Miracle-dz/UniFAR-FTUs huggingface dataset:
Imbalance Learning for Variable Star Classification

The accurate automated classification of variable stars into their respective subtypes is difficult. Machine learning–based solutions often fall foul of the imbalanced learning problem, which causes poor generalization performance in practice, especially on rare variable star subtypes. In previous work, we attempted to overcome such deficiencies via the development of a hierarchical machine learning classifier. This ‘algorithm-level’ approach to tackling imbalance yielded promising results on Catalina Real-Time Survey (CRTS) data, outperforming the binary and multiclass classification schemes previously applied in this area. In this work, we attempt to further improve hierarchical classification performance by applying ‘data-level’ approaches to directly augment the training data so that they better describe underrepresented classes. We apply and report results for three data augmentation methods in particular: Randomly Augmented Sampled Light curves from magnitude Error (RASLE), augmenting light curves with Gaussian Process modelling (GpFit) and the Synthetic Minority Oversampling Technique (SMOTE). When combining the ‘algorithm-level’ (i.e. the hierarchical scheme) together with the ‘data-level’ approach, we further improve variable star classification accuracy by 1–4 per cent. We found that a higher classification rate is obtained when using GpFit in the hierarchical model. Further improvement of the metric scores requires a better standard set of correctly identified variable stars, and perhaps enhanced features are needed.
On the Formation of PSR J1640+2224: A Neutron Star Born Massive?

PSR J1640+2224 is a binary millisecond pulsar (BMSP) with a white dwarf (WD) companion. Recent observations indicate that the WD is very likely to be a ∼0.7 M⊙ CO WD. Thus, the BMSP should have evolved from an intermediate-mass X-ray binary (IMXB). However, previous investigations on IMXB evolution predict that the orbital periods of the resultant BMSPs are generally < 40 days, in contrast with the 175 day orbital period of PSR J1640+2224. In this paper, we explore the influence of the mass of the neutron star (NS) and the chemical compositions of the companion star on the formation of BMSPs. Our results show that the final orbital period becomes longer with increasing NS mass, and the WD mass becomes larger with decreasing metallicity. In particular, to reproduce the properties of PSR J1640+2224, the NS was likely born massive (>2.0 M⊙).
Mass transfer of low-mass binaries and chemical anomalies among unevolved stars in globular clusters

While it is well known that mass transfer in binaries can pollute the surfaces of the accretors, it is still unclear whether this mechanism can reproduce the observed chemical inhomogeneities in globular clusters. We study the surface abundances of the accretors in low-mass binaries, as a first step towards understanding whether mass transfer in low-mass binaries is one of the potential origins of the aforementioned abundance anomalies in globular clusters. We use the Modules for Experiments in Stellar Astrophysics code to calculate binary evolutionary models with different initial donor masses between 0.9 and 1.9 $ \rm {M}_\odot$ for an initial metallicity of Z = 0.0034. The results show that in some low-mass binary systems, the accretors exhibit peculiar chemical patterns when they are still unevolved stars, e.g. C and O depletion; Na and N enhancement; constant Mg, Al and C+N+O. The abundance patterns of the accretors are significantly different from their initial abundances (or that of normal single stars), and can match the observed populations. These abundance patterns strongly depend not only on the initial parameters of binaries (donor mass, mass ratio, and orbital period), but also on the assumptions regarding mass-transfer efficiency and angular momentum loss. These results support the hypothesis that mass transfer in low-mass binaries is at least partly responsible for the unevolved anomalous stars in globular clusters. More work on binary evolutionary models and binary population synthesis is required to fully evaluate the contribution of this scenario.
A multiwavelength view of the open cluster NGC 2527: discovery of active stars

