Miracle-dz/UniFAR-FTUs huggingface dataset:
A study on distribution and population size of Androcymbium hierrense (Colchicaceae) an endangered taxon, in La Palma Biosphere Reserve (Canary Islands)

The scarce knowledge on the distribution of the natural populations of the Canarian endemic Androcymbium hierrense (Colchicaceae), an endangered species in the island of La Palma hampers the identification of priorities for undertaking actions for its conservation. Here we present a survey in which, the species is distributed in 14 locations grouped into two differentiated areas. This result represents an increase of ca. 200% in the occupancy area known for the species until now. A total of 15010 individuals were recorded: 1294 individuals in the northern parts of the island (Garafia), and 13716 in the east (Mazo). The species exhibits an average density of 8.80±24.03 SD (n=1559) and an estimated population size of 21593-24851 individuals (with a 95% confidence interval), given a total population of 217995 plants for the entire species distribution area on the island. Considering these estimates and the current distribution of the species, it is important to analyse its actual conservation status in La Palma both at regional and global scales, taking into account that the main threat for the species is habitat destruction by human activities.
Predictive modeling of I/O characteristics in high performance computing systems

Each of high performance computing, cloud computing, and computer security have their own interests in modeling and predicting the performance of computers with respect to how they are configured. An effective model might infer internal mechanics, minimize power consumption, or maximize computational throughput of a given system. This paper analyzes a four-dimensional dataset measuring the input/output (I/O) characteristics of a cluster of identical computers using the benchmark IOzone. The I/O performance characteristics are modeled with respect to system configuration using multivariate interpolation and approximation techniques. The analysis reveals that accurate models of I/O characteristics for a computer system may be created from a small fraction of possible configurations, and that some modeling techniques will continue to perform well as the number of system parameters being modeled increases. These results have strong implications for future predictive analyses based on more comprehensive sets of system parameters.
Automated image segmentation using support vector machines

Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.
Secure Credit Reporting on the Blockchain

We present a secure approach for maintaining and reporting credit history records on the Blockchain. Our approach removes third-parties such as credit reporting agencies from the lending process and replaces them with smart contracts. This allows customers to interact directly with the lenders or banks while ensuring the integrity, unmalleability and privacy of their credit data. Additionally, each customer has full control over complete or selective disclosure of her credit records, eliminating the risk of privacy violations or data breaches. Moreover, our approach provides strong guarantees for the lenders as well. A lender can check both correctness and completeness of the credit data disclosed to her. This is the first approach that can perform all credit reporting tasks without a central authority or changing the financial mechanisms1*An extended version of this article is accessible at [1].
Convolutive ICA for Audio Signals

The goal of Blind Source Separation (BSS) is to estimate latent sources from their mixed observations without any knowledge of the mixing process. Under the assumption of statistical independence of hidden sources, the task in BSS is to obtain Independent Components (IC) from the mixed signals. Such algorithms are called ICA-based BSS algorithms [1, 2]. ICA-based BSS has been well studied in the fields of statistics and information theory for different applications, including wireless communication and biomedicine. However, as speech and audio signal mixtures in a real reverberant environment are generally convolutive mixtures, they involve a structurally much more challenging task than instantaneous mixtures, which are prevalent in many other applications [3, 4]. Such a mixing situation is generally modeled with impulse responses from sound sources to microphones. In a practical room situation, such impulse responses can have thousands of taps even with an 8 kHz sampling rate, and this makes the convolutive problem difficult to solve. Blind speech separation is applicable to the realization of noise-robust speech recognition, high quality hands-free telecommunication systems and hearing aids.
The Next Generation Passive Optical Network: A Review

