Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting employee engagement and job satisfaction among cabin crew

Enhancing employee engagement and job satisfaction are desirable outcomes of every organization, since they are believed, on one hand, to reduce turnover intention, and on the other hand, to bring loyal customers and thus success to the organization. Cabin crew represent the front line of each airline and they play an essential role in delivering service quality, therefore their engagement and job satisfaction is vital for the organization. This thesis aims to examine the extentto which cabin crew engagement and job satisfactionare predicted by empowerment and leadership support, using a nonrandom sample of 103 flight attendants from an airline in the Middle East region. The research focuses on the linear relationships between leadership, empowerment, job satisfaction and employee engagement, and proposes theoretical concepts and validated measurement scales encompassing these variables. After testing the hypothesized relationships between the variables, using multiple regression analysis, the results of the first hypothesized model revealed that cabin crew engagement is mainly predicted by empowerment, followed by job satisfaction, whereas leadership support did not explain any variance in cabin crew engagement. While testing the second hypothesized model, the results demonstrated that both empowerment and leadership support predict cabin crew job satisfaction. Most findings were in line with a number of previous studies, however the nonsignificant prediction of employee engagement by leadership support was unexpected. This interesting result suggested that there might be some gap between employees and leadership, since cabin crew engagement is not predicted by leadership support to any extent. Thus, based on the current findings, the study elaborates on theoretical and practical implications and suggests future research directions. The findings of the current thesis are believed to contribute to the research on cabin crew engagement and job satisfaction. Furthermore, the researcher hopes that these findings would make the airline’s management consider more carefully the leadership training process so that leaders will learn how to inspire and engage cabin crew.
Orthogonal Inductive Matrix Completion

We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground-truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all components of the model simultaneously. We study the generalization capabilities of our method in both the distribution-free setting and in the case where the sampling distribution admits uniform marginals, yielding learning guarantees that improve with the quality of the injected knowledge in both cases. As particular cases of our framework, we present models that can incorporate user and item biases or community information in a joint and additive fashion. We analyze the performance of OMIC on several synthetic and real datasets. On synthetic datasets with a sliding scale of user bias relevance, we show that OMIC better adapts to different regimes than other methods. On real-life datasets containing user/items recommendations and relevant side information, we find that OMIC surpasses the state of the art, with the added benefit of greater interpretability.
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification

We propose a generative model of unordered point sets, such as point clouds, in the forms of an energy-based model, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network. The energy function learns a coordinate encoding of each point and then aggregates all individual point features into an energy for the whole point cloud. We call our model the Generative PointNet because it can be derived from the discriminative PointNet. Our model can be trained by MCMC-based maximum likelihood learning (as well as its variants), without the help of any assisting networks like those in GANs and VAEs. Unlike most point cloud generators that rely on hand-crafted distance metrics, our model does not require any hand-crafted distance metric for the point cloud generation, because it synthesizes point clouds by matching observed examples in terms of statistical properties defined by the energy function. Furthermore, we can learn a short-run MCMC toward the energy-based model as a flow-like generator for point cloud reconstruction and interpolation. The learned point cloud representation can be useful for point cloud classification. Experiments demonstrate the advantages of the proposed generative model of point clouds.
SiO maser astrometry of the red transient V838 Monocerotis

We present multiepoch observations with the Very Long Baseline Array of SiO maser emission in the v = 1, J = 1−0 transition at 43 GHz from the remnant of the red nova V838 Mon. We modeled the positions of maser spots to derive a parallax of 0.166 ± 0.060 mas. Combining this parallax with other distance information results in a distance of 5.6 ± 0.5 kpc, which is in agreement with an independent geometric distance of 6.1 ± 0.6 kpc from modeling polarimetry images of V838 Mon’s light echo. Combining these results, and including a weakly constraining Gaia parallax, yields a best estimate of distance of 5.9 ± 0.4 kpc. The maser spots are located close to the peaks of continuum at ∼225 GHz and SiO J = 5−4 thermal emission detected with the Atacama Large (sub)Millimeter Array. The proper motion of V838 Mon confirms its membership in a small open cluster in the Outer spiral arm of the Milky Way.
Problem of heart salvation during reperfusion. Opioid receptor agonists as a possible solution

