Assimilation of remote sensing and hydrological data using adaptive filtering techniques for watershed modelling

The knowledge of hydrological variables (e. g. soil moisture, evapotranspiration) are of pronounced importance in various applications including flood control, agricultural production and effective water resources management. These applications require the accurate prediction of hydrological variables spatially and temporally in watershed/basin. Though hydrological models can simulate these variables at desired resolution (spatial and temporal), often they are validated against the variables, which are either sparse in resolution (e. g. soil moisture) or averaged over large regions (e. g. runoff). A combination of the distributed hydrological model (DHM) and remote sensing (RS) has the potential to improve resolution. Data assimilation schemes can optimally combine DHM and RS. Retrieval of hydrological variables (e. g. soil moisture) from remote sensing and assimilating it in hydrological model requires validation of algorithms using field studies. Here we present a review of methodologies developed to assimilate RS in DHM and demonstrate the application for soil moisture in a small experimental watershed in south India.

A cloud-based intelligent computing system for contextual exploration on personal sleep-tracking data using association rule mining

With the development of wearable and mobile computing technology, more and more people start using sleep-tracking tools to collect personal sleep data on a daily basis aiming at understanding and improving their sleep. While sleep quality is influenced by many factors in a person’s lifestyle context, such as exercise, diet and steps walked, existing tools simply visualize sleep data per se on a dashboard rather than analyse those data in combination with contextual factors. Hence many people find it difficult to make sense of their sleep data. In this paper, we present a cloud-based intelligent computing system named SleepExplorer that incorporates sleep domain knowledge and association rule mining for automated analysis on personal sleep data in light of contextual factors. Experiments show that the same contextual factors can play a distinct role in sleep of different people, and SleepExplorer could help users discover factors that are most relevant to their personal sleep.

Combination of Tevatron Searches for the Standard Model Higgs Boson in the W+W- Decay Mode

We combine searches by the CDF and D0 collaborations for a Higgs boson decaying to W+W-. The data correspond to an integrated total luminosity of 4.8 (CDF) and 5.4 (D0) fb-1 of p-pbar collisions at sqrt{s}=1.96 TeV at the Fermilab Tevatron collider. No excess is observed above background expectation, and resulting limits on Higgs boson production exclude a standard-model Higgs boson in the mass range 162-166 GeV at the 95% C.L.

Combination antiretroviral therapy -associated lipodystrophy : insights into pathogenesis and treatment

Introduction: Combination antiretroviral therapy (cART) has decreased morbidity and mortality of individuals infected with human immunodeficiency virus type 1 (HIV-1). Its use, however, is associated with adverse effects which increase the patients risk of conditions such as diabetes and coronary heart disease. Perhaps the most stigmatizing side effect is lipodystrophy, i.e., the loss of subcutaneous adipose tissue (SAT) in the face, limbs and trunk while fat accumulates intra-abdominally and dorsocervically. The pathogenesis of cART-associated lipodystrophy is obscure. Nucleoside reverse transcriptase inhibitors (NRTI) have been implicated to cause lipoatrophy via mitochondrial toxicity. There is no known effective treatment for cART-associated lipodystrophy during unchanged antiretroviral regimen in humans, but in vitro data have shown uridine to abrogate NRTI-induced toxicity in adipocytes. Aims: To investigate whether i) cART or lipodystrophy associated with its use affect arterial stiffness; ii) lipoatrophic SAT is inflamed compared to non-lipoatrophic SAT; iii) abdominal SAT from patients with compared to those without cART-associated lipoatrophy differs with respect to mitochondrial DNA (mtDNA) content, adipose tissue inflammation and gene expression, and if NRTIs stavudine and zidovudine are associated with different degree of changes; iv) lipoatrophic abdominal SAT differs from preserved dorsocervical SAT with respect to mtDNA content, adipose tissue inflammation and gene expression in patients with cART-associated lipodystrophy and v) whether uridine can revert lipoatrophy and the associated metabolic disturbances in patients on stavudine or zidovudine based cART. Subjects and methods: 64 cART-treated patients with (n=45) and without lipodystrophy/-atrophy (n=19) were compared cross-sectionally. A marker of arterial stiffness, heart rate corrected augmentation index (AgIHR), was measured by pulse wave analysis. Body composition was measured by magnetic resonance imaging and dual-energy X-ray absorptiometry, and liver fat content by proton magnetic resonance spectroscopy. Gene expression and mtDNA content in SAT were assessed by real-time polymerase chain reaction and microarray. Adipose tissue composition and inflammation were assessed by histology and immunohistochemistry. Dorsocervical and abdominal SAT were studied. The efficacy and safety of uridine for the treatment of cART-associated lipoatrophy were evaluated in a randomized, double-blind, placebo-controlled 3-month trial in 20 lipoatrophic cART-treated patients. Results: Duration of antiretroviral treatment and cumulative exposure to NRTIs and protease inhibitors, but not the presence of cART-associated lipodystrophy, predicted AgIHR independent of age and blood pressure. Gene expression of inflammatory markers was increased in SAT of lipodystrophic as compared to non-lipodystrophic patients. Expression of genes involved in adipogenesis, triglyceride synthesis and glucose disposal was lower and of those involved in mitochondrial biogenesis, apoptosis and oxidative stress higher in SAT of patients with than without cART-associated lipoatrophy. Most changes were more pronounced in stavudine-treated than in zidovudine-treated individuals. Lipoatrophic SAT had lower mtDNA than SAT of non-lipoatrophic patients. Expression of inflammatory genes was lower in dorsocervical than in abdominal SAT. Neither depot had characteristics of brown adipose tissue. Despite being spared from lipoatrophy, dorsocervical SAT of lipodystrophic patients had lower mtDNA than the phenotypically similar corresponding depot of non-lipodystrophic patients. The greatest difference in gene expression between dorsocervical and abdominal SAT, irrespective of lipodystrophy status, was in expression of homeobox genes that regulate transcription and regionalization of organs during embryonal development. Uridine increased limb fat and its proportion of total fat, but had no effect on liver fat content and markers of insulin resistance. Conclusions: Long-term cART is associated with increased arterial stiffness and, thus, with higher cardiovascular risk. Lipoatrophic abdominal SAT is characterized by inflammation, apoptosis and mtDNA depletion. As mtDNA is depleted even in non-lipoatrophic dorsocervical SAT, lipoatrophy is unlikely to be caused directly by mtDNA depletion. Preserved dorsocervical SAT of patients with cART-associated lipodystrophy is less inflamed than their lipoatrophic abdominal SAT, and does not resemble brown adipose tissue. The greatest difference in gene expression between dorsocervical and abdominal SAT is in expression of transcriptional regulators, homeobox genes, which might explain the differential susceptibility of these adipose tissue depots to cART-induced toxicity. Uridine is able to increase peripheral SAT in lipoatrophic patients during unchanged cART.

