Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs

The promise of increased agility, autonomy, scalability, and reusability has made the microservices architecture a de facto standard for the development of large-scale and cloud-native commercial applications. Software patterns are an important design tool, and often they are selected and combined with the goal of obtaining a set of desired quality attributes. However, from a research standpoint, many patterns have not been widely validated against industry practice, making them not much more than interesting theories. To address this, we investigated how practitioners perceive the impact of 14 patterns on 7 quality attributes. Hence, we conducted 9 semi-structured interviews to collect industry expertise regarding (1) knowledge and adoption of software patterns, (2) the perceived architectural trade-offs of patterns, and (3) metrics professionals use to measure quality attributes. We found that many of the trade-offs reported in our study matched the documentation of each respective pattern, and identified several gains and pains which have not yet been reported, leading to novel insight about microservice patterns.
Quarkyonic matter state of neutron stars

This work extends our previous study of isospin symmetric quarkyonic matter to quarkyonic neutron matter which might be relevant to the inner cores of neutron stars. The vector-isovector $\rho$ mesons are introduced to the model mainly to account for isospin density interactions, just like $\omega$ meson for baryon density interactions. The modified Lagrangian still preserves approximate chiral symmetry which could be significantly broken at lower density. And new free parameters are fixed by adopting the experimental constraints on the symmetry energy and its slope at saturation density. Eventually, the pressure, mass-radius relation, and tidal deformability are explored in advance for the quarkyonic neutron stars. While the pressure and tidal deformability are well consistent with experimental and observational restrictions, the mass-radius relation is unable to reproduce the observed two solar mass of PSR J0740+6620.
On A Class of Weakly Weighted Einstein Metrics

The notion of general weighted Ricci curvatures appears naturally in many problems. The N-Ricci curvature and the projective Ricci curvature are just two special ones with totally diﬀerent geometric meanings. In this paper, we study general weighted Ricci curvatures. We ﬁnd that Randers metrics of certain isotropic weighted Ricci curvature must have isotropic S-curvature. Then we classify them via their navigation expressions. We also ﬁnd equations that characterize Randers metrics of almost isotropic weighted Ricci curvature.
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python

PEPit is a Python package aiming at simplifying the access to worst-case analyses of a large family of first-order optimization methods possibly involving gradient, projection, proximal, or linear optimization oracles, along with their approximate, or Bregman variants. In short, PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods. The key underlying idea is to cast the problem of performing a worst-case analysis, often referred to as a performance estimation problem (PEP), as a semidefinite program (SDP) which can be solved numerically. For doing that, the package users are only required to write first-order methods nearly as they would have implemented them. The package then takes care of the SDP modelling parts, and the worst-case analysis is performed numerically via a standard solver.
Rapid and Scalable Fabrication of Low Impedance, 3D Dry Electrodes for Physiological Sensing

Medical electrophysiological sensors that can study the body and diagnose diseases depend on consistently low impedance electrode–skin interfaces. Clinical‐standard wet electrodes use hydrogels and skin abrasion to improve the interface and thus the recorded signal quality. These electrodes are challenging to self‐administer and impede in‐home care. Wearable dry electrodes are more practical; however, they show higher impedances than wet electrodes and are costly to customize. This work presents a fabrication method for rapidly producing low impedance, anatomically fit dry electrodes that do not require hydrogels. By using electroless copper and gold plating with 3D printing, biocompatible electrodes can be optimized for individuals at a fraction of the cost of existing vacuum deposition‐based techniques. Example 3D dry electrodes made with this process are evaluated alongside clinical‐standard devices in typical scenarios to compare electrical performance and comfort. The resulting dry electrodes exhibit an average electrode‐skin impedance of 66.7 kΩ at 50 Hz and DC offset of −20 mV without any hydrogel, which, when area normalized, is within the range achieved by wet electrodes without skin abrasion.
Effect of EGTS on airport taxi movements at AAS

KLM and Schiphol Group have requested an investigation into the impact of EGTS on the entire airport operations. Previous research into this technology has shown that the potential benefits for airlines, airports and society are decreased noise and emissions, fuel savings, and autonomous pushback possibilities that allow for greater versatility on the apron area. Drawbacks of the technology at this point, however, are the fact that the maximum speed of the aircraft is limited while utilizing EGTS, possibly impacting other airport traffic and even leading to taxiway congestion. Taking this problem statement as a starting point, this research is aimed at generating an impact study into the effects of EGTS on the airport operations with all the key stakeholders. In order to achieve this, the traffic situation at Schiphol airport is simulated in scenarios with and without EGTS traffic, results are compared and verified, and stakeholder discussions are subsequently held to validate the results. The main research aim is to provide Schiphol and KLM with a tangible tool that translates the concerns and interests of the main stakeholders into a value model, that can aid them in their further decision making process regarding the implementation of EGTS.
PrintsGAN: Synthetic Fingerprint Generator

