Miracle-dz/UniFAR-FTUs huggingface dataset:
Broad scope and narrow focus: On the contemporary linguistic and psycholinguistic study of third language acquisition

Aims: in this introduction we situate the seven articles in this special issue in terms of the connections between their themes and their individual contributions to the field of third language acquisition (L3A): new theoretical models, innovative methodologies, an epistemological commentary and new perspectives related to multilingual processing and cognitive function. Approach: we discuss important and often overlooked differences between bi- and multilingualism in the context of second language versus third or further language acquisition. We also provide a brief historical overview of the relatively young field of L3A and outline the three current models of linguistic transfer in L3 morphosyntax. Finally, we approach the issues of methodology, psychological complexity and cognitive implications that are discussed in some of these papers. Conclusions: the diversity of topics in these articles endows the issue with a broad approach to the field of L3A, while individual articles offer a narrow focus on specific theoretical and methodological issues. Significance: this special issue provides an accurate portrayal of the current interest in, and rapid expansion of, multilingualism within linguistic and psycholinguistic approaches.
Innate Immunity Interactome Dynamics

Innate immune response involves protein–protein interactions, deoxyribonucleic acid (DNA)–protein interactions and signaling cascades. So far, thousands of protein–protein interactions have been curated as a static interaction map. However, protein–protein interactions involved in innate immune response are dynamic. We recorded the dynamics in the interactome during innate immune response by combining gene expression data of lipopolysaccharide (LPS)-stimulated dendritic cells with protein–protein interactions data. We identified the differences in interactome during innate immune response by constructing differential networks and identifying protein modules, which were up-/down-regulated at each stage during the innate immune response. For each protein complex, we identified enriched biological processes and pathways. In addition, we identified core interactions that are conserved throughout the innate immune response and their enriched gene ontology terms and pathways. We defined two novel measures to assess the differences between network maps at different time points. We found that the protein interaction network at 1 hour after LPS stimulation has the highest interactions protein ratio, which indicates a role for proteins with large number of interactions in innate immune response. A pairwise differential matrix allows for the global visualization of the differences between different networks. We investigated the toll-like receptor subnetwork and found that S100A8 is down-regulated in dendritic cells after LPS stimulation. Identified protein complexes have a crucial role not only in innate immunity, but also in circadian rhythms, pathways involved in cancer, and p53 pathways. The study confirmed previous work that reported a strong correlation between cancer and immunity.
Tamoxifen: A Novel Approach for the Treatment of Estrogen Receptor Negative Cancers

Tamoxifen is a selective estrogen receptor modulator that is used to treat estrogen receptor positive cancers , specif ically breast cancer. Tamoxifen antagonizes the estrogen receptor and thus impedes estrogen induced growth of cancer cells. How ever, recent studies have shown that tamoxifen also inhibits protein kinase C (PKC), a protein that activates the PKC/ Akt/ PI3k pathw ay. Disrupting this pathway results in the inhibition of the dow nstream target of the PKC/ Akt/ PI3k pathw ay, mTOR, a protein that promotes cell survival and growth. The aim of this project was to determine the inhibitory activity of tamoxifen on an estrogen receptor negative (ER-) cell line, specif ically a Burkitt‘s lymphoma cell line, Ramos. Enzyme immunosorbent assays were used to measure activities of PKC and mTOR. The MTS assay was used to measure Ramos cell viability after tamoxifen treatment (1-25μM). Results showed that Ramos cell viability, PKC activity, and mTOR activity each decreased with statistical signif icance demonstrating a dose-response relationship (p < 0.05) after tamoxifen treatment (1-25μM). Tamoxifen may therefore have potential as a treatment for not just ER positive cancers, but cancers that have a constitutively activated PKC/PI3k/Akt pathw ay.
Associations of Stressful Life Events and Social Strain With Incident Cardiovascular Disease in the Women's Health Initiative

Background Epidemiologic studies have yielded mixed findings on the association of psychosocial stressors with cardiovascular disease (CVD) risk. In this study, we examined associations of stressful life events (SLE) and social strain with incident coronary heart disease (CHD) and stroke (overall, and for hemorrhagic and ischemic strokes) independent of sociodemographic characteristics, and we evaluated whether these relationships were explained by traditional behavioral and biological risk factors. Methods and Results Data from approximately 82 000 Women's Health Initiative Observational Study participants were used for the SLE and social strain analyses, respectively. Participants were followed for events for up to 18.0 years (median, 14.0). Separate Cox proportional hazards models were generated to estimate associations of each stress measure with incident CVD. After adjusting for sociodemographic characteristics and depressive symptoms, higher SLE and social strain were associated with higher incident CHD and stroke (each P trend <0.05). Hazard ratios and 95% confidence intervals were 1.12 (1.01, 1.25) for incident CHD and 1.14 (1.01, 1.28) for incident stroke among participants reporting high versus low SLE. Findings were similar for social strain. Associations were attenuated with further adjustment for mediating behavioral and biological risk factors. Findings were similar for associations of SLE with ischemic stroke and hemorrhagic stroke, but social strain was only associated with ischemic stroke. Conclusions Higher SLE and social strain were associated with higher incident CVD independent of sociodemographic factors and depressive symptoms, but not behavioral and biological risk factors.
Refactoring an Automotive Embedded Software Stack using the Component-Based Paradigm

