Miracle-dz/UniFAR-FTUs huggingface dataset:
Resilient personality : Is grit a source of resilience ?

Resilience, the ability to function under adversity, is important in most aspects of life, but especially so in organizations (Britt et al., 2016; Caza and Milton, 2012). Workers face mounting stress from chronic issues such as employment uncertainty, growing work demands, 24/7 connectivity, and blurring work boundaries (Ashford et al., 2018; Kolb et al., 2012; Kossek and Perrigino, 2016). Moreover, acute workplace crises may be increasing in severity and frequency (Williams et al., 2017). As a result, it seems hard to overstate the importance of being able to recover from challenges at work. Indeed, resilient individuals have been found to enjoy many positive outcomes, including greater wellbeing, better mental health, higher life satisfaction, and more self-efficacy (Lee et al., 2013; Liu et al., 2017; Mayordomo et al., 2016). Resilience is also positively associated with important attitudes and behaviors such as job satisfaction, engagement, organizational commitment, and job performance (Elitharp, 2005; Kossek and Perrigino, 2016; Cooke et al., 2016; Wang et al., 2017).
Auxiliary-Classifier GAN for Malware Analysis

Generative adversarial networks (GAN) are a class of powerful machine learning techniques, where both a generative and discriminative model are trained simultaneously. GANs have been used, for example, to successfully generate"deep fake"images. A recent trend in malware research consists of treating executables as images and employing image-based analysis techniques. In this research, we generate fake malware images using auxiliary classifier GANs (AC-GAN), and we consider the effectiveness of various techniques for classifying the resulting images. Our results indicate that the resulting multiclass classification problem is challenging, yet we can obtain strong results when restricting the problem to distinguishing between real and fake samples. While the AC-GAN generated images often appear to be very similar to real malware images, we conclude that from a deep learning perspective, the AC-GAN generated samples do not rise to the level of deep fake malware images.
Optimizing ROC Curves with a Sort-Based Surrogate Loss Function for Binary Classification and Changepoint Detection

Receiver Operating Characteristic (ROC) curves are plots of true positive rate versus false positive rate which are useful for evaluating binary classification models, but difficult to use for learning since the Area Under the Curve (AUC) is non-convex. ROC curves can also be used in other problems that have false positive and true positive rates such as changepoint detection. We show that in this more general context, the ROC curve can have loops, points with highly sub-optimal error rates, and AUC greater than one. This observation motivates a new optimization objective: rather than maximizing the AUC, we would like a monotonic ROC curve with AUC=1 that avoids points with large values for Min(FP,FN). We propose a convex relaxation of this objective that results in a new surrogate loss function called the AUM, short for Area Under Min(FP, FN). Whereas previous loss functions are based on summing over all labeled examples or pairs, the AUM requires a sort and a sum over the sequence of points on the ROC curve. We show that AUM directional derivatives can be efficiently computed and used in a gradient descent learning algorithm. In our empirical study of supervised binary classification and changepoint detection problems, we show that our new AUM minimization learning algorithm results in improved AUC and comparable speed relative to previous baselines.
The Effect of the Prior and the Experimental Design on the Inference of the Precision Matrix in Gaussian Chain Graph Models

Here, we investigate whether (and how) experimental design could aid in the estimation of the precision matrix in a Gaussian chain graph model, especially the interplay between the design, the effect of the experiment and prior knowledge about the effect. We approximate the marginal posterior precision of the precision matrix via Laplace approximation under different priors: a flat prior, the conjugate prior Normal-Wishart, the unconfounded prior Normal-Matrix Generalized Inverse Gaussian (MGIG) and a general independent prior. We show that the approximated posterior precision is not a function of the design matrix for the cases of the Normal-Wishart and flat prior, but it is for the cases of the Normal-MGIG and the general independent prior. However, for the Normal-MGIG and the general independent prior, we find a sharp upper bound on the approximated posterior precision that does not involve the design matrix which translates into a bound on the information that could be extracted from a given experiment. We confirm the theoretical findings via a simulation study comparing the Stein's loss difference between random versus no experiment (design matrix equal to zero). Our findings provide practical advice for domain scientists conducting experiments to decode the relationships between a multidimensional response and a set of predictors.
Scanning tunneling microscopy study of Ni2MnGa(100) surface

