Miracle-dz/UniFAR-FTUs huggingface dataset:
Large Interferometer For Exoplanets (LIFE). IV. Ideal kernel-nulling array architectures for a space-based mid-infrared nulling interferometer

Aims: Optical interferometry from space for the purpose of detecting and characterising exoplanets is seeing a revival, specifically from missions such as the proposed Large Interferometer For Exoplanets (LIFE). A default assumption since the design studies of Darwin and TPF-I has been that the Emma X-array configuration is the optimal architecture for this goal. Here, we examine whether new advances in the field of nulling interferometry, such as the concept of kernel nulling, challenge this assumption. Methods: We develop a tool designed to derive the photon-limited signal to noise ratio of a large sample of simulated planets for different architecture configurations and beam combination schemes. We simulate four basic configurations: the double Bracewell/X-array, and kernel nullers with three, four and five telescopes respectively. Results: We find that a configuration of five telescopes in a pentagonal shape, using a five aperture kernel nulling scheme, outperforms the X-array design in both search (finding more planets) and characterisation (obtaining better signal, faster) when total collecting area is conserved. This is especially the case when trying to detect Earth twins (temperate, rocky planets in the habitable zone), showing a 23% yield increase over the X-array. On average, we find that a five telescope design receives 1.2 times the signal over the X-array design. Conclusions: With the results of this simulation, we conclude that the Emma X-array configuration may not be the best architecture choice for the upcoming LIFE mission, and that a five telescope design utilising kernel nulling concepts will likely provide better scientific return for the same collecting area, provided that technical solutions for the required achromatic phase shifts can be implemented.
On Sampling Collaborative Filtering Datasets

We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms. Recommender systems are generally trained and evaluated on samples of larger datasets. Samples are often taken in a naive or ad-hoc fashion: e.g. by sampling a dataset randomly or by selecting users or items with many interactions. As we demonstrate, commonly-used data sampling schemes can have significant consequences on algorithm performance. Following this observation, this paper makes three main contributions: (1) characterizing the effect of sampling on algorithm performance, in terms of algorithm and dataset characteristics (e.g. sparsity characteristics, sequential dynamics, etc.); (2) designing SVP-CF, which is a data-specific sampling strategy, that aims to preserve the relative performance of models after sampling, and is especially suited to long-tailed interaction data; and (3) developing an oracle, DATA-GENIE, which can suggest the sampling scheme that is most likely to preserve model performance for a given dataset. The main benefit of DATA-GENIE is that it will allow recommender system practitioners to quickly prototype and compare various approaches, while remaining confident that algorithm performance will be preserved, once the algorithm is retrained and deployed on the complete data. Detailed experiments show that using DATA-GENIE, we can discard upto 5x more data than any sampling strategy with the same level of performance.
Using Deep Learning to Generate Complete Log Statements

Logging is a practice widely adopted in several phases of the software lifecycle. For example, during software development log statements allow engineers to verify and debug the system by exposing fine-grained information of the running software. While the benefits of logging are undisputed, taking proper decisions about where to inject log statements, what information to log, and at which log level (e.g., error, warning) is crucial for the logging effectiveness. In this paper, we present LANCE (Log stAtemeNt reCommEnder), the first approach supporting developers in all these decisions. LANCE features a Text-To-Text-Transfer-Transformer (T5) model that has been trained on 6,894,456 Java methods. LANCE takes as input a Java method and injects in it a full log statement, including a human-comprehensible logging message and properly choosing the needed log level and the statement location. Our results show that LANCE is able to (i) properly identify the location in the code where to inject the statement in 65.9% of Java methods requiring it; (ii) selecting the proper log level in 66.2% of cases; and (iii) generate a completely correct log statement including a meaningful logging message in 15.2% of cases.
Speech Resources in the Tamasheq Language

In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. These two datasets were made available for the IWSLT 2022 low-resource speech translation track, and they consist of collections of radio recordings from daily broadcast news in Niger (Studio Kalangou) and Mali (Studio Tamani). We share (i) a massive amount of unlabeled audio data (671 hours) in five languages: French from Niger, Fulfulde, Hausa, Tamasheq and Zarma, and (ii) a smaller 17 hours parallel corpus of audio recordings in Tamasheq, with utterance-level translations in the French language. All this data is shared under the Creative Commons BY-NC-ND 3.0 license. We hope these resources will inspire the speech community to develop and benchmark models using the Tamasheq language.
Conserved quantities in non-Hermitian systems via vectorization method

