Miracle-dz/UniFAR-FTUs huggingface dataset:
A method to estimate prediction intervals for artificial neural networks that is sensitive to the noise distribution in the outputs

The use of confidence estimation techniques on neural networks outputs plays an important role when these mathematical models are applied in many practical applications. However, few of these techniques have the capability to deal with variable noise rate in the predictions over the domain, making the assumptions about the reliability of these outputs become not suitable with their real accuracy. In this paper an extension to the non-linear regression method to estimate prediction intervals for feed forward neural networks is presented. The main idea of this method is that residuals variance should be estimated in function of the input data and not as a constant. Thus, using clustering techniques, distinct estimates of the residuals variance are made and then used to obtain new prediction intervals. Proceeding in this manner, the experiments results show that this approach can lead to prediction intervals that better reflect the confidence level of the neural network outputs.
Lectin-Dependent Enhancement of Ebola Virus Infection via Soluble and Transmembrane C-type Lectin Receptors

Mannose-binding lectin (MBL) is a key soluble effector of the innate immune system that recognizes pathogen-specific surface glycans. Surprisingly, low-producing MBL genetic variants that may predispose children and immunocompromised individuals to infectious diseases are more common than would be expected in human populations. Since certain immune defense molecules, such as immunoglobulins, can be exploited by invasive pathogens, we hypothesized that MBL might also enhance infections in some circumstances. Consequently, the low and intermediate MBL levels commonly found in human populations might be the result of balancing selection. Using model infection systems with pseudotyped and authentic glycosylated viruses, we demonstrated that MBL indeed enhances infection of Ebola, Hendra, Nipah and West Nile viruses in low complement conditions. Mechanistic studies with Ebola virus (EBOV) glycoprotein pseudotyped lentiviruses confirmed that MBL binds to N-linked glycan epitopes on viral surfaces in a specific manner via the MBL carbohydrate recognition domain, which is necessary for enhanced infection. MBL mediates lipid-raft-dependent macropinocytosis of EBOV via a pathway that appears to require less actin or early endosomal processing compared with the filovirus canonical endocytic pathway. Using a validated RNA interference screen, we identified C1QBP (gC1qR) as a candidate surface receptor that mediates MBL-dependent enhancement of EBOV infection. We also identified dectin-2 (CLEC6A) as a potentially novel candidate attachment factor for EBOV. Our findings support the concept of an innate immune haplotype that represents critical interactions between MBL and complement component C4 genes and that may modify susceptibility or resistance to certain glycosylated pathogens. Therefore, higher levels of native or exogenous MBL could be deleterious in the setting of relative hypocomplementemia which can occur genetically or because of immunodepletion during active infections. Our findings confirm our hypothesis that the pressure of infectious diseases may have contributed in part to evolutionary selection of MBL mutant haplotypes.
Using a Reference Ontology with Semantic Similarity in Ontology Alignment

The current use of semantic similarity with a reference ontology in ontology alignment (OA) systems is reviewed. An extended matcher is described that incorporates semantic similarity with the use of a reference ontology. This matcher has been implemented using as a basis AgreementMaker’s mediating matcher. Specific experiments using the OAEI anatomy track are performed using the Uberon ontology as the reference ontology. The results of these experiments are compared to the OAEI 2011 results for the anatomy track. These show that semantic similarity measures can be useful for discovering mappings missed by the original mediating matcher. The use of semantic similarity with a reference ontology should be further investigated in the effort to improve the OA process.
Activation of mitogen‐activated protein kinase by muscarinic receptors in astroglial cells: Role in DNA synthesis and effect of ethanol