Ischaemia/reperfusion cardiac injury contributes to morbidity and mortality during percutaneous coronary intervention, heart surgery and transplantation. Even when the recanalization of an infarct-related coronary artery is carried out successfully, there is still a risk of death due to reperfusion injury. Numerous pharmacological interventions have been found in experiments on animals. However, the translation of these interventions to clinical practice has been disappointing. None of the drug treatment has been able to improve in-hospital mortality of patients with acute myocardial infarction. The search for pharmacological agents able to salvage myocardium during reperfusion continues. Opioid receptor (OR) agonists represent one of the promising group of drugs for treatment of patients with myocardial infarction. It has been found that µ-, δ- and κ-OR agonists are able to attenuate heart injury when administered before or at the beginning of reperfusion. However, what kind of OR receptors need to be activated in order to protect the heart during reperfusion and the precise mechanism of this effect have yet to be elucidated. RICICd has large experience in the development of products for cardiovascular surgery, in particular, the bioprosthesis heart valves and blood vessels. Since 2010, under the leadership of the academician of RAS L. S. Barbarash researches to create bioresorbable vascular prostheses of small diameter began. the primary purpose – to create polymer structures with bio-directed action capable to replace individual structures of a living organism, in particular, of cardiovascular system. Scientific novelty of the problem lies in using a new approach for creation an organ directly in the patient’s body by biofunctional and bioresorbable features of polymer structures. the long-term patency of PCL vascular grafts and PHBV and PCL composition grafts had proved. the in vitro experiments had proved that growth factors incorporated into composition of biodegradable grafts retain their biological activity. the long-term in vivo experiments had demonstrated that vascular endothelial growth factor had accelerated endothelization and had improved vascular patency of biodegradable polymer grafts.
Predicting the outputs of finite networks trained with noisy gradients

A recent line of studies has focused on the infinite width limit of deep neural networks (DNNs) where, under a certain deterministic training protocol, the DNN outputs are related to a Gaussian Process (GP) known as the Neural Tangent Kernel (NTK). However, finite-width DNNs differ from GPs quantitatively and for CNNs the difference may be qualitative. Here we present a DNN training protocol involving noise whose outcome is mappable to a certain non-Gaussian stochastic process. An analytical framework is then introduced to analyze this resulting non-Gaussian process, whose deviation from a GP is controlled by the finite width. Our work extends upon previous relations between DNNs and GPs in several ways: (a) In the infinite width limit, it establishes a mapping between DNNs and a GP different from the NTK. (b) It allows computing analytically the general form of the finite width correction (FWC) for DNNs with arbitrary activation functions and depth and further provides insight on the magnitude and implications of these FWCs. (c) It appears capable of providing better performance than the corresponding GP in the case of CNNs. We are able to predict the outputs of empirical finite networks with high accuracy, improving upon the accuracy of GP predictions by over an order of magnitude. Overall, we provide a framework that offers both an analytical handle and a more faithful model of real-world settings than previous studies in this avenue of research.
Relative Error Streaming Quantiles

Estimating ranks, quantiles, and distributions over streaming data is a central task in data analysis and monitoring. Given a stream of n items from a data universe equipped with a total order, the task is to compute a sketch (data structure) of size polylogarithmic in n. Given the sketch and a query item y, one should be able to approximate its rank in the stream, i.e., the number of stream elements smaller than or equal to y.
Amenable dynamical systems over locally compact groups

Abstract We establish several new characterizations of amenable $W^*$ - and $C^*$ -dynamical systems over arbitrary locally compact groups. In the $W^*$ -setting we show that amenability is equivalent to (1) a Reiter property and (2) the existence of a certain net of completely positive Herz–Schur multipliers of $(M,G,\alpha )$ converging point weak* to the identity of $G\bar {\ltimes }M$ . In the $C^*$ -setting, we prove that amenability of $(A,G,\alpha )$ is equivalent to an analogous Herz–Schur multiplier approximation of the identity of the reduced crossed product $G\ltimes A$ , as well as a particular case of the positive weak approximation property of Bédos and Conti [On discrete twisted $C^*$ -dynamical systems, Hilbert $C^*$ -modules and regularity. Münster J. Math. 5 (2012), 183–208] (generalized to the locally compact setting). When $Z(A^{**})=Z(A)^{**}$ , it follows that amenability is equivalent to the 1-positive approximation property of Exel and Ng [Approximation property of $C^*$ -algebraic bundles. Math. Proc. Cambridge Philos. Soc. 132(3) (2002), 509–522]. In particular, when $A=C_0(X)$ is commutative, amenability of $(C_0(X),G,\alpha )$ coincides with topological amenability of the G-space $(G,X)$ .
Assessing Impact of Data Partitioning for Approximate Memory in C/C++ Code

