Miracle-dz/UniFAR-FTUs huggingface dataset:
Trend of production and export of commercial crops of Vanuatu: 1991-2012

Agriculture is the mainstay of the rural population of Vanuatu. Obviously, improvements in crop production and exports will enhance standard of living of rural people and help support economic development. In Vanuatu beef, copra, cocoa and kava account for the bulk of export earnings. This study analysed trends in area, production and export of coconut, cocoa and kava crops during the 1991-2012 period. The study revealed that area under cocoa and kava crops has been declining during the study period, while the area under coconut crop was stagnant. The production of cocoa and kava showed negative trends. Copra production also showed a declining trend during the study period. Consequently, exports of cocoa and coconut products were declining during the reference period. However, exports of kava showed increasing trend but it also in the later part of the study period started declining. Evidently there is a positive relation between quantity of crop produced and exported. Vanuatu’s climate and land are conducive to produce these export-oriented crops, but detailed investigations are required to understand the problems and constraints faced by farmers in crop production, processing and export. In reversing the downward trend of agriculture export sector in Vanuatu, the integration of production and marketing will prove to be a crucial element of the agricultural development strategy.
Fuzzy soft subspace clustering method for gene co-expression network analysis

Clustering techniques for building gene co-expression networks suffer greatly from biological complexities. This paper proposes a fuzzy soft subspace clustering method for detecting overlapped clusters of locally co-expressed genes that may participate in multiple cellular processes and take on different biological functions. Process-specific feature subspaces of clusters and interrelations among different clusters can be extracted by this method, providing useful clues for gene co-expression network analysis. Experiments on yeast cell cycle data have shown that this method is effective in extracting biological relationships between functional gene clusters, and enhancing gene co-expression network analysis.
Ultra Fast Optical Sectioning: Signal preserving filtering and surface reconstruction

In 3D surface scanning it is desirable to filter away bad data without altering the quality of the remaining good data. Filtering of raw scanner data before surface reconstruction can minimize the induced error and improve on the probability of reconstructing the true surface. If outliers consist of actual data such as hair, and not just evenly distributed noise, these outliers tend to err smoothing algorithms away from the wanted result. We present a novel algorithm based on a Markov Random Field that uses a distance constraint to robustly classify a 3D scan volume. Through this classification a signal preserving filtering of the data set is done. The remaining data are used for a smooth surface reconstruction creating very plausible surfaces. The data used in our work comes from a newly developed hand held 3D scanner. The scanner is an Ultra Fast Optical Sectioning scanner, which is able to extract high quality 3D surface points from 2D images recorded at over 3000 fps. The scanner has been developed for digital impression taking in the dental area. Our work relates to future in-ear scanning for fitting custom hearing aids without impression taking. -
Consensus Building for Environmental Issues: Marine Protected Areas as a Case Study

This paper describes the key components of a consensus-building activity on environmental issues; while we present one example, we describe how similar activities can be built around other environmental issues. The activity’s primary goals are for students to build critical thinking skills solving real-world problems, and to appreciate the complexity of creating environmental policy given the often-difficult communication between scientists, policymakers, resource managers, and the general public. Our case study concerns the creation of marine protected areas (MPAs), in which students negotiate the terms of use of an MPA while assuming the identities of individuals with interests in the use and/or protection of the MPA.
A comparison of multiple classifier combinations using different voting-weights for remote sensing image classification

ABSTRACT Remote sensing image classification is a common application of remote sensing images. In order to improve the performance of Remote sensing image classification, multiple classifier combinations are used to classify the Landsat-8 Operational Land Imager (Landsat-8 OLI) images. Some techniques and classifier combination algorithms are investigated. The classifier ensemble consisting of five member classifiers is constructed. The results of every member classifier are evaluated. The voting strategy is experimented to combine the classification results of the member classifier. The results show that all the classifiers have different performances and the multiple classifier combination provides better performance than a single classifier, and achieves higher overall accuracy of classification. The experiment shows that the multiple classifier combination using producer’s accuracy as voting-weight (MCCmod2 and MCCmod3) present higher classification accuracy than the algorithm using overall accuracy as voting-weight (MCCmod1). And the multiple classifier combinations using different voting-weights affected the classification result in different land-cover types. The multiple classifier combination algorithm presented in this article using voting-weight based on the accuracy of multiple classifier may have stability problems, which need to be addressed in future studies.
3D Games for Tuning and Learning about Hearing Aids

