Miracle-dz/UniFAR-FTUs huggingface dataset:
A Naming Scheme for P 2 P Web Hosting

The peer–to–peer paradigm has great potential of providing the next generation Web hosting infrastructure. Profound advancements in P2P technology in the last decade have proven its capability to provide functionality similar to traditional client–server systems at a much larger scale with relatively lower cost. Existing centralized website hosting technology has a number of inherent deficiencies including scalability, single point of failure, administration overhead, hosting expenses etc. P2P Web hosting can effectively address these problems and hence open a new era for next generation Web hosting. However peer availability and content location are highly dynamic in a P2P network. This dynamism raises a number of research challenges related to naming, addressing, indexing, and searching in a P2P environment. In this paper we identify the practical requirements for devising a secure, persistent and human friendly naming scheme for P2P Web hosting and propose a novel naming scheme that satisfies all these requirements. We also present extensive simulation results validating the accuracy, scalability and faultresilience of the proposed naming scheme.
Petrology and Geochemistry of Mafic Lavas near Glacier Peak , North Cascades

Major element, trace element, and mineral compositions have been determined for four Quaternary mafic monogenetic cinder cones and flows south of Glacier Peak, a dacitic stratovolcano in the northern Cascade arc. The flows are the Whitechuck basalt, and the basaltic andesites of Indian Pass, Lightning Creek, and Dishpan Gap. Whitechuck has high concentrations of AI2O3 (> 18 wt.%) and low concentrations of K2O (< 0.45 wt.%) and shares similar trace element characteristics with high alumina olivine tholeiites reported in the central and southern Cascades. The three basaltic andesites are calcalkaline. Indian Pass and Lighting Creek have primitive compositions, with Mg# [100*Mg/(Mg+Fe*)] > 65, Cr > 200 ppm, and Ni > 100 ppm. Most samples have olivine in equilibrium with bulk rock compositions. Rb and Sr are relatively low in most flows, especially at Whitechuck and Dishpan Gap. Sr and La decrease with increasing Si02 in the Lightning Creek and Dishpan Gap cinder cones, indicating possible mixing with a Sr and La-poor felsic component represented by the most evolved Glacier Peak dacites. Petrographic observations of disequilibrium textures such as xenocrysts, xenoliths, quenched glass inclusions, and strongly-zoned phenocrysts in these two flows also indicate possible melting. All flows are enriched in large ion lithophile elements and light rare earth elements relative to high field strength elements, showing that the mantle beneath Glacier Peak has been fluxed by a hydrous subduction component. Ba/Nb ratios (-40-110) are highest for the three basaltic andesites, indicating that they had the greatest amount of subduction component enrichment. Nb and Ta abundances are highest at Indian Pass (Nb =~7 ppm) and lowest at Whitechuck (Nb = -2-3), indicating that Indian Pass and the other two basaltic andesites were produced by relatively low degree hydrous
Roles of Inflammasomes in Inflammatory Kidney Diseases

The immune system has a central role in eliminating detrimental factors, by frequently launching inflammatory responses towards pathogen infection and inner danger signal outbreak. Acute and chronic inflammatory responses are critical determinants for consequences of kidney diseases, in which inflammasomes were inevitably involved. Inflammasomes are closely linked to many kidney diseases such as acute kidney injury and chronic kidney diseases. Inflammasomes are macromolecules consisting of multiple proteins, and their formation initiates the cleavage of procaspase-1, resulting in the activation of gasdermin D as well as the maturation and release of interleukin-1β and IL-18, leading to pyroptosis. Here, we discuss the mechanism in which inflammasomes occur, as well as their roles in inflammatory kidney diseases, in order to shed light for discovering new therapeutical targets for the prevention and treatment of inflammatory kidney diseases and consequent end-stage renal disease.
2 Optimal Transport : Primal , Dual and Semi-dual Formulations

Optimal transport (OT) defines a powerful framework to compare probability distributions in a geometrically faithful way. However, the practical impact of OT is still limited because of its computational burden. We propose a new class of stochastic optimization algorithms to cope with large-scale OT problems. These methods can handle arbitrary distributions (either discrete or continuous) as long as one is able to draw samples from them, which is the typical setup in highdimensional learning problems. This alleviates the need to discretize these densities, while giving access to provably convergent methods that output the correct distance without discretization error. These algorithms rely on two main ideas: (a) the dual OT problem can be re-cast as the maximization of an expectation; (b) the entropic regularization of the primal OT problem yields a smooth dual optimization which can be addressed with algorithms that have a provably faster convergence. We instantiate these ideas in three different setups: (i) when comparing a discrete distribution to another, we show that incremental stochastic optimization schemes can beat Sinkhorn’s algorithm, the current state-of-the-art finite dimensional OT solver; (ii) when comparing a discrete distribution to a continuous density, a semidiscrete reformulation of the dual program is amenable to averaged stochastic gradient descent, leading to better performance than approximately solving the problem by discretization ; (iii) when dealing with two continuous densities, we propose a stochastic gradient descent over a reproducing kernel Hilbert space (RKHS). This is currently the only known method to solve this problem, apart from computing OT on finite samples. We backup these claims on a set of discrete, semi-discrete and continuous benchmark problems.
Altitudinal Effect on Diversity and Distribution of Araceae in Doi Suthep-Pui National Park, Chiang Mai Province in Thailand