An embedded grid adaptation strategy for unstructured data based finite volume computations

A grid adaptation strategy for unstructured data based codes, employing a combination of hexahedral and prismatic elements, generalizable to tetrahedral and pyramidal elements has been developed.

Robust Approach for Identification of Bad Data in State Estimation Using SLP Technique

This paper proposes a new approach for solving the state estimation problem. The approach is aimed at producing a robust estimator that rejects bad data, even if they are associated with leverage-point measurements. This is achieved by solving a sequence of Linear Programming (LP) problems. Optimization is carried via a new algorithm which is a combination of “upper bound optimization technique" and “an improved algorithm for discrete linear approximation". In this formulation of the LP problem, in addition to the constraints corresponding to the measurement set, constraints corresponding to bounds of state variables are also involved, which enables the LP problem more efficient in rejecting bad data, even if they are associated with leverage-point measurements. Results of the proposed estimator on IEEE 39-bus system and a 24-bus EHV equivalent system of the southern Indian grid are presented for illustrative purpose.

Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.

Improved modeling of groundwater recharge in agricultural watersheds using a combination of crop model and remote sensing

For improved water management and efficiency of use in agriculture, studies dealing with coupled crop-surface water-groundwater models are needed. Such integrated models of crop and hydrology can provide accurate quantification of spatio-temporal variations of water balance parameters such as soil moisture store, evapotranspiration and recharge in a catchment. Performance of a coupled crop-hydrology model would depend on the availability of a calibrated crop model for various irrigated/rainfed crops and also on an accurate knowledge of soil hydraulic parameters in the catchment at relevant scale. Moreover, such a coupled model should be designed so as to enable the use/assimilation of recent satellite remote sensing products (optical and microwave) in order to model the processes at catchment scales. In this study we present a framework to couple a crop model with a groundwater model for applications to irrigated groundwater agricultural systems. We discuss the calibration of the STICS crop model and present a methodology to estimate the soil hydraulic parameters by inversion of crop model using both ground and satellite based data. Using this methodology we demonstrate the feasibility of estimation of potential recharge due to spatially varying soil/crop matrix.

Generating Efficient Data Movement Code for Heterogeneous Architectures with Distributed-Memory

Programming for parallel architectures that do not have a shared address space is extremely difficult due to the need for explicit communication between memories of different compute devices. A heterogeneous system with CPUs and multiple GPUs, or a distributed-memory cluster are examples of such systems. Past works that try to automate data movement for distributed-memory architectures can lead to excessive redundant communication. In this paper, we propose an automatic data movement scheme that minimizes the volume of communication between compute devices in heterogeneous and distributed-memory systems. We show that by partitioning data dependences in a particular non-trivial way, one can generate data movement code that results in the minimum volume for a vast majority of cases. The techniques are applicable to any sequence of affine loop nests and works on top of any choice of loop transformations, parallelization, and computation placement. The data movement code generated minimizes the volume of communication for a particular configuration of these. We use a combination of powerful static analyses relying on the polyhedral compiler framework and lightweight runtime routines they generate, to build a source-to-source transformation tool that automatically generates communication code. We demonstrate that the tool is scalable and leads to substantial gains in efficiency. On a heterogeneous system, the communication volume is reduced by a factor of 11X to 83X over state-of-the-art, translating into a mean execution time speedup of 1.53X. On a distributed-memory cluster, our scheme reduces the communication volume by a factor of 1.4X to 63.5X over state-of-the-art, resulting in a mean speedup of 1.55X. In addition, our scheme yields a mean speedup of 2.19X over hand-optimized UPC codes.

