Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Real-Time Detection in Fusion-Based Sensor Networks

Real-time detection is an important requirement of many mission-critical wireless sensor network applications such as battlefield monitoring and security surveillance. Due to the high network deployment cost, it is crucial to understand and predict the real-time detection capability of a sensor network. However, most existing real-time analyses are based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of detection. In practice, data fusion has been adopted in a number of sensor systems to deal with sensing uncertainty and enable efficient collaboration among resource-limited sensors. However, real-time performance analysis of sensor networks designed based on data fusion has received little attention. In this paper, we bridge this gap by investigating the fundamental real-time detection performance of large-scale sensor networks under stochastic sensing models. In particular, we consider two basic data fusion schemes, i.e., value fusion and decision fusion. Our results show that data fusion is effective in achieving stringent performance requirements such as short detection delay and low false alarm rates. Moreover, value fusion and decision fusion are suitable for low and high signal-to-noise ratio scenarios, respectively. Our results help understand the impact of data fusion and provide important guidelines for the design of real-time wireless sensor networks for intrusion detection. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection. The results show that data fusion can reduce the network density by about 60 percent compared with the disc model while detecting any intruder within one detection period at a false alarm rate lower than five percent.
Activation of the caudal anterior cingulate cortex due to task‐related interference in an auditory Stroop paradigm

Successful information processing requires the focusing of attention on a certain stimulus property and the simultaneous suppression of irrelevant information. The Stroop task is a useful paradigm to study such attentional top‐down control in the presence of interference. Here, we investigated the neural correlates of an auditory Stroop task using fMRI. Subjects focused either on tone pitch (relatively high or low; phonetic task) or on the meaning of a spoken word (high/low/good; semantic task), while ignoring the other stimulus feature. We differentiated between task‐related (phonetic incongruent vs. semantic incongruent) and sensory‐level interference (phonetic incongruent vs. phonetic congruent). Task‐related interference activated similar regions as in visual Stroop tasks, including the anterior cingulate cortex (ACC) and the presupplementary motor‐area (pre‐SMA). More specifically, we observed that the very caudal/posterior part of the ACC was activated and not the dorsal/anterior region. Because identical stimuli but different task demands are compared in this contrast, it reflects conflict at a relatively high processing level. A more conventional contrast between incongruent and congruent phonetic trials was associated with a different cluster in the pre‐SMA/ACC which was observed in a large number of previous studies. Finally, functional connectivity analysis revealed that activity within the regions activated in the phonetic incongruent vs. semantic incongruent contrast was more strongly interrelated during semantically vs. phonetically incongruent trials. Taken together, we found (besides activation of regions well‐known from visual Stroop tasks) activation of the very caudal and posterior part of the ACC due to task‐related interference in an auditory Stroop task. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.
Unemployment by gender and gender catching-up: Empirical evidence from the Italian regions

We examine the dynamic behaviour of unemployment rates by gender and of the gender unemployment gap over the 1992–2009 period by Italian regions. The results from unit root tests with unknown structural breaks indicate that the gender unemployment gap has narrowed in most cases. However, the pace of catching-up and the dynamic behaviour characterizing the individual unemployment series differ substantially from one region to another.We comment on our results in the light of the reforms applied in the Italian labour market over the last two decades.
Discriminative Approach to Fill-in-the-Blank Quiz Generation for Language Learners

We propose discriminative methods to generate semantic distractors of fill-in-theblank quiz for language learners using a large-scale language learners’ corpus. Unlike previous studies, the proposed methods aim at satisfying both reliability and validity of generated distractors; distractors should be exclusive against answers to avoid multiple answers in one quiz, and distractors should discriminate learners’ proficiency. Detailed user evaluation with 3 native and 23 non-native speakers of English shows that our methods achieve better reliability and validity than previous methods.
Beliefs and practices of anti-bias curriculum in early childhood settings

The ever‐changing composition of early childhood classrooms brings many challenges as well as chances for teachers to be more responsive to the needs of children from different backgrounds. In order to better prepare early childhood teachers to meet the challenges presented by an increasingly diverse student body, this case study explored teachers’ beliefs and practices of anti‐bias curriculum in stated funded preschool, kindergarten, and 2‐3 grade settings. Data of this study were collected through document analysis, interviews, observations, and surveys. Qualitative analysis of the data revealed the factors that challenged teachers’ practices of anti‐bias teaching as well as the aspects that contributed to their continuous efforts to implement an anti‐bias curriculum with the regular curriculum. The results of the study indicate teachers’ classroom practice was strongly influenced by their beliefs as suggested by the belief systems theory (Rokeach, 1968) and their beliefs and practices were congruent across the three settings. The results of this study ought to be considered to be the thinking tool for future discussion and research. This study seeks to support the need for better teacher preparation in the area of diversity education and multicultural studies. Additionally, the need for collaboration among colleagues, children’s families, and support from the administration is called for. The results of the study also led to recommendations for future research in the areas of anti‐bias teaching and early childhood education.
A Novel Genetic Screen Implicates Elm1 in the Inactivation of the Yeast Transcription Factor SBF

Background Despite extensive large scale analyses of expression and protein-protein interactions (PPI) in the model organism Saccharomyces cerevisiae, over a thousand yeast genes remain uncharacterized. We have developed a novel strategy in yeast that directly combines genetics with proteomics in the same screen to assign function to proteins based on the observation of genetic perturbations of sentinel protein interactions (GePPI). As proof of principle of the GePPI screen, we applied it to identify proteins involved in the regulation of an important yeast cell cycle transcription factor, SBF that activates gene expression during G1 and S phase. Methodology/Principle Findings The principle of GePPI is that if a protein is involved in a pathway of interest, deletion of the corresponding gene will result in perturbation of sentinel PPIs that report on the activity of the pathway. We created a fluorescent protein-fragment complementation assay (PCA) to detect the interaction between Cdc28 and Swi4, which leads to the inactivation of SBF. The PCA signal was quantified by microscopy and image analysis in deletion strains corresponding to 25 candidate genes that are periodically expressed during the cell cycle and are substrates of Cdc28. We showed that the serine-threonine kinase Elm1 plays a role in the inactivation of SBF and that phosphorylation of Elm1 by Cdc28 may be a mechanism to inactivate Elm1 upon completion of mitosis. Conclusions/Significance Our findings demonstrate that GePPI is an effective strategy to directly link proteins of known or unknown function to a specific biological pathway of interest. The ease in generating PCA assays for any protein interaction and the availability of the yeast deletion strain collection allows GePPI to be applied to any cellular network. In addition, the high degree of conservation between yeast and mammalian proteins and pathways suggest GePPI could be used to generate insight into human disease.
Moving objects detection and tracking in infrared or thermal image

Moving objects detection and tracking in infrared images is an important goal in most of the practical applications of the thermo vision systems. For these thermo vision applications here is proposed to apply a cost function associated with the minimization of a global criterion for simultaneous estimation of the optical flow and detection of the moving objects in infrared images. The optical flow and moving objects detection and tracking in infrared images are modeled with an appropriate neural network. The thermo vision or infrared images, captured from thermo camera, are first partitioned in rectangular blocks. The blocks are described with a number of parameters placed in the corresponding feature vectors. It is proposed to apply as parameters of the blocks the following important in thermal images characteristics: the position, the gray level and the local motion information. It is chosen the classification of the feature vectors by considering the displaced frame difference, according to Bayesian theory of decision criterion and representing a metric in the parameter space.
Deep Sequencing of MYC DNA-Binding Sites in Burkitt Lymphoma

Background MYC is a key transcription factor involved in central cellular processes such as regulation of the cell cycle, histone acetylation and ribosomal biogenesis. It is overexpressed in the majority of human tumors including aggressive B-cell lymphoma. Especially Burkitt lymphoma (BL) is a highlight example for MYC overexpression due to a chromosomal translocation involving the c-MYC gene. However, no genome-wide analysis of MYC-binding sites by chromatin immunoprecipitation (ChIP) followed by next generation sequencing (ChIP-Seq) has been conducted in BL so far. Methodology/Principal Findings ChIP-Seq was performed on 5 BL cell lines with a MYC-specific antibody giving rise to 7,054 MYC-binding sites after bioinformatics analysis of a total of approx. 19 million sequence reads. In line with previous findings, binding sites accumulate in gene sets known to be involved in the cell cycle, ribosomal biogenesis, histone acetyltransferase and methyltransferase complexes demonstrating a regulatory role of MYC in these processes. Unexpectedly, MYC-binding sites also accumulate in many B-cell relevant genes. To assess the functional consequences of MYC binding, the ChIP-Seq data were supplemented with siRNA- mediated knock-downs of MYC in BL cell lines followed by gene expression profiling. Interestingly, amongst others, genes involved in the B-cell function were up-regulated in response to MYC silencing. Conclusion/Significance The 7,054 MYC-binding sites identified by our ChIP-Seq approach greatly extend the knowledge regarding MYC binding in BL and shed further light on the enormous complexity of the MYC regulatory network. Especially our observations that (i) many B-cell relevant genes are targeted by MYC and (ii) that MYC down-regulation leads to an up-regulation of B-cell genes highlight an interesting aspect of BL biology.
Volumetric analysis of digital objects using distance transformation : performance issues and applications

In binary images, the distance transformation (DT) and the geometrical medial axis are classic tools for shape analysis. In the digital geometry literature, recent articles have demonstrated that fast algorithms can be designed without any approximation of the Euclidean metric. The aim of the paper is to first give an overview of separable techniques to compute the distance transformation, the reverse distance transformation and a discrete medial axis extraction with the Euclidean metric. Then we will focus on performance issues and different applications in shape analysis.
Walking the Decidability Line for Rules with Existential Variables

We consider positive rules in which the conclusion may contain existentially quantified variables, which makes reasoning tasks (such as Deduction) undecidable. These rules, called ∀∃-rules, have the same logical form as TGD (tuple-generating dependencies) in databases and as conceptual graph rules. The aim of this paper is to provide a clearer picture of the frontier between decidability and non-decidability of reasoning with these rules. We show that Deduction remains undecidable with a single ∀∃-rule; then we show that none of the known abstract decidable classes is recognizable. Turning our attention to concrete decidable classes, we provide new classes and classify all known classes by inclusion. Finally, we study, in a systematic way, the question "given two decidable sets of ∀∃-rules, is their union decidable? ", and provide an answer for all known decidable cases except one.
Dynamic visual metaphors for news story abstractions

We describe a dynamic document genre for online news, specifically designed to support end users in both easily skimming news stories as well as reading them in-depth. Through careful analysis of the needs of online news readers, and understanding the opportunities and constraints of the computational medium, we set out to define characteristics of a document management environment which uses linguistically-intensive content analysis techniques to derive document abstractions usable as 'zooming' windows into and out of documents. The notion of flexible mediation between progressively richer, and more informative, layers of document content defines the core underlying characteristic of a dynamic document genre. We prototyped and deployed three examples of dynamic document viewers that characterize this new genre. The design incorporates a novel mix of: state-of-the-art linguistic analysis techniques that produces capsule document overviews, novel temporal typography techniques that allows us to dynamically change how these overviews are presented to the reader, specialized search and filter mechanisms to cull collections of relevant documents from the Web, and large screen displays to facilitate peripheral awareness of constantly changing news feeds.
Wiener filtering of meshes

This work investigates smoothing, fairing, or, more generally, filtering of mesh geometry. The approach transfers the ideas of optimal (Wiener) filtering to the setting of meshes. It extends fairing approaches that use only first order neighborhoods and allows to assume arbitrary local spectral properties of the mesh geometry. The definition of the local autocorrelation allows the design of filters for smoothing as well as for special effects in shape modeling.
Funding Costs and Loan Pricing by Multinational Bank Affiliates

We conduct a theoretical and empirical investigation of the influence which the financial condition of a multinational bank group may have on the lending rates of its affiliates. We first propose a model of bank lending to risky clients in which the implicit opportunity costs of lending by a foreign bank affiliate are influenced by the abundance/scarcity of funds within the multinational conglomerate. The model predicts that parent banksâ€™ influence should be stronger in loan segments with more pronounced information asymmetry problems. We then formulate an empirical model of the spread charged by the affiliate to clients over the local interbank rate as a function of affiliate-level controls and a parent influence variable. This model is tested for three categories of commercial non-financial borrowers (domestically owned firms, foreign-owned firms and the self-employed) from the ten biggest banks in the Czech Republic under foreign control. Evidence of parent influence on lending spread is found in a limited number of cases of banks and borrower classes for which the constraint on fund flow within the parent bank group is likely to be tight, particularly when the borrower class is of strategic importance for the affiliateâ€™s overall performance. Therefore the parent bank influence probably is not a dominating factor in interest-rate setting on aggregate, but it can influence the cost of credit in borrower categories that are of major importance for the affiliate.
Solving Structural Engineering Optimization Problems with an Amalgam Social Cultural Algorithms

This paper presents a new method to solve complex engineering optimization problems. This method is based on intermingling Cultural Algorithms with Tabu search meta-heuristic. The proposed approach uses a specified set of knowledge sources to update agent’s information, as well as employs the diffusion factor to propagate the best solutions in the social network. This approach avoids premature convergence through seeking the best neighboring solutions using Tabu search in case of stagnation, which uses the best solution found so far using social Cultural Algorithms. The Algorithm is validated on a set of structural engineering benchmarks from literature. The obtained results are compared with those generated from other state-of-the-art algorithms in the optimization field. These outcomes are discussed to demonstrate that the technique improves performance in terms of the found optima and convergence speed at less function evaluations. Key-Words: Constrained optimization, Cultural Algorithms, Tabu search, Knowledge swarming, Social integration, Knowledge integration
Physically-Based Analysis and Rendering of Bidirectional Texture Functions Data

In pursuit of photorealistic rendering of surface material, Bidirectional Texture Function (BTF) has been used frequently in recent years. Its main drawback is its massive data size. To solve this, the Spatial Bidirectional Reflectance Function (SBRDF) techniques compress BTFs into reflectance model parameters. However, SBRDF cannot produce the self-shadowing and self-occlusion mesostructure effects in many real-world surfaces. Aiming at this drawback, we investigate how self-shadowing and self-occlusion affect the surface appearance by additional physically-based analysis. We mainly rely on two physical phenomena to separate self-shadowing and self-occlusion into two independent effects. First, the self-shadowing is view independent. Second, the self-occlusion is independent of changes in lighting direction. After those analyses, we are able to add self-shadowing and self-occlusion effects to SBRDF to achieve rendering quality that is much closer to the original uncompressed BTF data than SBRDF.
PM-based indexing for Chinese text retrieval

This paper focused on introducing a novel PM indexing schema for Chinese text retrieval. Different with the Western languages, there is no delimiter between words in Chinese texts. The indexing is based either on the characters or on the segmented words. For the word-based indexing, the out-of-vocabulary words, such as the proper nouns, or domain terminology, are usually mis-segmented due to the limited vocabulary coverage of the segmentation dictionaries and thus impair the query precision. In this paper, several indexing and ranking methods, including the novel PM-based ranking, were tested so as to compare their efficiency in dealing with the new words in Chinese text retrieval. The experiment has shown that the query precision of the PM + word method is 10% higher than the word indexing.
LIDS Technical Report # 2779 1 Constrained Consensus ∗

We study the problem of reaching a consensus in the estimates generated by multiple agents forming a network with time-varying connectivity. Our main focus is on constrained consensus problems where the estimates of different agents are constrained to lie in different constraint sets. We consider a distributed “projected consensus algorithm” in which agents combine their local averaging operation with projection onto their individual constraint sets. This algorithm can be viewed as a version of an alternating projection method with weights that are varying over time and across agents. We establish convergence of the projected consensus algorithm. We also provide convergence rate estimates for the cases when the weights are constant and equal, and when the weights are time-varying but all agents have the same constraint set.
A reference architecture for integrated development and run-time environments

The need to support different stages of a software system's life-cycle in a software development environment has resulted in the emergence of plug-ins that can monitor and adapt the resulting system's run-time environment. In several recent self-adaptive systems, the existence of these plug-ins has resulted in the integration of the development and runtime environments. We introduce a novel reference architecture that captures the architectures of disparate development environments - from those, such as traditional development environments, that have no run-time plug-ins, to the ones, such as the recently emerging development environments, that are fully integrated with the run-time environments. This reference architecture aids the study and understanding of development environments. More importantly, adherence to the architecture improves the run-time availability of certain critical system facilities while reducing the resource consumption of the integrated development and run-time environments.