Miracle-dz/UniFAR-FTUs huggingface dataset:
A posteriori Trading-inspired Model-free Time Series Segmentation

Within the context of multivariate time series segmentation this paper proposes a method inspired by a posteriori optimal trading. After a normalization step time series are treated channel-wise as surrogate stock prices that can be traded optimally a posteriori in a virtual portfolio holding either stock or cash. Linear transaction costs are interpreted as hyperparameters for noise filtering. Resulting trading signals as well as resulting trading signals obtained on the reversed time series are used for unsupervised labeling, before a consensus over channels is reached that determines segmentation time instants. The method is model-free such that no model prescriptions for segments are made. Benefits of proposed approach include simplicity, adaptability to a wide range of different shapes of time series, and in particular computational efficiency that make it suitable for big data. Performance is demonstrated on synthetic and real-world data, including a large-scale dataset comprising a multivariate time series of dimension 1000 and length 2709. Proposed method is compared to a popular model-based bottom-up approach fitting piecewise affine models and to a state-of-the-art model-based top-down approach fitting Gaussian models, and found to be consistently faster while producing more intuitive results.
Characterizing the dynamics of learning in repeated reference games

The language we use over the course of conversation changes as we establish common ground and learn what our partner finds meaningful. Here we draw upon recent advances in natural language processing to provide a finer-grained characterization of the dynamics of this learning process. We release an open corpus (>15,000 utterances) of extended dyadic interactions in a classic repeated reference game task where pairs of participants had to coordinate on how to refer to initially difficult-to-describe tangram stimuli. We find that different pairs discover a wide variety of idiosyncratic but efficient and stable solutions to the problem of reference. Furthermore, these conventions are shaped by the communicative context: words that are more discriminative in the initial context (i.e., that are used for one target more than others) are more likely to persist through the final repetition. Finally, we find systematic structure in how a speaker's referring expressions become more efficient over time: Syntactic units drop out in clusters following positive feedback from the listener, eventually leaving short labels containing open-class parts of speech. These findings provide a higher resolution look at the quantitative dynamics of ad hoc convention formation and support further development of computational models of learning in communication.
Imaging the Sun's Far-side Active Regions by Applying Multiple Measurement Schemes on Multiskip Acoustic Waves

Being able to image active regions (ARs) on the Sun’s far side is useful for modeling the global-scale magnetic field around the Sun and for predicting the arrival of major ARs that rotate around the limb onto the near side. Helioseismic methods have already been developed to image the Sun’s far-side ARs using near-side high-cadence Doppler-velocity observations; however, the existing methods primarily explore the 3-, 4-, and 5-skip helioseismic waves, leaving room for further improvement in the imaging quality by including waves with more multiskip waves. Taking advantage of the fact that 6-skip waves have the same target-annuli geometry as 3- and 4-skip waves and that 8-skip waves have the same target-annuli geometry as 4-skip waves, we further develop a time–distance helioseismic code to include a total of 14 sets of measurement schemes. We then apply the new code on the Solar Dynamics Observatory/Helioseismic and Magnetic Imager observed Dopplergrams and find that the new code provides substantial improvements over the existing codes in mapping newly emerged ARs and ARs near both far-side limbs. Comparing 3 months of far-side helioseismic images with the Solar TErrestrial RElations Observatory/Extreme UltraViolet Imager observed 304 Å images, we find that 97.3% of the helioseismically detected far-side ARs that are larger than a certain size correspond to an observed region with strong EUV brightening. The high reliability of the new imaging tool will potentially allow us to further calibrate the far-side helioseismic images into maps of magnetic flux.
Developing multivariate distributions using Dirichlet generator

