Miracle-dz/UniFAR-FTUs huggingface dataset:
The Scaling of Abundance in Consumers and Their Resources: Implications for the Energy Equivalence Rule

The negative scaling of plant and animal abundance with body mass is one of the most fundamental relationships in ecology. However, theoretical approaches to explain this phenomenon make the unrealistic assumption that species share a homogeneous resource. Here we present a simple model linking mass and metabolism with density that includes the effects of consumer size on resource characteristics (particle size, density, and distribution). We predict patterns consistent with the energy equivalence rule (EER) under some scenarios. However, deviations from EER occur as a result of variation in resource distribution and productivity (e.g., due to the clumping of prey or variation in food particle size selection). We also predict that abundance scaling exponents change with the dimensionality of the foraging habitat. Our model predictions explain several inconsistencies in the observed scaling of vertebrate abundance among ecological and taxonomic groups and provide a broad framework for understanding variation in abundance.
The Architecture Of A Fault-tolerant Cached RAID Controller

RAID-5 arrays need 4 disk accesses to update a data block—2 to read old data and parity, and 2 to write new data and parity. Schemes previously proposed to improve the update performance of such arrays are the Log-Structured File System [10] and the Floating Parity Approach [6]. Here, we consider a third approach, called Fast Write, which eliminates disk time from the host response time to a write, by using a Non-Volatile Cache in the disk array controller. We examine three alternatives for handling Fast Writes and describe a hierarchy of destage algorithms with increasing robustness to failures. These destage algorithms are compared against those that would be used by a disk controller employing mirroring. We show that array controllers require considerably more (2 to 3 times more) bus bandwidth and memory bandwidth than do disk controllers that employ mirroring. So, array controllers that use parity are likely to be more expensive than controllers that do mirroring, though mirroring is more expensive when both controllers and disks are considered.
Benchmarking Classifier Performance with Sparse Measurements

WWW home page: http://relational.cvut. cz Abstract: The presented paper describes a methodology, how to perform benchmarking, when classifier perfor- mance measurements are sparse. The described methodol- ogy is based on missing value imputation and was demon- strated to work, even when 80% of measurements are missing, for example because of unavailable algorithm im- plementations or unavailable datasets. The methodology was then applied on 29 relational classifiers & proposi- tional tools and 15 datasets, making it the biggest meta- analysis in relational classification up to date.
Modulation of Protein Fermentation Does Not Affect Fecal Water Toxicity: A Randomized Cross-Over Study in Healthy Subjects

Objective Protein fermentation results in production of metabolites such as ammonia, amines and indolic, phenolic and sulfur-containing compounds. In vitro studies suggest that these metabolites might be toxic. However, human and animal studies do not consistently support these findings. We modified protein fermentation in healthy subjects to assess the effects on colonic metabolism and parameters of gut health, and to identify metabolites associated with toxicity. Design After a 2-week run-in period with normal protein intake (NP), 20 healthy subjects followed an isocaloric high protein (HP) and low protein (LP) diet for 2 weeks in a cross-over design. Protein fermentation was estimated from urinary p-cresol excretion. Fecal metabolite profiles were analyzed using GC-MS and compared using cluster analysis. DGGE was used to analyze microbiota composition. Fecal water genotoxicity and cytotoxicity were determined using the Comet assay and the WST-1-assay, respectively, and were related to the metabolite profiles. Results Dietary protein intake was significantly higher during the HP diet compared to the NP and LP diet. Urinary p-cresol excretion correlated positively with protein intake. Fecal water cytotoxicity correlated negatively with protein fermentation, while fecal water genotoxicity was not correlated with protein fermentation. Heptanal, 3-methyl-2-butanone, dimethyl disulfide and 2-propenyl ester of acetic acid are associated with genotoxicity and indole, 1-octanol, heptanal, 2,4-dithiapentane, allyl-isothiocyanate, 1-methyl-4-(1-methylethenyl)-benzene, propionic acid, octanoic acid, nonanoic acid and decanoic acid with cytotoxicity. Conclusion This study does not support a role of protein fermentation in gut toxicity. The identified metabolites can provide new insight into colonic health. Trial Registration ClinicalTrial.gov NCT01280513
Opportunities and challenges of the digital lifespan: views of service providers and citizens in the UK

