Miracle-dz/UniFAR-FTUs huggingface dataset:
Unveiling short-period binaries in the inner VVV bulge

Most of our knowledge of the structure of the Milky Way has come from the study of variable stars. Among these variables, mimicking the periodic variation of pulsating stars, are eclipsing binaries. These stars are important in astrophysics because they allow us to directly measure the radii and masses of the components, as well as the distance to the system, thus being useful in studies of Galactic structure alongside pulsating RR Lyrae and Cepheids. Using the distinguishing features of their light curves, one can identify them using a semi-automated process. In this work, we present a strategy to search for eclipsing variables in the inner VVV bulge across an area of 13.4 deg2 within 1.68° < l < 7.53° and −3.73° < b < −1.44°, corresponding to the VVV tiles b293–b296 and b307–b310. We accurately classify 212 previously unknown eclipsing binaries, including six very reddened sources. A preliminary analysis suggests that these eclipsing binaries are located in the most obscured regions of the foreground disc and bulge of the Galaxy. This search is therefore complementary to other variable-star searches carried out at optical wavelengths.
Joint Resource Allocation and Transceiver Design for Sum-Rate Maximization Under Latency Constraints in Multicell MU-MIMO Systems

Due to the continuous advancements of orthogonal frequency division multiplexing (OFDM) and multiple antenna techniques, multiuser multiple input multiple output (MU-MIMO) OFDM is a key enabler of both fourth and fifth generation networks. In this paper, we consider the problem of weighted sum-rate maximization under latency constraints in finite buffer multicell MU-MIMO OFDM systems. Unlike previous works, the optimization variables include the transceiver beamforming vectors, the scheduled packet size and the resources in the frequency and power domains. This problem is motivated by the observation that multicell MU-MIMO OFDM systems serve multiple quality of service classes and the system performance depends critically on both the transceiver design and the scheduling algorithm. Since this problem is non-convex, we resort to the max-plus queuing method and successive convex approximation. We propose both centralized and decentralized solutions, in which practical design aspects, such as signaling overhead, are considered. Finally, we compare the proposed framework with state-of-the-art algorithms in relevant scenarios, assuming a realistic channel model with space, frequency and time correlations. Numerical results indicate that our design provides significant gains over designs based on the wide-spread saturated buffers assumption, while also outperforming algorithms that consider a finite-buffer model. Due to the continuous advancements of orthogonal frequency division multiplexing (OFDM) and multiple antenna techniques, multiuser multiple input multiple output (MU-MIMO) OFDM is a key enabler of both fourth and fifth generation networks. In this paper, we consider the problem of weighted sum-rate maximization under latency constraints in finite buffer multicell MU-MIMO OFDM systems. Unlike previous works, the optimization variables include the transceiver beamforming vectors, the scheduled packet size and the resources in the frequency and power domains. This problem is motivated by the observation that multicell MU-MIMO OFDM systems serve multiple quality of service classes and the system performance depends critically on both the transceiver design and the scheduling algorithm. Since this problem is non-convex, we resort to the max-plus queuing method and successive convex approximation. We propose both centralized and decentralized solutions, in which practical design aspects, such as signaling overhead, are considered. Finally, we compare the proposed framework with state-of-the-art algorithms in relevant scenarios, assuming a realistic channel model with space, frequency and time correlations. Numerical results indicate that our design provides significant gains over designs based on the wide-spread saturated buffers assumption, while also outperforming algorithms that consider a finite-buffer model.
Time-domain Speech Enhancement with Generative Adversarial Learning

