Miracle-dz/UniFAR-FTUs huggingface dataset:
Moderating Role of Emotional Labour in Job Characteristics and Organizational Commitment Relations

This cross-sectional study investigated the moderating role of emotional labour in job characteristics and organizational commitment relations among secondary school teachers in Enugu urban area of Enugu State, Nigeria. One hundred and eighty (180) teachers, comprising 62 men and 118 women between the ages of 22 to 61 years were sampled using multi-stage sampling techniques. The 18-item Organizational Commitment Scale, 21-item Job Characteristics Scale and 10-item Emotional Labour Scale were the measures for data collection. Results of moderated regression analysis revealed that the various dimensions of job characteristics and emotional labour did not equally predict organizational commitment. And the two components of emotional labour did not equally moderate. Job characteristics and emotional labour accounted for 9.6% of the variance in affective commitment of the teachers, 10.6 % of variance in continuance commitment and 18.6% in normative commitment. Specifically, feedback from agentsâ€™ dimension of job characteristics independently predicted normative commitment while the other dimensions did not predict any dimension of organizational commitment. Surface acting negatively predicted affective commitment while deep acting positively predicted continuance commitment. Deep acting strengthened the relationship between feedback from agents and affective commitment while surface acting weakened the relationship between feedback from agents and continuance commitment. Surface acing strengthened the relationship between feedback from job and continuance commitment. Surface acting also strengthened the relationship between dealing with others and normative commitment. This study has recommended that policy makers in teaching/education should consider job characteristics and emotional labour in order to enhance organizational commitment.
Über das zähe Fließen in Metallen und verwandte Erscheinungen = On the viscous flow in metals, and allied phenomena

It has been observed that for a lead wire, loaded well beyond the elastic limit, the extension after some time becomes proportional to the time, or the flow becomes viscous in character. The rate of this viscous flow varies with the load, and the following work was undertaken to investigate the law of this variation, and the phenomenon in general. Methods of Experiment. The Hyperbolic Weight . The experiments were all done on wires of the metal, and the preliminary observations were all made on lead. To suspend the wires, they were soldered into stout brass hooks. In the first rough experiments the extension registered itself on a clockwork-driven drum; these experiments showed that the rate of extension for a given load became constant for a time, but finally increased, and continued increasing. This is due to two causes: (1) As the wire stretches, the length of wire being experimented on at any moment increases. (2) As the wire stretches, the cross-section diminishes, and thus the load per unit area or stress increases. This is by far the more disturbing cause, since, as shown later, the rate increases much more rapidly with the stress than would be given by a linear law. To obviate this difficulty an automatic method of keeping the stress constant was devised.
Emotion Awareness in Design-Based Learning

This Research Full Paper presents a case study where university students (N=13) use a paper diary called EmoForm to self-track their emotions over a ten-week-long design-based learning course. Design-Based Learning (DBL) is a learning approach that enables students to learn through a sequence of design activities in a project-based learning or problem-based learning environment. Student’s emotions are known to play an essential role in learning settings. We argue that students engaging in DBL will benefit from self-tracking of emotions. Such self-tracking will enable students to identify and control their emotions during the DBL process. The results of this study confirm that self-tracking with EmoForm enabled students’ emotion awareness in DBL. In particular, our results illustrate how self-tracking with EmoForm impacted on students’ DBL from three major aspects. We discuss design challenges regarding tools to support emotion awareness in DBL, and we present a summary of strategies conducive towards emotion awareness in DBL and the implications for future research.
Reputation, Innovation, and Externalities in Venture Capital

I introduce a dynamic model of random search where ex ante heterogeneous venture capitalists (investors) with unknown abilities match with a variety of startups (projects). There is incomplete yet symmetric information about investors’ types, whereas the projects’ types are publicly observable to all investors. In the unique stationary equilibrium, the matching sets, value functions and steady state distributions are endogenously determined. Interpreting the market posterior belief about the venture capitalists’ ability as their reputation, I study the outcomes of the economy when the success or failure of the projects create feedback effects: innovation spillovers and reputational externalities. When there are positive spillovers from successful early stage projects to late stage business opportunities, I show increased levels of search frictions could save the market from breakdown caused by the neglect of spillover effect. When the reputational externality is at play, namely when the deal flow of each investor is inversely impacted by the distribution of other investors’ reputation, I show the proportion of the high ability inactive investors is inefficiently high, and the projects suffer from early termination. JEL classification: C78; D83; G24; M13; O31
Freeze-in generation of lepton asymmetries after baryogenesis in the $\nu$MSM

The $\nu$MSM -- an extension of the Standard Model by three relatively light singlet Majorana fermions $N_{1,2,3}$ -- allows for generation of lepton asymmetry which is several orders of magnitude larger than the observed baryon asymmetry of the Universe. The lepton asymmetry is produced in interactions of $N_{2,3}$ (with masses in the GeV region) at temperatures below the sphaleron freeze out $ T < 130$ GeV and can enhance the cosmological production of dark matter (DM) sterile neutrinos $N_1$ (with mass of the keV scale) happening at $T \sim 200$ MeV due to active-sterile neutrino mixing. In this work we address the question of the magnitude of late-time asymmetry (LTA) generated by the heavy neutral leptons $N_{2,3}$ during their freeze-in at $T\sim 20$ GeV and study how much of it can survive down to the lower temperatures relevant for the sterile neutrino DM creation. We find that this LTA could result in production of a sizeable fraction of dark matter. We also examine a role played by magnetic fields and the Abelian chiral anomaly in generation of LTA, not accounted for in the previous studies. We argue that the production of LTA can be increased significantly and make an estimate of the influence of this effect.
Recalibration of Neural Networks for Point Cloud Analysis

Spatial and channel re-calibration have become powerful concepts in computer vision. Their ability to capture long-range dependencies is especially useful for those networks that extract local features, such as CNNs. While recalibration has been widely studied for image analysis, it has not yet been used on shape representations. In this work, we introduce re-calibration modules on deep neural networks for 3D point clouds. We propose a set of re-calibration blocks that extend Squeeze and Excitation blocks [11] and that can be added to any network for 3D point cloud analysis that builds a global descriptor by hierarchically combining features from multiple local neighborhoods. We run two sets of experiments to validate our approach. First, we demonstrate the benefit and versatility of our proposed modules by incorporating them into three state-of-the-art networks for 3D point cloud analysis: PointNet++ [22], DGCNN [29], and RSCNN [18]. We evaluate each network on two tasks: object classification on ModelNet40, and object part segmentation on ShapeNet. Our results show an improvement of up to 1% in accuracy for ModelNet40 compared to the baseline method. In the second set of experiments, we investigate the benefits of re-calibration blocks on Alzheimer’s Disease (AD) diagnosis. Our results demonstrate that our proposed methods yield a 2% increase in accuracy for diagnosing AD and a 2.3% increase in concordance index for predicting AD onset with time-to-event analysis. Concluding, re-calibration improves the accuracy of point cloud architectures, while only minimally increasing the number of parameters.
Functional principal component analysis for cointegrated functional time series

Functional principal component analysis (FPCA) has played an important role in the development of functional time series (FTS) analysis. This paper investigates how FPCA can be used to analyze cointegrated functional time series and propose a modification of FPCA as a novel statistical tool. Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel KPSS-type tests for examining some essential properties of cointegrated time series. As an empirical illustration, our methodology is applied to the time series of log-earning densities.
Auto Graph Encoder-Decoder for Model Compression and Network Acceleration

Model compression aims to deploy deep neural networks (DNN) to mobile devices with limited computing power and storage resource. However, most of the existing model compression methods rely on manually defined rules, which requires domain expertise. In this paper, we propose an Auto Graph encoder-decoder Model Compression (AGMC) method combined with graph neural networks (GNN) and reinforcement learning (RL) to find the best compression policy. We model the target DNN as a graph and use GNN to learn the embeddings of the DNN automatically. In our experiments, we first compared our method with rule-based DNN embedding methods to show the graph auto encoder-decoder's effectiveness. Our learning-based DNN embedding achieved better performance and a higher compression ratio with fewer search steps. Moreover, we evaluated the AGMC on CIFAR-10 and ILSVRC-2012 datasets and compared handcrafted and learning-based model compression approaches. Our method outperformed handcrafted and learning-based methods on ResNet-56 with 3.6% and 1.8% higher accuracy, respectively. Furthermore, we achieved a higher compression ratio than state-of-the-art methods on MobileNet-V2 with just 0.93% accuracy loss.
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning

There has recently been an increasing interest in computationally-efficient learning methods for resource-constrained applications, e.g., pruning, quantization and channel gating. In this work, we advocate a holistic approach to jointly train the backbone network and the channel gating which can speed up subnet selection for a new task at the resource-limited node. In particular, we develop a federated meta-learning algorithm to jointly train good meta-initializations for both the backbone networks and gating modules, by leveraging the model similarity across learning tasks on different nodes. In this way, the learnt meta-gating module effectively captures the important filters of a good meta-backbone network, and a task-specific conditional channel gated network can be quickly adapted from the meta-initializations using data samples of the new task. The convergence of the proposed federated meta-learning algorithm is established under mild conditions. Experimental results corroborate the effectiveness of our method in comparison to related work.
The Global Stability of M33 in MOND

The dynamical stability of disk galaxies is sensitive to whether their anomalous rotation curves are caused by dark matter halos or Milgromian dynamics (MOND). We investigate this by setting up a MOND model of M33. We first simulate it in isolation for 6 Gyr, starting from an initial good match to the rotation curve (RC). Too large a bar and bulge form when the gas is too hot, but this is avoided by reducing the gas temperature. A strong bar still forms in 1 Gyr, but rapidly weakens and becomes consistent with the observed weak bar. Previous work showed this to be challenging in Newtonian models with a live dark matter halo, which developed strong bars. The bar pattern speed implies a realistic corotation radius of 3 kpc. However, the RC still rises too steeply, and the central line-of-sight velocity dispersion (LOSVD) is too high. We then add a constant external acceleration field of 8.4 × 10−12 m s−2 at 30° to the disk as a first-order estimate for the gravity exerted by M31. This suppresses buildup of material at the center, causing the RC to rise more slowly and reducing the central LOSVD. Overall, this simulation bears good resemblance to several global properties of M33, and highlights the importance of including even a weak external field on the stability and evolution of disk galaxies. Further simulations with a time-varying external field, modeling the full orbit of M33, will be needed to confirm its resemblance to observations.
Hadron-Quark Phase Transition in the SU (3) Local Nambu-Jona-Lasinio (NJL) Model with Vector Interaction

We study the hadron–quark hybrid equation of state (EOS) of compact-star matter. The Nambu–Jona-Lasinio (NJL) local SU (3) model with vector-type interaction is used to describe the quark matter phase, while the relativistic mean field (RMF) theory with the scalar-isovector δ-meson effective field is adopted to describe the hadronic matter phase. It is shown that the larger the vector coupling constant GV, the lower the threshold density for the appearance of strange quarks. For a sufficiently small value of the vector coupling constant, the functions of the mass dependence on the baryonic chemical potential have regions of ambiguity that lead to a phase transition in nonstrange quark matter with an abrupt change in the baryon number density. We show that within the framework of the NJL model, the hypothesis on the absolute stability of strange quark matter is not realized. In order to describe the phase transition from hadronic matter to quark matter, Maxwell’s construction is applied. It is shown that the greater the vector coupling, the greater the stiffness of the EOS for quark matter and the phase transition pressure. Our results indicate that the infinitesimal core of the quark phase, formed in the center of the neutron star, is stable.
Osseous Healing With Nonrigid Suture Fixation in the Arthroscopic Latarjet Procedure

Background: The Latarjet procedure is a popular means to surgically address anterior glenohumeral joint instability. Although the Latarjet procedure is becoming increasingly common, challenges persist and include postoperative complications secondary to use of the conventional 2 bicortical fixation screws. Recently, a novel surgical technique using a guided surgical approach for graft positioning with nonrigid fixation via a suture suspensory system has been described. Purpose: To evaluate healing rates and stability of the grafts in patients who underwent this new Latarjet technique. Study Design: Case series; Level of evidence, 4. Methods: We retrospectively gathered anonymized computed tomography (CT) data sets from a total of 107 patients who underwent nonrigid suture fixation using a cortical button fixation for anterior glenohumeral instability. Of the 107 patients, 45 had CT scans performed at 2 different time periods. The CT scans of each patient were compared by 2 fellowship-trained musculoskeletal radiologists. Data recorded included age, sex, date of scan, initial graft position on the glenoid, presence and degree of graft migration relative to the equator on follow-up scan, and percentage of osseous healing (as assessed by osseous bridging) on the follow-up scan. Descriptive statistics were calculated to evaluate the average migration and average percentage of healing at both time points. Results: Our population (n = 45) consisted of 38 men (84.4%) and 7 women (15.6%). The mean age was 27.1 ± 1.1 years. The mean time between initial CT scan (2 weeks postoperatively) and follow-up CT scan was 26 ± 2 weeks. On follow-up scan, reviewer 1 found 75.6% of patients had greater than 75% healing, and reviewer 2 found 70.2% of patients had greater than 75% healing. The center of the graft was measured at or below the equator on follow-up examination in 43 of 45 (95.6%) patients by reviewer 1 and 44 of 45 (97.8%) patients by reviewer 2. Conclusion: Based on these findings, nonrigid suture fixation using a cortical button device offers an effective alternative to traditional screw fixation for the Latarjet procedure with a high level of osseous healing and minimal graft migration.
Assessing the Impact of COVID-19 on the Mental Health of Healthcare Workers in Three Metropolitan Cities of Pakistan

Purpose The COVID-19 (coronavirus disease-2019) has been associated with psychological distress during its rapid rise period in Pakistan. The present study aimed to assess the mental health of healthcare workers (HCWs) in the three metropolitan cities of Pakistan. Methods A cross-sectional, web-based study was conducted in 276 HCWs from April 10, 2020, to June 5, 2020. Depression, anxiety, and stress scale (DASS-21) were used for the mental health assessment of the HCWs. Multivariable logistic regression analysis (MLRA) was performed to measure the association between the demographics and the occurrence of depression, anxiety, and stress (DAS). Results The frequency of DAS in the HCWs was 10.1%, 25.4%, and 7.3%, respectively. The MLRA showed that the depression in HCWs was significantly associated with the profession (P<0.001). The anxiety in HCWs was significantly associated with their age (P=0.005), profession (P<0.05), and residence (P<0.05). The stress in HCWs was significantly associated with their age (P<0.05). Limitation This study was conducted in the early phase of the COVID-19 pandemic, when the number of COVID-19 cases was on the rise in Pakistan and it only represents a definite period (April to June 2020). Conclusion The symptoms of DAS are present in the HCWs of Pakistan and to manage the psychological health of HCWs, there is a need for the initiation of psychological well-being programs.
Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample

The generation of synthetic images is currently being dominated by Generative Adversarial Networks (GANs). Despite their outstanding success in generating realistic looking images, they still suffer from major drawbacks, including an unstable and highly sensitive training procedure, mode-collapse and mode-mixture, and dependency on large training sets. In this work we present a novel non-adversarial generative method - Clustered Optimization of LAtent space (COLA), which overcomes some of the limitations of GANs, and outperforms GANs when training data is scarce. In the full data regime, our method is capable of generating diverse multi-class images with no supervision, surpassing previous non-adversarial methods in terms of image quality and diversity. In the small-data regime, where only a small sample of labeled images is available for training with no access to additional unlabeled data, our results surpass state-of-the-art GAN models trained on the same amount of data. Finally, when utilizing our model to augment small datasets, we surpass the state-of-the-art performance in small-sample classification tasks on challenging datasets, including CIFAR-10, CIFAR-100, STL-10 and Tiny-ImageNet. A theoretical analysis supporting the essence of the method is presented.
Fast Object Segmentation Learning with Kernel-based Methods for Robotics

Object segmentation is a key component in the visual system of a robot that performs tasks like grasping and object manipulation, especially in presence of occlusions. Like many other computer vision tasks, the adoption of deep architectures has made available algorithms that perform this task with remarkable performance. However, adoption of such algorithms in robotics is hampered by the fact that training requires large amount of computing time and it cannot be performed on-line.In this work, we propose a novel architecture for object segmentation, that overcomes this problem and provides comparable performance in a fraction of the time required by the state-of-the-art methods. Our approach is based on a pre-trained Mask R-CNN, in which various layers have been replaced with a set of classifiers and regressors that are retrained for a new task. We employ an efficient Kernel-based method that allows for fast training on large scale problems. Our approach is validated on the YCB-Video dataset which is widely adopted in the computer vision and robotics community, demonstrating that we can achieve and even surpass performance of the state-of-the-art, with a significant reduction (~6×) of the training time.The code to reproduce the experiments is publicly available on GitHub1.
Higher-order correlations in transition metal dichalcogenides

We analyze the many-particle correlations that affect the optical properties of transition-metal dichalcogenide monolayers. These correlations manifest themselves through the specific optical resonances such as excitons, trions, etc. As the starting point the conventional electron-hole Hamiltonian with potential in the Keldysh-Rytova form is considered. By means of this model and microscopic Heisenberg equation of motion the infinite hierarchy of differential equations can be obtained. In order to decouple a such system we address the cluster expansion technique which provides a regular procedure of consistent accounting of many-particle correlation contributions into the interband polarization dynamics. In particular, the partially taken into account three-particle correlations modify the behavior of absorption spectra with the emergence of a trion-like peak additional to excitonic ones.
Community Energy Storage-based Energy Trading Management for Cost Benefits and Network Support

In this paper, the extent to which the integration of rooftop photovoltaic (PV) power with a community energy storage (CES) system can reduce energy cost and distribution network (DN) loss is explored. To this end, three energy trading systems (ETSs) are compared; first, an ETS where PV users exchange energy with the CES system in addition to the grid, second, an ETS where PV users merely exchange energy with the CES system, and third, an ETS where PV users only exchange energy with the grid. A multi-objective optimization framework, combined with a linear distribution network power flow model, is developed to study the trade-off between the energy cost and network power loss reductions while satisfying the DN voltage and current flow limits. Simulations, with real energy demand and PV power data, highlight that enabling the energy exchange between the users and the CES system can give a better trade-off between the DN power loss and energy cost reductions. Further, simulations demonstrate that all three ETSs deliver nearly 85% DN energy loss reduction with significantly increased revenues compared to an ETS without a CES system.