Miracle-dz/UniFAR-FTUs huggingface dataset:
Discriminative sparse-based feature extraction and dictionary learning for sound classification applications

This paper presents a novel sparse-based classification algorithm for audio applications such as sound classification. We propose performing the sparse feature extraction, the dictionary learning, and classification processes simultaneously. This discriminative learning procedure for adapting the dictionaries and classifier to each specified audio task, instead of employing pre-defined dictionaries is the main novelty of our work. According to our experiments, applying this algorithm on some Mel-scale spectral features, such as MFCC (Mel Frequency Cepstral Coefficient), instead of raw temporal data can improve the accuracy and execution time significantly. Our proposed discriminative MFCC-sparse features when evaluated on real data consisting of five audio classes, substantially out-performed the non-discriminative ones. The lengths of test segments in our method are less than 0.5 second. This potential of usage for real-time applications is another advantage of our proposed approach.
Ionic and osmotic relations in quinoa (Chenopodium quinoa Willd.) plants grown at various salinity levels

Ionic and osmotic relations in quinoa (Chenopodium quinoa Willd.) were studied by exposing plants to six salinity levels (0–500 mM NaCl range) for 70 d. Salt stress was administered either by pre-mixing of the calculated amount of NaCl with the potting mix before seeds were planted or by the gradual increase of NaCl levels in the irrigation water. For both methods, the optimal plant growth and biomass was achieved between 100 mM and 200 mM NaCl, suggesting that quinoa possess a very efficient system to adjust osmotically for abrupt increases in NaCl stress. Up to 95% of osmotic adjustment in old leaves and between 80% and 85% of osmotic adjustment in young leaves was achieved by means of accumulation of inorganic ions (Na+, K+, and Cl–) at these NaCl levels, whilst the contribution of organic osmolytes was very limited. Consistently higher K+ and lower Na+ levels were found in young, as compared with old leaves, for all salinity treatments. The shoot sap K+ progressively increased with increased salinity in old leaves; this is interpreted as evidence for the important role of free K+ in leaf osmotic adjustment under saline conditions. A 5-fold increase in salinity level (from 100 mM to 500 mM) resulted in only a 50% increase in the sap Na+ content, suggesting either a very strict control of xylem Na+ loading or an efficient Na+ removal from leaves. A very strong correlation between NaCl-induced K+ and H+ fluxes was observed in quinoa root, suggesting that a rapid NaCl-induced activation of H+-ATPase is needed to restore otherwise depolarized membrane potential and prevent further K+ leak from the cytosol. Taken together, this work emphasizes the role of inorganic ions for osmotic adjustment in halophytes and calls for more in-depth studies of the mechanisms of vacuolar Na+ sequestration, control of Na+ and K+ xylem loading, and their transport to the shoot.
Literature review on cutback management

Introduction The literature review at hand is the first deliverable of Work Package no. 7 The global financial crisis in the public sector as an emerging coordination challenge of the EU Seventh Framework program project Coordinating for Cohesion in the Public Sector of the Future (COCOPS). The main purpose of the review is to give an overview of the cutback management literature and its findings of cutback strategies in the public sector in the 1970-80s. The aim is to look at the application and impact of various cutback management and decision-making practices and discuss whether lessons could be learnt for the current crisis. The recent global financial and economic crisis, followed by fiscal crisis, have lifted the topic of cutback management back on the research agenda, as many governments in Europe and elsewhere either plan or have already implemented austerity measures in order to cope with the concurrent problems of lower revenues and high public debt. It can be expected that the large-scale cutbacks, undertaken by numerous governments, would lead to changes in public administration practices. Thus, the impacts of the global crisis and the subsequent retrenchment on public administration and governance is and will continue to be a challenging issue for several years to come (Pollitt 2010; Thynne 2011; Coen and Roberts 2012). Looking at the patterns of cutback management during the previous era(s) of austerity can therefore provide useful insights for coping with the current crisis.
Making and breaking governments

