Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy-coverage tradeoff of nocturnal vital sign estimation in smart beds

We introduce a novel evaluation approach for smart bed systems that continuously measure vital signs. In particular, we demonstrate that estimation accuracy (or error) and measurement coverage time are key performance metrics, describing a performance tradeoff in practical smart bed systems. Based on a typical smart bed system that uses a force sensor array placed between bed mattress and frame, we evaluate the effect of different signal filtering options to illustrate viable design choices using our accuracy-coverage tradeoff analysis. In a full night recording study with six participants focusing on respiration rate estimation, we show that measurement coverage is an essential metric that should be analysed together with accuracy, when assessing the performance of smart bed systems.
Mapping of elements distribution in plant samples using LIBS

This work is focused on application of LIBS technique for the study of plant samples. The elemental mapping on cross section throughout the annual terminal twigs of Picea abies was performed using double pulse Laser Induced Breakdown Spectroscopy (DP LIBS). 2D maps were created, where distribution of Cu and Ca in the plant tissue was observed. After mineralization of twig parts originated in the vicinity of the implementation of cross section, ICP–MS analysis was used for determination of total content of investigated elements.
Feature extraction and model-based noise compensation for noisy speech recognition evaluated on AURORA 2 task

We have evaluated several feature-based and a model-based method for robust speech recognition in noise. The evaluation was performed on Aurora 2 task. We show that after a subband based spectral subtraction, features can be more robust to additive noise. We also report a robust feature set derived from differential power spectrum (DPS), which is not only robust to additive noise, but also robust to spectrum colorization due to channel effects. When the clean training set is available, we show that a model-based noise compensation method can be effective to improve system robustness to noise. Given the testing sets, as a whole, the feature-based methods can yield about 22% relative improvement in accuracy for multi-condition training task, and the model-based method can have about 63% relative performance improvement when systems were trained on clean training set.
Entropic Regularization of Mixed-membership Network Models using Pseudo-observations

Mixed-membership network models permit a node in a graph to take on different latent roles in different interactions. However, while mixed-membership block models often do out-perform classical network models, the actual degree of mixed-membership in many graphs is small, with nodes usually taking on only a handful of many possible roles. We thus present a novel slightly mixed membership stochastic block model, in which the degree of mixed-membership can be controlled. This model is based on a novel regularization method, where the generative model is extended to include variables that measure aggregate statistics (e.g., the entropy of the distribution of latent roles assigned to nodes), as well as “noisy copies” of these aggregates. We then pseudo-observe a desired value for the noisy copies, which has the effect of penalizing models whose aggregates differ greatly from the desired value. Here we demonstrate two applications of this technique: one which encourages slightly-mixed membership, and one which encourages balanced clusters. Experiments with several networks from different domains show that the new models improve performance, as measured by link perplexity and cluster recovery.
Polymerase chain reaction‐restriction fragment length polymorphism of ribosomal internal transcribed spacer region analysis on polyacrylamide gel electrophoresis reveals two haplotypes coexisting in Myzus persicae

Aphids are geographically and economically the most important group of phytophagous insects. They are responsible for various damage on cultivated plants. In our laboratory, we are interested in their ability to transmit phytoviruses such as Rhabdovirus, Cucumovirus, Potyvirus, and Caulimavirus which cause many crop diseases. The present work deals with the study of vectors as analyzed by polymerase chain reaction‐restriction fragment length polymorphism (PCR‐RFLP) of internal transcribed spacer (ITS) regions. Electrophoresis of PCR‐RFLP products using EcoRV, AluI and DraI restriction enzymes on 10% polyacrylamid gel reveals the coexistence of two different haplotypes in the same individual genome. This result seems to explain some features of aphid evolution.
Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.
Temperature insensitive current reference for the 6.27 MHz oscillator

This paper describes a circuit, which generates temperature-independent bias currents. In this paper, low-temperature coefficient reference is presented. The circuit is firstly employed to generate a current reference with temperature compensation, then combining the opposite characteristic curve current reference to minimize the variation of temperature. The proposed circuit has been design by a 0. 18μm CMOS technology process and using computer simulation to evaluate the thermal drift of the reference current. This current reference is used to provide a stable current for a current controlled oscillator(CCO). The proposed CCO achieves temperature coefficients of 22.3 ppm/°C in the temperature range between −25 and 75°C.
Fast liveness checking for ssa-form programs

