Miracle-dz/UniFAR-FTUs huggingface dataset:
$\ell_p$-Norm Multiple Kernel One-Class Fisher Null-Space.

The paper addresses the multiple kernel learning (MKL) problem for one-class classification (OCC). For this purpose, based on the Fisher null-space one-class classification method, we present a multiple kernel learning algorithm where a general $\ell_p$-norm constraint ($p\geq1$) on kernel weights is considered. The proposed approach is then extended to learn several related one-class MKL problems jointly by constraining them to share common kernel weights. We pose the one-class MKL task as a min-max saddle point Lagrangian optimisation problem and propose an efficient alternating optimisation method to solve it. An extensive assessment of the proposed method on ten data sets from different application domains in one-class classification confirms its merits against the baseline and several other one-class multiple kernel learning methods.
Period and Signal Reconstruction from the Curve of Sample-Sequences.

A sequence of samples of a periodic signal can be treated as a point in a multi-dimensional space. All such sequences of a given length and taken at a given sampling rate form a closed curve. We prove that this curve determines the period of the sampled signal, even if the sequences are of short length and are taken at a sub-Nyquist rate. This result is obtained with a help of the theory of rotation numbers developed by Poincare. We also prove that the curve of sample-sequences determines the sampled signal up to a time shift provided that the ratio of the sampling period to the period of the signal is irrational. Eventually, we give an example, which shows that the same is not true if this ratio is rational.
A comparative study of similarity-based and GNN-based link prediction approaches

The task of inferring the missing links in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature. They show good prediction performance in many real-world graphs though they are heuristics and lack of universal applicability. On the other hand, the success of neural networks for classification tasks in various domains leads researchers to study them in graphs. When a neural network can operate directly on the graph, then it is termed as the graph neural network (GNN). GNN is able to learn hidden features from graphs which can be used for link prediction task in graphs. Link predictions based on GNNs have gained much attention of researchers due to their convincing high performance in many real-world graphs. This appraisal paper studies some similarity and GNN-based link prediction approaches in the domain of homogeneous graphs that consists of a single type of (attributed) nodes and single type of pairwise links. We evaluate the studied approaches against several benchmark graphs with different properties from various domains.
Improving Text to Image Generation using Mode-seeking Function

Generative Adversarial Networks (GANs) have long been used to understand the semantic relationship between the text and image. However, there are problems with mode collapsing in the image generation that causes some preferred output modes. Our aim is to improve the training of the network by using a specialized mode-seeking loss function to avoid this issue. In the text to image synthesis, our loss function differentiates two points in latent space for the generation of distinct images. We validate our model on the Caltech Birds (CUB) dataset and the Microsoft COCO dataset by changing the intensity of the loss function during the training. Experimental results demonstrate that our model works very well compared to some state-of-the-art approaches.
Switching Model Predictive Control for Online Structural Reformations of a Foldable Quadrotor

The aim of this article is the formulation of a switching model predictive control framework for the case of a foldable quadrotor with the ability to retain the overall control quality during online structural reformations. The majority of the related scientific publications consider fixed morphology of the aerial vehicles. Recent advances in mechatronics have brought novel considerations for generalized aerial robotic designs with the ability to alter their morphology in order to adapt to their environment, thus enhancing their capabilities. Simulation results are provided to prove the efficacy of the selected control scheme.
Agricultural watershed modeling: a review for hydrology and soil erosion processes