Star clusters are ideal platforms for categorising X-ray emitting stars and to study X-ray emission as a function of stellar age and activity. We present a comprehensive study of an open star cluster, NGC2527, by combining data from XMM-UVOT-Gaia. Cluster membership of stars and their photometry are taken from Gaia and cross-matched with XMM and UVOT detections. We estimate the age of NGC2527 as ~630 Myr, reddening as E(B-V)=0.13 mag, and a distance of 642+/-30 pc using PARSEC isochrones. We detect 5 sub-subgiants and 5 bandgap stars, which defy single star evolution. We estimate the temperature, mass, radius, and luminosity of 53 single stars and 10 potential binary stars using a python code which fits single and composite Kurucz spectra to broad-band Spectral Energy Distribution. Among the 12 X-ray emitting members, we find 5 are potential RS CVn type binaries, 2 are potential FK Comae type of RGB stars, and 5 are main sequence (MS) stars with high coronal activity. Members with strong UV emission comprise of 1 RGB star, and several MS stars with UV excess suggestive of chromospheric activity. Based on comparison with other clusters, we tentatively suggest that X-ray luminosity of both RS CVn and contact binaries increases with age suggesting more active binaries are present in older clusters as compared to younger clusters. This study suggests the possible presence of W UMa and FK Comae type stars in younger (age~630 Myr) clusters.
Distributionally Robust Chance Constrained Programming with Generative Adversarial Networks (GANs)

This paper presents a novel deep learning based data-driven optimization method. A novel generative adversarial network (GAN) based data-driven distributionally robust chance constrained programming framework is proposed. GAN is applied to fully extract distributional information from historical data in a nonparametric and unsupervised way without a priori approximation or assumption. Since GAN utilizes deep neural networks, complicated data distributions and modes can be learned, and it can model uncertainty efficiently and accurately. Distributionally robust chance constrained programming takes into consideration ambiguous probability distributions of uncertain parameters. To tackle the computational challenges, sample average approximation method is adopted, and the required data samples are generated by GAN in an end-to-end way through the differentiable networks. The proposed framework is then applied to supply chain optimization under demand uncertainty. The applicability of the proposed approach is illustrated through a county-level case study of a spatially explicit biofuel supply chain in Illinois.
‘Do the Right Thing’ for Whom? An Experiment on Ingroup Favouritism, Group Assorting and Moral Suasion

In this paper we investigate the effect of moral suasion on ingroup favouritism. We report a well-powered, pre-registered, two-stage 2x2 mixed-design experiment. In the first stage, groups are formed on the basis of how participants answer to a set of questions, concerning non-morally relevant issues in one treatment (assortativity on non-moral preferences), and morally relevant issues in another treatment (assortativity on moral preferences). In the second stage, participants choose how to split a given amount of money between participants of their own group and participants of the other group, first in the baseline setting and then in a setting where they are told to do what they believe to be morally right (moral suasion). Our main results are: (i) in the baseline, participants tend to favour their own group to a greater extent when groups are assorted according to moral preferences, compared to when they are assorted according to non-moral preferences; (ii) the net effect of moral suasion is to decrease ingroup favouritism, but there is also a non-negligible proportion of participants for whom moral suasion increases ingroup favouritism; (iii) the effect of moral suasion is substantially stable across group assortativity and four pre-registered individual characteristics (gender, political orientation, religiosity, pro-life vs pro-choice ethical convictions).
Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration

This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes.
Metaphoric Paraphrase Generation

This work describes the task of metaphoric paraphrase generation, in which we are given a literal sentence and are charged with generating a metaphoric paraphrase. We propose two different models for this task: a lexical replacement baseline and a novel sequence to sequence model, 'metaphor masking', that generates free metaphoric paraphrases. We use crowdsourcing to evaluate our results, as well as developing an automatic metric for evaluating metaphoric paraphrases. We show that while the lexical replacement baseline is capable of producing accurate paraphrases, they often lack metaphoricity, while our metaphor masking model excels in generating metaphoric sentences while performing nearly as well with regard to fluency and paraphrase quality.
MINA: Convex Mixed-Integer Programming for Non-Rigid Shape Alignment

We present a convex mixed-integer programming formulation for non-rigid shape matching. To this end, we propose a novel shape deformation model based on an efficient low-dimensional discrete model, so that finding a globally optimal solution is tractable in (most) practical cases. Our approach combines several favourable properties, namely it is independent of the initialisation, it is much more efficient to solve to global optimality compared to analogous quadratic assignment problem formulations, and it is highly flexible in terms of the variants of matching problems it can handle. Experimentally we demonstrate that our approach outperforms existing methods for sparse shape matching, that it can be used for initialising dense shape matching methods, and we showcase its flexibility on several examples.
An Improved Generic ER Schema for Conceptual Modeling of Information Systems

