Miracle-dz/UniFAR-FTUs huggingface dataset:
Reaction to political and socioeconomic transition and self-perceived health status in the adult population of Gjilan region, Kosovo

Aim: The objective of our study was to assess the association of reaction to political and socioeconomic transition with self-perceived general health status in adult men and women in a region of Kosovo, a post-war country in the Western Balkans which has proclaimed independence in 2008. Methods: This was a cross-sectional study carried out in Gjilan region of Kosovo in 2014, including a representative sample of 867 primary health care users aged ≥35 years (419 men aged 54.3±10.9 years and 448 women aged 54.0±10.1 years; overall response rate: 87%). Reaction to political and socioeconomic aspects of transition was assessed by a three-item scale (trichotomized in the analysis into positive attitude, intermediate attitude, and negative attitude towards transition), which was previously used in the neighbouring Albania. Self-reported health status was measured on a 5-point scale which was dichotomized in the analysis into “good” vs. “poor” health. Demographic and socioeconomic data were also collected. Binary logistic regression was used to assess the association of reaction to transition with self-rated health status. Results: In crude/unadjusted models, negative attitude to transition was a “strong” predictor of poor self-perceived health (OR=2.5, 95%CI=1.7-3.8). Upon multivariable adjustment for all the demographic factors and socioeconomic characteristics, the association was attenuated and was only borderline statistically significant (OR=1.6, 95%CI=1.0-2.6, P=0.07). Conclusion: Our findings indicate an important association between reaction to transition and self-perceived health status in the adult population of the newly independent Kosovo. Policymakers and decision-makers in post-war countries such as Kosovo should be aware of the health effects of attitudes towards political and socioeconomic aspects of transition, which is seemingly an important psychosocial factor.
Revocable Federated Learning: A Benchmark of Federated Forest

A learning federation is composed of multiple participants who use the federated learning technique to collaboratively train a machine learning model without directly revealing the local data. Nevertheless, the existing federated learning frameworks have a serious defect that even a participant is revoked, its data are still remembered by the trained model. In a company-level cooperation, allowing the remaining companies to use a trained model that contains the memories from a revoked company is obviously unacceptable, because it can lead to a big conflict of interest. Therefore, we emphatically discuss the participant revocation problem of federated learning and design a revocable federated random forest (RF) framework, RevFRF, to further illustrate the concept of revocable federated learning. In RevFRF, we first define the security problems to be resolved by a revocable federated RF. Then, a suite of homomorphic encryption based secure protocols are designed for federated RF construction, prediction and revocation. Through theoretical analysis and experiments, we show that the protocols can securely and efficiently implement collaborative training of an RF and ensure that the memories of a revoked participant in the trained RF are securely removed.
Nonlinear system identification in Sobolev spaces

We consider the problem of approximating an unknown function from experimental data, while approximating at the same time its derivatives. Solving this problem is useful, for instance, in the context of nonlinear system identification, for obtaining models that are more accurate and reliable than the traditional ones based on plain function approximation. Indeed, models identified by accounting for the derivatives can provide improved performance in several endeavours, such as in multi-step prediction, simulation, Nonlinear Model Predictive Control, and control design in general. In this paper, we propose a novel approach based on convex optimisation, allowing us to solve the aforementioned identification problem. We develop an optimality analysis, showing that models derived using this approach enjoy suitable optimality properties in Sobolev spaces. The optimality analysis also leads to the derivation of tight uncertainty bounds on the unknown function and its derivatives. We demonstrate the effectiveness of the approach with three numerical examples, concerned with univariate function identification, multi-step prediction of the Chua chaotic circuit, and control of the inverted pendulum.
Implementation of a Transmon Qubit Using Superconducting Granular Aluminum

The high kinetic inductance offered by granular aluminum (grAl) has recently been employed for linear inductors in superconducting high-impedance qubits and kinetic inductance detectors. Due to its large critical current density compared to typical Josephson junctions, its resilience to external magnetic fields, and its low dissipation, grAl may also provide a robust source of non-linearity for strongly driven quantum circuits, topological superconductivity, and hybrid systems. Having said that, can the grAl non-linearity be sufficient to build a qubit? Here we show that a small grAl volume ($10 \times 200 \times 500 \,\mathrm{nm^3}$) shunted by a thin film aluminum capacitor results in a microwave oscillator with anharmonicity $\alpha$ two orders of magnitude larger than its spectral linewidth $\Gamma_{01}$, effectively forming a transmon qubit. With increasing drive power, we observe several multi-photon transitions starting from the ground state, from which we extract $\alpha = 2 \pi \times 4.48\,\mathrm{MHz}$. Resonance fluorescence measurements of the $|0> \rightarrow |1>$ transition yield an intrinsic qubit linewidth $\gamma = 2 \pi \times 10\,\mathrm{kHz}$, corresponding to a lifetime of $16\,\mathrm{\mu s}$. This linewidth remains below $2 \pi \times 150\,\mathrm{kHz}$ for in-plane magnetic fields up to $\sim70\,\mathrm{mT}$.
Not Enough Data? Deep Learning to the Rescue!

Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks. We use a powerful pre-trained neural network model to artificially synthesize new labeled data for supervised learning. We mainly focus on cases with scarce labeled data. Our method, referred to as language-model-based data augmentation (LAMBADA), involves fine-tuning a state-of-the-art language generator to a specific task through an initial training phase on the existing (usually small) labeled data. Using the fine-tuned model and given a class label, new sentences for the class are generated. Our process then filters these new sentences by using a classifier trained on the original data. In a series of experiments, we show that LAMBADA improves classifiers' performance on a variety of datasets. Moreover, LAMBADA significantly improves upon the state-of-the-art techniques for data augmentation, specifically those applicable to text classification tasks with little data.
Branching laws for classical groups: the non-tempered case

This paper generalizes the Gan–Gross–Prasad (GGP) conjectures that were earlier formulated for tempered or more generally generic L-packets to Arthur packets, especially for the non-generic L-packets arising from Arthur parameters. The paper introduces the key notion of a relevant pair of Arthur parameters that governs the branching laws for ${{\rm GL}}_n$ and all classical groups over both local fields and global fields. It plays a role for all the branching problems studied in Gan et al. [Symplectic local root numbers, central critical L-values and restriction problems in the representation theory of classical groups. Sur les conjectures de Gross et Prasad. I, Astérisque 346 (2012), 1–109] including Bessel models and Fourier–Jacobi models.
Exotic domain walls in Bose-Einstein condensates with double-well dispersion

We study the domain walls which form when Bose condensates acquire a double-well dispersion. Experiments have observed such domain walls in condensates driven across a $\mathbb{Z}_2$ symmetry-breaking phase transition in a shaken optical lattice. We derive a generic model to describe the dispersion and to compute the wavefunctions and energies of the domain walls. We find two distinct regimes which demand different physical pictures. In the weak coupling regime, where interactions are weak compared to the kinetic energy barrier, "density wave domain walls" form that support an extended density wave and a series of phase steps. These features can be understood as the quantum interference between domains with distinct momenta. In the strong coupling regime where interaction dominates, the system forms "phase domain walls" which have the minimum width allowed by the uncertainty principle and suppressed density modulation. Analytic results for the domain wall wavefunctions are obtained in the two regimes. The energy of domain walls behaves similarly to that of topological defects in paradigmatic field theories.
Quality of Service (QoS) Modelling in Federated Cloud Computing

Building around the idea of a large scale server infrastructure with a potentially large number of tailored resources, which are capable of interacting to facilitate the deployment, adaptation, and support of services, cloud computing needs to frequently reschedule and manage various application tasks in order to accommodate the requests of a wide range and number of users. One of the challenges of cloud computing is to support and manage Quality-of-Service (QoS) by designing efficient techniques for the allocation of tasks between users and the cloud virtual resources, as well as assigning virtual resources to the cloud physical resources. The migration of virtual resources across physical resources is another challenge that requires considerable attention; especially in federated cloud computing environments wherein, providers might be willing to offer their unused resources as a service to the federation (cooperative allocation) and pull back these resources for their own use when they are needed (competitive allocation). This paper revisits the issue of QoS in cloud computing by formulating and presenting i) a multi-QoS task allocation model for the assignment of tasks to virtual machines and ii) a virtual machine migration model for a federated cloud computing environment by considering cases where resource providers are operating in cooperative or competitive mode. A new differential evolution (DE) based binding policy for task allocation and a novel virtual machine model are proposed as solutions for the problem of QoS support in federated cloud environments. The experimental results show that the proposed solutions improved the quality of service in the cloud computing environment and reveal the relative advantages of operating a mixed cooperation and competition model in a federated cloud environment.
Beacon-referenced pursuit for collective motions in three dimensions

