Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling neural representation using statistical features of natural scenes

A primary focus of neuroscience is understanding how information about the world is encoded in the activity of neurons. The classic approach to studying neural representation is to observe how activity in a neural system is modulated when presented with stimuli that vary along a specific, hypothesis-driven dimension (e.g. luminance, contrast, orientation, spatial frequency). This approach offers straight-forward interpretation and has allowed neuroscientists to develop computational models of the stimulus-response function for neurons in early processing stages of the visual system. However, the interpretations of these studies often do not generalize to other stimuli such as natural images, and the corresponding computational models often fail to explain later processing stages where the stimulus-response function is strikingly nonlinear. What is needed is an alternative approach that can a) inform new hypotheses without relying on the intuitions of the experimenter, b) generate tractable and interpretable nonlinear models of neural function based on these hypotheses, and c) directly assess model accuracy at predicting neural function under natural conditions. In this dissertation I propose a simple but powerful framework that uses statistical feature learning to automatically derive potential forms of neural representation and generates predictive and testable models of neural function based on these potential representations. The framework is general and can potentially be applied to data acquired using any physiological measurement technique (e.g. neurophysiology, neuroimaging, behavioral) in order to model neural representation throughout the brain. I demonstrate the potential of the proposed framework by first modeling the neural representation of image structure in early cortical stages of the macaque visual system (areas V1 and V2). I then apply the framework to investigate high-level scene processing in the human visual system. In both case studies, I am able to objectively derive accurate and interpretable nonlinear models of neural representation. In each study, I am also able to replicate and extend findings from a multitude of classic neurophysiology and neuroimaging studies using a single, simple experiment based on natural visual stimuli.
Dynamical theory of artificial optical magnetism produced by rings of plasmonic nanoparticles

We present a detailed analytical theory for the plasmonic nanoring configuration first proposed in [A. Alu, A. Salandrino, N. Engheta, Opt. Expr. 14, 1557 (2006)], which is shown to provide negative magnetic permeability and negative index of refraction at infrared and optical frequencies. We show analytically how the nanoring configuration may provide superior performance when compared to some other solutions for optical negative index materials, offering a more 'pure' magnetic response at these high frequencies, which is necessary for lowering the effects of radiation losses and absorption. Sensitivity to losses and the bandwidth of operation of this magnetic inclusion are also investigated in details and compared with other available setups.
Reconstruction of structural controllability over Erdős-Rényi graphs via power dominating sets

Controllability, or informally the ability to force a system into a desired state in a finite time or number of steps, is a fundamental problem studied extensively in control systems theory with structural controllability recently gaining renewed interest. In distributed control systems, possible control relations are limited by the underlying network (graph) transmitting the control signals from a single controller or set of controllers. Attackers may seek to disrupt these relations or compromise intermediate nodes, thereby gaining partial or total control. For a defender to re-gain full or partial control, it is therefore critical to rapidly reconstruct the control graph as far as possible. Failing to achieve this may allow the attacker to cause further disruptions, and may --- as in the case of electric power networks --- also violate real-time constraints leading to catastrophic loss of control. However, as this problem is known to be computationally hard, approximations are required particularly for larger graphs. We therefore propose a reconstruction algorithm for (directed) control graphs of bounded tree width embedded in Erdős-Rényi random graphs based on recent work by Aazami and Stilp as well as Guo et al.
In-Situ visualization in fluid mechanics using Catalyst: A case study for Code Saturne

Numerical simulations using supercomputers are producing an increasingly larger volume of data to be visualized. In this context, Catalyst is a prototype In-Situ visualization library developed by Kitware to help reduce the data post-treatment overhead. On the other side, Code Saturne is a Computational Fluid Dynamics code used at Eléctricité de France (EDF), one of the biggest electricity producers in Europe, for its large scale numerical simulations. In this article we present a study case where Catalyst is integrated into Code Saturne. We evaluate the feasibility and performance of this integration by running two test cases in one of our corporate supercomputers.
A Comparison of the Cost-Effectiveness of Day Treatment and Residential Treatment for Children with Severe Behaviour Problems *

This study compares the cost of treating 23 children admitted to a residential treatment unit in a psychiatric hospital and 23 children admitted to the same unit after it was converted to a day treatment program, through a retrospective chart review. The two groups were similar in age, gender, diagnosis, severity of pathology, family functioning and support, the number of subjects who dropped-out, and treatment outcome. The average length of stay on the unit dropped from 19.6 to 6.1 months, and the average cost of treatment per child decreased from $61,412 to $9,213 (Canadian dollars, adjusted for inflation). The sharp decrease in treatment time with day treatment may be the result of close links with community schools and maintaining the child in the family and community. The cost savings can be attributed to the shorter hospital stays and the lower operating costs of day treatment. Implications of these findings will be discussed with respect to health care policy including the need to raise awareness of day treatment as a cost-effective alternative to residential hospital treatment.
Using WikiProjects to Measure the Health of Wikipedia

In this paper we examine WikiProjects, an emergent, community-driven feature of Wikipedia. We analysed 3.2 million Wikipedia articles associated with 618 active Wikipedia projects. The dataset contained the logs of over 115 million article revisions and 15 million talk entries both representing the activity of 15 million unique Wikipedians altogether. Our analysis revealed that per WikiProject, the number of article and talk contributions are increasing, as are the number of new Wikipedians contributing to individual WikiProjects. Based on these findings we consider how studying Wikipedia from a sub-community level may provide a means to measure Wikipedia activity.
Determinants of Diet for Urban Aboriginal Youth

Overweight and obesity are associated with several life-threating comorbidities, including type 2 diabetes and cardiovascular disease. Obesity is a growing health concern in North America, with some groups experiencing higher levels of obesity than others. One group of particular interest is urban Aboriginal youth because they are a quickly growing population who experience high rates of obesity. Obesity is a complex condition with many contributing factors, diet being one of the primary contributors. In this article, we discuss the findings from an ethnographic study that examined determinants of diet for urban Aboriginal youth. Results revealed two themes: (a) Traditions and Sharing, and (b) The Struggle. The findings with Traditions and Sharing showed that food-sharing networks are often used to acquire traditional food. Traditional foods were believed to be healthy and desired by the participants. The theme The Struggle provides insight into the daily challenges the participants faced with food insecurity. Health promotion professionals need to consider the multiplicity of determinants of diet for urban Aboriginal youth in order to plan and implement culturally appropriate health promotion programs.
Degradation of Id proteins by the ubiquitin‐proteasome pathway

Id proteins act as negative regulators of bHLH transcription factors by forming transcriptionally inactive protein complexes. The proposed function of these proteins includes promotion of cell growth and cell cycle progression, induction of apo‐ptosis, and inhibition of cellular differentiation. We investigated the role of the ubiquitin‐mediated proteolytic pathway in the degradation of the Id3 protein. We found Id3 to be a short‐lived protein and estimated the half‐life to be ~20 min in 293 cells. Using specific inhibitors of the 26S proteasome and mutant fibroblast cells with a temperature‐sensitive defect in the essential E1 ubiquitin‐activating enzyme, we show that Id3 and the related Id1 and Id2 proteins are degraded through the ubiquitin‐proteasome pathway. We found the Id4 protein to be much less sensitive to inhibitors of the 26S proteasome, but its degradation was dependent on the E1 enzyme. In addition, we observed that coexpression of the bHLH protein E47 with Id3 significantly reduced the rate of degradation of Id3, suggesting that Id3 is less susceptible to degradation by the 26S proteasome when complexed to a bHLH protein. —Bounpheng, M. A. , Dimas, J. J., Dodds, S. G. , Christy, B. A. Degradation of Id proteins by the ubiquitin‐proteasome pathway. FASEB J. 13, 2257–2264 (1999)
Novel Synthetic (Poly)Glycerolphosphate-Based Antistaphylococcal Conjugate Vaccine

ABSTRACT Staphylococcal infections are a major source of global morbidity and mortality. Currently there exists no antistaphylococcal vaccine in clinical use. Previous animal studies suggested a possible role for purified lipoteichoic acid as a vaccine target for eliciting protective IgG to several Gram-positive pathogens. Since the highly conserved (poly)glycerolphosphate backbone of lipoteichoic acid is a major antigenic target of the humoral immune system during staphylococcal infections, we developed a synthetic method for producing glycerol phosphoramidites to create a covalent 10-mer of (poly)glycerolphosphate for potential use in a conjugate vaccine. We initially demonstrated that intact Staphylococcus aureus elicits murine CD4+ T cell-dependent (poly)glycerolphosphate-specific IgM and IgG responses in vivo. Naive mice immunized with a covalent conjugate of (poly)glycerolphosphate and tetanus toxoid in alum plus CpG-oligodeoxynucleotides produced high secondary titers of serum (poly)glycerolphosphate-specific IgG. Sera from immunized mice enhanced opsonophagocytic killing of live Staphylococcus aureus in vitro. Mice actively immunized with the (poly)glycerolphosphate conjugate vaccine showed rapid clearance of staphylococcal bacteremia in vivo relative to mice similarly immunized with an irrelevant conjugate vaccine. In contrast to purified, natural lipoteichoic acid, the (poly)glycerolphosphate conjugate vaccine itself exhibited no detectable inflammatory activity. These data suggest that a synthetic (poly)glycerolphosphate-based conjugate vaccine will contribute to active protection against extracellular Gram-positive pathogens expressing this highly conserved backbone structure in their membrane-associated lipoteichoic acid.
Model-based deployment generation for safety-critical avionics systems