Speech enhancement aims to obtain speech signals with high intelligibility and quality from noisy speech. Recent work has demonstrated the excellent performance of time-domain deep learning methods, such as Conv-TasNet. However, these methods can be degraded by the arbitrary scales of the waveform induced by the scale-invariant signal-to-noise ratio (SI-SNR) loss. This paper proposes a new framework called Time-domain Speech Enhancement Generative Adversarial Network (TSEGAN), which is an extension of the generative adversarial network (GAN) in time-domain with metric evaluation to mitigate the scaling problem, and provide model training stability, thus achieving performance improvement. In addition, we provide a new method based on objective function mapping for the theoretical analysis of the performance of Metric GAN, and explain why it is better than the Wasserstein GAN. Experiments conducted demonstrate the effectiveness of our proposed method, and illustrate the advantage of Metric GAN.
4D Dual-Tree Complex Wavelets for Time-Dependent Data

The dual-tree complex wavelet transform (DT-ℂWT) is extended to the 4D setting. Key properties of 4D DT-ℂWT, such as directional sensitivity and shift-invariance, are discussed and illustrated in a tomographic application. The inverse problem of reconstructing a dynamic three-dimensional target from X-ray projection measurements can be formulated as 4D space-time tomography. The results suggest that 4D DT-ℂWT offers simple implementations combined with useful theoretical properties for tomographic reconstruction.
A general framework of nonparametric feature selection in high‐dimensional data

Nonparametric feature selection for high‐dimensional data is an important and challenging problem in the fields of statistics and machine learning. Most of the existing methods for feature selection focus on parametric or additive models which may suffer from model misspecification. In this paper, we propose a new framework to perform nonparametric feature selection for both regression and classification problems. Under this framework, we learn prediction functions through empirical risk minimization over a reproducing kernel Hilbert space. The space is generated by a novel tensor product kernel, which depends on a set of parameters that determines the importance of the features. Computationally, we minimize the empirical risk with a penalty to estimate the prediction and kernel parameters simultaneously. The solution can be obtained by iteratively solving convex optimization problems. We study the theoretical property of the kernel feature space and prove the oracle selection property and Fisher consistency of our proposed method. Finally, we demonstrate the superior performance of our approach compared to existing methods via extensive simulation studies and applications to two real studies.
New Experimentally Observable Gamma-ray Emissions from 241Am Nuclear Decay

With the high resolution of microcalorimeter detectors, previously unresolvable gamma-ray lines are now clearly resolvable. A careful measurement of Am decay with a large microcalorimeter array has yielded never before seen or predicted gamma lines at 207.72±0.02 keV and 208.21±0.01 keV. These have been made possible because of new microwave-multiplexing readout and improved analysis algorithms for microcalorimeters.
Model-based clustering of partial records

Partially recorded data are frequently encountered in many applications. In practice, such datasets are usually clustered by removing incomplete cases or features with missing values, or by imputing missing values, followed by application of a clustering algorithm to the resulting altered data set. Here, we develop clustering methodology through a model-based approach using the marginal density for the observed values, using a ﬁnite mixture model of multivariate t distributions. We compare our algorithm to the corresponding full expectation-maximization (EM) approach that considers the missing values in the incomplete data set and makes a missing at random (MAR) assumption, as well as case deletion and imputation. Since only the observed values are utilized, our approach is computationally more efﬁcient than imputation or full EM. Simulation studies demonstrate that our approach has favorable recovery of the true cluster partition compared to case deletion and imputation under various missingness mechanisms, and is more robust to extreme MAR violations than the full EM approach since it does not use the observed values to inform those that are missing. Our methodology is demonstrated on a problem of clustering gamma-ray bursts and is implemented at https://github.com/emilygoren/MixtClust.
Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation

Temporal action proposal generation (TAPG) is a fundamental and challenging task in media interpretation and video understanding, especially in temporal action detection. Most previous works focus on capturing the local temporal context and can well locate simple action instances with clean frames and clear boundaries. However, they generally fail in complicated scenarios where interested actions involve irrelevant frames and background clutters, and the local temporal context becomes less effective. To deal with these problems, we present an augmented transformer with adaptive graph network (ATAG) to exploit both long-range and local temporal contexts for TAPG. Specifically, we enhance the vanilla transformer by equipping a snippet actionness loss and a front block, dubbed augmented transformer, and it improves the abilities of capturing long-range dependencies and learning robust feature for noisy action instances. Moreover, an adaptive graph convolutional network (GCN) is proposed to build local temporal context by mining the position information and difference between adjacent features. The features from the two modules carry rich semantic information of the video, and are fused for effective sequential proposal generation. Extensive experiments are conducted on two challenging datasets, THUMOS14 and ActivityNet1.3, and the results demonstrate that our method outperforms state-of-the-art TAPG methods. Our code will be released soon.
Roadmap for Rare-earth Quantum Computing

1. Division of Atomic physics, Lund University, P.O. Box 118, 221 00, Lund, Sweden 2. Karlsruhe Institute of Technology, Physikalisches Institut, Institute for Quantum Materials and Technologies, Wolfgang-Gaede Str. 1, 76131 Karlsruhe, Germany 3. 3 Institute of Physics, University of Stuttgart, Pfaffenwaldring 57, 70569 Stuttgart, Germany 4. Center for Complex Quantum Systems, Department of Physics and Astronomy, Aarhus University, DK 8000 Aarhus C, Denmark 5. ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Technology, Mediterranean Technology Park, 08860 Castelldefels (Barcelona), Spain 6. ICREA-Institució Catalana de Recerca i Estudis Avançats, 08015 Barcelona, Spain 7. Chimie ParisTech, PSL University, CNRS, Institut de Recherche de Chimie Paris, 75005 Paris, France 8. Thales Research and Technology, 1 Avenue Augustin Fresnel, 91767 Palaiseau, France 9. Attocube Systems AG, Eglfinger Weg 2, 85540 Haar, Germany
Symbol-Level Precoding Through the Lens of Zero Forcing and Vector Perturbation

Symbol-level precoding (SLP) has recently emerged as a new paradigm for physical-layer transmit precoding in multiuser multi-input-multi-output (MIMO) channels. It exploits the underlying symbol constellation structure, which the conventional paradigm of linear precoding does not, to enhance symbol-level performance such as symbol error probability (SEP). This paper aims to better understand the relationships between SLP and linear precoding, subsequent design implications, and further connections beyond the existing SLP scope. Focused on the quadrature amplitude modulation (QAM) constellations, our study is built on a basic signal observation, namely, that SLP can be equivalently represented by a zero-forcing (ZF) linear precoding scheme augmented with some appropriately chosen symbol-dependent perturbation terms, and that some extended form of SLP is equivalent to a vector perturbation (VP) nonlinear precoding scheme augmented with the above-noted perturbation terms. We examine how insights arising from this perturbed ZF and VP interpretations can be leveraged to i) substantially simplify the optimization of certain SLP design criteria, namely, total or peak power minimization subject to SEP quality guarantees; and ii) draw connections with some existing SLP designs. We also touch on the analysis side by showing that, under total power minimization, the basic ZF scheme is a near-optimal SLP scheme when the QAM order is very high—which gives a vital implication that SLP is more useful for lower-order QAM cases. Numerical results further indicate the merits and limitations of the different SLP designs derived from the perturbed ZF and VP interpretations.
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity

Greedy-GQ is a value-based reinforcement learning (RL) algorithm for optimal control. Recently, the finite-time analysis of Greedy-GQ has been developed under linear function approximation and Markovian sampling, and the algorithm is shown to achieve an $\epsilon$-stationary point with a sample complexity in the order of $\mathcal{O}(\epsilon^{-3})$. Such a high sample complexity is due to the large variance induced by the Markovian samples. In this paper, we propose a variance-reduced Greedy-GQ (VR-Greedy-GQ) algorithm for off-policy optimal control. In particular, the algorithm applies the SVRG-based variance reduction scheme to reduce the stochastic variance of the two time-scale updates. We study the finite-time convergence of VR-Greedy-GQ under linear function approximation and Markovian sampling and show that the algorithm achieves a much smaller bias and variance error than the original Greedy-GQ. In particular, we prove that VR-Greedy-GQ achieves an improved sample complexity that is in the order of $\mathcal{O}(\epsilon^{-2})$. We further compare the performance of VR-Greedy-GQ with that of Greedy-GQ in various RL experiments to corroborate our theoretical findings.
Linear Collider Signals of $Z'$ Bosons in GUT Inspired Gauge-Higgs Unification

