Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Agricultural Knowledge and Adoption

Understanding the trade-offs in improving the precision of agricultural measures through survey design is crucial. Yet, standard indicators used to determine program effectiveness may be flawed and at a differential rate for men and women. The authors use a household survey from Mozambique to estimate the measurement error from male and female self-reports of their adoption and knowledge of three practices: intercropping, mulching, and strip tillage. Despite clear differences in human and physical capital, there are no obvious differences in the knowledge, adoption, and error in self-reporting between men and women. Having received training unanimously lowers knowledge misreports and increases adoption misreports. Other determinants of reporting error differ by gender. Misreporting is positively associated with a greater number of plots for men. Recall decay on measures of knowledge appears prominent among men but not women. Findings from regression and cost-effectiveness analyses always favor the collection of objective measures of knowledge. Given the lowest rate of accuracy for adoption was around 80 percent, costlier objective adoption measures are recommended for a subsample in regions with heterogeneous farm sizes.
Sex differences in hypothalamic–pituitary–adrenal axis function in patients with chronic pain syndrome

Chronic pain is often equated with chronic stress yet the relationship between chronic pain and hypothalamic–pituitary–adrenal (HPA) axis activity is poorly understood. The objective of this study was to examine diurnal functioning of the HPA axis in patients with clinically defined non-inflammatory chronic pain syndrome (CPS) compared to controls. The sample consisted of 37 adults with CPS and 47 healthy controls. All participants provided saliva samples at awakening, 12:00, 18:00 and 21:00 h on two consecutive days, as well as completing self-report questionnaires relating to anxiety and depression. The CPS group had a significantly lower overall mean diurnal salivary cortisol concentration compared to the control group (p < 0.01) but no significant differences were found between the two groups for repeated cortisol sampling across the day. However, a three-way interaction of time of day by patient status by sex was found (p < 0.032), with lower cortisol concentration in male patients compared to female patients in the afternoon period. No significant group effect was found for the rate of decline in the circadian rise in cortisol concentration. These data demonstrate that CPS is associated with a degree of hypocortisolemia, particularly in male patients. The altered dynamics of cortisol secretion in CPS in relation to the onset and duration of pain in patients remains to be determined.
A model-driven approach for context-aware recommendation

Information overload on the Web has created enormous challenges to users searching for relevant information, goods or services. Moreover, online businesses are often overwhelmed by the complex, but rich, data they have in their information systems and find it difficult to target consumers with the appropriate content. In this paper, we propose a statistical approach for online goods and services recommendation. The recommendation model inspires from consumer psychology and relies on several factors influencing individuals' interests and purchase decisions such as consumers' demographics and intentions, items properties and contextual information. Recommendations are generated using a discriminative model which evaluates consumers' purchases probabilities based on a set of observed variables. In this work, two variants of the proposed recommendation model are detailed and evaluated on different datasets of consumers' navigations and purchases.
Using an Information Retrieval Technique to Discover Malicious Software

This paper describes a research effort to detect unknown, known or variances of known malicious software using an information retrieval technique known as cosine similarity. Document similarity techniques, such as cosine similarity, have been used with great success in several document retrieval applications. By following the standard information retrieval methodology, software, in machine readable format, is regarded as documents in the corpus. These “documents” may or may not have a known malicious intent. The query is a piece of software, again in machine readable format, which contains a certain type of malicious software. This methodology provides an ability to search the corpus with a query and retrieve/identify potentially malicious software as well as other instances of the same type of vulnerability. This retrieval is based on the similarity of the query to a given document in the corpus. The subsequent use of an information visualization technique will allow for quickly and clearly finding the malicious software and will provide the ability for finding similar, potentially new types or variances of malicious behavior.
Adaptive Shadows Detection Algorithm Based on Gaussian Mixture Model

This paper proposed an adaptive shadows detection algorithm based on Gaussian Mixture Model to improve the performance of video object segmentation. This method takes advantage of luminance weight to model the background of the image and obtains a primary segmentation in CIE Luv color space. In this way, it improves the real-time ability of detection. It also becomes more efficient, comparing with the existing shadow detection algorithms which often need to set the threshold manually or get them through a training process. By using the Gaussian distribution, it is able to realize an adaptive shadow detection. At same time, the authors deal with the noise or the aim points uneven distribution by using horizontal filling and vertical filling. It improves the accuracy of segmentation. The experimental results have shown that this method achieves adaptive shadows detection and has strong robustness, high segmentation accuracy.
On the inequality of the 3V's of Big Data Architectural Paradigms: A case for heterogeneity

The well-known 3V architectural paradigm for Big Data introduced by Laney (2011) provides a simplified framework for defining the architecture of a big data platform to be deployed in various scenarios tackling processing of massive datasets. While additional components such as Variability and Veracity have been discussed as an extension to the 3V model, the basic components (Volume, Variety, and Velocity) provide a quantitative framework while varia-bility and veracity target a more qualitative approach. In this paper we argue why the basic 3Vs are not equal due to the different requirements that need to be covered in case higher demands for a particular “V”. Similar to other conjectures such as the CAP theorem, 3V based architectures differ on their implementation. We call this paradigm heterogeneity and we provide a taxonomy of the existing tools (as of 2013) covering the Hadoop ecosystem from the perspective of heterogeneity. This paper contributes on the understanding of the Hadoop ecosystem from the perspective of different workloads and aims to help researchers and practitioners on the design of scalable platforms targeting different operational needs.
Proteomic analysis of synaptic structures

Proteomics is the large-scale analysis of protein expression in a biological system under defined conditions or factors which determine the expression level of proteins. This is complementary to DNA or RNA based genomic approaches and can lead to valuable information about the cellular function of proteins. The introduction of proteomics technologies into neuroscience research has now given new opportunities to study fundamental questions in neurotoxicology, neurometabolism and to investigate the molecular basis of brain function and diseases or mental disorders. Using proteomic protein profiling a more comprehensive view of functional diversity and specialisation of different brain regions, subregions, cell types or even organelles will broaden our understanding of brain physiology and pathology. The basic element to study mechanisms of neuronal plasticity is the synapse. Therefore this study focusses on proteomic approaches to different types of synaptic structures or biochemically defined synapse-enriched fractions.
SHOULD NOT BE USED FOR AUTOREGRESSIVE MODELLING

Autoregressive modelling of noise data is widely used for system identification, surveillance, malfunctioning detection and diagnosis. Several methods are available to estimate an autoregressive model. Usually, the so-called Yule-Walker method is employed. The various estimation methods generally yield comparable parameter estimates. In some special cases however, involving nearly periodic signals, the Yule-Walker approach may lead to incorrect parameter estimates. Burg’s method offers the best alternative to Yule-Walker. In this paper a theoretical explanation of this phenomenon is given, while the 1994 IAEA Benchmark test is presented as a practical example of Yule-Walker yielding poor parameter estimates.
Persistent patterns of interconnection in time-varying cortical networks estimated from high-resolution EEG recordings in humans during a simple motor act

In this work, a novel approach based on the estimate of time-varying graph indices is proposed in order to capture the basic schemes of communication within the functional brain networks during a simple motor act. To achieve this, we used a cascade of computational tools able to estimate first the electrical activity of the cortical surface by using high-resolution EEG techniques. From the cortical signals of different regions of interests we estimated the time-varying functional connectivity patterns by means of the adaptive partial directed coherence. The time-varying connectivity estimation returns a series of networks evolving during the examined task which can be summarized and interpreted with the aid of mathematical indices based on graph theory. The combination of all these methods is demonstrated on a set of high-resolution EEG data recorded from a group of healthy subjects performing a simple foot movement. It can be anticipated that the combination of the time-varying connectivity with the theoretical graph analysis is able to reveal precious information about the interconnections of the cerebral network as the significant persistence of mutual links and three-node motifs.
Feasibility of using a dual-promoter recombinant baculovirus vector to coexpress EGFP and GDNF in mammalian cells

Vectors that are capable of coexpressing two or more exogenous genes for in vitro and in vivo gene delivery are being increasingly studied. The aim of the present study was to explore the feasibility of using the pFastBac™ Dual vector, under the control of two cytomegalovirus (CMV) promoters with opposite directions, to coexpress enhanced green fluorescent protein (EGFP) and glial cell line-derived neurotrophic factor (GDNF) in the same mammalian cell. In the study, two promoters in the pFastBac Dual vector were replaced with CMV-EGFP and CMV-GDNF, whose directions were consistent with the initial directions. The pFastBac Dual-CMV-EGFP-CMV-GDNF plasmid was constructed and then transfected into human embryonic kidney (HEK) 293T cells. The recombinant virus, Bac Dual-CMV-EGFP-CMV-GDNF, was generated with the Bac-to-Bac Baculovirus Expression system and used to transduce HeLa cells. Immunofluorescence was applied to examine the coexpression of EGFP and GDNF in transfected or transduced mammalian cells, while western blot analysis was used to confirm the expression of GDNF in transduced HeLa cells. The recombinant plasmid was constructed and the recombinant baculovirus was successfully generated. Immunofluorescence observations demonstrated that EGFP and GDNF were simultaneously expressed in the same transfected HEK 293T cell and in a single transduced HeLa cell. Western blot analysis revealed that GDNF was expressed accurately in the transduced cells. Therefore, the pFastBac Dual vector is an efficient gene transfer vector that is able to coexpress two target proteins in mammalian cells and serve as a platform for combining reporter or/and therapy genes used in molecular imaging and dual-gene therapy. Thus, the current study presents a new coexpression strategy for dual-gene delivery in vitro and in vivo.
LITHIUM IN THE INTERMEDIATE-AGE OPEN CLUSTER, NGC 3680

