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While digital infrastructure is clearly a critical factor in addressing the digital divide for rural society, it is only one component in realising the benefits of information and communication technology (ICT). It is increasingly acknowledged that citizens, governments, and businesses need to develop skills and motivations to use technologies. It is also recognised that young people and their rural communities are among those who gain the least from opportunities to engage in and benefit from an ever-evolving digital society. As with other areas of rural development, local community institutions and actors assume their own leadership in developing initiatives to overcome challenges and advance digital literacy and in this regard, public libraries have led and continue to hold considerable potential to champion this area. This article reports on the experiences of a 14-month community-based collaborative research project with public libraries engaged in a process of developing coding clubs for children and youth in rural Manitoba, Canada. Our research sets out to answer the questions: first, whether it is viable for public libraries to cultivate advanced digital skills among rural youth and contribute to bridging the rural-urban digital divide by running coding clubs following the CoderDojo model? And second, what are the critical conditions to ensure the success of public library coding clubs? In examining some of the experiences encountered in adopting the coding club as a model of digital literacy building, we discuss wider themes for rural public libraries interested in advancing digital literacy building within their communities.

The prognosis for pancreatic ductal adenocarcinoma (PDAC) patients has not significantly improved in the past 3 decades, highlighting the need for more effective treatment approaches. Poor patient outcomes and lack of response to therapy can be attributed, in part, to the dense, fibrotic nature of PDAC tumours, which impedes the uptake of systemically administered drugs. Wet-spun alginate fibres loaded with the chemotherapeutic agent gemcitabine have been developed as a potential tool for overcoming the physical and biological barriers presented by the PDAC tumour microenvironment and deliver high concentrations of drug to the tumour directly over an extended period of time. While exciting, the practicality, safety, and effectiveness of these devices in a clinical setting requires further investigation. Furthermore, an in-depth assessment of the drug-release rate from these devices needs to be undertaken to determine whether an optimal release profile exists. Using a hybrid computational model (agent-based model and partial differential equation system), we developed a simulation of pancreatic tumour growth and response to treatment with gemcitabine loaded alginate fibres. The model was calibrated using in vitro and in vivo data and simulated using a finite volume method discretization. We then used the model to compare different intratumoural implantation protocols and gemcitabine-release rates. In our model, the primary driver of pancreatic tumour growth was the rate of tumour cell division and degree of extracellular matrix deposition. We were able to demonstrate that intratumoural placement of gemcitabine loaded fibres was more effective than peritumoural placement. Additionally, we found that an exponential gemcitabine release rate would improve the tumour response to fibres placed peritumourally. Altogether, the model developed here is a tool that can be used to investigate other drug delivery devices to improve the arsenal of treatments available for PDAC and other difficult-to-treat cancers in the future.Author SummaryPancreatic cancer has a dismal prognosis with a median survival of 3-5 months for untreated disease. The treatment of pancreatic cancer is challenging due to the dense nature of pancreatic tumours which impedes retention of drug at the tumour site. As such, systemic administration of chemotherapies, such as gemcitabine, has a limited efficacy. To overcome this, sustained-release devices have been proposed. These devices are injected locally and release drug slowly over time, providing a concentrated local, sustained drug concentration. To investigate the possible efficacy of these devices, we developed a mathematical model that would allow us to probe treatment perturbations in silico. We modelled the individual cancer cells and their growth and death from gemcitabine loaded into the sustained delivery devices. Our platform allows future investigations for these devices to be run in silico so that we may better understand the forms of the drug release-profile that are necessary for optimal treatment.

We find that individuals are confused by the titles used by investment professionals and cannot distinguish between investment advisers and brokers. This confusion persists even among those with in...

Similarity-based search of sequence collections is a core task in bioinformatics, one dominated for most of the genomic era by exact and heuristic alignment-based algorithms. However, even efficient heuristics such as BLAST may not scale to the data sets now emerging, motivating a range of alignment-free alternatives exploiting the underlying lexical structure of each sequence.In this paper, we introduce SuperVec, a novel supervised approach to learning sequence embeddings. Our method extends earlier Representation Learning (RL) based methods to include jointly contextual and class-related information for each sequence during training. This ensures that related sequence fragments have proximal representations in the target space, better reflecting the structure of the domain.Such representations may be used for downstream machine learning tasks or employed directly. Here, we apply SuperVec embeddings to a sequence retrieval task, where the goal is to retrieve sequences with the same family label as a given query. The SuperVec approach is extended further through H-SuperVec, a tree-based hierarchical method which learns embeddings across a range of feature spaces based on the class labels and their exclusive and exhaustive subsets.Experiments show that supervised learning of embeddings based on sequence labels using SuperVec and H-SuperVec provides a substantial improvement in retrieval performance over existing (unsupervised) RL-based approaches. Further, the new methods are an order of magnitude faster than BLAST for the database retrieval task, supporting hybrid approaches in which SuperVec rapidly filters the collection so that only potentially relevant records remain, allowing slower, more accurate methods to be executed quickly over a far smaller dataset. Thus, we may achieve faster query processing and higher precision than before.Finally, for some problems, direct use of embeddings is already sufficient to yield high levels of precision and recall. Extending this work to encompass weaker homology is the subject of ongoing research.

