Roland Leißa

In recent years, the rapidly increasing number of reads produced by next-generation sequencing (NGS) technologies has driven the demand for efficient implementations of sequence alignments in bioinformatics. However, current state-of-the-art approaches are not able to leverage the massively parallel processing capabilities of modern GPUs with close-to-peak p

Javier Jiménez-Garrido, Ignacio Miguel-Cantero, Javier Sanz, Gerhard Schindl

We construct optimal flat functions in Carleman-Roumieu ultraholomorphic classes associated to general strongly nonquasianalytic weight sequences, and defined on sectors of suitably restricted opening. A general procedure is presented in order to obtain linear continuous extension operators, right inverses of the Borel map, for the case of regular weight seq

Mark Anderson, Jose Camacho-Collados

The increase in performance in NLP due to the prevalence of distributional models and deep learning has brought with it a reciprocal decrease in interpretability. This has spurred a focus on what neural networks learn about natural language with less of a focus on how. Some work has focused on the data used to develop data-driven models, but typically this l

Jianjun Jin

In this paper we introduce and study several new Hardy-Littlewood-P\'olya-type operators. In particular, we study a Hardy-Littlewood-P\'olya-type operator induced by a positive Borel measure on $[0,1)$. We establish some sufficient and necessary conditions for the boundedness (compactness) of these operators. We also determine the exact values of the norms o

In-soo Kim, Mehdi Bennis, Junil Choi

In this paper, we consider the uplink channel estimation phase and downlink data transmission phase of cell-free millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with low-capacity fronthaul links and low-resolution analog-to-digital converters/digital-to-analog converters (ADC/DACs). In cell-free massive MIMO, a control unit dic

Nuno Crokidakis

We propose a simple mathematical model to describe the evolution of violent crimes. For such purpose, we built a model based on ordinary differential equations that take into account the number of violent crimes and the number of legal and illegal guns. The dynamics is governed by probabilities, modeling for example the police action, the risk perception reg

Lei Yu

In this paper, we derive variational formulas for the asymptotic exponents (i.e., convergence rates) of the concentration and isoperimetric functions in the product Polish probability space under certain mild assumptions. These formulas are expressed in terms of relative entropies (which are from information theory) and optimal transport cost functionals (wh

Sebastiano von Fellenberg, Stefan Gillessen, Julia Stadler, Michi Bauböck

We present a large ${\sim 30" \times 30"}$ spectroscopic survey of the Galactic Center using the SINFONI IFU at the VLT. Combining observations of the last two decades we compile spectra of over $2800$ stars. Using the Bracket-$\gamma$ absorption lines we identify $195$ young stars, extending the list of known young stars by $79$. In order to explore the ang

Corentin Le Bars

Let $G$ be a discrete group, $\mu$ a measure on $G$ and $X$ a proper CAT(0) space. We show that if $G$ acts non-elementarily with a rank one element on $X$, then the pushforward $\{Z_n o \}_n$ to $X$ of the random walk generated by $\mu$ converges almost surely to a rank one point of the boundary. We also show that in this context, there is a unique stationa

Mélanie Cambus, Fabian Kuhn, Etna Lindy, Shreyas Pai

Grouping together similar elements in datasets is a common task in data mining and machine learning. In this paper, we study streaming algorithms for correlation clustering, where each pair of elements is labeled either similar or dissimilar. The task is to partition the elements and the objective is to minimize disagreements, that is, the number of dissimil

Nicola Milano, Stefano Nolfi

In this paper we analyze the qualitative differences between evolutionary strategies and reinforcement learning algorithms by focusing on two popular state-of-the-art algorithms: the OpenAI-ES evolutionary strategy and the Proximal Policy Optimization (PPO) reinforcement learning algorithm -- the most similar methods of the two families. We analyze how the m

Kazuki Y. Nishida, Tsutomu T. Takeuchi, Takuma Nagata, Ryosuke S. Asano

The spectral energy distribution (SED) of galaxies provides fundamental information on the related physical processes. However, the SED is significantly affected by dust in its interstellar medium. Dust is mainly produced by asymptotic giant branch stars and Type II supernovae. In addition, the dust mass increases through the metal accretion, and the grain s