There exist several endeavors proposing a new family of extended distributions using the beta-generating technique. This is a well-known mechanism in developing flexible distributions, by embedding the cumulative distribution function (cdf) of a baseline distribution within the beta distribution that acts as a generator. Univariate beta-generated distributions offer many fruitful and tractable properties and have applications in hydrology, biology and environmental sciences amongst other fields. In the univariate cases, this extension works well, however, for multivariate cases, the beta distribution generator delivers complex expressions. In this document, the proposed extension from the univariate to the multivariate domain addresses the need of flexible multivariate distributions that can model a wide range of multivariate data. This new family of multivariate distributions, whose marginals are beta-generated distributed, is constructed with the function H(x_{1},.,x_{p})=F(G_{1}(x_{1}),G_{2}(x_{2}),. ,G_{p}(x_{p})), where $G_{i}(x_{i})$ are the cdfs of the gamma (baseline) distribution and F(.) as the cdf of the Dirichlet distribution. Hence as the main example, a general model having the support [0,1]^{p} (for p variates), using the Dirichlet as the generator, is developed together with some distributional properties, such as the moment generating function. The proposed Dirichlet-generated distributions can be applied to compositional data. The parameters of the model are estimated by using the maximum likelihood method. The effectiveness and prominence of the proposed family are illustrated by analyzing simulated as well as two real datasets. A new model testing technique is introduced to evaluate the performance of the multivariate models.
The Cayley transform in complex, real and graded K-theory

We use the Cayley transform to provide an explicit isomorphism at the level of cycles from van Daele $K$-theory to $KK$-theory for graded $C^*$-algebras with a real structure. Isomorphisms between $KK$-theory and complex or real $K$-theory for ungraded $C^*$-algebras are a special case of this map. In all cases our map is compatible with the computational techniques required in physical and geometrical applications, in particular index pairings and Kasparov products. We provide applications to real $K$-theory and topological phases of matter.
Common Voice: A Massively-Multilingual Speech Corpus

The Common Voice corpus is a massively-multilingual collection of transcribed speech intended for speech technology research and development. Common Voice is designed for Automatic Speech Recognition purposes but can be useful in other domains (e.g. language identification). To achieve scale and sustainability, the Common Voice project employs crowdsourcing for both data collection and data validation. The most recent release includes 29 languages, and as of November 2019 there are a total of 38 languages collecting data. Over 50,000 individuals have participated so far, resulting in 2,500 hours of collected audio. To our knowledge this is the largest audio corpus in the public domain for speech recognition, both in terms of number of hours and number of languages. As an example use case for Common Voice, we present speech recognition experiments using Mozilla’s DeepSpeech Speech-to-Text toolkit. By applying transfer learning from a source English model, we find an average Character Error Rate improvement of 5.99 ± 5.48 for twelve target languages (German, French, Italian, Turkish, Catalan, Slovenian, Welsh, Irish, Breton, Tatar, Chuvash, and Kabyle). For most of these languages, these are the first ever published results on end-to-end Automatic Speech Recognition.
Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection

Hierarchical Clustering is an unsupervised data analysis method which has been widely used for decades. Despite its popularity, it had an underdeveloped analytical foundation and to address this, Dasgupta recently introduced an optimization viewpoint of hierarchical clustering with pairwise similarity information that spurred a line of work shedding light on old algorithms (e.g., Average-Linkage), but also designing new algorithms. Here, for the maximization dual of Dasgupta's objective (introduced by Moseley-Wang), we present polynomial-time . 4246 approximation algorithms that use Max-Uncut Bisection as a subroutine. The previous best worst-case approximation factor in polynomial time was . 336, improving only slightly over Average-Linkage which achieves 1/3. Finally, we complement our positive results by providing APX-hardness (even for 0-1 similarities), under the Small Set Expansion hypothesis.
Pairwise-Based Multi-Attribute Decision Making Approach for Wireless Network

