Planetary benchmarks

Design criteria and technology requirements for a system of radar reference devices to be fixed to the surfaces of the inner planets are discussed. Offshoot applications include the use of radar corner reflectors as landing beacons on the planetary surfaces and some deep space applications that may yield a greatly enhanced knowledge of the gravitational and electromagnetic structure of the solar system. Passive retroreflectors with dimensions of about 4 meters and weighing about 10 kg are feasible for use with orbiting radar at Venus and Mars. Earth-based observation of passive reflectors, however, would require very large and complex structures to be delivered to the surfaces. For Earth-based measurements, surface transponders offer a distinct advantage in accuracy over passive reflectors. A conceptual design for a high temperature transponder is presented. The design appears feasible for the Venus surface using existing electronics and power components

Опухоль брюшной стенки - грыжа спигелевой линии, содержащая метастатическую карциному с невыявленным первичным очагом

АДЕНОКАРЦИНОМАБРЮШНАЯ СТЕНКАГРЫЖИКАРЦИНОМАКОЛОРЕКТАЛЬНЫЕ НОВООБРАЗОВАНИЯНОВООБРАЗОВАНИЯТОЛСТАЯ КИШК

Comparison of Inedible Egg Product and Spray-Dried Plasma as Sources of Protein for Weanling Pigs

Pigs were weaned at approximately 18 days of age and fed diets containing inedible egg product, spray-dried plasma (SDP), or the combination of both for 2 weeks postweaning. They then received a common diet for an additional 2 weeks after the treatment period. The impact of these dietary ingredients on growth performance was evaluated. Inedible egg product did not improve growth or feed efficiency of pigs compared with those fed the control diet during the 2-week treatment period. However, SDP increased body weight gain and feed efficiency during the treatment period. The improved performance over the control group that resulted from feeding a combination of SDP and egg product was primarily dependent upon the SDP. In the third week, a trend occurred for improved performance of pigs fed the egg product compared with those fed 4% SDP; however, the difference was not significant. During this same period, pigs previously fed SDP gained weight slower and consumed less feed than those that had not been fed SDP. Therefore, the response to SDP was partially lost when it was removed from the diet. In summary, the outcome of this study demonstrated that inedible egg product was an adequate source of protein for the weanling pig but did not provide measurable improvements over the control diet. Inedible egg product did not have an additive effect when combined with SDP. The improved feed efficiency associated with SDP resulted in increased weanling pig growth

Why We Read Wikipedia

Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, we observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Our findings advance our understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.Comment: Published in WWW'17; v2 fixes caption of Table

Wavelet Based Fractal Analysis of Airborne Pollen

The most abundant biological particles in the atmosphere are pollen grains and spores. Self protection of pollen allergy is possible through the information of future pollen contents in the air. In spite of the importance of airborne pol len concentration forecasting, it has not been possible to predict the pollen concentrations with great accuracy, and about 25% of the daily pollen forecasts have resulted in failures. Previous analysis of the dynamic characteristics of atmospheric pollen time series indicate that the system can be described by a low dimensional chaotic map. We apply the wavelet transform to study the multifractal characteristics of an a irborne pollen time series. We find the persistence behaviour associated to low pollen concentration values and to the most rare events of highest pollen co ncentration values. The information and the correlation dimensions correspond to a chaotic system showing loss of information with time evolution.Comment: 11 pages, 7 figure

Asynchronous Training of Word Embeddings for Large Text Corpora

Word embeddings are a powerful approach for analyzing language and have been widely popular in numerous tasks in information retrieval and text mining. Training embeddings over huge corpora is computationally expensive because the input is typically sequentially processed and parameters are synchronously updated. Distributed architectures for asynchronous training that have been proposed either focus on scaling vocabulary sizes and dimensionality or suffer from expensive synchronization latencies. In this paper, we propose a scalable approach to train word embeddings by partitioning the input space instead in order to scale to massive text corpora while not sacrificing the performance of the embeddings. Our training procedure does not involve any parameter synchronization except a final sub-model merge phase that typically executes in a few minutes. Our distributed training scales seamlessly to large corpus sizes and we get comparable and sometimes even up to 45% performance improvement in a variety of NLP benchmarks using models trained by our distributed procedure which requires $1/10$ of the time taken by the baseline approach. Finally we also show that we are robust to missing words in sub-models and are able to effectively reconstruct word representations.Comment: This paper contains 9 pages and has been accepted in the WSDM201

Domain-independent Extraction of Scientific Concepts from Research Articles

We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. First, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process. This set of concepts is utilised to annotate a corpus of scientific abstracts from 10 domains of Science, Technology and Medicine at the phrasal level in a joint effort with domain experts. The resulting dataset is used in a set of benchmark experiments to (a) provide baseline performance for this task, (b) examine the transferability of concepts between domains. Second, we present two deep learning systems as baselines. In particular, we propose active learning to deal with different domains in our task. The experimental results show that (1) a substantial agreement is achievable by non-experts after consultation with domain experts, (2) the baseline system achieves a fairly high F1 score, (3) active learning enables us to nearly halve the amount of required training data.Comment: Accepted for publishing in 42nd European Conference on IR Research, ECIR 202

A survey of location inference techniques on Twitter

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such as earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or an author’s location remains a challenge, thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state of the art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features

Communication calls produced by electrical stimulation of four structures in the guinea pig brain

One of the main central processes affecting the cortical representation of conspecific vocalizations is the collateral output from the extended motor system for call generation. Before starting to study this interaction we sought to compare the characteristics of calls produced by stimulating four different parts of the brain in guinea pigs (Cavia porcellus). By using anaesthetised animals we were able to reposition electrodes without distressing the animals. Trains of 100 electrical pulses were used to stimulate the midbrain periaqueductal grey (PAG), hypothalamus, amygdala, and anterior cingulate cortex (ACC). Each structure produced a similar range of calls, but in significantly different proportions. Two of the spontaneous calls (chirrup and purr) were never produced by electrical stimulation and although we identified versions of chutter, durr and tooth chatter, they differed significantly from our natural call templates. However, we were routinely able to elicit seven other identifiable calls. All seven calls were produced both during the 1.6 s period of stimulation and subsequently in a period which could last for more than a minute. A single stimulation site could produce four or five different calls, but the amygdala was much less likely to produce a scream, whistle or rising whistle than any of the other structures. These three high-frequency calls were more likely to be produced by females than males. There were also differences in the timing of the call production with the amygdala primarily producing calls during the electrical stimulation and the hypothalamus mainly producing calls after the electrical stimulation. For all four structures a significantly higher stimulation current was required in males than females. We conclude that all four structures can be stimulated to produce fictive vocalizations that should be useful in studying the relationship between the vocal motor system and cortical sensory representation

Electrons in High-Tc Compounds: Ab-Initio Correlation Results

Electronic correlations in the ground state of an idealized infinite-layer high-Tc compound are computed using the ab-initio method of local ansatz. Comparisons are made with the local-density approximation (LDA) results, and the correlation functions are analyzed in detail. These correlation functions are used to determine the effective atomic-interaction parameters for model Hamiltonians. On the resulting model, doping dependencies of the relevant correlations are investigated. Aside from the expected strong atomic correlations, particular spin correlations arise. The dominating contribution is a strong nearest neighbor correlation that is Stoner-enhanced due to the closeness of the ground state to the magnetic phase. This feature depends moderately on doping, and is absent in a single-band Hubbard model. Our calculated spin correlation function is in good qualitative agreement with that determined from the neutron scattering experiments for a metal.Comment: 21pp, 5fig, Phys. Rev. B (Oct. 98