Multi-view Machines

ABSTRACT With rapidly growing amount of data available on the web, it becomes increasingly likely to obtain data from different perspectives for multi-view learning. Some successive examples of web applications include recommendation and target advertising. Specifically, to predict whether a user will click an ad in a query context, there are available features extracted from user profile, ad information and query description, and each of them can only capture part of the task signals from a particular aspect/view. Different views provide complementary information to learn a practical model for these applications. Therefore, an effective integration of the multi-view information is critical to facilitate the learning performance. In this paper, we propose a general predictor, named multiview machines (MVMs), that can effectively explore the full-order interactions between features from multiple views. A joint factorization is applied for the interaction parameters which makes parameter estimation more accurate under sparsity and renders the model with the capacity to avoid overfitting. Moreover, MVMs can work in conjunction with different loss functions for a variety of machine learning tasks. The advantages of MVMs are illustrated through comparison with other methods for multi-view prediction, including support vector machines (SVMs), support tensor machines (STMs) and factorization machines (FMs). A stochastic gradient descent method and a distributed implementation on Spark are presented to learn the MVM model. Through empirical studies on two real-world web application datasets, we demonstrate the effectiveness of MVMs on modeling feature interactions in multi-view data. A 3.51% accuracy improvement is shown on MVMs over FMs for the problem of movie rating prediction, and 0.57% for ad click prediction

Seasonal variations in the fatty acid profile of milk from yaks grazing on the Qinghai-Tibetan plateau

An experiment was conducted to study the seasonal changes in the fatty acid profile of milk from yaks (Bos grunniens) when kept at altitudes of 3000m above sea level (a.s.l.) and higher. Data and samples were collected in summer (July), autumn (September), winter (November) and spring (March) from ten lactating yaks (four in spring). The yaks grazed pastures adjacent to the farm building throughout the year. In spring only they received 0·6kg crop by-products per day (dry matter basis). Fresh alpine grasses, available in summer and autumn, showed high concentrations of α-linolenic acid (46-51g/100g lipids) compared with the dry, yellow vegetation of winter and spring (16g/100g lipids). In autumn and summer, the milk fat had higher concentrations of polyunsaturated fatty acids than in winter. These polyunsaturated fatty acids were comprised of vaccenic acid, rumenic acid and α-linolenic acid, which are all considered beneficial to human health. The rare fatty acid, γ-linolenic acid, was also detected in yak milk, especially in the milk obtained in spring. The results suggest that yak milk, which is the most important basic food of the Tibetan herders, has the most favourable fatty acid profile when yaks grazed green pasture, which also corresponds to the period of highest milk productio

(±)-Peniorthoesters A and B, Two Pairs of Novel Spiro-Orthoester en-antiomers With an Unusual 1,4,6-Trioxaspi-ro[4.5]decane-7-One Unit From Penicillium minioluteum

(±)-Peniorthoesters A and B (±1 and ±2), two pairs of unprecedented spiro-orthoester enantiomers with a 1,4,6-trioxaspiro[4. 5]decane-7-one unit, were obtained from Penicillium minioluteum. Their structures were determined by spectroscopic methods, X-ray diffraction analyses, and ECD calculations. (±)-Peniorthoesters A and B are the first examples of spiro-orthoester enantiomers, and they represent the first spiro-orthoesters originating from fungi. All compounds showed potential inhibitory activities comparable to dexamethasone against NO production with IC50 values ranging from 14.2 to 34.5 μM

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements