Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of kernel clustering on an RBFN

Classical radial basis function network (RBFN) is widely used to process the non-linear separable data sets with the introduction of activation functions. However, the setting of parameters for activation functions is random and the distribution of patterns is not taken into account. To process this issue, some scholars introduce the kernel clustering into the RBFN so that the clustering results are related to the parameters about activation functions. On the base of the original kernel clustering, this study further discusses the influence of kernel clustering on an RBFN when the setting of kernel clustering is changing. The changing involves different kernel-clustering ways [bubble sort (BS) and escape nearest outlier (ENO)], multiple kernel-clustering criteria (static and dynamic) etc. Experimental results validate that with the consideration of distribution of patterns and the changes of setting of kernel clustering, the performance of an RBFN is improved and is more feasible for corresponding data sets. Moreover, though BS always costs more time than ENO, it still brings more feasible clustering results. Furthermore, dynamic criterion always cost much more time than static one, but kernel number derived from dynamic criterion is fewer than the one from static.
Autoencoding variational Bayes for latent Dirichlet allocation

Many posterior distributions take intractable forms and thus require variational inference where analytical solutions cannot be found. Variational Inference and Monte Carlo Markov Chains (MCMC) are established mechanism to approximate these intractable values. An alternative approach to sampling and optimisation for approximation is a direct mapping between the data and posterior distribution. This is made possible by recent advances in deep learning methods. Latent Dirichlet Allocation (LDA) is a model which oﬀers an intractable posterior of this nature. In LDA latent topics are learnt over unlabelled documents to soft cluster the documents. This paper assesses the viability of learning latent topics leveraging an autoencoder (in the form of Autoencoding variational Bayes) and compares the mimicked posterior distributions to that achieved by VI. After conducting various experiments the proposed AEVB delivers inadequate performance. Under Utopian conditions comparable conclusion are achieved which are generally unattainable. Further, model speciﬁcation becomes increasingly complex and deeply cir-cumstantially dependant - which is in itself not a deterrent but does warrant consideration. In a recent study, these concerns were highlighted and discussed theoretically. We conﬁrm the argument empirically by dissecting the autoencoder’s iterative process. In investigating the autoencoder, we see performance degrade as models grow in dimensionality. Visualization of the autoencoder reveals a bias towards the initial randomised topics.
Evaluation of combined bi-directional branching entropy language models for morphological segmentation of isiXhosa

An evaluation of the IsiXhosa Branching Entropy Segmenter (XBES), an unsupervised morphological segmenter for isiXhosa, is presented. The segmenter contributes a combined bi-directional branching entropy language model with an option for modi ﬁ ed Kneser-Ney (mKN) smoothing. XBES’s boundary identi ﬁ cation accuracy of 77 . 44 ± 0 . 32% is comparable to the benchmark Morfessor-Baseline’s 77 . 2 ± 0 . 10%. XBES’s f1 score, of 58 ± 0 . 10%, is signi ﬁ cantly better than Morfessor-Baseline’s 48 . 9 ± 0 . 75%. The study shows that mKN smoothing degrades performance on branching entropy-based segmentation of isiXhosa, and suggests that better segmentation performance could be achieved in the unsupervised morphological segmentation of isiXhosa, given more data.
Extended Hα over compact far-infrared continuum in dusty submillimeter galaxies

By using data from the Atacama Large Millimeter/submillimeter Array and near-infrared (NIR) integral field spectrographs, including both Spectrograph for INtegral Field Observations in the Near Infrared and K-band Multi Object Spectrograph on the Very Large Telescope, we investigate the two-dimensional distributions of Hα and rest-frame far-infrared (FIR) continuum in six submillimeter galaxies (SMGs) at z ∼ 2. At a similar spatial resolution (∼0.″5 FWHM; ∼4.5 kpc at z = 2), we find that the half-light radius of Hα is significantly larger than that of the FIR continuum in half of the sample, and on average Hα is a median factor of 2.0 ± 0.4 larger. Having explored various ways to correct for the attenuation, we find that the attenuation-corrected Hα-based star-formation rates (SFRs) are systematically lower than the infrared (IR)-based SFRs by at least a median factor of 3 ± 1, which cannot be explained by the difference in half-light radius alone. In addition, we find that in 40% of cases the total V-band attenuation (AV) derived from energy balance modeling of the full ultraviolet (UV)-to-FIR spectral energy distributions (SEDs) is significantly higher than what is derived from SED modeling using only the UV-to-NIR part of the SEDs, and the discrepancy appears to increase with increasing total infrared luminosity. Finally, in considering all of our findings along with the studies in the literature, we postulate that the dust distributions in SMGs, and possibly also in less IR luminous z ∼ 2 massive star-forming galaxies, can be decomposed into the following three main components: the diffuse dust heated by older stellar populations, the more obscured and extended young star-forming H II regions, and the heavily obscured central regions that have a low filling factor but dominate the infrared luminosity in which the majority of attenuation cannot be probed via UV-to-NIR emissions.
A Review on Outlier/Anomaly Detection in Time Series Data

