Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical parametric mapping of FMRI data using sparse dictionary learning

Statistical parametric mapping (SPM) of functional magnetic resonance imaging (fMRI) uses a canonical hemodynamic response function (HRF) to construct the design matrix within the general linear model (GLM) framework. Recently, there has been many research on data-driven method on fMRI data, such as the independence component analysis (ICA). The main weakness of ICA for fMRI is its restrictive assumption, especially independence. Furthermore, recent study demonstrated that sparsity is more important than independency in ICA analysis for fMRI. Hence, we propose sparse learning algorithm, such as K-SVD, as an alternative, that decomposes the dictionary-atoms using sparsity rather than independence of the components. For the fMRI finger tapping task data, we employed the K-SVD algorithm to extract the time-course signal atoms of brain activation. The activation maps using trained dictionary as a design matrix showed tightly localized signals in a small set of brain areas.
Scribbler: From Collaborative Sketching to Formal Domain Specific Models and Back Again

Most of the time developers make extensive use of software tools in a software development process to support them in their day-to-day work. One of the first and most important phases of this process is the design phase, but within this phase intuitive and easy to use tools, which support the creative but also collaborative workflow (parallel/distributed), are missing. At the moment, developers use whiteboards to express their ideas in team meetings. Subsequently a coworker takes a picture of the sketches and remodels them with a modeling tool. That procedure is very inconvenient, error-prone and hindering in a creative modeling cycle. For overcoming this ineffective process this paper shows a new software tool using digital whiteboards to transform free hand sketches in formal models and back again during modeling in a distributed team. The transformation is completely independent from a pre-defined modeling language. The tool provides also a training mode to learn new graphical syntax elements and map these to formal metamodel entities. Video: https://www.youtube. com/watch? v=0i3M9djPrRM [Mirror: http://sse-world.de/index.php? cID=3611]
Linguistic Template Extraction for Recognizing Reader-Emotion and Emotional Resonance Writing Assistance

In this paper, we propose a flexible principle-based approach (PBA) for reader-emotion classification and writing assistance. PBA is a highly automated process that learns emotion templates from raw texts to characterize an emotion and is comprehensible for humans. These templates are adopted to predict reader-emotion, and may further assist in emotional resonance writing. Results demonstrate that PBA can effectively detect reader-emotions by exploiting the syntactic structures and semantic associations in the context, thus outperforming wellknown statistical text classification methods and the state-of-the-art reader-emotion classification method. Moreover, writers are able to create more emotional resonance in articles under the assistance of the generated emotion templates. These templates have been proven to be highly interpretable, which is an attribute that is difficult to accomplish in traditional statistical methods.
Boosting Genetic Algorithms with Self-Adaptive Selection

In this paper we evaluate a new approach to selection in genetic algorithms (GAs). The basis of our approach is that the selection pressure is not a superimposed parameter defined by the user or some Boltzmann mechanism. Rather, it is an aggregated parameter that is determined collectively by the individuals in the population. We implement this idea in two different ways and experimentally evaluate the resulting genetic algorithms on a range of fitness landscapes. We observe that this new style of selection can lead to 30-40% performance increase in terms of speed.
Discriminatively trained joint speaker and environment representations for adaptation of deep neural network acoustic models

A recent trend in normalization of factors extraneous to a speech recognition task has been to explicitly introduce features related to the unwanted variability in the training of Deep Neural Networks (DNN). Typically, this is done by either perturbing the training set with models of these extraneous factors such as vocal tract length and environmental noise or augmenting the conventional spectral features with auxiliary information such as i-vector, noise spectrum, etc. Another emerging approach is to derive low dimensional representations of the factors from the hidden layers of DNN and use it for normalization of the acoustic model. Almost all of these approaches focus on either speaker or environment normalization. In this paper we propose a novel approach for estimating a compact joint representation of speakers and environment by training a DNN, with a bottleneck layer, to classify the i-vector features into speaker and environment labels by Multi-Task Learning (MTL). Another novelty is to learn this compact representation while learning to map the i-vector of a noisy utterance into its corresponding clean speaker i-vector and noise-only i-vector. Experiments were conducted on an artificially noise-corrupted version of the WSJ corpus. The proposed compact joint speaker-environment representations show promising gains.
Noncanonical Decapentaplegic Signaling Activates Matrix Metalloproteinase 1 To Restrict Hedgehog Activity and Limit Ectopic Eye Differentiation in Drosophila

One of the pertinent issues associated with cellular plasticity is to understand how the delicate balance between the determined state of cells and the extent to which they can transdetermine is maintained. Employing the well-established model of generating ectopic eyes in developing wing discs of Drosophila by ectopic eyeless expression, we provide evidence for the genetic basis of this mechanism. By both loss-of-function and gain-of-function genetic analyses, we demonstrate that Matrix metalloproteinase 1 (Mmp1) plays an important role in regulating the extent of ectopic ommatidial differentiation. Transcriptional activation of ectopic Mmp1 by the morphogen Decapentaplegic (Dpp) is not triggered by its canonical signaling pathway which involves Mad. Rather, Dpp activates an alternate cascade involving dTak1 and JNK, to induce ectopic Mmp1 expression. Mutational analyses reveal that Mmp1 negatively regulates ectopic eye differentiation by restricting the rate of proliferation and the levels of expression of retinal-determining genes dachshund and eyes absent. This is primarily achieved by restricting the range of Hedgehog (Hh) signaling. Importantly, the increase in proliferation and upregulation of target retinal-determining genes, as observed upon attenuating Mmp1 activity, gets significantly rescued when ectopic eyes are generated in wing discs of hh heterozygous mutants. In conjunction with the previously established instructive and permissive roles of Dpp in facilitating ectopic eye differentiation in wing discs, the outcome of this study sheds light on a mechanism by which Dpp plays a dual role in modulating the delicate balance between the determined state of cells and the extent they can transdetermine.
Preparation, characterization and in-vitro drug release evaluation of Ganciclovir loaded Chitosan nanoparticles

Chitosan Nanoparticles (CSNPs), as drug carrier, can be utilized for enhancing permeability of poorly absorbed drugs. The aim of this work was to enhance the permeability of Ganciclovir (GCV) by loading into CSNPs. An ionic gelation method was undertaken to develop GCV loaded CSNPs. Several process and formulation parameters were screened and optimized through 2 5-2 fractional factorial design and Box-Behnken design respectively. The CSNPs were evaluated and characterized for their particle size and shape, surface charge, entrapment efficiency, crosslinking mechanism (dried CSNPs) and drug release study. The optimized CSNPs were found with particle size of 121.20 ± 2.7 and entrapment efficiency (% EE) of 85.15 ± 1.1%. Transmission electron microscopy, scanning electron microscopy and dynamic light scattering technique revealed spherical particles with uniform size. The in-vitro release profile was found to be sustained up to 24 hr. Thus, incorporation of GCV into CSNPs results in enhanced permeability, that may in turn increase overall oral absorption of the drug.
Undamped nonlinear consensus using integral Lyapunov functions

This paper analyzes a class of nonlinear consensus algorithms where the input of an agent can be decoupled into a product of a gain function of the agents own state, and a sum of interaction functions of the relative states of its neighbors. We prove the stability of the protocol for both single and double integrator dynamics using novel Lyapunov functions, and provide explicit formulas for the consensus points. The results are demonstrated through simulations of a realistic example within the framework of our proposed consensus algorithm.
When “good” is not always right: effect of the consequences of motor action on valence-space associations

Since the work of Casasanto (2009), it is now well established that valence and laterality are associated. Participants tend to prefer objects presented on their dominant side over items presented on their non-dominant side, and to place good items on their dominant side and bad items on the other side. Several studies highlight that those associations of valence and laterality are accounted for by the greater motor fluency of the dominant hand and various studies noted that these associations could be reversed depending on the way people interact with their environment. Consistently with the Theory of Event Coding, the aim of this work is to show that the consequences of motor actions could also reverse the associations between valence and laterality. Thus, if participants had to place two animals (one good, one bad) on two supports, one stable (no risk of falling), one unstable (risk of falling), we hypothesized that the good item would be placed on the stable support, regardless of the side where it would be put (i.e., on the dominant or non-dominant side). We expected the opposite for the bad item. The results of two experiments are consistent with this prediction and support the claim that the consequences of motor action bias the hedonic connotation of our dominant side.
Increases in human plasma antioxidant capacity after consumption of controlled diets high in fruit and vegetables.

BACKGROUND The putative beneficial effects of an increased consumption of fruit and vegetables have been associated with antioxidant nutrients. However, the effect of fruit and vegetable consumption on the overall antioxidant status in humans is unclear. OBJECTIVE The objective of this study was to investigate whether a diet rich in fruit and vegetables would affect the antioxidant capacity of human plasma. DESIGN Thirty-six healthy nonsmokers resided in a metabolic research unit and consumed 2 sets of controlled diets. Diet A contained 10 servings of fruit and vegetables each day for 15 d. Diet B was the same as diet A, except diet B also provided 2 servings of broccoli each day on days 6-10. There was a free-living period of a minimum of 6 wk between the 2 experiments using either diet A or diet B. Fasting plasma antioxidant capacity, measured as oxygen radical absorbance capacity (ORAC), and alpha-tocopherol concentrations were determined on days 1, 6, 11, and 16. RESULTS The fasting baseline plasma ORAC of these subjects was significantly correlated with their estimated daily intake of total antioxidants from fruit and vegetables during the previous year. Plasma ORAC of these subjects was significantly increased by both diets A and B. This increase in ORAC could not be explained by the increase in the plasma alpha-tocopherol concentration. CONCLUSION Increased consumption of fruit and vegetables can increase the plasma antioxidant capacity in humans.
Contextual reconstruction of cloud-contaminated multitemporal multispectral images

The frequent presence of clouds in passive remotely sensed imagery severely limits its regular exploitation in various application fields. Thus, the removal of cloud cover from this imagery represents an important preprocessing task consisting in the reconstruction of cloud-contaminated data. The intent of this study is to propose two novel general methods for the reconstruction of areas obscured by clouds in a sequence of multitemporal multispectral images. Given a cloud-contaminated image of the sequence, each area of missing measurements is reconstructed through an unsupervised contextual prediction process that reproduces the local spectro-temporal relationships between the considered image and an opportunely selected subset of the remaining temporal images. In the first method, the contextual prediction process is implemented by means of an ensemble of linear predictors, each trained over a local multitemporal region that is spectrally homogeneous in each temporal image of the selected subset. In order to obtain such regions, each temporal image is locally classified by an unsupervised classifier based on the expectation-maximization (EM) algorithm. In the second method, the local spectro-temporal relationships are reproduced by a single nonlinear predictor based on the support vector machines (SVM) approach. To illustrate the performance of the two proposed methods, an experimental analysis on a sequence of three temporal images acquired by the Landsat-7 Enhanced Thematic Mapper Plus sensor over a total period of four months is reported and discussed. It includes a detailed simulation study that aims at assessing with different reconstruction quality criteria the accuracy of the methods in different qualitative and quantitative cloud contamination conditions. Compared with two techniques based on compositing algorithms for cloud removal, the proposed methods show a clear superiority, which makes them a promising and useful tool in solving the considered problem, whose great complexity is commensurate with its practical importance.
Crowd video retrieval via deep attribute-embedding graph ranking

Since the number of surveillance cameras in public areas increases very fast, massive crowd videos are captured and shared, which brings an urgent need to retrieve these videos efficiently and effectively. However, most recent research on crowd video mainly focused on crowd behavior understanding and abnormal detection. In this study, as the very first attempt, we propose a crowd video retrieval method via deep attribute-embedding graph ranking. Group profiling attributes are capable of reflecting rich crowd patterns in videos. To deeply embed the specific relationship and manifold structure of crowd patterns, we integrate graph ranking, optimized weights learning and deep metric transforming in a unified regularization framework for crowd video retrieval. To sufficiently explore the effects of multiple attributes and their complementation in crowds, we devise several scene-independent visual descriptors specifically for each crowd video. Interpretable descriptors are categorized into different levels and structures as group profiling attributes, according to semantic properties of crowd patterns. Extensive experiments conducted on CUHK crowd dataset demonstrate the effectiveness and superiority of the proposed approach.
Perirhinal Cortex Removal Dissociates Two Memory Systems in Matching-to-Sample Performance in Rhesus Monkeys

Dissociations of memory systems are typically made using independent cognitive tests. For example, in monkeys habits have been inferred from performance in object discrimination tests, while non-matching-to-sample tests are thought to measure familiarity resulting from single exposures. Such tests cannot measure individual memory processes accurately when more than one memory process contributes to performance. In process dissociation procedures (PDPs), two memory processes cooperate and compete in the performance of a single cognitive task, allowing quantitative estimates of the contributions of each process. We used PDP to measure the contributions of habits and one-trial memory to visual matching-to-sample performance. Sets of test images were shown only once in each daily testing session but were repeated day after day. To produce habits, high-frequency images were correct more frequently than other images across days. Habits were manifest in the extent to which choices in the test phase of matching-to-sample trials were made to the high-frequency images, regardless of which image had been presented as the sample. One-trial memory was measured by the extent to which choices at test were made to the image that had appeared as the sample on that trial, regardless of habit. Perirhinal cortex removal reduced the contribution of one-trial memory to matching performance, but left both habits and the ability to discriminate images intact. PDP can be applied in monkeys in a way that parallels its use in humans, providing a new tool for investigating the neurobiology of memory in nonhuman animals and for comparing memory across species.
The search for superconductors through solid state chemistry

Despite intensive research in the field of superconductivity, there exists no "recipe" for finding new materials that superconduct. Although the most fundamental type of superconductivity is understood, we do not understand all the condition in which the phenomenon occurs. Therefore finding routes for discovering new superconductors is of high interest. This thesis discusses a systematic search for new superconductors. This is done by comparing known superconductors and finding common similarities. Chapter 1 explains the most famous families of superconductors and explain common features appearing in most superconducting materials. Two different routes for searching for new superconductors are discussed. Chapter 2 focuses on crystal structure families that are commonly adopted by superconducting materials. In particular the XYZ family, a family of intermetallic phases with 1:1:1 stoichiometry will be investigated. Chapter 2 discusses the discovery of a new XYZ compound, HfCuGe2, the investigation of superconducting NaAlSi under pressure, and the discovery of a new high pressure phase of CaAuBi. The structural chemistry of XYZ phases is also discussed along with its relation to ionic and metallic radii. Chapter 3 focuses on the idea that superconductivity often appears close to a structural, magnetic, or electronic instability. In this Chapter I predict superconductivity in Tl halide Perovskites, with the help of density functional theory. This is done by drawing an analogy to BaBiO3based superconductors. Furthermore the discovery of a new Hollandite phase, Rb0.17IrO2 is reported. Lastly, the magnetic instabilities and the newly discovered quantum phase transition in Fe doped Cr2B is presented.
On the integration of metamorphic testing and model checking