Series editors' preface Acknowledgements Part I. The Context: 1. Theory, institutions, and government formation 2. The social context of government formation 3. The government formation process Part II. The Model: 4. Government equilibrium 5. Strong parties Part III. Empirical Investigations: 6. Two cases: Germany, 1987 Ireland, 1992-3 7. Theoretical implications, data, and operationalization 8. Exploring the model: a comparative perspective 9. A multivariate investigation of portfolio allocation Part IV. Applications, Extensions, and Conclusions: 10. Party systems and cabinet stability 11. Making the model more realistic 12. Party politics and administrative reform 13. Governments and parliaments Bibliography.
Constrained Randomisation of Weighted Networks

We propose a Markov chain method to efficiently generate surrogate networks that are random under the constraint of given vertex strengths. With these strength-preserving surrogates and with edge-weight-preserving surrogates we investigate the clustering coefficient and the average shortest path length of functional networks of the human brain as well as of the International Trade Networks. We demonstrate that surrogate networks can provide additional information about network-specific characteristics and thus help interpreting empirical weighted networks.
Focused retrieval using topical language and structure

We investigate focused retrieval techniques that deal with the increasing amount of structure on the web. Our approach is to combine multiple representations of web information in a common framework based on statistical language models. In this framework, it will be possible to derive a topical language model of the actual language-use on web pages on a certain topic--such as arts, business, entertainment, education, etc.--using the unigrams and bigrams taken from the plain text of the web pages. Similarly, it will be possible to derive models of the structure of web pages to distinguish between blogs, FAQs, personal web pages, etc. Structural characteristics of a web page include, amongst others, tagname statistics and parent-child tags. We will build a multiple level language model to exploit the information contained in the topical language and structure models. The . GOV2 corpus will be used as a test collection on which queries will be run on different topical categories and on web pages with different structures. We plan to develop so-called parsimonious models to derive a compact representation and to handle dependencies between representations of the data.
INFLATION TARGETING AND MONETARY POLICY RULES FOR SMALL AND OPEN DEVELOPING ECONOMIES: SIMPLE ANALYTICS WITH APPLICATION TO THAILAND

Abstract This paper uses a simple stylized macro model to examine the case for and operational mechanics behind an open inflation targeting (IT) arrangement which has increasingly been advocated for small and open developing economies in Asia and elsewhere. The model is calibrated to Thailand, which was among the first Asian country to implement an IT arrangement. The numerical investigations are aimed at comparing conventional optimal monetary policy under commitment and discretion and the variations of the simple fixed MPRs (i.e. versions of “Taylor Rules”). The simple MPRs we will examine differ in how much the real exchange rate is represented in the rule. Will the general philosophy behind IT be suitable for small and open developing economies in Asia? Are some rules better than others? These are some of questions we attempt to explore in this paper. Keywords: Asia, developing economy, exchange rate, inflation targeting (IT), monetary policy rules (MPRs), Thailand JEL Class: E52, E58, F31, F41
Comparison of Mamdani-type and Sugeno- type FIS for Water Flow Rate Control in a Rawmill

Fuzzy inference systems (FIS) are developed for water flow rate control in a rawmill of cement industry using Mamdani- type and Sugeno-type fuzzy models. This concept is taken from ambuja cement plant. It is essential to control water flow rate efficiently to produce high quality cement. Rawmill is a mill which is used to grind the raw materials which are used to manufacture cement. Water flow rate control system is two input and one output system. In this paper, both the models are simulated using MATLAB Fuzzy logic Toolbox and the results of the two fuzzy inference systems are compared. This paper outlines the basic difference between the Mamdani-type FIS and Sugeno-type FIS.
Great Plains Hydroclimate Variability: The View from North American Regional Reanalysis

