Miracle-dz/UniFAR-FTUs huggingface dataset:
Bottom-up and top-down processes in reading : influences of frequency and predictability on event-related potentials and eye movements

In reading, word frequency is commonly regarded as the major bottom-up determinant for the speed of lexical access. Moreover, language processing depends on top-down information, such as the predictability of a word from a previous context. Yet, however, the exact role of top-down predictions in visual word recognition is poorly understood: They may rapidly affect lexical processes, or alternatively, influence only late post-lexical stages. To add evidence about the nature of top-down processes and their relation to bottom-up information in the timeline of word recognition, we examined influences of frequency and predictability on event-related potentials (ERPs) in several sentence reading studies. The results were related to eye movements from natural reading as well as to models of word recognition. As a first and major finding, interactions of frequency and predictability on ERP amplitudes consistently revealed top-down influences on lexical levels of word processing (Chapters 2 and 4). Second, frequency and predictability mediated relations between N400 amplitudes and fixation durations, pointing to their sensitivity to a common stage of word recognition; further, larger N400 amplitudes entailed longer fixation durations on the next word, a result providing evidence for ongoing processing beyond a fixation (Chapter 3). Third, influences of presentation rate on ERP frequency and predictability effects demonstrated that the time available for word processing critically co-determines the course of bottom-up and top-down influences (Chapter 4). Fourth, at a nearnormal reading speed, an early predictability effect suggested the rapid comparison of top-down hypotheses with the actual visual input (Chapter 5). The present results are compatible with interactive models of word recognition assuming that early lexical processes depend on the concerted impact of bottom-up and top-down information. We offered a framework that reconciles the findings on a timeline of word recognition taking into account influences of frequency, predictability, and presentation rate (Chapter 4).
Synthesis and structural characterization of bioactive peptide conjugates using thioether linkage approaches

Applications of cysteine‐insertion and thioether linkage approaches to the preparation of a number of bioactive peptide conjugates are reported. Peptides containing epitopes from (i) herpes simplex virus type 1 glycoprotein D, (ii) a specific N‐terminal β‐amyloid epitope recognized by therapeutically active antibodies, and (iii) a GnRH‐III peptide from sea lamprey with antitumour activity, were elongated with Cys residues and attached to a chloroacetylated tetratuftsin derivative carrier via a thioether linkage either directly, or by insertion of a spacer. The structures and molecular homogeneity of all the peptide conjugates were ascertained by HPLC, MALDI and electrospray mass spectrometry. The use of a spacer such as an oligoglycine or GFLG‐tetrapeptide gave an increased yield in the conjugation reaction and enhanced reaction rates. In the formation of cysteinyl‐thioether linkages, it was found that the position of flanking Cys residues markedly influenced the conjugation reaction and the formation of intermolecular epitope disulfide‐dimers. C‐terminal Cys residues gave thioether conjugates with significantly diminished epitope‐dimerization, while Cys at the N‐terminal caused rapid disulfide‐dimerization, thereby preventing efficient conjugation. Copyright © 2004 European Peptide Society and John Wiley & Sons, Ltd.
Selecting a "primary partition" in partitionable asynchronous distributed systems

We consider network applications that are based on the process group paradigm. When such applications are deployed over networks that are subject to failures, they may partition across several disconnected clusters resulting in multiple views of the group's current composition to exist concurrently. Application semantics determine which operations, if any, can be performed in different partitions without compromising consistency. For certain application classes, most (possibly all) operations need to be confined to a single primary partition while other partitions are allowed to service only a (possibly empty) subset of the operations. We propose a mechanism for deciding when a view constitutes the primary partition for the group. Our solution is highly flexible and has the following novel features: each group member can establish if it belongs to the primary partition or not, based solely on local information; the group can be dynamic as processes voluntarily join and leave it; the selection rule for establishing the primary partition need not be universal but can be decided on a per-application basis and can be modified at run time; the primary partition can be re-established even after total failures. Layering our solution on top of a partitionable group membership service allows a wide range of applications with different and possibly conflicting notions of "primary partition" to be supported on a common computing base.
Attributes for causal inference in electronic healthcare databases

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria.
Ieee Transactions on Software Engineering 1 Class Schema Evolution for Persistent Object-oriented Software: Model, Empirical Study, and Automated Support

—With the wide support for object serialization in object-oriented programming languages, persistent objects have become common place and most large object-oriented software systems rely on extensive amounts of persistent data. Such systems also evolve over time. Retrieving previously persisted objects from classes whose schema has changed is however difficult, and may lead to invalidating the consistency of the application. The ESCHER framework addresses these issues through an IDE-integrated approach that handles class schema evolution by managing versions of the code and generating transformation functions automatically. The infrastructure also enforces class invariants to prevent the introduction of potentially corrupt objects. This article describes a model for class attribute changes, a measure for class evolution robustness, four empirical studies, and the design and implementation of the ESCHER system.
Multimodal optimization using niching differential evolution with index-based neighborhoods

A new family of Differential Evolution mutation strategies (DE/nrand) that are able to handle multimodal functions, have been recently proposed. The DE/nrand family incorporates information regarding the real nearest neighborhood of each potential solution, which aids them to accurately locate and maintain many global optimizers simultaneously, without the need of additional parameters. However, these strategies have increased computational cost. To alleviate this problem, instead of computing the real nearest neighbor, we incorporate an index-based neighborhood into the mutation strategies. The new mutation strategies are evaluated on eight well-known and widely used multimodal problems and their performance is compared against five state-of-the-art algorithms. Simulation results suggest that the proposed strategies are promising and exhibit competitive behavior, since with a substantial lower computational cost they are able to locate and maintain many global optima throughout the evolution process.
Multimodal analysis and digital technology

" Assessments of change, dynamics, and cause and effect are at the heart of thinking and explanation. To understand is to know what cause provokes what effect, by what means, at what rate. How then is such knowledge to be represented? " (Tufte, 1997: 10) The aim of this paper is to explore possible directions for advancing multimodal analysis through the use of digital technology. Historically, the impact of technology on knowledge is evident from the introduction of the printing press in the late fifteenth century, which led to the close study and subsequent development of mathematics and science (Eisenstein, 1979). Today, " computerization turns media into computer data " (Manovich, 2001: 45). The recursive nature of the computerisation process, where " images scatter into data, data gathers into images " , (Galison, 2002: 322) permits the visual display of data flow which is experienced over time, " be it in science, statistics, architecture, design, digital art, or any combination of these " (Paul, 2003: 175). There are few constants in the digital environment where everything is variable and open to manipulation and recontextualisation. However,
Design of Low-Power High-Speed Truncation-Error-Tolerant Adder and Its Application in Digital Signal Processing

In modern VLSI technology, the occurrence of all kinds of errors has become inevitable. By adopting an emerging concept in VLSI design and test, error tolerance (ET), a novel error-tolerant adder (ETA) is proposed. The ETA is able to ease the strict restriction on accuracy, and at the same time achieve tremendous improvements in both the power consumption and speed performance. When compared to its conventional counterparts, the proposed ETA is able to attain more than 65% improvement in the Power-Delay Product (PDP). One important potential application of the proposed ETA is in digital signal processing systems that can tolerate certain amount of errors.
Suffragettes to she-devils : women's liberation and beyond

The fight for women's rights has been one of the great struggles of the last century, and its graphic expression has been central to this battle. Suffragettes to She-Devils captures the excitement of revolutionary campaigns and movements, from the vibrant visual identity of the militant Suffragettes, through the humour and sniping of the cartoons of 1960s Women's Lib, to the virtual-reality explorations of end-of-the-century cyberfeminists. It explores the developing role of graphics and related media in the struggle for women's liberation. This book covers a wide and international range of issues including women and war work; 1950's consumerism; women's rights in the workplace; gay rights and gay pride; and questions of identity, race, violence, health and welfare. An enormous variety of posters, billboards, publications, logos, artworks and computer graphics are illustrated, reflecting the ways in which women of all ages and cultures have created avenues for communication and fostered social change through graphic design.
Group Testing using left-and-right-regular sparse-graph codes

We consider the problem of non-adaptive group testing of $N$ items out of which $K$ or less items are known to be defective. We propose a testing scheme based on left-and-right-regular sparse-graph codes and a simple iterative decoder. We show that for any arbitrarily small $\epsilon>0$ our scheme requires only $m=c_\epsilon K\log \frac{c_1N}{K}$ tests to recover $(1-\epsilon)$ fraction of the defective items with high probability (w.h.p) i.e., with probability approaching $1$ asymptotically in $N$ and $K$, where the value of constants $c_\epsilon$ and $\ell$ are a function of the desired error floor $\epsilon$ and constant $c_1=\frac{\ell}{c_\epsilon}$ (observed to be approximately equal to 1 for various values of $\epsilon$). More importantly the iterative decoding algorithm has a sub-linear computational complexity of $\mathcal{O}(K\log \frac{N}{K})$ which is known to be optimal. Also for $m=c_2 K\log K\log \frac{N}{K}$ tests our scheme recovers the \textit{whole} set of defective items w.h. p. These results are valid for both noiseless and noisy versions of the problem as long as the number of defective items scale sub-linearly with the total number of items, i.e., $K=o(N)$. The simulation results validate the theoretical results by showing a substantial improvement in the number of tests required when compared to the testing scheme based on left-regular sparse-graphs.
An empirical study of the relationship between corporate dividend policy and financial performance of chinese listed companies

A lot of empirical studies have been done on corporate dividend policy using firm-data, mostly for the US but seldom for emerging markets. In China, corporate dividend policy is still a new phenomenon. Based on the Signaling Theory (Lintner, 1956), two models are established to examine the relationship between corporate dividend policy and financial performance of Chinese listed companies. Sample data are from CSI 300 Index, which is representative because it includes most large companies in all sectors in China. By employing the method of analysis of variance (ANOVA), this study finds a significant positive relationship exists between dividends per share and financial performance. Chinese listed companies' dividend policies have strong effects on their share prices, while the market has little reflection on cash dividend, and bonus share is more popular in market than cash bonus. The results suggest that corporate management should consider the interests of the company and its shareholders as well when paying dividends. Cash or stock dividends payout should be in the principle of the company's sound development.
Genome‐wide linkage meta‐analysis identifies susceptibility loci at 2q34 and 13q31.3 for genetic generalized epilepsies

Purpose: Genetic generalized epilepsies (GGEs) have a lifetime prevalence of 0.3% with heritability estimates of 80%. A considerable proportion of families with siblings affected by GGEs presumably display an oligogenic inheritance. The present genome‐wide linkage meta‐analysis aimed to map: (1) susceptibility loci shared by a broad spectrum of GGEs, and (2) seizure type–related genetic factors preferentially predisposing to either typical absence or myoclonic seizures, respectively.
NRC Publications Archive Archives des publications du CNRC

The growing use of commercial products in large systems makes evaluation and selection of appropriate products an increasingly essential activity. However, many organizations struggle in their attempts to select an appropriate product for use in systems. As part of a cooperative effort, the Software Engineering Institute (SEI) and the National Research Council Canada (NRC) have defined a tailorable software product evaluation process that can support organizations in making carefully reasoned and sound product decisions. This paper describes that process.
A step-by-step debugging technique to facilitate mashup development and maintenance

With Web mashups, data from different Web documents and services are "mashed" together to create a new functionality. The mashup developer usually has a clear vision of the desired output, i.e., the resulting Web page to present to the end user. Complex mashups require multiple processing steps, and become hard to debug if the delivered result is not as expected. In this paper we propose an approach that supports step-by-step debugging for declarative development of data mashups. A dependency graph is constructed from the mashup definition, and developers are able to define breakpoints to inspect a snapshot of the running mashup execution. A Web 2.0 application visualizes the intermediate results in each processing step. On top of that, it is possible to specify expected and unexpected result elements. If the result does not comply with the specifications, the platform helps to identify the processing step which caused the error.
Persistence Lenses: Segmentation, Simplification, Vectorization, Scale Space and Fractal Analysis of Images

A persistence lens is a hierarchy of disjoint upper and lower level sets of a continuous luminance image's Reeb graph. The boundary components of a persistence lens's interior components are Jordan curves that serve as a hierarchical segmentation of the image, and may be rendered as vector graphics. A persistence lens determines a varilet basis for the luminance image, in which image simplification is a realized by subspace projection. Image scale space, and image fractal analysis, result from applying a scale measure to each basis function.
Structure and Dynamic Regulation of Abl Kinases*

The c-abl proto-oncogene encodes a unique protein-tyrosine kinase (Abl) distinct from c-Src, c-Fes, and other cytoplasmic tyrosine kinases. In normal cells, Abl plays prominent roles in cellular responses to genotoxic stress as well as in the regulation of the actin cytoskeleton. Abl is also well known in the context of Bcr-Abl, the oncogenic fusion protein characteristic of chronic myelogenous leukemia. Selective inhibitors of Bcr-Abl, of which imatinib is the prototype, have had a tremendous impact on clinical outcomes in chronic myelogenous leukemia and revolutionized the field of targeted cancer therapy. In this minireview, we focus on the structural organization and dynamics of Abl kinases and how these features influence inhibitor sensitivity.
An efficient framework on large-scale video genre classification

Efficient data mining and indexing is important for multimedia analysis and retrieval. In the field of large-scale video analysis, effective genre categorization plays an important role and serves one of the fundamental steps for data mining. Existing works utilize domain-knowledge dependent feature extraction, which is limited from genre diversification as well as data volume scalability. In this paper, we propose a systematic framework for automatically classifying video genres using domain-knowledge independent descriptors in feature extraction, and a bag-of-visualwords (BoW) based model in compact video representation. Scale invariant feature transform (SIFT) local descriptor accelerated by GPU hardware is adopted for feature extraction. BoW model with an innovative codebook generation using bottom-up two-layer K-means clustering is proposed to abstract the video characteristics. Besides the histogram-based distribution in summarizing video data, a modified latent Dirichlet allocation (mLDA) based distribution is also introduced. At the classification stage, a k-nearest neighbor (k-NN) classifier is employed. Compared with state of art large-scale genre categorization in [1], the experimental results on a 23-sports dataset demonstrate that our proposed framework achieves a comparable classification accuracy with 27% and 64% expansion in data volume and diversity, respectively.
Flexible Multivariate GARCH Modeling with an Application to International Stock Markets

This paper offers a new approach to estimating time-varying covariance matrices in the framework of the diagonal-vech version of the multivariate GARCH(1,1) model. Our method is numerically feasible for large-scale problems, produces positive semidefinite conditional covariance matrices, and does not impose unrealistic a priori restrictions. We provide an empirical application in the context of international stock markets, comparing the new estimator with a number of existing ones.
Computational support for compelling story telling

The Internet is a living, breathing reflection of who we are, what we think, and how we feel. The pages that make up the Web form the book of our contemporary life and culture. They are the ongoing and changing buzz of our world. The latest embodiment of this cultural reflection is found in blogs. Not only are blogs widespread, but they are incredibly dynamic, with hundreds updated per minute. The existence of millions of blogs on the web has resulted in more than the mere presence of millions of online journals, but rather, they generate a collective buzz around the events in the world.
Guidelines and recommendations for perfusion imaging in cerebral ischemia: A scientific statement for healthcare professionals by the writing group on perfusion imaging, from the Council on Cardiovascular Radiology of the American Heart Association.

A number of techniques have been developed during the past four decades to evaluate cerebral perfusion. The oldest used 133Xe, a lipophilic radioactive tracer that easily diffuses through the blood-brain barrier (BBB). It was either injected or inhaled, and probes placed over the scalp were used to measure perfusion to the cerebral cortex.1,2 In the mid-1970s, the development of a scanner to detect the emission of positrons led to positron emission tomography (PET) in humans.3 Using a number of radioisotopes, this technology can measure cerebral blood flow (CBF) and various metabolic processes, but until recently it has been primarily used as a research tool. Stable (“cold”) xenon was found to attenuate x-rays in a manner similar to iodine, and there were a number of projects in the 1970s to use this gas as a contrast agent for the rapidly emerging technology of computed tomography (CT), particularly as a perfusion tracer.4 This resulted in the development of the xenon-enhanced CT (XeCT) technique to calculate CBF in patients.5 With improvements in single photon emission CT (SPECT) during the 1980s, a number of compounds that are metabolized in the central nervous system (CNS) were found to be appropriate for perfusion imaging.6,7 Perfusion-weighted and diffusion-weighted magnetic resonance (MR) imaging (PWI and DWI) were developed in the late 1980s,8,9 and that technology has continued to improve. Finally, with the evolution of helical and spiral multislice CT technology, CT perfusion (CTP) imaging is becoming a potentially important clinical technique.10 Although the development of these technologies has been fascinating, their role in evaluating a variety of diseases of the CNS is controversial. It might seem obvious that a disorder of blood flow, such as acute stroke or chronic vascular occlusive disease, should be studied with a perfusion imaging technique. …
ON FAULT DIAGNOSIS OF ANALOGUE ELECTRONIC CIRCUITS WITH ACCESSIBILITY TO INTERNAL NODES BASED ON TRANSFORMATIONS IN MULTIDIMENSIONAL SPACES

In the paper new methods of fault localisation and identification in linear electronic circuits (two-port or multiport type) based on bilinear transformations in multidimensional spaces are presented. The novelty of these methods lies in transferring family of identification loci from a plane to multidimensional spaces. It implies greater distances between the loci and, in consequence, better fault resolution as well as robustness against non-faulty component tolerances and measurement errors. The methods can be used for diagnosis of electronic circuits in conventional testing systems and neural networks. They may be also useful in one or two-parameter identification measurements of other multi-parameter objects modelled by electrical circuits.