Liveness analysis is an important analysis in optimizing compilers. Liveness information is used in several optimizations and is mandatory during the code-generation phase. Two drawbacks of conventional liveness analyses are that their computations are fairly expensive and their results are easily invalidated by program transformations. We present a method to check liveness of variables that overcomes both obstacles. The major advantage of the proposed method is that the analysis result survives all program transformations except for changes in the control-flow graph. For common program sizes our technique is faster and consumes less memory than conventional data-flow approaches. Thereby, we heavily make use of SSA-form properties, which allow us to completely circumvent data-flow equation solving. We evaluate the competitiveness of our approach in an industrial strength compiler. Our measurements use the integer part of the SPEC2000 benchmarks and investigate the liveness analysis used by the SSA destruction pass. We compare the net time spent in liveness computations of our implementation against the one provided by that compiler. The results show that in the vast majority of cases our algorithm, while providing the same quality of information, needs less time: an average speed-up of 16%.
Hand-Held Tidal Breathing Nasal Nitric Oxide Measurement – A Promising Targeted Case-Finding Tool for the Diagnosis of Primary Ciliary Dyskinesia

Background Nasal nitric oxide (nNO) measurement is an established first line test in the work-up for primary ciliary dyskinesia (PCD). Tidal breathing nNO (TB-nNO) measurements require minimal cooperation and are potentially useful even in young children. Hand-held NO devices are becoming increasingly widespread for asthma management. Therefore, we chose to assess whether hand-held TB-nNO measurements reliably discriminate between PCD, and Healthy Subjects (HS) and included Cystic Fibrosis (CF) patients as a disease control group known to have intermediate nNO levels. Methods In this cross sectional, single centre, single occasion, proof-of-concept study in children and adults with PCD and CF, and in HS we compared feasibility, success rates, discriminatory capacity, repeatability and agreement between a hand-held electrochemical device equipped with a nNO software application sampling at flow rates 2 ml/s or 5 ml/s, and two stationary chemiluminescence devices, applying both tidal breathing and velum closure techniques. Results Measurements were done in 16 PCD patients, 21 patients with CF and 20 HS aged between 3.8 and 60.9 years. Hand-held TB-nNO showed high success rate (96.5–100%) vs. velum closure nNO techniques (70.2–89.5%). Hand-held TB-nNO sampling at flow rate 5 ml/s showed equally high discriminative power (PCD vs. HS [p<0.0001] and PCD vs. CF [p<0.0001]) and reaching close to 100% sensitivity and specificity, superior repeatability (CV% = 10%) and equal limits of agreement compared to TB-nNO by stationary devices and even compared to velum closure sampling. Conclusion Hand-held TB-nNO discriminates significantly between PCD, CF and HS and shows promising potential as a widespread targeted case-finding tool for PCD, although further studies are warranted before implementation.
Are Standards Always Protectionist?

We analyze the effect of heterogeneity of foreign and domestic producers on product standard and investigate whether the standard chosen by the welfare-maximizing policymaker is protectionist. In a partial-equilibrium set-up, both domestic and foreign producers compete in selling a product in the domestic market, in the presence of consumption externalities. The policymaker chooses a minimum domestic standard that has to be met by both domestic and foreign producers. Protectionism occurs when the welfare-maximizing domestic standard is higher than the international standard maximizing welfare inclusive of foreign profits. We show that the standard is anti-protectionist when foreign producers are much more efficient at addressing the externality than are domestic producers. Possible exclusion of domestic or foreign producers arises with large standards, which may alter the classification of a standard as protectionist or non-protectionist. The paper identifies multiple caveats for the estimation of tariff equivalents of nontariff barriers.
Electrodermal activity processing: A convex optimization approach

This paper reports on a novel model based on convex optimization methods for the analysis of the skin conductance (SC) as response of the electrodermal activity (EDA) to affective stimuli. Starting from previous assessed methodological approaches, this new model proposes a decomposition of SC into tonic and phasic components through the solution of a convex optimization problem. Previous knowledge about the physiology of the EDA is accounted for by means of an appropriate choice of constraints and regularizers. In order to test the effectiveness of the new approach, an experimental session in which 9 healthy subjects were stimulated using affective pictures gathered from the IAPS database was designed and carried out. The experimental session included series of negative-valence high-arousal images and series of neutral images, with an inter-stimulus interval of about 2 seconds for both neutral and high arousal pictures. Next, a statistical analysis was performed on a set of features extracted from the phasic driver and the tonic signal estimated by the model. Results showed that the phasic driver extracted from the model was able to strongly distinguish arousal sessions from neutral ones. Conversely, no significant difference was found for the tonic components. This experimental findings are consistent with the literature and confirm that the phasic component is strictly related to changes in the sympathetic activity of the autonomic nervous system. Although preliminary, these results are very encouraging and future work will progress to further validate the model through specific and controlled experiments.
Men with Sickle Cell Anemia and Priapism Exhibit Increased Hemolytic Rate, Decreased Red Blood Cell Deformability and Increased Red Blood Cell Aggregate Strength