Modelos tem sido usados pelo homem ha milhares de anos para controlar seu ambiente de uma maneira favoravel para melhores condicoes de vida para os humanos. O uso de modelos hidrologicos tem sido uma ferramenta muito eficaz para apoiar os decisores que lidam com as bacias hidrograficas para subsidiar diversas atividades economicas e sociais, como o abastecimento publico de agua, geracao de energia, e a disponibilidade de agua para a agricultura, entre outros. Objetivou-se, nesta revisao, discutir brevemente alguns modelos muito aplicados ao estudo do movimento da agua e solos em paisagens (RUSLE, WEPP, GeoWEPP, LASH, DHSVM and AnnAGNPS), para fornecer informacoes sobre os mesmos, para auxiliar no entendimento adequado de problemas especificos relacionados com os processos de hidrologia e erosao do solo. Modelos tem sido alterados e avaliados de forma significativa nos ultimos anos, com destaque para o uso de sensoriamento remoto, GIS e processo de calibracao automatica, permitindo aos mesmos que sejam capazes de simular bacias hidrograficas nas suas condicoes atuais de uso do solo e mudancas climaticas. No entanto, os modelos hidrologicos tem quase a mesma estrutura fisica, o que nao e suficiente para simular problemas relacionados com os efeitos a longo prazo de diferentes usos do solo. Esse tem sido um dos principais desafios para o futuro: compreender inteiramente o ciclo hidrologico, tendo como referencia a zona critica, na qual os processos hidrologicos agem em conjunto a partir do dossel ate a base dos aquiferos.
An Overview of Generalized Frequency Division Multiplexing (GFDM)

As a candidate waveform for next-generation wireless communications, generalized frequency division multiplexing (GFDM) features several decent properties which make it promising. In this paper, we systematically overview the research about GFDM. We start with GFDM transceivers with their main components, which consist of prototype ﬁlter design, low-complexity transceiver implementation, and symbol detection algorithms. Then, we investigate a couple of non-ideal issues of GFDM, including synchronization issues, channel estimation, and in-phase/quadrature (I/Q) imbalance compensation. Lastly, we study the applications of GFDM-based cognitive radio and full-duplex radio which boast of a high spectral efﬁciency.
Quantum phase diagram of a Moiré-Hubbard model

We theoretically study a generalized Hubbard model on moire superlattices of twisted bilayers, and find very rich filling-factor-dependent quantum phase diagrams tuned by interaction strength and twist angle. Strong long-range Coulomb interaction in the moire-Hubbard model induces Wigner crystals at a series of fractional filling factors. The effective lattice of the Wigner crystal is controlled by the filling factor, and can be triangle, rectangle, honeycomb, kagome, etc, providing a single platform to realize many different spin models on various lattices by simply tuning carrier density. In addition to Wigner crystals that are topologically trivial, interaction-induced Chern insulators emerge in the phase diagram. This finding paves a way for engineering interaction-induced quantum anomalous Hall effect in moire-Hubbard systems where the corresponding single-particle moire band is topologically trivial.
The stellar rotation–activity relation for a sample of SuperWASP and ASAS-SN field stars

Abstract It is well established that late-type main-sequence (MS) stars display a relationship between X-ray activity and the Rossby number, Ro, the ratio of rotation period to the convective turnover time. This manifests itself as a saturated regime (where X-ray activity is constant) and an unsaturated regime (where X-ray activity anti-correlates with Rossby number). However, this relationship breaks down for the fastest rotators. We cross-correlated SuperWASP visually classified photometric light curves and All-Sky Automated Survey for Supernovae automatically classified photometric light curves with XMM-Newton X-ray observations to identify 3 178 stars displaying a photometrically defined rotational modulation in their light curve and corresponding X-ray observations. We fitted a power-law to characterise the rotation–activity relation of 900 MS stars. We identified that automatically classified rotationally modulated light curves are not as reliable as visually classified light curves for this work. We found a power-law index in the unsaturated regime of G- to M-type stars of $\beta=-1.84\,\pm\,0.18$ for the SuperWASP catalogue, in line with the canonical value of $\beta=-2$ . We find evidence of supersaturation in the fastest rotating K-type stars, with a power-law index of $\beta_{s}=1.42\pm0.26$ .
On some polynomial version on the sum-product problem for subgroups