In wireless network applications, such as routing decision, network selection, etc., the Multi-Attribute Decision Making (MADM) is widely used. The MADM approach can address the multi-objective decision making issues effectively. However, when the parameters vary greatly, the traditional MADM algorithm is not effective anymore. To solve this problem, in this paper, we propose the pairwise-based MADM algorithm. In the PMADM, only two nodes’ utilities are calculated and compared at each time. The PMADM algorithm is much more accurate than the traditional MADM algorithm. Moreover, we also prove that the PMADM algorithm is sensitive to the parameters which vary seriously and insensitive to the parameters which change slightly. This property is better than that of the traditional MADM algorithm. Additionally, the PMADM algorithm is more stable than traditional MADM algorithm. For reducing the computational complexity of the PMADM algorithm, we propose the low-complexity PMADM algorithm. For analyzing the computational complexity of the <inline-formula> <tex-math notation="LaTeX">${l}$ </tex-math></inline-formula> PMADM algorithm, we propose the tree-based decomposing algorithm in this paper. The <inline-formula> <tex-math notation="LaTeX">${l}$ </tex-math></inline-formula> PMADM algorithm has the same properties and performances as that of the PMADM algorithm; however, it is simpler than the PMADM algorithm. The simulation results show that the PMADM and <inline-formula> <tex-math notation="LaTeX">${l}$ </tex-math></inline-formula> PMADM algorithms are much more effective than the traditional MADM algorithm.
High Energy Radiation from Spider Pulsars

The population of millisecond pulsars (MSPs) has been expanded considerably in the last decade. Not only is their number increasing, but also various classes of them have been revealed. Among different classes of MSPs, the behaviours of black widows and redbacks are particularly interesting. These systems consist of an MSP and a low-mass companion star in compact binaries with an orbital period of less than a day. In this article, we give an overview of the high energy nature of these two classes of MSPs. Updated catalogues of black widows and redbacks are presented and their X-ray/ γ -ray properties are reviewed. Besides the overview, using the most updated eight-year Fermi Large Area Telescope point source catalog, we have compared the γ -ray properties of these two MSP classes. The results suggest that the X-rays and γ -rays observed from these MSPs originate from different mechanisms. Lastly, we will also mention the future prospects of studying these spider pulsars with the novel methodologies as well as upcoming observing facilities.
Pneumothorax Segmentation: Deep Learning Image Segmentation to predict Pneumothorax

Computer vision has shown promising results in medical image processing. Pneumothorax is a deadly condition and if not diagnosed and treated at time then it causes death. It can be diagnosed with chest X-ray images. We need an expert and experienced radiologist to predict whether a person is suffering from pneumothorax or not by looking at the chest X-ray images. Everyone does not have access to such a facility. Moreover, in some cases, we need quick diagnoses. So we propose an image segmentation model to predict and give the output a mask that will assist the doctor in taking this crucial decision. Deep Learning has proved their worth in many areas and outperformed man state-of-the-art models. We want to use the power of these deep learning model to solve this problem. We have used U-net [13] architecture with ResNet [17] as a backbone and achieved promising results. U-net [13] performs very well in medical image processing and semantic segmentation. Our problem falls in the semantic segmentation category.
Enhanced superconductivity in few-layer TaS2 due to healing by oxygenation.

When approaching the atomically thin limit, defects and disorder play an increasingly important role in the properties of two-dimensional materials. While defects are generally thought to negatively affect superconductivity in 2D materials, here we demonstrate the contrary in the case of oxygenation of ultrathin tantalum disulfide (TaS2). Our first-principles calculations show that incorporation of oxygen into the TaS2 crystal lattice is energetically favourable and effectively heals sulfur vacancies typically present in these crystals, thus restoring the electronic band structure and the carrier density to the intrinsic characteristics of TaS2. Strikingly, this leads to a strong enhancement of the electron-phonon coupling, by up to 80% in the highly-oxygenated limit. Using transport measurements on fresh and aged (oxygenated) few-layer TaS2, we found a marked increase of the superconducting critical temperature (Tc) upon aging, in agreement with our theory, while concurrent electron microscopy and electron-energy loss spectroscopy confirmed the presence of sulfur vacancies in freshly prepared TaS2 and incorporation of oxygen into the crystal lattice with time. Our work thus reveals the mechanism by which certain atomic-scale defects can be beneficial to superconductivity and opens a new route to engineer Tc in ultrathin materials.
Restricted Extension of Sparse Partial Edge Colorings of Complete graphs

