Miracle-dz/UniFAR-FTUs huggingface dataset:
ADQUEX: Adaptive Processing of Federated Queries over Linked Data based on Tuple Routing

Recent achievements of linked data implementations and the increased number of datasets available on the web as linked data, has given rise to the need and tendency toward processing federated queries over these datasets. Due to the d istribution of linked data across the web, the methods that process federated queries through a di stributed approach are more attractive to the users and have gained more prosperity. In distribut ed processing of federated queries, we need methods and procedures to execute the query in an o ptimal manner. Most of the existing methods perform the optimization task based on some statist ical information, whereas the query processor does not have precise statistical information about their properties, since the data sources are autonomous. When precise statistics are not availab le, the possibility of wrong estimations will highl y increase, and may lead to inefficient execution of the query at runtime. Another problem of the existing methods is that in the optimization phase, they assume that runtime conditions of query execution are stable, while the environment in whic h federated queries are executed over linked data is dynamic and non-predictable. By considering thes e two problems, there is a great potential for exploiting the federated query processing technique s in an adaptive manner. In this paper, an adaptive method is proposed for processing federate d queries over linked data, based on the concept of routing the tuples. The proposed method, named A DQUEX, is able to execute the query effectively without any prior statistical information. This met hod can change the query execution plan at runtime so that less intermediate results are produced. It can also adapt the execution plan to new situation if unpredicted network latencies arise. Extensive eval uation of our method by running real queries over well-known linked datasets shows very good results especially for complex queries.
Distributed under Creative Commons Cc-by 4.0 Co-father Relationships among the Suruí (paiter) of Brazil

Partible paternity refers to the conception belief that children can have multiple fathers (" co-fathers ") and is common to indigenous cultures of lowland South America. The nature of social relationships observed between co-fathers reveals information about the reproductive strategies underlying partible paternity. Here we analyze clan, genealogical, and social relationships between co-fathers for the Suruí, an indigenous horticultural population in Brazil. We show that co-fathers roughly assort into two separate categories. In the affiliative category, co-father relationships are amicable when they are between close kin, namely brothers and father-son. In the competitive category, relationships are more likely of avoidance or open hostility when between more distant kin such as cousins or unrelated men of different clans. Results therefore imply multiple types of relationships, including both cooperative and competitive contexts, under the rubric of partible paternity. These complexities of partible paternity institutions add to our knowledge of the full range of cross-cultural variation in human mating/marriage arrangements and speak to the debate on whether or not humans should be classified as cooperative breeders.
Multiple Interkingdom Horizontal Gene Transfers in Pyrenophora and Closely Related Species and Their Contributions to Phytopathogenic Lifestyles

Many studies have reported horizontal gene transfer (HGT) events from eukaryotes, especially fungi. However, only a few investigations summarized multiple interkingdom HGTs involving important phytopathogenic species of Pyrenophora and few have investigated the genetic contributions of HGTs to fungi. We investigated HGT events in P. teres and P. tritici-repentis and discovered that both species harbored 14 HGT genes derived from bacteria and plants, including 12 HGT genes that occurred in both species. One gene coding a leucine-rich repeat protein was present in both species of Pyrenophora and it may have been transferred from a host plant. The transfer of genes from a host plant to pathogenic fungi has been reported rarely and we discovered the first evidence for this transfer in phytopathogenic Pyrenophora. Two HGTs in Pyrenophora underwent subsequent duplications. Some HGT genes had homologs in a few other fungi, indicating relatively ancient transfer events. Functional analyses indicated that half of the HGT genes encoded extracellular proteins and these may have facilitated the infection of plants by Pyrenophora via interference with plant defense-response and the degradation of plant cell walls. Some other HGT genes appeared to participate in carbohydrate metabolism. Together, these functions implied that HGTs may have led to highly efficient mechanisms of infection as well as the utilization of host carbohydrates. Evolutionary analyses indicated that HGT genes experienced amelioration, purifying selection, and accelerated evolution. These appeared to constitute adaptations to the background genome of the recipient. The discovery of multiple interkingdom HGTs in Pyrenophora, their significance to infection, and their adaptive evolution, provided valuable insights into the evolutionary significance of interkingdom HGTs from multiple donors.
Incremental Learning and Novelty Detection of Gestures in a Multi-class System

The difficulties of data streams, i.e. Infinite length, the occurrence of concept-drift and the possible emergence of novel classes, are topics of high relevance in the field of recognition systems. To overcome all of these problems, the system should be updated continuously with new data while the amount of processing time should be kept small. We propose an incremental Parzen window kernel density estimator (IncPKDE) which addresses the problems of data streaming using a model that is insensitive to the training set size and has the ability to detect novelties within multi-class recognition systems. The results show that the IncPKDE approach has superior properties especially regarding processing time and that it is robust to wrongly labelled samples if used in a semi-supervised learning scenario.
A new dimensionality analysis algorithm for hyperspectral imagery