Background Sequencing highly-variable 16S regions is a common and often effective approach to the study of microbial communities, and next-generation sequencing (NGS) technologies provide abundant quantities of data for analysis. However, the speed of existing analysis pipelines may limit our ability to work with these quantities of data. Furthermore, the limited coverage of existing 16S databases may hamper our ability to characterise these communities, particularly in the context of complex or poorly studied environments. Results In this article we present the SigClust algorithm, a novel clustering method involving the transformation of sequence reads into binary signatures. When compared to other published methods, SigClust yields superior cluster coherence and separation of metagenomic read data, while operating within substantially reduced timeframes. We demonstrate its utility on published Illumina datasets and on a large collection of labelled wound reads sourced from patients in a wound clinic. The temporal analysis is based on tracking the dominant clusters of wound samples over time. The analysis can identify markers of both healing and non-healing wounds in response to treatment. Prominent clusters are found, corresponding to bacterial species known to be associated with unfavourable healing outcomes, including a number of strains of Staphylococcus aureus. Conclusions SigClust identifies clusters rapidly and supports an improved understanding of the wound microbiome without reliance on a reference database. The results indicate a promising use for a SigClust-based pipeline in wound analysis and prediction, and a possible novel method for wound management and treatment.

Microservice migration is a promising technique to incrementally modernize monolithic legacy enterprise applications and enable them to exploit the benefits of cloud-computing environments. This article elaborates on three challenges of microservice migration: multitenancy, statefulness, and data consistency. The authors show how to identify each of these challenges in legacy code and explain refactoring and architectural pattern-based migration techniques relevant to microservice architectures. They explain how multitenancy enables microservices to be utilized by different organizations with distinctive requirements, why statefulness affects both the availability and reliability of a microservice system, and why data consistency challenges are encountered when migrating legacy code that operates on a centralized data repository to microservices operating on decentralized data repositories. They also explain the interdependencies between multitenancy, statefulness, and data consistency.

All-to-all comparison problems represent a class of big data processing problems widely found in many application domains. To achieve high performance for distributed computing of such problems, storage usage, data locality and load balancing should be considered during the data distribution phase in the distributed environment. Existing data distribution strategies, such as the Hadoop one, are designed for problems with MapReduce pattern and do not consider comparison tasks at all. As a result, a huge amount of data must be re-arranged at runtime when the comparison tasks are executed, degrading the overall computing performance significantly. Addressing this problem, a scalable and efficient data distribution strategy is presented in this paper with comparison tasks in mind for distributed computing of all-to-all comparison problems. Specifically designed for problems with all-to-all comparison pattern, it not only saves storage space and data distribution time but also achieves load balancing and good data locality for all comparison tasks of the all-to-all comparison problems. Experiments are conducted to demonstrate the presented approaches. It is shown that about 90% of the ideal performance capacity of the multiple machines can be achieved through using the approach presented in this paper.

This paper presents a change detection system using Planet’s mosaic dataset. This dataset has higher resolution but fewer bands than data captured from Landsat or Sentinel satellites. Here, an object-based random forest regressor is used to detect vegetation change. The mosaic was separated into individual images, which were then ranked in a list. Results indicate adequate performance with a tradeoff in precision and recall.