A major impediment to researchers working in the area of fingerprint recognition is the lack of publicly available, large-scale, fingerprint datasets. The publicly available datasets that do exist contain very few identities and impressions per finger. This limits research on a number of topics, including e.g., using deep networks to learn fixed length fingerprint embeddings. Therefore, we propose PrintsGAN, a synthetic fingerprint generator capable of generating unique fingerprints along with multiple impressions for a given fingerprint. Using PrintsGAN, we synthesize a database of 525k fingerprints (35K distinct fingers, each with 15 impressions). Next, we show the utility of the PrintsGAN generated dataset by training a deep network to extract a fixed-length embedding from a fingerprint. In particular, an embedding model trained on our synthetic fingerprints and fine-tuned on a small number of publicly available real fingerprints (25K prints from NIST SD 302) obtains a TAR of 87.03% @ FAR=0.01% on the NIST SD4 database (a boost from TAR=73.37% when only trained on NIST SD 302). Prevailing synthetic fingerprint generation methods do not enable such performance gains due to i) lack of realism or ii) inability to generate multiple impressions per finger. Our dataset is released to the public: https://biometrics.cse. msu.edu/Publications/Databases/MSU_PrintsGAN/.
Learning Fair Node Representations with Graph Counterfactual Fairness

Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender. Among the many existing fairness notions, counterfactual fairness measures the model fairness from a causal perspective by comparing the predictions of each individual from the original data and the counterfactuals. In counterfactuals, the sensitive attribute values of this individual had been modified. Recently, a few works extend counterfactual fairness to graph data, but most of them neglect the following facts that can lead to biases: 1) the sensitive attributes of each node's neighbors may causally affect the prediction w.r. t. this node; 2) the sensitive attributes may causally affect other features and the graph structure. To tackle these issues, in this paper, we propose a novel fairness notion - graph counterfactual fairness, which considers the biases led by the above facts. To learn node representations towards graph counterfactual fairness, we propose a novel framework based on counterfactual data augmentation. In this framework, we generate counterfactuals corresponding to perturbations on each node's and their neighbors' sensitive attributes. Then we enforce fairness by minimizing the discrepancy between the representations learned from the original graph and the counterfactuals for each node. Experiments on both synthetic and real-world graphs show that our framework outperforms the state-of-the-art baselines in graph counterfactual fairness, and also achieves comparable prediction performance.
Directional variations of cosmological parameters from the 
Planck
 CMB data

Recent observations suggest that there are violations of the isotropy of the universe at large scales, an important part of the cosmological principle. In this paper, we use the Cosmic Microwave Background (CMB) data to search for spatial variations of the cosmological parameters in the ΛCDM model. We fit the Planck temperature angular power spectrum CTT ` for 48 different halfskies, centering on 48 different directions, to search for directional dependences of the standard cosmological parameters. There are 3(2)σ-level directional variations in Ωbh 2, Ωch 2, ns, 100θMC, and H0 (τ and ln(10 As)). Furthermore, the directional distributions of the parameters follow a dipole form to good approximation. The Bayes factor between the isotropic and anisotropic hypotheses is 0.0041, strongly disfavouring the former. The best-fit dipole axes for Ωbh 2, Ωch 2, ns, 100θMC, and Ase −2τ all generally align with the mean direction of V ≡ (b = −5.6 −17.4°, l = 48.8 −14.4°), which is roughly perpendicular to the dipole of the variation in fine structure constant, and is about 45° to the directions of the CMB kinematic dipole, CMB parity asymmetry, and polarization of QSOs. Our results suggest either significant violation of the cosmological principle, or previously unknown systematic errors in the standard CMB analysis.
Theoretical and Observational Evidence for Coriolis Effects in Coronal Magnetic Fields via Direct Current Driven Flaring Events

All stars produce explosive surface events such as flares and coronal mass ejections. These events are driven by the release of energy stored in coronal magnetic fields, generated by the stellar dynamo. However, it remains unclear if the energy deposition in the magnetic fields is driven by direct or alternating currents. Recently, we presented observational measurements of the flare intensity distributions for a sample of ∼105 stars across the main sequence observed by TESS, all of which exhibited power-law distributions similar to those observed in the Sun, albeit with varying slopes. Here we investigate the mechanisms required to produce such a distribution of flaring events via direct current energy deposition, in which coronal magnetic fields braid, reconnect, and produce flares. We adopt a topological model for this process, which produces a power-law distribution of energetic flaring events. We expand this model to include the Coriolis effect, which we demonstrate produces a shallower distribution of flare energies in stars that rotate more rapidly (corresponding to a weaker decline in occurrence rates toward increasing flare energies). We present tentative evidence for the predicted rotation-power-law index correlation in the observations. We advocate for future observations of stellar flares that would improve our measurements of the power-law exponents, and yield key insights into the underlying dynamo mechanisms that underpin the self-similar flare intensity distributions.
Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms

We consider a platform's problem of collecting data from privacy sensitive users to estimate an underlying parameter of interest. We formulate this question as a Bayesian-optimal mechanism design problem, in which an individual can share her (verifiable) data in exchange for a monetary reward or services, but at the same time has a (private) heterogeneous privacy cost which we quantify using differential privacy. We consider two popular differential privacy settings for providing privacy guarantees for the users: central and local. In both settings, we establish minimax lower bounds for the estimation error and derive (near) optimal estimators for given heterogeneous privacy loss levels for users. Building on this characterization, we pose the mechanism design problem as the optimal selection of an estimator and payments that will elicit truthful reporting of users' privacy sensitivities. Under a regularity condition on the distribution of privacy sensitivities we develop efficient algorithmic mechanisms to solve this problem in both privacy settings. Our mechanism in the central setting can be implemented in time O (n log n) where n is the number of users and our mechanism in the local setting admits a Polynomial Time Approximation Scheme (PTAS). The full paper is available at: https://arxiv.org/abs/2201.03968
Emotion Estimation from EEG - A Dual Deep Learning Approach Combined with Saliency

—Emotion estimation is an active ﬁeld of research that has an important impact on the interaction between human and computer. Among the different modality to assess emotion, electroencephalogram (EEG) representing the electrical brain activity presented motivating results during the last decade. Emotion estimation from EEG could help in the diagnosis or rehabilitation of certain diseases. In this paper, we propose a dual method considering the physiological knowledge deﬁned by specialists combined with novel deep learning (DL) models initially dedicated to computer vision. The joint learning has been enhanced with model saliency analysis. To present a global approach, the model has been evaluated on four publicly available datasets and achieves similar results to the state-of-the-art approaches and outperforming results for two of the proposed datasets with a lower standard deviation that reﬂects higher stability. For sake of reproducibility, the codes and models proposed in this paper are available at github.com/VDelv/Emotion-EEG.
Stratified Graph Spectra

In classic graph signal processing, given a real-valued graph signal, its graph Fourier transform is typically defined as the series of inner products between the signal and each eigenvector of the graph Laplacian. Unfortunately, this definition is not mathematically valid in the cases of vector-valued graph signals which however are typical operands in the stateof-the-art graph learning modeling and analyses. Seeking a generalized transformation decoding the magnitudes of eigencomponents from vector-valued signals is thus the main objective of this paper. Several attempts are explored, and also it is found that performing the transformation at hierarchical levels of adjacency help profile the spectral characteristics of signals more insightfully. The proposed methods are introduced as a new tool assisting on diagnosing and profiling behaviors of graph learning models.
Low-Energy Interactions of Mesons with Participation of the First Radially Excited States in U(3) × U(3) NJL Model

The U(3)×U(3) chiral symmetric NJL model describing pseudoscalar, vector, and axial-vector mesons in both the ground state and first radially excited states is shortly presented in this review. In this model, it is possible to describe a large number of low-energy interactions of mesons, τ lepton decays into mesons, and processes of meson production in electron–positron annihilations in satisfactory agreement with the experiments. In describing a number of processes, it turned out to be necessary to take into account the interactions of mesons in the final state.
Competing Mutual Information Constraints with Stochastic Competition-based Activations for Learning Diversified Representations

This work aims to address the long-established problem of learning diversified representations. To this end, we combine information-theoretic arguments with stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA) units. In this context, we ditch the conventional deep architectures commonly used in Representation Learning, that rely on non-linear activations; instead, we replace them with sets of locally and stochastically competing linear units. In this setting, each network layer yields sparse outputs, determined by the outcome of the competition between units that are organized into blocks of competitors. We adopt stochastic arguments for the competition mechanism, which perform posterior sampling to determine the winner of each block. We further endow the considered networks with the ability to infer the sub-part of the network that is essential for modeling the data at hand; we impose appropriate stick-breaking priors to this end. To further enrich the information of the emerging representations, we resort to information-theoretic principles, namely the Information Competing Process (ICP). Then, all the components are tied together under the stochastic Variational Bayes framework for inference. We perform a thorough experimental investigation for our approach using benchmark datasets on image classification. As we experimentally show, the resulting networks yield significant discriminative representation learning abilities. In addition, the introduced paradigm allows for a principled investigation mechanism of the emerging intermediate network representations.
Positive Time Series Regression Models