Motivated by real-world applications of unmanned aerial vehicles, this paper introduces a decentralized control mechanism to guide steering control of autonomous agents manoeuvring in the vicinity of multiple moving entities (e.g. other autonomous agents) and stationary entities (e.g. fixed beacons or points of reference) in a three-dimensional environment. The proposed control law, which can be perceived as a modification of the three-dimensional constant bearing (CB) pursuit law, provides a means to allocate simultaneous attention to multiple entities. We investigate the behaviour of the closed-loop dynamics for a system with one agent referencing two beacons, as well as a two-agent mutual pursuit system wherein each agent employs the beacon-referenced CB pursuit law with regards to the other agent and a stationary beacon. Under certain assumptions on the associated control parameters, we demonstrate that this problem admits circling equilibria with agents moving on circular orbits with a common radius, in planes perpendicular to a common axis passing through the beacons. As the common radius and distances from the beacon are determined by the choice of parameters in the pursuit law, this approach provides a means to engineer desired formations in a three-dimensional setting.
The Systemic Approach to Teaching and Learning [SATL]: A 10-Year Review

The Systemic Approach to Teaching and Learning (SATL) is based on constructivist principles and involves the creation of closed cluster concept maps called systemic diagrams. The SATL technique encourages deep learning, as opposed to rote learning. Examples in the use of SATL methods in teaching chemistry are presented. Experimental evidence collected in Egyptian schools is presented to illustrate the efficacy of SATL methods on student achievement. It is suggested that SATL methods mimic our current understanding of how the human brain functions, as the basic reason that SAL methods are successful.
Towards a feasible implementation of quantum neural networks using quantum dots

We propose an implementation of quantum neural networks using an array of quantum dots with dipole-dipole interactions. We demonstrate that this implementation is both feasible and versatile by studying it within the framework of GaAs based quantum dot qubits coupled to a reservoir of acoustic phonons. Using numerically exact Feynman integral calculations, we have found that the quantum coherence in our neural networks survive for over a hundred ps even at liquid nitrogen temperatures (77 K), which is three orders of magnitude higher than current implementations which are based on SQUID-based systems operating at temperatures in the mK range.
Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation

The quality of neural machine translation can be improved by leveraging additional monolingual resources to create synthetic training data. Source-side monolingual data can be (forward-)translated into the target language for self-training; target-side monolingual data can be back-translated. It has been widely reported that back-translation delivers superior results, but could this be due to artefacts in the test sets? We perform a case study using French-English news translation task and separate test sets based on their original languages. We show that forward translation delivers superior gains in terms of BLEU on sentences that were originally in the source language, complementing previous studies which show large improvements with back-translation on sentences that were originally in the target language. To better understand when and why forward and back-translation are effective, we study the role of domains, translationese, and noise. While translationese effects are well known to influence MT evaluation, we also find evidence that news data from different languages shows subtle domain differences, which is another explanation for varying performance on different portions of the test set. We perform additional low-resource experiments which demonstrate that forward translation is more sensitive to the quality of the initial translation system than back-translation, and tends to perform worse in low-resource settings.
Benchmark Solution for a Three-Dimensional Mixed-Convection Flow, Part 2: Analysis of Richardson Extrapolation in the Presence of a Singularity

A reference solution to a benchmark problem for a three-dimensional mixed-convection flow in a horizontal rectangular channel differentially heated (Poiseuille-Rayleigh-Bénard flow) has been proposed in Part 1 of the present article (Numer. Heat Transfer B, vol. 60, pp. 325–345, 2011). Since mixed Dirichlet and Neumann thermal boundary conditions are used on the horizontal walls of the channel, a temperature gradient discontinuity is generated. The aim of this article is to analyze the consequences of this singularity on Richardson extrapolation (RE) of the numerical solutions. The convergence orders of the numerical methods used (finite difference, finite volume, finite element), observed from RE of local and integral quantities are discussed with an emphasis on singularity influence. With the grids used, it is shown that RE can increase the accuracy of the discrete solutions preferentially with the discretization methods of low space accuracy order, but only in some part of the channel and for a restricted range of the extrapolation coefficient. A correction to the Taylor expansion involved in the RE formalism is proposed to take into account the singularity and to explain the majority of the RE behaviors observed.
Lock-Free Hopscotch Hashing

In this paper we present a lock-free version of Hopscotch Hashing. Hopscotch Hashing is an open addressing algorithm originally proposed by Herlihy, Shavit, and Tzafrir, which is known for fast performance and excellent cache locality. The algorithm allows users of the table to skip or jump over irrelevant entries, allowing quick search, insertion, and removal of entries. Unlike traditional linear probing, Hopscotch Hashing is capable of operating under a high load factor, as probe counts remain small. Our lock-free version improves on both speed, cache locality, and progress guarantees of the original, being a chimera of two concurrent hash tables. We compare our data structure to various other lock-free and blocking hashing algorithms and show that its performance is in many cases superior to existing strategies. The proposed lock-free version overcomes some of the drawbacks associated with the original blocking version, leading to a substantial boost in scalability while maintaining attractive features like physical deletion or probe-chain compression.
Coupled transport of phonons and carriers in semiconductors: A case study of 
n
-doped GaAs