Solving large-scale all-to-all comparison problems using distributed computing is increasingly significant for various applications. Previous efforts to implement distributed all-to-all comparison frameworks have treated the two phases of data distribution and comparison task scheduling separately. This leads to high storage demands as well as poor data locality for the comparison tasks, thus creating a need to redistribute the data at runtime. Furthermore, most previous methods have been developed for homogeneous computing environments, so their overall performance is degraded even further when they are used in heterogeneous distributed systems. To tackle these challenges, this paper presents a data-aware task scheduling approach for solving all-to-all comparison problems in heterogeneous distributed systems. The approach formulates the requirements for data distribution and comparison task scheduling simultaneously as a constrained optimization problem. Then, metaheuristic data pre-scheduling and dynamic task scheduling strategies are developed along with an algorithmic implementation to solve the problem. The approach provides perfect data locality for all comparison tasks, avoiding rearrangement of data at runtime. It achieves load balancing among heterogeneous computing nodes, thus enhancing the overall computation time. It also reduces data storage requirements across the network. The effectiveness of the approach is demonstrated through experimental studies. Abstraction of all-to-all comparison computing pattern with big data sets.Formulation of all-to-all comparisons in distributed systems as a constrained optimization.Data-aware task scheduling approach for solving the all-to-all comparison problem.Metaheuristic data pre-scheduling and dynamic task scheduling in the approach.

MPSoCs with hierarchical communication infrastructures are promising architectures for low power embedded systems. Multiple CPU clusters are coupled using an Network-on-Chip (NoC). Our CoreVA-MPSoC targets streaming applications in embedded systems, like signal and video processing. In this work we introduce a tightly coupled shared data memory to each CPU cluster, which can be accessed by all CPUs of a cluster and the NoC with low latency. The main focus is the comparison of different memory architectures and their connection to the NoC. We analyze memory architectures with local data memory only, shared data memory only, and a hybrid architecture integrating both. Implementation results are presented for a 28 nm FD-SOI standard cell technology. A CPU cluster with shared memory shows similar area requirements compared to the local memory architecture. We use post place and route simulations for precise analysis of energy consumption on both cluster and NoC level using the different memory architectures. An architecture with shared data memory shows best performance results in combination with a high resource efficiency. On average, the use of shared memory shows a 17.2 percent higher throughput for a benchmark suite of 10 applications compared to the use of local memory only.

Chronic wounds present a significant risk to the patient and a substantial drain on health budgets, with the problem likely to worsen markedly with increased incidence of type II diabetes. The wound fluid microbiome is known to influence wound healing outcomes, but is poorly characterised. Next Generation Sequencing approaches yield abundant data from wound samples, but progress in understanding these microbial communities may be hampered by the speed of existing analysis pipelines and limitations on coverage by 16S databases. This paper presents SigClust, a novel clustering method based on binary signatures derived from sequence reads. SigClust yields superior cluster coherence and separation of metagenomic read data in timeframes substantially reduced from those of alternative methods. We demonstrate its utility in the wound context on a preliminary set of labelled patient data. We show how a time course analysis based on tracking the dominant clusters over successive wound samples can identify markers of both successful wound healing and wounds refractory to treatment. Clusters prominent in these analyses are found to correspond to bacterial species known to be implicated as a determinant of wound outcomes, notably a number of strains of Staphylococcus aureus. The clusters obtained rapidly via SigClust support improved understanding of the wound microbiome without direct reliance on a reference database, offering the promise of a SigClust-based pipeline for wound analysis and prediction, and potentially novel methods for wound treatment and management.

We examine market value implications of managing liquidity via supplier financing. Results suggest a direct link between shareholder wealth and use of trade credit, and the relation exhibits significant cross-sectional variation. In particular, the market value of trade credit varies with the liquidity of goods sold and competition in product markets. Evidence also indicates the value-supplier financing association strengthens with financial constraint, which supports the financing motive for trade credit. Further findings are consistent with the transaction cost motive. Overall, we conclude that shareholders value the strategic benefits associated with supplier financing and that downstream firms’ characteristics influence this value.

We examine the determinants and value effects of corporate lobbying, controlling for corporate political action committee (PAC) campaign contributions. We find evidence that firms with greater potential payoffs from favorable policy and regulations lobby most actively, and that managers often utilize both lobbying and campaign contribution channels to influence the political climate affecting the firm. We also find that shareholders value the lobbying activities pursued by management on their behalf, particularly if the firm does not have a PAC that contributed to an election campaign. The results are robust to a number of tests designed to mitigate potential omitted-variable and self-selection bias. [ABSTRACT FROM AUTHOR]

This study examines the determinants of direct costs for real estate investment trust (REIT) seasoned equity offerings. These costs are not related to information asymmetries, unlike non- REIT firms. Gross spreads vary inversely with stock liquidity, price, and industry activity. Concerning REIT-specific heterogeneity, gross spreads are generally insensitive to property type and operating partnership structure. Still, the findings suggest managers can influence costs as higher fees are directly related to the use of underwriting syndicates and more reputable investment banks. Finally, a test for differences in direct costs across REIT and comparable industrials shows significantly lower direct issuance costs for REITs.