– In this a review is given on the Next Generation Passive Optical Network (NG-PON). Recently, a WDM-PON system has gained significant attention to support high data rate transmission. To overcome the several effects occurring in optical transmission, OFDM has been introduced to improve the overall performance of the system. In this paper, NG-PON1 and NG-PON2 technology has been reviewed and studied their compatibility with the deployed networks, bandwidth efficiency, capacity and its efficiency for the cost. In it , the design principles and prospective technologies for NG-PONs has been described which introduced a views of NG-PON evolution, by focusing on the discussion and evaluation of various technologies. The discussion have been taken under the FSAN and ITU-T framework of NG-PON recommendations.
Representation Balancing MDPs for Off-Policy Policy Evaluation

We study the problem of off-policy policy evaluation (OPPE) in RL. In contrast to prior work, we consider how to estimate both the individual policy value and average policy value accurately. We draw inspiration from recent work in causal reasoning, and propose a new finite sample generalization error bound for value estimates from MDP models. Using this upper bound as an objective, we develop a learning algorithm of an MDP model with a balanced representation, and show that our approach can yield substantially lower MSE in common synthetic benchmarks and a HIV treatment simulation domain.
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials

Dense conditional random fields (CRFs) with Gaussian pairwise potentials have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segmentation. By modelling long-range interactions, dense CRFs provide a more detailed labelling compared to their sparse counterparts. Currently the state-of-the-art algorithm performs mean-field inference using a filter-based method to obtain accurate segmentations, but fails to provide strong theoretical guarantees on the quality of the solution. Whilst the underlying model of a dense CRF provides enough information to yield well defined segmentations, it lacks the richness introduced via higher order potentials. The mean-field inference strategy was also extended to incorporate higher order potentials, but again failed to obtain a bound on the quality of the solution. To this extent, we show that a dense CRF can be aggregated with sparse higher order potentials in a way that is amenable to continuous relaxations. We will then show that, by using a filter-based method, these continuous relaxations can be optimised efficiently using state-of-the-art algorithms. Specifically we will solve a quadratic programming (QP) relaxation using the Frank-Wolfe algorithm and a linear programming (LP) relaxation by developing a proximal minimisation framework. By exploiting labelling consistency in the higher order potentials and utilising the filter-based method, we are able to formulate the above algorithms such that each iteration has a complexity linear in the number of classes and random variables. The experiments are performed on the standard publicly available MSRC data set and demonstrate the low energies achieved from the minimisation and the accuracy of the resulting segmentations.
New high-performance CAD scheme for the detection of polyps in CT colonography

We developed a new method for automated detection of colonic polyps in CT colonography. The colon is extracted from CT images by use of a centerline-based colon segmentation method. Polyp candidates are detected by use of hysteresis thresholding and fuzzy merging. The regions of the polyp candidates are segmented by use of conditional morphological dilation. False-positive polyp candidates are reduced by a region-based supine-prone correspondence method and by a Bayesian neural network with shape and texture features. To evaluate the method, CT colonography was performed for 121 patients with standard technique and single- and multi-detector helical scanners by use of 2.5-5.0 mm collimations, 1.0-5.0 mm reconstruction intervals, and 60-100 mA tube currents. Twenty-eight patients had a total of 42 polyps: 22 polyps were 5-10 mm, and 20 polyps were 11-25 mm in size. A leave-one-out evaluation of the CAD scheme with by-patient elimination yielded 93% by-polyp and by-patient detection sensitivities with 2.0 false-positive detections per data set on average. The average computation time was 4 minutes per data set. The results indicate that the CAD scheme may be useful in improving the performance of computer-aided detection for colon cancer in a clinical screening setting.
Origin and Evolution of Insect Metamorphosis

Insect metamorphosis can be classified into three modalities: ametabolan (no changes), hemimetabolan (progressive changes) and holometabolan (dramatical changes at the end of the cycle). The metamorphic changes are mainly regulated by two hormones: the moulting hormone, which promotes the moults, and the juvenile hormone (JH), which represses the transformation into the adult. The action of these two hormones is mediated by a number of transcription factors, and the molecular mechanismsregulating the expressionof theseand ofthe corresponding target genes are finally refined by the action of micro ribonucleic acids. Among the different transcription factors involved, those of the Broad-Complex group are especially interesting because they have a differential expression and JH dependency in holometabolans and hemimetabolans. Holometabolan metamorphosis probably evolved from hemimetabolan ancestors, although the mechanisms underlying such a transition are still obscure.
Formulation Development and Quality Evaluation of Polyherbal Toothpaste “Oral S”

Objective: To Formulate and Develop Polyherbal toothpaste formulation and then evaluating its antimicrobial activity. Method: Methanol extract of Polyherbal formulations was prepared. Standard cultures of Streptococcus mutans(MTCC 890), Streptococcus oralis(MTCC 2696) ,staphylococcus aureus(MTCC 7443), Candida albicans (MTCC 183), and Lactobacillus acidophilus(MTCC 10307), gram positive was used for the study. The antibacterial tests used was the agar well diffusion method. Methanol and Gentamicin were used as the negative and positive control respectively. Marketed formulations were compared. Also Quality control parameters like physiochemical, phytochemical and HPTLC(for identification of compounds) were done on developed polyherbal formulations. Results: The methanol extract of Polyherbal formulation 2 showed maximum activity against Streptococcus mutans and minimum activity against Candida Albicans . Whereas Methanol extract of polyherbal formulation 1 and 3 showed lesser activity when compared with Polyherbal formulation 2 as shown in table 3 and 4. However, the activity was less than the standard Gentamicin. The extract shows increasing inhibitory activity with increase in concentration (50%-100%). Quality parameters were conducted on polyherbal formulations shown in table 5, 6 and figure 3. Conclusion: The results
Monte Carlo Tree Search for Asymmetric Trees

We present an extension of Monte Carlo Tree Search (MCTS) that strongly increases its efficiency for trees with asymmetry and/or loops. Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account. Our first algorithm (MCTS-T), which assumes a non-stochastic environment, backs-up tree structure uncertainty and leverages it for exploration in a modified UCB formula. Results show vastly improved efficiency in a well-known asymmetric domain in which MCTS performs arbitrarily bad. Next, we connect the ideas about asymmetric termination to the presence of loops in the tree, where the same state appears multiple times in a single trace. An extension to our algorithm (MCTS-T+), which in addition to non-stochasticity assumes full state observability, further increases search efficiency for domains with loops as well. Benchmark testing on a set of OpenAI Gym and Atari 2600 games indicates that our algorithms always perform better than or at least equivalent to standard MCTS, and could be first-choice tree search algorithms for non-stochastic, fully-observable environments.
Evaluation of accuracy and workflow between different alignment techniques for correction of CTAC and PET misalignment in cardiac PET-CT imaging

Small errors in the alignment between CT Attenuation Correction (CTAC) images and Positron Emission Tomography (PET) acquisitions can result in significant changes in PET attenuation corrected images. Misalignment due to respiratory or cardiac motion can produce mismatch between the PET and CTAC acquisitions. This contributes to artifactual hypoperfusion defects that are interpretable as myocardial ischemia or infarct. Correction for the misalignment between the PET and CTAC images can eliminate these false positive artifacts. Two methods for correcting for this respiratory and cardiac misalignment were compared. The first was an existing procedure, the manual-shift method, using point-to-point, in-plane, two-dimensional (2D) measurements of the shifts in axial, sagittal, and coronal planes. A new PET image reconstruction using the corrected attenuation map shifted by the 2D measurements was then performed. In the second method, the Interactive ACQC method, visual alignment was performed between the left ventricle boundaries on fused images and automated calculation of necessary rigid three-dimensional (3D) alignment parameters was performed. A new PET image reconstruction was then performed with an attenuation map shifted by the prescribed alignment parameters. The two methods were compared for accuracy and workflow efficiency using five cardiac PET/CT cases, scanned on GE Discovery VCT and Discovery ST systems. Alignment measurements using the visual alignment process (the interactive ACQC method) improved productivity by over five minutes, on average. The results show that the interactive ACQC procedure yields similar results to those of the point-to-point procedure while providing improved workflow for cardiac PET attenuation correction quality control.
Gr upSM Therapeutic Targeting of Vascular Senescence Pathways