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on unsupervised outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.
Self-concordant analysis of Frank-Wolfe algorithms

Projection-free optimization via different variants of the Frank-Wolfe (FW), a.k. a. Conditional Gradient method has become one of the cornerstones in optimization for machine learning since in many cases the linear minimization oracle is much cheaper to implement than projections and some sparsity needs to be preserved. In a number of applications, e.g. Poisson inverse problems or quantum state tomography, the loss is given by a self-concordant (SC) function having unbounded curvature, implying absence of theoretical guarantees for the existing FW methods. We use the theory of SC functions to provide a new adaptive step size for FW methods and prove global convergence rate O(1/k) after k iterations. If the problem admits a stronger local linear minimization oracle, we construct a novel FW method with linear convergence rate for SC functions.
CGCNN: Complex Gabor Convolutional Neural Network on Raw Speech

Convolutional Neural Networks (CNN) have been used in Automatic Speech Recognition (ASR) to learn representations directly from the raw signal instead of hand-crafted acoustic features, providing a richer and lossless input signal. Recent researches propose to inject prior acoustic knowledge to the first convolutional layer by integrating the shape of the impulse responses in order to increase both the interpretability of the learnt acoustic model, and its performances. We propose to combine the complex Gabor filter with complex-valued deep neural networks to replace usual CNN weights kernels, to fully take advantage of its optimal time-frequency resolution and of the complex domain. The conducted experiments on the TIMIT phoneme recognition task shows that the proposed approach reaches top-of-the-line performances while remaining interpretable.
Potential energy of complex networks: a novel perspective.

We present a novel characterization of complex networks, based on the potential of an associated Schrodinger equation. The potential is designed so that the energy spectrum of the Schrodinger equation coincides with the graph spectrum of the normalized Laplacian. Crucial information is retained in the reconstructed potential, which provides a compact representation of the properties of the network structure. The median potential over several random network realizations is fitted via a Landau-like function, and its length scale is found to diverge as the critical connection probability is approached from above. The ruggedness of the median potential profile is quantified using the Higuchi fractal dimension, which displays a maximum at the critical connection probability. This demonstrates that this technique can be successfully employed in the study of random networks, as an alternative indicator of the percolation phase transition. We apply the proposed approach to the investigation of real-world networks describing infrastructures (US power grid). Curiously, although no notion of phase transition can be given for such networks, the fractality of the median potential displays signatures of criticality. We also show that standard techniques (such as the scaling features of the largest connected component) do not detect any signature or remnant of criticality.
The Obelisk simulation: galaxies contribute more than AGN to HI reionization of protoclusters

We present the Obelisk project, a cosmological radiation-hydrodynamics simulation following the assembly and reionization of a protocluster progenitor during the first two billions of years from the big bang, down to $z = 3.5$. The simulation resolves haloes down to the atomic cooling limit, and tracks the contribution of different sources of ionization: stars, active galactic nuclei, and collisions. The Obelisk project is designed specifically to study the coevolution of high redshift galaxies and quasars in an environment favouring black hole growth. In this paper, we establish the relative contribution of these two sources of radiation to reionization and their respective role in establishing and maintaining the high redshift ionizing background. Our volume is typical of an overdense region of the Universe and displays star formation rate and black hole accretion rate densities similar to high redshift protoclusters. We find that hydrogen reionization happens inside-out and is completed by $z \sim 6$ in our overdensity, and is predominantly driven by galaxies, while accreting black holes only play a role at $z \sim 4$.
Background Matting

The current state of the art alpha matting methods mainly rely on the trimap as the secondary and only guidance to estimate alpha. This paper investigates the effects of utilising the background information as well as trimap in the process of alpha calculation. To achieve this goal, a state of the art method, AlphaGan is adopted and modified to process the background information as an extra input channel. Extensive experiments are performed to analyse the effect of the background information in image and video matting such as training with mildly and heavily distorted backgrounds. Based on the quantitative evaluations performed on Adobe Composition-1k dataset, the proposed pipeline significantly outperforms the state of the art methods using AlphaMatting benchmark metrics.
Connecting GANs and MFGs

Generative Adversarial Networks (GANs), introduced in 2014 [12], have celebrated great empirical success, especially in image generation and processing. Meanwhile, Mean-Field Games (MFGs), established in [17] and [16] as analytically feasible approximations for N-player games, have experienced rapid growth in theoretical studies. In this paper, we establish theoretical connections between GANs and MFGs. Interpreting MFGs as GANs, on one hand, allows us to devise GANs-based algorithm to solve MFGs. Interpreting GANs as MFGs, on the other hand, provides a new and probabilistic foundation for GANs. Moreover, this interpretation helps establish an analytical connection between GANs and Optimal Transport (OT) problems.
Minimax optimal goodness-of-fit testing for densities under a local differential privacy constraint

