Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite Element Modeling of the Pulse Wave propagation in the aorta for simulation of the Pulse Wave Imaging (PWI) method

A large number of pathological conditions result in significant changes of the mechanical properties of the aortic wall. Using the Pulse Wave Velocity (PWV) as an indicator of aortic stiffness has been proposed for several decades. Pulse Wave Imaging (PWI) is an ultrasonography-based imaging method that has been developed to map and quantify the pulse wave (PW) propagation along the abdominal aortic wall and measure its local properties. We present a finite-element-based approach that aims at improving our understanding of the complex PW patterns observed by PWI and their relationship to the underlying mechanical properties. A Fluid-Structure Interaction (FSI) coupled model was developed based on an idealized axisymmetric aorta geometry. The accuracy of the model as well as its ability to reproduce realistic PW propagation were evaluated by performing a parametric analysis on aortic elasticity, by varying the aortic Young�s modulus between 20 kPa and 2000 kPa. The Finite-Element model was able to predict with good accuracy the expected PWV values in different theoretical cases, with an averaged relative difference of 14% in the 20kPa-100kPa, which corresponds to a wide physiologic range for stiffness of the healthy aorta. This study allows to validate the proposed FE model as a tool that is capable of representing quantitatively the pulse wave patterns in the aorta.
EVA-1 Functions as an UNC-40 Co-receptor to Enhance Attraction to the MADD-4 Guidance Cue in Caenorhabditis elegans

We recently discovered a secreted and diffusible midline cue called MADD-4 (an ADAMTSL) that guides migrations along the dorsoventral axis of the nematode Caenorhabditis elegans. We showed that the transmembrane receptor, UNC-40 (DCC), whose canonical ligand is the UNC-6 (netrin) guidance cue, is required for extension towards MADD-4. Here, we demonstrate that MADD-4 interacts with an EVA-1/UNC-40 co-receptor complex to attract cell extensions. EVA-1 is a conserved transmembrane protein with predicted galactose-binding lectin domains. EVA-1 functions in the same pathway as MADD-4, physically interacts with both MADD-4 and UNC-40, and enhances UNC-40's sensitivity to the MADD-4 cue. This enhancement is especially important in the presence of UNC-6. In EVA-1's absence, UNC-6 interferes with UNC-40's responsiveness to MADD-4; in UNC-6's absence, UNC-40's responsiveness to MADD-4 is less dependent on EVA-1. By enabling UNC-40 to respond to MADD-4 in the presence of UNC-6, EVA-1 may increase the precision by which UNC-40-directed processes can reach their MADD-4-expressing targets within a field of the UNC-6 guidance cue.
Tunable inductance using transmission lines

This paper presents a concept of a tunable inductance using coupled transmission lines. The proposed method suggests applying signal at one line of a shorted coupled transmission line and injecting current at the other line of the coupled pair. This way the inductance value can vary proportionally with the ratio of the currents in the coupled lines. A detailed analytical discussion is presented. A circuit level implementation of this concept is also presented. In order to confirm this effect in measurement, we have realized a test circuit in 28 nm CMOS technology. Measurements show that equivalent inductance seen at the input of the transmission lines can be controlled by input voltage. Equivalent inductance can be tuned continuously and an inductance tuning factor higher than 10 is achieved.
Machine Learning Across Cultures: Modeling the Adoption of Financial Services for the Poor

Recently, mobile operators in many developing economies have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, a consistent difficulty has been identifying individuals most likely to benefit from access to the new technology. Here, we combine terabyte-scale data from three different mobile phone operators from Ghana, Pakistan, and Zambia, to better understand the behavioral determinants of mobile money adoption. Our supervised learning models provide insight into the best predictors of adoption in three very distinct cultures. We find that models fit on one population fail to generalize to another, and in general are highly context-dependent. These findings highlight the need for a nuanced approach to understanding the role and potential of financial services for the poor.
Model-based detection and tracking of vehicle using a scanning laser rangefinder: A particle filtering approach

A method derived from the Sequential Monte Carlo approaches is proposed here to solve the vehicle detection and tracking problem using a scanning laser rangefinder. The originality of this approach lies in a joint detection and tracking of the objects that avoid the usual pre-detection stage. The proposed modeling is strongly nonlinear. To improve the efficiency of the solution, we use a Rao-Blackwell particle filter: the non-linearity of the state-space equations is taken into account by a particle filter and the linearity is optimally processed by a Kalman filter. The solution of the proposed modeling is based on a matched filter (to the object) which uses a predefined vehicle model. A central point here is to calculate the weights of the matched particle filter according to the vehicle model. The efficiency of the method is shown in terms of estimation accuracies and detection.
COMMENTARY Measurement bias and fluctuating asymmetry estimates

luctuating asymmetry (FA), that is, small, randomdeviation from symmetry in bilateral characters (VanValen 1962; Palmer & Strobeck 1986), is an increasinglypopular measure of developmental instability. As such, itis used for many purposes in relation to the evaluation ofenvironmental or genetic variation in population or indi-vidual quality (Markow 1995; Moller & Swaddle 1998). Itmay also be involved in mate choice, as an honest signalof individual quality (Watson & Thornhill 1994). Anumber of theoretical studies has shown that measure-ment error (i.e. random variation arising from inaccuracyof measurement devices) can lead to overestimation of FA(Palmer & Strobeck 1986; Palmer 1994; Swaddle et al.1994; Merila¨ & Bjo¨rklund 1995; Bjo¨rklund & Merila¨1997). Standard methods based on replicate measures arenow available to avoid this pitfall and produce FA esti-mates as free from measurement error as possible (Palmer& Strobeck 1986). These methods usually assume that the right and leftsides of an individual are measured independently andare therefore subject to uncorrelated measurement errors(Merila¨ & Bjo¨rklund 1995; Bjo¨rklund & Merila¨ 1997; andimplicit in Palmer & Strobeck 1986). However, in manycases, practical constraints may lead researchers tomeasure the left and right sides of a given individualsuccessively or during the same session (when sample sizeis too big to measure all individuals at once). Therefore,the data are in the form of pairs of left and right valuesthat share potential session bias, such as the calibration ofan optical system, the light and position in which objectsare observed and the state of mind of the experimenter.The influence of such biases has never been modelled,although they have been mentioned by Moller & Swaddle(1998, page 29). Here we describe the statistical conse-quences of such biases and show: (1) that they are notfatal to the estimation of FA; (2) that there are somesimple ways to account for them when estimating eitherindividual asymmetry or population FA; and (3) that thebest strategy is always to take left and right values ofan individual during the same session rather than indifferent sessions.We assume that several replicate measures of the sameindividual are taken on different sessions, although leftand right values for that individual are measured duringthe same session. Replicate
Performance and Cost Evaluation of an Adaptive Encryption Architecture for Cloud Databases

The cloud database as a service is a novel paradigm that can support several Internet-based applications, but its adoption requires the solution of information confidentiality problems. We propose a novel architecture for adaptive encryption of public cloud databases that offers an interesting alternative to the tradeoff between the required data confidentiality level and the flexibility of the cloud database structures at design time. We demonstrate the feasibility and performance of the proposed solution through a software prototype. Moreover, we propose an original cost model that is oriented to the evaluation of cloud database services in plain and encrypted instances and that takes into account the variability of cloud prices and tenant workloads during a medium-term period.
Preliminary Study on Wilcoxon Learning Machines

As is well known in statistics, the resulting linear regressors by using the rank-based Wilcoxon approach to linear regression problems are usually robust against (or insensitive to) outliers. This motivates us to introduce in this paper the Wilcoxon approach to the area of machine learning. Specifically, we investigate four new learning machines, namely Wilcoxon neural network (WNN), Wilcoxon generalized radial basis function network (WGRBFN), Wilcoxon fuzzy neural network (WFNN), and kernel-based Wilcoxon regressor (KWR). These provide alternative learning machines when faced with general nonlinear learning problems. Simple weights updating rules based on gradient descent will be derived. Some numerical examples will be provided to compare the robustness against outliers for various learning machines. Simulation results show that the Wilcoxon learning machines proposed in this paper have good robustness against outliers. We firmly believe that the Wilcoxon approach will provide a promising methodology for many machine learning problems.
Eosinophils But Not of Neutrophils Stimulates Effector Functions of Human Interaction with Secretory Component

Youichi Motegi and Hirohito Kitahttp://www.jimmunol.org/content/161/8/4340J Immunol€1998; 161:4340-4346; ;Referenceshttp://www.jimmunol.org/content/161/8/4340.full#ref-list-1This article cites 49 articles, 21 of which you can access for free at: Subscriptionshttp://jimmunol.org/subscriptionsInformation about subscribing to The Journal of Immunology is online at: Permissionshttp://www.aai. org/ji/copyright.htmlSubmit copyright permission requests at: Email Alertshttp://jimmunol.org/cgi/alerts/etocReceive free email-alerts when new articles cite this article. Sign up at:
Optimal Campaigning in Presidential Elections: The Probability of Being Florida