To reduce the growth in size, weight and power of avionic electronic systems, the avionics industry tends to integrate more and more functions on less electronic computing units. Particularly, the use of multicore processors seems ineluctable in this domain. These changes, however, bring new challenges regarding, among others, methods and tools to proof safety and timing requirements for new platform architectures. One complex task to be solved during the design phase is the so-called deployment problem, which analyzes possible mappings between software applications and hardware execution units. Without the help of dedicated tools and due to the large number of constraints avionics systems need to fulfill, this problem becomes intractable. In this paper, we employ a model-based development tool, called AutoFOCUS 3, to generate deployment solutions for an industrial case study based on a flight control system with strict requirements regarding safety, timing, storage and communication aspects. We have also customized AutoFOCUS 3 to seek optimized deployment solutions with respect to the number of cores and other hardware resources.
System of Enterprise-Systems Integration Issues: an Engineering Perspective

Today's needs for more capable enterprise systems in a short timeframe is leading more organizations toward the integration of existing product-systems into broader intra-organizational enterprise-systems and their integration into inter-organizational systems of enterprise-systems. Although important R&D efforts lead to a general consensus that Enterprise Modeling and IT Systems Interoperability are keys to Enterprise Integration to align the human-intensive infrastructure with the information-intensive architecture, these integration issues are not handled well in industrial systems engineering practices. Bridging the gap from an integrated system to a system of interoperable systems for architecting such broader enterprise-systems or complex systems of enterprise-systems requires ‘system thinking' beyond ‘system process'. This survey paper discusses some key problems, trends and accomplishments for Enterprise Integration with a systemic approach before advocating the System of Systems (SoS) paradigm to explore some rationales and forecasts in research and engineering.
A Study on Product Association Modeling in Collaborative Product Design

Many customers are no longer satisfied with mass-produced products; they are demanding customization and rapid delivery of innovative products. A new category of software, Collaborative Product Design (CPD), uses Internet technology to deliver product innovation at Internet speed and tie together product design, engineering, marketing, and customers into a global knowledge net. CPD enables organizations to quickly respond to the market change, so it has become increasingly important. Though CPD permits manufactures to significantly improve the core processes of the complete product life cycle, its information system will store a large number of product data in database day by day. R&D engineers are difficult to retrieve useful design data to cope with customers’ demand. Therefore, the purpose of this research is to apply the association rule that belongs to one of the data mining techniques to analyze the product feature association. This research also applies object-oriented technique of Unified Modeling Language, (UML) to establish a reference model integrated with CPD product feature association rules. In the end, this research cites an example of cellular phone model design and applies association rule technique to analyze product feature association rule of cellular phones. The results help cellular phone developers designing good products to conform to customers’ different demands.
Census of the Viral Metagenome within an Activated Sludge Microbial Assemblage

ABSTRACT The viral metagenome within an activated sludge microbial assemblage was sampled using culture-dependent and culture-independent methods and compared to the diversity of activated sludge bacterial taxa. A total of 70 unique cultured bacterial isolates, 24 cultured bacteriophages, 829 bacterial metagenomic clones of 16S rRNA genes, and 1,161 viral metagenomic clones were subjected to a phylogenetic analysis.
Predictive Coding with Neural Nets: Application to Text Compression

To compress text files, a neural predictor network P is used to approximate the conditional probability distribution of possible "next characters", given n previous characters. P's outputs are fed into standard coding algorithms that generate short codes for characters with high predicted probability and long codes for highly unpredictable characters. Tested on short German newspaper articles, our method outperforms widely used Lempel-Ziv algorithms (used in UNIX functions such as "compress" and "gzip").
A Hybrid Deep Representation Learning Model for Time Series Classification and Prediction

Rapid increase in connectivity of physical sensors and Internet of Things (IoT) systems is enabling large-scale collection of time series data, and the data represents the working patterns and internal evolutions of observed objects. Recognizing and forecasting the underlying high-level states from raw sensory data are useful for daily activity recognition of humans and predictive maintenance of machines. Deep Learning (DL) methods have been proved efficient in computer vision, natural language processing, and speech recognition, and these model are also applied to time series analysis. Since time series are multi-dimensional and sequential with long-term temporal dependency, current DL-based model could not well learn the spatial and temporal features inside and between states, thus there is still plenty of room for improvement of recognizing and predicting high-level states. In this paper, a hybrid deep architecture named Long-term Recurrent Convolutional LSTM Network (LR-ConvLSTM) is proposed. The model is composed of Convolutional LSTM layers to extract features inside a high-level state, and extra LSTM layers to capture temporal dependencies between high-level states. We evaluate our model on the Opportunity dataset that has once been used in public activity recognition challenge. The results show that the proposed model has a good performance both in time series classification and prediction tasks.
Robust Covariate‐Adjusted Log‐Rank Statistics and Corresponding Sample Size Formula for Recurrent Events Data

Summary Recurrent events data are frequently encountered in clinical trials. This article develops robust covariate‐adjusted log‐rank statistics applied to recurrent events data with arbitrary numbers of events under independent censoring and the corresponding sample size formula. The proposed log‐rank tests are robust with respect to different data‐generating processes and are adjusted for predictive covariates. It reduces to the Kong and Slud (1997, Biometrika84, 847–862) setting in the case of a single event. The sample size formula is derived based on the asymptotic normality of the covariate‐adjusted log‐rank statistics under certain local alternatives and a working model for baseline covariates in the recurrent event data context. When the effect size is small and the baseline covariates do not contain significant information about event times, it reduces to the same form as that of Schoenfeld (1983, Biometrics39, 499–503) for cases of a single event or independent event times within a subject. We carry out simulations to study the control of type I error and the comparison of powers between several methods in finite samples. The proposed sample size formula is illustrated using data from an rhDNase study.
Challenges in Researching the Remarried

Despite the growing number of books and articles which document, explore and analyze steprelationships, much of our understanding of the stepfamily continues to be based on anecdotal reports. The scarcity of rigorous studies examining stepfamily functioning and adjustment reflects the methodological complexities of studying families generally, in addition to problems that are specific to research with stepfamily populations. Here difficulties in doing research with stepfamilies are discussed at each stage of the research process: (1) formulating the research problem, (2) selecting a research sample and design, (3) collecting the data, (4) analyzing the data, and (5) interpreting the results. Practical guidelines are outlined and recommendations are given to overcome common methodological obstacles.
Scalable analytics to detect DNS misuse for establishing stealthy communication channels

The Domain Name System (DNS) protocol is one of the few application protocols that are allowed to cross network perimeters of organizations. However, comprehensive monitoring of DNS traffic has been often overlooked in many organizations' cybersecurity strategies. As such, DNS provides a highly attractive channel for advanced threat actors and botnet operators to establish hard-to-block and stealthy communication channels between infected devices and command-and-control (C&C) infrastructures. Fast-fluxing (FF) and domain name generation algorithms (DGAs) are two well-known public DNS exploitation techniques to build agile C&C infrastructures. The detection of FF and DGA domain names is a big data problem, as it requires analyzing millions of DNS queries and replies over extended time periods. In this paper, we propose two algorithms to perform DNS analytics and effectively detect FF and DGA domain names. More importantly, we describe how the algorithms are implemented using two big data processing models: MapReduce and Feature Collection and Correlation Engine. The algorithms and implementation proposed are iterative and scale over long analysis periods. We describe the implementations and provide an evaluation complemented with case studies on 50 days of real-world DNS data consisting of more than 40 billion events, collected within a large corporate network.
Introducing COMPARA: the Portuguese-English Parallel Corpus

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This paper explores the suitability of commonly employed classification methods to action-selection tasks in robotics, and argues that a classifier’s introspective capacity is a vital but as yet largely under-appreciated attribute. As illustration we propose an active learning framework for semantic mapping in mobile robotics and demonstrate it in the context of autonomous driving. In this framework, data are selected for label disambiguation by a human supervisor using uncertainty sampling. Intuitively, an introspective classification framework – i.e. one which moderates its predictions by an estimate of how well it is placed to make a call in a particular situation – is particularly well suited to this task. To achieve an efficient implementation we extend the notion of introspection to a particular sparse Gaussian Process Classifier, the Informative Vector Machine (IVM). Furthermore, we leverage the information-theoretic nature of the IVM to formulate a principled mechanism for forgetting stale data, thereby bounding memory use and resulting in a truly life-long learning system. Our evaluation on a publicly available dataset shows that an introspective active learner asks more informative questions compared to a more traditional non-introspective approach like a Support Vector Machine (SVM) and in so doing, outperforms the SVM in terms of learning rate while retaining efficiency for practical use.