High-dispersion spectra centered on the Li 6708 Å line have been obtained for 70 potential members of the intermediate-age open cluster NGC 3680, with an emphasis on stars in the turnoff region of the cluster color–magnitude diagram (CMD). A measurable Li abundance has been derived for 53 stars, 39 of which have radial velocities and proper motions consistent with cluster membership. After being transferred to common temperature and abundance scales, previous Li estimates have been combined to generate a sample of 49 members, 40 of which bracket the cluster Li-dip. Spectroscopic elemental analysis of eight giants and five turnoff stars produces [Fe/H] = −0.17 ± 0.07 (sd) and −0.07 ± 0.02 (sd), respectively. We also report measurements of Ca, Si, and Ni which are consistent with scaled-solar ratios within the errors. Adopting [Fe/H] = −0.08 (Section 3.6), Y2 isochrone comparisons lead to an age of 1.75 ± 0.1 Gyr and an apparent modulus of (m − M) = 10.30 ± 0.15 for the cluster, placing the center of the Li-dip at 1.35 ± 0.03 M☉. Among the giants, five of the nine cluster members are now known to have measurable Li with A(Li) near 1.0. A combined sample of dwarfs in the Hyades and Praesepe is used to delineate the Li-dip profile at 0.7 Gyr and [Fe/H] = +0.15, establishing its center at 1.42 ± 0.02 M☉ and noting the possible existence of a secondary dip on its red boundary. When evolved to the typical age of the clusters NGC 752 (age = 1.45 Gyr, (m − M) = 8.4), IC 4651 (age = 1.5 Gyr, (m − M) = 10.4), and NGC 3680, the Hyades/Praesepe Li-dip profile reproduces the observed morphology of the combined Li-dip within the CMDs of the intermediate-age clusters while implying a metallicity dependence for the central mass of the Li-dip given by M/M☉ = 1.38 ± 0.04 + 0.4 ± 0.2 [Fe/H]. The implications of the similarity of the Li-dichotomy among giants in NGC 752 and IC 4651 and the disagreement with the pattern among NGC 3680 giants are discussed.
Computing the Discrete Fourier Transform on FPGA Based Systolic Arrays

Reconfigurable logic arrays allow for the creation on the one physical hardware platform many different virtual circuits. A configuration bit-stream loaded into the logic array specifies the virtual circuit implemented. This paper addresses the problem of implementing FFTs using virtual computers based on Xilinx FPGAs. A systolic array processor architecture consisting of processing elements (PEs) employing CORDIC arithmetic is presented. The CORDIC approach removes the requirement for area consuming multipliers in the design. The method is suitable for handling power-of-2 and non power-of-2 transform lengths. The modular nature of the design provides for a highly scalable architecture that provides the system designer with a flexible mechanism for making cost-performance tradeoffs. The array processor and PE architecture are described. Based on simulation results, FPGA device utilization and transform execution time are calculated.
Reducing Latency in Internet Access Links with Mechanisms in Endpoints and within the Network

Excessive and unpredictable end-to-end latency is a major problem for today’s Internet performance, affecting a range of applications from real-time multimedia to web traffic. This is mainly attributed to the interaction between the TCP congestion control mechanism and the unmanaged large buffers deployed across the Internet. This dissertation investigates transport and link layer solutions to solve the Internet’s latency problem on the access links. These solutions operate on the sender side, within the network or use signaling between the sender and the network based on Explicit Congestion Notification (ECN). By changing the sender’s reaction to ECN, a method proposed in this dissertation reduces latency without harming link utilization. Real-life experiments and simulations show that this goal is achieved while maintaining backward compatibility and being gradually deployable on the Internet. This mechanism’s fairness to legacy traffic is further improved by a novel use of ECN within the network.
Analysis of Muscle and Ovary Transcriptome of Sus scrofa: Assembly, Annotation and Marker Discovery