We generalize two results about subgroups of multiplicative group of finite field of prime order. In particular, the lower bound on the cardinality of the set of values of polynomial $P(x,y)$ is obtained under the certain conditions, if variables $x$ and $y$ belong to a subgroup $G$ of the multiplicative group of the filed of residues. Also the paper contains a proof of the result that states that if a subgroup $G$ can be presented as a set of values of the polynomial $P(x,y)$, where $x\in A$, and $y\in B$ then the cardinalities of sets $A$ and $B$ are close (in order) to a square root of the cardinality of subgroup $G$.
Lattice gauge theory and dynamical quantum phase transitions using noisy intermediate-scale quantum devices

With noisy intermediate scale quantum devices in mind, we propose a class of superconducting circuits for the general implementation of U(1) lattice gauge models via the formalism of quantum link models. The circuit can be modularly scaled to any lattice configuration. Simulating the circuit dynamics with realistic circuit parameters we find that it implements the target dynamics with a steady average fidelity of $ 99.5\% $ or higher. The principles of these circuits can be generalized to implement other, more complicated gauge symmetries. Finally, we consider readout of the circuit using a method that yields information about all the degrees of freedom with resonators coupled dispersively to only a subset of them. As an example of interesting physics to study with this we show how such a quantum link model on a periodic chain exhibits dynamical quantum phase transitions by studying the Loschmidt amplitude and a novel gauge invariant string order parameter. The zeros of the Loschmidt amplitude as well as the zeros of our order parameter are revealed by vortices in their phases, which can be counted by a topologically invariant winding number.
Spherical collapse in DGP braneworld cosmology

The DGP (Dvali, Gabadaze and Porrati) braneworld cosmology gives an alternative to dark energy. In DGP cosmology the alleged cosmic acceleration is generated by the modification of gravity theory. Nonlinear evolution of matter density contrast in DGP braneworld cosmology is studied in the present work. The semi-analytic approach of spherical collapse of matter overdensity is adopted in the present context to study the nonliner evolution. Further, the number count of galaxy clusters along the redshift is studied for the DGP cosmology using Press-Schechter and Sheth-Tormen mass function formalisms. It is observed that for same values of cosmological parameters, the DGP model enhances the number of galaxy cluster compared to the standard $\Lambda$CDM scenario.
Radiation from an Inertial Mirror Horizon

The purpose of this study is to investigate radiation from asymptotic zero acceleration motion where a horizon is formed and subsequently detected by an outside witness. A perfectly reflecting moving mirror is used to model such a system and compute the energy and spectrum. The trajectory is asymptotically inertial (zero proper acceleration)—ensuring negative energy flux (NEF), yet approaches light-speed with a null ray horizon at a finite advanced time. We compute the spectrum and energy analytically.
Inducing Language-Agnostic Multilingual Representations

Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world. However, they currently require large pretraining corpora or access to typologically similar languages. In this work, we address these obstacles by removing language identity signals from multilingual embeddings. We examine three approaches for this: (i) re-aligning the vector spaces of target languages (all together) to a pivot source language; (ii) removing language-specific means and variances, which yields better discriminativeness of embeddings as a by-product; and (iii) increasing input similarity across languages by removing morphological contractions and sentence reordering. We evaluate on XNLI and reference-free MT evaluation across 19 typologically diverse languages. Our findings expose the limitations of these approaches—unlike vector normalization, vector space re-alignment and text normalization do not achieve consistent gains across encoders and languages. Due to the approaches’ additive effects, their combination decreases the cross-lingual transfer gap by 8.9 points (m-BERT) and 18.2 points (XLM-R) on average across all tasks and languages, however.
Data-driven quark- and gluon-jet modification in heavy-ion collisions