H earing aid technology has dramatically advanced in the last 25 years since the commercialization of the first digital hearing aid. The majority of individuals with digital hearing aids, however, use these devices as if they are a standard analogue hearing aid, (i.e., only for their amplification and equalisation features). New algorithms are under-used or not exploited to their full potential (Figure 1). This could be because of inefficient training. Traditional gaming technologies have been successfully employed in non-leisure scenarios for learning and skill acquisition, empowerment, and social inclusion (McMorris. Eur J Phys Edu 1998;3[1]:65-74; Johnson. “Serious games for language learning: How much game, how much AI? ” Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology 2005:306-313). Studies have shown games have benefits for enabling product familiarization specifically for older users (Darzentas. “Mastering Technology for Greater Autonomy: Device Familiarization for Older Users via Games.” 6th European Conference on Games Based Learning 2012:131). These authors describe how a game-based approach can be applied to the process of device familiarization to provide motivation and reduce anxiety and uncertainty associated with the use of technology. Dryburgh notes technology use is self-taught by either watching others, asking advice from others, trial and error, or reading the manual (Dryburgh. Can Soc Trends 2002:20-24). 3D Tune-In (3DTI) is an EU Horizon 2020 funded project (2015-2018) that brings together the relevant stakeholders from traditional gaming industries, academic institutions, a large hearing aid manufacturer, and hearing communities to produce digital games in the field of hearing aid technologies and hearing loss in children and older adults. Five games aimed at children or adults will provide accurate 3D sound simulations to demonstrate and provide training on the different features of digital hearing aids in everyday contexts.
STRING-ing together protein complexes: extracting physical protein interactions from the literature

Understanding biological processes relies heavily on curated knowledge of physical interactions between proteins. Yet, a notable gap remains between the information stored in databases of curated knowledge and the plethora of interactions documented in the scientific literature. To bridge this gap, we introduce ComplexTome, a corpus designed to facilitate the development of text-mining methods for the extraction of complex formation relationships among biomedical entities. This novel corpus comprises 1,287 documents with ∼ 3 , 500 relationships. We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score=82.8%). We additionally enhance the model’s capabilities through unsupervised trigger word detection and apply it to extract relations and trigger words for these relations from all open publications in the domain literature. This information has been fully integrated in the latest version of the STRING database, and all introduced resources are openly accessible via Zenodo and GitHub.
Manufacturing supply chain applications 3: multi-agent simulation of purchasing activities in organizations

In this paper we present a Multi-Agent simulation model to investigate purchasing activities in an organizational environment. Starting point is the observation that the majority of purchasing activities in organizations is usually performed without any involvement of the organization's purchasing department. The purpose of the experiments is to investigate if and how certain factors determine the degree to which Purchasing professionals become involved in the purchasing of Non-Product Related (NPR) items and services. Among the factors investigated are: corporate purchasing policies, available information, and the nature of the various purchasing activities. Preliminary results show that the behavior of the multi-agent simulation model is an acceptable representation of reality. Furthermore, the results show the limits of a Purchasing department's added value and the important role of organizational learning in that respect. The paper provides directions for further research.
Paleofire activity in tropical America during the last 21 ka: A regional synthesis based on sedimentary charcoal

mitChell J. PoWer1, m.b. bush2, h. behling3, s.P. horn4, F.e. mayle5 and d.h. urrego2 Utah Museum of Natural History, Department of Geography, University of Utah, USA; mitchell.power@geog.utah. edu Department of Biological Sciences, Florida Institute of Technology, USA; Department of Palynology and Climate Dynamics, University of Göttingen, Germany; Department of Geography, University of Tennessee, USA; School of Geosciences, University of Edinburgh, UK
Enhanced Coinduction

