Miracle-dz/UniFAR-FTUs huggingface dataset:
Active colonization dynamics and diversity patterns are influenced by dendritic network connectivity and species interactions

Habitat network connectivity influences colonization dynamics, species invasions, and biodiversity patterns. Recent theoretical work suggests dendritic networks, such as those found in rivers, alter expectations regarding colonization and dispersal dynamics compared with other network types. As many native and non-native species are spreading along river networks, this may have important ecological implications. However, experimental studies testing the effects of network structure on colonization and diversity patterns are scarce. Up to now, experimental studies have only considered networks where sites are connected with small corridors, or dispersal was experimentally controlled, which eliminates possible effects of species interactions on colonization dynamics. Here, we tested the effect of network connectivity and species interactions on colonization dynamics using continuous linear and dendritic (i.e., river-like) networks, which allow for active dispersal. We used a set of six protist species and one rotifer species in linear and dendritic microcosm networks. At the start of the experiment, we introduced species, either singularly or as a community within the networks. Species subsequently actively colonized the networks. We periodically measured densities of species throughout the networks over 2 weeks to track community dynamics, colonization, and diversity patterns. We found that colonization of dendritic networks was faster compared with colonization of linear networks, which resulted in higher local mean species richness in dendritic networks. Initially, community similarity was also greater in dendritic networks compared with linear networks, but this effect vanished over time. The presence of species interactions increased community evenness over time, compared with extrapolations from single-species setups. Our experimental findings confirm previous theoretical work and show that network connectivity, species-specific dispersal ability, and species interactions greatly influence the dispersal and colonization of dendritic networks. We argue that these factors need to be considered in empirical studies, where effects of network connectivity on colonization patterns have been largely underestimated.
Evaluating and Optimizing the Storage Strategies for an Elastic Object Store

In this paper, we focus on evaluating different storage strategies of different kinds of data and their index stored in Punt Table. Punt Table is a NoSQL database designed for elastic objects storage. Punt Table uses a schema-free way to store and get the objects and builds indices to support querying the fields inside the objects. In order to achieve high throughput and low latency, Punt Table is designed using multiple content storage engine and index storage engine through two interfaces, Punt Store and Punt Index. Punt Store and Punt Index are designed as the storage layer of Punt Table. Both the objects content and their indices could choose to adopt the most suitable storage layer for current data set. Punt Table was tested and evaluated for the performance of object data and index store combination under test data sets with different single record sizes that are picked to simulate the real application scenarios. The result reveals that the proper configuration of storage layer for a particular data set could improve the throughput and drop the latency dramatically.
DDL : Extending Dynamic Linking for Program Customization , Analysis , and Evolution

While new software languages and environments have moved towards richer introspective and manipulatable runtime environments, there is still much traditional software that is compiled into platformspecific executables and runs in a context that does not easily offer such luxuries. Yet even in these environments, mechanisms such as dynamic link libraries do offer the potential of building more control over the deployed and running system, and can offer opportunities for supporting dynamic evolution of such systems. Indeed, the basic dynamic linking mechanisms can be exploited to offer a rich component-based execution environment. In this paper, we present our initial explorations into building such support. Our approach is to extend the Gnu open-source dynamic loader to give the deployer control over the configuration of the system, and to be able to dynamically evolve that system. Applications of this capability include runtime component configuration, program evolution and version management, and runtime monitoring.
Compressed Differential Erasure Codes for Efficient Archival of Versioned Data

In this paper, we study the problem of storing an archive of versioned data in a reliable and efficient manner in distributed storage systems. We propose a new storage technique called differential erasure coding (DEC) where the differences (deltas) between subsequent versions are stored rather than the whole objects, akin to a typical delta encoding technique. However, unlike delta encoding techniques, DEC opportunistically exploits the sparsity (i.e., when the differences between two successive versions have few non-zero entries) in the updates to store the deltas using compressed sensing techniques applied with erasure coding. We first show that DEC provides significant savings in the storage size for versioned data whenever the update patterns are characterized by in-place alterations. Subsequently, we propose a practical DEC framework so as to reap storage size benefits against not just in-place alterations but also real-world update patterns such as insertions and deletions that alter the overall data sizes. We conduct experiments with several synthetic workloads to demonstrate that the practical variant of DEC provides significant reductions in storage overhead (up to 60\% depending on the workload) compared to baseline storage system which incorporates concepts from Rsync, a delta encoding technique to store and synchronize data across a network.
Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models