In gauge-Higgs unification (GHU), the 4D Higgs boson appears as a part of the fifth dimensional component of 5D gauge field. Recently, an SO(11) GUT inspired SO(5) × U(1) × SU(3) GHU model has been proposed. In the GHU, Kaluza-Klein (KK) excited states of neutral vector bosons, photon, Z boson and ZR boson, appear as neutral massive vector bosons Z ′s. The Z ′ bosons in the GHU couple to quarks and leptons with large parity violation, which leads to distinctive polarization dependence in, e.g., cross sections and forward-backward asymmetries in e−e+ → μ−μ+, qq̄ processes. In the talk, we discuss fermion pair production in e−e+ linear collider experiments with polarized e− and e+ beams in the GUT inspired GHU. Deviations from the SM are shown in the early stage of planned international linear collider (ILC) with 250 GeV experiments. The deviations can be tested for the KK mass scale up to about 15 TeV. This talk is mainly based on Phys.Rev.D102(2020)015029 (Ref. [1]). ∗Talk presented at the International Workshop on Future Linear Colliders (LCWS2021), 15-18 March 2021. C21-03-15.1. †Speaker ar X iv :2 10 3. 16 32 0v 1 [ he pph ] 3 0 M ar 2 02 1
Adaptive Boosting for Domain Adaptation: Toward Robust Predictions in Scene Segmentation

Domain adaptation is to transfer the shared knowledge learned from the source domain to a new environment, i.e., target domain. One common practice is to train the model on both labeled source-domain data and unlabeled target-domain data. Yet the learned models are usually biased due to the strong supervision of the source domain. Most researchers adopt the early-stopping strategy to prevent over-fitting, but when to stop training remains a challenging problem since the lack of the target-domain validation set. In this paper, we propose one efficient bootstrapping method, called Adaboost Student, explicitly learning complementary models during training and liberating users from empirical early stopping. Adaboost Student combines deep model learning with the conventional training strategy, i.e., adaptive boosting, and enables interactions between learned models and the data sampler. We adopt one adaptive data sampler to progressively facilitate learning on hard samples and aggregate “weak” models to prevent over-fitting. Extensive experiments show that (1) Without the need to worry about the stopping time, AdaBoost Student provides one robust solution by efficient complementary model learning during training. (2) AdaBoost Student is orthogonal to most domain adaptation methods, which can be combined with existing approaches to further improve the state-of-the-art performance. We have achieved competitive results on three widely-used scene segmentation domain adaptation benchmarks.
Hubble diagram at higher redshifts: model independent calibration of quasars

In this paper, we present a model-independent approach to calibrate the largest quasar sample. Calibrating quasar samples is essentially constraining the parameters of the linear relation between the log of the ultraviolet (UV) and X-ray luminosities. This calibration allows quasars to be used as standardized candles. There is a strong correlation between the parameters characterizing the quasar luminosity relation and the cosmological distances inferred from using quasars as standardized candles. We break this degeneracy by using Gaussian process regression to model-independently reconstruct the expansion history of the Universe from the latest type Ia supernova observations. Using the calibrated quasar dataset, we further reconstruct the expansion history up to redshift of z ∼ 7.5. Finally, we test the consistency between the calibrated quasar sample and the standard ΛCDM model based on the posterior probability distribution of the GP hyperparameters. Our results show that the quasar sample is in good agreement with the standard ΛCDM model in the redshift range of the supernova, despite of mildly significant deviations taking place at higher redshifts. Fitting the standard ΛCDM model to the calibrated quasar sample, we obtain a high value of the matter density parameter Ωm = 0.382 +0.045 −0.042, which is marginally consistent with the constraints from other cosmological observations.
A Systematic Survey on Multi-relational Community Detection