Roberto Di Pietro, Stefano Cresci

The metaverse promises a host of bright opportunities for business, economics, and society. Though, a number of critical aspects are still to be considered and the analysis of their impact is almost non-existent. In this paper, we provide several contributions. We start by analysing the foundations of the metaverse, later we focus on the novel privacy and se

Denise M. Reeves

Finding discriminant functions of minimum risk binary classification systems is a novel geometric locus problem -- which requires solving a system of fundamental locus equations of binary classification -- subject to deep-seated statistical laws. We show that a discriminant function of a minimum risk binary classification system is the solution of a locus eq

Evagoras Stylianou, Charalambos D. Charalambous, Jan H. van Schuppen

Examined in this paper, is the Gray and Wyner achievable lossy rate region for a tuple of correlated multivariate Gaussian random variables (RVs) $X_1 : \Omega \rightarrow {\mathbb R}^{p_1}$ and $X_2 : \Omega \rightarrow {\mathbb R}^{p_2}$ with respect to square-error distortions at the two decoders. It is shown that among all joint distributions induced by

Antonio Pich, Antonio Rodríguez-Sánchez

Using the spectral functions measured in $\tau$ decays, we investigate the actual numerical impact of duality violations on the extraction of the strong coupling. These effects are tiny in the standard $\alpha_s(m_\tau^2)$ determinations from integrated distributions of the hadronic spectrum with pinched weights, or from the total $\tau$ hadronic width. The

Jun-Tao Zhu, Shu-Yi Kong, Lin-Qing Song, Jun He

In this work, the $\Omega_c$-like molecular states are systematically investigated in a quasipotential Bethe-Salpeter equation approach. The relevant interactions $\Xi_c^{(*,')}\bar{K}^{(*)}$, $\Xi^{(*)}D^{(*)}$, and $\Omega^{(*)}_c(\pi/\eta/\rho/\omega)$ are described by light meson exchanges with the help of the effective Lagrangians with SU(3), chiral, an

Manjunath Bhat, Wojciech Chomicki, Krzysztof Cichy, Martha Constantinou

Precise quantification of the structure of nucleons is one of the crucial aims of hadronic physics for the coming years. The expected progress related to ongoing and planned experiments should be accompanied by calculations of partonic distributions from lattice QCD. While key insights from the lattice are expected to come for distributions that are difficul

Berent Ånund Strømnes Lunde, Feda Curic, Sondre Sortland

GraphSPME is an open source Python, R and C++ header-only package implement-ing non-parametric sparse precision matrix estimation along with asymptotic Stein-type shrinkage estimation of the covariance matrix. The user defines a potential neighbourhood structure and provides data that potentially are p >> n. This paper introduces a novel approach for finding

Omar Lakkis, Amireh Mousavi

We propose a conforming finite element method to approximate the strong solution of the second order Hamilton-Jacobi-Bellman equation with Dirichlet boundary and coefficients satisfying Cordes condition. We show the convergence of the continuum semismooth Newton method for the fully nonlinear Hamilton-Jacobi-Bellman equation. Applying this linearization for

Yi Yu, Karl Borjesson

Transformer models have been developed in molecular science with excellent performance in applications including quantitative structure-activity relationship (QSAR) and virtual screening (VS). Compared with other types of models, however, they are large, which results in a high hardware requirement to abridge time for both training and inference processes. I

Shaheen Nazir

For a poset $P$ and an integer $r\geq 1$, let $P_r$ be a collection of all $r$-multichains in $P$. Corresponding to each strictly increasing map $\i:[r]\rightarrow [2r]$, there is an order $\preceq_{\i}$ on $P_r$. Let $\D(G_{\i}(P_r))$ be the clique complex of the graph $G_{\i}$ associated to $P_r$ and $\i$. In a recent paper \cite{NW}, it is shown that $\D(

Iskander Gazizov, Sergei Zenevich, Alexander Rodin

We demonstrate the imaging capability of a frequency modulated continuous wave lidar based on a fiber bundle. The lidar constructs velocity and range images for hard targets at a rate of 60 Hz. The sensing range is up to 30 m with 20 mW of output power. The instrument employs custom electronics with seven parallel heterodyne receivers. An example of image re