Whether quark- and gluon-initiated jets are modified differently by the quark-gluon plasma produced in heavy-ion collisions is a long-standing question that has thus far eluded a definitive experimental answer. A crucial complication for quark-gluon discrimination in both proton-proton and heavy-ion collisions is that all measurements necessarily average over the (unknown) quark-gluon composition of a jet sample. In the heavy-ion context, the simultaneous modification of both the fractions and substructure of quark and gluon jets by the quark-gluon plasma further obscures the interpretation. Here, we demonstrate a fully data-driven method for separating quark and gluon contributions to jet observables using a statistical technique called topic modeling. Assuming that jet distributions are a mixture of underlying "quark-like" and "gluon-like" distributions, we show how to extract quark and gluon jet fractions and constituent multiplicity distributions as a function of the jet transverse momentum. This proof-of-concept study is based on proton-proton and heavy-ion collision events from the Monte Carlo event generator Jewel with statistics accessible in Run 4 of the Large Hadron Collider. These results suggest the potential for an experimental determination of quark and gluon jet modifications.
Symmetries and turbulence modeling. A critical examination

The recent study by Klingenberg, Oberlack & Pluemacher (2020) proposes a new strategy for modeling turbulence in general. A proof-of-concept is presented therein for the particular flow configuration of a spatially evolving turbulent planar jet flow, coming to the conclusion that their model can generate scaling laws which go beyond the classical ones. Our comment, however, shows that their proof-of-concept is flawed and that their newly proposed scaling laws do not go beyond any classical solutions. Hence, their argument of having established a new and more advanced turbulence model cannot be confirmed. The problem is already rooted in the modeling strategy itself, in that a nonphysical statistical scaling symmetry gets implemented. Breaking this symmetry will restore the internal consistency and will turn all self-similar solutions back to the classical ones. To note is that their model also includes a second nonphysical symmetry. One of the authors already acknowledged this fact for turbulent jet flow in a formerly published Corrigendum (Sadeghi, Oberlack & Gauding, 2020). However, the Corrigendum is not cited and so the reader is not made aware that their method has fundamental problems that lead to inconsistencies and conflicting results. Instead, the very same nonphysical symmetry gets published again.
The time-scales probed by star formation rate indicators for realistic, bursty star formation histories from the FIRE simulations

Understanding the rate at which stars form is central to studies of galaxy formation. Observationally, the star formation rates (SFRs) of galaxies are measured using the luminosity in different frequency bands, often under the assumption of a time-steady SFR in the recent past. We use star formation histories (SFHs) extracted from cosmological simulations of star-forming galaxies from the FIRE project to analyse the time-scales to which the H α and far-ultraviolet (FUV) continuum SFR indicators are sensitive. In these simulations, the SFRs are highly time variable for all galaxies at high redshift, and continue to be bursty to z = 0 in dwarf galaxies. When FIRE SFHs are partitioned into their bursty and time-steady phases, the best-fitting FUV time-scale fluctuates from its ∼10 Myr value when the SFR is time-steady to ≳100 Myr immediately following particularly extreme bursts of star formation during the bursty phase. On the other hand, the best-fitting averaging time-scale for H α is generally insensitive to the SFR variability in the FIRE simulations and remains ∼5 Myr at all times. These time-scales are shorter than the 100 and 10 Myr time-scales sometimes assumed in the literature for FUV and H α, respectively, because while the FUV emission persists for stellar populations older than 100 Myr, the time-dependent luminosities are strongly dominated by younger stars. Our results confirm that the ratio of SFRs inferred using H α versus FUV can be used to probe the burstiness of star formation in galaxies.
Effect of pore-size disorder on the electronic properties of semiconducting graphene nanomeshes

