Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques

For a solar-like star, the surface rotation evolves with time, allowing in principle to estimate the age of a star from its surface rotation period. Here we are interested in measuring surface rotation periods of solar-like stars observed by the NASA Kepler mission. Different methods have been developed to track rotation signals in Kepler photometric light curves: time-frequency analysis based on wavelet techniques, autocorrelation and composite spectrum. We use the learning abilities of random forest classifiers to take decisions during two crucial steps of the analysis. First, given some input parameters, we discriminate the considered Kepler targets between rotating MS stars, non-rotating MS stars, red giants, binaries and pulsators. We then use a second classifier only on the MS rotating targets to decide the best data-analysis treatment.
Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization

Intelligent reflecting surface (IRS) is a promising new technology for achieving both spectrum and energy efficient wireless communication systems in the future. However, existing works on IRS mainly consider frequency-flat channels and assume perfect knowledge of channel state information (CSI) at the transmitter. Motivated by the above, in this paper we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system under frequency-selective channels and propose a practical transmission protocol with channel estimation. First, to reduce the overhead in channel training as well as exploit the channel spatial correlation, we propose a novel IRS elements grouping method, where each group consists of a set of adjacent IRS elements that share a common reflection coefficient. Based on this method, we propose a practical transmission protocol where only the combined channel of each group needs to be estimated, thus substantially reducing the training overhead. Next, with any given grouping and estimated CSI, we formulate the problem to maximize the achievable rate by jointly optimizing the transmit power allocation and the IRS passive array reflection coefficients. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the power allocation and the passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the case without using IRS. Moreover, it is shown that there exists an optimal size for IRS elements grouping which achieves the maximum achievable rate due to the practical trade-off between the training overhead and IRS passive beamforming flexibility.
Recurrent Adversarial Service Times

Service system dynamics occur at the interplay between customer behaviour and a service provider's response. This kind of dynamics can effectively be modeled within the framework of queuing theory where customers' arrivals are described by point process models. However, these approaches are limited by parametric assumptions as to, for example, inter-event time distributions. In this paper, we address these limitations and propose a novel, deep neural network solution to the queuing problem. Our solution combines a recurrent neural network that models the arrival process with a recurrent generative adversarial network which models the service time distribution. We evaluate our methodology on various empirical datasets ranging from internet services (Blockchain, GitHub, Stackoverflow) to mobility service systems (New York taxi cab).
Robust convex clustering: How does fusion penalty enhance robustness?

Convex clustering has gained popularity recently due to its desirable performance in empirical studies. It involves solving a convex optimization problem with the cost function being a squared error loss plus a fusion penalty that encourages the estimated centroids for observations in the same cluster to be identical. However, when data are contaminated, convex clustering with a squared error loss will fail to identify correct cluster memberships even when there is only one arbitrary outlier. To address this challenge, we propose a robust convex clustering method. Theoretically, we show that the new estimator is resistant to arbitrary outliers: it does not break down until more than half of the observations are arbitrary outliers. In particular, we observe a new phenomenon that the fusion penalty can help enhance robustness. Numerical studies are performed to demonstrate the competitive performance of the proposed method.
Improving Description-Based Person Re-Identification by Multi-Granularity Image-Text Alignments

Description-based person re-identification (Re-id) is an important task in video surveillance that requires discriminative cross-modal representations to distinguish different people. It is difficult to directly measure the similarity between images and descriptions due to the modality heterogeneity (the cross-modal problem). And all samples belonging to a single category (the fine-grained problem) makes this task even harder than the conventional image-description matching task. In this paper, we propose a Multi-granularity Image-text Alignments (MIA) model to alleviate the cross-modal fine-grained problem for better similarity evaluation in description-based person Re-id. Specifically, three different granularities, i.e., global-global, global-local and local-local alignments are carried out hierarchically. Firstly, the global-global alignment in the Global Contrast (GC) module is for matching the global contexts of images and descriptions. Secondly, the global-local alignment employs the potential relations between local components and global contexts to highlight the distinguishable components while eliminating the uninvolved ones adaptively in the Relation-guided Global-local Alignment (RGA) module. Thirdly, as for the local-local alignment, we match visual human parts with noun phrases in the Bi-directional Fine-grained Matching (BFM) module. The whole network combining multiple granularities can be end-to-end trained without complex pre-processing. To address the difficulties in training the combination of multiple granularities, an effective step training strategy is proposed to train these granularities step-by-step. Extensive experiments and analysis have shown that our method obtains the state-of-the-art performance on the CUHK-PEDES dataset and outperforms the previous methods by a significant margin.
Emergence of localized persistent weakly–evanescent cortical brain wave loops