Ni2MnGa(100) surface has been investigated in the premartensite and martensite phase by using scanning tunneling microscopy. The presence of twined morphology is observed in the premartensite phase for Mn excess surface which exhibit non-equispaced parallel bands in one side of the twin boundary. Moreover, in the flat region of the surface two domains of non-periodic parallel bands corresponding to the incommensurate CDW is observed. Although, stoichiometric surface also exhibits twining but the parallel bands are equispaced and have equal corrugation. Most interestingly, coexistence of twined morphology and the CDW pattern is observed in the premartensite phase for Ni excess surface which was not reported till date. In the martensite phase for Mn excess surface, incommensurate CDW is transformed to commensurate CDW corresponding to the equispaced parallel bands. In stark contrast, stoichiometric surface exhibit parallel bands that have different periodicity in different regions. Both the voltage dependent STM and STS measurement establishes that this morphology is also related to the CDW.
Real-time measurement of full spectrum polarization states in dissipative soliton fiber lasers

There has been tremendous progress in multi-parameter measurement of ultrafast laser, including optical spectrum and waveform. However, real-time measurement of full spectrum polarization state of ultrafast laser has not been reported. We simultaneously measure laser intensities of four channels by utilizing division-of-amplitude. Combining dispersive Fourier transform, dissipative soliton mode-locked by carbon nanotube can be easily detected by high-speed photodetector. By calibrating the system with tunable laser, we reconstruct the system matrix of each wavelength. According to intensity vector of dissipative soliton and the inverse matrix of the system, we get the full spectrum state of polarization in real time.
Optimum GSSK Transmission in Massive MIMO Systems Using the Box-LASSO Decoder

We propose in this work to employ the Box-LASSO, a variation of the popular LASSO method, as a low-complexity decoder in a massive multiple-input multiple-output (MIMO) wireless communication system. The Box-LASSO is mainly useful for detecting simultaneously structured signals such as signals that are known to be sparse and bounded. One modulation technique that generates essentially sparse and bounded constellation points is the so-called generalized space-shift keying (GSSK) modulation. In this direction, we derive high dimensional sharp characterizations of various performance measures of the Box-LASSO such as the mean square error, probability of support recovery, and the element error rate, under independent and identically distributed (i.i.d.) Gaussian channels that are not perfectly known. In particular, the analytical characterizations can be used to demonstrate performance improvements of the Box-LASSO as compared to the widely used standard LASSO. Then, we can use these measures to optimally tune the involved hyper-parameters of Box-LASSO such as the regularization parameter. In addition, we derive optimum power allocation and training duration schemes in a training-based massive MIMO system. Monte Carlo simulations are used to validate these premises and to show the sharpness of the derived analytical results.
Layer-by-Layer Assembled Microcapsules:Fabrication,Stimuli-responsivity,Loading and Release

Layer-by-layer(LBL) assembly of polymers and polymers/small molecules on colloidal particles forms core-shell particles, which produce hollow microcapsules with a layered structure after core removal. This article reviews the recent progress in driving forces for fabricating the LBL microcapsules, intelligent stimuli-responsivity and loading and release properties. The manipulation of the electrostatic and hydrogen bonded microcapsules by chemical crosslinking in terms of their structures and properties is first presented. Then the microcapsules based on new driving forces, i.e. covalent interaction, base pair and host-guest interaction, are introduced. The microcapsules thus obtained are responsive to pH, temperature, charge, light, electricity, magnetic field and chemical compounds. The typical loading and release properties and techniques established so far, such as direct encapsulation by the multilayers, pre-adsorption or pre-incorporation into the templates and charge controlled deposition, are discussed subsequently. Finally, aiming at the target delivery of the microcapsules and functional capsule devices, the methods for arraying the microcapsules on the substrates based on the electrostatic interaction and bioconjugation are described.
A theory of higher-order subtyping with type intervals