Finding anonymization mechanisms to protect personal data is at the heart of machine learning research. Here we consider the consequences of local differential privacy constraints on goodness-of-fit testing, i.e. the statistical problem assessing whether sample points are generated from a fixed density $f_0$, or not. The observations are hidden and replaced by a stochastic transformation satisfying the local differential privacy constraint. In this setting, we propose a new testing procedure which is based on an estimation of the quadratic distance between the density $f$ of the unobserved sample and $f_0$. We establish minimax separation rates for our test over Besov balls. We also provide a lower bound, proving the optimality of our result. To the best of our knowledge, we provide the first minimax optimal test and associated private transformation under a local differential privacy constraint, quantifying the price to pay for data privacy.
Optimizing item and subgroup configurations for social-aware VR shopping

Shopping in VR malls has been regarded as a paradigm shift for E-commerce, but most of the conventional VR shopping platforms are designed for a single user. In this paper, we envisage a scenario of VR group shopping, which brings major advantages over conventional group shopping in brick-and-mortar stores and Web shopping: 1) configure flexible display of items and partitioning of subgroups to address individual interests in the group, and 2) support social interactions in the subgroups to boost sales. Accordingly, we formulate the Social-aware VR Group-Item Configuration (SVGIC) problem to configure a set of displayed items for flexibly partitioned subgroups of users in VR group shopping. We prove SVGIC is APX-hard and also NP-hard to approximate within [EQUATION]. We design a 4-approximation algorithm based on the idea of Co-display Subgroup Formation (CSF) to configure proper items for display to different subgroups of friends. Experimental results on real VR datasets and a user study with hTC VIVE manifest that our algorithms outperform baseline approaches by at least 30.1% of solution quality.
Combining full-shape and BAO analyses of galaxy power spectra: a 1.6% CMB-independent constraint on H0

We present cosmological constraints from a joint analysis of the pre- and post-reconstruction galaxy power spectrum multipoles from the final data release of the Baryon Oscillation Spectroscopic Survey (BOSS). Geometric constraints are obtained from the positions of BAO peaks in reconstructed spectra, which are analyzed in combination with the unreconstructed spectra in a full-shape (FS) likelihood using a joint covariance matrix, giving stronger parameter constraints than FS-only or BAO-only analyses. We introduce a new method for obtaining constraints from reconstructed spectra based on a correlated theoretical error, which is shown to be simple, robust, and applicable to any flavor of density-field reconstruction. Assuming ΛCDM with massive neutrinos, we analyze clustering data from two redshift bins zeff=0.38,0.61 and obtain 1.6% constraints on the Hubble constant H0, using only a single prior on the current baryon density ωb from Big Bang Nucleosynthesis (BBN) and no knowledge of the power spectrum slope ns. This gives H0 = 68.6±1.1 km s−1Mpc−1, with the inclusion of BAO data sharpening the measurement by 40%, representing one of the strongest current constraints on H0 independent of cosmic microwave background data, comparable with recent constraints using BAO data in combination with other data-sets. Restricting to the best-fit slope ns from Planck (but without additional priors on the spectral shape), we obtain a 1% H0 measurement of 67.8± 0.7 km s−1Mpc−1. Finally, we find strong constraints on the cosmological parameters from a joint analysis of the FS, BAO, and Planck data. This sets new bounds on the sum of neutrino masses ∑ mν < 0.14 eV (at 95% confidence) and the effective number of relativistic degrees of freedom Neff = 2.90+0.15−0.16, though contours are not appreciably narrowed by the inclusion of BAO data.
Metrics of Pinched Curvature on Heintze Spaces of Carnot-type

Rank-one symmetric spaces have a generalization to a larger class of Lie groups that are one-dimensional extensions of nilpotent groups. By examining some metric properties of these symmetric spaces, we motivate and prove the existence of analogous metrics on Heintze spaces of Carnot-type. As an application of this construction, we see that Heintze spaces with H-metrics are of Iwasawa type, fail to have the Einstein condition, and admit a useful symmetric adjoint action. We also introduce the definition of square-pinched algebras, to generalize the quarter-pinched property explored by Eberlein and Heber, and use H-metrics to prove that the Lie algebras corresponding to Heintze spaces of Carnot-type are square-pinched. We show in a special case that this pinching is optimal.
Shapes of stellar activity cycles

