Miracle-dz/UniFAR-FTUs huggingface dataset:
Water Vapour Variability in the High-Latitude Upper Troposphere- Part 2: Impact of Volcanic Eruptions

Abstract. The impact of volcanic eruptions on water vapour in the high-latitude upper troposphere is studied using deseasonalized time series based on observations by the Atmospheric Chemistry Experiment (ACE) water vapour sensors, namely MAESTRO (Measurements of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation) and the Fourier Transform Spectrometer (ACE-FTS). The two eruptions with the greatest impact on the high-latitude upper troposphere during the time frame of this satellite-based remote sensing mission are chosen. The Puyehue–Cordon Caulle volcanic eruption in June 2011 was the most explosive in the past 24 years and is shown to be able to account for the observed (50 ± 12) % increase in water vapour in the southern high-latitude upper troposphere in July 2011 after a minor adjustment for the simultaneous influence of the Antarctic oscillation. Eyjafjallajokull erupted in the spring of 2010, increasing water vapour in the upper troposphere at northern high latitudes significantly for a period of ∼ 1 month. These findings imply that extratropical volcanic eruptions in windy environments can lead to significant perturbations to high-latitude upper tropospheric humidity mostly due to entrainment of lower tropospheric moisture by wind-blown plumes. The Puyehue–Cordon Caulle eruption must be taken into account to properly determine the magnitude of the trend in southern high-latitude upper tropospheric water vapour over the last decade.
Low Rank Matrix Approximation

Low Rank Approximation is a fundamental computation across a broad range of applications where matrix dimension reduction is required. This survey paper on the Low Rank Approximation (LRA) of matrices provides a broad overview of recent progress in the field with perspectives from both the Theoretical Computer Science (TCS) and Numerical Linear Algebra (NLA) points of view. While traditional application areas of LRA have come from scientific, engineering, and statistical disciplines, a plethora of recent activity has been seen in image processing, machine learning, and climate informatics to name just a few of the emerging modern technologies. All of these disciplines and technologies are increasingly challenged by the scale of modern massive data sets (MMDS) collected from a variety of sources including sensor measurement, computational models, and Internet. At the same time, applied mathematicians and computer scientists have been seeking alternatives to the standard numerical linear algebra (NLA) algorithms that are fundamentally incapable to handle the sheer size of these MMDS’s. Against the backdrop of the disparity between increasing computer processing power and disk storage capabilities on the one hand versus memory bandwidth limitations on the other, a further research goal is to find flexible new LRA approaches that leverage the strengths of modern hardware architectures while limiting exposure to data communication bottlenecks. Central to new approaches with regard to MMDS’s is the approximation of a matrix by one of much smaller dimension facilitated by randomization techniques. The term ”low rank” in the context of LRA refers to the inherent ”dimension” of the matrix which may possibly be much smaller than both the actual number of rows and columns in the matrix. Thus, a matrix may be approximated, if not exactly represented, by another matrix containing significantly less rows and columns while still preserving the salient characteristics of the original matrix. Recent results have shown that it is possible to attain this dimension reduction using alternative approximation and probabilistic techniques where randomization plays a key role. This paper surveys classical and recent results in LRA and presents practical algorithms representative of the recent research progress. A broad understanding of potential future research directions in LRA should also be evident to readers from theoretical, algorithmic, and computational backgrounds.
Non-Dominated Sorting Harmony Search Differential Evolution (NS-HS-DE): A Hybrid Algorithm for Multi-Objective Design of Water Distribution Networks

We developed a hybrid algorithm for multi-objective design of water distribution networks (WDNs) in the present study. The proposed algorithm combines the global search schemes of differential evolution (DE) with the local search capabilities of harmony search (HS) to enhance the search proficiency of evolutionary algorithms. This method was compared with other multi-objective evolutionary algorithms (MOEAs) including NSGA2, SPEA2, MOEA/D and extended versions of DE and HS combined with non-dominance criteria using several metrics. We tested the compared algorithms on four benchmark WDN design problems with two objective functions, (i) the minimization of cost and (ii) the maximization of resiliency as reliability measure. The results showed that the proposed hybrid method provided better optimal solutions and outperformed the other algorithms. It also exhibited significant improvement over previous MOEAs. The hybrid algorithm generated new optimal solutions for a case study that dominated the best-known Pareto-optimal solutions in the literature
Clustering LaTeX Solutions to Machine Learning Assignments for Rapid Assessment

Math assignments in machine learning (ML) classes allow students to gain competency in performing derivations. These derivations are graded by hand, which is a time-consuming process. We present our on-going work on clustering derivations so that they can be graded by cluster, rather than one at a time. This paper describes our current algorithms and interface for merging mathematically equivalent expressions, a first step toward the goal of clustering solutions.
Impact of Individual Feelings of Energy on Creative Work Involvement: A Mediating Role of Leader-Member Exchange

