Miracle-dz/UniFAR-FTUs huggingface dataset:
The Analysis of the Impact of Regulatory Environment on the Pace of Economic Growth of the World Countries According to the Bayesian Model Averaging

The study examines the relationship between the regulatory variables and economic growth on the basis of Bayesian model pooling applied to Blundell and Bond’s GMM system estimator. The areas of regulations (institutions) are measured by the following indicators: index of economic freedom, worldwide governance indicators, democracy index, doing business indicators, transition indicators. The models are estimated based on overlapping panel data and they include nonlinearities. In general, regulatory environment is an important determinant of economic growth. To achieve rapid growth, it is necessary to increase economic freedom, quality of governance, and market reforms. The association between regulatory variables and GDP dynamics is mostly nonlinear.The countries with greater scope of economic freedom record more rapid GDP growth but a given increase in economic freedom has a higher impact on growth in those countries that are economically not (or partly) free. However, the results are not robust in a lot of areas – with regard to the sample of countries, the exact measure of the regulatory variable, and the type of nonlinear impact (concave vs. convex functions). There are many factors affecting both regulations and GDP dynamics as well as many transmission channels between these areas and the results sometimes are mixed.
Survey on network lifetime research for wireless sensor networks

Wireless sensor network consists of large number of sensor nodes randomly distributed in some regions. Each node has a limited energy supply and generates information when some events that need to be communicated to a sink node occur. It has been proved that the nodes closer to the sink node will use up their energy more quickly, as a result, the network lifetime will be affected, some bad consequences will happen. Therefore how to optimize the network lifetime become an important problem. According to the reasearch fruit many reaserchers have contributed, we concluded that the method of optimizing the network life included keeping energy effectifcy, improving or creating some optimized algorithms, making use of the inherent mechanism of nodes and so on. The network lifetime can depend on many other factors too. In the paper we give out the survey of the sensor network lifetime and do some analysis.
Efficient First Order Methods for Linear Composite Regularizers

A wide class of regularization problems in machine learning and statistics employ a regularization term which is obtained by composing a simple convex function \omega with a linear transformation. This setting includes Group Lasso methods, the Fused Lasso and other total variation methods, multi-task learning methods and many more. In this paper, we present a general approach for computing the proximity operator of this class of regularizers, under the assumption that the proximity operator of the function \omega is known in advance. Our approach builds on a recent line of research on optimal first order optimization methods and uses fixed point iterations for numerically computing the proximity operator. It is more general than current approaches and, as we show with numerical simulations, computationally more efficient than available first order methods which do not achieve the optimal rate. In particular, our method outperforms state of the art O(1/T) methods for overlapping Group Lasso and matches optimal O(1/T^2) methods for the Fused Lasso and tree structured Group Lasso.
Determinants of accepting non-invasive ventilation treatment in motor neurone disease: a quantitative analysis at point of need

Objectives: Motor neurone disease (MND) progressively damages the nervous system causing wasting to muscles, including those used for breathing. There is robust evidence that non-invasive ventilation (NIV) relieves respiratory symptoms and improves quality of life in MND. Nevertheless, about a third of those who would benefit from NIV decline the treatment. The purpose of the study was to understand this phenomenon. Design: A cross-sectional quantitative analysis. Methods: Data including age, sex, MND symptomatology, general physical and mental health and psychological measures were collected from 27 patients and their family caregivers at the point of being offered ventilatory support based on physiological markers. Results: Quantitative analyses indicated no difference in patient characteristics or symptomatology between those who tolerated (n = 17) and those who declined (n = 10) NIV treatment. A comparison of family caregivers found no differences in physical or mental health or in caregiving distress, emphasising that this was high in both groups; however, family caregivers supporting NIV treatment were significantly more resilient, less neurotic and less anxious than family caregivers who did not. Regression analyses, forcing MND symptoms to enter the equation first, found caregiver resilience:commitment the strongest predictor of uptake of NIV treatment adding 22% to the 56% explained variance. Conclusion: Patients who tolerated NIV treatment had family caregivers who cope through finding meaning and purpose in their situation. Psychological support and proactive involvement for family caregivers in the management of the illness situation is indicated if acceptance of NIV treatment is to be maximised in MND.
Measuring Retrieval Effectiveness Based on User Preference of Documents

The notion of user preference is adopted for the representation, interpretation, and measurement of the relevance or usefulness of documents. User judgments on documents may be formally described by a weak order (i.e., user ranking) and measured using an ordinal scale. Within this framework, a new measure of system performance is suggested based on the distance between user ranking and system ranking. It only uses the relative order of documents and therefore confirms to the valid use of an ordinal scale measuring relevance. It is also applicable to multilevel relevance judgments and ranked system output. The appropriateness of the proposed measure is demonstrated through an axiomatic approach. The inherent relationships between the new measure and many existing measures provide further supporting evidence. © 1995 John Wiley & Sons, Inc.
Cross-validation of multiple language recognition systems using pseudo keys

