Miracle-dz/UniFAR-FTUs huggingface dataset:
Rasch measurement model: a review of Bayesian estimation for estimating the person and item parameters

This paper focuses on the methods used for estimating the parameters in Rasch Measurement Model (RMM). These include the MLE and Bayesian Estimation (BE) techniques. The accuracy and precision of the parameter estimates based on these two MLE and BE were discussed and compared. A questionnaire is a well-known measurement instrument used by most of the researchers. It is a powerful tool for collecting data in survey research. It should be noted that the quality of a measurement instrument used plays a key role in ensuring the quality of data obtained in the survey. Therefore, it has become essential for the researchers to carefully design their questionnaire so that the quality of the data obtained can be preserved. Then, it is also vital for the researchers to assess the quality of the data obtained before it can be successfully used for further analysis. Review of the literature shows that RMM is a psychometric approach widely used as an assessment tool of many measurement instruments developed in various fields of study. At present, the Maximum Likelihood Estimation (MLE) techniques were used to estimate the parameters in the RMM. In order to obtain more precise and accurate parameter estimates, a certain number of sample size and normality assumption are usually required. However, in a small sample, MLE could produce bias, imprecise and less accurate estimates with bigger standard error. A proper selection of the parameter estimation techniques to deal with small sample and non-normality of data is required to obtain more precise and accurate parameter estimates. From the review, it reveals that BE has successfully dealt with the issues of small sample and non-normality of the data. It produced a more accurate parameter estimate with smallest Mean Squared Error (MSE), particularly in a small sample compared to MLE.
Modelling the sugarcane value chain: an interactive decision-support tool

Sugarcane is a high-volume, low-value crop that is highly susceptible to damage during harvest and deterioration after harvest. Included crop residues negatively impact on milling performance. Mechanical harvesting and transport costs in Australia can approach 20% of total product value, and avoidable losses in the form of reduced cane quality and reduced yields can demonstrably exceed direct harvesting costs. Managing harvesting costs by increasing equipment output then typically exacerbates the potential for loss of value. Harvest Best Practice emerged as a concept to reduce harvesting losses by limiting harvesting speed and extractor system settings. This increases harvesting costs, however, the net impact on the value chain is highly variable and difficult to quantify. Significant previous work in Australia and internationally has generated datasets that correlate aspects of harvester operation with losses, cane quality and ultimately sugar recovery, but limited work has been undertaken to understand the complexity of the interactions among multiple factors in the value chain. A spreadsheet model was developed that has been used in the Australian industry and internationally. The Sugarcane Harvesting and Logistics Optimisation Tool (SCHLOT) is a web-based multi-parametric mathematical tool developed from the model. Input on physical and agronomic properties of the crop, field parameters and harvester configuration and operational parameters allows probable cane yield, composition and quality, and, from that, final sugar, molasses and bagasse production to be derived. Cost data are also derived from the relevant inputs and known relationships. The paper briefly describes the methodologies by which the model determines the impact of harvesting, transport and milling on both subsequent operations and direct costs and allows users to balance direct costs of an operational strategy against loss of value. This allows harvesting strategies to be optimised on a field-by-field, or even time-of-day basis.
Sequence analyses at mitochondrial and nuclear loci reveal a novel Theileria sp. and aid in the phylogenetic resolution of piroplasms from Australian marsupials and ticks

The order Piroplasmida encompasses two main families: Babesiidae and Theileriidae, containing tick-borne pathogens of veterinary and medical importance worldwide. While only three genera (Babesia, Cytauxzoon and Theileria) comprising piroplasm parasites are currently recognised, phylogenetic studies at the 18S rRNA (18S) gene suggest that these organisms represent at least ten lineages, one of which comprises the relatively unique and highly diverse Theileria spp. from Australian marsupials and ticks. As an alternative to analysing 18S sequences alone, sequencing of mitochondrial genes has proven to be useful for the elucidation of evolutionary relationships amongst some groups of piroplasms. This research aimed to characterise piroplasms from Australian native mammals and ticks using multiple genetic markers (18S, cytochrome c, oxidase subunit III (cox3) and cytochrome B (cytB)) and microscopy. For this, nearly complete piroplasm-18S sequences were obtained from 32 animals belonging to six marsupial species: eastern bettong (Bettongia gaimardi), eastern quoll (Dasyurus viverrinus), eastern grey kangaroo (Macropus giganteus), swamp wallaby (Wallabia bicolor), quokka (Setonix brachyurus) and Gilbert’s potoroo (Potorous gilbertii). The organisms detected represented eight novel Theileria genotypes, which formed five sub-clades within the main marsupial clade containing previously reported Australian marsupial and tick-derived Theileria spp. A selection of both novel and previously described Australian piroplasms at the 18S were also successfully characterised, for the first time, at the cox3 and cytB loci, and corroborated the position of Australian native theilerias in a separate, well-supported clade. Analyses of the cox3 and cytB genes also aided in the taxonomic resolution within the clade of Australian Piroplasmida. Importantly, microscopy and molecular analysis at multiple loci led to the discovery of a unique piroplasm species that clustered with the Australian marsupial theilerias, for which we propose the name Theileria lupei n. sp.
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization

In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc. Many convolutional neural networks (CNN) are designed for tackling this task. However, currently released datasets are so small-scale that they can not meet the needs of the supervised CNN-based algorithms. To remedy this problem, we construct a large-scale congested crowd counting and localization dataset, NWPU-Crowd, consisting of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range (<inline-formula><tex-math notation="LaTeX">$0\! \sim \! 20,033$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>0</mml:mn><mml:mspace width="-0.166667em"/><mml:mo>∼</mml:mo><mml:mspace width="-0.166667em"/><mml:mn>20</mml:mn><mml:mo>,</mml:mo><mml:mn>033</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-3013269.gif"/></alternatives></inline-formula>). Besides, a benchmark website is developed for impartially evaluating the different methods, which allows researchers to submit the results of the test set. Based on the proposed dataset, we further describe the data characteristics, evaluate the performance of some mainstream state-of-the-art (SOTA) methods, and analyze the new problems that arise on the new data. What’s more, the benchmark is deployed at <uri>https://www.crowdbenchmark. com/</uri>, and the dataset/code/models/results are available at <uri>https://gjy3035.github. io/NWPU-Crowd-Sample-Code/</uri>.
Pre-Defined Sparsity for Low-Complexity Convolutional Neural Networks

The high energy cost of processing deep convolutional neural networks impedes their ubiquitous deployment in energy-constrained platforms such as embedded systems and IoT devices. This article introduces convolutional layers with pre-defined sparse 2D kernels that have support sets that repeat periodically within and across filters. Due to the efficient storage of our periodic sparse kernels, the parameter savings can translate into considerable improvements in energy efficiency due to reduced DRAM accesses, thus promising significant improvements in the trade-off between energy consumption and accuracy for both training and inference. To evaluate this approach, we performed experiments with two widely accepted datasets, CIFAR-10 and Tiny ImageNet in sparse variants of the ResNet18 and VGG16 architectures. Compared to baseline models, our proposed sparse variants require up to <inline-formula><tex-math notation="LaTeX">$\mathord {\sim }82\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>82</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="kundu-ieq1-2972520.gif"/></alternatives></inline-formula> fewer model parameters with <inline-formula><tex-math notation="LaTeX">$5.6\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>5</mml:mn><mml:mo>. </mml:mo><mml:mn>6</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="kundu-ieq2-2972520.gif"/></alternatives></inline-formula> fewer FLOPs with negligible loss in accuracy for ResNet18 on CIFAR-10. For VGG16 trained on Tiny ImageNet, our approach requires <inline-formula><tex-math notation="LaTeX">$5.8 \times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>5</mml:mn><mml:mo>. </mml:mo><mml:mn>8</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="kundu-ieq3-2972520.gif"/></alternatives></inline-formula> fewer FLOPs and up to <inline-formula><tex-math notation="LaTeX">$\mathord {\sim }83.3\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>83</mml:mn><mml:mo>. </mml:mo><mml:mn>3</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="kundu-ieq4-2972520.gif"/></alternatives></inline-formula> fewer model parameters with a drop in top-5 (top-1) accuracy of only 1.2% (<inline-formula><tex-math notation="LaTeX">$\mathord {\sim }2.1\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>2</mml:mn><mml:mo>.</mml:mo><mml:mn>1</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="kundu-ieq5-2972520.gif"/></alternatives></inline-formula>). We also compared the performance of our proposed architectures with that of ShuffleNet and MobileNetV2. Using similar hyperparameters and FLOPs, our ResNet18 variants yield an average accuracy improvement of <inline-formula><tex-math notation="LaTeX">$\mathord {\sim }2.8\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>2</mml:mn><mml:mo>. </mml:mo><mml:mn>8</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="kundu-ieq6-2972520.gif"/></alternatives></inline-formula>.
Automated lumbar vertebral level identification using ultrasound

Spinal needle procedures require identification of the vertebral level for effectiveness and safety. E.g. in obstetric epidurals, the preferred target is between the third and fourth lumbar vertebra. The current clinical standard involves “blind” identification of the level through manual palpation, which only has a 30% reported accuracy. Therefore, there is a need for better anatomical identification prior to needle insertion. Ultrasound provides anatomical information to physicians, which is not obtainable via manual palpation. However, due to artifacts and the complex anatomy of the spine, ultrasound is not commonly used for pre-puncture planning. This thesis describes two machine learning based systems that can aid physicians to utilize ultrasound for lumbar level identification. The first system, LIT, is proposed to identify vertebrae, assigning them to their respective levels and tracking them in a sequence of ultrasound images in the paramedian plane. A deep sparse auto-encoder network learns to extract anatomical features from pre-processed ultrasound images. A feasibility study (n=15) evaluated performance. The second system, SLIDE, identifies vertebral levels from a sequence of ultrasound images in the transverse plane. The system uses a deep convolutional neural network (CNN) to classify transverse planes of the lower spine. In conjunction, a novel state-machine is developed to automatically identify vertebral levels as the transducer moves. A feasibility study (n=20) evaluated performance. The CNN achieves 88% accuracy in discriminating images from three planes of the spine. As a system, SLIDE successfully identifies all lumbar levels in 17 of 20 test scans, processed at real-time speed. A clinical study with 76 parturient patients was performed. The study com-
Nouragues: dynamics and plant-animal interactions in a neotropical rainforest.

Editors Preface. Part I: Introduction. 1. The field station P.Charles-Dominique. 2. Geography and climate M. Grimaldi, B. Riera. 3. Scales of ambient light variation F. Bongers, et al. Part II: Plant Communities. 4. The lowland high rainforest: structure and tree species diversity O. Poncy, et al. 5. The low forest (Nouragues Inselberg) D. Larpin. 6. Plant communities on a granitic outcrop C. Sarthou. 7. Palaeoclimates and their consequences on forest composition P. Charles-Dominique, et al. Part III: Vertebrate Communities and Evolutionary Ecology. 8. Diet and population densities of the primate community in relation to fruit supplies B. Simmen, et al. 9. Comparative positional behaviour of five primates D. Youlatos, J.-P. Gasc. 10. The bat community A. Brosset, et al. 11. The marsupial community M. -L. Guillemin, et al. 12. The avian community: an overview of species composition and guild structure J.-M. Thiollay, et al. 13. The adaptive significance of flocking in tropical understorey forest birds: the field evidence M. Julien, J.-M. Thiollay. 14. Habitat selection, ambient light and colour patterns in some lek-displaying birds M. Thery, J.A. Endler. 15. Distribution and life histories of amphibians and reptiles M. Born, P. Gaucher. 16. Fishes of the Arataye river and their space-time organization F.J. Meunier, T. Boujard. Part IV: Frugivory and Seed Dispersal.17. Relationships between seed dispersal and behavioural ecology P. Charles-Dominique. 18. Frugivory and seed dispersal by three neotropical primates: impact on plant regeneration C. Julliot, et al. 19. Frugivory and seed dispersal by bats P. Charles-Dominique, A. Cockle. 20. Frugivory and seed dispersal by Kinkajous D. Julien-Laferriere. 21. Frugivory and seed dispersal by terrestrial mammals F. Feer, et al. 22. Vegetarian species in the bird community with an emphasis on frugivory and seed dispersal C. Erard, M. Thery. Part V: Forest Dynamics and Recruitment. 23. Tree-falls and canopy gaps: patterns of natural disturbance P.J. van der Meer, F. Bongers. 24. The dispersal and recruitment of Cyclanthaceae and Philodendron (Araceae) understorey root-climbing vines A. Cockle. 25. Post-dispersal seed removal in four frugivore-dispersed tree species P.-M. Forget, et al. 26. Scatterhoarding and tree regeneration P.A. Jansen, P.-M. Forget. 27. Effects of tree height and light availability on plant traits at different organisation levels F.J. Sterck, et al. Appendices. References. Index.
Fungal biofilm architecture produces hypoxic microenvironments that drive antifungal resistance