Naicheng Guo, Xiaolei Liu, Shaoshuai Li, Qiongxu Ma

Sequential recommendation (SR) learns users' preferences by capturing the sequential patterns from users' behaviors evolution. As discussed in many works, user-item interactions of SR generally present the intrinsic power-law distribution, which can be ascended to hierarchy-like structures. Previous methods usually handle such hierarchical information by mak

Marc Martí-Sabaté, Sébastien Guenneau, Dani Torrent

Multiple scattering theory is applied to the study of clusters of point-like scatterers attached to a thin elastic plate and arranged in quasi-periodic distributions. Two type of structures are specifically considered: the twisted bilayer and the quasi-periodic line. The former consists in a couple of two-dimensional lattices rotated a relative angle, so tha

Francesco Giancaterini, Alain Hecq, Claudio Morana

This paper proposes strategies to detect time reversibility in stationary stochastic processes by using the properties of mixed causal and noncausal models. It shows that they can also be used for non-stationary processes when the trend component is computed with the Hodrick-Prescott filter rendering a time-reversible closed-form solution. This paper also li

Xin Chen, Sam Toyer, Cody Wild, Scott Emmons

Imitation learning often needs a large demonstration set in order to handle the full range of situations that an agent might find itself in during deployment. However, collecting expert demonstrations can be expensive. Recent work in vision, reinforcement learning, and NLP has shown that auxiliary representation learning objectives can reduce the need for la

Howard E. Haber

The properties of the Higgs boson discovered at the Large Hadron Collider are very well described by the Standard Model (SM). Thus, any theory that invokes an extended Higgs sector must explain why the neutral scalar observed at the LHC so closely resembles the SM Higgs boson. In this talk, I review the Higgs alignment limit, in which one neutral scalar stat

Pantelis Pnigouras, Fabian Gittins, Amlan Nanda, Nils Andersson

We carefully develop the framework required to model the dynamical tidal response of a spinning neutron star in an inspiralling binary system, in the context of Newtonian gravity, making sure to include all relevant details and connections to the existing literature. The tidal perturbation is decomposed in terms of the normal oscillation modes, used to deriv

Ville Laitinen, Leo Lahti

A broad range of natural and social systems from human microbiome to financial markets can go through critical transitions, where the system suddenly collapses to another stable configuration. Critical transitions can be unexpected, with potentially catastrophic consequences. Anticipating them early and accurately can facilitate controlled system manipulatio

Andrea Urru, Ayako Nakaki, Oualid Benkarim, Francesca Crovetto

The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. In this work, a new pipeline for fetal and neon

Zikang Chen, Jiajun Liao, Jiajie Ling, Baobiao Yue

The presence of a super-light sterile neutrino can lead to a dip in the survival probability of solar neutrinos, and explain the suppression of the upturn in the low energy solar neutrino data. In this work, we systematically study the survival probabilities in the 3+1 framework by taking into account of the non-adiabatic transitions and the coherence effect

Sean Eberhard, Daniele Garzoni

Suppose $\pi$ and $\pi'$ are two random elements of $S_n$ with constrained cycle types such that $\pi$ has $x n^{1/2}$ fixed points and $yn/2$ two-cycles, and likewise $\pi'$ has $x' n^{1/2}$ fixed points and $y'n/2$ two-cycles. We show that the events that $G = \langle \pi, \pi' \rangle$ is transitive and $G \geq A_n$ both have probability approximately \[(

Donna Calhoun, Erik Chudzik, Christiane Helzel

We present the first implementation of the Active Flux method on adaptively refined Cartesian grids. The Active Flux method is a third order accurate finite volume method for hyperbolic conservation laws, which is based on the use of point values as well as cell average values of the conserved quantities. The resulting method has a compact stencil in space a

Janko Marovt, Dijana Mosić, Insa Cremer

Let $\mathcal{R}$ be a unital ring with involution. The notions of 1MP-inverse and MP1-inverse are extended from $M_{m,n}(\mathbb{C)}$, the set of all $m\times n $ matrices over $\mathbb{C}$, to the set $\mathcal{R}% ^{\dagger}$ of all Moore-Penrose invertible elements in $\mathcal{R}$. We study partial orders on $\mathcal{R}^{\dagger}$ that are induced by 1