Metamorphic testing, an innovative software testing technique, generates test cases based on domain specific properties. Model checking is a technique that verifies software designs against system properties. Motivated by the fact that both techniques are based on some properties of software under development, we investigate how to integrate metamorphic testing and model checking. This paper will introduce and discuss some potential topics on this new research direction.
A comparison of two strategies for ASR in additive noise: missing data and spectral subtraction

Keywords: speech ; kermorvant ; morris Reference EPFL-CONF-82532 URL: http://publications.idiap. ch/downloads/papers/1999/kermorvant_eurospeech99. pdf Record created on 2006-03-10, modified on 2017-05-10
Building a Java™ Virtual Machine for Non-Cache-Coherent Many-core Architectures

This work presents the key challenges that Java Virtual Machines (JVMs) implementers face when targeting future non-cache-coherent architectures. It discusses the techniques used to overcome these challenges by distributed JVMs in the literature, and examines their applicability on future non-cache-coherent architectures. It presents new algorithms for software caching, monitor management, and thread scheduling, that take advantage of the hierarchical nature of future non-cache-coherent architectures with coherent-islands. It also builds a proof-of-concept JVM that runs on a 512-core, non-cache-coherent, many-core prototype and present early evaluation results for parts of the proposed algorithms.
Determinants of vitamin D status in young children: results from the Belgian arm of the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study

Abstract Objective To describe the vitamin D status of Belgian children and examine the influence of non-nutritional determinants, in particular of anthropometric variables. Design Cross-sectional data of Belgian participants of the EU 6th Framework Programme IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study. Setting 25-Hydroxyvitamin D (25(OH)D) was measured using RIA. Vitamin D status was categorized as deficient (<25 nmol/l), insufficient (25–50 nmol/l), sufficient (50–75 nmol/l) and optimal (≥75 nmol/l). Anthropometric measurements included height, weight, waist and hip circumferences and triceps and subscapular skinfold thicknesses. Subjects Children (n 357) aged 4–11 years. Results Serum 25(OH)D ranged from 13·6 to 123·5 nmol/l (mean 47·2 (sd 14·6) nmol/l); with 5 % deficient, 53 % insufficient, 40 % sufficient and 2 % optimal. No significant differences were found by age and gender. Significant differences in 25(OH)D were observed for month of sampling (P < 0·001), number of hours playing outside per week (r = 0·140), weight (r = −0·121), triceps (r = −0·112) and subscapular (r = −0·119) skinfold thickness, sum of two skinfold thicknesses (r = −0·125) and waist circumference (r = −0·108). Linear regression analysis of 25(OH)D adjusted for age, month of sampling and hours playing outside per week suggested that (i) weight, (ii) BMI Z-score, (iii) waist circumference and (iv) triceps and subscapular skinfold thickness (as well as the sum of both) independently influenced 25(OH)D. Conclusions The majority of Belgian children had a suboptimal vitamin D status, with more than half having an insufficient status in winter and spring. Month of the year, weekly number of hours playing outside and body composition – both central and abdominal obesity – were identified as important determinants of vitamin D status in Belgian children.
Predicting thermal cracking of asphalt pavements from bitumen and mix properties

The objective of this study was to develop approximate relationships for prediction of the rheological properties and tensile strength of asphalt concrete with conventional bitumen binder to address the low-temperature cracking analysis. The parameters of modified Christensen–Anderson–Marasteanu model for relaxation modulus of bitumen were related with its penetration index and softening point by approximate formulas based on Van Der Poel's nomograph for determining bitumen stiffness. Hot mix asphalt relaxation function has been described by the Christensen–Bonaquist model. Hot mix asphalt tensile strength as a function of loading time and temperature was related to the bitumen stiffness by equation based on Heukelom's data and the Molenaar-Li empirical formula. The strength of hot mix asphalt has been also studied by performing tensile tests at constant strain rate and temperatures. The results were analysed considering the calculated stress increase with temperature drop at various cooling rates. Critical temperature was estimated from comparison of thermal stress and tensile strength of asphalt concrete at constant stress rate.