Abstract Interannual variability of warm-season rainfall over the Great Plains is analyzed using the recently released North American Regional Reanalysis (NARR). The new dataset differs from its global counterparts in the additional assimilation of precipitation and radiances. This along with the use of a more comprehensive land surface model in generation of NARR offers the prospect of obtaining improved estimates of surface hydrologic and near-surface meteorological fields. NARR’s representation of hydroclimate is used to weigh in on the authors’ recent finding of the dominance of large-scale moisture flux convergence over evaporation in accounting for Great Plains precipitation variations. Evaporation estimates are notoriously uncertain and, while the NARR ones are not assured to be realistic, they are more constrained than those diagnosed before from inline and offline assessments. NARR’s portrayal of warm-season hydroclimate variability corroborates the importance of remote water sources in generatio.
Optimization and modeling of FPGA implementation of the Katan Cipher

Lightweight ciphers (e.g., Katan) are crucial for secure communication for resource-constrained devices. The Katan cipher algorithm was proposed for low-resource devices. This paper examines implementing Katan Cipher on field programmable gate array (FPGA) platform. The paper discusses several implementations, with 80-bits key size and 64-bits block size. The energy and power dissipations are examined to select the optimum design. Models for resources and power are derived with average error of 12% and 17%.
Periodic Thermonuclear X-Ray Bursts from GS 1826–24 and the Fuel Composition as a Function of Accretion Rate

We analyze 24 type I X-ray bursts from GS 1826-24 observed by the Rossi X-Ray Timing Explorer between 1997 November and 2002 July. The bursts observed between 1997 and 1998 were consistent with a stable recurrence time of 5.74 ± 0.13 hr. The persistent intensity of GS 1826-24 increased by 36% between 1997 and 2000, by which time the burst interval had decreased to 4.10 ± 0.08 hr. In 2002 July the recurrence time was shorter again, at 3.56 ± 0.03 hr. The bursts within each epoch had remarkably identical light curves over the full ≈150 s burst duration; both the initial decay timescale from the peak and the burst fluence increased slightly with the rise in persistent flux. The decrease in the burst recurrence time was proportional to -1.05±0.02 (assuming that is linearly proportional to the X-ray flux), so that the ratio α between the integrated persistent and burst fluxes was inversely correlated with . The average value of α was 41.7 ± 1.6. Both the α-value and the long burst durations indicate that the hydrogen is burning during the burst via the rapid-proton (rp) process. The variation in α with implies that hydrogen is burning stably between bursts, requiring solar metallicity (Z ~ 0.02) in the accreted layer. We show that solar metallicity ignition models naturally reproduce the observed burst energies but do not match the observed variations in recurrence time and burst fluence. Low-metallicity models (Z ~ 0.001) reproduce the observed trends in recurrence time and fluence but are ruled out by the variation in α. We discuss possible explanations, including extra heating between bursts or that the fraction of the neutron star covered by the accreted fuel increases with .
Factor H–Related Protein 5 Interacts with Pentraxin 3 and the Extracellular Matrix and Modulates Complement Activation

The physiological roles of the factor H (FH)-related proteins are controversial and poorly understood. Based on genetic studies, FH-related protein 5 (CFHR5) is implicated in glomerular diseases, such as atypical hemolytic uremic syndrome, dense deposit disease, and CFHR5 nephropathy. CFHR5 was also identified in glomerular immune deposits at the protein level. For CFHR5, weak complement regulatory activity and competition for C3b binding with the plasma complement inhibitor FH have been reported, but its function remains elusive. In this study, we identify pentraxin 3 (PTX3) as a novel ligand of CFHR5. Binding of native CFHR5 to PTX3 was detected in human plasma and the interaction was characterized using recombinant proteins. The binding of PTX3 to CFHR5 is of ∼2-fold higher affinity compared with that of FH. CFHR5 dose-dependently inhibited FH binding to PTX3 and also to the monomeric, denatured form of the short pentraxin C–reactive protein. Binding of PTX3 to CFHR5 resulted in increased C1q binding. Additionally, CFHR5 bound to extracellular matrix in vitro in a dose-dependent manner and competed with FH for binding. Altogether, CFHR5 reduced FH binding and its cofactor activity on pentraxins and the extracellular matrix, while at the same time allowed for enhanced C1q binding. Furthermore, CFHR5 allowed formation of the alternative pathway C3 convertase and supported complement activation. Thus, CFHR5 may locally enhance complement activation via interference with the complement-inhibiting function of FH, by enhancement of C1q binding, and by activating complement, thereby contributing to glomerular disease.
Impact of Lipid Nutrition on Neural Stem/Progenitor Cells