Demi Allen, Benjamin Ward

In this note, we use the mass transference principle for rectangles, recently obtained by Wang and Wu (Math. Ann., 2021), to study the Hausdorff dimension of sets of "weighted $\Psi$-well-approximable" points in certain self-similar sets in $\mathbb{R}^{d}$. Specifically, we investigate weighted $\Psi$-well-approximable points in "missing digit" sets in $\ma

Jianlu Zhang

In the paper we prove the convergence of viscosity solutions $u_{\lambda}$ as $\lambda\rightarrow0_+$ for the parametrized degenerate viscous Hamilton-Jacobi equation \[ H(x,d_x u, \lambda u)=\alpha(x)\Delta u,\quad \alpha(x)\geq 0,\quad x\in \mathbb T^n \] under suitable convex and monotonic conditions on $H: T^*M\times\mathbb R\rightarrow\mathbb R$. Such a

Bailiang Jian, Mohammad Farid Azampour, Francesca De Benetti, Johannes Oberreuter

CT and MRI are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. Thus, fusing the information of both modalities can be very beneficial. Registration is the first step for this fusion. While the soft tissues around the vertebra a

Qiqi Dai, Yee Hui Lee, Hai-Han Sun, Genevieve Ow

Traditional ground-penetrating radar (GPR) data inversion leverages iterative algorithms which suffer from high computation costs and low accuracy when applied to complex subsurface scenarios. Existing deep learning-based methods focus on the ideal homogeneous subsurface environments and ignore the interference due to clutters and noise in real-world heterog

Chuanxun Du, Gang Wang, Yan Zhang, Jin-Hui Wu

We propose two non-Hermitian arrays consisting of $N=2l+1$ waveguides and exhibiting parity-time ($\mathcal{PT}$) or anti-$\mathcal{PT}$ symmetry for investigating light transfer dynamics based on $N$th-order exceptional points (EPs). The $\mathcal{PT}$-symmetric array supports two $N$th-order EPs separating an unbroken and a broken phase with real and imagi

Jingwen Zhu, Suiyi Ling, Yoann Baveye, Patrick Le Callet

Just Noticeable Difference (JND) model developed based on Human Vision System (HVS) through subjective studies is valuable for many multimedia use cases. In the streaming industries, it is commonly applied to reach a good balance between compression efficiency and perceptual quality when selecting video encoding recipes. Nevertheless, recent state-of-the-art

Miguel Angel Crespo, Julio Bernués

We show the relevance of the logarithmic integral function in the development of mathematics in the first half of the 19th century. Its importance involved first level mathematicians such as Euler, Gauss, Bessel, Riemann. Our perspective is the result of a detailed study of the original sources. We manage to establish the timeline of how the advances took pl

Aleksejus Kononovicius, Rytis Kazakevičius, Bronislovas Kaulakys

We analyze the statistical properties of a temporal point process driven by a confined fractional Brownian motion. The event count distribution and power spectral density of this non--Markovian point process exhibit power--law scaling. We show that a nonlinear Markovian point process can reproduce the same scaling behavior. This result indicates a possible l

Alejandro Romero, Gianluca Baldassarre, Richard J. Duro, Vieri Giuliano Santucci

Autonomous open-ended learning is a relevant approach in machine learning and robotics, allowing the design of artificial agents able to acquire goals and motor skills without the necessity of user assigned tasks. A crucial issue for this approach is to develop strategies to ensure that agents can maximise their competence on as many tasks as possible in the

Kotub Uddin, James Schofield, W. Dhammika Widanage

This work presents an effective state of health indicator to indicate lithium-ion battery degradation based on a long short-term memory (LSTM) recurrent neural network (RNN) coupled with a sliding-window. The developed LSTM RNN is able to capture the underlying long-term dependencies of degraded cell capacity on battery degradation stress factors. The learni

Leonardo Scandurra

The interaction between the foundation structures and the soil has been developed for many engineering applications. For the determination of the stress in foundation structure it is needed to determine the influence of the stiffness of soil with respect to the displacement w of the deformable plate (direct problem), and viceversa, how the stiffness of the f

Dye SK Sato, Yukitoshi Fukahata, Yohei Nozue

Bayesian inversion generates a posterior distribution of model parameters from an observation equation and prior information both weighted by hyperparameters. The prior is also introduced for the hyperparameters in fully Bayesian inversions and enables us to evaluate both the model parameters and hyperparameters probabilistically by the joint posterior. Howe

Kajal Kumari, Mayank Goswami

Computed Tomography is one of the efficient and vital modalities of non-destructive techniques (NDT). Various factors influence the CT reconstruction result, including limited projection data, detector electronics optimization, background noise, detection noise, discretized nature of projection data, and many more. Radiation hardening and other aging factors

Martin Blaschke, Zdeněk Stuchlík, Sudipta Hensh

We study evolution of the braneworld Kerr--Newman (K-N) naked singularities, namely their mass $M$ , spin $a$, and tidal charge $b$ characterizing the role of the bulk space, due to matter in-falling from Keplerian accretion disk. We construct the evolution in two limiting cases applied to the tidal charge. In the first case we assume $b$ = const during the

Dominik Stammbach, Maria Antoniak, Elliott Ash

This paper shows how to use large-scale pre-trained language models to extract character roles from narrative texts without training data. Queried with a zero-shot question-answering prompt, GPT-3 can identify the hero, villain, and victim in diverse domains: newspaper articles, movie plot summaries, and political speeches.

Fangxin Shang, Siqi Wang, Xiaorong Wang, Yehui Yang

We present an effective method for Intracranial Hemorrhage Detection (IHD) which exceeds the performance of the winner solution in RSNA-IHD competition (2019). Meanwhile, our model only takes quarter parameters and ten percent FLOPs compared to the winner's solution. The IHD task needs to predict the hemorrhage category of each slice for the input brain CT.

Liwei Wu, Dan Huang, Qipeng Ma, Zhiyuan Li

In this paper, a modified intermediately homogenized peridynamic (IH-PD) model for analyzing impact failure of wet concrete has been presented under the configuration of ordinary state-based peridynamic theory. The meso-structural properties of concrete are linked to the macroscopic mechanical behavior in the IH-PD model, where the heterogeneity of concrete

J. Nousiainen, C. Rajani, M. Kasper, T. Helin

The direct imaging of potentially habitable Exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based extremely large telescopes. To reach this demanding science goal, the instruments are equipped with eXtreme Adaptive Optics (XAO) systems which will control thousands of actuators at a framerate of kilo

Anabelle Wong, Laura Barrero, Elizabeth Goult, Michael Briga

Despite the availability of effective vaccines, the persistence of SARS-CoV-2 suggests that co-circulation with other pathogens and resulting multi-epidemics may become increasingly frequent. To better forecast and control the risk of such multi-epidemics, it is essential to elucidate the potential interactions of SARS-CoV-2 with other pathogens; these inter

Thibault Scoquart, Dominique Delande, Nicolas Cherroret

We study the dynamical evolution of a two-dimensional Bose gas after a disorder potential quench. Depending on the initial conditions, the system evolves either to a thermal or a superfluid state. Using extensive quasi-exact numerical simulations, we show that the two phases are separated by a Kosterlitz-Thouless transition. The thermalization time is shown

Matthias Bentert, Niclas Boehmer, Klaus Heeger, Tomohiro Koana

We study stable matching problems where agents have multilayer preferences: There are $\ell$ layers each consisting of one preference relation for each agent. Recently, Chen et al. [EC '18] studied such problems with strict preferences, establishing four multilayer adaptions of classical notions of stability. We follow up on their work by analyzing the compu

Thomas Eiter, Nelson Higuera, Johannes Oetsch, Michael Pritz

We present a neuro-symbolic visual question answering (VQA) pipeline for CLEVR, which is a well-known dataset that consists of pictures showing scenes with objects and questions related to them. Our pipeline covers (i) training neural networks for object classification and bounding-box prediction of the CLEVR scenes, (ii) statistical analysis on the distribu

Yuhta Takida, Takashi Shibuya, WeiHsiang Liao, Chieh-Hsin Lai

One noted issue of vector-quantized variational autoencoder (VQ-VAE) is that the learned discrete representation uses only a fraction of the full capacity of the codebook, also known as codebook collapse. We hypothesize that the training scheme of VQ-VAE, which involves some carefully designed heuristics, underlies this issue. In this paper, we propose a new