Mitogen‐activated protein kinase (MAPK) can be phosphorylated by mitogens binding to G‐protein‐coupled receptors and is considered a major pathway involved in cell proliferation. In this study, we report on the activation of MAPK by muscarinic acetylcholine receptors in astroglial cells, namely the 1321N1 human astrocytoma cell line, primary rat cortical astrocytes, and fetal human astrocytes. Carbachol caused a rapid and transient phorphorylation of MAPK (ERK1/2) in all cell types, with an increase in MAPK activity, without changing the levels of MAPK proteins. Human astrocytoma cells were used to characterize the effect of carbachol on MAPK. Experiments with M2‐ and M3‐receptor subtype‐selective antagonists, and with pertussis toxin, indicated that the M3 subtype is responsible for activating MAPK in glial cells. Pretreatment of cells with the protein kinase C (PKC) inhibitor bisindolylmaleimide I, or downregulation of PKC by 24‐h treatment with the phorbol ester TPA inhibited carbachol‐induced MAPK activation. Additional experiments with PKC α‐ or PKC ϵ‐specific compounds indicated that the ϵ isozyme of PKC is primarily involved in MAPK activation by carbachol. Chelation of calcium also inhibited MAPK activation by carbachol. Two MEK (MAPK kinase) inhibitors inhibited carbachol‐induced DNA synthesis but only at concentrations that exceeded those sufficient to block carbachol‐induced MAPK activation. Ethanol (≤200 mM) had no effect on MAPK when present alone and did not affect carbachol‐induced MAPK activation under various experimental conditions, although it inhibits carbachol‐induced DNA synthesis at low concentrations (10–100 mM). These results suggest that activation of MAPK by carbachol may be necessary but not sufficient for its mitogenic effect in astroglial cells, and that does not represent a target for ethanol‐induced inhibition of DNA synthesis elicited by muscarinic receptors. GLIA 35:111–120, 2001. © 2001 Wiley‐Liss, Inc.
A Survey on Swarm Robotics

In this chapter, the current work on swarm robotics is briefly reviewed. Swarm robotics, inspired from nature swarm, is a combination of swarm intelligence and robotic, and shows great potential in several aspects. Firstly of all, the cooperation in nature swarm and swarm intelligence is briefly introduced, and the special features of the swarm robotics compared with single robot and other multi-individual systems is also presented. Then we describe the modeling method for swarm robotics and list several widely used swarm robotics entity projects and simulation platforms for interested researchers. Finally, as the main point of this chapter, we summarize the current researches on swarm robotic algorithms, i.e., cooperative control mechanisms for swarm robotics for flocking, navigating and searching applications.
A 3D pattern matching algorithm for DNA sequences

MOTIVATION Biologists usually work with textual DNA sequences (succession of A, C, G and T). This representation allows biologists to study the syntax and other linguistic properties of DNA sequences. Nevertheless, such a linear coding offers only a local and a one-dimensional vision of the molecule. The 3D structure of DNA is known to be very important in many essential biological mechanisms. By using 3D conformation models, one is able to construct a 3D trajectory of a naked DNA molecule. From the various studies that we performed, it turned out that two very different textual DNA sequences could have similar 3D structures. RESULTS In this article, we address a new research work on 3D pattern matching for DNA sequences. The aim of this work is to enhance conventional pattern matching analyses with 3D-augmented criteria. We have developed an algorithm, based on 3D trajectories, which compares angles formed by these trajectories and thus quantifies the difference between two 3D DNA sequences. This analysis performs from a global scale to al local one. AVAILABILITY Available on request from the authors.
A Two-Stage Beamforming Approach for Noise Reduction and Dereverberation