ABSTRACT Information about UK citizens’ use of digital technologies is often expressed in statistics – x% lack Internet access; y% get online to engage in online banking, update social media sites, or participate in online auctions. There are many social implications to digital technology use, however – individuals may communicate online as a major way to stay in touch with friends and family, and as Internet access rises and government and public sector budgets shrink, online services become an increasingly attractive way for government and public sector service providers to communicate with citizens. This paper presents selected results of an exploratory study designed to investigate the digital personhood of UK citizens through interviews with participants at three life transitions: leaving secondary school, becoming a parent, and retiring from work. Digital personhood in this paper implies identity information online, and some interaction with others around that information. We then report on our presentation of a selection of these results to thirteen stakeholders who represented UK government departments, public sector organisations, and industry. We found that citizen and stakeholder concerns were quite different, especially at the new parent life transition, and that stakeholders tended to underestimate the willingness and ability of citizens to become involved online with the government and public sector, and overestimate citizens’ vulnerability online. Future research should investigate practical strategies for increasing communication between stakeholders and citizens, and also how to encourage stakeholders to work together to benefit their common clientele – the citizens.
Negative affectivity in females’ identification of their nonconsensual sexual experiences and sexual dissatisfaction

Very little research has examined the adjustment differences between those who identify their non-consensual sexual experiences (NSEs) as sexual assault and those who do not, despite both groups meeting the legal criteria for having experienced sexual assault. Identifying differences between NSE identifiers and non-identifiers may help to illuminate psychosexual factors in NSE construal, emotional reactivity, and resilience in sexual outcomes. This study examines the association of individuals’ NSE self-identification and negative affectivity with women’s sexual dissatisfaction in an undergraduate sample. Participants (N = 126) completed measures of negative affectivity five months before completing measures of sexual satisfaction and NSE history. Results indicated that negative affectivity has a robust independent association with NSE identification and sexual concerns for women who report NSEs, corresponding to legal definitions of sexual assault. In addition, no differences were found between identifiers and non-identifiers on sexual dissatisfaction, suggesting NSE history may have more to do with sexual satisfaction in these women than the construal of the event.
Patient Engagement in Cancer Survivorship Care through mHealth: A Consumer-centered Review of Existing Mobile Applications

With improvements in early detection and treatment, the number of cancer survivors has been on the rise. Studies suggest that cancer survivors do not often receive proper follow-up care despite existing guidelines. Patient engagement is key to healthy survivorship, and mHealth provides a viable platform to empower survivors with just- in-time personalized support. However, our understanding of existing mHealth solutions in cancer survivorship is limited. In this paper, we use Patient Engagement Framework to investigate existing apps to bridge this knowledge gap. App features are mapped to the framework components to determine the level of engagement facilitated. Ability to record treatment summaries has been found in five out of seven apps examined. While collaborative care and social engagement are found minimally, the majority of features (95%) are limited to information and way finding, e-tools, and interactive forms. Limitations of the existing apps and possible improvements to the framework are discussed.
Convergence of Modal Electromagnetic Fields in a B-spline Finite Element Method

Convergence of finite-element-method solutions for electric field distributions of resonant modes is discussed and analyzed in two canonical microwave cavity problems when B-splines are utilized for geometrical modeling of elements. While the analyzed problems, namely, those of a spherical cavity and a ridged cavity, respectively, are relatively simple, they still provide valuable benchmarks for novel numerical methods, allowing for early estimates of accuracy, efficiency, and convergence properties of the method. Furthermore, curved geometry (in the case of a spherical cavity) and geometry with reentrant corners (in the case of a ridged cavity) illustrate versatile and flexible uses of B-splines for geometrical modeling of solids.
Social Cognitive Career Theory in a Diverse World

We were delighted to receive an invitation from Bruce Walsh, editor of the Journal of Career Assessment, to assemble a special issue devoted to advances in social cognitive career theory (SCCT). This was an opportunity that was too good for us to pass up. The only question was, how should we organize the issue? We ultimately decided to emphasize SCCT’s relevance to the career development of a diverse range of persons (e.g., students of color; those facing economic challenges; lesbian, gay, bisexual, and transgender [LGBT] workers; international students and workers) and to issues touching on social justice (e.g., inclusion of underrepresented persons in science, technology, engineering, and mathematics [STEM] fields; understanding the career needs of the unemployed; management of multiple roles in the service of gender equity). Much of the earliest impetus for developing SCCT was to extend the study of career behavior to a broader range of persons and situations than those highlighted by earlier career theories. Indeed, career scholars had exhorted the field to move beyond its primary fixation with the career development of White, male, middle class, and relatively well-educated persons (e.g., see Fitzgerald & Betz, 1994). Vocational psychology inquiry had also often focused nearly exclusively on students and workers in the United States. Fortunately, the past 20 or so years have seen much progress in the diversification of career development theory and research, and the field seems poised to dramatically extend these advances, relying on a new generation of social justice-minded and internationally oriented theorists and researchers. Career self-efficacy theory (Hackett & Betz, 1981), the precursor of SCCT, emphasized the ways in which women’s career behavior can be limited (and also fostered) through social, familial, and educational influences that become internalized, in part, through perceptions of personal capabilities, or self-efficacy beliefs—a construct adapted from Bandura’s (1986) general social cognitive theory. Hackett and Betz’s seminal insights were later generalized to the experiences of other understudied groups, such as women of color (e.g., Hackett & Byars, 1996). SCCT built on this sturdy foundation by incorporating additional features of Bandura’s theory (e.g., outcome expectations, goals) and highlighting a range of ‘‘person input’’ (e.g., gender, race/ethnicity, ability, trait)
An Improved KNN Algorithm Based on Minority Class Distribution for Imbalanced Dataset

K-nearest neighbor (KNN) is a popular classification algorithm with good scalability, which has been widely used in many fields. When dealing with imbalanced data, minority examples are given the same weight as majority examples in the existing KNN algorithm. In this paper, we pay more attention to the minority class than the majority class, and we increase the weight of minority class according to the local characteristic of minority class distribution. In addition, we compare the proposed algorithm with the existing Weighted Distance K-nearest neighbor (WDKNN). Experimental results show that our algorithm performs better than WDKNN in imbalanced data sets.
Circuit Modeling of Ultra Wideband (UWB) Microstrip Patch Antenna Bending Effect

This study describes the bending effect on an Ultra Wideband (UWB) microstrip patch antenna on the antenna performance in terms of impedance bandwidth and radiation pattern. Comparison is done between the UWB planar antenna and the bent UWB antenna. The bending effect causes shift in resonant frequencies with slight change in the impedance bandwidth. A circuit model comprising lumped elements is also developed to represent the antenna design to analyze the variation of the input matching of the antenna during bending. The finding shows that the UWB antenna bending is suitable for UWB application even though slight degradation is observed compared to the planar UWB antenna. The simulated results are discussed in detail for the planar UWB antenna and the bending effect on the antenna performance as according to UWB characteristics.
Color Image Quantization Techniques Based on Image Decomposition for Memory Consumption in Cloud Environment

Cloud computing becomes a hot research topic in the recent years. In the cloud computing, software applications and databases are moved to the centralized large data centres, which is called cloud. In the cloud, due to lack of physical possession of the data and the machine, the video and audio data’s may possess more memory space. Existing work on cloud mainly focuses on cloud storage without taking memory consumption into consideration. In this paper, we propose a new Colour Quantisation (CQ) technique is introduced. The CQ technique is based on video/image split into sub-images and the use of self-organised neural network classifiers (SONNC). Initially, the dominant colours of each sub-image are extracted through SONNCs and then are used for the quantisation of the colours of the entire image scheme to consume memory in cloud computing based on decomposition technique. We also discuss how to optimize memory for cloud users to minimize the utility cost. Detailed analysis and simulations have demonstrated the effectiveness and efficiency of the proposed scheme. Applying a systematic design methodology to the developed CQ algorithm, an efficient cloud architecture achieving high-speed processing and less memory consumption, is derived.
A Complicated Task Solution Scheme Based on Node Cooperation for Wireless Sensor Networks

Traditional task solution schemes in Wireless Sensor Networks (WSNs) are mainly not suitable for complicated task processing due to high energy consumption and long processing delay. In this paper, we proposed an energy efficient Complicated Task Solution scheme for real-time task processing based on node Cooperation (CTSC), which consists of two main phases: task grouping and task allocation. In the task grouping phase, complicated tasks are divided into different groups based on task graph. In the task allocation phase, based on node cooperation, different group tasks are allocated to different nodes by using bid invitation. Thus, multiple tasks can be processed in parallel, which ultimately reduces task processing delay and limits communication overheads. Simulation results show that CTSC is much more suitable for large scale WSNs. In addition, CTSC outperforms related works in terms of shorter response time of task processing and less energy consumption.
Saving the whales with image processing

At the moment, only 500 North Atlantic right whales are left in the world. To ensure the survival of this endangered species, marine biologists are tracking all of them to know their status and health at all times. However, manual recognition is tricky and very few researchers can perform it on the fly [1]. In this project, we implement a whale recognition system that allows researchers to reliably identify the individual whales from aerial photographs. This project is intended to be an entry to the Kaggle right whale recognition competition for which data set of labeled aerial photographs of North Atlantic right whales is provided. Throughout this paper, we will propose a whale recognition system based on two main steps: head detection and recognition. As part of this pipeline, a novel image segmentationbased filtering method is applied. We evaluate our system and compare it to the top submissions on Kaggle discussing further improvements towards the end.
QoS provisioning in mobile video services with satellite sources

In this paper, we propose a real-time transcoding system to generate contents for mobile networks with QoS provisioning. Video transcoding is widely used to change storage formats. However, as far as the authors know, it has not been proposed to generate real-time contents. Although the philosophy in this paper can be extended to alternative video sources (all major open TV channels are broadcasted via terrestrial radio, cable and satellite), we have chosen satellite TV because of its ubiquitous access. We present an indoor video service that delivers transcoded contents to wireless Pocket PCs via IEEE 802.11b multicast. We have focused on IEEE 802.11b because it has been the first legal sub-standard in the European Union. Due to EU regulations, IEEE 802.11a was not immediately adopted and this situation influenced the development of the new sub-standard IEEE 802.11h, a variation of IEEE 802.11a [3]. However, the extension of this work to new sub-standards like IEEE 802.11g is straightforward.
Preserving Local Structure in Gaussian Process Latent Variable Models

The Gaussian Process Latent Variable Model (GPLVM) is a non-linear variant of probabilistic Principal Components Analysis (PCA). The main advantage of the GPLVM over probabilistic PCA is that it can model non-linear transformations from the latent space to the data space. An important disadvantage of the GPLVM is its focus on preserving global data structure in the latent space, whereas preserving local data structure is generally considered to be more important in dimensionality reduction. In this paper, we present an extension of the GPLVM that encourages the preservation of local structure in the latent space. The extension entails the introduction of a prior distribution over the parameters of the GPLVM that measures the divergence between the pairwise distances in the data space and the latent space. We show that the proposed extension leads to strong results.
Histological Changes on Liver Glycogen Storage in Mice (Mus musculus) Caused by Unbalanced Diets

Weight-losing diets have appealed to people who want to lose weight in the short-term. They usually apply high-protein (HP) diets (like Atkin's, Stillman's, Scarsdale) which they practice for 2 weeks or so. Unfortunately, these people who have rapid weight loss return to their old habits and quickly regain the weight lost. We have shown in previous work that actually these weight losses have been associated with body fluids, protein and glycogen storage. In our study, we examined the effect of unbalanced diet–-related to an HP diet- on liver glycogen storage. For this study 40 Swiss albino mice consisting of two groups were used. The first group (HPSD) was fed with 25% HP for fifteen days and then were fed standard meals for the remaining 15 days; the other group was fed with standard meals throughout. The two groups were fed their respective diets for 30 days. At the end of 15th, 20th, 25th and 30th days 5 from each group were killed with cervical dislocation. The livers were removed perfused and then fixated. There were major differences in weight between the first and the fifteenth days. We detected remarkable increase in the weight gain of mice in the remaining 15 days. Glycogen storage was significantly reduced in HPSD (15) stained with PAS. In the others 20th, 25th and 30th days abnormally dense glycogen deposits were observed. Vacuoles in the hepatocyte cytoplasm, brownish deposits within hepatocytes, wide sinusoids, macrovesiculler steatosis structures and hydropic degeneration were observed in PAS and H&E stained HPSD group. As a result for the HPSD group a significant decrement in glycogen storage at the 15th day and also an accumulation of excessive amounts of glycogen deposits in mice liver was observed in the normal feeding phase.
Dynamic reduce task adjustment for hadoop workloads

In recent years, we observe an increasing demand for systems that are capable of efficiently managing and processing huge amounts of data. Apache's Hadoop, an open-source implementation of Google's MapReduce programming model, has emerged as one of the most popular systems for Big Data processing and is supported by major companies like Facebook, Yahoo! and Amazon. One of the most challenging aspects of executing a Hadoop job, is to configure appropriately the number of reduce tasks. The problem is exacerbated when multiple jobs are executing concurrently competing for the available system resources. Our approach consists of the following components: (i) an algorithm for computing the appropriate number of reduce tasks per job, (ii) the usage of profiler-jobs for gathering information necessary for the reduce task computation and (iii) two different policies for fragmenting the reduce tasks to the available system resources when multiple jobs execute concurrently in the cluster. Our detailed experimental evaluation using traffic monitoring Hadoop jobs on our local cluster, illustrates that our approach is practical and exhibits solid performance.
VIP: an FPGA-based processor for image processing and neural networks

The present in this paper the architecture and implementation of the Virtual Image Processor (VIP) which is an SIMD multiprocessor build with large FPGAs. The SIMD architecture, together with a 2D torus connection topology, is well suited for image processing, pattern recognition and neural network algorithms. The VIP board can be programmed on-line at the logic level, allowing optimal hardware dedication to any given algorithm.
Significance of Mobility on Received Signal Strength: An Experimental Investigation

In this paper, estimation of mobility using received signal strength is presented. In contrast to standard methods, speed can be inferred without the use of any additional hardware like accelerometer, gyroscope or position estimator. The strength of Wi-Fi signal is considered herein to compute the time-domain features such as mean, minimum, maximum, and autocorrelation. The experiments are carried out in different environments like academic area, residential area and in open space. The complexity of the algorithm in training and testing phase are quadratic and linear with the number of Wi-Fi samples respectively. The experimental results indicate that the average error in the estimated speed is 12 % when the maximum signal strength features are taken into account. The proposed method is cost-effective and having a low complexity with reasonable accuracy in a Wi-Fi or cellular environment. Additionally, the proposed method is scalable that is the performance is not affected in a multi-smartphones scenario.