Graphene nanomeshes (GNMs) are novel materials that recently raised a lot of interest. They are fabricated by forming a lattice of pores in graphene. Depending on the pore size and pore lattice constant, GNMs can be either semimetallic or semiconducting with a gap large enough (∼ 0.5 eV) to be considered for transistor applications. The fabrication process is bound to produce some structural disorder due to variations in pore size. Recent electronic transport measurements in GNM devices (ACS Appl. Mater. Interfaces 10, 10 362, 2018) show a degradation of their bandgap in devices having pore-size disorder. It is therefore important to understand the effect of such variability on the electronic properties of semiconducting GNMs. In this work we use the density functional-based tight binding formalism to calculate the electronic properties of GNM structures with different pore sizes, pore densities, and with hydrogen and oxygen pore edge passivations. We find that structural disorder reduces the electronic gap and the carrier group velocity, which may interpret recent transport measurements in GNM devices. Furthermore, the trend of the bandgap with structural disorder is not significantly affected by the change in pore edge passivation. Our results show that even with structural disorder, GNMs are still attractive from a transistor device perspective.
Distributed Stochastic Subgradient Optimization Algorithms Over Random and Noisy Networks

We study distributed stochastic optimization by networked nodes to cooperatively minimize a sum of convex cost functions. The network is modeled by a sequence of time-varying random digraphs with each node representing a local optimizer and each edge representing a communication link. We consider the distributed subgradient optimization algorithm with noisy measurements of local cost functions' subgradients, additive and multiplicative noises among information exchanging between each pair of nodes. By stochastic Lyapunov method, convex analysis, algebraic graph theory and martingale convergence theory, it is proved that if the local subgradient functions grow linearly and the sequence of digraphs is conditionally balanced and uniformly conditionally jointly connected, then proper algorithm step sizes can be designed so that all nodes' states converge to the global optimal solution almost surely.
Field-Induced Magnetic Monopole Plasma in Artificial Spin Ice

Artificial spin ices (ASIs) are interacting arrays of lithographically-defined nanomagnets in which novel frustrated magnetic phases can be intentionally designed. A key emergent description of fundamental excitations in ASIs is that of magnetic monopoles -- mobile quasiparticles that carry an effective magnetic charge. Here we demonstrate that the archetypal square ASI lattice can host, in specific regions of its magnetic phase diagram, high-density plasma-like regimes of mobile magnetic monopoles. By passively "listening" to spontaneous monopole noise in thermal equilibrium, we reveal their intrinsic dynamics and show that monopole kinetics are minimally correlated (that is, most diffusive) in the plasma phase. These results open the door to on-demand monopole regimes having field-tunable densities and dynamic properties, thereby providing a new paradigm for probing the physics of effective magnetic charges in synthetic matter.
Spatial-spectral FFPNet: Attention-Based Pyramid Network for Segmentation and Classification of Remote Sensing Images

We consider the problem of segmentation and classification of high-resolution and hyperspectral remote sensing images. Unlike conventional natural (RGB) images, the inherent large scale and complex structures of remote sensing images pose major challenges such as spatial object distribution diversity and spectral information extraction when existing models are directly applied for image classification. In this study, we develop an attention-based pyramid network for segmentation and classification of remote sensing datasets. Attention mechanisms are used to develop the following modules: i) a novel and robust attention-based multi-scale fusion method effectively fuses useful spatial or spectral information at different and same scales; ii) a region pyramid attention mechanism using region-based attention addresses the target geometric size diversity in large-scale remote sensing images; and iii cross-scale attention} in our adaptive atrous spatial pyramid pooling network adapts to varied contents in a feature-embedded space. Different forms of feature fusion pyramid frameworks are established by combining these attention-based modules. First, a novel segmentation framework, called the heavy-weight spatial feature fusion pyramid network (FFPNet), is proposed to address the spatial problem of high-resolution remote sensing images. Second, an end-to-end spatial--spectral FFPNet is presented for classifying hyperspectral images. Experiments conducted on ISPRS Vaihingen and ISPRS Potsdam high-resolution datasets demonstrate the competitive segmentation accuracy achieved by the proposed heavy-weight spatial FFPNet. Furthermore, experiments on the Indian Pines and the University of Pavia hyperspectral datasets indicate that the proposed spatial--spectral FFPNet outperforms the current state-of-the-art methods in hyperspectral image classification.