An inhomogeneous anisotropic physical model of the brain cortex is presented that predicts the emergence of non–evanescent (weakly damped) wave–like modes propagating in the thin cortex layers transverse to both the mean neural fiber direction and to the cortex spatial gradient. Although the amplitude of these modes stays below the typically observed axon spiking potential, the lifetime of these modes may significantly exceed the spiking potential inverse decay constant. Full brain numerical simulations based on parameters extracted from diffusion and structural MRI confirm the existence and extended duration of these wave modes. Contrary to the commonly agreed paradigm that the neural fibers determine the pathways for signal propagation in the brain, the signal propagation due to the cortex wave modes in the highly folded areas will exhibit no apparent correlation with the fiber directions. The results are consistent with numerous recent experimental animal and human brain studies demonstrating the existence electrostatic field activity in the form of traveling waves (including studies where neuronal connections were severed) and with wave loop induced peaks observed in EEG spectra. The localization and persistence of these cortical wave modes has significant implications in particular for neuroimaging methods that detect electromagnetic physiological activity, such as EEG and MEG, and for the understanding of brain activity in general, including mechanisms of memory.
Mechanical and Optical Properties of Amorphous Oxide Thin Films: Experimental Correlation Between Internal Friction and Urbach Energy

Alex Amato,∗ Silvana Terreni, Massimo Granata, Christophe Michel, Benoit Sassolas, Laurent Pinard, Maurizio Canepa, and Gianpietro Cagnoli Laboratoire des Matériaux Avancés, CNRS/IN2P3, F-69622 Villeurbanne (FR). OPTMATLAB, Dipartimento di Fisica, Università di Genova, Via Dodecaneso 33, 16146 Genova (IT). INFN, Sezione di Genova, Via Dodecaneso 33, 16146 Genova, Italy. and Institut Lumière et Matière, CNRS, Université de Lyon, F-69622 Villeurbanne (FR). (Dated: June 25, 2019)
SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors

There is more and more evidence that machine learning can be successfully applied in materials science and related fields. However, datasets in these fields are often quite small (from tens to several thousands of samples). This means the most advanced machine learning techniques remain neglected, as they are considered to be applicable to big data only. Moreover, materials informatics methods often rely on human-engineered descriptors, that should be carefully chosen, or even created, to fit the physicochemical property that one intends to predict. In this article, we propose a new method that tackles both the issue of small datasets and the difficulty of developing task-specific descriptors. The SMILES-X is an autonomous pipeline for molecular compounds characterisation based on a {Embed-Encode-Attend-Predict} neural architecture with a data-specific Bayesian hyper-parameters optimisation. The only input to the architecture—the SMILES strings—are de-canonicalised in order to efficiently augment the data. One of the key features of the architecture is the attention mechanism, which enables the interpretation of output predictions without extra computational cost. The SMILES-X achieves state-of-the-art results in the inference of aqueous solubility ( RMSE¯test≃0.57±0.07 mols/L), hydration free energy ( RMSE¯test≃0.81±0.22 kcal/mol, which is ∼24.5% better than molecular dynamics simulations), and octanol/water distribution coefficient ( RMSE¯test≃0.59±0.02 for LogD at pH 7.4) of molecular compounds. The SMILES-X is intended to become an important asset in the toolkit of materials scientists and chemists. The source code for the SMILES-X is available at github.com/GLambard/SMILES-X.
TopoLines: Topological Smoothing for Line Charts

Line charts are commonly used to visualize a series of data samples. When the data are noisy, smoothing is applied to make the signal more apparent. However, the difference between signal and noise is ill-defined, as it depends upon the application domain of the data. Common methods used to smooth line charts, such as rank filters, convolutional filters, frequency domain filters, and subsampling, optimize on various properties of the data. However, these methods are only ideal in certain limited scenarios. We present TopoLines, a method for smoothing line charts by leveraging techniques from Topological Data Analysis. The design goal of TopoLines is to smooth line charts while maintaining prominent peaks, as defined by persistent homology, in the data. We compare TopoLines to 5 popular line smoothing methods using data from 4 application domains. We evaluate TopoLines in terms of $l^2$-norm of the residual as the simplification threshold is varied, and we perform a user study to evaluate users' perception of the accuracy of TopoLines.
Multi-Cell Sparse Activity Detection for Massive Random Access: Massive MIMO Versus Cooperative MIMO