GSOS Recall from Section 3.5.2 that an abstract GSOS specification is a natural transformation of the form ρ : Σ(B×Id)⇒ BΣ∗, where Σ∗ is the free monad for Σ: A → A (the (−)∗ notation is used both to denote reindexing functors of morphisms in A and to denote free monads of endofunctors, but the distinction should be clear). Any such specification induces a distributive law ρ† : Σ∗(B × Id)⇒ (B × Id)Σ∗. To prove compatibility of the contextual closure for bialgebras for a distributive law ρ† generated from an abstract GSOS specification, one could exhibit a natural transformation ρ† : Σ∗(B × Id) ⇒ (B × Id)Σ∗ above ρ† directly, and then apply Theorem 5.2. 7. We next show how to simplify such a task by proving that, under mild additional conditions, it suffices to show that there exists ρ : Σ(B×Id)⇒ B Σ∗ above ρ. The lifting of Σ∗ here is induced by the given lifting of Σ; the functor Id lifts the identity (it does not need to be the identity itself), and will be subject to a condition involving Σ. The construction of ρ† from ρ is similar to the construction of ρ† from ρ. In order to show that it is a lifting, we need some properties relating algebras in the total category E to those in the base category A. Lemma 5.2. 8. Consider a lifting Σ of an A-endofunctor Σ and assume Σ has free algebras. 1. The functor p : E → A has a right adjoint 1 : A → E , and this adjunction lifts 104 Chapter 5. Coinduction up-to
Comparative study between radiofrequency ablation and Comparative study between radiofrequency ablation and endovenous laser ablation in the management of primary great endovenous laser ablation in the management of primary great saphenous varicose veins saphenous varicose veins

Background : Varicose veins are a common presentation of chronic venous disease. Multiple risk factors have been speci ﬁ ed by epidemiological studies such as female sex, pregnancy, obesity, and prior deep venous thrombosis. Objective : This is a prospective, comparative study to compare radiofrequency (RF) ablation versus endovenous laser ablation (EVLA) in the management of primary varicose veins of great saphenous vein (GSV). Patients and methods : This study was conducted to 130 patients who had primary great saphenous varicose veins who attended to the vascular department of Ain Shams University Hospitals and El-Sahel Teaching Hospital. The study is a prospective comparative clinical trial. Results : This study compares RF and EVLA in the management of primary GSV varicose veins. Age, sex, BMI, and comorbidities are of no clinical signi ﬁ cance in this study. GSV diameter is a signi ﬁ cant factor that is important in follow-up to detect the successfulness of the procedure and risk of recurrence. Three diameters of GSV were recorded preoperatively and compared at 1 week, 3 months, and 6 months postoperatively. Classi ﬁ cation of varicose veins through the Clinical, Etiological, Anatomical, and Pathophysiological characters of varicose veins (CEAP) and venous clinical severity score (VCSS) were used for clinical assessment of the patients. Most of the cases presented with CEAP clas-si ﬁ cation C2, VCSS score 9. Clinical improvement can be detected by the change of CEAP to C1 or C0, and the VCSS from score 9 to 2 in both study groups.
Retrieving Reservoir-only Reflection and Transmission Responses from Target-enclosing Extended Images

The Marchenko redatuming approach reconstructs wavefields at depth that contain not only primary reflections, but also multiply-scattered waves. While such fields in principle contain additional subsurface information, conventional imaging approaches cannot tap into the information encoded in internal multiples in a trivial manner. We discuss a new approach that uses the full information contained in Marchenko-redatumed fields, whose output are local reflection and transmission responses that fully enclose a target volume at depth, without contributions from over- or under-burden structures. To obtain the Target-Enclosing Extended Images (TEEIs) we solve a multi-dimensional deconvolution (MDD) problem that can be severely ill-posed, so we offer stable estimates to the MDD problem that rely on the physics of the Marchenko scheme. We validate our method on ocean-bottom field data from the North Sea. In our field data example, we show that the TEEIs can be used for reservoir-targeted imaging using reflection and, for the first time, local transmission responses, shown to be the direct by-product of using internal multiples in the redatuming scheme. Finally, we present local, TEEI-derived reflection and transmission images of the target volume at depth that are structurally consistent with a benchmark image from conventional migration of surface data.
A SITE-BASED CONSERVATION APPROACH TO PROMOTE THE RECOVERY OF BANGGAI CARDINALFISH (Pterapogon kauderni) ENDEMIC POPULATIONS