Given a partial edge coloring of a complete graph $K_n$ and lists of allowed colors for the non-colored edges of $K_n$, can we extend the partial edge coloring to a proper edge coloring of $K_n$ using only colors from the lists? We prove that this question has a positive answer in the case when both the partial edge coloring and the color lists satisfy certain sparsity conditions.
Enlightening the dark ages with dark matter

Constraints on dark matter annihilation or decay offer unique insights into the nature of dark matter. We illustrate how surveys dedicated to detect the highly redshifted 21 cm signal from the dark ages will offer a new window into properties of particle dark matter. The 21 cm intensity mapping signal and its fluctuations are sensitive to energy injection from annihilation or decay of long-lived particles in a way that is complementary to other probes. We present forecasted constraints from forthcoming and next-generation radio surveys. We show that, while SKA might be capable of a detection for some cases, the most promising opportunity to detect dark matter in the 21 cm intensity mapping signal is with a futuristic radio array on the lunar far-side, with the potential to detect a signal many orders of magnitude weaker than current or maximal constraints from other probes.
HILUCSI: Simple, robust, and fast multilevel ILU for large‐scale saddle‐point problems from
 PDEs

Incomplete factorization is a widely used preconditioning technique for Krylov subspace methods for solving large‐scale sparse linear systems. Its multilevel variants, such as ILUPACK, are more robust for many symmetric or unsymmetric linear systems than the traditional, single‐level incomplete LU (or ILU) techniques. However, the previous multilevel ILU techniques still lacked robustness and efficiency for some large‐scale saddle‐point problems, which often arise from systems of PDEs. We introduce HILUCSI, or Hierarchical Incomplete LU‐Crout with Scalability‐oriented and Inverse‐based dropping. As a multilevel preconditioner, HILUCSI statically and dynamically permutes individual rows and columns to the next level for deferred factorization. Unlike ILUPACK, HILUCSI applies symmetric preprocessing techniques at the top levels but always uses unsymmetric preprocessing and unsymmetric factorization at the coarser levels. The deferring combined with mixed preprocessing enabled a unified treatment for nearly or partially symmetric systems and simplified the implementation by avoiding mixed 1×1 and 2×2 pivots for symmetric indefinite systems. We show that this combination improves robustness for indefinite systems without compromising efficiency. Furthermore, to enable superior efficiency for large‐scale systems with millions or more unknowns, HILUCSI introduces a scalability‐oriented dropping in conjunction with a variant of inverse‐based dropping. We demonstrate the effectiveness of HILUCSI for dozens of benchmark problems, including those from the mixed formulation of the Poisson equation, Stokes equations, and Navier–Stokes equations. We also compare its performance with ILUPACK and the supernodal ILUTP in SuperLU.
Triggering Mechanism and Material Transfer of a Failed Solar Filament Eruption

Solar filament eruptions are often associated with solar flares and coronal mass ejections, which have the greatest impact on space weather. However, the fine structures and the trigger mechanisms of solar filaments are still unclear. To address these issues, we studied a failed solar active-region filament eruption associated with a C-class flare by using high-resolution Hα images from the New Vacuum Solar Telescope, supplemented by EUV observations from the Solar Dynamics Observatory. Before the filament eruption, a small bipolar magnetic field emerged below the filament. Then magnetic reconnection between the filament and the emerging bipolar magnetic field triggered the filament eruption. During the filament eruption, the untwisting motion of the filament can be clearly traced by the eruptive threads. Moreover, the footpoints of the eruptive threads are determined by tracing the descending filament material. Note that the twisted structure of the filament and the right part of the eruptive filament threads cannot be seen before the filament eruption. These eruptive threads in the right part of the filament are found to be rooting in the weak negative polarities near the main negative sunspot. Moreover, a new filament formed in the filament channel due to material injection from the eruptive filament. The above observations and the potential field extrapolations are inclined to support the idea that the filament materials were transferred into the overlying magnetic loops and the nearby filament channel by magnetic reconnection. These observations improve our understanding of the complexity of filament eruptions.
Incorporation of erbium ions into thin-film lithium niobate integrated photonics

As an active material with favorable linear and nonlinear optical properties, thin-film lithium niobate has demonstrated its potential in integrated photonics. Integration with rare-earth ions, which are promising candidates for quantum memories and transducers, will enrich the system with new applications in quantum information processing. Here, we investigate the optical properties at 1.5 micron wavelengths of rare-earth ions (Er$^{3+}$) implanted in thin-film lithium niobate waveguides and micro-ring resonators. Optical quality factors near a million after post annealing show that ion implantation damage can be successfully repaired. The transition linewidth and fluorescence lifetime of erbium ions are characterized, revealing values comparable to bulk-doped crystals. The ion-cavity coupling is observed through a Purcell enhanced fluorescence, from which a Purcell factor of ~3.8 is extracted. This platform is compatible with top-down lithography processes and leads to a scalable path for controlling spin-photon interfaces in photonic circuits.
Extremes, Extremal Index Estimation, Records, Moment Problem for the Pseudo-Lindley Distribution and Applications

The pseudo-Lindley distribution which was introduced in Zeghdoudi and Nedjar (2016) is studied with regards to its upper tail. In that regard, and when the underlying distribution function follows the Pseudo-Lindley law, we investigate the behavior of its values, the asymptotic normality of the Hill estimator and the double-indexed generalized Hill statistic process (Ngom and Lo), the asymptotic normality of the records values and the moment problem.
Study of Adaptive Activity-Aware Constellation List-Based Detection for Massive Machine-Type Communications

In this work, we propose an adaptive list-based decision feedback detector along with an $l_0$-norm regularized recursive least-squares algorithm that only requires pilot symbols (AA-CL-DF). The proposed detector employs a list strategy based on the signal constellation points to generate different candidates for detection. Simulation results show that the proposed AA-CL-DF successfully mitigates the error propagation and approaches the performance for the oracle LMMSE algorithm.
XMP gas-rich dwarfs in nearby voids: results of SALT spectroscopy

In the framework of an ongoing project aimed at searching for and studying eXtremely Metal-Poor (XMP) very gas-rich blue dwarfs in nearby voids, we conducted spectroscopy with the 11-m Southern African Large Telescope (SALT) of 26 candidates, preselected in the first paper of this series (PEPK19). For 23 of them, we detected oxygen lines, allowing us to estimate the gas O/H ratio. For 10 of them, the oxygen abundance is found to be very low, in the range of 12 + log (O/H) = 6.95–7.30 dex. Of those, four void dwarfs have 12 + log (O/H) < 7.19, or Z < Z⊙/30. For the majority of observed galaxies, the faint line [O iii] λ4363 Å used to estimate O/H with the direct Te method appeared either too noisy or was not detected. We therefore use the semi-empirical method of Izotov & Thuan for these spectra, or, when applicable, the new ‘Strong line’ method of Izotov et al. We present and discuss the results for all void dwarfs observed in this work. We also compare their O/H values with O/H values of ∼140 void galaxies available from our recent papers. We address the properties of the newly found unusual void XMP dwarfs and compare them with those for 10 known prototype void XMP objects. The latter small group is outstanding based on their very small mass fraction of stars (only 0.01–0.02 of the baryonic mass), the blue colours of stars in the outer body indicating a non-cosmological age for the main star-forming episode), and the low gas metallicity (several times lower than expected for their luminosity).