The relationships between Araceae diversity and altitude gradients in mountain ecosystem at Doi Suthep-Pui National Park, Chiang Mai Province in Thailand, were studied during January 2016-March 2018. The strip-plots, 10 m × 1,000 m along the five nature trails, were established from 300-500, 501-800, 801-1,100, 1,101-1,400, 1,401-1,685 m above mean sea level (amsl). Twenty species of 11 genera were found. Alocasia navicularis and Lemna sp. were found at 300-500 m amsl, wild taro (Colocasia esculenta) was at 300-800 m amsl, A. acuminata was at 300-1,400 m amsl, while Amorphophallus krausei, Hapaline benthamiana, Lasia spinosa, Rhaphidophora chevalieri and R. megaphylla were at 501-800 m amsl, but dwarf taro (C. affinis) was at 501-1,400 m amsl, Homalomena aromatica was at 801-1,100 m amsl, A. fuscus, A. thaiensis were at 801-1,685 m amsl, A. yunnanensis was at 1,101-1,685 m amsl, only five species, Arisaema consanguineum, A. kerrii, Remusatia hookeriana, R. peepla and Sauromatum horsfieldii were found at 1,401-1,685 m amsl. Species diversity in the rainy season was the highest (1.75), the top five dominant species were dwarf taro (C. affinis), wild taro (C. esculenta), A. fuscus, A. acuminata and A. thaiensis (importance value index (IVI%): 61.54, 24.13, 17.12, 16.52 and 9.60, respectively). In dry season, the dominant species was wild taro. The altitude gradients showed the main effect on diversity and distribution of Araceae.
DR AF T Car Parking Occupancy Detection Using Smart Camera Networks and Deep Learning

This paper presents an approach for real-time car parking occupancy detection that uses a Convolutional Neural Network (CNN) classifier running on-board of a smart camera with limited resources. Experiments show that our technique is very effective and robust to light condition changes, presence of shadows, and partial occlusions. The detection is reliable, even when tests are performed using images captured from a viewpoint different than the viewpoint used for training. In addition, it also demonstrates its robustness when training and tests are executed on different parking lots. We have tested and compared our solution against state of the art techniques, using a reference benchmark for parking occupancy detection. We have also produced and made publicly available an additional dataset that contains images of the parking lot taken from different viewpoints and in different days with different light conditions. The dataset captures occlusion and shadows that might disturb the classification of the parking spaces status.
Omni-gravity Hydroponics for Space Exploration

As part of the NASA Plant Water Management technology demonstration experiments, a capillary fluidics hydroponic system that can function in a variety of gravity environments has been developed and tested for crop production in space. A passive liquid delivery method is employed that drastically reduces the number of contaminable moving parts providing a high reliability solution requiring minimal resources for operation. The terrestrial, lunar, and Martian environments are managed in a ‘gravity-dominated mode,’ while the low-gravity transit and orbit environments are managed in a ‘capillary fluidics mode,’ where the role of gravity is replaced by the equally passive effects of surface tension, conduit shape, and wettability. The unique considerations for priming, germination, aeration, nutrient supply, root accommodation, layout, crew interaction, etc. are highlighted. Design guides for system function are provided along with high Technology Readiness Level demonstrations of the system during terrestrial and drop tower tests. Long duration tests are planned on short schedule aboard the International Space Station in 2019.
Genetical Genomics : A Few Critical Points

The regions identified through quantitative trait loci (QTL) methods are quite large and a considerable amount of effort is required to clone functional genes. Fortunately, during the past two decades, numerous advances in both technology and methodology have greatly improved our ability to collect, measure, and analyze the data necessary to clone gene. The genomic analysis of transcripts as individual phenotypes has led to the emerging field of expression QTL (eQTL) analysis. If gene expression levels are heritable, one can consider each gene’s expression level to be a quantitative trait that can be combined with marker data for a linkage study to identify the loci influence variation in the gene’s expression. A locus affecting the expression level of a gene has been named eQTL, and the genetic variation causing differences in gene expression are called expression-level polymorphisms.
Modeling Expected Utility : Considering Cycle and Chain Failure

Copyright: © 2018 INFORMS Abstract. Algorithmic matches in fielded kidney exchanges do not typically result in an actual transplant.We address the problem of cycles and chains in proposedmatches failing after the matching algorithm has committed to them. We show that failure-aware kidney exchange can significantly increase the expected number of lives saved (i) in theory, on random graph models; (ii) on real data from kidney exchange match runs between 2010 and 2014; and (iii) on synthetic data generated via a model of dynamic kidney exchange. This gain is robust to uncertainty over the true underlying failure rate.We design a branchand-price–based optimal clearing algorithm specifically for the probabilistic exchange clearing problem and show that this new solver scales well on large simulated data, unlike prior clearing algorithms. Finally, we show that failure-awarematching can increase overall system efficiency and simultaneously increase the expected number of transplants to highly sensitized patients, in both static and dynamic models.
Superparamagnetic Manganese Ferrite Nanoparticles : Synthesis and Magnetic Properties

In this paper, manganese ferrite nanoparticles of controlled size were synthesized using chemical co-precipitation method and characterized for its structural and magnetic properties. The method is relatively simple, low cost and their particle size can be easily controlled. The X-ray diffraction pattern confirms the synthesis of single crystalline phase of MnFe2O4 nanoparticles with lattice parameter 8. 5075Å. From TEM analysis it is seen that the sample contains nanoparticles of almost spherical in shape with the average particle size of 5 nm. The HRTEM image of the nanoparticles indicates the single crystalline nature which implies that high quality uniform manganese ferrite is obtained with our facile wet chemical method. Spectroscopic study implies that oleic acid is coated on the surface of the MnFe2O4 nanoparticles. The magnetic measurements show superparamagnetic nature of the sample. The saturation magnetization is 69emu/g. The superparamagnetic nature with high saturation magnetization makes these nanoparticles suitable for the synthesis of ferrofluid.
Reliable , Intelligent , and Large Scale E-Health Architecture using Wireless Sensor Networks

Recently Wireless Sensor Networks (WSNs) are used in many applications concerned with e-health, military among others. In this paper a new architecture for e-health is presented. This architecture uses multiple WSNs. Also, the architecture is controlled by a network management system which regulates the transmission of data between patients and doctors remotely in secure manner. Furthermore, the gathered information by sensors is analyzed and reported using an intelligent expert system. Finally, the proposed architecture is considered as large scale due to using the Internet as a connection for the WSNs. keywords: E-Health, WSN, Network Management,
Personalization, Collaboration, and Recommendation in the Digital Library Environment CYCLADES

CYCLADES is a system, which combines several technologies from the Information Retrieval and Digital Library area, where users and user communities may deal with a quite large set of heterogeneous digital archives. CYCLADES provides a highly personalized environment where not only users may organize (and search into) the information space according to their individual taste and use, but, and more importantly, also provides advanced features of collaborative work among the users. It is up then to the system to discover interesting properties about the users’ interests, relationship between users and user communities, as well as meaningful events that happen in user shared workspaces, and finally to notify the involved users according to their own preferences.
Regular Paper Simultaneous Segmentation of Multiple Organs Using Random Walks C

Random walks-based (RW) segmentation methods have been proven to have a potential application in segmenting the medical image with minimal interactive guidance. However, the approach leads to large-scale graphs due to number of nodes equal to voxel number. Also, segmentation is inaccurate because of the unavailability of appropriate initial seed points. It is a challenge to use the RW-based segmentation algorithm to segment organ regions from 3D medical images interactively. In this paper, a knowledge-based segmentation framework for multiple organs is proposed based on random walks. This method employs the previous segmented slice as prior knowledge (the shape and intensity constraints) for automatic segmentation of other slices, which can reduce the graph scale and significantly speed up the optimization procedure of the graph. To assess the efficiency of our proposed method, experiments were performed on liver tissues, spleen tissues and hepatic cancer and it was extensively evaluated both quantitatively and qualitatively. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for multi-organ segmentation (p < 0.001).
Context Effects on Human Judgments of Similarity

The semantic similarity of words forms the basis of many natural language processing methods. These computational similarity measures are often based on a mathematical comparison of vector representations of word meanings, while human judgments of similarity differ in lacking geometrical properties, e.g., symmetric similarity and triangular similarity. In this study, we propose a novel task design to further explore human behavior by asking whether a pair of words is deemed more similar depending on an immediately preceding judgment. Results from a crowdsourcing experiment show that people consistently judge words as more similar when primed by a judgment that evokes a relevant relationship. Our analysis further shows that word2vec similarity correlated significantly better with the out-of-context judgments, thus confirming the methodological differences in human-computer judgments, and offering a new testbed for probing the differences.
Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4