In general, the signal-to-noise ratio as well as the signal-to-reverberation ratio of speech received by a microphone decrease when the distance between the talker and microphone increases. Dereverberation and noise reduction algorithm are essential for many applications such as videoconferencing, hearing aids, and automatic speech recognition to improve the quality and intelligibility of the received desired speech that is corrupted by reverberation and noise. In the last decade, researchers have aimed at estimating the reverberant desired speech signal as received by one of the microphones. Although this approach has let to practical noise reduction algorithms, the spatial diversity of the received desired signal is not exploited to dereverberate the speech signal. In this paper, a two-stage beamforming approach is presented for dereverberation and noise reduction. In the first stage, a signal-independent beamformer is used to generate a reference signal which contains a dereverberated version of the desired speech signal as received at the microphones and residual noise. In the second stage, the filtered microphone signals and the noisy reference signal are used to obtain an estimate of the dereverberated desired speech signal. In this stage, different signal-dependent beamformers can be used depending on the desired operating point in terms of noise reduction and speech distortion. The presented performance evaluation demonstrates the effectiveness of the proposed two-stage approach.
Ultra low power capacitive power management unit in 0:18μm CMOS

This paper presents the design and simulation of a two stage power management circuit implemented in 0:18μm CMOS that operates from very low voltages starting from 460mV and higher up to a maximum of 800mV. The proposed capacitive power management unit consumes very low power of 11μW @ 500mV sufficient to be operated from tiny photovoltaic cells, dimensions of few mm2. In addition to the lower power consumption, the proposed circuit does not need any off chip components; ideal for ultra low power wireless sensor nodes.
Education—Migration Nexus: Understanding Youth Migration in Southern Ethiopia

The purpose of this study is to unravel the education–migration nexus in the African context, specifically Ethiopia. It examines why young people terminate their education to migrate out of the country. The study applies de Haas’ aspiration—capability framework and Turner’s macro, meso and micro sociology as its analytical lenses. It offers unique insight into the terrain of youth migration in southern Ethiopia based on empirical data obtained from two rural sub-districts known for high levels of youth out-migration. Data are generated based on interviews with would-be migrant youth, parents, teachers and school principals. The findings reveal that education has both direct and indirect impacts on youth migration. On the other hand, the results indicate that though terminating school could have negative ramifications on human capital accumulation at micro and macro levels, migration can positively impact households and local communities through investments made by individual migrants, migrant-returnees, and remittance-receiving households in small businesses or community development projects, which included better resourced schools.
SAGE-based estimation algorithms for time-varying channels in Amplify-and-forward cooperative networks

Cooperative communication is a technique that achieves spatial diversity by exploiting the presence of other nodes in the network. Most analyses of such networks are conducted under the simplifying assumption of perfect channel knowledge. In this paper we focus on the popular Amplify-and-forward (AF) cooperative protocol. We propose several SAGE-based iterative algorithms with different complexities for estimating the channel gain and noise variance in the case of time-varying channels. Computer simulations are provided to evaluate their performance over Rice-fading channels. We point out a low-complexity estimation algorithm yielding an error performance that (for Rayleigh fading) is only about 0.5 dB worse than in the case of perfect estimation, while outperforming a pilot-based estimation algorithm by about 1.5 dB.
Investment Strategies with VIX and VSTOXX

VIX and VSTOXX derivatives have been the story of success in terms of product innovation over the last five years. In this paper we use historical data on S&P500 and EURO STOXX 50, VIX and VSTOXX, as well as VIX and VSTOXX Futures to reveal linkages between these important series that can be used by equity investors to generate alpha and protect their investments during turbulent times. We consider for comparative performance purposes investment portfolios in U.S. and EU zone and also a long-short cross border portfolio. The econometric analysis is spanned by a battery of GARCH models from which we have selected the GARCH (1,1), the EGARCH and the GJR model as the best models for our data. Overall, investors with EURO STOXX 50 exposure can improve greatly the performance of their portfolio by adding VSTOXX futures.
The Disposition Effect and Realization Preferences: a Direct Test∗