Approximate memory is a technique to mitigate the performance gap between memory subsystems and CPUs with its reduced access latency at a cost of data integrity. To gain benefit from approximate memory for realistic applications, it is crucial to partition applications' data to approximate data and critical data and apply different error rates. However, error rates cannot be controlled in a fine-grained manner (e.g., per byte) due to fundamental limitations of how approximate memory can be realized. Due to this, if approximate data and critical data are interleaved in a data structure (e.g., a C struct that has a pointer and an approximatable number as its members), data partitioning may degrade the application's performance because the data structure must be split to separate memory regions that have different error rates. This paper is the first to conduct an analysis of realistic C/C++ code to assess the impact of this problem. First, we find the type of data (e.g., "int", "struct point") that is assessed by the instruction that incurs the largest number of cache misses in a benchmark, which we refer to as the target data type. Second, we qualitatively estimate if the target data type of an application has approximate data and critical data interleaved. To this end, we set up three criteria to analyze it because definitively distinguishing a piece of data as approximate data or critical data is infeasible since it depends on each use-case. We analyze 11 memory intensive benchmarks from SPEC CPU 2006 and 2 graph analytics frameworks, and show that the target data types of 9 benchmarks are either a C struct or a C++ class (criterion 1). Among them, two have a pointer and a non-pointer member together (criterion 2) and three have a floating point number and other members together (criterion 3).
A convenient category of locally stratified spaces

In this thesis we define the notion of a locally stratified space. Locally stratified spaces are particular kinds of streams and d-spaces which are locally modelled on stratified spaces. We construct a locally presentable and cartesian closed category of locally stratified spaces that admits an adjunction with the category of simplicial sets. Moreover, we show that the full subcategory spanned by locally stratified spaces whose associated simplicial set is an ∞-category has the structure of a category with fibrant objects. We define the fundamental category of a locally stratified space and show that the canonical functor θ_A from the fundamental category of a simplicial set A to the fundamental category of its realisation is essentially surjective. We show that the functor θ_A sends split monomorphisms to isomorphisms, in particular we show that θ_A is not necessarily an equivalence of categories. On the other hand, we show that the fundamental category of the realisation of the simplicial circle is equivalent to the monoid of the natural numbers. To conclude, we define left covers of locally stratified spaces and we show that, under suitable assumptions, the category of representations of the fundamental category of a simplicial set is equivalent to the category of left covers over its realisation.
Locally solvable and solvable-by-finite maximal subgroups of $GL_n(D)$.

This paper aims at studying solvable-by-finite and locally solvable maximal subgroups of an almost subnormal subgroup of the general skew linear group $GL_n(D)$ over a division ring $D$. It turns out that in the case $D$ is non-commutative, if such kinds of maximal subgroups exist, then either it is abelian or $[D:F]<\infty$. If $F$ is an infinite field and $n\geq 5$, then every locally solvable maximal subgroup of a normal subgroup of $GL_n(F)$ is abelian.
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations

The need for more transparency of the decision-making processes in artificial neural networks steadily increases driven by their applications in safety critical and ethically challenging domains such as autonomous driving or medical diagnostics. We address today's lack of transparency of neural networks and shed light on the roles of single neurons and groups of neurons within the network fulfilling a learned task. Inspired by research in the field of neuroscience, we characterize the learned representations by activation patterns and network ablations, revealing functional neuron populations that a) act jointly in response to specific stimuli or b) have similar impact on the network's performance after being ablated. We find that neither a neuron's magnitude or selectivity of activation, nor its impact on network performance are sufficient stand-alone indicators for its importance for the overall task. We argue that such indicators are essential for future advances in transfer learning and modern neuroscience.
Polariton interactions in microcavities with atomically thin semiconductor layers

We investigate the interactions between exciton-polaritons in N two-dimensional semiconductor layers embedded in a planar microcavity. In the limit of low-energy and low-momentum scattering, where we can ignore the composite nature of the excitons, we obtain exact analytical expressions for the spin-triplet and spin-singlet interaction strengths, which go beyond the Born approximation employed in previous calculations. Crucially, we find that the strong light-matter coupling enhances the strength of polariton-polariton interactions compared to that of the exciton-exciton interactions, with the latter vanishing in the zero-momentum limit. We furthermore show that the polariton interactions have a highly non-trivial dependence on the number of layers N, and we highlight the important role played by the optically dark states that exist in multiple layers. In particular, we predict that the singlet interaction strength is stronger than the triplet one for a wide range of parameters in most of the currently used transition medal dichalcogenides. This has consequences for the pursuit of polariton condensation and other interaction-driven phenomena in these materials.
INTENSIVE 2013