Individual feelings of energy (IFE), creative work involvement (CWI) and leader-member exchange (LMX) are understudied areas in Pakistan. The purposes of this study are: a) to analyze the mediating role of LMX on the relationship between IFE and CWI; and b) to evaluate whether the LMX relationship mediates differently for non-managerial staff, supervisors, middle and senior managers. A sample of 300 responses is drawn from the manufacturing companies of Karachi using a questionnaire administered to them. Exploratory factor analysis is used to extract three orthogonal constructs (namely, IFE, CWI, and LMX) and their convergent and discriminant validity are established through confirmatory factor analysis. Both composite and MaxR(H) statistics are estimated to ensure the reliability of the three constructs. The results show that LMX partially mediates the positive relationship between IFE and CWI. In addition, the results of the multi-group mediation analysis for different levels of responsibility show that LMX partially mediates in case of middle and senior management only. However, it does not mediate for non-managerial staff and supervisors. The most important theoretical contribution of the study in the existing knowledge of leadership-creativity relationship is that LMX partially mediates the relationship between IFE and CWI. Besides, this is one of the first reports in the context of the manufacturing companies of Pakistan.
Key Sectors in the Moroccan Economy: An Application of Input-Output Analysis

Abstract The exploration of the structural features and sectoral interdependences of and in an economy is fundamental for the understanding of its modes of functioning and of its transformations over time. Input-output analysis is largely used to fulfill this objective. Furthermore, information provided by the Leontief inverse matrix is useful for the identification of key sectors. This identification may guide policy makers in setting an adequate industrial strategy. In this paper, the classification of productive sectors is performed by using the Unweighted Rassmussen Approach. The ordering of sectors depends on the intensity of their links with other sectors. Two results ought to be highlighted. First, key sectors of the Moroccan economy (sectors that are classified in category II) reduced to two sectors in 2007 instead of four sectors in 1998. Second, the ordering of sectors is highly sensitive to the precision of the data and to the year in which the classification is realized.
Modifications of the quasi‐biennial oscillation by a geoengineering perturbation of the stratospheric aerosol layer

This paper examines the impact of geoengineering via stratospheric sulfate aerosol on the quasi‐biennial oscillation (QBO) using the NASA Goddard Earth Observing System version 5 Chemistry Climate Model. We performed four 30 year simulations with a continuous injection of sulfur dioxide on the equator at 0° longitude. The four simulations differ by the amount of sulfur dioxide injected (5 Tg/yr and 2.5 Tg/yr) and the altitude of the injection (16 km–25 km and 22 km–25 km). We find that such an injection dramatically alters the quasi‐biennial oscillation, prolonging the phase of easterly shear with respect to the control simulation. This is caused by the increased aerosol heating and associated warming in the tropical lower stratosphere and higher residual vertical velocity. In the case of maximum perturbation, i.e., highest stratospheric aerosol burden, the lower tropical stratosphere is locked into a permanent westerly QBO phase.
Two new sibling species of Mantidactylus cornutus from Madagascar (Amphibia, Anura, Ranidae)

A review of the Malagasy frogs which in the past were subsumed under the name Mantidactylus redimitus, together with new field data, led to the confirmation of the specific validity of M. cornutus and to the recognition of two new species: M. tschenki, spec. nov. from Ranomafana is morphologically similar to M. cornutus but shows distinct differences in advertisement call and has a slightly bilobed (instead of a roundish) subgular vocal sac. M. tandroka, spec. nov. from higher elevations of the Marojezy massif is distinguished by a wide head, distinct dorsal ridges, and colouration from M. cornutus, M. tschenki, and M. redimitus. The discovery of close syntopy of M. redimitus and M. cornutus in central eastern Madagascar confirms that they represent valid species and that advertisement calls are good indicators for specific distinctness in Malagasy anurans.
Polarization, Growth and Social Policy in the Case of Israel, 1997–2008

Abstract In this paper we study income polarization by first comparing the efficiency of two statistical models to identify the number of poles in the income distribution empirically. The statistical models used are a multi-resolution analysis (MRA) and a log-normal approach (LNA). We then apply the methodology to Israeli income data over the years 1997−2008 in order to empirically detect the number of income classes as sub-populations of incomes concentrated around an optimally determined number of poles. After that we compute polarization using a multiplicative normalized polarization measure, developed by Palacios-González and García- Fernández (An Intra-Group Variance Based Polarization Measure, 2010), which consists of three interacting components based on well-known axioms of Esteban and Ray (Extensions of a Measure of Polarization OCDE Countries, 1994). Finally we study the causes of the obtained polarization results in a multinomial logit analysis.
Anthropogenic CO2 in the oceans estimated using transit time distributions