Objectives To investigate the association between priapism in men with sickle cell anemia (SCA) and hemorheological and hemolytical parameters. Materials and Methods Fifty-eight men with SCA (median age: 38 years) were included; 28 who had experienced priapism at least once during their life (priapism group) and 30 who never experienced this complication (control group). Twenty-two patients were treated with hydroxycarbamide, 11 in each group. All patients were at steady state at the time of inclusion. Hematological and biochemical parameters were obtained through routine procedures. The Laser-assisted Optical Rotational Cell Analyzer was used to measure red blood cell (RBC) deformability at 30 Pa (ektacytometry) and RBC aggregation properties (laser backscatter versus time). Blood viscosity was measured at a shear rate of 225 s-1 using a cone/plate viscometer. A principal component analysis was performed on 4 hemolytic markers (i.e., lactate dehydrogenase (LDH), aspartate aminotransferase (ASAT), total bilirubin (BIL) levels and reticulocyte (RET) percentage) to calculate a hemolytic index. Results Compared to the control group, patients with priapism exhibited higher ASAT (p = 0.01), LDH (p = 0.03), RET (p = 0.03) levels and hemolytic indices (p = 0.02). Higher RBC aggregates strength (p = 0.01) and lower RBC deformability (p = 0.005) were observed in patients with priapism compared to controls. After removing the hydroxycarbamide-treated patients, RBC deformability (p = 0.01) and RBC aggregate strength (p = 0.03) were still different between the two groups, and patients with priapism exhibited significantly higher hemolytic indices (p = 0.01) than controls. Conclusion Our results confirm that priapism in SCA is associated with higher hemolytic rates and show for the first time that this complication is also associated with higher RBC aggregate strength and lower RBC deformability.
Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples

Feature extraction plays a central role in classification of hyperspectral data. We propose a clustering-based feature extraction (CBFE) method in this letter. The proposed method is supervised and only needs to calculate the first-order statistics. Thus, CBFE has better performance than some popular supervised feature extraction methods such as linear discriminant analysis, generalized discriminant analysis, and nonparametric weighted feature extraction in small sample size situation. In addition, CBFE works better than unsupervised approaches such as principal component analysis in classification applications. CBFE considers a vector associated with each band that is composed by the mean values of all classes in that band. Then, a clustering method such as k-means is run to group the similar bands in one cluster. The selected number of clusters is equal to the number of extracted features. Experiments carried out on two different hyperspectral data sets demonstrate that the CBFE has better performance in comparison with some conventional feature extraction methods.
Alternating guided image filtering

Edge preserving filters aim to simplify the representation of images (e.g., by reducing noise or eliminating irrelevant detail) while preserving their most significant edges. These filters are typically nonlinear and locally smooth the image structure while minimizing both blurring and over-sharpening of visually important edges. Here we present the Alternating Guided Filter (AGF) that achieves edge preserving smoothing by combining two recently introduced filters: the Rolling Guided Filter (RGF) and the Smooth and iteratively Restore Filter (SiR). We show that the integration of RGF and SiR in an alternating iterative framework results in a new smoothing operator that preserves significant image edges while effectively eliminating small scale details. The AGF combines the large scale edge and local intensity preserving properties of the RGF with the edge restoring properties of the SiR while eliminating the drawbacks of both previous methods (i.e., edge curvature smoothing by RGF and local intensity reduction and restoration of small scale details near large scale edges by SiR). The AGF is simple to implement and efficient, and produces high-quality results. We demonstrate the effectiveness of AGF on a variety of images, and provide a public code to facilitate future studies.
Scalable Discovery of Unique Column Combinations