This paper delivers a precise recommendation for how presidential candidates should allocate their resources to maximize the probability of gaining a majority in the Electoral College. A two-candidate, probabilistic-voting model reveals that more resources should be devoted to states which are likely to be decisive in the electoral college and, at the same time, have very close state elections. The optimal strategies are empirically estimated using state-level opinion-polls available in September of the election year. The model’s recommended campaign strategies closely resemble those used in actual campaigns. The paper also analyses how the allocation of resources would change under the alternative electoral rule of a direct national vote for president.
DIVERSE APPROACHES IN MINIMIZING THE BUILD TIME FOR DIFFERENT RAPID PROTOTYPING PROCESSES

Build time is a vital factor of layered manufacturing as it affects cost of the prototype. Reducing the manufacturing time of the products is an endless process, without compromising the quality of the model. Various approaches have been employed for reducing build time in different RP methodologies. Reduction of build time is a complicated task as one has to cope-up a contradicting objective like part surface finish. This paper describes the various attempts made to reduce the build time.
Improving the default data analysis workflow for large autoimmune biomarker discovery studies with ProtoArrays

Contemporary protein microarrays such as the ProtoArray® are used for autoimmune antibody screening studies to discover biomarker panels. For ProtoArray data analysis, the software Prospector and a default workflow are suggested by the manufacturer. While analyzing a large data set of a discovery study for diagnostic biomarkers of the Parkinson's disease (ParkCHIP), we have revealed the need for distinct improvements of the suggested workflow concerning raw data acquisition, normalization and preselection method availability, batch effects, feature selection, and feature validation. In this work, appropriate improvements of the default workflow are proposed. It is shown that completely automatic data acquisition as a batch, a re‐implementation of Prospector's pre‐selection method, multivariate or hybrid feature selection, and validation of the selected protein panel using an independent test set define in combination an improved workflow for large studies.
Geographic Named Entity Disambiguation with Automatic Profile Generation

Knowledge rich approach of processing documents has been viewed as a method to improve over simple bag-of-word representation. Extracting location information from documents and link them to some ontology such as world gazetteer through a disambiguation process becomes an interesting and important topic. Lacking of training data is a problem in disambiguation method. In this paper we described a method to automatically extract training data from large collection of documents based on local context disambiguation, and then sense profiles are generated automatically for disambiguation use. Another topic of this paper is to describe a linear combination method to combine different types of evidences of disambiguation. We explored three different evidences including location sense context in training documents, local neighbor context, and the popularity of individual location sense. Our results show that combining the three evidences generates reasonable results
Blind signal separation using fixed overcomplete basis function dictionaries

A solution for achieving blind separation for underdetermined systems is to use an overcomplete basis function set that has the ability to span all possible inputs. Ideally, such a basis would be learned for each set of inputs but this is computationally expensive. A less processor intensive system is shown using a fixed dictionary of basis functions learned from existing sources and reduced using a correlation-based method. The relation between dictionary size and separation performance for underdetermined scenarios is examined and we demonstrate that a reduced dictionary can produce comparable results using less computational power.
Large scale musical instrument identification

In this paper, automatic musical instrument identification using a variety of classifiers is addressed. Experiments are performed on a large set of recordings that stem from 20 instrument classes. Several features from general audio data classification applications as well as MPEG-7 descriptors are measured for 1000 recordings. Branch-and-bound feature selection is applied in order to select the most discriminating features for instrument classification. The first classifier is based on non-negative matrix factorization (NMF) techniques, where training is performed for each audio class individually. A novel NMF testing method is proposed, where each recording is pro- jected onto several training matrices, which have been Gram-Schmidt orthogonalized. Several NMF variants are utilized besides the standard NMF method, such as the local NMF and the sparse NMF. In addition, 3-layered multilayer perceptrons, normalized Gaussian radial basis function networks, and support vector machines employing a polynomial kernel have also been tested as classifiers. The classification accuracy is high, ranging between 88.7% to 95.3%, outperforming the state-of-the-art techniques tested in the aforementioned experiment. Keywords— Musical instrument identification, Non- negative matrix factorization, MPEG-7 audio descriptors.
Secure query-processing strategies