The calculus of Dependent Object Types (DOT) has enabled a more principled and robust implementation of Scala, but its support for type-level computation has proven insufficient. As a remedy, we propose F··ω, a rigorous theoretical foundation for Scala’s higher-kinded types. F··ω extends F<:ω with interval kinds, which afford a unified treatment of important type- and kind-level abstraction mechanisms found in Scala, such as bounded quantification, bounded operator abstractions, translucent type definitions and first-class subtyping constraints. The result is a flexible and general theory of higher-order subtyping. We prove type and kind safety of F··ω, as well as weak normalization of types and undecidability of subtyping. All our proofs are mechanized in Agda using a fully syntactic approach based on hereditary substitution.
Average-Case Communication Complexity of Statistical Problems

We study statistical problems, such as planted clique, its variants, and sparse principal component analysis in the context of average-case communication complexity. Our motivation is to understand the statistical-computational trade-offs in streaming, sketching, and query-based models. Communication complexity is the main tool for proving lower bounds in these models, yet many prior results do not hold in an average-case setting. We provide a general reduction method that preserves the input distribution for problems involving a random graph or matrix with planted structure. Then, we derive two-party and multi-party communication lower bounds for detecting or finding planted cliques, bipartite cliques, and related problems. As a consequence, we obtain new bounds on the query complexity in the edge-probe, vector-matrix-vector, matrix-vector, linear sketching, and $\mathbb{F}_2$-sketching models. Many of these results are nearly tight, and we use our techniques to provide simple proofs of some known lower bounds for the edge-probe model.
Exact Backpropagation in Binary Weighted Networks with Group Weight Transformations

Quantization based model compression serves as high performing and fast approach for inference that yields models which are highly compressed when compared to their full-precision floating point counterparts. The most extreme quantization is a 1-bit representation of parameters such that they have only two possible values, typically -1(0) or +1, enabling efficient implementation of the ubiquitous dot product using only additions. The main contribution of this work is the introduction of a method to smooth the combinatorial problem of determining a binary vector of weights to minimize the expected loss for a given objective by means of empirical risk minimization with backpropagation. This is achieved by approximating a multivariate binary state over the weights utilizing a deterministic and differentiable transformation of real-valued, continuous parameters. The proposed method adds little overhead in training, can be readily applied without any substantial modifications to the original architecture, does not introduce additional saturating nonlinearities or auxiliary losses, and does not prohibit applying other methods for binarizing the activations. Contrary to common assertions made in the literature, it is demonstrated that binary weighted networks can train well with the same standard optimization techniques and similar hyperparameter settings as their full-precision counterparts, specifically momentum SGD with large learning rates and $L_2$ regularization. To conclude experiments demonstrate the method performs remarkably well across a number of inductive image classification tasks with various architectures compared to their full-precision counterparts. The source code is publicly available at https://bitbucket.org/YanivShu/binary_weighted_networks_public.
Visual time series forecasting: an image-driven approach

Time series forecasting is essential for agents to make decisions. Traditional approaches rely on statistical methods to forecast given past numeric values. In practice, end-users often rely on visualizations such as charts and plots to reason about their forecasts. Inspired by practitioners, we re-imagine the topic by creating a novel framework to produce visual forecasts, similar to the way humans intuitively do. In this work, we leverage advances in deep learning to extend the field of time series forecasting to a visual setting. We capture input data as an image and train a model to produce the subsequent image. This approach results in predicting distributions as opposed to pointwise values. We examine various synthetic and real datasets with diverse degrees of complexity. Our experiments show that visual forecasting is effective for cyclic data but somewhat less for irregular data such as stock price. Importantly, when using image-based evaluation metrics, we find the proposed visual forecasting method to outperform various numerical baselines, including ARIMA and a numerical variation of our method. We demonstrate the benefits of incorporating vision-based approaches in forecasting tasks - both for the quality of the forecasts produced, as well as the metrics that can be used to evaluate them.
Detailed Abundances of Planet-hosting Open Clusters. The Praesepe (Beehive) Cluster