The distribution of anthropogenic carbon (Cant) in the oceans is estimated using the transit time distribution (TTD) method applied to global measurements of chlorofluorocarbon-12 (CFC12). Unlike most other inference methods, the TTD method does not assume a single ventilation time and avoids the large uncertainty incurred by attempts to correct for the large natural carbon background in dissolved inorganic carbon measurements. The highest concentrations and deepest penetration of anthropogenic carbon are found in the North Atlantic and Southern Oceans. The estimated total inventory in 1994 is 134 Pg-C. To evaluate uncertainties the TTD method is applied to output from an ocean general circulation model (OGCM) and compared the results to the directly simulated Cant. Outside of the Southern Ocean the predicted Cant closely matches the directly simulated distribution, but in the Southern Ocean the TTD concentrations are biased high due to the assumption of ‘constant disequilibrium’. The net result is a TTD overestimate of the global inventory by about 20%. Accounting for this bias and other centred uncertainties, an inventory range of 94–121 Pg-C is obtained. This agrees with the inventory of Sabine et al., who applied the ΔC* method to the same data. There are, however, significant differences in the spatial distributions: The TTD estimates are smaller than ΔC* in the upper ocean and larger at depth, consistent with biases expected in ΔC* given its assumption of a single parcel ventilation time.
A First Joint Look at DoS Atacks and BGP Blackholing in the Wild

BGP blackholing is an operational countermeasure that builds upon the capabilities of BGP to achieve DoS mitigation. Although empirical evidence of blackholing activities are documented in literature, a clear understanding of how blackholing is used in practice when attacks occur is still missing. This paper presents a frst joint look at DoS attacks and BGP blackholing in the wild. We do this on the basis of two complementary data sets of DoS attacks, inferred from a large network telescope and DoS honeypots, and on a data set of blackholing events. All data sets span a period of three years, thus providing a longitudinal overview of operational deployment of blackholing during DoS attacks.
The potential of global reanalysis datasets in identifying flood events in Southern Africa

Abstract. Sufficient and accurate hydro-meteorological data are essential to manage water resources. Recently developed global reanalysis datasets have significant potential in providing these data, especially in regions such as Southern Africa that are both vulnerable and data poor. These global reanalysis datasets have, however, not yet been exhaustively validated and it is thus unclear to what extent these are able to adequately capture the climatic variability of water resources, in particular for extreme events such as floods. This article critically assesses the potential of a recently developed global Water Resources Reanalysis (WRR) dataset developed in the European Union's Seventh Framework Programme (EU-FP7) eartH2Observe (E2O) project for identifying floods, focussing on the occurrence of floods in the Limpopo River basin in Southern Africa. The discharge outputs of seven global models and ensemble mean of those models as available in the WRR dataset are analysed and compared against two benchmarks of flood events in the Limpopo River basin. The first benchmark is based on observations from the available stations, while the second is developed based on flood events that have led to damages as reported in global databases of damaging flood events. Results show that, while the WRR dataset provides useful data for detecting the occurrence of flood events in the Limpopo River basin, variation exists amongst the global models regarding their capability to identify the magnitude of those events. The study also reveals that the models are better able to capture flood events at stations with a large upstream catchment area. Improved performance for most models is found for the 0.25° resolution global model, when compared to the lower-resolution 0.5° models, thus underlining the added value of increased-resolution global models. The skill of the global hydrological models (GHMs) in identifying the severity of flood events in poorly gauged basins such as the Limpopo can be used to estimate the impacts of those events using the benchmark of reported damaging flood events developed at the basin level, though this could be improved if further details on location and impacts are included in disaster databases. Large-scale models such as those included in the WRR dataset are used by both global and continental forecasting systems, and this study sheds light on the potential these have in providing information useful for local-scale flood risk management. In conclusion, this study offers valuable insights in the applicability of global reanalysis data for identifying impacting flood events in data-sparse regions.
Creative Accounting Practices and Measurement Methods: Evidence from Turkey

Abstract A fiscal rule imposed when the budget is not transparent yields more creative accounting to circumvent it and less fiscal adjustment, generating hidden deficits/debts in public sector. This study focuses on creative accounting practices of governments and adds to the literature by measuring hidden debts of the Turkish public sector ranging from the period 1989 to 2010. The author shows that the IMF has been misinformed, indeed has been misled by the Turkish authorities regarding the magnitude of public debt stock in the late 90’s. The lacking information deteriorated the IMF’s forecasts, which might be one of the main reasons for the failure of the IMF’s planned fiscal consolidation at the outset of 2000–2001 crisis. The author’s methodology can easily be adapted to any other country in order to identify the different margins on which governments can cheat and manipulate the Government Finance Statistics.
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables

Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We describe and study in these models a decomposition of predictive uncertainty into its epistemic and aleatoric components. First, we show how such a decomposition arises naturally in a Bayesian active learning scenario by following an information theoretic approach. Second, we use a similar decomposition to develop a novel risk sensitive objective for safe reinforcement learning (RL). This objective minimizes the effect of model bias in environments whose stochastic dynamics are described by BNNs with latent variables. Our experiments illustrate the usefulness of the resulting decomposition in active learning and safe RL settings.
Galaxy Zoo: Quantitative visual morphological classifications for 48 000 galaxies from CANDELS

We present quantified visual morphologies of approximately 48 000 galaxies observed in three Hubble Space Telescope legacy fields by the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) and classified by participants in the Galaxy Zoo project. 90 per cent of galaxies have z ≤ 3 and are observed in rest-frame optical wavelengths by CANDELS. Each galaxy received an average of 40 independent classifications, which we combine into detailed morphological information on galaxy features such as clumpiness, bar instabilities, spiral structure, and merger and tidal signatures. We apply a consensus-based classifier weighting method that preserves classifier independence while effectively down-weighting significantly outlying classifications. After analysing the effect of varying image depth on reported classifications, we also provide depth-corrected classifications which both preserve the information in the deepest observations and also enable the use of classifications at comparable depths across the full survey. Comparing the Galaxy Zoo classifications to previous classifications of the same galaxies shows very good agreement; for some applications, the high number of independent classifications provided by Galaxy Zoo provides an advantage in selecting galaxies with a particular morphological profile, while in others the combination of Galaxy Zoo with other classifications is a more promising approach than using any one method alone. We combine the Galaxy Zoo classifications of ‘smooth’ galaxies with parametric morphologies to select a sample of featureless discs at 1 ≤ z ≤ 3, which may represent a dynamically warmer progenitor population to the settled disc galaxies seen at later epochs.
Scitovsky, Behavioural Economics, and Beyond

Abstract Scitovsky is known as a forerunner of behavioural economics simply because he drew heavily on psychology and claimed that people’s choices may be ‘joyless’ (Scitovsky, The joyless economy, 1976). However, a careful reformulation of his analysis shows that he anticipated a number of insights (also with respect to Kahneman’s ‘two-systems of thought’) which suggest new lines of inquiry from an original and different perspective. These insights of Scitovsky regard the following aspects: Uncertainty as a condition where the outcomes of choosing a particular option (novelty) is partially unknown; the case of individual ‘consumption skill’ (inclusive of emotions) that finds this uncertainty desirable when it is challenging; the case of increasing such skill so as to change preferences and make choices more efficient; the case of failing to increase such skill so as to make addictive harmful products an alternative and more tempting option.
What you need to know about : High flow nasal oxygen therapy

Several options exist to deliver supplemental oxygen to patients, and each will provide a varying fraction of inspired oxygen (FiO2). The first distinction to make is between fixed and variable performance devices. In fixed performance devices FiO2 remains constant regardless of inspiratory flow rates; an example being the Venturi mask system. Variable performance devices deliver a variable FiO2 depending on the patient’s inspiratory flow rate, with examples including nasal cannulae and simple face masks. Increasing flow rates will deliver increasing FiO2 up to the limitations of the device and patient comfort, with much variation depending on the patient’s respiratory pattern. Table 1 summarises the devices available.
Effect of Nutrient Management and Planting Geometry on Productivity of Hybrid Rice (Oryza sativa L.) Cultivars

Field experiments were conducted during the wet seasons of 2006 and 2007 at the Agricultural Experimental Farm of the Indian Statistical Institute, Giridih, a part of eastern plateau region of India. The study was designed to investigate the effect of planting geometry and nutrient management practices on productivity of two hybrid rice cultivars. Split-plot design with three replications was adopted to carry out the experiment by allocating combinations of treatments of planting geometry and rice cultivar in main-plots and nutrient management treatments in sub-plots. “CNRH-3” rice proved its efficiency in terms of grain yield that was also reflected in yield attributing characters such as number of productive tillers, number of grains per panicle, length of panicle, panicle weight, test weight and harvest index. Higher rice grain yield was registered when the cultivars grown in 20 cm × 20 cm planting geometry. Rice cultivars grown with the application of inorganic fertilizers alone produced maximum grain yield and also recorded higher values of ancillary characters. The maximum amount of N, P and K was taken up by the “CNRH-3” rice, whereas maximum residual soil fertility was recorded in “Pro Agro 6201” rice. Maximum N, P and K uptake values were recorded in 20 cm × 20 cm crop geometry and inorganic fertilizers treatment.