The Entity-Relationship (ER) model is widely used for creating ER schemas for modeling application domains in the field of Information Systems development. However, when an ER schema is transformed to a Relational Database Schema (RDS), some important information on the ER schema may not be represented meaningfully on the RDS. This causes a loss of information during the transformation process. Although, several previous researches have proposed solutions to remedy the situation, the problem still exists. Thus, in this on-going research, we wish to improve the proposed solutions and maximize information preservation in the ER to relational transformation process. Cardinality ratio constraints, role names, composite attributes, and certain relationship types are among the information frequently lost in the transformation process. Deficiencies in the ER model and the transformation method seems to cause this situation. We take the view that if the information lost is resolved; a one-to-one mapping should exist from the ER schema to its RDS. We modified the ER model and the transformation algorithm following a heuristic research method with a view to eliminating the deficiencies and thereby achieving a one-to-one mapping. We should show that the mapping exists for any real-world application. We create a generic ER schema - an ER schema that represents any phenomena in symbolic form - and use it to show that a one-to-one mapping exists for any real-world application. In this paper, we explore our generic ER schema and its advantages over its predecessors in view of representing any real-world application.
Learning Directly from Grammar Compressed Text

Neural networks using numerous text data have been successfully applied to a variety of tasks. While massive text data is usually compressed using techniques such as grammar compression, almost all of the previous machine learning methods assume already decompressed sequence data as their input. In this paper, we propose a method to directly apply neural sequence models to text data compressed with grammar compression algorithms without decompression. To encode the unique symbols that appear in compression rules, we introduce composer modules to incrementally encode the symbols into vector representations. Through experiments on real datasets, we empirically showed that the proposal model can achieve both memory and computational efficiency while maintaining moderate performance.
Effects of device geometry and material properties on dielectric losses in superconducting coplanar-waveguide resonators

Superconducting coplanar-waveguide (CPW) resonators are one of the key devices in circuit quantum electrodynamics (cQED). Their performance can be limited by dielectric losses in the substrate and in the material interfaces. Reliable modeling is required to aid in the design of low-loss CPW structures for cQED. We analyze the geometric dependence of the dielectric losses in CPW structures using finite-element modeling of the participation ratios of the lossy regions. In a practical scenario, uncertainties in the the dielectric constants and loss tangents of these regions introduce uncertainties in the theoretically predicted participation ratios. We present a method for combining loss simulations with measurements of two-level-system-limited quality factors and resonance frequencies of CPW resonators. Namely, we solve an inverse problem to find model parameters producing the measured values. High quality factors are obtainable by properly designing the cross-sectional geometries of the CPW structures, but more accurate modeling and design methods for low-loss CPW resonators are called for major future improvements. Our nonlinear optimization methodology for solving the aforementioned inverse problem is a step in this direction.
Affinity guided Geometric Semi-Supervised Metric Learning

In this paper, we address the semi-supervised metric learning problem, where we learn a distance metric using very few labeled examples, and additionally available unlabeled data. To address the limitations of existing semi-supervised approaches, we integrate some of the best practices across metric learning, to achieve the state-of-the-art in the semi-supervised setting. In particular, we make use of a graph-based approach to propagate the affinities or similarities among the limited labeled pairs to the unlabeled data. Considering the neighborhood of an example, we take into account the propagated affinities to mine triplet constraints. An angular loss is imposed on these triplets to learn a metric. Additionally, we impose orthogonality on the parameters of the learned embedding to avoid a model collapse. In contrast to existing approaches, we propose a stochastic approach that scales well to large-scale datasets. We outperform various semi-supervised metric learning approaches on a number of benchmark datasets.
Supervised Enhanced Soft Subspace Clustering (SESSC) for TSK Fuzzy Classifiers

Fuzzy c-means based clustering algorithms are frequently used for Takagi-Sugeno-Kang (TSK) fuzzy classifier antecedent parameter estimation. One rule is initialized from each cluster. However, most of these clustering algorithms are unsupervised, which waste valuable label information in the training data. This paper proposes a supervised enhanced soft subspace clustering (SESSC) algorithm, which considers simultaneously the within-cluster compactness, between-cluster separation, and label information in clustering. It can effectively deal with high-dimensional data, be used as a classifier alone, or be integrated into a TSK fuzzy classifier to further improve its performance. Experiments on nine UCI datasets from various application domains demonstrated that SESSC based initialization outperformed other clustering approaches, especially when the number of rules is small.
KeypointNet: A Large-Scale 3D Keypoint Dataset Aggregated From Numerous Human Annotations

