Miracle-dz/UniFAR-FTUs huggingface dataset:
An Alternative Structure for the Abstract Factory Software Design Pattern

The abstract factory design pattern is a well known and widely applied design in the construction of object-oriented software. An amendment to the abstract factory is presented based on the \envelope/letter" idiom. This results in factory products being instantiated in application code in a manner apparently independent of the factory pattern. Application code can thus forget about the factory, and concentrate on using the product objects. E ort in library design thus bene ts application structure, always a worthy end result in program design. For C++ users, the resulting pattern makes factory-generated objects more amenable to language features including operator overloading and memory management.
Improving Parameters for Wireless Sensor Networks

The optimization of wireless sensor networks in terms of lifetime and quantity of information has been investigated recently through a large number of algorithms and routing techniques. The main contribution of the paper consists in studying the application parameters that can change the behavior of existing algorithms, as the network information collecting ability. Simulations show that due to this parameter the network decides itself when to stop operating according to the information it generates. The network lifetime becomes more flexible based on the specific requirements of user applications.
Stakeholder Analyses of Firm-Related Web Forums: Applications in Stock Return Prediction

In this study, we present stakeholder analyses of firm-related web forums. Prior analyses of firm-related forums have considered all participants in the aggregate, failing to recognize the potential for diversity within the populations. However, distinctive groups of forum participants may represent various interests and stakes in a firm worthy of consideration. To perform the stakeholder analyses, the Stakeholder Analyzer system for firm-related web forums is developed following the design science paradigm of information systems research. The design of the system and its approach to stakeholder analysis is guided by two kernel theories, the stakeholder theory of the firm and the systemic functional linguistic theory. A stakeholder analysis identifies distinctive groups of forum participants with shared characteristics expressed in discussion and evaluates their specific opinions and interests in the firm. Stakeholder analyses are performed in six major firm-related forums hosted on Yahoo Finance over a 3-month period. The relationships between measures extracted from the forums and subsequent daily firm stock returns are examined using multiple linear regression models, revealing statistically significant indicators of firm stock returns in the discussions of the stakeholder groups of each firm with stakeholder-model-adjusted R2 values reaching 0.83. Daily stock return prediction is also performed for 31 trading days, and stakeholder models correctly predicted the direction of return on 67% of trading days and generated an impressive 17% return in simulated trading of the six firm stocks. These evaluations demonstrate that the stakeholder analyses provided more refined assessments of the firm-related forums, yielding measures at the stakeholder group level that better explain and predict daily firm stock returns than aggregate forum-level information.
HGF-Induced PKCζ Activation Increases Functional CXCR4 Expression in Human Breast Cancer Cells

The chemokine receptor CXCR4 and its ligand CXCL12 have been shown to mediate the metastasis of many malignant tumors including breast carcinoma. Interaction between hepatocyte growth factor (HGF) and the Met receptor tyrosine kinase mediates development and progression of cancers. HGF is able to induce CXCR4 expression and contributes to tumor cell invasiveness in breast carcinoma. However, the mechanism of the CXCR4 expression modulated by c-Met-HGF axis to enhance the metastatic behavior of breast cancer cells is still unclear. In this study, we found that HGF induced functional CXCR4 receptor expression in breast cancer cells. The effect of HGF was specifically mediated by PKCζ activity. After transfection with PKCζ-siRNA, the phosphorylation of PKCζ and CXCR4 was abrogated in breast cancer cells. Interference with the activation of Rac1, a downstream target of HGF, prevented the HGF-induced increase in PKCζ activity and CXCR4 levels. The HGF-induced, LY294002-sensitive translocation of PKCζ from cytosol to plasma membrane indicated that HGF was capable of activating PKCζ, probably via phosphoinositide (PI) 3-kinases. HGF treatment also increased MT1-MMP secretion. Inhibition of PKCζ, Rac-1 and phosphatidylinositol 3-kinase may attenuate MT1-MMP expression in cells exposed to HGF. Functional manifestation of the effects of HGF revealed an increased ability for migration, chemotaxis and metastasis in MDA-MB-436 cells in vitro and in vivo. Our findings thus provided evidence that the process of HGF-induced functional CXCR4 expression may involve PI 3-kinase and atypical PKCζ. Moreover, HGF may promote the invasiveness and metastasis of breast tumor xenografts in BALB/c-nu mice via the PKCζ-mediated pathway, while suppression of PKCζ by RNA interference may abrogate cancer cell spreading.
Topical CpG Oligodeoxynucleotide Adjuvant Enhances the Adaptive Immune Response against Influenza A Infections

Current influenza vaccines generate humoral immunity, targeting highly variable epitopes and thus fail to achieve long-term protection. T cells recognize and respond to several highly conserved epitopes across influenza serotypes. A strategy of raising strong cytotoxic T cell memory responses to epitopes conserved across serotypes would provide cross serotype protection, eliminating the need for annual vaccination. We explored the adjuvant potential of epicutaneous (ec) and subcutaneous (sc) delivery of CpG oligodeoxynucleotide in conjunction with sc protein immunization to improve protection against influenza A virus (IAV) infections using a mouse model. We found enhanced long-term protection with epicutaneous CpG ODN (ecCpG) compared to subcutaneous CpG ODN (scCpG) as demonstrated by reduced viral titers in the lungs. This correlated with increased antigen-specific CD8 T cells in the airways and the lungs. The memory T cell response after immunization with ecCpG adjuvant was comparable to memory response by priming with IAV infection in the lungs. In addition, ecCpG was more efficient than scCpG in inducing the generation of IFN-γ producing CD4 T cells. The adjuvant effect of ecCpG was accompanied with its ability to modulate tissue-homing molecules on T cells that may direct them to the site of infection. Together, this work provides evidence for using ecCpG to induce strong antibody and memory T cell responses to confer protection against IAV infection.
The Neural Code for Auditory Space Depends on Sound Frequency and Head Size in an Optimal Manner

A major cue to the location of a sound source is the interaural time difference (ITD)–the difference in sound arrival time at the two ears. The neural representation of this auditory cue is unresolved. The classic model of ITD coding, dominant for a half-century, posits that the distribution of best ITDs (the ITD evoking a neuron’s maximal response) is unimodal and largely within the range of ITDs permitted by head-size. This is often interpreted as a place code for source location. An alternative model, based on neurophysiology in small mammals, posits a bimodal distribution of best ITDs with exquisite sensitivity to ITDs generated by means of relative firing rates between the distributions. Recently, an optimal-coding model was proposed, unifying the disparate features of these two models under the framework of efficient coding by neural populations. The optimal-coding model predicts that distributions of best ITDs depend on head size and sound frequency: for high frequencies and large heads it resembles the classic model, for low frequencies and small head sizes it resembles the bimodal model. The optimal-coding model makes key, yet unobserved, predictions: for many species, including humans, both forms of neural representation are employed, depending on sound frequency. Furthermore, novel representations are predicted for intermediate frequencies. Here, we examine these predictions in neurophysiological data from five mammalian species: macaque, guinea pig, cat, gerbil and kangaroo rat. We present the first evidence supporting these untested predictions, and demonstrate that different representations appear to be employed at different sound frequencies in the same species.
A Generalized Language Model as the Combination of Skipped n-grams and Modified Kneser Ney Smoothing

We introduce a novel approach for building language models based on a systematic, recursive exploration of skip n-gram models which are interpolated using modified Kneser-Ney smoothing. Our approach generalizes language models as it contains the classical interpolation with lower order models as a special case. In this paper we motivate, formalize and present our approach. In an extensive empirical experiment over English text corpora we demonstrate that our generalized language models lead to a substantial reduction of perplexity between 3.1% and 12.7% in comparison to traditional language models using modified Kneser-Ney smoothing. Furthermore, we investigate the behaviour over three other languages and a domain specific corpus where we observed consistent improvements. Finally, we also show that the strength of our approach lies in its ability to cope in particular with sparse training data. Using a very small training data set of only 736 KB text we yield improvements of even 25.7% reduction of perplexity.
A systematic investigation into the effects ofnanodal tDCS on healthy populations across measures of language, working memory & novel language acquisition

Transcranial direct current stimulation (tDCS) is a technique thought to modify cognition via a weak electric current applied to the scalp. Several thousand papers have been published since its inception in the early 2000s, with positive effects observed across healthy and patient samples in terms of language, memory, attention and various other executive functions. However, evidence is emerging that reported effects are exaggerated, and difficult to reproduce, especially in studies using single applications of anodal tDCS on healthy individuals. This thesis documents several studies that aimed to verify whether tDCS can modify word production, working memory and novel language acquisition in healthy participants when using conventional stimulation parameters, whilst considering factors that have driven its unreliability
The sublime attraction of active volcanoes: An exploration of tourists’ experiences during a long-distance hiking route in Iceland

This article attempts to understand the value and meaning of a hazardous natural environment for tourists. It focuses on the attraction of volcanic sites in the eyes of sensation-seeking hikers. The research is based on a participatory observation study and in-depth interviews with 11 hikers on the Laugavegur hiking trail, in the Highlands of Iceland. The research questions addressed in this article are, do hikers experience a threat from the natural environment, and does a hazardous environment contribute to a feeling of the sublime? In support of the theories put forth in the article about sensation seekers, risk perception, its heuristic traps and sublime feeling, the article argues that tourists perceive risks in the hazardous environment in a positive way, as something spectacular, unique and sublime. Thus, their positive risk perception of existing potential environmental hazards encourages tourists to ignore signs of risks and hazards and subsequently puts them in unnecessary danger.
Vision based mobile node localization using a landmark