Blood vessels become less flexible with age. Arteries narrow and become less flexible, hindering blood circulation in those with various vascular diseases including atherosclerosis. Mechanistically, vascular senescence plays important roles in the pathogenesis of normal aging and age-related cardiovascular diseases. Vascular senescence also causes vascular dysfunction, resulting in damage to the vessel wall. This review presents the various molecular biologic mechanisms of vascular senescence and their therapeutic implications. We discuss topics such as mitochondrial oxidative stress/anti-oxidant enzymes, nitric oxide synthase–nitric oxide signaling and the Akt-mTORC1-S6K1 pathway and sirtuin family members. keywords: Vascular diseases; Vascular senescence; Mitochondrial oxidative stress/antioxidant enzymes; NOS-NO; Akt-mTORC1-S6K1; Sirtuin Sungha Park1 and Byeong Mo Kim2* 1Division of Cardiology, Yonsei University College of Medicine, Republic of Korea 2Severance Integrative Research Institute for Cerebral & Cardiovascular Diseases (SIRIC), Yonsei University College of Medicine, Republic of Korea *Corresponding author: Byeong Mo Kim, Severance Integrative Research Institute for Cerebral & Cardiovascular Diseases (SIRIC), Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea, Tele Phone: 82-2-2228-0789, Fax: 82-22227-7906, E-mail: bkim2@yuhs.ac Published Date: July 09, 2016
Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures

Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.
Iterative Methods for Solving Factorized Linear Systems

Stochastic iterative algorithms such as the Kaczmarz and Gauss-Seidel methods have gained recent attention because of their speed, simplicity, and the ability to approximately solve large-scale linear systems of equations without needing to access the entire matrix. In this work, we consider the setting where we wish to solve a linear system in a large matrix X that is stored in a factorized form, X = UV; this setting either arises naturally in many applications or may be imposed when working with large low-rank datasets for reasons of space required for storage. We propose a variant of the randomized Kaczmarz method for such systems that takes advantage of the factored form, and avoids computing X. We prove an exponential convergence rate and supplement our theoretical guarantees with experimental evidence demonstrating that the factored variant yields significant acceleration in convergence.
ANTIMICROBIAL AND CYTOTOXIC ACTIVITY OF MEDICINAL PLANT SPECIES SUBJECTED TO ABIOTIC STRESS IN EASTERN GHATS

Abutilon indicum, Achyranthes aspera, Gomphrena celosioides, Ocimum sanctum and Azadirachta indica are chosen due to their previous history as the plants with various medicinal properties. In order to study the effect of metal stress (abiotic stress), the plants are selected from industrially polluted site. Agar well diffusion method was used to analyze broad spectrum antimicrobial potential of the extracts of the selected plants subjected to abiotic stress by industrial pollution. Of the important human pathogenic microorganisms selected, the gram positive bacteria, Bacillus cereus was proved to be highly susceptible to all the extracts followed by Enterococcus, Bacillus subtilis and Staphylococcus aureus. Among the gram negative bacteria, Klebsiella was resistant to all the extracts, whereas E.coli showed average response. The extracts of Azadirachta indica subjected to stress exhibited antifungal activity against Candida albicans. XTT assay was used to analyze the invitro cytotoxic activities of the extracts of Ocimum sanctum and Azadirachta indica against human hepatocarcinoma cell line (HepG2). Abiotic stress enhanced the phytochemicals and therapeutic activity in Ocimum sanctum only. These observations clearly indicate that Ocimum sanctum extracts possess anticancer activity suggesting wide application of the plant species in the above therapeutic preparations.