This paper considers sparse device activity detection for cellular machine-type communications with non-orthogonal signatures using the approximate message passing algorithm. This paper compares two network architectures, massive multiple-input-multiple-output (MIMO) and cooperative MIMO, in terms of their effectiveness in overcoming inter-cell interference. In the massive MIMO architecture, each base station (BS) detects only the users from its own cell while treating inter-cell interference as noise. In the cooperative MIMO architecture, each BS detects the users from neighboring cells as well; the detection results are then forwarded in the form of a log-likelihood ratio (LLR) to a central unit where final decisions are made. This paper analytically characterizes the probabilities of false alarm and missed detection for both architectures. The numerical results validate the analytic characterization and show that as the number of antennas increases, a massive MIMO system effectively drives the detection error to zero, while as the cooperation size increases, the cooperative MIMO architecture mainly improves the cell-edge user performance. Moreover, this paper studies the effect of LLR quantization to account for the finite-capacity fronthaul. The numerical simulations of a practical scenario suggest that in specific case cooperating three BSs in a cooperative MIMO system achieves about the same cell-edge detection reliability as a non-cooperative massive MIMO system with four times the number of antennas per BS.
Arnoux-Rauzy interval exchange transformations

The Arnoux-Rauzy systems are defined in [5], both as symbolic systems on three letters and exchanges of six intervals on the circle. In connection with a conjecture of S.P. Novikov, we investigate the dynamical properties of the interval exchanges, and precise their relation with the symbolic systems, which was known only to be a semi-conjugacy; in order to do this, we define a new system which is an exchange of nine intervals (it was described in [3] for a particular case). Our main result is that the semi-conjugacy determines a measure-theoretic isomorphism under an explicit (sufficient) condition, which is satisfied by almost all Arnoux-Rauzy systems for a suitable measure; but, under another condition, the interval exchanges are not uniquely ergodic and the isomorphism does not hold for all invariant measures; finally, we give conditions for these interval exchanges to be weakly mixing.
A computational model of early language acquisition from audiovisual experiences of young infants

Earlier research has suggested that human infants might use statistical dependencies between speech and non-linguistic multimodal input to bootstrap their language learning before they know how to segment words from running speech. However, feasibility of this hypothesis in terms of real-world infant experiences has remained unclear. This paper presents a step towards a more realistic test of the multimodal bootstrapping hypothesis by describing a neural network model that can learn word segments and their meanings from referentially ambiguous acoustic input. The model is tested on recordings of real infant-caregiver interactions using utterance-level labels for concrete visual objects that were attended by the infant when caregiver spoke an utterance containing the name of the object, and using random visual labels for utterances during absence of attention. The results show that beginnings of lexical knowledge may indeed emerge from individually ambiguous learning scenarios. In addition, the hidden layers of the network show gradually increasing selectivity to phonetic categories as a function of layer depth, resembling models trained for phone recognition in a supervised manner.
Specifying Search Queries for Web Service Discovery

Web services are meant to be easily accessible software systems. With the emergence of Web service technologies and the presence of thousands, or perhaps millions, of Web services in the coming years, the challenge is searching for a Web service that meets a specified requirement. In this paper, we propose methods and models for effectively specifying the search queries in a Web service discovery system. We show that while search queries should be specified precisely enough to contain information for appropriate matching, they should not be too detailed. Users often do not have a clear vision of their desired Web service and a search query should be at a high enough level of abstraction. We propose two models and show how they are appropriate for capturing search queries.
High-fidelity single-qubit gates in a strongly driven quantum-dot hybrid qubit with1/fcharge noise

Semiconductor double quantum dot hybrid qubits are promising candidates for high-fidelity quantum computing. However, their performance is limited by charge noise, which is ubiquitous in solid-state devices, and phonon-induced dephasing. Here we explore methods for improving the quantum operations of a hybrid qubit, using strong microwave driving to enable gate operations that are much faster than decoherence processes. Using numerical simulations and a theoretical method based on a cumulant expansion, we analyze qubit dynamics in the presence of $1/f$ charge noise, which forms the dominant decoherence mechanism in many solid-state devices. We show that, while strong-driving effects and charge noise both reduce the quantum gate fidelity, simple pulse-shaping techniques effectively suppress the strong-driving effects. Moreover, a broad AC sweet spot emerges when the detuning parameter and the tunneling coupling are driven simultaneously. Taking into account phonon-mediated noise, we find that it should be possible to achieve $X_{\pi}$ gates with fidelities higher than $99.9\%$.
Adaptive Period Embedding for Representing Oriented Objects in Aerial Images

