Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Spiritual Intelligence and Affective Commitment Among Police Officers in Malaysia: The Mediating Role of Work Engagement

The aim of this study is to investigate the direct relationship between spiritual intelligence and work engagement as well as the relationship between work engagement and affective commitment. In addition, this study also aims to examine the role of work engagement as a mediator on the relationship between spiritual intelligence and affective commitment. As there is scarce empirical evidence with regard to the role of work engagement as a mediator, particularly between spiritual intelligence and affective commitment, this study contributes towards enriching this field of study. The study was conducted on a sample of 372 police officers in five states, namely Kuala Lumpur, Selangor, Johor, Sarawak and Sabah. Hypothesis testing was analyzed using PLS-SEM analysis. Results indicated that: (1) three dimensions of spiritual intelligence consisting of critical existential thinking (CET), personal meaning making (PMP) and transcendental awareness (TA) has a positive direct and significant relationship with work engagement; (2) one dimension of spiritual intelligence consisting of conscious state expansion) (CSE) has nonsignificant relationship with work engagement. (3) Work engagement has a positive and significant direct relationship with affective commitment; (4) Work engagement has a significant role as mediators between the relationship of the three dimensions of spiritual intelligence (CET, PMP and TA) with affective commitment. (5) Work engagement has non-significant role as mediators between the relationships of one dimension of spiritual intelligence (CSE) with affective commitment. Results of this study have proven that work engagement was significant in the role of mediator to influence spiritual intelligence in the relationship with affective commitment. Findings of the study also indicate the importance of spiritual intelligence in influencing loyalty and commitment of the police officers.
Remittances, ICT and Pension Income Coverage: The International Evidence

This study examines the impact of remittances and information and communication technology (ICT) on pension at the country level. Our empirical evidence, based on data from 96 countries, indicate a significant non-linearity between remittances, ICT and pension income coverage. First, we find a convex relation between remittances and pension income coverage, indicating that increases in remittance, initially decreases pension income coverage, but as remittance increases beyond a certain point, so too does pension income coverage. This inflection point, where the effect of remittances turns from negative to positive, is estimated to be around 3.09% of GDP. Second, we document a concave relationship between ICT (i.e. mobile subscription and internet penetration) and pension income coverage. An increase in ICT results in increased pension income coverage. However, when ICT reaches a certain point, any further increase is associated with lower pension income coverage. The estimated optimal point is found to be around 140.14 subscriptions (per 100 people) for mobile phone and 27.93 (per 100 people) for internet penetration, respectively. Other implications are discussed.
Using Time-Varying Parameter VAR Approach to Assess the Effects of Government Spending Shocks in Korea

We apply Time Varying Parameter Structural Vector Autoregression (TVP-SVAR) model to Korea’s fiscal data from 1986 through 2011 to estimate the effect on output growth of expansionary government spending in Korea. Our estimation results show that the GDP growth has significantly increased for approximately three to four quarters after the shock arrives, suggesting that an increase in discretionary government spending has affected Korea’s economic growth in a favorable way over the sample period. Our fiscal multiplier estimates imply that the stimulating effect of a positive spending shock has declined over time. Furthermore, our regression analysis suggests that the government spending multiplier in Korea appears inversely related to the output gap, the degree of openness, whereas exhibiting positive relationships with the share of government spending on fixed capital formulations as well as the level of household debts. JEL classification: C5; E6; H5
Reprogramming cellular fate using defined factors

Embryonic stem (ES) cells have a vast therapeutic potential given their pluripotency, or the ability to differentiate into tissues from all three germ layers. One of the ultimate goals of regenerative medicine is to isolate pluripotent stem cells from patients. Nuclear reprogramming offers the possibility of creating patient-specific cell lines, thus abrogating the need for immunosuppressants following cell transplantation therapy. It was recently reported that the forced expression of four transcription factors, Oct4, Sox2, c-Myc and Klf4 can induce a pluripotent state in somatic cells, without the need for embryo destruction. The work presented here aims to characterize reprogramming using defined factors and provide insight into the mechanisms governing this process. It also seeks to identify transient cues to induce reprogramming in somatic cells, alleviating the need for virally transduced transcription factors that hinder its eventual clinical use. Thesis Supervisor: Dr. Rudolf Jaenisch Title: Professor of Biology
Working Paper WP / 20 / 057 Information Asymmetry and Insurance in Africa

In this study, we assess the relevance of decreasing information asymmetry on life and nonlife insurance consumption, by using data from 48 African countries during the period 20042014. Reduced information asymmetry is proxied by information sharing offices, namely: public credit registries and private credit bureaus. The empirical evidence is based on the Generalised Method of Moments. The findings show that information sharing offices increase insurance consumption with a comparatively higher magnitude in life insurance penetration, relative to non-life insurance penetration. Practical and theoretical implications are discussed. JEL Classification: I30; G20; G22; O16; O55
Efficacy of organic mulches on soil properties, earthworm population, growth and yield of aonla cv. NA7 in semi-arid ecosystem.

The influence of different organic mulches viz, paddy straw, maize straw, rice husk, grasses, subabul loppings on soil properties, earthworm population, growth, yield and fruit quality attributes was studied. Various organic mulches significantly increased the soil moisture status at various soil depths. Paddy straw mulch recorded the maximum soil moisture content followed by maize straw and grasses, among the different evaluated organic mulches. The findings of the study indicated that paddy straw mulch followed by maize straw and grasses had given favourable results with regards to soil moisture and physico-chemical attributes of soil and earthworm population in basin soil. Paddy straw mulches were found to be more effective in producing maximum growth than rest of the mulching treatments. Plants treated with paddy straw mulch recorded highest yield 09.00 kg/plant and 42.15 kg/plant during 2005 and 2006. TSS, total sugar, total phenols and vitamin C were observed maximum with paddy straw followed by maize straw mulch. Among the evaluated organic mulches, paddy straw had showed better response followed by maize straw. In view of the superiority over other organic mulches, use of paddy straw as mulch material over long period needs to be popularized for increasing the productivity of aonla orchards in rainfed condition under semi-arid ecosystem.
Estimation of genetic and phenotypic parameters of economic traits in White Leghorn.

The data on age at first egg (ASM), 16 and 40 week body weight (Bw16, Bw40), 28 and 40 week egg weight (Ew 28, Ew40) and egg production up to 40 weeks age (En40) pertaining to 2558 progeny belonging to 50 sires and 300 dam of IWN strain were formed the basis of this study. The mean values ASM, Bw 16, Bw40, Ew28, Ew40 and En40 were 151.38 days, 1.097 kg, 1.563 kg, 50.47 g, 54.04g and 102.81, respectively. The heritabilities of ASM, egg weight and egg production was low to moderate. Higher heritability was recorded for body weight characters. Positive correlation was observed between both egg weight characters on genetic and phenotypic scale as well as between body weight characters. Negative correlation was noticed between ASM and En40 on both genetic and phenotypic scale.
The Origin of Elements from Carbon to Uranium

To reach a deeper understanding of the origin of elements in the periodic table, we construct Galactic chemical evolution (GCE) models for all stable elements from C (A = 12) to U (A = 238) from first principles, i.e., using theoretical nucleosynthesis yields and event rates of all chemical enrichment sources. This enables us to predict the origin of elements as a function of time and environment. In the solar neighborhood, we find that stars with initial masses of M > 30M ⊙ can become failed supernovae if there is a significant contribution from hypernovae (HNe) at M ∼ 20–50M ⊙. The contribution to GCE from super-asymptotic giant branch (AGB) stars (with M ∼ 8–10M ⊙ at solar metallicity) is negligible, unless hybrid white dwarfs from low-mass super-AGB stars explode as so-called Type Iax supernovae, or high-mass super-AGB stars explode as electron-capture supernovae (ECSNe). Among neutron-capture elements, the observed abundances of the second (Ba) and third (Pb) peak elements are well reproduced with our updated yields of the slow neutron-capture process (s-process) from AGB stars. The first peak elements (Sr, Y, Zr) are sufficiently produced by ECSNe together with AGB stars. Neutron star mergers can produce rapid neutron-capture process (r-process) elements up to Th and U, but the timescales are too long to explain observations at low metallicities. The observed evolutionary trends, such as for Eu, can well be explained if ∼3% of 25–50M ⊙ HNe are magneto-rotational supernovae producing r-process elements. Along with the solar neighborhood, we also predict the evolutionary trends in the halo, bulge, and thick disk for future comparison with Galactic archeology surveys.
Physicochemical models: source-tailored or generic?

Physicochemical models can be powerful tools to trace the chemical evolution of a protostellar system and allow to constrain its physical conditions at formation. The aim of this work is to assess whether source-tailored modelling is needed to explain the observed molecular abundances around young, low-mass protostars or if, and to what extent, generic models can improve our understanding of the chemistry in the earliest stages of star formation. The physical conditions and the abundances of simple, most abundant molecules based on three models are compared. After establishing the discrepancies between the calculated chemical output, the calculations are redone with the same chemical model for all three sets of physical input parameters. With the differences arising from the chemical models eliminated, the output is compared based on the influence of the physical model. Results suggest that the impact of the chemical model is small compared to the influence of the physical conditions, with considered time-scales having the most drastic effect. Source-tailored models may be simpler by design; however, likely do not sufficiently constrain the physical and chemical parameters within the global picture of star-forming regions. Generic models with more comprehensive physics may not provide the optimal match to observations of a particular protostellar system, but allow a source to be studied in perspective of other star-forming regions.
Arbitrarily Strong Utility-Privacy Tradeoff in Multi-Agent Systems

Each agent in a network makes a local observation that is linearly related to a set of public and private parameters. The agents send their observations to a fusion center to allow it to estimate the public parameters. To prevent leakage of the private parameters, each agent first sanitizes its local observation using a local privacy mechanism before transmitting it to the fusion center. We investigate the utility-privacy tradeoff in terms of the Cramér-Rao lower bounds for estimating the public and private parameters. We study the class of privacy mechanisms given by linear compression and noise perturbation, and derive necessary and sufficient conditions for achieving arbitrarily strong utility-privacy tradeoff in a multi-agent system for both the cases where prior information is available and unavailable, respectively. We also provide a method to find the maximum estimation privacy achievable without compromising the utility and propose an alternating algorithm to optimize the utility-privacy tradeoff in the case where arbitrarily strong utility-privacy tradeoff is not achievable.
BREEDS: Benchmarks for Subpopulation Shift

We develop a methodology for assessing the robustness of models to subpopulation shift---specifically, their ability to generalize to novel data subpopulations that were not observed during training. Our approach leverages the class structure underlying existing datasets to control the data subpopulations that comprise the training and test distributions. This enables us to synthesize realistic distribution shifts whose sources can be precisely controlled and characterized, within existing large-scale datasets. Applying this methodology to the ImageNet dataset, we create a suite of subpopulation shift benchmarks of varying granularity. We then validate that the corresponding shifts are tractable by obtaining human baselines for them. Finally, we utilize these benchmarks to measure the sensitivity of standard model architectures as well as the effectiveness of off-the-shelf train-time robustness interventions. Code and data available at this https URL .
ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows

Downscaling is a common task in climate science and meteorology in which the goal is to use coarse scale, spatio-temporal data to infer values at finer scales. Statistical downscaling aims to approximate this task using statistical patterns gleaned from an existing dataset of downscaled values, often obtained from observations or physical models. In this work, we investigate the application of domain alignment to the task of statistical downscaling. We present ClimAlign, a novel method for unsupervised, generative downscaling using adaptations of recent work in normalizing flows for variational inference. We evaluate the viability of our method using several different metrics on two datasets consisting of daily temperature and precipitation values gridded at low (1° latitude/longitude) and high ( and ) resolutions. We show that our method achieves comparable predictive performance to existing supervised statistical downscaling methods while simultaneously allowing for both conditional and unconditional sampling from the joint distribution over high and low resolution spatial fields. To the best of our knowledge, this is the first proposed method for unsupervised statistical downscaling, and one of very few proposed methods that allows for efficient sampling of synthetic data.
Testing jet geometries and disc–jet coupling in the neutron star LMXB 4U 0614 + 091 with the internal shocks model

Multi-wavelength spectral energy distributions of Low Mass X-ray Binaries in the hard state are determined by the emission from a jet, for frequencies up to mid-infrared, and emission from the accretion flow in the optical to X-ray range. In the last years, the flat radio-to-mid-IR spectra of Black Hole (BH) X-ray binaries was described using the internal shocks model, which assumes that the fluctuations in the velocity of the ejecta along the jet are driven by the fluctuations in the accretion flow, described by the X-ray Power Density Spectrum (PDS). In this work we attempt to apply this model for the first time to a Neutron Star (NS) LMXB, i.e. 4U 0614+091. We used the multi-wavelength data set obtained in 2006, comprising data from radio to X-ray, and applied a model which includes an irradiated disc model for the accretion flow and an updated version of the internal shocks code for the ejection. The new version of the code allows to change the geometry of the jet for the case of non-conical jets. Only two alternative scenarios provide a satisfactory description of the data: using the X-ray PDS but in a non-conical geometry for the jet, or either using a conical geometry but with a "flicker-noise" PDS. Both scenarios would imply some differences with the results obtained with similar models on BH X-ray binaries, shedding light on the possibility that jets in NS and BH binaries might somehow have a different geometry or a different coupling with the accretion flow.
Unconventional singularities and frictional rupture energy budget

A widespread framework for understanding frictional rupture, such as earthquakes along geological faults, invokes an analogy to ordinary cracks. In ordinary cracks, near rupture edge fields feature a conventional square root singularity, accompanied by an associated edge-localized energy balance. It has been recently demonstrated that this analogy holds to a very good approximation, yet that overall rupture (earthquake) energy budget includes also a significant non-edge-localized excess frictional dissipation, which is predicted to vanish in the crack analogy. We develop a macroscopic theory that address these puzzling observations and fully resolves the apparent contradiction. The theory identifies a "hidden" small parameter, which quantifies the deviation from a conventional singular behavior of near-edge fields, and predicts non-edge-localized excess frictional dissipation that is dominated by an accumulated spatially-extended contribution. The latter, which is also position dependent, is associated with the generic rate-dependent nature of friction and compensates the aforementioned smallness. The theoretical predictions are quantitatively supported by available numerical results, and their possible implications for earthquake physics are discussed.
tf-Darshan: Understanding Fine-grained I/O Performance in Machine Learning Workloads

Machine Learning applications on HPC systems have been gaining popularity in recent years. The upcoming large scale systems will offer tremendous parallelism for training through GPUs. However, another heavy aspect of Machine Learning is I/O, and this can potentially be a performance bottleneck. TensorFlow, one of the most popular Deep-Learning platforms, now offers a new profiler interface and allows instrumentation of TensorFlow operations. However, the current profiler only enables analysis at the TensorFlow platform level and does not provide system-level information. In this paper, we extend TensorFlow Profiler and introduce tf-Darshan, both a profiler and tracer, that performs instrumentation through Darshan. We use the same Darshan shared instrumentation library and implement a runtime attachment without using a system preload. We can extract Darshan profiling data structures during TensorFlow execution to enable analysis through the TensorFlow profiler. We visualize the performance results through TensorBoard, the web-based TensorFlow visualization tool. At the same time, we do not alter Darshan's existing implementation. We illustrate tf-Darshan by performing two case studies on ImageNet image and Malware classification. We show that by guiding optimization using data from tf-Darshan, we increase POSIX I/O bandwidth by up to 19% by selecting data for staging on fast tier storage. We also show that Darshan has the potential of being used as a runtime library for profiling and providing information for future optimization.
Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE)

This report considers the problem of Byzantine fault-tolerance in homogeneous multi-agent distributed learning. In this problem, each agent samples i.i. d. data points, and the goal for the agents is to compute a mathematical model that optimally fits, in expectation, the data points sampled by all the agents. We consider the case when a certain number of agents may be Byzantine faulty. Such faulty agents may not follow a prescribed learning algorithm. Faulty agents may share arbitrary incorrect information regarding their data points to prevent the non-faulty agents from learning a correct model. We propose a fault-tolerance mechanism for the distributed stochastic gradient descent (D-SGD) method -- a standard distributed supervised learning algorithm. Our fault-tolerance mechanism relies on a norm based gradient-filter, named comparative gradient elimination (CGE), that aims to mitigate the detrimental impact of malicious incorrect stochastic gradients shared by the faulty agents by limiting their Euclidean norms. We show that the CGE gradient-filter guarantees fault-tolerance against a bounded number of Byzantine faulty agents if the stochastic gradients computed by the non-faulty agents satisfy the standard assumption of bounded variance. We demonstrate the applicability of the CGE gradient-filer for distributed supervised learning of artificial neural networks. We show that the fault-tolerance by the CGE gradient-filter is comparable to that by other state-of-the-art gradient-filters, namely the multi-KRUM, geometric median of means, and coordinate-wise trimmed mean. Lastly, we propose a gradient averaging scheme that aims to reduce the sensitivity of a supervised learning process to individual agents' data batch-sizes. We show that gradient averaging improves the fault-tolerance property of a gradient-filter, including, but not limited to, the CGE gradient-filter.
Grasping Field: Learning Implicit Representations for Human Grasps

Robotic grasping of house-hold objects has made remarkable progress in recent years. Yet, human grasps are still difficult to synthesize realistically. There are several key reasons: (1) the human hand has many degrees of freedom (more than robotic manipulators); (2) the synthesized hand should conform to the surface of the object; and (3) it should interact with the object in a semantically and physically plausible manner. To make progress in this direction, we draw inspiration from the recent progress on learning-based implicit representations for 3D object reconstruction. Specifically, we propose an expressive representation for human grasp modelling that is efficient and easy to integrate with deep neural networks. Our insight is that every point in a three-dimensional space can be characterized by the signed distances to the surface of the hand and the object, respectively. Consequently, the hand, the object, and the contact area can be represented by implicit surfaces in a common space, in which the proximity between the hand and the object can be modelled explicitly. We name this 3D to 2D mapping as Grasping Field, parameterize it with a deep neural network, and learn it from data. We demonstrate that the proposed grasping field is an effective and expressive representation for human grasp generation. Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud. The extensive experiments demonstrate that our generative model compares favorably with a strong baseline and approaches the level of natural human grasps. Furthermore, based on the grasping field representation, we propose a deep network for the challenging task of 3D hand-object interaction reconstruction from a single RGB image. Our method improves the physical plausibility of the hand-object contact reconstruction and achieves comparable performance for 3D hand reconstruction compared to state-of-the-art methods. Our model and code are available for research purpose at https://github.com/korrawe/grasping_field.