In the procedure of hyperspectral data dimensionality reduction (DR), intrinsic dimensionality (ID) of high-dimensional hyperspectral data is normally obtained through the linear dimensionality analysis methods. This article applies a kind of unsupervised learning method, manifold learning method, to the dimensionality analysis for hyperspectral data and gives a manifold-learning-based algorithm for dimensionality analysis of hyperspectral data. The experiments use ISOMAP, LLE, LE and LTSA algorithms to estimate the intrinsic dimensionality of hyperspectral simulated data and real data, get the two-dimension manifold figures of high-dimensional data and discuss the advantages and disadvantages of these algorithms in hyperspectral dimensionality analysis.
A Tracking Model for Enhancing Social Video Integration and Sharing

With the growing of online multimedia and autonomous management of web sites, how to enhance the integration and sharing level centered content without boundary are the key issues faced with the social media networks presently. Address these problems, a tracking model of social video services on the Web was proposed, which not only tracks logical relationships and user-generated content by the context-tracker component, but also forms an interconnecting network of social videos with global view. For implement better integration and sharing, the methods of wrapping, tracking and three-layer sharing to the social video were further proposed. Theoretical analysis and experimental results show that the tracking model can tremendously enhance the videos integrating and social sharing among users on the Web.
Multiscale biosystems integration: Coupling intracellular network analysis with tissue-patterning simulations

In order to achieve a comprehensive understanding of complex biological systems, researchers must develop new techniques that incorporate key features of the system across all relevant spatial and temporal scales. Recent advances in molecular biology and genetics have generated a wealth of experimental data that provides details with respect to gene-expression patterns and individual gene and protein functions, but integration of this information into meaningful knowledge of the complete system is a challenge borne by a new scientific era dependent on computational tools. In this paper, we review new computational techniques, developed to reconstruct single-cell biochemical networks for generating quantitative descriptions of network properties, and agent-based models designed to study multicell interactions important in tissue patterning. We also discuss the challenges and promises of combining these approaches in a single quantitative framework for advancing medical care for diseases that arise from a multitude of factors.
Improved denoising technique for natural and synthetic images

Image processings applications like in object tracking, medical imaging, satellite imaging, face recognition and segmentation requires image denoising as the preprocessing step. Problem with current image denoising methods are bluring and artifacts introduces after removal of noise from image. Current denoising methods are based on patches of image has well denoisinging ability but implemention of such methods are difficult. The patch-less Progressive Image Denoising(PID) is a dual-domain image denoising method which progressively removes reduce the noise from image in each iteration. It has simple implementation using robust noise estimation and deterministic annealing. Its results are artifacts free. It is better for the artifical images i.e. computer generated images or synthetic images. This paper presents comparatively results of PID, Dual-Domain Image Denoising (DDID) and Block Matching and 3D Filtering (BM3D) for both natural and synthetic images contaminated with different levels of AWGN noise.
School Leadership in North Carolina through the Eyes of Distinguished Leaders: A Thematic Analysis.

The purpose of this study was to interview and observe the principals selected to participate in the inaugural cohort of the North Carolina Distinguished Leadership in Practice (NCDLP) program to understand what leadership practices they applied within their schools that positively impacted teaching and learning. The major research question for this study was, How does a principal's leadership support high quality teaching and learning? From this major research question, five guiding questions emerged to serve as integral components of this study: 1. What do distinguished principals perceive to be the relationship between the principal leadership and student achievement in North Carolina? 2. What are the leadership perspectives and outlooks of NC DLP principals that support high quality teaching and learning in their schools? 3. What are NC DLP principals doing to develop leadership capacity that support high quality teaching and learning in their schools? 4. What competencies within the School Executive Standards do distinguished principals perceive to be the most important in their leadership? 5. What School Executive Standards do distinguished principals perceive to be the most important to improve student achievement? Qualitative data were collected from semi-structured interviews, leadership surveys, and site visit observations with six principals. Member-checking, document analysis, and field observations of principals during regular and staff development days were conducted to collect data on the principals' leadership practice. The focus of both the interviews and site visits centered on descriptive questions which revealed information about the hows and whys of changes in principal leadership behavior following participation in the NC DLP program as well as the impact of the leader's practice on teaching and learning and organizational structures. Key findings revealed that principals are expected to play an active role in leadership, consider the processes, activities, and relationships within their school and use those factors to positively affect teaching and learning (Bottoms & Schmidt-Davis, 2010). Overall, the findings from this study suggest that principals who center their attention on developing the culture of the school and creating synergy of individual skills of teachers, learning relationships among teachers, a relentless focus on instruction, and ongoing mobilization of resources will make significant contributions to teaching and learning in a school.
The Optimal Byrd Tariff in Vertical Markets

This paper develops the theory of the optimal tariff under the Byrd Amendment when producers are also processors. The optimal tariff in this case is equal to the optimal processor tariff (when the processor is not also a producer) and is smaller than the optimal producer tariff (when the producer is not also a processor). In a vertical market structure, the welfare of a producer/processor (P/P) increases if tariffs are imposed on raw product imports because a P/P keeps the tariff revenue under the Byrd Amendment. The P/P maximizes joint welfare under a positive Byrd tariff since the tariff allows the P/P to behave in a noncompetitive manner, but this is true only under competitive conditions. Under imperfect competition, the optimal Byrd tariff is zero.
Texture Content Based Successive Approximations for Image Compression and Recognition

The representation of an image as a collection of "words", also called "textons" or "primitives", from a predefined vocabulary has become an essential step in developing advanced computer vision algorithms. Such vocabularies are often derived via some form of unsupervised or supervised machine learning technique, and by arbitrarily choosing an optimal set of words, often referred to as a dictionary. Even though such representations have been shown to capture the 2D image content, textons or image descriptors were never used for image compression or for recognizing image content in the compressed domain. In this work we are presenting a novel texture based image representation technique that can be used for both image compression and recognition purposes. The developed algorithm requires no filter, no post processing (for image enhancements) and serves as a viable tool for image compression when compared to standards such as JPEG and JPEG2000. It is also a powerful tool for recognizing content in the compressed domain. Performance of the developed image compression descriptor is bench-marked for object recognition applications in the compressed domain using Caltech-101.
Corporate America and Environmental Policy: How Often Does Business Get Its Way?

@fmct:Contents @toc4:Preface iii List of Abbreviations iii @toc2:1 Business and American Democracy 000 2 Interest Groups and Environmental Policymaking 000 3 Theories of Issue Definition, Framing, and Agenda Building 000 4 Agenda Building in Congress 000 5 The Influence of Business in Federal Agencies and Courts 000 6 Corporate Involvement in Regulatory Policy 000 7 Corporate Involvement in Natural Resource Policy 000 8 Conclusion 000 @toc4:Notes 000 References 000 Index 000
Assessment of Anti-nutritive Activity of Tannins in Tea By-products Based on In vitro Rumen Fermentation

Nutritive values of green and black tea by-products and anti-nutritive activity of their tannins were evaluated in an in vitro rumen fermentation using various molecular weights of polyethylene glycols (PEG), polyvinyl pyrrolidone (PVP) and polyvinyl polypyrrolidone as tannin-binding agents. Significant improvement in gas production by addition of PEG4000, 6000 and 20000 and PVP was observed only from black tea by-product, but not from green tea by-product. All tannin binding agents increased NH3-N concentration from both green and black tea by-products in the fermentation medium, and the PEG6000 and 20000 showed relatively higher improvement in the NH3-N concentration. The PEG6000 and 20000 also improved in vitro organic matter digestibility and metabolizable energy contents of both tea by-products. It was concluded that high molecular PEG would be suitable to assess the suppressive activity of tannins in tea by-products by in vitro fermentation. Higher responses to gas production and NH3-N concentration from black tea by-product than green tea by-product due to PEG indicate that tannins in black tea by-product could suppress rumen fermentation more strongly than that in green tea by-product.
GROT in NICMOS Cameras

Grot is exhibited as small areas of reduced sensitivity, most likely due to flecks of antireflective paint scraped off the optical baffles as they were forced against each other. This paper characterizes grot associated with all three cameras. Flat field images taken from March 1997 through January 1999 have been investigated for changes in the grot, including possible wavelength dependency and throughput characteristics. The main products of this analysis are grot masks for each of the cameras which may also contain any new cold or dead pixels not specified in the data quality arrays.
A fuzzy logic approach to neuronal modelingW

W. Senn1;2, M. Jakob2, K. Wyler2, J. Kleinle2, H. -R. L uscher3, H. Mey2, L. M uller2 Abstract | Methods of fuzzy logic are commonly used to model a complex system by a set of rules provided by experts. But fuzzy methods can also be applied to the reverse problem: given the inputoutput behavior of a system, what are the rules which are governing that behavior. We introduce this reengineering method to neuronal modeling and, as an example, we analyze a biological neuron of the Hodgkin-Huxley type. Starting from measured data of the membrane potential and the sodiumand potassium-current through the membrane, we extract fuzzy rules which determine the time course of the membrane potential in terms of the actual values of the membrane current. These fuzzy rules allow not only to rebuild the behavior but also to understand the underlying membrane mechanisms. The approach o ers a new representation of neuronal mechanisms in terms of linguistic rules.
Non-cooperative foundations of hedonic equilibrium

This paper studies Bayesian equilibrium in a worker firm matching problem in which workers choose their human capi- tal investment and firms choose wages before the matching process occurs. Symmetric equilibrium exists, and supports assortative matching. However, when the number of traders is large, low types tend to invest too much, while higher types invest in a way that is bilaterally efficient. In this sense the upper end of the market be- haves in a manner that is similar to the way they would behave in a competitive (hedonic) equilibrium. The lower end of the market, however, does not. All types end up investing more and being paid higher wages than they are in a simple hedonic equilibrium. In the limit, the Bayesian game supports and outcome that looks like a Truncated Hedonic Equilibrium as described in Peters (2006).
Caspase-8 Binding to Cardiolipin in Giant Unilamellar Vesicles Provides a Functional Docking Platform for Bid

Caspase-8 is involved in death receptor-mediated apoptosis in type II cells, the proapoptotic programme of which is triggered by truncated Bid. Indeed, caspase-8 and Bid are the known intermediates of this signalling pathway. Cardiolipin has been shown to provide an anchor and an essential activating platform for caspase-8 at the mitochondrial membrane surface. Destabilisation of this platform alters receptor-mediated apoptosis in diseases such as Barth Syndrome, which is characterised by the presence of immature cardiolipin which does not allow caspase-8 binding. We used a simplified in vitro system that mimics contact sites and/or cardiolipin-enriched microdomains at the outer mitochondrial surface in which the platform consisting of caspase-8, Bid and cardiolipin was reconstituted in giant unilamellar vesicles. We analysed these vesicles by flow cytometry and confirm previous results that demonstrate the requirement for intact mature cardiolipin for caspase-8 activation and Bid binding and cleavage. We also used confocal microscopy to visualise the rupture of the vesicles and their revesiculation at smaller sizes due to alteration of the curvature following caspase-8 and Bid binding. Biophysical approaches, including Laurdan fluorescence and rupture/tension measurements, were used to determine the ability of these three components (cardiolipin, caspase-8 and Bid) to fulfil the minimal requirements for the formation and function of the platform at the mitochondrial membrane. Our results shed light on the active functional role of cardiolipin, bridging the gap between death receptors and mitochondria.
Asymptotic Effectiveness of the Event-Based Sampling according to the Integral Criterion

A rapid progress in intelligent sensing technology creates new interest in a development of analysis and design of non-conventional sampling schemes. The investigation of the event-based sampling according to the integral criterion is presented in this paper. The investigated sampling scheme is an extension of the pure linear send-on-delta/level-crossing algorithm utilized for reporting the state of objects monitored by intelligent sensors. The motivation of using the event-based integral sampling is outlined. The related works in adaptive sampling are summarized. The analytical closed-form formulas for the evaluation of the mean rate of event-based traffic, and the asymptotic integral sampling effectiveness, are derived. The simulation results verifying the analytical formulas are reported. The effectiveness of the integral sampling is compared with the related linear send-on-delta/level-crossing scheme. The calculation of the asymptotic effectiveness for common signals, which model the state evolution of dynamic systems in time, is exemplified.
An incremental learning algorithm of Recursive Fisher Linear Discriminant

This paper presents an online feature extraction method called Incremental Recursive Fisher Linear Discriminant (IRFLD) whose batch learning algorithm called RFLD has been proposed by Xiang et al. In the conventional Linear Discriminant Analysis (LDA), the number of discriminant vectors is limited to the number of classes minus one due to the rank of the between-class scatter matrix. RFLD and the proposed IRFLD can eliminate this limitation. In the proposed IRFLD, the Pang et al.'s Incremental Linear Discriminant Analysis (ILDA) is extended such that effective discriminant vectors are recursively searched for the complementary space of a conventional ILDA subspace. In addition, to estimate a suitable number of effective discriminant vectors, we also propose a convergence criterion for the recursive computations which is defined by using the class separability of discriminant features projected on the complementary subspace. The experimental results suggest that the recognition accuracies of IRFLD is improved as the learning proceeds. For several datasets, we confirm that the proposed IRFLD outperforms ILDA in terms of the recognition accuracy. However, the advantage of IRFLD against ILDA depends on datasets.