Significance Failure of antimicrobial therapies to clear an infection is in part due to a biofilm mode of bacterial and fungal growth. Understanding how microbial biofilms mediate antimicrobial treatment failure is critical for improving infection outcomes. The mechanisms by which filamentous fungal biofilms mediate drug resistance are particularly ill-defined. We observe that Aspergillus fumigatus biofilms develop hypoxic microenvironments during maturation that facilitate fungal survival in the face of antifungal treatments. Oxygenation of A. fumigatus biofilms, however, reduces fungal survival. These results extend the role of dynamic biofilm oxygen gradients found in bacterial pathogens to the fungal kingdom and provide insights into how to overcome fungal drug resistance through manipulation of biofilm oxygen availability and consumption. Human fungal infections may fail to respond to contemporary antifungal therapies in vivo despite in vitro fungal isolate drug susceptibility. Such a discrepancy between in vitro antimicrobial susceptibility and in vivo treatment outcomes is partially explained by microbes adopting a drug-resistant biofilm mode of growth during infection. The filamentous fungal pathogen Aspergillus fumigatus forms biofilms in vivo, and during biofilm growth it has reduced susceptibility to all three classes of contemporary antifungal drugs. Specific features of filamentous fungal biofilms that drive antifungal drug resistance remain largely unknown. In this study, we applied a fluorescence microscopy approach coupled with transcriptional bioreporters to define spatial and temporal oxygen gradients and single-cell metabolic activity within A. fumigatus biofilms. Oxygen gradients inevitably arise during A. fumigatus biofilm maturation and are both critical for, and the result of, A. fumigatus late-stage biofilm architecture. We observe that these self-induced hypoxic microenvironments not only contribute to filamentous fungal biofilm maturation but also drive resistance to antifungal treatment. Decreasing oxygen levels toward the base of A. fumigatus biofilms increases antifungal drug resistance. Our results define a previously unknown mechanistic link between filamentous fungal biofilm physiology and contemporary antifungal drug resistance. Moreover, we demonstrate that drug resistance mediated by dynamic oxygen gradients, found in many bacterial biofilms, also extends to the fungal kingdom. The conservation of hypoxic drug-resistant niches in bacterial and fungal biofilms is thus a promising target for improving antimicrobial therapy efficacy.
A systematic literature review of automatic Alzheimer’s disease detection from speech and language

Abstract Objective In recent years numerous studies have achieved promising results in Alzheimer’s Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. Materials and Methods We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? Results and Discussion We identified 33 eligible studies and 5 main findings: participants’ demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups. Conclusion The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.
Micro-Economic Benefits of Peer-Producing Containerized Network Functions

Discrete, non-virtualized network elements are characterized by large costs, limited functions, vendor lock-in, and limited orchestration. Virtualization technologies like virtual machines (VM) and containers have expanded the scope of virtual resource utilization through consolidation of workloads that were previously running on multiple servers by running them on a single server. With the advent of Network Functions Virtualization (NFV), industries are able to reduce the micro-economic factors associated with vendor proprietary model such as transaction costs and (physical and human) asset specificity to deal with vendor vulnerabilities in contractual relationships because Virtual Network Functions (VNFs) can virtualize dedicated networking functions that were traditionally performed by vendor appliances such as routers, switches, firewalls, and load balancers. Even though virtualization technologies (VMs and containers) and NFV have demonstrated their benefits in the market, little attention has been devoted to the development and adoption of containers to build VNFs. This research paper identifies micro-economic factors, such as transaction costs, associated with searching, buying, provisioning, and maintenance of vendor proprietary appliances and compares them with the coordination costs associated with the adoption of containerized VNFs. This comparative analysis could be used to identify the type of network operators that could serve as key organizers (the network operator who can benefit largely by adopting containerized VNFs) of an open source peer production model as well as other firms that could serve as individual contributors. Furthermore, to identify various rewards and incentives that a managerial firm can leverage to motivate its employees to participate in such an effort, a quantitative survey was conducted (with Tier 1, Tier 2 and Tier 3 Service Providers) to identify the managerial incentives such as bonuses, rewards, peer recognition, and promotion targeting varied network operator firms to accurately capture and analyze employee interests/motivation. Finally, the paper shows a measuring framework to evaluate individual contributors based on the project modularity and indicates the viability of this model.