The number of electronic systems in cars is continuously growing. Electronic systems, consisting of so-called electronic control units (ECUs) interconnected by a communication network, account for up to 30% of a modern car's worth. Consequently, software plays an ever more important role, both for the implementation of functions and the infrastructure. In order to benefit from the reuse of software modules, the major automotive companies have standardized a large number of these modules in the context of the AUTOSAR consortium. In this paper we propose the refactoring of the AUTOSAR stack of system software modules by applying the component-based paradigm in order to increase the scalability of the software stack according to the particular requirements of the application. We demonstrate the feasibility of this approach by performing the refactoring of the modules FlexRay Driver and FlexRay Interface as an example and by deploying the resulting refactored components in a sample automotive application. Finally, we measure the execution time as well as the memory consumption of the refactored components and compare these measures to the measures obtained from the corresponding ordinary AUTOSAR modules.
Structure Preserving Object Tracking

Model-free trackers can track arbitrary objects based on a single (bounding-box) annotation of the object. Whilst the performance of model-free trackers has recently improved significantly, simultaneously tracking multiple objects with similar appearance remains very hard. In this paper, we propose a new multi-object model-free tracker (based on tracking-by-detection) that resolves this problem by incorporating spatial constraints between the objects. The spatial constraints are learned along with the object detectors using an online structured SVM algorithm. The experimental evaluation of our structure-preserving object tracker (SPOT) reveals significant performance improvements in multi-object tracking. We also show that SPOT can improve the performance of single-object trackers by simultaneously tracking different parts of the object.
Block-level De-duplication with Encrypted Data

Deduplication is a storage saving technique which has been adopted by many cloud storage providers such as Dropbox. The simple principle of deduplication is that duplicate data uploaded by different users are stored only once. Unfortunately, deduplication is not compatible with encryption. As a scheme that allows deduplication of encrypted data segments, we propose ClouDedup, a secure and efficient storage service which guarantees blocklevel deduplication and data confidentiality at the same time. ClouDedup strengthens convergent encryption by employing a component that implements an additional encryption operation and an access control mechanism. We also propose to introduce an additional component which is in charge of providing a key management system for data blocks together with the actual deduplication operation. We show that the overhead introduced by these new components is minimal and does not impact the overall storage and computational costs.
Autonomous Band Registration for On-Board Applications

Automatic band-to-band registration of multispectral images is the most critical pre-processing requirement for imaging tasks performed on board satellites, such as data fusion, intra-band coding, change detection, spectral signature based classification, etc. The performance of these tasks depends on the robustness and accuracy of band registration. The a priori information of band misalignment is not accurate enough, due to spacecraft shaking or attitude changes. This paper proposes an autonomous band registration system, which uses a new gradient-based phase correlation method with added sub-pixel extension stage. A new automatic determination scheme for valid registration is also proposed. Experimental results on natural satellite images show that the proposed autonomous system has higher accuracy and better robustness.
Semi-Supervised Neural Networks for Efficient Hyperspectral Image Classification

A framework for semi-supervised remote sensing image classification based on neural networks is presented. The methodology consists of adding a flexible embedding regular izer to the loss function used for training neural networks. Training is done using stochastic gradient descent with additional balncing constraints to avoid falling into local minima. The method constitutes a generalization of both supervised and unsupe rvised methods, and can handle millions of unlabeled samples . Therefore, the proposed approach gives rise to an operation al classifier, as opposed to previously presented transductiv e or Laplacian support vector machines. The proposed methodolo gy constitutes a general framework for building computationally efficient semi-supervised methods. The method is compared t o LapSVM and TSVM in semi-supervised scenarios, to SVM in supervised settings, and to online and batchk-means for unsupervised learning. Results demonstrate the improved classific ation accuracy and scalability of this approach on several hypers pectral image classification problems.
On scheduling mesh-structured computations for Internet-based computing

Advances in technology have rendered the Internet a viable medium for employing multiple independent computers collaboratively in the solution of a single computational problem. A variety of mechanisms - e.g., Web-based computing, peer-to-peer computing, and grid computing - have been developed for such Internet-based computing (IC). Scheduling a computation for IC presents challenges that were not encountered with earlier modalities of parallel or distributed computing, especially when the computation's constituent tasks have interdependencies that constrain their order of execution. The process of scheduling such computations for IC is studied via a "pebble game" that abstracts the process of orchestrating the allocation of a computation's interdependent tasks to participating computers. A quality measure for plays of this game is developed that addresses the danger of "gridlock" in IC when a computation stalls because (due to dependencies) no tasks are eligible for execution. This measure rewards schedules that maximize the number of tasks that are eligible for execution at every step of the computation, one avenue for minimizing the likelihood of "gridlock. " The resulting formal setting is illustrated via the problem of scheduling computations whose intertask dependencies have the structure of "evolving" meshes of finite dimensionalities. Within an idealized setting, a simple scheduling strategy is shown to be optimal when the dependencies have the structure of a two-dimensional mesh and within a constant factor of optimal for meshes of higher dimensionalities. The strategy remains optimal for a generalization of two-dimensional meshes whose structures are determined by abelian monoids (a monoid-based version of Cayley graphs). The optimality results for the idealized setting provide scheduling guidelines for real settings.
An Application of Behavioral Activation Therapy for Major Depressive Disorder in the Context of Complicated Grief, Low Social-Economic Status, and Ethnic Minority Status

Behavioral activation (BA) has been shown to be an effective treatment for depression, especially for severe and/or chronic depression; however, there is little empirical guidance on how BA can be effectively utilized to treat chronic Major Depressive Disorder (MDD) in the context of complicated grief, low socioeconomic status, and ethnic minority status. This case study focuses on the treatment of Ms. X, a 51-year-old African American woman with chronic depression that was exacerbated by complicated grief. This case describes an effective course of a standard BA protocol for MDD in the context of complicated grief, low socioeconomic status, and ethnic minority status and illustrates how a BA protocol can be applied while incorporating important cultural considerations.
Poverty and suicide research in low- and middle-income countries: systematic mapping of literature published in English and a proposed research agenda

Approximately 75% of suicides occur in low- and middle-income countries (LMICs) where rates of poverty are high. Evidence suggests a relationship between economic variables and suicidal behaviour. To plan effective suicide prevention interventions in LMICs we need to understand the relationship between poverty and suicidal behaviour and how contextual factors may mediate this relationship. We conducted a systematic mapping of the English literature on poverty and suicidal behaviour in LMICs, to provide an overview of what is known about this topic, highlight gaps in literature, and consider the implications of current knowledge for research and policy. Eleven databases were searched using a combination of key words for suicidal ideation and behaviours, poverty and LMICs to identify articles published in English between January 2004 and April 2014. Narrative analysis was performed for the 84 studies meeting inclusion criteria. Most English studies in this area come from South Asia and Middle, East and North Africa, with a relative dearth of studies from countries in Sub-Saharan Africa. Most of the available evidence comes from upper middle-income countries; only 6% of studies come from low-income countries. Most studies focused on poverty measures such as unemployment and economic status, while neglecting dimensions such as debt, relative and absolute poverty, and support from welfare systems. Most studies are conducted within a risk-factor paradigm and employ descriptive statistics thus providing little insight into the nature of the relationship. More robust evidence is needed in this area, with theory-driven studies focussing on a wider range of poverty dimensions, and employing more sophisticated statistical methods.
Functional Imaging in Behavioral Neurology and Cognitive Neuropsychology

Introduction What can we hope to learn of the physical operation of the central nervous system and the mental processes that result? The fields of behavioral neurology and cognitive neuropsychology survey this relationship, and have at their disposal powerful intellectual and methodological tools. While the terrain of possible models of brain and behavior interaction seems boundless, particular tests of these models may exist beyond the reach of particular methods.
Segmentation of nuclei in digital pathology images

There are challenges for image cancer nuclei segmentation in clinical decision support systems for brain tumor diagnosis. In this study, we propose a method for segmentation of cancer nuclei when such conflicts of cancer nuclei involve ‘omics’ indicative of brain tumors pathologically. To constrain the problem space in the region of color information (i.e. cancer nuclei), we begin by converting the images into the V component of HSV (Hue, Saturation, Value) using the level-set segmentation (VLS) in the training stage, follow by applying the sparsity representation (SR) in the test stage. Via the SR, the proposed VLS-SR would exhibits an improved capability of searching recursively for the optimal threshold level-set in the working subsets of the SR for image cancer nuclei segmentation.
GENERALIZED FIBONACCI POLYNOMIALS AND THE FUNCTIONAL ITERATION OF RATIONAL FUNCTIONS OF DEGREE ONE

The results derived here are not new in the sense that they can be proven from existing work on generalized Fibonacci polynomials but the approach is entirely novel in that it provides a link between the analysis of generalized Fibonacci numbers and the theory of dynamical systems via the iteration of rational functions of degree one. Fundamental to the concept of the analysis of nonlinear dynamical systems is the functional iteration of the form
Heparin: A Mucopolysaccharide and an Active Antithrombotic Drug

H EPARIN, like so many other biological substances, was discovered incidentally. William 11. llowell, professor of physiology at Johns 11opkinis University, Baltimore, was in 1916 trying to isolate a thromboplastin, an accelerator of the coagulation of the blood, from the phosphatide fraction of the liver and the heart. His co-worker, Jay AMcLean, found instead a substance, later called heparin, which retarded the coagulation of blood. Also unexpected was the finding made almost 20 years later in 1935 that heparin is a mucopolysaceharide esterified with sulfuric acid to a quite extraordinary degree.
A new approximate likelihood estimator for ARMA-filtered hidden Markov models

Hidden Markov models (HMMs) are successfully applied in various fields of time series analysis. Colored noise, e.g., due to filtering, violates basic assumptions of the model. Although it is well known how to consider autoregressive (AR) filtering, there is no algorithm to take into account moving-average (MA) filtering in parameter estimation exactly. We present an approximate likelihood estimator for MA-filtered HMM that is generalized to deal with an autoregressive moving-average (ARMA) filtered HMM. The approximation order of the likelihood calculation can be chosen. Therefore, we obtain a sequence of estimators for the HMM parameters as well as for the filter coefficients. The recursion equations for an efficient algorithm are derived from exact expressions for the forward iterations. By simulations, we show that the derived estimators are unbiased in filter situations where standard HMM's are not able to recover the true dynamics. Special implementation strategies together with small approximations yield further acceleration of the algorithm.
Person Re-identification by Exploiting Spatio-Temporal Cues and Multi-view Metric Learning

In this letter, we introduce a new spatio-temporal feature, namely optical flow energy image (OFEI), for video-based person re-identification. OFEI aims to exploit spatio-temporally stable regions across frames, which can capture discriminative cues such as human body parts and carry-on stuffs. Furthermore, we propose a novel matching method, denoted by multi-view relevance metric learning with list-wise constraints (mvRMLLC), to integrate the spatio-temporal (i.e., OFEI) and appearance features. Unlike previous works, mvRMLLC assumes that multiple features are generated from different views with distinct data distributions, while their similarities should be globally consistent. Multiple similarity metrics are then learned and fused by maximizing their global consistency and simultaneously allowing local discrepancies. Extensive experiments on two benchmarks demonstrate that OFEI outperforms the state-of-the-art spatio-temporal features, and mvRMLLC could further enhance the overall performance significantly.
UPSILON: Universal Programming System with Incomplete Lazy Object Notation

This paper presents a new model of computation that differs from prior models in that it emphasizes data over flow control, has no named variables and has an object-oriented flavor. We prove that this model is a complete and confluent acceptable programming system and has a usable type theory. A new data synchronization primitive is introduced in order to achieve the above properties. Subtle variations of the model are shown to fall short of having all these necessary properties.
How to Transform Personal Knowledge into Collaborative Knowledge with a Wiki Dedicated to Microlearning

Residents, who are students and healthcare professionals, have specific needs to improve their knowledge. To be sure that they will not forget something considered as essential in their practice, they write important information in paper notebooks. Most of the residents consider these notebooks as indispensable. A previous analysis of residents’ activity and of their use of the notebooks let us think about an augmented notebook. An electronic device could be used to collect and to share essential information. In this paper, we will present our tool named WiSe (the Wiki for the Specialties). We decided to use a wiki as it is well adapted to the collaborative elaboration of knowledge, and as it supports semantic annotations. We present the issues related to the double nature of the augmented notebook: a personal resource as well as a shared environment. We describe the model of organization proposed to structure our wiki, and explain how we make the implementation of the prototype.
Vision-Based Interface: Using Face and Eye Blinking Tracking with Camera

To provide new type of interaction between human-machine, many devices which could substitute input equipments (e.g. mouse, keyboard etc.) have been invented. In this paper, we develop a novel robust camera mouse based on common Webcam which could be used by handicapped, or be used in some special application. We choose common Webcam to build the device in order to ensure the universality of our design. Combination between optical flow and Harris corner detector obtains good result, and an approach based on tracking fix feature point is used to detect eye blinking. The application can be applied to various illuminations even in a house with no lamp but only screen light. At the same time, this method just takes up less than 30% of system resource. Based on our approach we will present a library from which anybody could develop more application.