Open classical and quantum systems have attracted great interest in the past two decades. These include systems described by non-Hermitian Hamiltonians with parity-time $(\mathcal{PT})$ symmetry that are best understood as systems with balanced, separated gain and loss. Here, we present an alternative way to characterize and derive conserved quantities, or intertwining operators, in such open systems. As a consequence, we also obtain non-Hermitian or Hermitian operators whose expectations values show single exponential time dependence. By using a simple example of a $\mathcal{PT}$-symmetric dimer that arises in two distinct physical realizations, we demonstrate our procedure for static Hamiltonians and generalize it to time-periodic (Floquet) cases where intertwining operators are stroboscopically conserved. Inspired by the Lindblad density matrix equation, our approach provides a useful addition to the well-established methods for characterizing time-invariants in non-Hermitian systems.
Objective Point Symmetry Classifications/Quantifications of an Electron Diffraction Spot Pattern with Pseudo-Hexagonal Lattice Metric

The recently developed information-theoretic approach to crystallographic symmetry classifications and quantifications [1-4] in two dimensions (2D) from digital transmission electron and scanning probe microscope images is adapted here for the analysis of an experimental electron diffraction spot pattern, Fig. 1. Digital input data are in [1-3] considered to consist of the pixel-wise sums of approximately Gaussian distributed noise and an unknown underlying signal that is strictly 2D periodic. Structural defects within the crystals or on the crystal surfaces, instrumental image recording noise, slight deviations from zero-crystal-tilt conditions in transmission electron microscopy, and small inaccuracies in the algorithmic processing of the digital data all contribute to a single generalized noise term. The plane symmetry group and projected Laue class [1,3] (or 2D Bravais lattice type [4]) that is “anchored” to the least broken symmetries [1] are identified as genuine in the presence of generalized noise. More severely broken symmetries that are not anchored in this sense are identified as pseudosymmetries. Objective symmetry classifications/quantifications have been undertaken for three concentric regions of the electron in Fig. The well-known electron crystallography program CRISP/ELD for the extraction of the electron diffraction spot intensities in the program’s default (primitive These intensities were as * . hke details of the three regions are given in numerical results point symmetry
LARD: Large-scale Artificial Disfluency Generation

Disfluency detection is a critical task in real-time dialogue systems. However, despite its importance, it remains a relatively unexplored field, mainly due to the lack of appropriate datasets. At the same time, existing datasets suffer from various issues, including class imbalance issues, which can significantly affect the performance of the model on rare classes, as it is demonstrated in this paper. To this end, we propose LARD, a method for generating complex and realistic artificial disfluencies with little effort. The proposed method can handle three of the most common types of disfluencies: repetitions, replacements, and restarts. In addition, we release a new large-scale dataset with disfluencies that can be used on four different tasks: disfluency detection, classification, extraction, and correction. Experimental results on the LARD dataset demonstrate that the data produced by the proposed method can be effectively used for detecting and removing disfluencies, while also addressing limitations of existing datasets.
Time-varying Identification of Guided Wave Propagation under Varying Temperature via Non-Stationary Time Series Models

Modern-day civil, mechanical, and aeronautical structures are transitioning towards a continuous, online, and automated maintenance paradigm in order to ensure increased safety and reliability. The field of structural health monitoring (SHM) is playing a key role in this respect and active sensing acousto-ultrasound guided-wave based SHM techniques have shown great promise due to their potential sensitivity to small changes in the structure. However, the methods’ robustness and diagnosis capability become limited in the presence of environmental and operational variability such as changing temperature. In order to circumvent this difficulty, in this paper, a novel stochastic time series-based framework was adopted to model guided wave propagation under varying temperatures. Different stochastic time-varying time series models, such as Recursive Maximum Likelihood Time-varying Auto-Regressive (RML-TAR) and Recursive Maximum Likelihood Time-varying Auto-Regressive with Exogenous Excitation (RML-TARX) models are put forward to model and capture the underlying dynamics of guided wave propagation under varying temperatures. The steps and facets of the identification procedure are presented and clearly explained. Then the identified models are used to perform one-step-ahead prediction as well as “simulation” of the guided wave signals. In order to gain insight from a physics perspective, high-fidelity finite element (FE) models were also established to model the effect of temperature variation on guided wave propagation. Finally, surrogate models are formulated through the use of stochastic time-dependent RML-TARX models and compared with the FE models under varying temperatures. It is shown that by using only a limited number of data sets and interpolating the model parameters, it is possible to “simulate” guided wave signals for a wide range of temperatures. It is concluded that RML-TARXbased time series models can be used as a surrogate for FEM to simulate guided wave signals under varying temperatures, and therefore, saving a significant amount of computational time. Corresponding author. 1 ar X iv :2 20 1. 04 72 1v 1 [ ee ss . S P] 1 2 Ja n 20 22 Important conventions and symbols Definition is indicated by :=. Matrix transposition is indicated by the superscript T . Bold-face upper/lower case symbols designate matrix/column-vector quantities, respectively. A functional argument in parentheses designates function of a real variable; for instance P (x) is a function of the real variable x. A functional argument in brackets designates function of an integer variable; for instance x[t] is a function of normalized discrete time (t = 1, 2, . . .). The conversion from discrete normalized time to analog time is based on (t− 1)Ts, with Ts designating the sampling period. A hat designates estimator/estimate; for instance θ̂ is an estimator/estimate of θ.
Observability Analysis and Keyframe-Based Filtering for Visual Inertial Odometry With Full Self-Calibration