CLaC labs participated in Task 1 and 4 of SMM4H 2019. We pursed two main objectives in our submission. First we tried to use some textual features in a deep net framework, and second, the potential use of more than one word embedding was tested. The results seem positively affected by the proposed architectures.
Mismatch Detection Neural Circuit Applied to Navigation

Detecting mismatches between the world and our model of it is a fundamental aspect of our cognitive capacities. Mismatches reveal the presence of elements and situations that we did not expect, and thus, have not prepared for, often demanding changes in behavior. Additionally, mismatches frequently expose errors in our model of the world, thus providing a useful learning signal. Furthermore, mismatches between the world and our motivations directly indicate situations in which actions should be taken. In spite of the importance and pervasive character of mismatch detection in cognition, its neural implementation is not well understood. This contributes to the lack of common sense often displayed by artificial intelligence systems that are based on neural networks. In this thesis, we present a novel neural network architecture that has mismatch detection as the basis of its operation. The network will signal the presence of unexpected elements (mismatch by excess) and the absence of expected ones (mismatch by deficit). The neurons of the network include biologically inspired dendritic nonlinearities that increase their discriminatory capacity. We introduce a rule for learning the associations between concepts which operates using local information and resembles the mechanism of synaptic tagging and capture of biological neurons. This rule will ensure that connections from different input combinations cluster onto different dendrites, and can also be used to learn the association between desires and the actions that are needed in order to fulfill them. The model is applied to a navigation task in order to demonstrate its capabilities. A navigation system based on the mismatch detection circuit is proposed, where the environment is encoded as a series of landmarks and their relative positions, inspired by findings in animal spatial cognition. The system is simulated in a simple virtual environment, where it detects the addition and removal of landmarks. The mismatch detection network could have applications in artificial intelligence and may also offer insights about how learning and mismatch detection happen in biological brains.
EFFECT OF WEATHER CONDITIONS ON CONTENT OF CARBON DIOXIDE IN BARNS

The most important condition for optimization of the inside climate in livestock buildings is its compliance with the physiological state of animals. However, in most large and small-scale farms the microclimate in cow barns is far from the standard parameters. One reason for this discrepancy is the lack of the forecasting system of barn microclimate parameters variation depending on the changes in external conditions. The research was aimed at identifying the relationship between the level of carbon dioxide in the barn and external climatic conditions. The study results proved the concentration of carbon dioxide in the barn to be about 80 % dependent on the wind speed, outside and inside air temperature under natural ventilation of the barn.
Anisotropic velocity analysis for 4 C seismic data : a case study

We present a case study of polar anisotropy (or vertical transverse isotropy, VTI) and demonstrate how the anisotropic parameter and the NMO velocity ratio ( n) can be estimated from 4-C seaoor seismic data. This is achieved by performing a semblance analysis over n and using the anisotropic DSR equation of Li and Yuan (1999) for P-SV moveout (PS-moveout). The analysis requires the prior knowledge of the P wave NMO velocity vpn and the vertical velocity ratio 0. Compared with the existing methods, this approach improves the results of PS-moveout correction at far o sets, reduces error propagation and magni cation by using as the input for parameter inversion, and yields more stable estimates of anisotropic parameters.
Parsing Meaning Representations: Is Easier Always Better?

The parsing accuracy varies a great deal for different meaning representations. In this paper, we compare the parsing performances between Abstract Meaning Representation (AMR) and Minimal Recursion Semantics (MRS), and provide an in-depth analysis of what factors contributed to the discrepancy in their parsing accuracy. By crystalizing the trade-off between representation expressiveness and ease of automatic parsing, we hope our results can help inform the design of the next-generation meaning representations.
EIP on AHA Ontology for adherence: Knowledge representation advanced tools

Nowadays diseases tend to chronicle, mainly due to the increase in life expectancy and this leads to a state of polypharmacy. More than 1.5% of Spain’s GDP is spent on pharmaceuticals and healthcare products. Complex chronic patients (pluripathological and polymedicated) account for most of the expenditure. The “Action Group A1” of the European Innovation Partnership develops in the “Active and Healthy Ageing” programme actions to improve the quality of life and health outcomes of these patients. On the other hand, the PITeS TIiSS project develops decision support tools to improve this scenario. An ontology has been developed as a tool on adherence. The domain of this ontology is mainly focused on medication adherence and measurement methods. This ontology gathers the necessary knowledge about the domain allowing the use of the ontology as part for is possible.