Strategies for secure query processing multilevel-security database management systems are proposed. They are carried out by query modification, a technique that has been used for enforcing integrity constraints and providing view mechanisms. The technique consists of replacing the query the user presents with one that, when evaluated, will perform the desired function. In the case of a view mechanism, the names of views referenced in the query are replaced by the definitions of the views in terms of base relations. The basic strategy and two variants-adding environmental information and using graphs-are described. The performance of the strategies is examined. <<ETX>>
Hang the DJ: Automatic Sequencing and Seamless Mixing of Dance-Music Tracks

Many radio stations and night-clubs employ Disk-Jockeys (DJs) to provide a continuous stream or “mix” of music, built from a sequence of individual song-tracks. In the last decade, commercial pre-recorded compilation CDs of DJ mixes have become a booming market. DJs exercise skill in deciding an appropriate sequence of tracks and in mixing 'seamlessly' from one track to the next. Online access to large-scale archives of digitized music via automated music information retrieval systems offers users the possibility of discovering many new songs they like, but the majority of consumers are unlikely to want to learn the DJ skills of sequencing and mixing. This paper describes an automatic method by which compilations of dance-music can be sequenced and seamlessly mixed by computer, with minimal user involvement. The user may specify a selection of tracks, and may give a qualitative indication of the type of mix required. The resultant mix can be presented as a continuous single digital audio file, whether for burning to CD or for play-out from a virtual jukebox or personalized virtual radio station.
Stability Analysis of Discrete Hopfield Neural Network

The stability of neural networks is the foundation of the neural networks in various applications. But most interconnection matrixes are symmetric or antisymmetric. The dynamic behavior of asymmetric discrete Hopfield neural network (DHNN) is mainly studied in partial simultaneous updating mode and parallel mode, and using the Lyapunov method, some new simple stability conditions of DHNN are presented.The results established here improve and extend the corresponding results given in the earlier references.
Plasticity of the developing glutamate synapse in the hippocampus

Synapses are highly plastic, i.e. they have the ability to change their signaling strength both in the short- and long-term (e.g. long-term potentiation - LTP) in response to specific patterns of activity. In the developing brain synaptic plasticity promotes activity-dependent development, whereas in the mature brain synaptic plasticity forms the basis for learning and memory. Although both development and learning involve organization and reorganization of synaptic circuits, the extent to which the plasticity behind these two phenomena uses the same mechanisms is unknown. The glutamate synapse which represents > 90 % of the brain synapses signals mainly via postsynaptic AMPA and NMDA receptors. In the developing brain, sparse synaptic activation can make the synapse lose its AMPA signaling capacity, i.e. make it AMPA silent, while LTP can reinstall the AMPA signaling (unsilencing). The aim of this study was to investigate the possible role of the AMPA silent synapse, and its unsilencing, in developmental and mature synaptic plasticity. Electrophysiological recordings of synaptic transmission in the CA1 region and in the dentate gyrus of acute hippocampal slices were used for these studies. A new and unexpected finding was that AMPA unsilencing can also be induced by not activating the AMPA silent synapse for tens of minutes. Together with previous findings this suggests a model in which the glutamate synapse is born with a single AMPA labile module, i.e. the synapse cycles between an AMPA silent state, induced by sparse synaptic activity, and an AMPA signaling state, induced by the absence of synaptic activity. The results further suggest that AMPA silencing is a prerequisite for developmental LTP to occur. In other words, developmental LTP does not potentiate synaptic transmission but rather stabilizes the AMPA labile module. It can, however, transiently potentiate the synapse by the addition of a labile AMPA module to an existing synapse with a single stable AMPA module. After this initial period of synaptic stabilization there is an increase in synaptic connectivity between pre- and postsynaptic neurons. It is proposed that this increased connectivity can be explained, at least partly, by the addition of stable AMPA modules to existing synapses promoted by mature LTP. This thesis thus proposes that, using the same principle mechanism, namely the addition of stable AMPA modules, developmental LTP promotes initial synaptic stabilization while mature LTP promotes synaptic growth.
Impact of Congestion on Taxi Times, Fuel Burn, and Emissions at Major Airports

Taxiing aircraft contribute significantly to fuel burn and emissions at airports. This paper provides a comprehensive assessment of the impact of surface congestion on taxi times, fuel burn, and emissions through analysis of the departing traffic data from four major U.S. airports. Several metrics based on airport throughput and taxi-out times are introduced and studied, including one that considers the number of flights that encounter a congested airport, a second metric that compares the observed taxi-out times with the unimpeded ones, and a third that evaluates them in conjunction with the airport throughput.