We present a general coupled electron-phonon Boltzmann transport equations (BTEs) scheme designed to capture the mutual drag of the two interacting systems. By combining density functional theory based first principles calculations of anharmonic phonon-phonon interactions with physical models of electron-phonon interactions, we apply our implementation of the coupled BTEs to calculate the thermal conductivity, mobility, Seebeck and Peltier coefficients of n-doped gallium arsenide. The measured low temperature enhancement in the Seebeck coefficient is captured using the solution of the fully coupled electron-phonon BTEs, while the uncoupled electron BTE fails to do so. This work gives insights into the fundamental nature of charge and heat transport in semiconductors and advances predictive ab initio computational approaches. We discuss possible extensions of our work.
Discovering Invariances in Healthcare Neural Networks

We study the invariance characteristics of pre-trained predictive models by empirically learning transformations on the input that leave the prediction function approximately unchanged. To learn invariant transformations, we minimize the Wasserstein distance between the predictive distribution conditioned on the data instances and the predictive distribution conditioned on the transformed data instances. To avoid finding degenerate or perturbative transformations, we add a similarity regularization to discourage similarity between the data and its transformed values. We theoretically analyze the correctness of the algorithm and the structure of the solutions. Applying the proposed technique to clinical time series data, we discover variables that commonly-used LSTM models do not rely on for their prediction, especially when the LSTM is trained to be adversarially robust. We also analyze the invariances of BioBERT on clinical notes and discover words that it is invariant to.
Dipolar condensed atomic mixtures and miscibility under rotation

By considering symmetric- and asymmetric-dipolar coupled mixtures (with dysprosium and erbium isotopes), we report a study on relevant anisotropic effects, related to spatial separation and miscibility, due to dipole-dipole interactions (DDIs) in rotating binary dipolar Bose-Einstein condensates. The binary mixtures are kept in strong pancake-like traps, with repulsive two-body interactions modeled by an effective two-dimensional (2D) coupled Gross-Pitaevskii equation. The DDI are tuned from repulsive to attractive by varying the dipole polarization angle. A clear spatial separation is verified in the densities for attractive DDIs, being angular for symmetric mixtures and radial for asymmetric ones. Also relevant is the mass-imbalance sensibility observed by the vortex-patterns in symmetric- and asymmetric-dipolar mixtures. In an extension of this study, here we show how the rotational properties and spatial separation of these dipolar mixture are affected by a quartic term added to the harmonic trap of one of the components.
Heart rate measurement with photoplethysmography through SCADA

This paper describes the development of a heart rate monitor system based on a Arduino UNO. It offers the advantage of portability over tape-based recording systems. This paper explains how a Arduino UNO can be used to analyze heart beat rate signals in real-time. In addition, it allows doctors to get the heart beat rate file of the patient by email every twenty-four hours. It can also be used to control patients or athletic person over a long period. The system reads, stores and analyses the heart beat rate signals repetitively in real-time. The hardware and software design are oriented towards a Arduino UNO system, hence minimizing the size. It then processes on real-time the information to determine some heart diseases.
Synovia-Derived Mesenchymal Stem Cell Application in Musculoskeletal Injuries: A Review

Musculoskeletal injuries impact millions of people globally and affect their health and well-being as well as of their companion and athletic animals. Soft-tissue injuries rep resent almost half of these and are associated with unorganized scar tissue formation and long time-depending healing processes. Cell-based therapeutic strategies have been developed in the past decades aiming at the treatment and reversion of such disorders. Stem cells are fairly appealing in the field, being a responsive undifferentiated popula tion, with ability to self-renew and differentiate into different lineages. Mesenchymal stem cells (MSCs) can be obtained from several adult tissues, including the synovial mem -brane. Synovia-derived MSCs can be found in individuals of any age and are associated to intrinsic regenerative processes, through both paracrine and cell-to-cell interactions, thus, contributing to hosts’ healing capacity. Studies have demonstrated the potential benefit of synovia-derived MSCs in these regenerative processes in both human and veterinary medicine. The purpose of this chapter is to review the literature regarding SM-MSC thera - pies applied to musculoskeletal disorders, in both human and veterinary medicine.
What is the effect of spatial proximity on research collaboration in a small country? A gravity model for co-authored publications

In this study we fit a gravity model for knowledge production, where the number of co-authored publications is explained by the size of the affiliated organisations and the physical distance between them. We analyse 2247 publications on drinking water and wastewater in the period 2006-2008 with at least one author affiliation in the Netherlands. At this small spatial level we find a robust and significant effect in the expected direction: the larger the distance between two organisations, the less publications they co-author together. We extend the model and show that organisations of the same type (academia, governmental bodies, etc.) also collaborate more.