Zoledronic acid in combination with alfacalcidol has additive effects on trabecular microarchitecture and mechanical properties in osteopenic ovariectomized rats

We conducted the present study to investigate the therapeutic effects of the antiresorptive agent zoledronic acid (ZOL), alone and in combination with alfacalcidol (ALF), in a rat model of postmenopausal osteoporosis. Female Wistar rats were ovariectomized (OVX) or sham-operated at 3 months of age. Twelve weeks post surgery, rats were randomized into six groups: (1) sham + vehicle, (2) OVX + vehicle, (3) OVX + ZOL (100 mu g/kg, i.v. single dose), (4) OVX + ZOL (50 mu g/kg, i.v. single dose), (5) OVX + ALF (0.5 mu g/kg, oral gauge daily) and (6) OVX + ZOL (50 mu g/kg, i.v. single dose) + ALF (0.5 mu g/kg, oral gauge daily) for 12 weeks. After treatment, we evaluated the mechanical properties of the lumbar vertebra and femoral mid-shaft. Femurs were also tested for bone density, porosity and trabecular micro-architecture. Biochemical markers in serum and urine were also determined. With respect to improvement in the mechanical strength of the lumbar spine and the femoral mid-shaft, the combination treatment of ZOL and ALF was more effective than each administered as a monotherapy. Moreover, combination therapy using ZOL and ALF preserved the trabecular micro-architecture and cortical bone porosity. Furthermore, the combination treatment of ZOL and ALF corrected the decrease in serum calcium and increase in serum alkaline phosphatase and the tartarate-resistant acid phosphatase level better than single-drug therapy using ZOL or ALF in OVX rats. In addition, the combination treatment of ZOL and ALF corrected the increase in urine calcium, phosphorous and creatinine levels better than single-drug therapy using ZOL or ALF in OVX rats. These data suggest that the combination treatment of ZOL and ALF has a therapeutic advantage over each monotherapy for the treatment of osteoporosis.

Latent variable models for neural data analysis

The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 10^11 neurons, each making an average of 10^3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis.

It is divided into two parts. The first begins with an exposition of the general techniques of latent variable modeling. A new, extremely general, optimization algorithm is proposed - called Relaxation Expectation Maximization (REM) - that may be used to learn the optimal parameter values of arbitrary latent variable models. This algorithm appears to alleviate the common problem of convergence to local, sub-optimal, likelihood maxima. REM leads to a natural framework for model size selection; in combination with standard model selection techniques the quality of fits may be further improved, while the appropriate model size is automatically and efficiently determined. Next, a new latent variable model, the mixture of sparse hidden Markov models, is introduced, and approximate inference and learning algorithms are derived for it. This model is applied in the second part of the thesis.

The second part brings the technology of part I to bear on two important problems in experimental neuroscience. The first is known as spike sorting; this is the problem of separating the spikes from different neurons embedded within an extracellular recording. The dissertation offers the first thorough statistical analysis of this problem, which then yields the first powerful probabilistic solution. The second problem addressed is that of characterizing the distribution of spike trains recorded from the same neuron under identical experimental conditions. A latent variable model is proposed. Inference and learning in this model leads to new principled algorithms for smoothing and clustering of spike data.

New approach for improving transverse superresolution in optical data storage

We have proposed a new confocal readout system which is based on the combination of two N-zone circular phase-only transverse superresolving pupils to improve transverse superresolution. The procedure for designing such an improved system is presented. Results of comparisons between the performance of the proposed system and the transverse superresolving pupils indicate that with the same Strehl ratio the former has much higher transverse superresolution capacity and significantly lower sidelobe intensity. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

New approach for improving transverse superresolution in optical data storage

We have proposed a new confocal readout system which is based on the combination of two N-zone circular phase-only transverse superresolving pupils to improve transverse superresolution. The procedure for designing such an improved system is presented. Results of comparisons between the performance of the proposed system and the transverse superresolving pupils indicate that with the same Strehl ratio the former has much higher transverse superresolution capacity and significantly lower sidelobe intensity. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

Combination of web usage, content and structure information for diverse web mining applications in the tourism context and the context of users with disabilities

188 p.

Collective dynamics of glass-forming polymers at intermediate length scales: a synergetic combination of neutron scattering, atomistic simulations and theoretical modelling

Qens/wins 2014 - 11th International Conference on Quasielastic Neutron Scattering and 6th International Workshop on Inelastic Neutron Spectrometers / editado por:Frick, B; Koza, MM; Boehm, M; Mutka, H