The endemic Banggai cardinalfish (Pterapogon kauderni) is an Indonesian conservation priority with Endangered species. The goal of this research was to develop a site-based conservation concept appropriate from a bio-ecological viewpoint, based on the unusual characteristics of this species, in particular: (i) mouthbrooder with direct development, leading to reproductively isolated stocks and fine scale genetic structure; (ii) high level of reliance on habitat, in particular symbiosis with benthic animals providing protective micro-habitat. Methods used include review and analysis of published literature and unpublished data, including an analysis using the Marxan spatial planning software. We suggest several policy options and identify research needs, including: (i) base P. kauderni conservation (protection, rehabilitation and sustainable use) on stocks as the basic management unit; (ii) use data on P. kauderni genetic stocks in the zonation of the proposed Banggai Archipelago marine protected area (MPA); (iii) undertake further research to identify stocks/stock boundaries; (iv) apply the "BCF gardens" concept to fine-scale rebuilding of P. kauderni populations and enabling sustainable use through micro-habitat rehabilitation, with a community-based approach supported by a multi-phase scientific research program. The outputs from this study should support efforts towards sustainable management of the Banggai cardinalfish, particularly in the context of strategies to develop and manage an effective sub-national MPA.
Satellite Image Classification using Neural Network

The data from remote sensing have been used from so many years for image classification and its development algorithm, which can be applied to several different fields like forestry, educational purpose, management etc. In this paper, a classification method of a high resolution satellite image using neural network is proposed. First noisy bands were removed using dimensionality reduction technique. Minimum noise fraction (MNF) reduces the spatial dimension of hyperspectral image (HSI). Then, learning vector quantization (LVQ) based algorithm and some samples from groundtruth map are used to train the network for image classification and finally, accuracy is estimated. The main goal of this paper is to determine the ability of artificial neural network system for classifying satellite image by algorithm based on LVQ. General Terms Classification of hyperspectral image, neural network
TOPOLOGY INFERENCE IN EVOLUTIONARY GAMES ON GRAPHS

We consider the task of estimating weighted adjacency matrices in evolutionary games on graphs. In evolutionary games on graphs, nodes play games with their neighbors and update their strategies based on the outcomes of these games. Existing link prediction methods often have limitations. We propose a correlation matrix designed to estimate the weighted adjacency matrix using convolutional neural networks (CNNs). Our numerical experiments illustrate that this method can predict weighted adjacency matrices with high precision. Finally, we extend the correlation matrix to correlation tensor and apply to hypergraphs.
ARE TAX INCENTIVES STIMULATING SMEs’ COMPETITIVENESS IN NIGERIA? A CRITICAL EXAMINATION

A major objective of tax incentives is to stimulate the competitiveness of small and medium-scale enterprises (SMEs) to contribute meaningfully to economic development. However, it appears this objective has not been achieved as many SMEs in Nigeria struggle for their survival. Are the tax incentives given by the government actually meeting the objective? This study therefore investigated the extent of utilisation of various tax incentives by SMEs in Nigeria and examined the impact of these incentives on the competitiveness of SMEs. Data were collected through the administration of structured questionnaire to purposively selected respondents who are knowledgeable in tax incentives. Data collected were subjected to psychometric tests, and were analysed using Pearson correlation analysis and OLS multiple linear regression technique. The results showed that SMEs in Nigeria enjoy tax incentives such as capital allowance, reinvestment allowance, investment tax credit, reduced company income tax, tax holiday, and free trade zones and export incentives to a moderate extent. The study also revealed that tax incentives have fairly stimulated the competitiveness of SMEs in the areas of employment creation, investment opportunities, and production capability amongst others.
Efficiently Robustify Pre-Trained Models —Supplementary Material—

This supplementary contains more experiments involving ablations or analysis on various datasets, to further provide better understanding of our method as well as its advantages. We provide analysis of various ablations in our method in Sec. 1. Following this we provide further analysis of knowledge transfer for a given student w.r. t. various teachers in Sec. 2. We also show more results of our approach against the baselines, under transfer learning setup, in Sec. 3 along with extending it to other multi-modalities as well in Sec. 4. Finally, we further analyze our scheme and baselines on ImageNet-P dataset in Sec. 5 followed by amount of parameters we tune for different students (Sec. 6) and implementation details (Sec. 7) for reproducibility.
Exact algorithms for treewidth and minimum fill-in ∗ †

We show that there are O(1.8899) time algorithms to compute the treewidth and the minimum fill-in of each graph G on n vertices. Our result is based on a combinatorial proof that each graph on n vertices has at most n · 1.7087 minimal separators and that all potential maximal cliques can be listed in O(1.8899) time. For the class of AT-free graphs we obtain O(1.4142) time algorithms to compute treewidth and minimum fill-in.
e-Publications@Marquette e-Publications@Marquette

Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector.