Miracle-dz/UniFAR-FTUs huggingface dataset:
A land snail's view of a fragmented landscape.

Habitat fragmentation may influence the genetic structure of populations, especially of species with low mobility. So far, these effects have been mainly studied by surveying neutral markers, and much less by looking at ecologically relevant characters. Therefore, we aimed to explore eventual patterns of covariation between population structuring in neutral markers and variation in shell morphometrics in the forest-associated snail Discus rotundatus in relation to habitat fragment characteristics. To this end, we screened shell morphometric variability and sequence variation in a fragment of the mitochondrial 16S rDNA gene in D. rotundatus from the fragmented landscape of the Lower Rhine Embayment, Germany. The 16S rDNA of D. rotundatus was highly variable, with a total of 118 haplotypes (384 individuals) forming four clades and one unresolved group. There was a geographic pattern in the distribution of the clades with the river Rhine apparently separating two groups. Yet, at the geographic scale considered, there was no obvious effect of fragmentation on shell morphometrics and 16S rDNA variation because GST often was as high within, as between forests. Instead, the age of the habitat and (re-)afforestation events appeared to affect shell shape and 16S rDNA in terms of the number of clades per site. The ecologically relevant characters thus supported the presumably neutral mitochondrial DNA markers by indicating that populations of not strictly stenecious species may be (relatively) stable in fragments. However, afforestation after large clearcuts and habitat gain after the amendment of deforestation are accompanied by several, seemingly persistent peculiarities, such as altered genetic composition and shell characters (e.g. aperture size). © 2009 The Linnean Society of London, Biological Journal of the Linnean Society, 2009, 98, 839–850.
Yoga Pose Classification Using Deep Learning

Human pose estimation is a deep-rooted problem in computer vision that has exposed many challenges in the past. Analyzing human activities is beneficial in many fields like video- surveillance, biometrics, assisted living, at-home health monitoring etc. With our fast-paced lives these days, people usually prefer exercising at home but feel the need of an instructor to evaluate their exercise form. As these resources are not always available, human pose recognition can be used to build a self-instruction exercise system that allows people to learn and practice exercises correctly by themselves. This project lays the foundation for building such a system by discussing various machine learning and deep learning approaches to accurately classify yoga poses on prerecorded videos and also in real-time. The project also discusses various pose estimation and keypoint detection methods in detail and explains different deep learning models used for pose classification.
Understanding Farmer Needs and Unlocking Local Genetic Resources for Potato Improvement: A Case Study in Ethiopia

Understanding farmers’ needs and local genetic resources are crucial steps to improve and conserve the potato crop. A study was conducted to understand what potato traits Ethiopian farmers consider most important, and to characterize the diversity and distribution of local varieties. Growers from six districts were surveyed in 2012 and 2014. Based on the survey results, participatory variety selection (PVS) activities were conducted in two districts during two production seasons. Simultaneously, local varieties were collected from northwest and southern Ethiopia and characterized using molecular and morphological markers. Farmers identified 23 traits they considered important for variety selection, with the degree of importance for each trait varying across gender groups, agro-ecological zones, and growing seasons, as well as with extent of market access. The distribution of local varieties varied by agro-ecological zone, cropping system and proximity to markets. Our genetic fingerprinting and morphological characterization further revealed that, 34% of 44 local potato varieties collected are truly unique, the rest were duplicates, known by different names. These unique Ethiopian local varieties harbour considerable genetic variation, compared to the variation found in European and North American clones. Although local varieties may have lower yield than commercial varieties, they have other desirable attributes that make them well suited for alternative uses and different agro-ecologies.
CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users

A major drawback of cross-network recommender solutions is that they can only be applied to users that are overlapped across networks. Thus, the non-overlapped users, which form the majority of users are ignored. As a solution, we propose CnGAN, a novel multi-task learning based, encoder-GAN-recommender architecture. The proposed model synthetically generates source network user preferences for non-overlapped users by learning the mapping from target to source network preference manifolds. The resultant user preferences are used in a Siamese network based neural recommender architecture. Furthermore, we propose a novel user-based pairwise loss function for recommendations using implicit interactions to better guide the generation process in the multi-task learning environment. We illustrate our solution by generating user preferences on the Twitter source network for recommendations on the YouTube target network. Extensive experiments show that the generated preferences can be used to improve recommendations for non-overlapped users. The resultant recommendations achieve superior performance compared to the state-of-the-art cross-network recommender solutions in terms of accuracy, novelty and diversity.
An Investigation of AI enabled SoE Attacking Impact in Higher Learning Institute: Structural Equation Modeling (SEM) Approach

Theory of artificial enabled social engineering attacking risk factors are employed in this study to determine the impact that disturbed the personal productivity of higher learning institute of the user towards the AI enable SoE attacking. Five independent variable which are threat, vulnerability, valuation, countermeasure and personal disturbance factors using in this paper. Moreover using as an indicator in determining disturbance of personal productivity in an higher learning institute. Since multiple regression by using Structural Equation Modelling –Partial Least Square (SEM-PLS) is used to examine the collection of data by a questionnaire which is relevant with AI enable SE attacking risk. And the resulting point out three independent variable significantly influences the personal productivity in higher learning institute. As a matter of fact this study concludes that the foremost influence factor on disturbance of personal productivity in higher learning institute towards the AI enables SoE attacking risk factors such as threat, vulnerability, valuation and countermeasure. This study contributes to introductory study but vibrant understanding in stimulating the higher learning institute to become a worldwide institution. Journal of Applied & Computational Mathematics J o u r n a l o f A pp lie d & Computional M th e m a t i c s ISSN: 2168-9679
New Species of Day Gecko, Phelsuma Gray (Reptilia: Squamata: Gekkonidae), from the Réserve Naturelle Intégrale d'Andohahela, Southern Madagascar

Abstract Andohahela Réserve Naturelle Intégrale is located in southeastern Madagascar. It consists of three parcels, the largest of which (Parcel 1, 63,100 ha), lying on the eastern flank of the Anosyenne Mountains between 90 and 1972 m elevation, is dominated by rain forest. We surveyed the herpetofauna of Parcel 1 at the beginning of the warm, rainy season between 20 October and 5 December 1995. We recorded 45 amphibian and 32 reptile species in Parcel 1, including a new species of Phelsuma, which we describe in this paper. This new species, Phelsuma malamakibo, is a medium-sized, rock-dwelling species which occurs in primary rain forest at elevations between 810 m and 1940 m. It is identified by the presence of a distinct dark lateral stripe on the neck and body with a white lateral stripe beneath it and a second, thin dark lateral stripe below the white stripe; greenish dorsal ground color with reddish brown spots arranged into three longitudinal rows or lines on the neck and back; no sexual dichromatism; a median cleft dorsally on the rostral scale; nostrils centered above the first supralabial scale; subequal subcaudal scales; keeled gular and anterior chest scales; and smooth (unkeeled) scales on the ventral surfaces of the body and tail. In most respects, this new species is similar to Phelsuma lineata, P. pusilla, and P. quadriocellata; however, these species have keeled ventral scales. Phelsuma malamakibo is similar to P. modesta in having smooth ventral scales, but the latter species is sexually dichromatic and differs from P. malamakibo in details of coloration and nostril position, as well as habitat. Phelsuma modesta is largely arboricolous in variable, usually drier habitats, whereas P. malamakibo is saxicolous in rain forest.
Novel histone deacetylase (HDAC) inhibitors with improved selectivity for HDAC2 and 3 protect against neural cell death

Neurodegenerative disorders, such as motor neurone disease, Alzheimer’s disease and responses to brain traumas such as stroke, involve the unwanted death of neural cells. Although the exact underlying mechanisms leading to neural cell death are not well defined, one contributory event in many situations is the over-excitation of cells caused by too much of the neurotransmitter glutamate. Drugs that inhibit enzymes called histone deacetylases (HDACs) can protect neural cells from glutamate excitotoxicity. However, current inhibitors lack specificity and although they function in vitro, they have a substantial potential for adverse side effects in vivo. HDAC2 and 3 have been implicated in neurotoxicity and here we investigated the neuroprotective potential of three novel HDAC inhibitors that show selectivity for these. The ability of these HDAC inhibitors to protect against glutamate excitotoxicity was tested using cultured organotypic cerebral slices from 7-day-old (P7) Wistar rats. Glutamate excitotoxicity was induced by 200 µM of the glutamate transporter blocker, DL-threo-β-benzyloxyaspartate (DL-TBOA). This was applied alone and alongside 1 µM of the novel HDAC2 and 3 selective inhibitors AH51, AH61 and AH62. Neural cell viability in slices was quantified from assays using the fluorescent stains, 4′,6-diamidino-2-phenylindole and ethidium homodimer-1. The induction of glutamate excitotoxicity by DL-TBOA resulted in 41.3 ± 6.1% (n = 7, P < 0.01) loss in cell viability as judged by ethidium homodimer-1 staining. All three novel HDAC inhibitors significantly prevented neural cell death in response to DL-TBOA (P < 0.01), with cell viabilities of 107.5 ± 6.01% (n = 4), 97.1 ± 16.5% (n = 3) and 106.7 ± 6.45% (n = 4) for AH51, AH61 and AH62, respectively. This study has shown that inhibitors selective for HDAC2 and 3 can protect neural cells from death and thus have potential as therapeutic agents against neurotoxicity.
A New Approach for Text Documents Classification with Invasive Weed Optimization and Naive Bayes Classifier

With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features space in TDC. TDC includes different actions such as text processing, feature extraction, forming feature vectors, and final classification. In the presented model, the authors formed a feature vector for each document by means of weighting features use for IWO. Then, documents are trained with NB classifier; then using the test, similar documents are classified together. FS do increase accuracy and decrease the calculation time. IWO-NB was performed on the datasets Reuters-21578, WebKb, and Cade 12. In order to demonstrate the superiority of the proposed model in the FS, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been used as comparison models. Results show that in FS the proposed model has a higher accuracy than NB and other models. In addition, comparing the proposed model with and without FS suggests that error rate has decreased.
Analytic Hierarchy Process for Evaluation Weights on Occupational Safety and Hygiene Items in the Bridge Construction Site

This paper aims to evaluate key factors that associate Occupational Safety and Hygiene management during bridge constructions by the Analytic Hierarchy Process (AHP). Data were collected from experts’ questionnaires and conducted on the Expert Choice 2000 software. It finds that electrocution factor plays an important factor on this study rather than fall injury factor as one knows. The reason maybe cause by that electrocution events have less been reported by supervisor than fall injury events. This study found that the results from AHP analysis were partial consistent with traditional labor safety and hygiene assessment item that fall injury factor shows. Hence, it is ensured that AHP model has capacity to evaluate weights on occupational safety and hygiene management assessment items in bridge constructions and to provide more in-sighted objective (electrocution) on this topic.
Cascaded Multilevel Inverter Based on Quasi-Z-Source Converter: Analysis, Design and Study of Optimal Structures

In this paper, a new topology for cascaded multilevel inverter based on quasi-Zsource converter is proposed. In the proposed topology the magnitude of output DC voltage is not limited to the sum of magnitude of DC voltage sources. Moreover, the reliability of the circuit due to capability of short circuit by Z-source network is increased. The quasi-Zsource converter in different modes is analyzed and the voltage gain is obtained. Also, the values of quasi-Z-source network components are designed. In the proposed topology, the number of DC voltage sources, the number of switches, installation area and cost in comparison with conventional multilevel inverters are significantly reduced. Three algorithms to determine the magnitude of DC voltage sources are proposed. Then the optimal structures for the minimum number of switches and DC voltage sources to generate the maximum voltage levels are presented. Moreover, the control method for the proposed topology is described. To verify the performance of the proposed topology, simulation and experimental results of proposed topology are presented.
KNOWLEDGE-BASED FRAMEWORK FOR AUTOMATIC SEMANTISATION AND RECONSTRUCTION OF MILITARY ARCHITECTURE ON CITY-SCALE MODELS

Abstract. The scale models of fortified towns belonging to the Plans-Reliefs collection are exceptional witnesses of the formation of the French territory. The aim of the URBANIA project is the valorisation and the diffusion of this heritage through the creation of virtual models. The town scale model of Strasbourg at 1 : 600 currently exhibited in the Historical Museum of Strasbourg was selected as a case study. We develop and experiment an automatic procedure to identify and reconstruct military architecture works from point cloud digitisation of this fragile and bulky heritage. A priori knowledge formalized in a domain ontology informs the identification of the works – via geometrical feature comparison and consistency evaluation within the fortification system morphology – and their parametric 3D reconstruction refined by direct fit to the initial point cloud.
Accelerated Structure-Aware Sparse Bayesian Learning for Three-Dimensional Electrical Impedance Tomography

In this paper, we consider the reconstruction of three-dimensional (3-D) conductivity distribution using electrical impedance tomography (EIT) technique. A high-resolution and efficient algorithm is developed to solve the EIT inverse problem. The presented algorithm is extended upon a recently proposed novel EIT reconstruction approach based on structure-aware sparse Bayesian learning (SA-SBL). The correlation between proximal layers in the 3-D geometry are incorporated into the structure prior to improve the reconstruction accuracy. In addition, an efficient approach based on approximate message passing is developed to accelerate the large-scale 3-D learning process. To validate the algorithm, numerical experiments using real recorded data are conducted. The visual and quantitative-metric comparisons show that the proposed method outperforms the existing methods in terms of reconstruction accuracy and computational complexity in all test cases. The SA-SBL-based reconstruction approach can preserve the 3-D structure of medical volume, reduce the systematic artifacts, and improve the computational efficiency.
rexB of bacteriophage λ is an anti-cell death gene

