Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear mixed effects cross-sectional and time series models for unemployment rate estimation

Traditional small area estimators borrow strength either from similar small areas or from the same area across time, but not both. In the past ten years, several approaches to borrowing strength simultaneously across both space and time have been developed. Estimators based on the approach developed by Rao and Yu (1994), Datta et al. (1999) and You et al. (2000), successfully exploit the two dimensions simultaneously to produce improved estimates with desirable properties for small areas. Datta et al. (1999) applied their model to data from the U.S. Current Population Survey, while You et al. (2000) applied a similar model to the Canadian Labour Force Survey (LFS). Unlike Datta et al. (1999), the model proposed by You et al. (2000) does not contain seasonal parameters. This reduces substantially the number of parameters that need to be estimated. Despite this simplification, You et. al. (2000) obtained both an adequate model fit and large reductions in the coefficients of variation (CVs) of the small area estimators of the unemployment rate. However, a major limitation of the models proposed by Datta et al. (1999) and You et al. (2000) is that the linking model for the parameter of interest, the true unemployment rate, is a linear model with normal random effects. The linear linking model may lead to negative estimates for some small areas. To overcome this limitation, in this paper we propose a nonlinear linking model for the parameters of interest; see Section 2.
WaterWOLF: Water Watch On Load Flow

We have developed WaterWOLF, a new electronic system and signal processing algorithm for evaluating flow components in pipes. These techniques nonintrusively convert an existing flow meter into a high resolution wireless meter for determining flow rates in real-time. These techniques exploit new tunneling magneto-resistive materials (TMR) for detecting very small magnetic fields generated by certain types of flow meters. The sensors are nonintrusive, requiring no new access to the flow stream. A pipe distribution network, e.g., for water or oil production or potentially even gas utilities, can not only deliver a commodity like water but can also serve as its own sensor for monitoring water flow and the operation of individual water consuming appliances. Continuous measurements of water consumption can be fed back to the user to help find opportunities for conservation. Additionally, high resolution flow sensing can improve leak detection accuracy. This is important for countries with significant water challenges like Kuwait.
Would a real depreciation of the euro improve the French economy

In this paper, we use a Micro-Macro model to evaluate the effects of a euro's depreciation on the French economy, both at the macro and micro level. Our Micro-Macro model consists of a Microsimulation model that includes an arithmetical model for the French fiscal system and two behavioral models used to simulate the effects on consumption behavior and labor supply, and a multisectoral CGE model which simulates the macroeconomic effects of a reform or a shock. The integration of the two models is made using an iterative (or sequential) approach. We find that a 10% euro's depreciation stimulates the aggregate demand by increasing exports and reducing imports which increases production and reduces the unemployment rate in the economy. At the individual level, we find that the macroeconomic shock reduces poverty and, to a lesser extent, income inequality. In particular, the decrease in the equilibrium wage, determined in the macro model, slightly reduces the available income for people who have already a job, while the reduction in the level of unemployment permits to some individuals to find a job, substantially increasing their income and, in many cases, bringing them out of poverty.
Evolutionary Cluster Analysis

The determination of the number of groups in a dataset, their composition and the most relevant measurements to be considered in clustering the data, is a high-demanding task, especially when the a priori information on the dataset is limited. Some different genetic approaches are proposed as tools for automatic data clustering and features selection. They differ in the adopted codification of the grouping problem, not in the evolutionary operator and parameters. Two of them deals with the grouping problem in a deterministic framework. The first directly approaches the grouping problem as a combinatorial one. The second wants to determine some relevant points in the data domain to be used in clustering data. A probabilistic framework is then introduced with the third one which wants to specify the statistical model from which data are assumed to be drawn. The evolutionary approaches are compared with respect to classical partitional clustering algorithms on s imulated data and on Fisher’s Iris dataset.
Clique partitions, graph compression and speeding-up algorithms

We first consider the problem of partitioning the edges of a graph G into bipartite cliques such the total order of the cliques is minimized, where the order of a clique is the number of vertices in it. It is shown that the problem is NP-complete. We then prove the existence of a partition of small total order in a sufficiently dense graph and devise an efficient algorithm to compute such a partition and the running time. Next, we define the notion of a compression of a graph G and use the result on graph partitioning to efficiently compute an optimal compression for graphs of a given size. An interesting application of the graph compression result arises from the fact that several graph algorithms can be adapted to work with the compressed representation of the input graph, thereby improving the bound on their running times, particularly on dense graphs. This makes use of the trade-off result we obtain from our partitioning algorithm. The algorithms analyzed include those for matchings, vertex connectivity, edge connectivity, and shortest paths. In each case, we improve upon the running times of the best-known algorithms for these problems.
Improved Preconditioner for Hessian Free Optimization