—Systems with chip multi-processors are currently used for several applications that have real-time requirements. In chip multi-processor architectures, many hardware resources such as parts of the cache hierarchy are shared between cores and by using such resources, applications can signiﬁcantly interfere with each other. In previous work, we showed that a single X-ray imaging streaming applications can be executed with low jitter on such systems. However, it was assumed that only one application would be running on the system, which prevents system integration where multiple real-time and best-effort applications are executing on a single chip multi-processor. In this paper, we address the limited bandwidth in the cache hierarchy, which can cause threads to interfere with each other signiﬁcantly. We propose a technique that implements cache bandwidth reservation in software, by dynamically duty-cycling best-effort applications, based on their cache bandwidth usages using processor performance counters in order to control the inﬂuence of best-effort applications on real-time applications. With this technique we can control the latency increase of real-time applications that is caused by best-effort application in order to satisfy real-time requirements with a minimal reduction in best-effort performance. The results of the experiments with real-life applications indicate that we can control the increase of the latency to such an extent that we can almost completely eliminate the inﬂuence of bandwidth sharing in the cache at the cost of best-effort performance.
Predicting Labor Shortages from Labor Demand and Labor Supply Data: A Machine Learning Approach

This research develops a Machine Learning approach able to predict labor shortages for occupations. We compile a unique dataset that incorporates both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018. This includes data from 1.3 million job advertisements (ads) and 20 official labor force measures. We use these data as explanatory variables and leverage the XGBoost classifier to predict yearly labor shortage classifications for 132 standardized occupations. The models we construct achieve macro-F1 average performance scores of up to 86 per cent. However, the more significant findings concern the class of features which are most predictive of labor shortage changes. Our results show that job ads data were the most predictive features for predicting year-to-year labor shortage changes for occupations. These findings are significant because they highlight the predictive value of job ads data when they are used as proxies for Labor Demand, and incorporated into labor market prediction models. This research provides a robust framework for predicting labor shortages, and their changes, and has the potential to assist policy-makers and businesses responsible for preparing labor markets for the future of work.
Asymptotically Optimal Management of Heterogeneous Collectivised Investment Funds

A collectivised fund is a proposed form of pension investment, in which all investors agree that any funds associated with deceased members should be split among survivors. For this to be a viable financial product, it is necessary to know how to manage the fund even when it is heterogeneous: that is when different investors have different preferences, wealth and mortality. There is no obvious way to define a single objective for a heterogeneous fund, so this is not an optimal control problem. In lieu of an objective function, we take an axiomatic approach. Subject to our axioms on the management of the fund, we find an upper bound on the utility that can be achieved for each investor, assuming a complete markets and the absence of systematic longevity risk. We give a strategy for the management of such heterogeneous funds which achieves this bound asymptotically as the number of investors tends to infinity.
The origin of pulsating ultra-luminous X-ray sources: Low- and intermediate-mass X-ray binaries containing neutron star accretors

Context. Ultra-luminous X-ray sources (ULXs) are those X-ray sources located away from the centre of their host galaxy with luminosities exceeding the Eddington limit of a stellar-mass black hole (LX > 1039 erg s−1). Observed X-ray variability suggests that ULXs are X-ray binary systems. The discovery of X-ray pulsations in some of these objects (e.g. M82 X-2) suggests that a certain fraction of the ULX population may have a neutron star as the accretor. Aims. We present systematic modelling of low- and intermediate-mass X-ray binaries (LMXBs and IMXBs; donor-star mass range 0.92–8.0 M⊙ and neutron-star accretors) to explain the formation of this sub-population of ULXs. Methods. Using MESA, we explored the allowed initial parameter space of binary systems consisting of a neutron star and a low- or intermediate-mass donor star that could explain the observed properties of ULXs. These donors are transferring mass at super-Eddington rates while the accretion is limited locally in the accretion disc by the Eddington limit. Thus, our simulations take into account beaming effects and also include stellar rotation, tides, general angular momentum losses, and a detailed and self-consistent calculation of the mass-transfer rate. Results. Exploring the initial parameters that lead to the formation of neutron-star ULXs, we study the conditions that lead to dynamical stability of these systems, which depends strongly on the response of the donor star to mass loss. Using two values for the initial neutron star mass (1.3 M⊙ and 2.0 M⊙), we present two sets of mass-transfer calculation grids for comparison with observations of NS ULXs. We find that LMXBs/IMXBs can produce NS-ULXs with typical time-averaged isotropic-equivalent X-ray luminosities of between 1039 and 1041 erg s−1 on a timescale of up to ∼1.0 Myr for the lower luminosities. Finally, we estimate their likelihood of detection, the types of white-dwarf remnants left behind by the donors, and the total amount of mass accreted by the neutron stars. Conclusions. We show that observed super-Eddington luminosities can be achieved in LMXBs/IMXBs undergoing non-conservative mass transfer while assuming geometrical beaming. We also compare our results to the observed pulsating ULXs and infer their initial parameters. Our results suggest that a large subset of the observed pulsating ULX population can be explained by LMXBs/IMXBs in a super-Eddington mass-transfer phase.