Miracle-dz/UniFAR-FTUs huggingface dataset:
Darwish, Tamer K and Singh, Satwinder (2013). Does Strategic Human Resource Involvement and Devolvement Enhance Organisational Performance?

Purpose: The purpose of this paper is to test empirically the relationship between the strategic involvement and the devolvement of human resource functions with organisational performance. Design/Methodology/Approach: This paper is based on the primary data collected from the population of financial firms based in Jordan. The methodology adopted for the purpose of data analysis includes the use of basic statistics, zero-order correlations, confirmatory factor analysis and hierarchical regressions. Findings: The results provide strong support for the hypothesis that the involvement of human resource functions into the business and corporate strategy reduces employee turnover rate and enhances financial performance. The analysis does not support the second hypothesis that empowering day-to-day human resource functions to line managers impacts negatively on employee turnover and positively on financial performance. Practical Implications: Our results imply that financial performance can be enhanced and employee turnover rate decreased by involving human resource directors in the overall strategic decision-making process of companies. However, our results also imply that the devolvement of routine human resource issues to line managers is neither positively related to the financial performance of the companies nor negatively related to employee turnover. This raises doubts as to whether, after having involved human resource functions into the strategic affairs of the company, they are empowered enough to make a positive impact. Originality/Value: This is one of few papers conducted on this topic in a non-Western environment, and the first of its kind for the country of Jordan. This paper contributes to the field through its approach to measuring and testing strategic human resource management theory. The paper also successfully links the core aspects of strategic human resource management with objective indicators of financial performance of the companies.
Clearing the Smoke on Cannabis: Respiratory and Cardiovascular Effects of Cannabis Smoking [report]

• Cannabis smoke contains many of the same chemicals as tobacco smoke, several of which are known carcinogens. • Evidence for a link between cannabis smoking and stroke, heart attack and inflammation of the arteries is limited and still unclear. Further studies are required to clarify whether heavy cannabis smoking is a risk factor for the onset of these complications. • Cannabis smoking has been related to a greater incidence of coughing, wheezing, sore throat, chest tightness and hoarse voice. • Evidence for a link between cannabis smoking and serious lung conditions such as cancer or chronic obstructive pulmonary disease is unclear. Further research is needed to clarify whether cannabis smoke is a factor for lung cancer. • There is emerging evidence that quitting cannabis smoking can reverse some of the negative respiratory symptoms associated with its use. • Further research is needed to investigate the shortand long-term effects of vaping cannabis on both cardiovascular and respiratory systems. • It is essential for healthcare professionals to be aware of the impact of cannabis smoking on respiratory and cardiovascular health so that they can inform and advise their patients, as well as develop strategies to promote awareness and general cardiovascular and respiratory health.
Coworking Spaces as a Driver of the Post-Fordist City: a Tool for Building a Creative Ecosystem

Collaborative places nurture creativity and efficiency of cultural and creative industries. Research in collaborative places revealed they are essential for networking and cooperation in the creative ecosystem. The results of studies focusing on competitiveness of coworking spaces and their effect on boosting entrepreneurship are rather vague. Furthermore, an awareness of how coworking spaces stimulate coworkers to engage in urban regeneration through local community initiatives is limited. Hence, this study seeks to provide an insight into coworking spaces from the organizational perspective devoted to entrepreneurship and competitiveness. Simultaneously, the paper aims to reveal synergies between creative communities and local development. The method of data gathering consists of semi-structured in-depth interviews with managers and entrepreneurs from selected countries of the EU applying the grounded theory for their analysis. The results suggest that coworking spaces indicate a boosting of the entrepreneurship of the creative class through collective projects. These activities tend to stimulate knowledge creation and open innovation in the creative ecosystem that benefit local development. Coworking spaces also represent a driving force to initiate and maintain a dialogue between the creative ecosystem and local authorities for culture-led urban development.
INFLUENCE OF INM PRACTICES ON POST HARVEST SOIL NUTRIENT STATUS AND BIOLOGICAL

Field experiments were conducted for evaluating different organic manures on biology in rice during 2007 and 2008 to January in available P (20.9 kg ha randomized block absolute control, recommend dose of nitrogen alone and in combination with different organic manures namely green manure pressmud and vermicompost. Rice cultivar CO 43 was used a cultivar. recorded in 100% RDN along with vermicompost @ 5t ha the same treatment recorded significantly higher soil av after the harvest of the crop. But higher soil available P was registered under 100% RDN along with pressmud compost @ 10 t ha chemical fertiliz
Drug-eluting balloon treatment in femoropopliteal in-stent restenosis of different lengths.

Background In this study, we present one-year results of drugeluting balloon treatment of femoropopliteal in-stent restenosis. Methods A total of 62 patients (48 males, 14 females; mean age 64.2±9.1 years; range, 54 to 81 years) who underwent drugeluting balloon stenting for femoropopliteal in-stent restenosis between August 2013 and October 2017 were included in the study. The patients were classified into three groups based on the narrowing length of stenosis in the stents. Group/Class 1 (n=17): narrowing <1/2 of the stent length; Group/Class 2 (n=22): narrowing >1/2 of the stent length, not totally occluded; and Group/Class 3 (n=23): totally occluded. In-stent restenosis was treated with drug-eluting balloon treatment. Results There was a significant difference among all classes in terms of in-stent restenosis. The length of stenosis was a predictor for in-stent restenosis. The mean stent length was 107.7±24.6 mm in Group 1, 164.6±17.9 mm in Group 2, and 180±19.3 mm in Group 3. For non-occluded in-stent restenosis, restenosis rate at one year after balloon angioplasty was 47.1% in Group 1, 86.4% in Group 2, and 95.7% in Group 3. Femoropopliteal bypass was performed in five patients in whom treatment failed. None of the patients required amputation. Conclusion The length of in-stent restenosis in the femoropopliteal arterial stents is an important predictor for recurrent stenosis, when re-flow is achieved with drug-eluting balloons.
DeVLBert: Learning Deconfounded Visio-Linguistic Representations

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this problem are purely likelihood-based, leading to the spurious correlations and hurt the generalization ability when transferred to out-of-domain downstream tasks. By spurious correlation, we mean that the conditional probability of one token (object or word) given another one can be high (due to the dataset biases) without robust (causal) relationships between them. To mitigate such dataset biases, we propose a Deconfounded Visio-Linguistic Bert framework, abbreviated as DeVLBert, to perform intervention-based learning. We borrow the idea of the backdoor adjustment from the research field of causality and propose several neural-network based architectures for Bert-style out-of-domain pretraining. The quantitative results on three downstream tasks, Image Retrieval (IR), Zero-shot IR, and Visual Question Answering, show the effectiveness of DeVLBert by boosting generalization ability.
Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling

Recent techniques built on generative adversarial networks (GANs), such as cycle-consistent GANs, are able to learn mappings among different domains built from unpaired data sets, through min–max optimization games between generators and discriminators. However, it remains challenging to stabilize the training process and thus cyclic models fall into mode collapse accompanied by the success of discriminator. To address this problem, we propose an novel Bayesian cyclic model and an integrated cyclic framework for interdomain mappings. The proposed method motivated by Bayesian GAN explores the full posteriors of cyclic model via sampling latent variables and optimizes the model with maximum a posteriori (MAP) estimation. Hence, we name it Bayesian CycleGAN. In addition, original CycleGAN cannot generate diversified results. But it is feasible for Bayesian framework to diversify generated images by replacing restricted latent variables in inference process. We evaluate the proposed Bayesian CycleGAN on multiple benchmark data sets, including Cityscapes, Maps, and Monet2photo. The proposed method improve the per-pixel accuracy by 15% for the Cityscapes semantic segmentation task within origin framework and improve 20% within the proposed integrated framework, showing better resilience to imbalance confrontation. The diversified results of Monet2Photo style transfer also demonstrate its superiority over original cyclic model. We provide codes for all of our experiments in https://github.com/ranery/Bayesian-CycleGAN.
A Komar-like integral for mass and angular momentum of asymptotically AdS black holes in Einstein gravity

The purpose of this paper is to enhance the conventional Komar integral to asymptotically anti-de Sitter (AdS) black holes. In order to do so, we first obtain a potential that is the linear combination of the usual Komar potential with two third-order derivative terms generated by the action of the d’Alembertian operator and the exterior derivative upon a Killing vector. Then this higher-order corrected potential is extended to the Einstein gravity with a negative cosmological constant, yielding the potential that is the linear combination of the usual Komar one with it acted on by the d’Alembertian. It is demonstrated that the surface integral of the improved Komar potential can serve as a formula for conserved charges of asymptotically AdS spacetimes. Finally, to illustrate such a formula, we make use of it to compute the mass and the angular momentum of Schwarzschild-AdS black holes, regular AdS black holes, asymptotically AdS Kerr-Sen black holes, Kerr-NUT-AdS black holes, and Kerr-AdS black holes in arbitrary dimensions. The results are in agreement with the ones in the literature.
Bow shocks, nova shells, disc winds and tilted discs: the nova-like V341 Ara has it all

V341 Ara was recently recognized as one of the closest (d ≃ 150 pc) and brightest (V ≃ 10) nova-like cataclysmic variables. This unique system is surrounded by a bright emission nebula, likely to be the remnant of a recent nova eruption. Embedded within this nebula is a prominent bow shock, where the system’s accretion disc wind runs into its own nova shell. In order to establish its fundamental properties, we present the first comprehensive multiwavelength study of the system. Long-term photometry reveals quasi-periodic, super-orbital variations with a characteristic time-scale of 10–16 d and typical amplitude of ≃1 mag. High-cadence photometry from theTransiting Exoplanet Survey Satellite (TESS) reveals for the first time both the orbital period and a ‘negative superhump’ period. The latter is usually interpreted as the signature of a tilted accretion disc. We propose a recently developed disc instability model as a plausible explanation for the photometric behaviour. In our spectroscopic data, we clearly detect antiphased absorption and emission-line components. Their radial velocities suggest a high mass ratio, which in turn implies an unusually low white-dwarf mass. We also constrain the wind mass-loss rate of the system from the spatially resolved [O iii] emission produced in the bow shock; this can be used to test and calibrate accretion disc wind models. We suggest a possible association between V341 Ara and a ‘guest star’ mentioned in Chinese historical records in AD 1240. If this marks the date of the system’s nova eruption, V341 Ara would be the oldest recovered nova of its class and an excellent laboratory for testing nova theory.
Global 𝜀-regularity for 4-dimensional Ricci flow
with integral scalar curvature bound

Ge-Jiang (Geom Funct Anal 27:1231-1256, 2017) proved global $\varepsilon$-regularity for 4-dimensional Ricci flow with bounded scalar curvature. In this note, we extend this result to 4-dimensional Ricci flow with integral bound on the scalar curvature.
Spread of CTX-M-15 Extended Spectrum β-lactamases Encoding Genes Among Enterobacteriaceae in the Middle Eastern Region

The CTX-M type enzymes have become the most prevalent extended spectrumβ-lactamases (ESBLs) worldwide. Among the CTX-M type enzymes, CTX-M-15 is themost widespread and has been reported from all continents. It has been recoveredfrom different Enterobacteriaceae and has been isolated from both community andhospital acquired infections. This review primarily highlights the prevalence of CTXM-15 in addition to other ESBLs in the Middle East. Detection of any type of ESBL isof importance in therapeutic treatment.
Witnessing non-Markovian effects of quantum processes through Hilbert-Schmidt speed

Non-Markovian effects can speed up the dynamics of quantum systems while the limits of the evolution time can be derived by quantifiers of quantum statistical speed. We introduce a witness for characterizing the non-Markovianity of quantum evolutions through the Hilbert-Schmidt speed (HSS), which is a special type of quantum statistical speed. This witness has the advantage of not requiring diagonalization of evolved density matrix. Its sensitivity is investigated by considering several paradigmatic instances of open quantum systems, such as one qubit subject to phase-covariant noise and Pauli channel, two independent qubits locally interacting with leaky cavities, V-type and $\Lambda $-type three-level atom (qutrit) in a dissipative cavity. We show that the proposed HSS-based non-Markovianity witness detects memory effects in agreement with the well-established trace distance-based witness, being sensitive to system-environment information backflows.
Axisymmetry of critical points for the Onsager functional

A simple proof is given of the classical result (Fatkullin I, Slastikov V. 2005 Critical points of the Onsager functional on a sphere. Nonlinearity 18, 2565–2580 (doi:10.1088/0951-7715/18/6/008); Liu H et al. 2005 Axial symmetry and classification of stationary solutions of Doi-Onsager equation on the sphere with Maier-Saupe potential. Commun. Math. Sci. 3, 201–218 (doi:10.4310/CMS.2005.v3.n2.a7)) that critical points for the Onsager functional with the Maier-Saupe molecular interaction are axisymmetric, including the case of stable critical points with an additional dipole-dipole interaction (Zhou H et al. 2007 Characterization of stable kinetic equilibria of rigid, dipolar rod ensembles for coupled dipole-dipole and Maier-Saupe potentials. Nonlinearity 20, 277–297 (doi:10.1088/0951-7715/20/2/003)). The proof avoids spherical polar coordinates, instead using an integral identity on the sphere S2. For general interactions with absolutely continuous kernels the smoothness of all critical points is established, generalizing a result in (Vollmer MAC. 2017 Critical points and bifurcations of the three-dimensional Onsager model for liquid crystals. Archive for Rational Mechanics and Analysis 226, 851–922 (doi:10.1007/s00205-017-1146-8)) for the Onsager interaction. It is also shown that non-axisymmetric critical points exist for a wide variety of interactions including that of Onsager. This article is part of the theme issue ‘Topics in mathematical design of complex materials’.
Deep Reinforcement Learning with Robust and Smooth Policy

Deep reinforcement learning (RL) has achieved great empirical successes in various domains. However, the large search space of neural networks requires a large amount of data, which makes the current RL algorithms not sample efficient. Motivated by the fact that many environments with continuous state space have smooth transitions, we propose to learn a smooth policy that behaves smoothly with respect to states. We develop a new framework -- \textbf{S}mooth \textbf{R}egularized \textbf{R}einforcement \textbf{L}earning ($\textbf{SR}^2\textbf{L}$), where the policy is trained with smoothness-inducing regularization. Such regularization effectively constrains the search space, and enforces smoothness in the learned policy. Moreover, our proposed framework can also improve the robustness of policy against measurement error in the state space, and can be naturally extended to distribubutionally robust setting. We apply the proposed framework to both on-policy (TRPO) and off-policy algorithm (DDPG). Through extensive experiments, we demonstrate that our method achieves improved sample efficiency and robustness.
An Exact Quantum Query Algorithm Beyond Parity using Maiorana-McFarland (MM) type Bent functions

The Exact Quantum Query model is the least explored query model, and almost all of the functions for which non-trivial query algorithms exist are symmetric in nature. In this paper we explore the Maiorana-McFarland(MM) Bent functions, defined on all even $n$ variables. The Deterministic Query Complexity ($D(f)$) of all functions in this class is $n$. In this regard we construct a $\frac{n}{2} + \lceil \frac{n}{8} \rceil$ query exact quantum algorithm that is not a parity decision tree and evaluates a non-symmetric subclass of MM type Bent functions on $n$ variables consisting of $\left(2^{\lfloor \frac{n}{4} \rfloor}! \right)^2 2^{2^{\lfloor \frac{n}{4} \rfloor}}$ functions. Finally we also extend this technique for functions defined on odd values of $n$ using the Bent concatenation method. To the best of our knowledge, this is the first algorithm beyond parity for a general class of non-symmetric functions.
Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation

Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU). The recent advent of end-to-end neural models, self-supervised via language modeling (LM), and their success on a wide range of LU tasks, however, questions this belief. In this work, we empirically investigate the usefulness of supervised parsing for semantic LU in the context of LM-pretrained transformer networks. Relying on the established fine-tuning paradigm, we first couple a pretrained transformer with a biaffine parsing head, aiming to infuse explicit syntactic knowledge from Universal Dependencies treebanks into the transformer. We then fine-tune the model for LU tasks and measure the effect of the intermediate parsing training (IPT) on downstream LU task performance. Results from both monolingual English and zero-shot language transfer experiments (with intermediate target-language parsing) show that explicit formalized syntax, injected into transformers through IPT, has very limited and inconsistent effect on downstream LU performance. Our results, coupled with our analysis of transformers’ representation spaces before and after intermediate parsing, make a significant step towards providing answers to an essential question: how (un)availing is supervised parsing for high-level semantic natural language understanding in the era of large neural models?
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence

Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting. While most existing work studies the problem in the context of computer vision or console games, this paper focuses on reinforcement learning in autonomous cyber defence under partial observability. We demonstrate that under the black-box setting, where the attacker has no direct access to the target RL model, causative attacks—attacks that target the training process—can poison RL agents even if the attacker only has partial observability of the environment. In addition, we propose an inversion defence method that aims to apply the opposite perturbation to that which an attacker might use to generate their adversarial samples. Our experimental results illustrate that the countermeasure can effectively reduce the impact of the causative attack, while not significantly affecting the training process in non-attack scenarios.
Jointly Fine-Tuning "BERT-like" Self Supervised Models to Improve Multimodal Speech Emotion Recognition

Multimodal emotion recognition from speech is an important area in affective computing. Fusing multiple data modalities and learning representations with limited amounts of labeled data is a challenging task. In this paper, we explore the use of modality-specific "BERT-like" pretrained Self Supervised Learning (SSL) architectures to represent both speech and text modalities for the task of multimodal speech emotion recognition. By conducting experiments on three publicly available datasets (IEMOCAP, CMU-MOSEI, and CMU-MOSI), we show that jointly fine-tuning "BERT-like" SSL architectures achieve state-of-the-art (SOTA) results. We also evaluate two methods of fusing speech and text modalities and show that a simple fusion mechanism can outperform more complex ones when using SSL models that have similar architectural properties to BERT.
Natural Wake-Sleep Algorithm

The benefits of using the natural gradient are well known in a wide range of optimization problems. However, for the training of common neural networks the resulting increase in computational complexity sets a limitation to its practical application. Helmholtz Machines are a particular type of generative model composed of two Sigmoid Belief Networks (SBNs), acting as an encoder and a decoder, commonly trained using the Wake-Sleep (WS) algorithm and its reweighted version RWS. For SBNs, it has been shown how the locality of the connections in the graphical structure induces sparsity in the Fisher information matrix. The resulting block diagonal structure can be efficiently exploited to reduce the computational complexity of the Fisher matrix inversion and thus compute the natural gradient exactly, without the need of approximations. We present a geometric adaptation of well-known methods from the literature, introducing the Natural Wake-Sleep (NWS) and the Natural Reweighted Wake-Sleep (NRWS) algorithms. We present an experimental analysis of the novel geometrical algorithms based on the convergence speed and the value of the log-likelihood, both with respect to the number of iterations and the time complexity and demonstrating improvements on these aspects over their respective non-geometric baselines.