Parallel programming and effective partitioning of applications for embedded many-core architectures requires optimization algorithms. However, these algorithms have to quickly evaluate thousands of different partitions. We present a fast performance estimator embedded in a parallelizing compiler for streaming applications. The estimator combines a single execution-based simulation and an analytic approach. Experimental results demonstrate that the estimator has a mean error of 2.6% and computes its estimation 2848 times faster compared to a cycle accurate simulator.

We examine shareholder wealth implications of supplying financing to customers. Robust results suggest that excess returns and changes in trade receivables are directly and significantly related. Further evidence indicates the value of receivables is higher for suppliers with stronger motives relating to operating and contracting costs. The results also suggest a discounted value of receivables for financially unconstrained firms. Overall, we conclude that investors recognize trade credit as an effective instrument in mitigating frictions hindering sales growth. Thus, certain suppliers are positioned to derive increased strategic benefits from credit policy.

Net operating working capital captures multiple dimensions of firms’ adjustments to operating and financial conditions. Sales growth, uncertainty of sales, costly external financing, and financial distress encourage firms to pursue more aggressive working capital strategies. Firms with greater internal financing capacity and superior capital market access employ more conservative working capital policies. Results are robust to unobserved heterogeneity and industry effects. The evidence suggests that operating and financing conditions should be considered when evaluating working capital behavior, not just industry averages. Additionally, industry concentration magnifies the effect of sales growth.

Network Interfaces (NIs) are used in Multiprocessor System-on-Chips (MPSoCs) to connect CPUs to a packet switched Network-on-Chip. In this work we introduce a new NI architecture for our hierarchical CoreVA-MPSoC. The CoreVA-MPSoC targets streaming applications in embedded systems. The main contribution of this paper is a system-level analysis of different NI configurations, considering both software and hardware costs for NoC communication. Different configurations of the NI are compared using a benchmark suite of 10 streaming applications. The best performing NI configuration shows an average speedup of 20 for a CoreVA-MPSoC with 32 CPUs compared to a single CPU. Furthermore, we present physical implementation results using a 28 nm FD-SOI standard cell technology. A hierarchical MPSoC with 8 CPU clusters and 4 CPUs in each cluster running at 800 MHz requires an area of 4.56 mm².

Distributed systems are widely used for solving large-scale and data-intensive computing problems, including all-to-all comparison (ATAC) problems. However, when used for ATAC problems, existing computational frameworks such as Hadoop focus on load balancing for allocating comparison tasks, without careful consideration of data distribution and storage usage. While Hadoop-based solutions provide users with simplicity of implementation, their inherent MapReduce computing pattern does not match the ATAC pattern. This leads to load imbalances and poor data locality when Hadoop’s data distribution strategy is used for ATAC problems. Here we present a data distribution strategy which considers data locality, load balancing and storage savings for ATAC computing problems in homogeneous distributed systems. A simulated annealing algorithm is developed for data distribution and task scheduling. Experimental results show a significant performance improvement for our approach over Hadoop-based solutions.

Membrane filters are often used in the semiconductor industry to remove small particles from liquids. Filters have recently been developed that use adsorption as the dominant particle capture mechanism by creating a surface that is electrostatically neutral in the fluid of interest. Consequently, these filters provide high permeability as compared to traditional filters with equivalent particle retention because they do not rely solely on particle sieving. This paper describes efforts to elucidate the mechanistic relationship between the relevant membrane, particle and fluid properties that control retention in an adsorptive membrane. Experiments and modeling were focused on aqueous streams of varying pH and ionic strength. A network model was developed to describe the fluid (and particle) flow within the membrane on a microscopic level. This network model was combined with a DLVO-based particle adhesion model to form a complete representation of a membrane filtration process. Comparing this retention model with experimental results demonstrates the utility of this tool in predicting the effect of particle size, pore size and ionic strength on particle retention.

Distributed computation and storage have been widely used for processing of big data sets. For many big data problems, with the size of data growing rapidly, the distribution of computing tasks and related data can affect the performance of the computing system greatly. In this paper, a distributed computing framework is presented for high performance computing of All-to-All Comparison Problems. A data distribution strategy is embedded in the framework for reduced storage space and balanced computing load. Experiments are conducted to demonstrate the effectiveness of the developed approach. They have shown that about 88% of the ideal performance capacity can be achieved in multiple machines through using the approach presented in this paper.

The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.

Integer tuple relations can concisely summarize many types of information gathered from analysis of scientific codes. For example, they can be used to precisely describe which iterations of a statement are data dependent of which other iterations. It is generally not possible to represent these tuple relations by enumerating the related pairs of tuples. For example, it is impossible to enumerate the related pairs of tuples in relation {[i] ? [i + 2]|l ? i ? n ? 2}. Even when it is possible to enumerate the related pairs of tuples, such as for the relation {[i, j] ? [i?, j?]|l ? i, j, i?, j? ? 100}, it is often not practical to do so. We instead use a closed form description by specifying a predicate consisting of affine constraints on the related pairs of tuples. As we just saw, these affine constraints can be parameterized, so what we are really describing are infinite families of relations (or graphs). Many of our applications of tuple relations rely heavily on an operation called transitive closure. Computing the transitive closure of these "infinite graphs" is very different from the traditional problem of computing the transitive closure of a graph whose edges can be enumerated. For example, the transitive closure of the first relation above is the relation {[i] ? [i?]|?β s.t. i? ? i = 2β ? l ? i ? i? ? n}. As we will prove, transitive closure is not comutable in the general case. We have developed algorithms that produce exact results in most commonly occuring cases and produce upper of lower bounds (as necessary) in the other cases. This paper will describe ou algorithms for computing transitive closure and some of its applications such as determining which inter-processor synchronizations are redundant.

Using citation data collected from quantitative business-oriented journals, we addressed two separate issues in this study. First, inferences were drawn about the distribution of citation vintages of published research based upon parameters of the generalized gamma distribution. The exponential distribution, which is commonly used in this type of research, is a member of this family of distribution, and we argue that it is less well suited for this application than other members of the gamma family. Understanding the distributional properties of citation vintages and frequencies can improve our grasp of the process of acquiring new knowledge via research and simultaneously give us a means to evaluate this

We present a unified framework for applying iteration reordering transformations. This framework is able to represent traditional transformations such as loop interchange, loop skewing and loop distribution as well as compositions of these transformations. Using a unified framework rather than a sequence of ad-hoc transformations makes it easier to analyze and predict the effects of these transformations. Our framework is based on the idea that all reordering transformations can be represented as a mapping from the original iteration space to a new iteration space. An optimizing compiler would use our framework by finding a mapping that both corresponds to a legal transformation and produces efficient code. We present the mapping selection problem as a search problem by decomposing it into a sequence of smaller choices. We then characterize the set of all legal mappings by defining a search tree. As part of this process we use a new operation called affine closure.

With the emergence of multi-cores into the mainstream, there is a growing need for systems to allow programmers and automated systems to reason about data dependencies and inherent parallelism in imperative object-oriented languages. In this paper we exploit the structure of object-oriented programs to abstract computational side-effects. We capture and validate these effects using a static type system. We use these as the basis of sufficient conditions for several different data and task parallelism patterns. We compliment our static type system with a lightweight runtime system to allow for parallelization in the presence of complex data flows. We have a functioning compiler and worked examples to demonstrate the practicality of our solution.

The idea of stealing cycles has been hyped for some years, boasting unlimited potential by tapping the computational power of millions of under utilized PCs connected to the Internet. Despite a few spectacular success stories (e.g. SETI@HOME), cycle-stealing is today not a widely used technology. We believe two principal impediments need to be overcome. The first is ease of development and use. Most of the problems faced in developing cycle stealing applications are not specific to those applications, so generic cycle stealing frameworks such as our G2 framework can play a vital role in this regard. The second is uncertainty. Potential developers don't know whether if they went to the effort of developing a parallel application for a cycle stealing environment, it would pay off, i.e. whether they would get a reasonable speedup. To minimize this risk, we propose the development and use of detailed performance models

There has been a great amount of recent work toward unifying iteration reordering transformations. Many of these approaches represent transformations as affine mappings from the original iteration space to a new iteration space. These approaches show a great deal of promise, but they all rely on the ability to generate code that iterates over the points in these new iteration spaces in the appropriate order. This problem has been fairly well-studied in the case where all statements use the same mapping. We have developed an algorithm for the less well-studied case where each statement uses a potentially different mapping. Unlike many other approaches, our algorithm can also generate code from mappings corresponding to loop blocking. We address the important trade-off between reducing control overhead and duplicating code. >

We present a framework for unifying iteration reordering transformations such as loop interchange, loop distribution, skewing, tiling, index set splitting and statement reordering. The framework is based on the idea that a transformation can be represented as a mapping from the original iteration space to a new iteration space. The framework is designed to provide a uniform way to represent and reason about transformations. We also provide algorithms to test the legality of mappings, and to generate optimized code for mappings. >