Miracle-dz/UniFAR-FTUs huggingface dataset:
Boost.Compute: A parallel computing library for C++ based on OpenCL

Boost.Compute is a powerful C++ header-only template library for parallel computing based on OpenCL. It has a layered architecture and acts both as a thin C++ wrapper over the OpenCL API and as a feature-rich interface to high-level constructs that resemble the functionality of the STL and is just as easy to use. Through its template-based design, Boost.Compute seeks to further provide flexible OpenCL support in C++ projects via meta-programming. Meta-programming is not available in standard C-based OpenCL, and OpenCL C++ kernel language is currently only provisional. SYCL is intended as a prospective C++ abstraction layer over OpenCL, but it is still under development and requires a special compiler. Bolt and Thrust are C++-based parallel-computing libraries, but they are tied to specific hardware vendors. Boost.Compute on the other hand is a vendor-neutral solution for GPUs and multi-core CPUs that is available now, and it works with any standard C++ compiler and on all OpenCL platforms. Boost.Compute has been accepted for integration with the official Boost C++ libraries. With this step, and considering the large number of Boost users, usage of Boost.Compute and visibility of OpenCL among C++ developers is likely to increase. This technical presentation is therefore intended as a comprehensive overview of Boost.Compute for current and prospective users of the library and covers the library's overall architecture, its low-level and high-level functionality and advanced topics such as custom functions, closures and lambda expressions. The presentation also describes how a custom template-based OpenCL library can be designed on top of Boost.Compute. Examples are included throughout the presentation to aid in a better understanding. Among others, I will demonstrate how advanced features of the library can lead to a simple and efficient C++-only solution for BLAS calculations. The architectural presentation of the library will be followed by a presentation of current performance results of the library and a comparison with competing solutions. I will conclude the presentation with insights that I gained during Google Summer of Code '15 and my overall experience in contributing to Boost.Compute, which I hope to be of interest to the wider developer community.
Strategies To Improve Urban Water Delivery In West Bengal, India: An Analysis Of Water Institutions And Benchmarking Of Water Delivery Organizations

The study examines the status of urban water delivery in India through a case study of the state of West Bengal in India. The state has been selected as a unit of study to bring out the regional variances that are not captured at a more aggregate or country level study. The study focuses on various types of urban local bodies in the state, the organizational and institutional structure for water delivery and issues around them. The study also benchmarks various urban local bodies for their water service delivery through data envelopment analysis. The study finds that in the current state of fragmented institutions and shared responsibilities between various water organizations, there needs to be a focused policy guideline for integrated development of the water sector in the state. The study also identifies certain categories of urban local bodies, for which there are further scope for efficiency improvement.
The problem with embedded CRDT counters and a solution

Conflict-free Replicated Data Types (CRDTs) can simplify the design of deterministic eventual consistency. Considering the several CRDTs that have been deployed in production systems, counters are among the first. Counters are apparently simple, with a straightforward inc/dec/read API, but can require complex implementations and several variants have been specified and coded. Unlike sets and registers, that can be adapted to operate inside maps, current counter approaches exhibit anomalies when embedded in maps. Here, we illustrate the anomaly and propose a solution, based on a new counter model and implementation.
Composite Correlation Quantization for Efficient Multimodal Retrieval

Efficient similarity retrieval from large-scale multimodal database is pervasive in modern search engines and social networks. To support queries across content modalities, the system should enable cross-modal correlation and computation-efficient indexing. While hashing methods have shown great potential in achieving this goal, current attempts generally fail to learn isomorphic hash codes in a seamless scheme, that is, they embed multiple modalities in a continuous isomorphic space and separately threshold embeddings into binary codes, which incurs substantial loss of retrieval accuracy. In this paper, we approach seamless multimodal hashing by proposing a novel Composite Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds correlation-maximal mappings that transform different modalities into isomorphic latent space, and learns composite quantizers that convert the isomorphic latent features into compact binary codes. An optimization framework is devised to preserve both intra-modal similarity and inter-modal correlation through minimizing both reconstruction and quantization errors, which can be trained from both paired and partially paired data in linear time. A comprehensive set of experiments clearly show the superior effectiveness and efficiency of CCQ against the state of the art hashing methods for both unimodal and cross-modal retrieval.
Deep Learning for Information Retrieval