We investigate the use of Hessian Free optimization for learning deep autoencoders. One of the critical components in that algorithm is the choice of the preconditioner. We argue in this paper that the Jacobi preconditioner leads to faster optimization and we show how it can be accurately and efficiently estimated using a randomized algorithm.
Return of the State and attempts of centralisation in Italy

The paper analyses the unresolved question of urban and metropolitan governance in Italy in the light of current trends towards re-centralization at national level of policies and resources. This is part of a broader global-local issue. While the “global” has received an extraordinary recognition, a mistrust towards the “local” as and obstacle to development continues to be part of the political and economic dominant approaches in Italy. On this base, weaknesses and strengths of Italian regional and local policies are sketched. There is a growing need to conceive the “local” in a more universal way: the city as a complete society open to cross-border relations with other placesand cultures, and a node of wider global city-regions and networks. An approach towards macroregional governance in the framework of European place-based reform of territorial cohesion is finally proposed.
Were the clocks striking or surprising? Using WSD to improve MT performance

We report on a series of experiments aimed at improving the machine translation of ambiguous lexical items by using wordnet-based unsupervised Word Sense Disambiguation (WSD) and comparing its results to three MT systems. Our experiments are performed for the English-Slovene language pair using UKB, a freely available graph-based word sense disambiguation system. Results are evaluated in three ways: a manual evaluation of WSD performance from MT perspective, an analysis of agreement between the WSD-proposed equivalent and those suggested by the three systems, and finally by computing BLEU, NIST and METEOR scores for all translation versions. Our results show that WSD performs with a MT-relevant precision of 71% and that 21% of sense-related MT errors could be prevented by using unsupervised WSD.
THE TEMPERATURE–DENSITY RELATION OF THE INTERGALACTIC MEDIUM AFTER HYDROGEN REIONIZATION

We use an analytic model to study how inhomogeneous hydrogen reionization affects the temperature distribution of the intergalactic medium (IGM). During this process, the residual energy of each ionizing photon is deposited in the IGM as heat, increasing its temperature to 20,000–30,000 K; subsequent expansion of the universe then cools the gas. Because reionization most likely proceeds from high to low densities, underdense voids are ionized last, have less time to cool, and are (on average) warmer than mean-density gas immediately after reionization is complete (an “inverted” temperature–density relation). From this initial configuration, the low-density gas cools quickly and eventually returns to a more normal temperature–density relation. The rapidly evolving temperature introduces systematic uncertainties in measurements of the ionizing background at z ∼ 6. For example, late reionization implies rapid cooling, so that the ionizing background would have to evolve even more rapidly at z ∼ 5–6 than typically claimed. This degeneracy is difficult to disentangle, because the Lyα forest probes only a narrow range in densities (over which the gas is nearly isothermal). However, higher Lyman-series transitions probe wider density ranges, sampling different effective temperatures, and offer a new way to measure the IGM temperature–density relation that should work even where nearly saturated absorption precludes other methods. This will help to separate evolution in temperature from that in the ionizing background. While more detailed study with hydrodynamic simulations is needed, we show that such measurements could potentially distinguish early and late reionization using only a handful of lines of sight.
Polymerase Chain Reaction: A Better Method for Diagnosing Chronic Schistosoma mansoni Infections

For more effective diagnosis of the acute and chronic stages of Schistosoma mansoni infection in humans, the polymerase chain reaction (PCR) technique was compared with the Kato-Katz method. A total of 150 stool samples were collected from inpatient and outpatient clinics at the Department of Tropical Medicine, Minia University Hospital, Egypt. Three groups of patients, 50 with acute intestinal schistosomiasis, 70 with chronic intestinal schistosomiasis and 30 normal healthy controls were studied. Stool samples were analyzed by PCR and the Kato-Katz method. The mean number of eggs per gram of feces was 4.6 when estimated by the Kato-Katz method in positive stool samples from acute schistosomiasis cases but only 1.7 in chronic cases. In acute intestinal schistosomiasis, 15 and 45 out of 50 cases were positive by Kato-Katz and PCR, respectively. In the chronic intestinal schistosomiasis cases, 6 and 68 out of 70 cases were positive by the Kato-Katz and PCR methods, respectively. We conclude that PCR appears to be an effective diagnostic technique for S. mansoni infection, especially where a low worm burden exists, such as in chronic cases.
On the Distance Entropy of a Data Collection Network