Camera–inertial measurement unit (IMU) sensor fusion has been extensively studied in recent decades. Numerous observability analysis and fusion schemes for motion estimation with self-calibration have been presented. However, it has been uncertain whether the intrinsic parameters of both the camera and the IMU are observable under general motion. To answer this question, by using the Lie derivatives, we first prove that for a rolling shutter (RS) camera–IMU system, all the intrinsic and extrinsic parameters, camera time offset, and readout time of the RS camera are observable with an unknown landmark. To our knowledge, we are the first to present such a proof. Next, to validate this analysis and to solve the drift issue of a structureless filter during standstills, we develop a keyframe-based sliding window filter (KSWF) for odometry and self-calibration, which works with a monocular RS camera or stereo RS cameras. Though the keyframe concept is widely used in vision-based sensor fusion, to our knowledge, the KSWF is the first of its kind to support self-calibration. Our simulation and real data tests have validated that it is possible to fully calibrate the camera–IMU system using the observations of opportunistic landmarks under diverse motion. Real data tests confirmed previous allusions that keeping landmarks in the state vector can remedy the drift in standstill and showed that the keyframe-based scheme is an alternative solution.
Interactive Data Analysis with Next-step Natural Language Query Recommendation

Natural language interfaces (NLIs) provide users with a convenient way to interactively analyze data through natural language queries. Nevertheless, interactive data analysis is a demanding process, especially for novice data analysts. When exploring large and complex SQL databases from different domains, data analysts do not necessarily have sufficient knowledge about different data tables and application domains. It makes them unable to systematically elicit a series of topically-related and meaningful queries for insight discovery in target domains. We develop a NLI with a step-wise query recommendation module to assist users in choosing appropriate next-step exploration actions. The system adopts a data-driven approach to suggest semantically relevant and context-aware queries for application domains of users' interest based on their query logs. Also, the system helps users organize query histories and results into a dashboard to communicate the discovered data insights. With a comparative user study, we show that our system can facilitate a more effective and systematic data analysis process than a baseline without the recommendation module.
Detection of Gravity Modes in RR Lyrae Stars

We report the detection of gravity modes in RR Lyrae stars. Thanks to Photometer AntarctIca eXtinction (PAIX), the first Antarctic polar photometer. Unprecedented and uninterrupted UBVRI time-series photometric ground-based data are collected during 150 days from the highest plateau of Antarctica. PAIX light-curve analyses reveal an even richer power spectrum with mixed modes in RR Lyrae stars. The nonlinear nature of several dominant peaks, showing lower and higher frequencies, occurs around the dominant fundamental radial pressure mode. These lower frequencies and harmonics linearly interact with the dominant fundamental radial pressure mode and its second and third overtone pressure modes, as well. Half-integer frequencies are also detected, likewise side-peak structures, demonstrating that HH Puppis is a bona-fide Blazhko star. Fourier correlations are used to derive underlying physical characteristics for HH Puppis. The most striking finding is the direct detection of gravity waves. We interpret the excitation mechanism of gravity waves in RR Lyrae stars by the penetrative convection-driving mechanism. We demonstrate that RR Lyrae stars’ pulsation is excited by several distinct mechanisms, and hence RR Lyrae stars are simultaneously g-mode and p-mode pulsators. Our discoveries make RR Lyrae stars very challenging stellar objects, and provide their potential to undergo at the same time g and p modes toward an advancement of the theory of stellar evolution and a better understanding of the universe.
ChartText: Linking Text with Charts in Documents