In this paper we discuss dynamic ARMA-type regression models for time series taking values in $(0,\infty)$. In the proposed model, the conditional mean is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and link functions. We introduce the new class of models and discuss partial maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting.
Fitting AGN/Galaxy X-Ray-to-radio SEDs with CIGALE and Improvement of the Code

Modern and future surveys effectively provide a panchromatic view for large numbers of extragalactic objects. Consistently modeling these multiwavelength survey data is a critical but challenging task for extragalactic studies. The Code Investigating GALaxy Emission (cigale) is an efficient python code for spectral energy distribution (SED) fitting of galaxies and active galactic nuclei (AGNs). Recently, a major extension of cigale (named x-cigale) has been developed to account for AGN/galaxy X-ray emission and improve AGN modeling at UV-to-IR wavelengths. Here, we apply x-cigale to different samples, including Cosmological Evolution Survey (COSMOS) spectroscopic type 2 AGNs, Chandra Deep Field-South X-ray detected normal galaxies, Sloan Digital Sky Survey quasars, and COSMOS radio objects. From these tests, we identify several weaknesses of x-cigale and improve the code accordingly. These improvements are mainly related to AGN intrinsic X-ray anisotropy, X-ray binary emission, AGN accretion-disk SED shape, and AGN radio emission. These updates improve the fit quality and allow for new interpretation of the results, based on which we discuss physical implications. For example, we find that AGN intrinsic X-ray anisotropy is moderate, and can be modeled as LX(θ)∝1+cosθ , where θ is the viewing angle measured from the AGN axis. We merge the new code into the major branch of cigale, and publicly release this new version as cigale v2022.0 on https://cigale.lam. fr.
Improved Neural Distinguishers with (Related-key) Differentials: Applications in SIMON and SIMECK

In CRYPTO 2019, Gohr made a pioneering attempt, and successfully applied deep learning to the diﬀerential cryptanalysis against NSA block cipher Speck 32/64, achieving higher accuracy than the pure diﬀerential distinguishers. By its very nature, mining eﬀective features in data plays a crucial role in data-driven deep learning. In this paper, in addition to considering the integrity of the information from the training data of the ciphertext pair, domain knowledge about the structure of diﬀerential cryptanalysis is also considered into the training process of deep learning to improve the performance. Besides, based on the SAT/SMT solvers, we ﬁnd other high probability compatible diﬀeren-tial characteristics which eﬀectively improve the performance compared with previous work. We build neural distinguishers (NDs) and related-key neural distinguishers (RKNDs) against Simon and Simeck . The ND and RKND for Simon 32/64 reach 11, 11-round with an accuracy of 59.55% and 97.90%, respectively. For Simon 64/128, the ND achieve an accuracy of 60.32% in 13-round, while it is 95.49% for the RKND. For Simeck
Linear Stability of Black Holes and Naked Singularities

A review of the current status of the linear stability of black holes and naked singularities is given. The standard modal approach, that takes advantage of the background symmetries and analyze separately the harmonic components of linear perturbations, is briefly introduced and used to prove that the naked singularities in the Kerr–Newman family, as well as the inner black hole regions beyond Cauchy horizons, are unstable and therefore unphysical. The proofs require a treatment of the boundary condition at the timelike boundary, which is given in detail. The nonmodal linear stability concept is then introduced, and used to prove that the domain of outer communications of a Schwarzschild black hole with a non-negative cosmological constant satisfies this stronger stability condition, which rules out transient growths of perturbations, and also to show that the perturbed black hole settles into a slowly rotating Kerr black hole. The encoding of the perturbation fields in gauge invariant curvature scalars and the effects of the perturbation on the geometry of the spacetime is discussed. These notes follow from a course delivered at the V José Plínio Baptista School of Cosmology, held at Guarapari (Espírito Santo) Brazil, from 30 September to 5 October 2021.
Feature Space Hijacking Attacks against Differentially Private Split Learning

Split learning and differential privacy are technologies with growing potential to help with privacy-compliant advanced analytics on distributed datasets. Attacks against split learning are an important evaluation tool and have been receiving increased research attention recently. This work's contribution is applying a recent feature space hijacking attack (FSHA) to the learning process of a split neural network enhanced with differential privacy (DP), using a client-side off-the-shelf DP optimizer. The FSHA attack obtains client's private data reconstruction with low error rates at arbitrarily set DP epsilon levels. We also experiment with dimensionality reduction as a potential attack risk mitigation and show that it might help to some extent. We discuss the reasons why differential privacy is not an effective protection in this setting and mention potential other risk mitigation methods.