Pig (Sus scrofa) is an important organism for both agricultural and medical purpose. This study aims to investigate the S. scrofa transcriptome by the use of Roche 454 pyrosequencing. We obtained a total of 558 743 and 528 260 reads for the back-leg muscle and ovary tissue each. The overall 1 087 003 reads give rise to 421 767 341 bp total residues averaging 388 bp per read. The de novo assemblies yielded 11 057 contigs and 60 270 singletons for the back-leg muscle, 12 204 contigs and 70 192 singletons for the ovary and 18 938 contigs and 102 361 singletons for combined tissues. The overall GC content of S. scrofa transcriptome is 42.3% for assembled contigs. Alternative splicing was found within 4394 contigs, giving rise to 1267 isogroups or genes. A total of 56 589 transcripts are involved in molecular function (40 916), biological process (38 563), cellular component (35 787) by further gene ontology analyses. Comparison analyses showed that 336 and 553 genes had significant higher expression in the back-leg muscle and ovary each. In addition, we obtained a total of 24 214 single-nucleotide polymorphisms and 11 928 simple sequence repeats. These results contribute to the understanding of the genetic makeup of S. scrofa transcriptome and provide useful information for functional genomic research in future.
Increasing Consistency of Disease Biomarker Prediction Across Datasets

Microarray studies with human subjects often have limited sample sizes which hampers the ability to detect reliable biomarkers associated with disease and motivates the need to aggregate data across studies. However, human gene expression measurements may be influenced by many non-random factors such as genetics, sample preparations, and tissue heterogeneity. These factors can contribute to a lack of agreement among related studies, limiting the utility of their aggregation. We show that it is feasible to carry out an automatic correction of individual datasets to reduce the effect of such ‘latent variables’ (without prior knowledge of the variables) in such a way that datasets addressing the same condition show better agreement once each is corrected. We build our approach on the method of surrogate variable analysis but we demonstrate that the original algorithm is unsuitable for the analysis of human tissue samples that are mixtures of different cell types. We propose a modification to SVA that is crucial to obtaining the improvement in agreement that we observe. We develop our method on a compendium of multiple sclerosis data and verify it on an independent compendium of Parkinson's disease datasets. In both cases, we show that our method is able to improve agreement across varying study designs, platforms, and tissues. This approach has the potential for wide applicability to any field where lack of inter-study agreement has been a concern.
Hebbian algorithms for a digital library recommendation system

This paper proposes a set of algorithms to extract metadata about the documents in a digital library from the way these documents are used. Inspired by the learning of connections in the brain, the system assumes that documents develop stronger associations as they are more frequently co-activated. Co-activation corresponds to consultation by the same user, and decreases exponentially with the time interval between consultations. The strength of activation is proportional to the user's interest for the document, either evaluated explicitly, or inferred implicitly from user actions or the duration of the consultation. Co-activation values are added, producing a matrix of associations. This matrix can be used to recommend the documents that are most strongly related to a given document, most relevant to the user's implicit interest profile, or most interesting to users overall. Moreover, it allows the calculation of document similarity values, which in turn can be used to cluster similar documents. The data needed to feed such a recommendation system are readily extracted from the usage logs of document servers, and can be processed either in a centralized or a distributed manner.
Information based adaptive robotic exploration

Exploration involving mapping and concurrent localization in an unknown environment is a pervasive task in mobile robotics. In general, the accuracy of the mapping process depends directly on the accuracy of the localization process. This paper address the problem of maximizing the accuracy of the map building process during exploration by adaptively selecting control actions that maximize localisation accuracy. The map building and exploration task is modeled using an Occupancy Grid (OG) with concurrent localisation performed using a feature-based Simultaneous Localisation And Mapping (SLAM) algorithm. Adaptive sensing aims at maximizing the map information by simultaneously maximizing the expected Shannon information gain (Mutual Information) on the OG map and minimizing the uncertainty of the vehicle pose and map feature uncertainty in the SLAM process. The resulting map building system is demonstrated in an indoor environment using data from a laser scanner mounted on a mobile platform.
A Fiber Optic Doppler Sensor and Its Application in Debonding Detection for Composite Structures

Debonding is one of the most important damage forms in fiber-reinforced composite structures. This work was devoted to the debonding damage detection of lap splice joints in carbon fiber reinforced plastic (CFRP) structures, which is based on guided ultrasonic wave signals captured by using fiber optic Doppler (FOD) sensor with spiral shape. Interferometers based on two types of laser sources, namely the He-Ne laser and the infrared semiconductor laser, are proposed and compared in this study for the purpose of measuring Doppler frequency shift of the FOD sensor. Locations of the FOD sensors are optimized based on mechanical characteristics of lap splice joint. The FOD sensors are subsequently used to detect the guided ultrasonic waves propagating in the CFRP structures. By taking advantage of signal processing approaches, features of the guided wave signals can be revealed. The results demonstrate that debonding in the lap splice joint results in arrival time delay of the first package in the guided wave signals, which can be the characteristic for debonding damage inspection and damage extent estimation.