This paper develops a new laboratory test of the hypothesis that individual investors have an irrational preference for selling winning stocks vis-à-vis selling losing stocks. In the experiment, subjects invest in a security that bundles a risky asset, whose price evolves in near-continuous time, with a perpetual put option. Optimal behavior is characterized by an upper and a lower selling thresholds in the asset price space, thus producing a clear rational benchmark and eliminating known confounds. Subjects indeed tend to delay selling losers beyond the optimal point and to sell winners before reaching the optimal liquidation point. The median liquidation points imply the probability of realizing a gain conditional on a sale is 56% larger than optimal. Such behavior is shown to be consistent with a realization utility model and structural estimates reveal that the sensitivity of realization utility to gains and losses decreases faster than what is implied by canonical estimates of prospect-theoretic value functions. A direct estimate of the degree of diminishing sensitivity is an important input for behavioral finance theory as even qualitative results of realization utility models, such as whether investors voluntary realize losses or not, depend on the value of the sensitivity parameter.
Comparison Between Various Application Independent Data Aggregation Techniques in Sensor Networks

Summary Sensor networks, a novel paradigm in distributed wireless communication technology, have been proposed for various applications including military surveillance and environmental monitoring. Such systems suffer bandwidth, energy, and throughput constraints that limit the quantity of information transferred from end-to-end. Mechanisms to perform data centric aggregation utilizing application specific knowledge provide a means to augmenting throughput, but have limitations due to their lack of adaptation and reliance on application specific decisions. We, therefore, propose three novel Application Independent data aggregation schemes :Signal Strength (aggregator with high signal strength), Fixed(pre-defined aggregators), Fly (adaptive) to perform application independent data aggregation in a time sensitive manner. Our work isolates aggregation decisions into a module that resides between the network and the data link layer and does not require any modifications to the currently existing MAC and network layer protocols. We take advantage of queuing delay and the broadcast nature of wireless communication to concatenate network units into an aggregate using a novel adaptive feedback scheme to schedule the delivery of this aggregate to the MAC layer for transmission. In our evaluation we show that bandwidth utilization has increased by as much as 80% , packet loss has been reduced by 55% in Fixed Application Independent Data Aggregation(FX-AIDA) , 65% in Signal Strength Application Independent Data Aggregation(SSB-AIDA) & 80% in Fly Application Independent Data Aggregation(FLY-AIDA) under heavy traffic loads. Average response time is reduced to a mark up difference Additionally, we show as much as average 70% reduction in transmission energy consumption with an overall reduction in header overhead . Time delay is reduced to 90% in case of mobility of the events by Fly Based Application Independent Data Aggregation. We also prove 95 % reduction in cost with FLY-AIDA that is major emphasis in sensor networks . We conclude our evaluation by proposing scheme Fly Based Application Independent Data Aggregation as best scheme through which sensor nodes can dynamically change from one aggregation technique to the other in an unpredictable environment and adapt to dynamic changes in the network and also support real time communication .
Design of Large Field-of-View High-Resolution Miniaturized Imaging System

Steps are taken to design the optical system of lenslet array/photoreceptor array plexus on curved surfaces to achieve a large field of view (FOV) with each lenslet capturing a portion of the scene. An optimal sampling rate in the image plane, as determined by the pixel pitch, is found by the use of an information theoretic performance measure. Since this rate turns out to be sub-Nyquist, superresolution techniques can be applied to the multiple low-resolution (LR) images captured on the photoreceptor array to yield a single high-resolution (HR) image of an object of interest. Thus, the computational imaging system proposed is capable of realizing both the specified resolution and specified FOV.
The Applications and Methods of Pedestrian Automated Detection

Pedestrian safety is a primary traffic issue in urban environment. The use of modern sensing technologies to improve pedestrian safety has remained an active research topic for years. The applications and difficulties of pedestrian automated detection were investigated, and the existing sensing technologies such as piezoelectric sensor, infrared sensor, ultrasonic sensor, microwave radar, laser scanner and computer vision were assessed for their suitability to pedestrian automated detection, their advantages and limitations were analyzed. As any one particular type of technology may have difficulties meeting all necessary requirements in various conditions, It is pointed that that a complete and reliable sensing system for pedestrian detection can benefit from the combined use of multiple sensors.
Prediction of Thematic Rank for Structured Semantic Role Labeling