We study the communication cost of collecting correlated data at a sink over a network. To do so, we introduce Distance Entropy, an intrinsic quantity that characterizes the data gathering limit of networked sources. We demonstrate that, for any network embedded with any set of sources and a cost function [cost]=[data rate]×[link weight], distance entropy is a lower bound on the optimal communication cost. This is true for the most general data collection schemes that allow arbitrary routing and coding operations, including network coding and source coding. This lower bound can be matched using optimal rate Slepian-Wolf encoding plus shortest path routing. For more general communication cost functions, we show that the optimal scheme among schemes using Slepian-Wolf codes is also optimal over all of the possible schemes. Our results imply that for collecting data from correlated sources at a single sink, Network Coding does not help in the sense of lowering the optimal communication cost. We then extend our results to the case that includes broadcast links in the network. We show that the same optimal cost holds even if we allow broadcasting. In other words, neither broadcasting nor Network Coding improves the total cost of collecting data from correlated sources at a single sink.
mSwasthya: A mobile-enabled personal health record management system

The increase in number of hospitals with functional hospital management information systems has helped to increase efficiency and quality of care, thereby improving healthcare service delivery. However, with the prevalent issues that the Indian healthcare industry is still grappling with, there is a growing need for each citizen's proactive responsibility towards managing his/her health. Moreover, with changes in lifestyle and with increase in the occurrence of cardio vascular diseases and chronic ailments, the patients need to adopt a preventive style of treatment for managing their own personal health. This has led to the emergence of a personal health record (PHR), which is focused on a citizen-centric healthcare system rather than an institution-centric system. This paper highlights mSwasthya, which is a mobile-enabled personal health record management system designed for individuals. The PHR as part of mSwasthya is integrated with various health, wellness and fitness-oriented mobile applications, which the user can install and use them `on-the-fly' as part of their daily routines to manage their own health.
Coupling of Navier–Stokes equations with protein molecular dynamics and its application to hemodynamics

The red blood cell (RBC) aggregation plays an important role in many physiological phenomena, in particular the atherosclerosis and thrombotic processes. In this research, we introduce a new modelling technique that couples Navier–Stokes equations with protein molecular dynamics to investigate the behaviours of RBC aggregates and their effects on the blood rheology. In essence, the Lagrangian solid mesh, which represents the immersed deformable cells, is set to move on top of a background Eulerian mesh. The effects of cell–cell interaction (adhesive/repulsive) and hydrodynamic forces on RBC aggregates are studied by introducing equivalent protein molecular potentials into the immersed finite element method. The aggregation of red blood cells in quiescent fluids is simulated. The de‐aggregation of a RBC cluster at different shear rates is also investigated to provide an explanation of the shear‐rate‐dependence of the blood viscoelastic properties. Finally, the influences of cell–cell interaction, RBC rigidity, and vessel geometry are addressed in a series of test cases with deformable cells (normal and sickle RBCs) passing through micro‐ and capillary vessels. Copyright © 2004 John Wiley & Sons, Ltd.
Personality and an Engaged Response Style in Patients Diagnosed with Multiple Sclerosis

Multiple sclerosis MS) is an autoimmune disease that impacts the central nervous system (CNS). The disease’s attack on the CNS results in a wide range of symptoms and an unpredictable disease course. The combined uncertainty of the disease and its progression over time, make adjustment to MS a complex process. Despite its prevalence and long-term consequences, few studies have been conducted to provide comprehensive understanding of the psychological factors contributing to the adjustment process. Acceptance and Commitment Therapy (ACT) is considered one of the newer forms of CBT, often referred to as the “third wave. ” The core principle of ACT is the development of psychological flexibility through the six components of acceptance, cognitive defusion, present moment awareness, self as observer, values clarification, and committed action. When faced with a diagnosis of a chronic, life-changing illness, individuals may re-examine their goals and values. In addition, MS may make it more difficult to move toward one’s values and may require determination and commitment to overcome barriers related to the disease. The current study examined the relationship between values, committed action, personality, and quality of life in a sample of individuals diagnosed with MS. The present study found that committed action was a significant predictor in all four of the QoL domains. However, it was most strongly associated with psychological and social QoL. The content and importance of the values that individuals with MS held predicted much less variance in QoL domains than committed action. Implications for future studies and clinical interventions targeting individuals with MS as well as the present study’s limitations were also discussed.
Interaction of Cellular and Network Mechanisms in Spatiotemporal Pattern Formation in Neuronal Networks