We suggest quantile regression methods for a class of smooth coefficient time series models. We use both local polynomial and local constant fitting schemes to estimate the smooth coefficients in a quantile framework. We establish the asymptotic properties of both the local polynomial and local constant estimators for α-mixing time series. We also suggest a bandwidth selector based on the nonparametric version of the Akaike information criterion, along with a consistent estimate of the asymptotic covariance matrix. We evaluate the asymptotic behaviors of the estimators at boundaries and compare the local polynomial quantile estimator and the local constant estimator. A simulation study is carried out to illustrate the performance of estimates. An empirical application of the model to real data further demonstrates the potential of the proposed modeling procedures.
Interface Automata with Complex Actions: Limiting Interleaving in Interface Automata

Many formalisms use interleaving to model concurrency. To describe some system behaviours appropriately, we need to limit interleaving. For example, in a component-based system, we might wish to limit interleaving to force the inputs to a method to arrive together in order. InWeb services, the arrival of XML messages consisting of multiple simple parts should not be interleaved with the behaviour of another component. We introduce interface automata with complex actions (IACA), which adds complex actions to de Alfaro and Henzinger's interface automata (IA). A complex action is a sequence of actions that may not be interleaved with actions from other components. The composition operation and refinement relation are more involved in IACA compared to IA, and we must sacrifice associativity of composition. However, we argue that the advantages of having complex actions make it a useful formalism. We provide proofs of various properties of IACA and discuss the use of IACA for modellingWeb services.
Music genre classification using novel features and a weighted voting method

This paper proposes a novel music genre classification system based on two novel features and a weighted voting. The proposed features, modulation spectral flatness measure (MSFM) and modulation spectral crest measure (MSCM), represent the time-varying behavior of a music and indicate the beat strength. The weighted voting method determines the music genre by summarizing the classification results of consecutive time segments. Experimental results show that the proposed features give more accurate classification results when combined with traditional features than the octave-based modulation spectral contrast (OMSC) does in spite of short feature vector and that the weighted voting is more effective than statistical method and majority voting.
Color Gaussian Jet Features For No-Reference Quality Assessment of Multiply-Distorted Images

In this letter we present a novel no-reference image quality assessment (NR-IQA) method for the visual quality prediction of multiply-distorted color images. In the proposed method, to describe the image structure, a number of feature maps are first calculated based on the color Gaussian jet of the image. The popular local binary pattern operator is then applied on the computed feature maps to measure any potential structural degradations caused by multiple distortions. The performance of the proposed method was compared with 14 prominent full-reference IQA methods as well as 11 NR-IQA methods on two multidistortion IQA databases. The results indicate that the proposed method outperforms all the compared methods with a high accuracy at a moderate complexity.
Exemplar-Based Colour Constancy and Multiple Illumination

—Exemplar-based learning or, equally, nearest neighbour methods have recently gained interest from researchers in a variety of computer science domains because of the prevalence of large amounts of accessible data and storage capacity. In computer vision, these types of technique have been successful in several problems such as scene recognition, shape matching, image parsing, character recognition and object detection. Applying the concept of exemplar-based learning to the problem of colour constancy seems odd at ﬁrst glance since, in the ﬁrst place, similar nearest neighbour images are not usually affected by precisely similar illuminants and, in the second place, gathering a dataset consisting of all possible real-world images, including indoor and outdoor scenes and for all possible illuminant colours and intensities, is indeed impossible. In this paper we instead focus on surfaces in the image and address the colour constancy problem by unsupervised learning of an appropriate model for each training surface in training images. We ﬁnd nearest neighbour models for each surface in a test image and estimate its illumination based on comparing the statistics of pixels belonging to nearest neighbour surfaces and the target surface. The ﬁnal illumination estimation results from combining these estimated illuminants over surfaces to generate a unique estimate. We show that it performs very well, for standard datasets, compared to current colour constancy algorithms, including when learning based on one image dataset is applied to tests from a different dataset. The proposed method has the advantage of overcoming multi-illuminant situations, which is not possible for most current methods since they assume the colour of the illuminant is constant all over the image. We show a technique to overcome the multiple illuminant situation using the proposed method and test our technique on images with two distinct sources of illumination using a multiple-illuminant colour constancy dataset. The concept proposed here is a completely new approach to the colour constancy problem and provides a simple learning-based framework.
Spatially Adapted Total Variation Model to Remove Multiplicative Noise