Recent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being successfully applied into almost all of their major tasks. The key to the success of deep learning is its capability of accurately learning distributed representations (vector representations or structured arrangement of them) of natural language expressions such as sentences, and effectively utilizing the representations in the tasks. This tutorial aims at summarizing and introducing the results of recent research on deep learning for information retrieval, in order to stimulate and foster more significant research and development work on the topic in the future. The tutorial mainly consists of three parts. In the first part, we introduce the fundamental techniques of deep learning for natural language processing and information retrieval, such as word embedding, recurrent neural networks, and convolutional neural networks. In the second part, we explain how deep learning, particularly representation learning techniques, can be utilized in fundamental NLP and IR problems, including matching, translation, classification, and structured prediction. In the third part, we describe how deep learning can be used in specific application tasks in details. The tasks are search, question answering (from either documents, database, or knowledge base), and image retrieval.
Amazon Search: The Joy of Ranking Products

Amazon is one of the world's largest e-commerce sites and Amazon Search powers the majority of Amazon's sales. As a consequence, even small improvements in relevance ranking both positively influence the shopping experience of millions of customers and significantly impact revenue. In the past, Amazon's product search engine consisted of several hand-tuned ranking functions using a handful of input features. A lot has changed since then. In this talk we are going to cover a number of relevance algorithms used in Amazon Search today. We will describe a general machine learning framework used for ranking within categories, blending separate rankings in All Product Search, NLP techniques used for matching queries and products, and algorithms targeted at unique tasks of specific categories --- books and fashion.
Closed Finite Queuing Networks with Time Lags

Consider a closed finite queuing system in which m units move among stations 1, 2, ', NN â§ 1. A unit that completes service at station i moves to station ji, j = 1, 2, ', N with probability ejâ£i âj = Nj =1ejâ£i=1, i = 1, 2, ', N where it either commences service immediately if there is a server available, or queues to await service. The time taken for a unit to move from station i to station j is a random variable with distribution function Gi,jâ¯. At each station, the service times are independent and exponentially distributed, with the instantaneous service rate at a station being an arbitrary function of the number of units at that station. The positions of the units enroute between stations, measured in time units, are introduced as supplementary variables yielding a Markov process with states having a combination of discrete and continuous components. The integro-differential difference equations for the state variables are derived, and the steady-state solution is determined. The marginal joint distribution for the number of units at each station is found, and shown to depend, not on the distributions of the time lags, but rather on some linear combination of the means of the time lag distributions. An approximate technique is outlined for determining the absolute probability distribution of the number of units at a station, and some of the limitations of this method are discussed.
"Ethnoveterinary Practices for Herbal Cure of Livestock Used by Rural Populace of Hamirpur, (H.P.), India."

Despite the undoubted success of a scientific approach to pharmaceuticals, the last few decades have reformed towards the spectacular rise in interest in herbal medicinal products for cure of livestock by the indigenous people of Hamirpur (H.P.), India. The present study was carried out between 2008 to 2011.Recording of indigenous medicinal plants used for common diseases in animals along with the interviews with elderly persons, cattle owners, traditional farmers, healers and house wives was done. A total of 123 plant spp. belonging to 49 families wild in occurrence, were reported to be used in more than 60 ailments of livestock. Low cost and almost no side effects of these medicines make them acceptable by the local community. Ethnomedicine is an integral part of traditional medical practices in many countries of the developing world. Livestock is a major asset for resource poor small farm holders and pastoralists throughout the world. The present study is aimed at providing an overview of the ethnoveterinary preparations for various ailments of livestocks particularly in Hamirpur District of Himachal Pradesh.
Shortest Paths in Probabilistic Graphs

This paper considers the problem of finding shortest-path probability distributions in graphs whose branches are weighted with random lengths, examines the consequences of various assumptions concerning the nature of the available statistical information, and gives an exact method for computing the probability distribution, as well as methods based on hypothesis testing and statistical estimation. It presents Monte Carlo results and, based on these results, it develops an efficient method of hypothesis testing. Finally, it discusses briefly the pairwise comparison of paths.
The Traveling-Salesman Problem and Minimum Spanning Trees