The neural system originates from neural stem/progenitor cells (NSPCs). Embryonic NSPCs first proliferate to increase their numbers and then produce neurons and glial cells that compose the complex neural circuits in the brain. New neurons are continually produced even after birth from adult NSPCs in the inner wall of the lateral ventricle and in the hippocampal dentate gyrus. These adult-born neurons are involved in various brain functions, including olfaction-related functions, learning and memory, pattern separation, and mood control. NSPCs are regulated by various intrinsic and extrinsic factors. Diet is one of such important extrinsic factors. Of dietary nutrients, lipids are important because they constitute the cell membrane, are a source of energy, and function as signaling molecules. Metabolites of some lipids can be strong lipid mediators that also regulate various biological activities. Recent findings have revealed that lipids are important regulators of both embryonic and adult NSPCs. We and other groups have shown that lipid signals including fat, fatty acids, their metabolites and intracellular carriers, cholesterol, and vitamins affect proliferation and differentiation of embryonic and adult NSPCs. A better understanding of the NSPCs regulation by lipids may provide important insight into the neural development and brain function.
Performance Evaluation of Shortest Path Routing Strategy using Cognitive Radios

To have an efficient and successful communication in ad hoc networks, the use of Cognitive Radios (CR) has gained popularity in the recent past. These radios have spectrum sensing capabilities and thus can easily switch to free spectrum band. The use of such type of radios not only improves the routing performance but at the same time improves the overall throughput of the network. To show the same, we implement shortest path routing strategy in MATLAB-7.01. The routing scheme uses two types of radios one with spectrum sensing capability such as cognitive radios and the other which doesn’t. The following performance metrics such as Packet Delivery Ratio (PDR), Delay, Success Ratio and Hop count are taken into consideration for measuring the efficiency. The result shows that a node having spectrum sensing capability outperforms the other one.
Nogo enhances the adhesion of olfactory ensheathing cells and inhibits their migration

The migration of olfactory ensheathing cells (OECs) is essential for pioneering the olfactory nerve pathway during development and for promoting axonal regeneration when implanted into the injured central nervous system (CNS). In the present study, recombinant Nogo-66 enhanced the adhesion of OECs and inhibited their migration. Using immunocytochemistry and western blot, we showed that the Nogo-66 receptor (NgR) was expressed on OECs. When NgR was released from the cell surface with phosphatidylinositol-specific phospholipase C or neutralized by NgR antibody, the effect of Nogo-66 on OEC adhesion and migration was markedly attenuated. Nogo-66 was found to promote the formation of focal adhesion in OECs and inhibited their membrane protrusion through the activation of RhoA. Furthermore, the co-culture migration assay demonstrated that OEC motility was significantly restricted by Nogo-A expressed on Cos7 cell membranes or oligodendrocytes. Moreover, treatment with anti-NgR antibody facilitated migration of implanted OECs in a spinal cord hemisection injury model. Taken together, we demonstrate, for the first time, that Nogo, a myelin-associated inhibitor of axon regeneration in the CNS, enhances the adhesion and inhibits the migration of OECs via NgR regulation of RhoA.
Neural Network-Based Adaptive Feedback Linearization Control of Antilock Braking System