Mobile nodes localization scheme using a landmark is proposed. It is based on the shape/size changes of object with its perspective view and trigonometric concepts. It incorporates vision based localization and projective geometry concepts and elaborates that how shape/ size changes of an object by varying distances/ view angle with changed position provides distinct position coordinates. The problem has been modeled to confirm authenticity of proposed solution and various features have been critically brought to lime light.
Using Tensor Analysis to characterize Contact-map Dynamics of Proteins

Molecular dynamics simulations provide vast amount of information about a protein’s dynamics. To interpret a protein’s dynamics and how it may relate to its function, traditionally, two-way analysis techniques such as principal component analysis have been used. However, two-way analysis techniques are usually limited by the fact that they have to be done post-process, i.e., after the simulations have been run and also cannot provide insights into temporal behavior of a protein. To overcome these limitations, we are proposing to use multi-way analysis techniques to understand and interpret protein dynamics as and when the simulations are progressing i.e., online. We model MD simulations in terms of a collection of contact maps and then modeling them as tensors to capture multiple dependencies. Using two recently developed techniques to perform online analysis of streaming data, we illustrate the use of this technique to describe and interpret the behavior of a protein complex in real time. We provide both experimental evidence to support our claims and also discuss the potential advantages and disadvantages of using tensor analysis techniques.
CMOS IC technology scaling and its impact on burn-in

This article describes how CMOS IC technology scaling impacts semiconductor burn-in and burn-in procedures. Burn-in is a quality improvement procedure challenged by the high leakage currents that are rapidly increasing with IC technology scaling. These currents are expected to increase even more under the new burn-in environments leading to higher junction temperatures, possible thermal runaway, and yield loss of good parts during burn-in. The paper discusses the effect of junction temperature on device reliability, aging, and burn-in procedure optimization. The effect of device thermal runaway and the requirements it forces on commercial burn-in ovens, device package, and device cooling are also described.
Characterization of Gene Expression Associated with Drought Avoidance and Tolerance Traits in a Perennial Grass Species

To understand molecular mechanisms of perennial grass adaptation to drought stress, genes associated with drought avoidance or tolerance traits were identified and their expression patterns were characterized in C4 hybrid bermudagrass [Cynodon dactylon (L.) Pers. ×C. transvaalensis Burtt Davy, cv. Tifway] and common bermudagrass (C. dactylon, cv. C299). Plants of drought-tolerant ‘Tifway’ and drought-sensitive ‘C299’ were exposed to drought for 5 d (mild stress) and 10 d (severe stress) by withholding irrigation in a growth chamber. ‘Tifway’ maintained significantly lower electrolyte leakage and higher relative water content than ‘C299’ at both 5 and 10 d of drought stress. Four cDNA libraries via suppression subtractive hybridization analysis were constructed and identified 277 drought-responsive genes in the two genotypes at 5 and 10 d of drought stress, which were mainly classified into the functional categories of stress defense, metabolism, osmoregulation, membrane system, signal and regulator, structural protein, protein synthesis and degradation, and energy metabolism. Quantitative-PCR analysis confirmed the expression of 36 drought up-regulated genes that were more highly expressed in drought-tolerant ‘Tifway’ than drought-sensitive ‘C299’, including those for drought avoidance traits, such as cuticle wax formation (CER1 and sterol desaturase), for drought tolerance traits, such as dehydration-protective proteins (dehydrins, HVA-22-like protein) and oxidative stress defense (superoxide dismutase, dehydroascorbate reductase, 2-Cys peroxiredoxins), and for stress signaling (EREBP-4 like protein and WRKY transcription factor). The results suggest that the expression of genes for stress signaling, cuticle wax accumulation, antioxidant defense, and dehydration-protective protein accumulation could be critically important for warm-season perennial grass adaptation to long-term drought stress.
Ablation of the epithelial‐specific splicing factor Esrp1 results in ureteric branching defects and reduced nephron number

Abnormalities in ureteric bud (UB) branching morphogenesis lead to congenital anomalies of the kidney and reduced nephron numbers associated with chronic kidney disease (CKD) and hypertension. Previous studies showed that the epithelial fibroblast growth factor receptor 2 (Fgfr2) IIIb splice variant supports ureteric morphogenesis in response to ligands from the metanephric mesenchyme during renal organogenesis. The epithelial‐specific splicing regulator Esrp1 is required for expression of Fgfr2‐IIIb and other epithelial‐specific splice variants. Our objective was to determine whether Esrp1 is required for normal kidney development.
Generating and Maintaining a Distinctive Feature: Lessons for Urban New Schools

As the number of new schools explodes in urban centers, educators need to understand the processes and challenges faced by schools trying to develop a central focus for curriculum and instruction. Using case study methods including interviews and document analysis, the current study explores the ways in which a highly successful magnet high school for science and technology developed and maintains its Technology Laboratories as its distinctive feature. Issues such as funding, staffing, leadership, and competition are explored. Recommendations are made for schools seeking to organize themselves around a distinctive center of excellence and questions posed for further exploration.
Comparing Document Segmentation Strategies for Passage Retrieval in Question Answering

Information retrieval (IR) techniques are used in question answering (QA) to retrieve passages from large document collections which are relevant to answering given natural language questions. In this paper we investigate the impact of document segmentation approaches on the retrieval performance of the IR component in our Dutch QA system. In particular we compare segmentations into discourse-based passages and window-based passages with either fixed sizes or variable sizes. We also look at the effect of overlapping passages and sliding window approaches. Finally, we evaluate the different strategies by applying them to our question answering system in order to see the impact of passage retrieval on the overall QA accuracy.
Role-equivalent Actors in Networks ? Group Structures Beyond Dense Communities

Communities in social networks are often defined as groups of densely connected actors. However, members of the same dense group are not equal but may dier largely in their social position or in the role they play. Furthermore, the same positions can be found across the bor- ders of dense communities so that networks contain a significant group structure which does not coincide with the structure of dense groups. This papers gives a survey over formalizations of network-positions with a special emphasis on the use of algebraic notions.
Foreign Exchange Interventions as an (Un)Conventional Monetary Policy Tool

The zero level of interest rates constitutes a limit of this standard monetary policy instrument. Based on the example of the Czech Republic we argue that in such a situation foreign exchange interventions represent a meaningful monetary policy tool for small open economies not facing serious liquidity problems. Existing studies also suggest that potential financial losses of central banks stemming from the buildup of international reserves do not necessarily compromise credibility or the ability to pursue monetary policy objectives. We provide an overview of the evidence on the functioning of FX interventions and the exchange rate pass-through to consumer prices. Using simulations which take into account the existence of the zero lower bound, we argue that in such a situation the pass-through can be much larger than the existing evidence suggest.Full publication: Market Volatility and Foreign Exchange Intervention in EMEs: What Has Changed?
Towards earlier detection of Alzheimer's disease using magnetic resonance images

Early detection of Alzheimer’s disease (AD) is essential to provide the patients with adequate and timely treatments and to help researchers monitor their effectiveness. Structural Magnetic Resonance Imaging (MRI) is a diagnostic tool that provides high-resolution images and a high brain tissue contrast. Classification methods have been proposed that use MRI-based biomarkers as features to distinguish between normal controls and AD patients. Most approaches rely substantially on the quality of at least one of the following: 1) the assumptions of which brain regions are affected; 2) the segmentation of these brain structures, which suffers from large variability across studies; 3) a voxelwise inter-subject correspondence, which is difficult to achieve, particularly considering the large anatomical variability of the brain across different subjects. Also, such methods focus on structural (volume, shape, density) changes only. It has recently been considered that also the MR image intensities and textures can provide complementary information that is overlooked by the structural-based features. In this thesis, we build on the existing literature on classification approaches that use MR image textures for early detection of AD. Firstly, we focus our analysis on a type of lesions in the white matter, which are thought to play a role in cognitive decline. Results show that the lesion textures are more discriminative than the widely used lesion volumes and locations. Secondly, we propose three approaches that use texture descriptors, determined at local patches over the entire brain. Results show that: 1) texture descriptors are able to achieve high classification rates, comparably to structural-based features; 2) no assumptions need to be made about the expectedly affected brain regions, and consequently no prior segmentations are needed; 3) by only affine-registering the images we are still able to localize discriminative regions using finely sampled patches in the brain.
The Division of Labor Between the Neocortex and Hippocampus

This chapter presents an overview of a computational approach towards understanding the different contributions of the neocortex and hippocampus in learning and memory. The approach is based on a set of principles derived from converging biological, psychological, and computational constraints. The most central principles are that the neocortex employs a slow learning rate and overlapping distributed representations to extract the general statistical structure of the environment, while the hippocampus learns rapidly using separated representations to encode the details of specific events while suffering minimal interference. Additional principles concern the nature of learning (error-driven and Hebbian), and recall of information via pattern completion. These principles are demonstrated through neocortical and hippocampal models of a well-known memory task, the AB-AC paired associates task. The results of applying these principles to a wide range of phenomena in conditioning, habituation, contextual learning, recognition memory, recall, and retrograde amnesia, are also summarized.