Multiplicative noise removal based on total variation (TV) regularization has been widely researched in image science. In this paper, inspired by the spatially adapted methods for denoising Gaussian noise, we develop a variational model, which combines the TV regularizer with local constraints. It is also related to a TV model with spatially adapted regularization parameters. The automated selection of the regularization parameters is based on the local statistical characteristics of some random variable. The corresponding subproblem can be efficiently solved by the augmented Lagrangian method. Numerical examples demonstrate that the proposed algorithm is able to preserve small image details, whereas the noise in the homogeneous regions is sufficiently removed. As a consequence, our method yields better denoised results than those of the current state-of-the-art methods with respect to the signal-to-noise-ratio values.
Improved sparse channel estimation for multi-user massive MIMO systems with compressive sensing

Massive multiple input multiple output (MIMO) is a promising technology for the next generation communication system to increase data rate and throughput. To fully enhance the performance of massive MIMO and improve the quality of service, accurate channel state information (CSI) is required for coherent detection. However, due to the overwhelming pilot overhead, conventional pilot aided channel estimation (PACE) approaches are not suitable for massive MIMO systems, especially for frequency-division duplexing (FDD) systems. In this paper, we consider the channel estimation problem in FDD multiuser massive MIMO systems. A spatial correlated channel is first modeled. By exploiting the spatial correlation, the channel can be represented in a sparse form in spatial-frequency domain. Then, the theory of compressive sensing (CS) is applied to develop an effective method for channel estimation. Moreover, based on the inherent common sparsity in the user channel matrices, this paper proposes an improved sparse channel estimation orthogonal matching pursuit (OMP) algorithm to reduce the pilot overhead and improve the channel estimation accuracy. Simulation results demonstrate that the proposed algorithm can significantly reduce the pilot overhead and have the superior performance in greatly elevating the accuracy of channel estimation.
Quantitative I/O-controllability assessment for uncertain plants

This paper deals with the calculation of the optimal control performance for linear time invariant plant models in the context of the screening of possible control structures. Compared to other approaches, e.g. the use of the relative gain array or other controllability indices, the proposed computation of the attainable control performance gives a quantitative assessment of a control structure without reference to a particular controller structure other than the assumption of linear control. In the time domain, control performance is measured in a least squares sense while constraints on manipulated and controlled variables can be included. The extension of the method presented here integrates the frequency domain condition for robust stability. Uncertainties are represented by the well known MDelta structure which renders the method generically applicable
Knowledge Integration in Software Teams: An Assessment of Team Project and IT-related Issues

Organizational knowledge resources typically exist in specialized pockets scattered across the firm. As distributed knowledge systems, firms’ capacity to manage their knowledge resources is linked with their ability to integrate these pockets of specialized knowledge. Firms are increasingly depending on teams to strategically consolidate their dispersed knowledge into productive outcomes. Teams integrate knowledge from external sources with internal knowledge such as skills, know-how, and expertise of their members to create project outcomes. The aim of this research in progress will be to examine how knowledge integration in software teams is influenced by various team, project, and IT-related issues.
Application of Image Processing and Artificial Neural Networks to Identify Ripeness and Maturity of the Lime (citrus medica)

The object of this study is to identify maturity and ripeness of lime (citrus medica) using Image Processing and Artificial Neural Network. Image processing method was developed and applied to samples of lime from three levels of maturity and ripeness based on their harvest time. The area, shape factor, energy and color indexes were extracted from image processing development system. The feature extracted from image processing were used as artificial neural network inputs and trained by using back propagation methods with 4 model neural network and used momentum constant’s value 0.8, learning rate constant value 0.8 with 1 until 2000 iteration.The result showed that the use of 4 back propagations neural network models with 3, 6, 9 and 12 hidden layers provided the 100% accurateness in classifying the lime based on their maturity and ripeness.
Microfluidic Biosensor Array with Integrated Poly(2,7-Carbazole)/Fullerene-Based Photodiodes for Rapid Multiplexed Detection of Pathogens

A multiplexed microfluidic biosensor made of poly(methylmethacrylate) (PMMA) was integrated into an array of organic blend heterojunction photodiodes (OPDs) for chemiluminescent detection of pathogens. Waterborne Escherichia coli O157:H7, Campylobacter jejuni and adenovirus were targeted in the PMMA chip, and detection of captured pathogens was conducted by poly(2,7-carbazole)/fullerene OPDs which showed a responsivity over 0.20 A/W at 425 nm. The limits of chemiluminescent detection were 5 × 105 cells/mL for E. coli, 1 × 105 cells/mL for C. jejuni, and 1 × 10−8 mg/mL for adenovirus. Parallel analysis for all three analytes in less than 35 min was demonstrated. Further recovery tests illustrated the potential of the integrated biosensor for detecting bacteria in real water samples.
The Open University ’ s repository of research publications and other research outputs Alleviating data sparsity for Twitter sentiment analysis Conference Item

Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifiers from tweets data often faces the data sparsity problem partly due to the large variety of short and irregular forms introduced to tweets because of the 140-character limit. In this work we propose using two different sets of features to alleviate the data sparseness problem. One is the semantic feature set where we extract semantically hidden concepts from tweets and then incorporate them into classifier training through interpolation. Another is the sentiment-topic feature set where we extract latent topics and the associated topic sentiment from tweets, then augment the original feature space with these sentiment-topics. Experimental results on the Stanford Twitter Sentiment Dataset show that both feature sets outperform the baseline model using unigrams only. Moreover, using semantic features rivals the previously reported best result. Using sentimenttopic features achieves 86.3% sentiment classification accuracy, which outperforms existing approaches.
Detecting Design Errors in Composite Events for Event Triggered Real-Time Systems Using Timed Automata

Many applications need to detect and respond to occurring events and combine these event occurrences into new events with a higher level of abstraction. Specifying how events can be combined is often supported by design tools specific to the current event processing engine. However, the issue of ensuring that the combinations of events provide the system with the correct combination of information is often left to the developer to analyze. We argue that analyzing correctness of event composition is a complex task that needs tool support. In this paper we present a novel development tool for specifying composition of events with time constraints. One key feature of our tool is to automatically transform composite events for real-time systems into a timed automaton representation. The timed automaton representation allow us to check for design errors, for example, whether the outcome of combining events with different operators in different consumption policies is consistent with the requirement specification
Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate.

Abstract. We analysed 1040 individual trees, located in 62 plots across the Amazon Basin for leaf mass per unit area (MA), foliar carbon isotopic composition (δ13C) and leaf level concentrations of C, N, P, Ca, Mg, K and Al. All trees were identified to the species level with the dataset containing 58 families, 236 genera and 508 species, distributed across a wide range of soil types and precipitation regimes. Some foliar characteristics such as MA, [C], [N] and [Mg] emerge as highly constrained by the taxonomic affiliation of tree species, but with others such as [P], [K], [Ca] and δ13C also strongly influenced by site growing conditions. By removing the environmental contribution to trait variation, we find that intrinsic values of most trait pairs coordinate, although different species (characterised by different trait suites) are found at discrete locations along a common axis of coordination. Species that tend to occupy higher fertility soils are characterised by a lower MA and have a higher intrinsic [N], [P], [K], [Mg] and δ13C than their lower fertility counterparts. Despite this consistency, different scaling patterns were observed between low and high fertility sites. Inter-relationships are thus substantially modified by growth environment. Analysing the environmental component of trait variation, we found soil fertility to be the most important predictor, influencing all leaf nutrient concentrations and δ13C and reducing MA. Mean annual temperature was negatively associated with leaf level [N], [P] and [K] concentrations. Total annual precipitation positively influences MA, [C] and δ13C, but with a negative impact on [Mg]. These results provide a first basis for understanding the relationship between the physiological functioning and distribution of tree species across Amazonia.
On the Role of the Galactic Magnetic Halo in Ultra-High-Energy Cosmic-Ray Propagation

The study of the propagation of ultra-high-energy cosmic rays (UHECRs) is a key step to unveiling the secret of their origin. Up to now only the influence of the galactic and extragalactic magnetic fields was considered. In this article we focus our analysis on the influence of the magnetic field of the galaxies standing between possible UHECR sources and us. Our main approach is to start from the well-known galaxy distribution up to 120 Mpc. We use the most complete galaxy catalog: the LEDA catalog. Inside a sphere of 120 Mpc, we extract 60,130 galaxies with known positions. In our simulations we assign a halo dipole magnetic field (HDMF) to each galaxy. The code developed is able to retro-propagate a charged particle from the arrival points of UHECR data across our galaxy sample. We present simulations in the case of the Virgo Cluster and show that there is a nonnegligible deviation in the case of protons of 7 × 1019 eV, even if the B value is conservative. Then special attention is devoted to the AGASA triplet, where we find that NGC 3998 and NGC 3992 could be possible source candidates.