In Escherichia coli, programmed cell death is mediated through “addiction modules” consisting of two genes; the product of one gene is long-lived and toxic, whereas the product of the other is short-lived and antagonizes the toxic effect. Here we show that the product of λrexB, one of the few genes expressed in the lysogenic state of bacteriophage λ, prevents cell death directed by each of two addiction modules, phd-doc of plasmid prophage P1 and the rel mazEF of E. coli, which is induced by the signal molecule guanosine 3′,5′-bispyrophosphate (ppGpp) and thus by amino acid starvation. λRexB inhibits the degradation of the antitoxic labile components Phd and MazE of these systems, which are substrates of ClpP proteases. We present a model for this anti-cell death effect of λRexB through its action on the ClpP proteolytic subunit. We also propose that the λrex operon has an additional function to the well known phenomenon of exclusion of other phages; it can prevent the death of lysogenized cells under conditions of nutrient starvation. Thus, the rex operon may be considered as the “survival operon” of phage λ.
Subsampling CMOS Frontends for Multistandard Reconfigurable RF Radios

With the evolution of wireless systems, more communication standards are being proposed while maintaining backward compatibility, therefore, there is an ever growing need for wideband multistandard receivers similar to software defined radio (SDR) receivers. For many years now, SDR or digitally reconfigurable radio research has been quite challenging with only a few reported practical prototypes with limited success. The basic demand of the SDR has been the need for a giant analog-to-digital converter (ADC) and a powerful enough digital signal processing (DSP) so that it serves as a universal radio platform receiving almost all radio standards and services. These demands are difficult to meet even in modern CMOS technologies leading to the necessity of highly flexible architectures compared to traditional receivers. To achieve this objective, digital intensive receiver architectures with passive structures are proposed. Radio frequency (RF) sampling receivers using time-interleaved (TI), RF sampling and discrete-time mixing also provide additional flexibility by employing discrete-time signal processing in analog domain. Owing to the oversampling ratio of up to 8 times in these receivers, high-performance complex frequency synthesizers are needed with stringent phase noise requirement. Moreover, the clocking schemes employed in TI RF sampling architectures result in additional timing jitter and mismatch offsets. These challenges are alleviated in a class of sampling receivers, called subsampling receivers, where the employed bandpass sampling principle results in less than the Nyquist sampling frequency. This leads to the less-complex and less power hungry frequency synthesizers. In this thesis, an intermediate solution to SDR receiver implementation called “Mini-SDR” architecture is proposed by exploiting the bandpass sampling concept. Based on the proposed Mini-SDR architecture, three CMOS radio frontends are proposed for standalone electronic attack (EA) transceiver system, dynamically reconfigurable multistandard subsampling (DRMS) radio receiver and a dual-band subsampling receiver for IEEE WLAN 802.11ac standard.
Energy Storage Control and Requirements For Inverter-Based Microgrids

The intermittent nature of distributed renewable sources such as wind or solar requires integration of energy storage systems. In this dissertation a distributed form of the Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) method is used to determine the energy storage requirements of three-phase inverter-based microgrids. The overall control is appropriate to be integrated into a hierarchical control system. As the primary control, a novel dq droop control sets the local references and is supported by a level-zero Hamiltonian controller which includes energy storage feedforward and feedback, and an inverter feed-forward controls. Here, the energy storage element performs as the sole actuator of the system and enforces the references that are set by the droop control while the inverter feed-forward matches the voltage levels of the inverter to the local bus. The control method as well as the power flow and energy transfer model of the microgrid system enables the capacity and bandwidth of the storage system to be determined. The Hamiltonian control is further derived for parallel operation of hybrid, bandlimited and reduced-order battery and flywheel storage systems. Moreover, a control scheme is proposed to enable sharing of power between parallel battery and flywheel storage systems according to their bandwidth support capabilities. Here, battery
Texture synthesis with neural networks

Texture Synthesis with Neural Networks S. Schreiber Thesis: M.Sc (Computer Science) December 2018 Creating detailed texture maps for virtual environments is often a timeconsuming process. Procedural texture generation enables the creation of more rich and detailed virtual environments with minimal input needed from an artist. However, finding a flexible generative model of real world textures remains an open problem. There are currently two key limiting factors. The first key limitation is a lack of available knowledge on the capability of the various neural network based techniques and how the components associated with each technique affects the quality of synthesized textures. The second key limitation in modern generative models is the inability to apply localized constraints in situations where there are complex interactions between two regions within a texture. To address these limitations, three areas of interest (training set, network architecture, and texture representation) involving the synthesis process are identified specifically for neural network-based techniques and their effects on the synthesized textures are investigated. Included in this investigation is a comparative study focusing on subjective quality and quantitative error measurement between the currently available techniques. Second, a novel convolutional neural network-based texture model is proposed, consisting of four summary statistics (content or feature maps, Gramian matrices, transformed Gramian matrices, and total variation), as well as spectrum constraints. The Fourier transform and windowed Fourier transform are investigated in applying spectrum constraints, and it is found that the windowed Fourier transform improved the quality and consistency of the generated textures. During the component investigation, it was identified that the VGG-19 network still produces comparable results when compared to more modern network architectures. Additionally, it was also demonstrated that direct methods ii Stellenbosch University https://scholar.sun.ac.za