Complex networks contain various interactions among similar or different entities. These kinds of networks are called multi-relational networks, in which each layer corresponds to a special type of interaction. Multi-relational networks are a particular type of multilayer networks in which nodes are similar entities; however, edges or communications demonstrate different types of interactions among similar entities. In this survey, we study community detection methods for multi-relational networks. The considered models are divided into two main groups, namely, direct methods and indirect methods. We put indirect methods in two classes: flattening and ensembling, and the direct methods are further divided into four groups which are: probabilistic methods, algebraic methods, modular-based methods, and graph feature-based methods. For each approach and each method, we explain their pros and cons. Additionally, all the used datasets in the multilayer community detection studies are categorized into synthetic and real data. We elaborate on the most important datasets. Afterward, the utilized evaluation metrics by the papers are described. Finally, the current models' challenges and shortcomings are discussed. Finally, some suggestions for future research are developed. Putting all this together, this study, to the best of our knowledge, is the most comprehensive survey dedicated to multi-relational networks community detection.
A Regulatory System for Optimal Legal Transaction Throughput in Cryptocurrency Blockchains

Permissionless blockchain consensus protocols have been designed primarily for defining decentralized economies for the commercial trade of assets, both virtual and physical, using cryptocurrencies. In most instances, the assets being traded are regulated, which mandates that the legal right to their trade and their trade value are determined by the governmental regulator of the jurisdiction in which the trade occurs. Unfortunately, existing blockchains do not formally recognise proposal of legal cryptocurrency transactions, as part of the execution of their respective consensus protocols, resulting in rampant illegal activities in the associated crypto-economies. In this contribution, we motivate the need for regulated blockchain consensus protocols with a case study of the illegal, cryptocurrency based, Silk Road darknet market. We present a novel regulatory framework for blockchain protocols, for ensuring legal transaction confirmation as part of the blockchain distributed consensus. As per our regulatory framework, we derive conditions under which legal transaction throughput supersedes throughput of traditional transactions, which are, in the worst case, an indifferentiable mix of legal and illegal transactions. Finally, we show that with a small change to the standard blockchain consensus execution policy (appropriately introduced through regulation), the legal transaction throughput in the blockchain network can be maximized.
Cut finite element methods and ghost stabilization techniques for space‐time discretizations of the Navier–Stokes equations

We propose and analyze computationally a new fictitious domain method, based on higher order space‐time finite element discretizations, for the simulation of the nonstationary, incompressible Navier–Stokes equations on evolving domains. The physical domain is embedded into a fixed computational mesh such that arbitrary intersections of the moving domain's boundaries with the background mesh occur. The potential of such cut finite element techniques for higher order space‐time finite element methods has rarely been studied in the literature so far and deserves further elucidation. The key ingredients of the approach are the weak formulation of Dirichlet boundary conditions by Nitsche's method, the flexible and efficient integration over all types of intersections of cells by moving boundaries and the spatial extension of the discrete physical quantities to the entire computational background mesh including fictitious (ghost) subdomains of fluid flow. Thereby, an expensive remeshing and adaptation of the sparse matrix data structure are avoided and the computations are accelerated. To prevent spurious oscillations caused by irregular intersections of mesh cells, a penalization, defining also implicitly the extension to ghost domains, is added. These techniques are embedded in an arbitrary order, discontinuous Galerkin discretization of the time variable and an inf‐sup stable discretization of the spatial variables. The parallel implementation of the matrix assembly is described. The optimal order convergence properties of the algorithm are illustrated in a numerical experiment for an evolving domain. The well‐known 2d benchmark of flow around a cylinder as well as flow around moving obstacles with arising cut cells and fictitious domains are considered further.