We propose a novel method for representing oriented objects in aerial images named adaptive period embedding (APE). Although traditional object detection methods represent objects using horizontal bounding boxes, the objects in aerial images are oriented. Calculating the angle of the object is a yet challenging task. Almost all previous object detectors for aerial images directly regress the angle of objects, they use complex rules to calculate the angle, and their performance is limited by the rule design. In contrast, our method is based on the angular periodicity of oriented objects. The angle is represented by two 2-D periodic vectors the periods of which are different, so the vector is continuous as the shape changes. The label generation rule is simpler and more reasonable compared with previous methods. The proposed method is general and can be applied to other oriented detector. Besides, we propose a novel intersection over union (IoU) calculation method for long objects named length-independent IoU (LIIoU). We intercept part of the long side of the target box to get the maximum IoU between the proposed box and intercepted target box. Thereby, some long boxes will have corresponding positive samples. Our method reaches the first place of DOAI2019 competition task1 (oriented object) held in a workshop on detecting objects in aerial images in conjunction with IEEE CVPR 2019.
Data Poisoning Attack against Knowledge Graph Embedding

Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its effectiveness in a benign environment, KGE's robustness to adversarial attacks is not well-studied. Existing attack methods on graph data cannot be directly applied to attack the embeddings of knowledge graph due to its heterogeneity. To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph. The effectiveness and efficiency of the proposed attack strategies are verified by extensive evaluations on two widely-used benchmarks.
Managing Email Overload in the Workplace

The problem of email overload in the workplace, the feeling of being overwhelmed by the constant flow of messages appearing in the inbox and the inability to manage the high volume of messages effectively, is a current area of research in the field of human–computer interaction. Most current literature reflects three types of recommendations related to how to cope with or mitigate email overload in the workplace: email management techniques for the employee, strategies for the organization or employer, and inbox usability improvements. The state of current research related to email overload is reviewed and avenues for future research are suggested.
Distribution-Independent PAC Learning of Halfspaces with Massart Noise

We study the problem of {\em distribution-independent} PAC learning of halfspaces in the presence of Massart noise. Specifically, we are given a set of labeled examples $(\mathbf{x}, y)$ drawn from a distribution $\mathcal{D}$ on $\mathbb{R}^{d+1}$ such that the marginal distribution on the unlabeled points $\mathbf{x}$ is arbitrary and the labels $y$ are generated by an unknown halfspace corrupted with Massart noise at noise rate $\eta<1/2$. The goal is to find a hypothesis $h$ that minimizes the misclassification error $\mathbf{Pr}_{(\mathbf{x}, y) \sim \mathcal{D}} \left[ h(\mathbf{x}) \neq y \right]$. We give a $\mathrm{poly}\left(d, 1/\epsilon \right)$ time algorithm for this problem with misclassification error $\eta+\epsilon$. We also provide evidence that improving on the error guarantee of our algorithm might be computationally hard. Prior to our work, no efficient weak (distribution-independent) learner was known in this model, even for the class of disjunctions. The existence of such an algorithm for halfspaces (or even disjunctions) has been posed as an open question in various works, starting with Sloan (1988), Cohen (1997), and was most recently highlighted in Avrim Blum's FOCS 2003 tutorial.
Regularity Properties for a Class of Non-uniformly Elliptic Isaacs Operators

Abstract We consider the elliptic differential operator defined as the sum of the minimum and the maximum eigenvalue of the Hessian matrix, which can be viewed as a degenerate elliptic Isaacs operator, in dimension larger than two. Despite of nonlinearity, degeneracy, non-concavity and non-convexity, such an operator generally enjoys the qualitative properties of the Laplace operator, as for instance maximum and comparison principles, ABP and Harnack inequalities, Liouville theorems for subsolutions or supersolutions. Existence and uniqueness for the Dirichlet problem are also proved as well as local and global Hölder estimates for viscosity solutions. All results are discussed for a more general class of weighted partial trace operators.
On a problem of De Koninck

Let $\sigma(n)$ and $\gamma(n)$ denote the sum of divisors and the product of distinct prime divisors of $n$ respectively. We shall show that, if $n\neq 1, 1782$ and $\sigma(n)=(\gamma(n))^2$, then there exist odd (not necessarily distinct) primes $p, p^\prime$ and (not necessarily odd) distinct primes $q_i (i=1, 2, \ldots, k)$ such that $p, p^\prime\mid\mid n$, $q_i^2\mid\mid n (i=1, 2, \ldots, k)$ and $q_1\mid \sigma(p^2), q_{i+1}\mid\sigma(q_i^2) (i=1, 2, \ldots, k-1), p^\prime \mid\sigma(q_k^2)$.