Rui Yan, Gabriel Santos, Xiaoming Duan, David Parker

We present novel techniques for neuro-symbolic concurrent stochastic games, a recently proposed modelling formalism to represent a set of probabilistic agents operating in a continuous-space environment using a combination of neural network based perception mechanisms and traditional symbolic methods. To date, only zero-sum variants of the model were studied

Nan Bai, Pirouz Nourian, Renqian Luo, Ana Pereira Roders

Values (why to conserve) and Attributes (what to conserve) are essential concepts of cultural heritage. Recent studies have been using social media to map values and attributes conveyed by public to cultural heritage. However, it is rare to connect heterogeneous modalities of images, texts, geo-locations, timestamps, and social network structures to mine the

Boris T. Polyak, Ilia A. Kuruzov, Fedor S. Stonyakin

We study the gradient method under the assumption that an additively inexact gradient is available for, generally speaking, non-convex problems. The non-convexity of the objective function, as well as the use of an inexactness specified gradient at iterations, can lead to various problems. For example, the trajectory of the gradient method may be far enough

Christoph Rettinger, Ulrich Rüde, Stefan Vollmer, Roy M. Frings

Porosity is one of the key properties of dense particle packings like sediment deposits and is influenced by a multitude of grain characteristics such as their size distribution and shape. In the present work, we focus on the form, a specific aspect of the overall shape, of sedimentary grains in order to investigate and quantify its effect on porosity, ultim

C. Charalambous, C. A. Giuppone, O. M. Guilera

Satellite systems around giant planets are immersed in a region of complex resonant configurations. Understanding the role of satellite resonances contributes to comprehending the dynamical processes in planetary formation and posterior evolution. Our main goal is to analyse the resonant structure of small moons around Uranus and propose different scenarios

Dongge Ma, Yuhang Qian, Mingyang Ji, Jiani Li

We synthesized a pure organic non-metal crystalline covalent organic framework TAPA-BTD-COF by bottom-up Schiff base chemical reaction. And this imine-based COF is stable in aerobic condition and room-temperature. We discovered that this TAPA-BTD-COF exhibited strong magneticity in 300 K generating magnetic hysteresis loop in M-H characterization and giant c

Anaïs Tack, Chris Piech

How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability. This paper reports on a first a

Takuma Hayashi

Fabian Januszewski and the author established the theory of twisted D-modules over general base schemes. In this short note, we construct a $K$-invariant positive exhaustive filtration on the globalization of the twisted D-module on a smooth quasi-compact $K$-scheme over a Dedekind scheme $S$ obtained by the direct image of a $K$-equivariant twisted integrab

Antonio Bevilacqua, Lisa Alcock, Brian Caulfield, Eran Gazit

Remote monitoring of motor functions is a powerful approach for health assessment, especially among the elderly population or among subjects affected by pathologies that negatively impact their walking capabilities. This is further supported by the continuous development of wearable sensor devices, which are getting progressively smaller, cheaper, and more e

Wei Xiong, Peng Liu, Chao Niu, Cheng-Yong Zhang

We study the linear instability and the nonlinear dynamical evolution of the Reissner-Nordstr\"om (RN) black hole in the Einstein-Maxwell-scalar theory in asymptotic flat spacetime. We focus on the coupling function $f(\phi)=e^{-b\phi^2}$ which allows both the scalar-free RN solution and scalarized black hole solution. We first present the evolution of syste

Mohammed M. S. El-Kholany, Martin Gebser, Konstantin Schekotihin

The Job-shop Scheduling Problem (JSP) is a well-known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible. In this paper, we investigate problem decomposition into time windows whose operations can be successively scheduled and o

Dongjie Yu, Haitong Ma, Shengbo Eben Li, Jianyu Chen

Constrained reinforcement learning (CRL) has gained significant interest recently, since safety constraints satisfaction is critical for real-world problems. However, existing CRL methods constraining discounted cumulative costs generally lack rigorous definition and guarantee of safety. In contrast, in the safe control research, safety is defined as persist

Xiaofeng Han, Amjed Tahir, Peng Liang, Steve Counsell

Code review that detects and locates defects and other quality issues plays an important role in software quality control. One type of issue that may impact the quality of software is code smells. Yet, little is known about the extent to which code smells are identified during modern code review. To investigate the concept behind code smells identified in mo