Safety systems in road vehicles are categorised into two main types: passive and active systems. The antilock braking system (ABS) is an active safety system in road vehicles, which senses the slip value between the tyre and the road and uses these values to determine the optimum braking force. Due to the high non-linearity of the tyre and road interaction, and uncertainties from vehicle dynamics, standard control methods: like PID, sliding mode control and feedback linearization will not suffice. This paper, therefore proposes a neural network-based feedback linearization control design method. The experimental results reveal that slip regulation using neural network-based control scheme is feasible for different slip values (road conditions) and robust to external disturbances.
Progression of early structural lung disease in young children with cystic fibrosis assessed using CT

Background Cross-sectional studies implicate neutrophilic inflammation and pulmonary infection as risk factors for early structural lung disease in infants and young children with cystic fibrosis (CF). However, the longitudinal progression in a newborn screened population has not been investigated. Aim To determine whether early CF structural lung disease persists and progresses over 1 year and to identify factors associated with radiological persistence and progression. Methods 143 children aged 0.2–6.5 years with CF from a newborn screened population contributed 444 limited slice annual chest CT scans for analysis that were scored for bronchiectasis and air trapping and analysed as paired scans 1 year apart. Logistic and linear regression models, using generalised estimating equations to account for multiple measures, determined associations between persistence and progression over 1 year and age, sex, severe cystic fibrosis transmembrane regulator (CFTR) genotype, pancreatic sufficiency, current respiratory symptoms, and neutrophilic inflammation and infection measured by bronchoalveolar lavage. Results Once detected, bronchiectasis persisted in 98/133 paired scans (74%) and air trapping in 178/220 (81%). The extent of bronchiectasis increased in 139/227 (63%) of paired scans and air trapping in 121/264 (47%). Radiological progression of bronchiectasis and air trapping was associated with severe CFTR genotype, worsening neutrophilic inflammation and pulmonary infection. Discussion CT-detected structural lung disease identified in infants and young children with CF persists and progresses over 1 year in most cases, with deteriorating structural lung disease associated with worsening inflammation and pulmonary infection. Early intervention is required to prevent or arrest the progression of structural lung disease in young children with CF.
Humanising GrabCut: Learning to segment humans using the Kinect

The Kinect provides an opportunity to collect large quantities of training data for visual learning algorithms relatively effortlessly. To this end we investigate learning to automatically segment humans from cluttered images (without depth information) given a bounding box. For this algorithm, obtaining a large dataset of images with segmented humans is crucial as it enables the possible variations in human appearances and backgrounds to be learnt. We show that a large dataset of roughly 3400 humans can be automatically acquired very cheaply using the Kinect. Segmenting humans is then cast as a learning problem with linear classifiers trained to predict segmentation masks from sparsely coded local HOG descriptors. These classifiers introduce top-down knowledge to obtain a crude segmentation of the human which is then refined using bottom up information from local color models in a Snap-Cut [2] like fashion. The method is quantitatively evaluated on images of humans in cluttered scenes, and a high performance obtained (88:5% overlap score). We also show that the method can be completely automated - segmenting humans given only the images, without requiring a bounding box, and compare with a previous state of the art method.
A Survey of Analyzing the Efficiency Based Feature Selection of High Dimensional Data using Fast Algorithm

There are unit many algorithms applied to seek out the potency and effectiveness. Here we have a tendency to take into account the potency because the time taken to retrieve the data’s from the information and effectiveness is from the foremost datasets (or) subsets that area unit relevant to the users search. By mistreatment FAST algorithmic rule we are able to retrieve the data’s while not the orthogonal options. Here the orthogonal options area unit disbursed by means that of assorted levels of the question input and therefore the output the relevant data is disbursed just in case of the set choice and clump ways. These is fashioned in well equipped format and therefore the time taken for retrieve the knowledge are going to be short time and therefore the FAST algorithmic rule calculate the retrieval time of the information from the dataset. This algorithmic rule formulates as per the information out there within the dataset. During this paper, in the main focus regarding the small array pictures that don't seem to be mentioned within the previous work. By analyzing the potency of the projected work and therefore the existing work, the time taken to retrieve the information are going to be higher within the projected by removing all the orthogonal options that area unit gets analyzed.