In this paper, we present a pseudo-key analysis approach for cross-validation of language recognition systems before the ground truth (true key) becomes available. A state-of-the-art language recognition system typically employs multiple language recognition classifiers which are fused to form a mixture of experts. The individual classifiers are also called subsystems. To avoid the fused system from being brought down by some outlier classifiers, pseudo keys are designed to cross-examine the integrity of individual classifier candidates. The language recognition experiments are conducted on the NIST 2007 Language Recognition Evaluation (LRE) corpus using the subsystems in the primary submission from the Institute for Infocomm Research (IIR).
Using store-forward technique to conserve energy in wireless sensor networks: Initial step for routing mechanism

Wireless sensor network (WSN) is a technology which consists a number of sensor nodes distributed among of an area usually for monitoring purposes. As the emerging of this technology has been rapidly increased nowadays, the successful of the wireless sensor networks application is highly depends on the reliable communication among the sensor nodes. One major problem in wireless sensor networks environment is the limitation of the physical resource in sensor nodes which energy resource has been identified as a critical constraint for achieving the demanding capabilities in wireless sensor networks application. Packet dissemination is one of the data transmission method in wireless sensor networks routing protocol where it would cause high energy consumption in sensor node due to the high energy consuming for disseminate the packet through the network. Initial step in routing protocol using packet dissemination method for wireless sensor networks could contribute energy efficiency if it has the ability to reduce the energy consumption. The proposed store-forward technique is to define the initial step in packet dissemination routing protocol whereas to identify the neighbor nodes without consuming high energy in sensor nodes. The result shows using the store-forward technique gave significant impact in conserving the energy in sensor nodes where it slightly reduce the energy consumption in terms of power availability after the routing initialization process is executed.
Identification of volatile compounds produced by the bacterium Burkholderia tropica that inhibit the growth of fungal pathogens

It has been documented that bacteria from the Burkholderia genera produce different kinds of compounds that inhibit plant pathogens, however in Burkholderia tropica, an endophytic diazotrophic and phosphate-solubilizing bacterium isolated from a wide diversity of plants, the capacity to produce antifungal compounds has not been evaluated. In order to expand our knowledge about Burkholderia tropica as a potential biological control agent, we analyzed 15 different strains of this bacterium to evaluate their capacities to inhibit the growth of four phytopathogenic fungi, Colletotrichum gloeosporioides, Fusarium culmorum, Fusarium oxysporum and Sclerotium rolffsi. Diverse analytical techniques, including plant root protection and dish plate growth assays and gas chromatography-mass spectroscopy showed that the fungal growth inhibition was intimately associated with the volatile compounds produced by B. tropica and, in particular, two bacterial strains (MTo293 and TTe203) exhibited the highest radial mycelial growth inhibition. Morphological changes associated with these compounds, such as disruption of fungal hyphae, were identified by using photomicrographic analysis. By using gas chromatography-mass spectroscopy technique, 18 volatile compounds involved in the growth inhibition mechanism were identified, including α-pinene and limonene. In addition, we found a high proportion of bacterial strains that produced siderophores during growth with different carbon sources, such as alanine and glutamic acid; however, their roles in the antagonism mechanism remain unclear.
A platform-independent API for quality of service management

The differentiated services approach to QoS in the Internet poses new challenges on the configuration and service provisioning side. The approach relies on an entity often referred to as a bandwidth broker. It configures the network elements so that guaranteed networking services are provided to customers. However, the DiffServ capable routers have a large variety of hardware configurations and different configuration interfaces (e.g. SNMP, CORBA, CLI). Therefore, we propose a QoS management API which is used by bandwidth broker implementations in order to configure the underlying routers. In our prototype we implemented a proprietary configuration interface to our Linux-based DiffServ router implementation.
Signal model and receiver algorithms for a transmit-reference ultra-wideband communication system

A communication system based on transmit-reference (TR) ultra-wideband (UWB) is studied and further developed. Introduced by Hoctor and Tomlinson, the aim of the TR-UWB transceiver is to provide a straightforward impulse radio system, feasible to implement with current technology, and to achieve either high data rate transmissions at short distances or low data rate transmissions in typical office or industrial environments. The main contribution in this paper is the derivation of a signal processing model that takes into account the effects of the radio propagation channel, in particular, for the case where the two pulses in a doublet are closely spaced. Several receivers based on the code-division multiple-access-like properties of the proposed model are derived, and the performance of the algorithms is tested in a simulation.
A Real Time 6DOF Visual SLAM System Using a Monocular Camera

One of the most important properties that a robot must have in order to be considered autonomous is the ability to localize by itself in an unknown environment, using the information gathered by its sensors. The system uses a cheap web camera, carried by a mobile robot or by a person, while it builds a map and at the same time estimates its localization with respect to this map. To develop such system, problems such as camera calibration, corners detection, features parametrization, motion and measurement models, and the estimation of the joint state camera-features using the extended Kalman Filter, must be tackled.
Complex Networks, Communities and Clustering: A survey

This paper is an extensive survey of literature on complex network communities and clustering. Complex networks describe a widespread variety of systems in nature and society especially systems composed by a large number of highly interconnected dynamical entities. Complex networks like real networks can also have community structure. There are several types of methods and algorithms for detection and identification of communities in complex networks. Several complex networks have the property of clustering or network transitivity. Some of the important concepts in the field of complex networks are small-world and scale-robustness, degree distributions, clustering, network correlations, random graph models, models of network growth, dynamical processes on networks, etc. Some current areas of research on complex network communities are those on community evolution, overlapping communities, communities in directed networks, community characterization and interpretation, etc. Many of the algorithms or methods proposed for network community detection through clustering are modified versions of or inspired from the concepts of minimum-cut based algorithms, hierarchical connectivity based algorithms, the original GirvanNewman algorithm, concepts of modularity maximization, algorithms utilizing metrics from information and coding theory, and clique based algorithms.
Sparse Spectral-based Feature Selection with Side Information

We address the problem of selecting a subset of the most relevant features from a set of sample data in cases where there are multiple (equally reasonable) solutions. In particular, this topic includes the suppression of one of the solutions given “side” data, i.e., when one is given information about undesired aspects of the data. Such situations often arise when there are several, even conflicting, dimensions to the data. For example, documents can be clustered based on topic, authorship or writing style; images of human faces can be clustered based on illumination conditions, facial expressions or by person identity; gene expressions levels can be clustered by pathologies or by correlations that also exist in other conditions, and so forth. We address this problem by extending the recently introduced algorithm for feature selection [10]. The algorithm uses spectral properties of the affinity (or Laplacian) matrix of the input data sample to guide the search for a relevant subset of features (coordinates). The most relevant subset of coordinates is the one that induces the most coherent collection of clusters in the subspace defined by the selected coordinates. In this work we address the problem which arises when different coordinate selections induce equally good clusters in their respective subspaces. We derive a principled manner to represent unwanted cluster arrangements, such as “side” data in various forms, as part of the optimization functional for feature selection and also derive a topographical structure of all selected feature subsets.
Evaluation of clustering and summarizing in distributed latent semantic indexing

Latent Semantic Indexing is a conceptual method in information retrieval systems. In this method, a term-document matrix is built through term weighting techniques. This matrix is mapped to a conceptual space by mathematical decomposition techniques like Singular Value Decomposition. The more documents and key terms collection are, the more element of term-document matrix is created, causes difficulty to manage. Such a huge size of matrix needs more memory space to save and more calculation to find out the solutions. With the assumption of using distribution in order to decrease the required memory space and to reduce the run-time problem, we did a research and implemented distributed LSI. To meet a better improvement, clustering is concerned for document too. In this combination, term-document matrix is recreated for each cluster and retrieval is accomplished on these set of term-document matrices. We evaluate our combinational method on Hamshahri Collection which is the largest collection in Persian language. Evaluation shows remarkable improvement in contrast with non-combinational LSI method.
Capital Structure and Labour Demand: Investigations Using German Micro Data

The paper analyses the interaction between capital structure and employment decisions of firms. For this purpose, a theoretical model is developed in which a firm determines employment along an optimal path taking into account financial considerations. The empirical analysis using West German micro data proves that a negative relationship exists between employment and the debt-asset ratio of the firm. We also demonstrate that as real wages (sales) increase, employment decreases (increases).
Distance-Based Outlier Detection on Uncertain Data

The technique of outlier detection is useful in many real world applications such as detection of network intrusion. It has been studied intensively on deterministic data. However, it is still a novel research field on uncertain data. To our best knowledge, this paper is the firstone to focus on distance-based outlier detection on uncertain data, in which each data is affiliated with a certain confidence value. In this paper, we propose a new definition of outlier on uncertain data. Based on the properties we discovered, both dynamic programming approach (DPA) and grid-based pruning approach(GPA) are used for detecting outliers on uncertain dataefficiently. Detailed analysis and thorough experimental results demonstrate the efficiency and scalability of our method.
FARMER PERCEPTIONS ON CLIMATE CHANGE AND VARIABILITY IN SEMI-ARID ZIMBABWE IN RELATION TO CLIMATOLOGY EVIDENCE

Farmers in semi-arid Zimbabwe prioritise climate variability as their major agricultural productivity-reducing problem. This paper raises the importance of considering local farmers’ perceptions on climate risk, as this greatly influences on-farm investments and decision-making in agricultural management and production in semi-arid Zimbabwe. A study was conducted in two districts of semi-arid Zimbabwe using participatory research techniques, to investigate farmers’ perceptions of climate variability and whether these perceptions correspond with historical climatic data. The study showed that farmers perceived climatic and weather patterns to have changed over the past decade or two, as indicated by erratic rainfall patterns, decreased rainfall and temperature increases, leading to crop productivity decline and increased livestock morbidity and mortality. Majority of respondents (75%; n=81) were highly risk-averse, perceiving that most of the seasons in any ten given years could be poor. The climatic data show no evidence that corroborates the farmers’ perceptions, with only temperature showing a clear signal, indicating the influence of other non-climatic factors. The climate data show rainfall variability to be a normal characteristic of the study sites, with deviations from the climatic rainfall means (or the poor seasons) being cyclical and occurring once in every three seasons over the past 40 years. The study highlights strategies that farmers could implement to enhance agricultural productivity in the semi-arid areas to adapt to climate change and variability. M. MOYO et al. 318 claire indiquant l’influence d’autres facteurs non climatiques. Les données climatiques montrent que la variabilité pluviométrique dans les sites d’étude présente une caractéristique normale, avec des déviations cycliques des moyennes des précipitations (les saisons pauvres) apparaissant une fois toutes les trois saisons au cours de 40 ans. L’étude met en évidence les stratégies auxquelles les paysans pourraient recourir pour améliorer la productivité des cultures en milieux semi arides pour l’adaptation au changement climatique et sa variabilité. Mots Cés : variabilité et changements climatiques, évidence climatologique, pluies erratiques, Perceptions paysannes, risque d’aversion, augmentation de la température
Entrepreneurship and UK Doctoral Graduates

This paper discusses the experience of UK doctoral graduates in pursuing entrepreneurial careers: there is evidence that this applies to a substantial number – about 10% – of doctoral graduates. The nature of their experience was explored using 37 interviews with doctoral entrepreneurs. The research was funded by Vitae (www.vitae.ac.uk), an organization championing the personal, professional and career development of doctoral researchers and research staff in UK higher education. The stories that the participants tell suggest that doctoral entrepreneurship develops out of a complex interaction between the personality and skills of the entrepreneurs and the environment in which they operate. In particular, the authors argue that the participants have mobilized a mix of financial, social and educational capital in order to create and sustain their enterprises successfully.
The Consequences of Uncertainty and of Irreversibility on Optimal Intertemporal Co2 Emission Policies

This paper investigates how uncertainty affects the optimal intertemporal accumulation of a stock externality using CO2 emissions and the greenhouse effect as a topical pars pro toto. More precisely, the evolution of the future temperature is assumed to follow an Ito-process with the drift provided by current carbon-dioxide emissions. In this context, the paper emphasizes two different kinds of irreversibilities. First, CO2 once dissolved in the air cannot be collected later which rules out quadratic value functions even in linear quadratic applications. Second, stopping CO2 emissions (i.e. a shut down of the entire fossil fuel industry) is irreversible; this introduces the technical complication that 'smooth pasting' is not applicable. Hence, this paper applies techniques that contrast both the deterministic set ups and the stochastic applications so far in the literature. The supposition that stopping emissions is reversible (i.e. a costless re-start of emissions is possible) leads to termination at a temperature level where the marginal benefit of emission still exceeds the net present value of marginal external costs. Although irreversibility is expected to mitigate the usual consequences of uncertainty (e.g. the conservative stopping rule above), it actually further lowers the threshold temperature at which the fossil fuel industry should be shut down forever.
Financial Development and Financing Constraints in a Developing Country: The Case of Bangladesh

This paper examines the sensitivity of investment to available cash-stock, a measure for internal funds, for 192 listed non-financial firms of Bangladesh from 1992 to 2002. The empirical results show that smaller firms have greater financing constraints to investment than larger firms due to financial market imperfection and unequal access to external finance. We also find that financing constraints of investment by small firms are eased along with financial development. It is likely that financial development encourages efficiency of the financial market in Bangladesh, and hence decreases cash stock sensitivity of investment of small firms. Our finding demonstrates the importance of financial development for economic growth even in a developing country like Bangladesh.