Miracle-dz/UniFAR-FTUs huggingface dataset:
Calculation of altitude-dependent tikhonov constraints for TES nadir retrievals

A key component in the regularization of vertical atmospheric trace gas retrievals is the construction of constraint matrices. We introduce a novel method for developing a constraint matrix based on altitude-varying combinations of zeroth-, first-, and second-order derivatives of the trace gas profile. This constraint matrix can be optimized to minimize the diagonal a posteriori error covariance and can also consider other factors such as degrees of freedom. This approach is applied to the calculation of constraint matrices for Tropospheric Emission Spectrometer nadir retrievals of atmospheric temperature, H/sub 2/O, O/sub 3/, CO, and CH/sub 4/. The retrieval error achieved with these constraints is comparable to the error achieved with the classical Bayesian constraint. Furthermore, these constraints are shown to be robust under uncertainty in the climatological conditions.
On the peak factors of sampled and continuous signals

Motivated by a recent letter on an infinite difference between the peak-to-average power ratios (PAPRs) of samples (a series) and a band-limited function constructed from that series, we investigate the amplitude and variations of a band-limited function and present some relevant bounds. Related aspects on sampling theorems and sampling series are also discussed.
Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles

In this paper, a real-time path-planning algorithm that provides an optimal path for off-road autonomous driving with static obstacles avoidance is presented. The proposed planning algorithm computes a path based on a set of predefined waypoints. The predefined waypoints provide the base frame of a curvilinear coordinate system to generate path candidates for autonomous vehicle path planning. Each candidate is converted to a Cartesian coordinate system and evaluated using obstacle data. To select the optimal path, the priority of each path is determined by considering the path safety cost, path smoothness, and path consistency. The proposed path-planning algorithms were applied to the autonomous vehicle A1, which won the 2010 Autonomous Vehicle Competition organized by the Hyundai-Kia Automotive Group in Korea.
Greedy algorithm for subspace clustering from corrupted and incomplete data

We describe the Fast Greedy Sparse Subspace Clustering (FGSSC) algorithm providing an efficient method for clustering data belonging to a few low-dimensional linear or affine subspaces. FGSSC is a modification of the SSC algorithm. The main difference of our algorithm from predecessors is its ability to work with noisy data having a high rate of erasures (missed entries at the known locations) and errors (corrupted entries at unknown locations). The algorithm has significant advantage over predecessor on synthetic models as well as for the Extended Yale B dataset of facial images. In particular, the face recognition misclassification rate turned out to be 6-20 times lower than for the SSC algorithm.
The Structural Correlates of Statistical Information Processing during Speech Perception

The processing of continuous and complex auditory signals such as speech relies on the ability to use statistical cues (e.g. transitional probabilities). In this study, participants heard short auditory sequences composed either of Italian syllables or bird songs and completed a regularity-rating task. Behaviorally, participants were better at differentiating between levels of regularity in the syllable sequences than in the bird song sequences. Inter-individual differences in sensitivity to regularity for speech stimuli were correlated with variations in surface-based cortical thickness (CT). These correlations were found in several cortical areas including regions previously associated with statistical structure processing (e.g. bilateral superior temporal sulcus, left precentral sulcus and inferior frontal gyrus), as well other regions (e.g. left insula, bilateral superior frontal gyrus/sulcus and supramarginal gyrus). In all regions, this correlation was positive suggesting that thicker cortex is related to higher sensitivity to variations in the statistical structure of auditory sequences. Overall, these results suggest that inter-individual differences in CT within a distributed network of cortical regions involved in statistical structure processing, attention and memory is predictive of the ability to detect structural structure in auditory speech sequences.
Yeast cell mortality related to a high‐pressure shift: occurrence of cell membrane permeabilization

The shrinkage of yeast cells caused by high‐pressure treatment (250 MPa, 15 min) was investigated using direct microscopic observation. A viable staining method after treatment allowed the volume variation of two populations to be distinguished: an irreversible volume decrease (about 35% of the initial volume) of pressure‐inactivated cells during pressure holding time, and viable cells, which were less affected. A mass transfer was then induced during high‐pressure treatment. Causes of this transfer seem to be related to a pressure‐induced membrane permeabilization, allowing a subsequent leakage of internal solutes, where three ions (Na+, K+ and Ca2+), plus endogenous glycerol, were verified. This glycerol leakage was found to occur after yeast pressurization in a medium having low water activity, although the yeast was not inactivated. All these observations lead to the hypothesis that pressure‐induced cell permeabilization could be the cause of yeast inactivation under pressure.
Resilience of nurses who work in community mental health workplaces in Palestine

People in Palestine live and work in a significantly challenging environment. As a result of these challenges they have developed resilient responses which are embedded in their cultural context. 'Sumud', in particular, is a socio-political concept which refers to ways of surviving in the context of occupation, chronic adversity, lack of resources and limited infrastructure. Nurses' work in Palestine is an under-researched subject and very little is known about how nurses adjust to such challenging environments. To address this gap in the literature this study aimed to explore the resilience of community mental health nurses (CMHNs) who work in Palestine. An interpretive qualitative design was chosen. Fifteen face-to-face interviews were completed with participants. Thirty-two hours of observations of the day-to-day working environment and workplace routines were conducted in two communities' mental health centres. Written documents relating to practical job-related policies were also collected from various workplaces. Thematic analysis was used across all data sources resulting in four main themes, which describe the sources of resilience among CMHNs. These sources are 'Sumud and Islamic cultures', 'Supportive relationships', 'Making use of the available resources', and 'Personal capacity'. The study concludes with a better understanding of resilience in nursing, which draws on wider cultural contexts and social ecological responses. The outcomes from this study will be used to develop the resilience of CMHNs in Palestine.
ATL 313, A Selective A2A Adenosine Receptor Agonist, Reduces Myocardial Infarct Size in a Rat Ischemia/Reperfusion Model

Objective: The cardioprotective effects of activation of the A2A adenosine receptor (A2AAR) on ischemia/reperfusion injury in the heart remain controversial. We investigated whether ATL 313, a new selective A2AAR agonist, could reduce myocardial infarct size in a rat ischemia/reperfusion model. Methods: Sprague-Dawley rats were subjected to a 40 minute occlusion of the left coronary artery followed by 3 hours reperfusion. Hemodynamics were monitored during the procedure. The rats were divided into 3 groups: Group 1 received continuous intravenous infusion of saline given 10 min prior to ischemia and throughout reperfusion (n=8); Group 2 received continuous intravenous infusion of 10 ng/kg/min of ATL 313 given 10 min prior to ischemia, and throughout reperfusion (n=8); and group 3 received an intravenous bolus of ATL 313 (900 ng/Kg body weight) given 10 min prior to ischemia, and continuous intravenous infusion of 10 ng/kg/min of ATL 313 started at 20 min after ischemia and throughout reperfusion (n=8). After euthanasia of the rats, the hearts were harvested for the assessment of risk zone and zone of necrosis of the left ventricle. Results: The percentage of risk zone in the left ventricle was similar among group 1 (47 ± 3.7 %), group 2 (41.5 ± 4.2 %) and group 3 (42.4 ± 3.8 %). However, the infarct size, expressed as a percentage of the risk zone, was significantly decreased in group 3 (30.6 ± 5 %, P=0.01) compared with group 1 (53.8 ± 6.2 %) and group 2 (52.1 ± 4.8 %). In group 3, the bolus injection of ATL 313 caused a reduction in blood pressure during the procedure, and decreased heart rate and LV ±dp/dt before coronary artery occlusion; but increased LV +dp/dt at the end of reperfusion compared to the other 2 groups. Conclusion: A2AAR agonist ATL313 significantly reduced infarct size and improved LV contractility at the end of reperfusion assessed by LV dp/dt at a dose of 900 ng/Kg. The mechanisms for the observed cardioprotection effect of ATL313 remain to be determined.
Novel Extension of SBR–PO Method for Solving Electrically Large and Complex Electromagnetic Scattering Problem in Half-Space

To extend hybrid high-frequency shooting and bouncing rays–physical optics (SBR–PO) method to the modeling of electromagnetic (EM) scattering from an object in half-space, this paper focuses on addressing two key issues: 1) how to accurately obtain the induced electric currents on the object surface by taking into account the EM interactions between the object and half-space and 2) how to accurately calculate far-fields in half-space configuration. First, by introducing virtual interface of half-space, the proposed method avoids physically modeling the underlying ground, and only induced electric currents on the object are effectively taken into account in the SBR process. Second, the closed-form half-space Green’s function is used to calculate scattered far-fields to avoid the time-consuming evaluation of the Sommerfeld integrals. The computational errors are only from the approximation of induced electric currents with the same level of the SBR–PO method for the solution of the free-space problem, so the proposed method is more accurate than the conventional SBR–PO technique. Besides, performing the proposed method on the graphics processing unit platform to further accelerate the ray-tracing process makes the method even more powerful to solve the extra-large-scale scattering problems in the half-space.
Wavelet-based image coding using fuzzy inference and adaptive quantization

A class of image compression algorithms has been developed for exploiting dependencies between the hierarchical wavelet coefficients using zerotrees. The paper deals with a fuzzy inference filter for image entropy coding by choosing significant coefficients and zerotree roots in the higher frequency wavelet subbands. Moreover, an adaptive quantization is proposed to improve the coding performance. Evaluating with the popular images, the proposed approach is comparable or superior to most state-of-the-art coders.
Handbook of Solid-State Lighting and LEDs

III-nitride emitters with nanostructures have generated tremendous interest in the research community due to a variety of novel properties that their planar counterparts do not have. As a result, these devices demonstrate great potentials in application in solid-state lighting with highbrightness and full color. This chapter reviews the up-to-date technologies for the nanofabrication of III-nitride light-emitting diodes. The emission properties and related mechanisms have also been discussed. At the end of this review, we display the outlook of high-efficiency III-nitride-based lightemitting diodes with nanostructures, particularly for the aim of white light illumination.
Using SVD for Segmentation and Classification of Human Hand Actions

An automated strategy for decomposing time series into small, elementary subsequences is proposed. This is accomplished in two steps: first the time series must be decomposed into simpler sub-series (segmentation), next each sub series has to be suitably modeled or uniquely characterized (classification). In this paper, an approximation employing the first right singular vector of the data matrix is considered, and two new criteria for segmenting data are proposed and compared. The effectiveness of the proposed strategy is shown on a time series resulting from sensory data on a data-glove when a human picks a tin can. The strategy proves to be simple and reliable, and can be used as a basic ingredient for real-time detection and interpretation of human gestures.
Application of boundary element method to reinforced concrete

The Dual Boundary Element formulation for modelling of crack growth in reinforced concrete is presented. The influence of reinforcements on the concrete can be considered as a body forces or attachment forces which are confined to a straight line instead of being distributed over the whole domain. The solution for these attachment forces is obtained from the condition that the deformations of the concrete and the reinforcement under the action of the external loading are compatible. The fictitious crack model is employed to represent the fracture of concrete in which the fracture zone is replaced by a closing forces acting on both crack surfaces. The numerical results obtained are compared to the FEM analysis and experimental results.
An effective diagnosis method to support yield improvement

The ability to achieve and maintain high yield levels depends on the capability of detecting, analyzing and correcting repetitive failure mechanisms. In this paper, a statistical fault diagnosis method based on using only the first or the first few failing test vectors is presented. The new approach is analyzing the failing vectors from an entire lot and produces a finite list of suspect locations, which are then subjected to further statistical and physical analysis. The results of the performed case studies show the usefulness of this method when applied in a production environment. We were able to detect repetitive failure mechanisms and accurately correlate electrical fail locations to in-line inspection data and thus greatly improve the accuracy of the determined kill ratio.
Local Markov Property for Models Satisfying Composition Axiom

The local Markov condition for a DAG to be an independence map of a probability distribution is well known. For DAGs with latent variables, represented as bi-directed edges in the graph, the local Markov property may invoke exponential number of conditional independencies. This paper shows that the number of conditional independence relations required may be reduced if the probability distributions satisfy the composition axiom. In certain types of graphs, only linear number of conditional independencies are required. The result has applications in testing linear structural equation models with correlated errors.
On PPP , unit roots and panels Reihe

This paper re-assesses the panel (unit root test) evidence for PPP on four monthly data sets. We discuss and illustrate that commonly-used first generation panel unit root tests are inappropriate for PPP analysis since they are constructed for cross-sectionally uncorrelated panels. Given that real exchange rate panel data sets are – almost by construction – highly cross-sectionally correlated, so called second generation panel unit root methods that allow for and model cross-sectional dependence should be applied. Using inappropriate first generation tests, quite strong evidence for PPP is found. However, this evidence vanishes entirely when resorting to an appropriate method (e.g. the one developed in Bai and Ng, 2004a) for nonstationary cross-sectionally correlated panels. We strongly believe that our findings are relevant beyond the data sets investigated here for illustration.
Joint angle estimation in rehabilitation with inertial sensors and its integration with Kinect

In this paper, we explore the combined use of inertial sensors and the Kinect for applications on rehabilitation robotics and assistive devices. In view of the deficiencies of each individual system, a new method based on Kalman filtering was developed in order to perform online calibration of sensor errors automatically whenever measurements from Kinect are available. The method was evaluated on experiments involving healthy subjects performing multiple DOF tasks.
Memory consolidation during sleep: Interactive effects of sleep stages and HPA regulation

Sleep is critically involved in the consolidation of previously acquired memory traces. However, nocturnal sleep is not uniform but is subject to distinct changes in electrophysiological and neuroendocrine activity. Specifically, the first half of the night is dominated by slow wave sleep (SWS), whereas rapid eye movement (REM) sleep prevails in the second half. Concomitantly, hypothalamo-pituitary–adrenal (HPA) activity as indicated by cortisol release is suppressed to a minimum during early sleep, while drastically increasing during late sleep. We have shown that the different sleep stages and the concomitant glucocorticoid release are interactively involved in the consolidation of different types of memories. SWS-rich early sleep has been demonstrated to benefit mainly the consolidation of hippocampus-dependent declarative memories (i.e. facts and episodes). In contrast, REM sleep-rich late sleep was shown to improve in particular emotional memories involving amygdalar function, as well as procedural memories (for skills) not depending on hippocampal or amygdalar function. Enhancing plasma glucocorticoid concentrations during SWS-rich early sleep counteracted hippocampus-dependent declarative memory consolidation, but did not affect hippocampus-independent procedural memory. Preventing the increase in cortisol during late REM sleep-rich sleep by administration of metyrapone impaired hippocampus-dependent declarative memory but enhanced amygdala-dependent emotional aspects of memory. The data underscore the importance of pituitary–adrenal inhibition during early SWS-rich sleep for efficient consolidation of declarative memory. The increase in cortisol release during late REM sleep-rich sleep may counteract an overshooting consolidation of emotional memories.
Multiple hypotheses data association propagation for robust monocular-based SLAM algorithms

Data Association is probably the most important step of every monocular Simultaneous Localization and Mapping (SLAM) algorithm because it provides the basic information to the estimation module, independently on the estimation algorithm of choice. Although important, it is also a difficult task because the analytic solution is NP-Hard. The usual approximation is obtaining only one data association hypothesis per frame which affects the robustness of the result [1][2][3][4][5]. In this paper, a data association approach is presented, where multiple hypotheses are propagated between frames using a probabilistic framework. Experimental results, using real and synthetic data, show that the proposed algorithm produces promising results with respect to other state of the art methods.
State-space Representation of Closed Contours in Electrical Capacitance To- Mography

In Electrical Capacitance Tomography (ECT) the main focus is on the reconstruction of distinct objects with sharp transitions between the phases. Being inherently ill-posed, the reconstruction algorithm requires some sort of regularization to stabilize the solution of the inverse problem. However, introducing regularization may counteract the reconstruction of well-defined contours for grid-based methods. Level set propagation approaches able to model sharp phase boundaries suffer from high computational demands. In this contribution two different state-space representations of closed contours based on B-Splines and on Fourier descriptors are investigated. Both approaches allow to describe the problem with only a small set of state-space variables. Regularization is incorporated implicitly which can be directly interpreted in the object domain as it relates to smooth contours. To solve the inverse problem, statistical inversion is performed by means of particle filtering providing the opportunity to incorporate prior information conveniently and to take measurement uncertainties into account. 1. INTRODUCTION Electrical Capacitance Tomography (ECT) is a non-invasive image-based technique that aims at estimating the permittivity distribution (ε r-distribution) within closed objects [14]. Such objects are, for instance, pipelines in the oil industry or chambers and vessels in the food industry. Voltage patterns are applied to the electrodes which are mounted along the circumference. The resulting potentials which depend on the materials inside the closed object are measured. Based on these measurements, the spatial material distribution is reconstructed. However, this reconstruction task is a nonlinear and severely ill-posed inverse problem. A lot of different algorithms have been developed and applied to tomographic tasks for two-phase flow fields in recent years with the objective to improve the image quality and to reduce the computational effort in order to approach real-time. Finite Element Methods (FEM) which are widely in use are not well suited when the reconstruction of phase boundaries is of interest due to the need of regularization. Regularization simply incorporates some a priori assumption on the material distribution and is done by adding a regularization term to the original functional to be minimized. The amount of regularization is crucial for the reconstruction task and, therefore, many different approaches have been proposed [4]. But up to now there is no universally valid rule to adjust this parameter. Another drawback of regularization for grid-based methods is that it causes a blurring of object interfaces. However, in process tomography the main focus is on the identification of distinct objects with sharp boundaries …
Confusion assessment method: a systematic review and meta-analysis of diagnostic accuracy

Background Delirium is common in the early stages of hospitalization for a variety of acute and chronic diseases. Objectives To evaluate the diagnostic accuracy of two delirium screening tools, the Confusion Assessment Method (CAM) and the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Methods We searched MEDLINE, EMBASE, and PsychInfo for relevant articles published in English up to March 2013. We compared two screening tools to Diagnostic and Statistical Manual of Mental Disorders IV criteria. Two reviewers independently assessed studies to determine their eligibility, validity, and quality. Sensitivity and specificity were calculated using a bivariate model. Results Twenty-two studies (n = 2,442 patients) met the inclusion criteria. All studies demonstrated that these two scales can be administered within ten minutes, by trained clinical or research staff. The pooled sensitivities and specificity for CAM were 82% (95% confidence interval [CI]: 69%–91%) and 99% (95% CI: 87%–100%), and 81% (95% CI: 57%–93%) and 98% (95% CI: 86%–100%) for CAM-ICU, respectively. Conclusion Both CAM and CAM-ICU are validated instruments for the diagnosis of delirium in a variety of medical settings. However, CAM and CAM-ICU both present higher specificity than sensitivity. Therefore, the use of these tools should not replace clinical judgment.