In Semantic Role Labeling (SRL), it is reasonable to globally assign semantic roles due to strong dependencies among arguments. Some relations between arguments significantly characterize the structural information of argument structure. In this paper, we concentrate on thematic hierarchy that is a rank relation restricting syntactic realization of arguments. A loglinear model is proposed to accurately identify thematic rank between two arguments. To import structural information, we employ re-ranking technique to incorporate thematic rank relations into local semantic role classification results. Experimental results show that automatic prediction of thematic hierarchy can help semantic role classification.
Tracking adaptive performance models using dynamic clustering of user classes

Estimation techniques have been largely applied to track hidden performance parameters (e.g. service demands) of web based software systems. In this paper we investigate dynamic multiclass modeling of such systems, with variable classes of service, aiming at finding a low complexity model yet with enough accuracy. We propose a combination of clustering algorithm and tracking filter for effective grouping of classes of services. The tracking estimator is based on a layered queuing model with parameters for CPU demands and the user load intensity of each class of service. Clustering uses the K-means algorithm. The target application is autonomic control of web clusters, where changes occur at different rates and amplitudes and at random time instants. Experiments show that the tracking is effective, and reveal good filter settings for different variations.
Capturing drinking and nightlife behaviours and their social and physical context with a smartphone application – investigation of users’ experience and reactivity

Abstract Background: Many addictive behaviours are influenced by the context in which they occur, but methods for simultaneously capturing the characteristics of a behaviour and its context are scarce. This study describes a smartphone application developed to document young adults’ nightlife and drinking behaviours and investigates its impact on participants’ lives. Methods: 241 participants, aged 16–25 (46.5% women), were asked to document 10 Friday and Saturday nights over seven weekends. Using their own smartphones, they documented the beverages consumed and the social and physical context by means of questionnaires, photos, and video clips, while phone sensors (e.g., GPS, Bluetooth, accelerometer) were running in the background. Quantitative and additional qualitative data (40 in-depth interviews) were used to investigate response burden, assessment reactivity, and disruption of usual activities among three participant groups, arranged according to the number of reports submitted during the study. Results: 69% of participants documented 10 or more nights. Compared with the most frequent contributors, regular and irregular participants reported similar numbers of non-alcoholic drinks per night, but lower numbers of alcoholic drinks. Within each group, the number of drinks consumed did not change over the course of the study. Taking pictures and video clips was sometimes perceived as inappropriate and potentially disruptive to the ongoing social activities. Conclusion: The application required a high but sustainable degree of commitment and did not induce reactivity. The method might be adapted to study other context-dependent addictive behaviours. Measures to decrease response burden and disruption of usual activities are proposed.
A Comparison of Some Error Estimates for Neural Network Models

We discuss a number of methods for estimating the standard error of predicted values from a multilayer perceptron. These methods include the delta method based on the Hessian, bootstrap estimators, and the sandwich estimator. The methods are described and compared in a number of examples. We find that the bootstrap methods perform best, partly because they capture variability due to the choice of starting weights.
Mining bilingual linguistic patterns with aligned and parsed bilingual corpus

Classical grammar for natural languages, which is defined by the linguistics, is widely used in many natural languages processing (NLP) tasks, such as information extraction, machine translation and parsing. The classical grammar is well defined but is context free and does not include the complex patterns which contain multiple linguistic units. On the other hand, there are also many simple patterns which are not included in the classical grammar but are useful in the NLP tasks. Therefore, the recognition of special linguistic patterns from natural language is an important step in various NLP systems. We propose an unsupervised method to automatically discover the complex monolingual linguistic patterns from a classically parsed and aligned bilingual corpus. And all the patterns in one language are qualified by the other parallel language. A specialized and efficient algorithm is applied to mine the frequent bilingual subtrees in the forest and the found subtrees are formalized as the linguistic patterns.