Detecting 3D objects keypoints is ofgreat interest to the areas of both graphics and computer vision. There have been several 2D and 3D keypoint datasets aiming to address this problem in a data-driven way. These datasets, however, either lack scalability or bring ambiguity to the definition of keypoints. Therefore, we present KeypointNet: the first large-scale and diverse 3D keypoint dataset that contains 83,231 keypoints and 8,329 3D models from 16 object categories, by leveraging numerous human annotations. To handle the inconsistency between annotations from different people, we propose a novel method to aggregate these keypoints automatically, through minimization of a fidelity loss. Finally, ten state-of-the-art methods are benchmarked on our proposed dataset.
One-dimensional Janus fluids. Exact solution and mapping from the quenched to the annealed system

The equilibrium properties of a Janus fluid confined to a one-dimensional channel are exactly derived. The fluid is made of particles with two faces (active and passive), so that the pair interaction is that of hard spheres, except if the two active faces are in front of each other, in which case the interaction has a square-well attractive tail. Our exact solution refers to quenched systems (i.e., each particle has a fixed face orientation), but we argue by means of statistical–mechanical tools that the results also apply to annealed systems (i.e., each particle can flip its orientation) in the thermodynamic limit. Comparison between theoretical results and Monte Carlo simulations for quenched and annealed systems, respectively, shows an excellent agreement.
Artificial intelligence for high-throughput discovery of topological insulators: The example of alloyed tetradymites

Significant advances have been made in predicting new topological materials using high-throughput empirical descriptors or symmetry-based indicators. To date, these approaches have been applied to materials in existing databases, and are severely limited to systems with well-defined symmetries, leaving a much larger materials space unexplored. Using tetradymites as a prototypical class of examples, we uncover a novel two-dimensional descriptor by applying an artificial intelligence (AI) based approach for fast and reliable identification of the topological characters of a drastically expanded range of materials, without prior determination of their specific symmetries and detailed band structures. By leveraging this descriptor that contains only the atomic number and electronegativity of the constituent species, we have readily scanned a huge number of alloys in the tetradymite family. Strikingly, nearly half of which are identified to be topological insulators, revealing a much larger territory of the topological materials world. The present work also attests the increasingly important role of such AI-based approaches in modern materials discovery.
Formal Synthesis of Monitoring and Detection Systems for Secure CPS Implementations

We consider the problem of securing a given control loop implementation of a cyber-physical system (CPS) in the presence of Man-in-the-Middle attacks on data exchange between plant and controller over a compromised network. To this end, there exists various detection schemes which provide mathemat¬ical guarantees against such attacks for the theoretical control model. However, such guarantees may not hold for the actual control software implementation. In this article, we propose a formal approach towards synthesizing attack detectors with varying thresholds which can prevent performance degrading stealthy attacks while minimizing false alarms.
Entanglement-based quantum deep learning

Classical deep learning algorithms have aroused great interest in both academia and industry for their utility in image recognition, language translation, decision-making problems and more. In this work, we have provided a quantum deep learning scheme based on multi-qubit entanglement states, including computation and training of neural network in full quantum process. In the course of training, efficient calculation of the distance between unknown unit vector and known unit vector has been realized by proper measurement based on the Greenberger–Horne–Zeilinger entanglement states. An exponential speedup over classical algorithms has been demonstrated. In the process of computation, quantum scheme corresponding to multi-layer feedforward neural network has been provided. We have shown the utility of our scheme using Iris dataset. The extensibility of the present scheme to different types of model has also been analyzed.
C-CoCoA: A Continuous Cooperative Constraint Approximation Algorithm to Solve Functional DCOPs

Distributed Constraint Optimization Problems (DCOPs) have been widely used to coordinate interactions (i.e. constraints) in cooperative multi-agent systems. The traditional DCOP model assumes that variables owned by the agents can take only discrete values and constraints' cost functions are defined for every possible value assignment of a set of variables. While this formulation is often reasonable, there are many applications where the variables are continuous decision variables and constraints are in functional form. To overcome this limitation, Functional DCOP (F-DCOP) model is proposed that is able to model problems with continuous variables. The existing F-DCOPs algorithms experience huge computation and communication overhead. This paper applies continuous non-linear optimization methods on Cooperative Constraint Approximation (CoCoA) algorithm. We empirically show that our algorithm is able to provide high-quality solutions at the expense of smaller communication cost and execution time compared to the existing F-DCOP algorithms.