Spatiotemporal patterning of neuronal activity is considered to be an important feature of cognitive processing in the brain as well as pathological brain states, such as seizures. Here, we investigate complex interactions between intrinsic properties of neurons and network structure in the generation of network spatiotemporal patterning in the context of seizure-like synchrony. We show that membrane excitability properties have differential effects on network activity patterning for different network topologies. We consider excitatory networks consisting of neurons with excitability properties varying between type I and type II that exhibit significantly different spike frequency responses to external current stimulation, especially at firing threshold. We find that networks with type II-like neurons show higher synchronization and bursting capacity across a range of network topologies than corresponding networks with type I-like neurons. These differences in activity patterning are persistent across different network sizes, connectivity strengths, magnitudes of random external input, and the addition of inhibitory interneurons to the network, making them highly likely to be relevant to brain function. Furthermore, we show that heterogeneous networks of mixed cell types show emergent dynamical patterns even for very low mixing ratios. Specifically, the addition of a small percentage of type II-like cells into a network of type I-like cells can markedly change the patterning of network activity. These findings suggest that cellular as well as network mechanisms can go hand in hand, leading to the generation of seizure-like discharges, suggesting that a single ictogenic mechanism alone may not be responsible for seizure generation.
Efficient MPI Implementation upon DSP / BIOS TM on Sundance TM Multi-computers

The high computational complexity of real-time video applications is currently being faced by task distribution on DSP multi-computers. Unfortunately, DSP vendors offer these platforms nowadays with proprietary kernels and communication libraries. As a result, current software is hardware targeted and not portable at all. We understand that the introduction of a well established communication standard as MPI in the signal-processing world should help to improve this scenery. However, a light and fast implementation of MPI is a must to meet this goal in the embedded world. This article describes eMPI, a preliminary implementation of MPI for Sundance™ machines. Performance figures are given.
A shadow elimination algorithm for vehicle detection by merging projection features and HSV color space

This paper presents a novel algorithm based on projector features to eliminate dynamic shadow. Through the projected image, this method will split the vehicle, and extract the single-vehicle image. Then using the gradient projection separates regions of shadow and vehicle. Effect of shadow elimination of the algorithm is better, and separated image of vehicles has not holes. In addition, this paper improves the algorithm of shadow elimination based HSV color space by optimizing the Ostu algorithm. Using merging algorithm integrates these two algorithms to further improve the effect of shadow elimination. And performance of the algorithm is more accurate description by using performance parameters of F.
Technology Adoption, Bubbles, Network Externalities and Growth

Can stock market bubbles accelerate long term growth? Do such bubbles indicate irrational behavior? This paper studies the effect of innovation uncertainty on the concomitant time path of firm valuations, technology adoption and growth in a setting that incorporates positive network externalities. In such a setting a bubble may form under rational expectations. The resulting valuation bubble, induced by uncertainty and accelerated by positive externalities, may serve as a coordination mechanism supporting accelerated post-bubble growth. Such a path may compensate for the inability of market participants to internalized and exploit the full potential of positive externalities. It may also overcome the chilling effects often attributed to the adoption process of externality intensive innovations. Under the framework proposed, uncertainty may improve long-term growth. This setup may also be used to explain the accelerated productivity that followed the bursting of notable technology related bubbles such as the railway bubble of 19th century England and the 20th century ICT bubble. Attributing a productive role to certain bubbles may have far-reaching policy implications.
The Communication of Multiple Sclerosis Diagnosis: The Patients' Perspective

Background. Multiple sclerosis (MS) is the leading cause of nontraumatic neurological disability in young adults in Europe and in the United States. The uncertainty regarding its evolution makes the diagnosis disclosure a difficult process. Objective. The aim of the study was to provide patients' global perspective towards MS diagnosis communication. Methods. 150 consecutive patients, recently diagnosed with CIS or MS, were asked to complete a 17-item questionnaire assessing factors influencing their satisfaction with the information provided. Results. Eighty-six patients fulfilled diagnostic criteria for MS and 64 for CIS. Diagnosis disclosure took place in a private setting and required in most cases (87.3%) less than 30 minutes. Most patients reported being moderately or highly satisfied with the information provided (75%). The degree of satisfaction seems significantly related to patients' younger age, a longer time dedicated to disclose the diagnosis, a CIS diagnosis, and, above all, tailored information and an adequate emotional support. Conclusion. Most patients reported a good degree of satisfaction about the communication of MS or CIS diagnosis. A fruitful relationship between patient and neurologist is essential to obtain a better acceptance of the disease, patients' compliance with chronic treatments and to improve patients' quality of life.