This paper explores new approaches to the symmetric traveling-salesman problem in which 1-trees, which are a slight variant of spanning trees, play an essential role. A 1-tree is a tree together with an additional vertex connected to the tree by two edges. We observe that i a tour is precisely a 1-tree in which each vertex has degree 2, ii a minimum 1-tree is easy to compute, and iii the transformation on "intercity distances" cij â Cij + πi + πj leaves the traveling-salesman problem invariant but changes the minimum 1-tree. Using these observations, we define an infinite family of lower bounds wπ on C*, the cost of an optimum tour. We show that maxπwπ = C* precisely when a certain well-known linear program has an optimal solution in integers. We give a column-generation method and an ascent method for computing maxπwπ, and construct a branch-and-bound method in which the lower bounds wπ control the search for an optimum tour.
Measuring the mind’s ear: EEG of music imagery

In the current study we use electroencephalography (EEG) to detect heard music from the brain signal, hypothesizing that the time structure in music makes it especially suitable for decoding perception from EEG signals. While excluding music with vocals, we classified the perception of seven different musical fragments of about three seconds, both individually and cross-participants, using only time domain information (the event-related potential, ERP). The best individual results are 70% correct in a seven-class problem while using single trials, and when using multiple trials we achieve 100% correct after 6 presentations of the stimulus. When classifying across participants, a maximum rate of 53% was reached, supporting a general representation of each musical fragment over participants. While for some music stimuli the amplitude envelope correlated well with the ERP, this was not true for all stimuli. Aspects of the stimulus that may contribute to the differences between the EEG responses to the pieces of music are discussed. 2
Semantic representation and scale-up of integrated air traffic management data

Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.
Factores asociados con el incumplimiento del tratamiento antipalúdico en pacientes ecuatorianos

Un total de 249 personas residentes del noroccidente del Ecuador, con diagnóstico clínico de paludismo, confirmado mediante tis de gota gruesa, recibieron tratamiento con cloroquina o primaquina según el régimen terapéutico vigente en el Servicio Nacion de Erradicación de la Malaria. Se realizó nueva valoración clínica y frotis de gota gruesa 4 d después en los casos de P. falciparum (n = 120) y 8 d más tarde en los de P.vivax (n = 129); se averiguó mediante interrogatorio directo si el paciente había o no cumplido con el tratamiento y se investigaron las causas para haber actuado en uno u otro sentido. La información se procesó en pa EPI-INFO 6.04 y SPSS PC 7,0, se utilizaron la prueba de bondad de ajuste y el análisis factorial de correspondencias. Se defini ó como paciente cumplidor a quien tomó diariamente el número de pastillas prescrito, durante el número de días indicado, con el inte alo establecido y en el momento preciso. Por cada 3 pacientes cumplidores hubo 2 incumplidores; el incumplimiento no se a significativamente con la edad, el sexo, la escolaridad, la etnia, procedencia urbana o rural o el nivel de ingreso monetari El conocimiento de la gravedad de la infección sí contribuyó al cumplimiento. Las razones del incumplimiento se asociaron principa lmente con los medicamentos (efectos secundarios/renuencia a tomarlos); el olvido y el hecho de "curarse enseguida" fueron otras ra es. El perfil del incumplidor se identificó con un adolescente, varón, mestizo, pobre y proveniente del área rural.
Interactive and Connected Tableware for Promoting Children's Vegetable-Eating and Family Interaction

In this paper, we present an interactive and connected tableware system for promoting children to eat vegetables more. Our system aims to encourage children to eat vegetables more and educate them to understand the benefits of those vegetables. Our system comprises a food tray, a spoon and a smartphone cradle, which named "Healthy Tray", "Healthy Spoon" and "Healthy Cradle" respectively. We also present a mobile application with education and gamification elements connected to our tableware. Healthy Cradle is particularly designed to enhance the interaction and communication between children and their parents while playing the game. We conducted a pilot study with two families. Through the study the child participants who used to hate eating vegetables (e.g., broccoli) changed their attitudes; they had vegetable on their own will while playing the presented education game. After the game ends, they also showed a good knowledge on the benefits of the vegetable that they just had. In addition, we could observe the children participants actively interact with mothers while eating vegetables and playing the game.
DroidRA: taming reflection to support whole-program analysis of Android apps

Android developers heavily use reflection in their apps for legitimate reasons, but also significantly for hiding malicious actions. Unfortunately, current state-of-the-art static analysis tools for Android are challenged by the presence of reflective calls which they usually ignore. Thus, the results of their security analysis, e.g., for private data leaks, are inconsistent given the measures taken by malware writers to elude static detection. We propose the DroidRA instrumentation-based approach to address this issue in a non-invasive way. With DroidRA, we reduce the resolution of reflective calls to a composite constant propagation problem. We leverage the COAL solver to infer the values of reflection targets and app, and we eventually instrument this app to include the corresponding traditional Java call for each reflective call. Our approach allows to boost an app so that it can be immediately analyzable, including by such static analyzers that were not reflection-aware. We evaluate DroidRA on benchmark apps as well as on real-world apps, and demonstrate that it can allow state-of-the-art tools to provide more sound and complete analysis results.
The fraud behind the MMR scare

This week we begin a special series of articles by the journalist Brian Deer. It focuses on what may seem a familiar story—the scare linking the MMR vaccine with autism, launched at a press conference in 1998 after the Lancet published a paper by Andrew Wakefield and colleagues describing 12 children with brain and bowel disease. Deer’s investigation into Wakefield’s work has taken seven years. It led to the UK General Medical Council’s longest ever fitness to practise hearing, after which Wakefield and his senior co-author, John Walker-Smith, were struck off and the paper was retracted. That surely should be the end of the story. It turns out not to …
The Standardization of Bronchoscopic Techniques for Ventilator-Associated Pneumonia

During the last decade, several centers have directed their research efforts at defining more efficient modalities for the accurate diagnosis of ventilator-associated pneumonia (VAP). Accurate diagnosis is an essential prerequisite for clinical investigation. Initial studies, which correlated histologic findings and quantitative bacterial cultures of the lung with the findings of protected specimen brush cultures have stimulated clinical investigation of VAF! Two diagnostic techniques, protected specimen brushing (PSB) and bronchoalveolar lavage (BAL), have been extensively investigated and have shown promising results. Advancements in the field, however, have been limited by the lack of standardization, particularly for BAL. In this communication, we attempt to standardize methodology for presently available bronchoscopic techniques and interpretation of data obtained by bronchoscopy to facilitate comparisons among investigations and to promote multicenter collaboration.
Molecular Species Identification of Cryptic Apple and Snowberry Maggots (Diptera: Tephritidae) in Western and Central Washington

ABSTRACT In Washington state, identification of the quarantine apple pest Rhagoletis pomonella (Walsh) is complicated by the presence of the cryptic species Rhagoletis zephyria Snow (Diptera: Tephritidae). Distinguishing the two flies is important because there is a zero tolerance policy for R. pomonella in apple production for export. Here, we attempt to distinguish the two species by scoring R. pomonella and R. zephyria populations from western and south—central Washington for a set of 11 nuclear markers, including four single nucleotide polymorphisms (SNPs) developed for rapid and inexpensive genotyping using Taqman real-time quantitative—polymerase chain reaction. We show that the four SNPs maybe adequate in most cases for distinguishing whether a fly originated from apple or black hawthorn (the two major host plants for R. pomonella representing an economic risk) versus snowberry (the major host for R. zephyria, and not a commercial threat). However, directional introgression of R. zephyria alleles into R. pomonella can complicate the identification of flies of mixed ancestry based only on the four SNPs. Moreover, this problem is more acute in the sensitive apple-growing regions of central Washington where our results imply hybridization is common. Consequently, application of the four SNP quantitative—polymerase chain reaction assay can immediately assist ongoing apple maggot monitoring, while the development of additional genetic markers through next-generation sequencing would be valuable for increasing confidence in species identification and for assessing the threat posed by hybridization as R. pomonella further spreads into the more arid apple-growing regions of central Washington.
N-Butyl Sulfide as an Attractant and Coattractant for Male and Female Codling Moth (Lepidoptera: Tortricidae)

ABSTRACT Research to discover and develop attractants for the codling moth, Cydia pomonella L. , has involved identification of the chemicals eliciting moth orientation to conspecific female moths, host fruits, fermented baits, and species of microbes. Pear ester, acetic acid, and N-butyl sulfide are among those chemicals reported to attract or enhance attractiveness to codling moth. We evaluated the trapping of codling moth with N-butyl sulfide alone and in combination with acetic acid and pear ester in apple orchards. Acetic acid was attractive in two tests and N-butyl sulfide was attractive in one of two tests. N-Butyl sulfide increased catches of codling moth when used with acetic acid to bait traps. N-Butyl sulfide also increased catches of codling moth when added to traps baited with the combination of acetic acid and pear ester. Male and female codling moth both responded to these chemicals and chemical combinations. These results provide a new three-component lure comprising N-butyl sulfide, acetic acid, and pear ester that is stronger for luring codling moth females than other attractants tested.