The discovery of all unique (and non-unique) column combinations in a given dataset is at the core of any data profiling effort. The results are useful for a large number of areas of data management, such as anomaly detection, data integration, data modeling, duplicate detection, indexing, and query optimization. However, discovering all unique and non-unique column combinations is an NP-hard problem, which in principle requires to verify an exponential number of column combinations for uniqueness on all data values. Thus, achieving efficiency and scalability in this context is a tremendous challenge by itself. In this paper, we devise Ducc, a scalable and efficient approach to the problem of finding all unique and non-unique column combinations in big datasets. We first model the problem as a graph coloring problem and analyze the pruning effect of individual combinations. We then present our hybrid column-based pruning technique, which traverses the lattice in a depth-first and random walk combination. This strategy allows Ducc to typically depend on the solution set size and hence to prune large swaths of the lattice. Ducc also incorporates row-based pruning to run uniqueness checks in just few milliseconds. To achieve even higher scalability, Ducc runs on several CPU cores (scale-up) and compute nodes (scale-out) with a very low overhead. We exhaustively evaluate Ducc using three datasets (two real and one synthetic) with several millions rows and hundreds of attributes. We compare Ducc with related work: Gordian and HCA. The results show that Ducc is up to more than 2 orders of magnitude faster than Gordian and HCA (631x faster than Gordian and 398x faster than HCA). Finally, a series of scalability experiments shows the efficiency of Ducc to scale up and out.
Improvement in Toughness and Crystallization of Poly(L-lactic acid) by Melt Blending with Ethylene/Methyl Acrylate/Glycidyl Methacrylate Terpolymer

Melt blending of poly(lactic acid) (PLA) and ethylene/methyl acrylate/glycidyl methacrylate terpolymer (EGA) containing relatively high-concentration epoxide groups (8 wt%) was performed to improve the toughness and crystallization of PLA. The results of nonisothermal and isothermal crystallization investigation showed that the addition of EGA accelerated the crystallization rate and increased the final crystallinity of PLA in the blends. Significant enhancement in toughness and flexibility of PLA were achieved by the incorporation of the EGA elastomer. When 20 wt% EGA added, the impact strength increased from 3.0 kJ m(-2) of neat PLA to 59.8 kJ m(-2) and the elongation at break increased from 4.9 to 232.0%. The failure mode changed from brittle fracture of neat PLA to ductile fracture of the blend. (c) 2013 Society of Plastics Engineers
New technologies to diagnose and monitor infectious diseases of livestock: challenges for sub-Saharan Africa.

Using foot-and-mouth disease (FMD) as an example, this review describes new tools that can be used to detect and characterise livestock diseases. In recent years, molecular tests that can detect and characterise pathogens in a diverse range of sample types have revolutionised laboratory diagnostics. In addition to use in centralised laboratories, there are opportunities to locate diagnostic technologies close to the animals with suspected clinical signs. Work in this area has developed simple-to-use lateral-flow devices for the detection of FMD virus (FMDV), as well as new hardware platforms to allow molecular testing to be deployed into the field for use by non-specialists. Once FMDV has been detected, nucleotide sequencing is used to compare field strains with reference viruses. Transboundary movements of FMDV are routinely monitored using VP1 sequence data, while higher resolution transmission trees (at the farm-to-farm level) can be reconstructed using full-genome sequencing approaches. New technologies such as next-generation sequencing technologies are now being applied to dissect the viral sequence populations that exist within single samples. The driving force for the use of these technologies has largely been influenced by the priorities of developed countries with FMD-free (without vaccination) status. However, it is important to recognise that these approaches also show considerable promise for use in countries where FMD is endemic, although further modifications (such as sample archiving and strain and serotype characterisation) may be required to tailor these tests for use in these regions. Access to these new diagnostic and sequencing technologies in sub-Saharan Africa have the potential to provide novel insights into FMD epidemiology and will impact upon improved strategies for disease control.Effective control of infectious diseases is reliant upon accurate diagnosis of clinical cases using laboratory tests, together with an understanding of factors that impact upon the epidemiology of the infectious agent. A wide range of new diagnostic tools and nucleotide sequencing methods are used by international reference laboratories to detect and characterise the agents causing outbreaks of infectious diseases. In the past, high costs (initial capital expenses, as well as day-to-day maintenance and running costs) and complexity of the protocols used to perform some of these tests have limited the use of these methods in smaller laboratories. However, simpler and more cost-effective formats are now being developed that offer the prospect that these technologies will be even more widely deployed into laboratories particularly those in developing regions of the world such as sub-Saharan Africa.