Recent works show that interactive documents connecting text with visualizations facilitate reading comprehension. However, creating this type of content requires specialized knowledge. We present ChartText, a method that links text with visualizations in this work. Our approach supports documents that include bar charts, line charts, and scatter plots. ChartText receives the visual encoding of the visualization and its associated text as input. It then performs the linking in two stages: The matching stage creates individual links relating simple phrases between the text and the chart. Then, it combines the individual links according to the visual channels in the grouping stage, building more meaningful connections. We use two datasets to design and evaluate our method; the first comes from web documents (24 bar charts and texts) and the second from academic documents (25 bar charts, 25 line charts, and 25 scatter plots with their texts). Our experiments show that our method obtains F1 scores of 0.50 and 0.66 on both datasets. We can also use a semi-automatic approach correcting individual links; in this case, the scores rise to 0.68 and 0.84, respectively. To show the usefulness of our technique, we implement two proofs of concept. We create interactive documents using graphic overlays in the first one, facilitating the reading experience. We use voice instead of text to annotate charts in real-time in the second. For example, in a videoconference, our technique can automatically annotate a chart following the presenter's description.
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

Multivariate time series (MTS) forecasting plays an important role in the automation and optimization of intelligent applications. It is a challenging task, as we need to consider both complex intra-variable dependencies and inter-variable dependencies. Existing works only learn temporal patterns with the help of single inter-variable dependencies. However, there are multi-scale temporal patterns in many real-world MTS. Single inter-variable dependencies make the model prefer to learn one type of prominent and shared temporal patterns. In this article, we propose a multi-scale adaptive graph neural network (MAGNN) to address the above issue. MAGNN exploits a multi-scale pyramid network to preserve the underlying temporal dependencies at different time scales. Since the inter-variable dependencies may be different under distinct time scales, an adaptive graph learning module is designed to infer the scale-specific inter-variable dependencies without pre-defined priors. Given the multi-scale feature representations and scale-specific inter-variable dependencies, a multi-scale temporal graph neural network is introduced to jointly model intra-variable dependencies and inter-variable dependencies. After that, we develop a scale-wise fusion module to effectively promote the collaboration across different time scales, and automatically capture the importance of contributed temporal patterns. Experiments on six real-world datasets demonstrate that MAGNN outperforms the state-of-the-art methods across various settings.
Deep clustering with fusion autoencoder

Embracing the deep learning techniques for representation learning in clustering research has attracted broad attention in recent years, yielding a newly developed clustering paradigm, viz. the deep clustering (DC). Typically, the DC models capitalize on autoencoders to learn the intrinsic features which facilitate the clustering process in consequence. Nowadays, a generative model named variational autoencoder (VAE) has got wide acceptance in DC studies. Nevertheless, the plain VAE is insufficient to perceive the comprehensive latent features, leading to the deteriorative clustering performance. In this paper, a novel DC method is proposed to address this issue. Specifically, the generative adversarial network and VAE are coalesced into a new autoencoder called fusion autoencoder (FAE) for discerning more discriminative representation that benefits the downstream clustering task. Besides, the FAE is implemented with the deep residual network architecture which further enhances the representation learning ability. Finally, the latent space of the FAE is transformed to an embedding space shaped by a deep dense neural network for pulling away different clusters from each other and collapsing data points within individual clusters. Experiment conducted on several image datasets demonstrate the effectiveness of the proposed DC model against the baseline methods.
How to make the toss fair in cricket?

In the sport of cricket, the side that wins the toss and has the first choice to bat or bowl can have an unfair or a critical advantage. The issue has been discussed by International Cricket Council committees, as well as several cricket experts. In this article, I outline a method to make the toss fair in cricket. The method is based on ideas from the academic fields of game theory and fair division.
Approximate the individually fair k-center with outliers

In this paper, we propose and investigate the individually fair $k$-center with outliers (IF$k$CO). In the IF$k$CO, we are given an $n$-sized vertex set in a metric space, as well as integers $k$ and $q$. At most $k$ vertices can be selected as the centers and at most $q$ vertices can be selected as the outliers. The centers are selected to serve all the not-an-outlier (i.e., served) vertices. The so-called individual fairness constraint restricts that every served vertex must have a selected center not too far way. More precisely, it is supposed that there exists at least one center among its $\lceil (n-q) / k \rceil$ closest neighbors for every served vertex. Because every center serves $(n-q) / k$ vertices on the average. The objective is to select centers and outliers, assign every served vertex to some center, so as to minimize the maximum fairness ratio over all served vertices, where the fairness ratio of a vertex is defined as the ratio between its distance with the assigned center and its distance with a $\lceil (n - q )/k \rceil_{\rm th}$ closest neighbor. As our main contribution, a 4-approximation algorithm is presented, based on which we develop an improved algorithm from a practical perspective.