Magnetic activity cycles are an important phenomenon in both the Sun and other stars. The shape of the solar cycle is commonly characterised by a fast rise and a slower decline, but not much attention has been paid to the shape of cycles in other stars. Our aim is to study whether the asymmetric shape of the solar cycle is common in other stars as well, and compare the cycle asymmetry to other stellar parameters. We also study the differences in the shape of the solar cycle, depending on what activity indicator is used. The observations are also compared to simulated activity cycles. We use the chromospheric Ca II H&K data from the Mount Wilson Observatory HK Project. From this data set we identify 47 individual cycles from 18 stars. We use the statistical skewness of a cycle as a measure of its asymmetry, and compare this to other stellar parameters. A similar analysis has been done to magnetic cycles extracted from direct numerical magnetohydrodynamic simulations of solar-type convection zones. The shape of the solar cycle (fast rise and slower decline) is common in other stars as well, although the Sun has particularly asymmetric cycles. Cycle-to-cycle variations are large, but the average shape of a cycle is still fairly well represented by a sinusoid. We find only slight correlations between the cycle asymmetry and other stellar parameters. There are large differences in the shape of the solar cycle, depending on what activity indicator is used. In the simulated cycles, there is a difference in the symmetry of global simulations covering the full longitudinal range, hence capable of exciting non-axisymmetric dynamo modes, versus wedge simulations covering a partial extent in longitude, where only axisymmetric modes are possible. The former produce preferentially positive skewness, while the latter a negative one.
Automatic Discourse Segmentation: an evaluation in French

In this article, we describe some discursive segmentation methods as well as a preliminary evaluation of the segmentation quality. Although our experiment were carried for documents in French, we have developed three discursive segmentation models solely based on resources simultaneously available in several languages: marker lists and a statistic POS labeling. We have also carried out automatic evaluations of these systems against the Annodis corpus, which is a manually annotated reference. The results obtained are very encouraging.
Human-to-Robot Attention Transfer for Robot Execution Failure Avoidance Using Stacked Neural Networks

Due to world dynamics and hardware uncertainty, robots inevitably fail in task executions, leading to undesired or even dangerous executions. To avoid failures for improved robot performance, it is critical to identify and correct robot abnormal executions in an early stage. However, limited by reasoning capability and knowledge level, it is challenging for a robot to self diagnose and correct their abnormal behaviors. To solve this problem, a novel method is proposed, human-to-robot attention transfer (H2R-AT) to seek help from a human. H2R-AT is developed based on a novel stacked neural networks model, transferring human attention embedded in verbal reminders to robot attention embedded in robot visual perceiving. With the attention transfer from a human, a robot understands what and where human concerns are to identify and correct its abnormal executions. To validate the effectiveness of H2R-AT, two representative task scenarios, "serve water for a human in a kitchen" and "pick up a defective gear in a factory" with abnormal robot executions, were designed in an open-access simulation platform V-REP; $252$ volunteers were recruited to provide about 12000 verbal reminders to learn and test the attention transfer model H2R-AT. With an accuracy of $73.68\%$ in transferring attention and accuracy of $66.86\%$ in avoiding robot execution failures, the effectiveness of H2R-AT was validated.
On the monodromy group of desingularised moduli spaces of sheaves on K3 surfaces

In this paper we prove a conjecture of Markman about the shape of the monodromy group of irreducible holomorphic symplectic manifolds of OG10 type. As a corollary, we also compute the locally trivial monodromy group of the underlying singular symplectic variety.
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss

Neural networks trained to minimize the logistic (a.k.a. cross-entropy) loss with gradient-based methods are observed to perform well in many supervised classification tasks. Towards understanding this phenomenon, we analyze the training and generalization behavior of infinitely wide two-layer neural networks with homogeneous activations. We show that the limits of the gradient flow on exponentially tailed losses can be fully characterized as a max-margin classifier in a certain non-Hilbertian space of functions. In presence of hidden low-dimensional structures, the resulting margin is independent of the ambiant dimension, which leads to strong generalization bounds. In contrast, training only the output layer implicitly solves a kernel support vector machine, which a priori does not enjoy such an adaptivity. Our analysis of training is non-quantitative in terms of running time but we prove computational guarantees in simplified settings by showing equivalences with online mirror descent. Finally, numerical experiments suggest that our analysis describes well the practical behavior of two-layer neural networks with ReLU activation and confirm the statistical benefits of this implicit bias.
Modular Path Queries with Arithmetic

We propose a new approach to querying graph databases. Our approach balances competing goals of expressive power, language clarity and computational complexity. A distinctive feature of our approach is the ability to express properties of minimal (e.g. shortest) and maximal (e.g. most valuable) paths satisfying given criteria. To express complex properties in a modular way, we introduce labelling-generating ontologies. The resulting formalism is computationally attractive - queries can be answered in non-deterministic logarithmic space in the size of the database.