It is not yet fully understood how planet formation affects the properties of host stars, in or out of a cluster; however, abundance trends can help us understand this process. We present a detailed chemical abundance analysis of six stars in Praesepe, a planet-hosting open cluster. Pr0201 is known to host a close-in (period of 4.4 days) giant planet (mass of 0.54 M J), while the other five cluster members in our sample (Pr0133, Pr0081, Pr0208, Pr0051, and Pr0076) have no detected planets according to measurements of radial velocity. Using high-resolution echelle spectra with high signal-to-noise ratio obtained with Keck/HIRES and a novel approach to measurements of equivalent width (XSpect-EW), we derived abundances of up to 20 elements spanning a range of condensation temperatures (T C). We find a mean cluster metallicity of [Fe/H] = +0.21 ± 0.02 dex, in agreement with most previous determinations. We find most of our elements show an [X/Fe] scatter of ∼0.02–0.03 dex and conclude that our stellar sample is chemically homogeneous. The T C slope for the cluster mean abundances is consistent with zero and none of the stars in our sample exhibit individually a statistically significant T C slope. Using a planet engulfment model, we find that the planet host, Pr0201, shows no evidence of significant enrichment in its refractory elements when compared to the cluster mean that would be consistent with a planetary accretion scenario.
Searching Ternary Oxides and Chalcogenides as Positive Electrodes for Calcium Batteries

The identification of alternatives to the Lithium-ion battery architecture remains a crucial priority in the diversification of energy storage technologies. Accompanied by the low reduction potential of Ca2+/Ca, –2.87 V vs. SHE, metal-anode-based rechargeable Calcium (Ca) batteries appear competitive in terms of energy densities. However, the development of Ca-batteries lacks high-energy density intercalation cathode materials. Using first-principles methodologies, we screen a large chemical space for potential Cabased cathode chemistries, with composition of CaiTMjZk, where TM is a 1st or 2nd row transition metal and Z is oxygen, sulfur, selenium or tellurium. 10 materials are selected and their Ca intercalation properties are investigated. We identify two previously unreported promising electrode compositions: the post-spinel CaV2O4 and the 1 ar X iv :2 10 7. 01 36 5v 1 [ co nd -m at . m tr lsc i] 3 J ul 2 02 1 layered CaNb2O4, with Ca migration barriers of ∼654 meV and ∼785 meV, respectively. Finally, we analyse the geometrical features of the Ca migration pathways across the 10 materials studied and provide an updated set of design rules for the identification of good ionic conductors, especially with large mobile cations.
About the value of the two dimensional Levy's constant

We give a numerical approximation of the Lévy constant on the growth of the denominators of the best Diophantine approximations in dimension 2 with respect to the euclidean norm. This constant is expressed as an integral on a surface of dimension 7. We reduce the computation of this integral to a triple integral, whose numerical evaluation was carried out in [3].
Dissecting the Gaia HR diagram within 200 pc

We analyse the high-quality Hertzsprung–Russell diagram (HRD) derived from Gaia data release 2 for the Solar Neighbourhood. We start building an almost complete sample within 200 pc and for |b| > 25○, so as to limit the impact of known errors and artefacts in the Gaia catalogue. Particular effort is then put into improving the modelling of population of binaries, which produce two marked features in the HRD: the sequence of near-equal mass binaries along the lower main sequence, and the isolated group of hot subdwarfs. We describe a new tool, BinaPSE, to follow the evolution of interacting binaries in a way that improves the consistency with PARSEC evolutionary tracks for single stars. BinaPSE is implemented into the TRILEGAL code for the generation of ‘partial models’ for both single and binary stellar populations, taking into account the presence of resolved and unresolved binaries. We then fit the Gaia HRD via Markov chain Monte Carlo methods that search for the star formation history and an initial binary fraction (by mass) that maximize the likelihood. The main results are (i) the binary fraction derived from the lower MS is close to 0.4, while twice larger values are favoured when the upper part of the HRD is fitted; (ii) present models predict the observed numbers of hot subdwarfs to within a factor of 2; and (iii) irrespective of the prescription for the binaries, the star formation rate peaks at values $\sim \! 1.5\times 10^{-4}{\rm{M}_{\odot }}\mathrm{yr}^{-1}$ at ages slightly above 2 Gyr, and then decreases to $\sim \! 0.8\times 10^{-4}{\rm{M}_{\odot }}\mathrm{yr}^{-1}$ at very old ages.
FISTA and Extensions -- Review and New Insights

The purpose of this technical report is to review the main properties of an accelerated composite gradient (ACG) method commonly referred to as the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). In addition, we state a version of FISTA for solving both convex and strongly convex composite minimization problems and derive its iteration complexities to generate iterates satisfying various stopping criteria, including one which arises in the course of solving other composite optimization problems via inexact proximal point schemes. This report also discusses different reformulations of the convex version of FISTA and how they relate to other formulations in the literature.
Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation

In the past few years, transformers have achieved promising performances on various computer vision tasks. Unfortunately, the immense inference overhead of most existing vision transformers withholds their from being deployed on edge devices such as cell phones and smart watches. Knowledge distillation is a widely used paradigm for compressing cumbersome architectures via transferring information to a compact student. However, most of them are designed for convolutional neural networks (CNNs), which do not fully investigate the character of vision transformer (ViT). In this paper, we utilize the patch-level information and propose a fine-grained manifold distillation method. Specifically, we train a tiny student model to match a pre-trained teacher model in the patch-level manifold space. Then, we decouple the manifold matching loss into three terms with careful design to further reduce the computational costs for the patch relationship. Equipped with the proposed method, a DeiT-Tiny model containing 5M parameters achieves 76.5% top-1 accuracy on ImageNet-1k, which is +2.0% higher than previous distillation approaches. Transfer learning results on other classification benchmarks and downstream vision tasks also demonstrate the superiority of our method over the state-of-the-art algorithms.
SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification

Person re-identification via 3D skeletons is an emerging topic with great potential in security-critical applications. Existing methods typically learn body and motion features from the body-joint trajectory, whereas they lack a systematic way to model body structure and underlying relations of body components beyond the scale of body joints. In this paper, we for the first time propose a Self-supervised Multi-scale Skeleton Graph Encoding (SM-SGE) framework that comprehensively models human body, component relations, and skeleton dynamics from unlabeled skeleton graphs of various scales to learn an effective skeleton representation for person Re-ID. Specifically, we first devise multi-scale skeleton graphs with coarse-to-fine human body partitions, which enables us to model body structure and skeleton dynamics at multiple levels. Second, to mine inherent correlations between body components in skeletal motion, we propose a multi-scale graph relation network to learn structural relations between adjacent body-component nodes and collaborative relations among nodes of different scales, so as to capture more discriminative skeleton graph features. Last, we propose a novel multi-scale skeleton reconstruction mechanism to enable our framework to encode skeleton dynamics and high-level semantics from unlabeled skeleton graphs, which encourages learning a discriminative skeleton representation for person Re-ID. Extensive experiments show that SM-SGE outperforms most state-of-the-art skeleton-based methods. We further demonstrate its effectiveness on 3D skeleton data estimated from large-scale RGB videos. Our codes are open at https://github.com/Kali-Hac/SM-SGE.