Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Multitask Compressive Sensing

Abstract: Compressive sensing (CS) is a framework whereby one performs N nonadaptive measurements to constitute a vector v isin RN used to recover an approximation u isin RM desired signal u isin RM with N 1 sets of compressive measurements {vi}i=1,L are performed, each of the associated {ui}i=1,Lare recovered one at a time, independently. In many applications the L ldquotasksrdquo defined by the mappings virarrui are not statistically independent, and it may be possible to improve the performance of the inversion if statistical interrelationships are exploited. In this paper, we address this problem within a multitask learning setting, wherein the mapping vrarru for each task corresponds to inferring the parameters (here, wavelet coefficients) associated with the desired signal vi, and a shared prior is placed across all of the L tasks. Under this hierarchical Bayesian modeling, data from all L tasks contribute toward inferring a posterior on the hyperparameters, and once the shared prior is thereby inferred, the data from each of the L individual tasks is then employed to estimate the task-dependent wavelet coefficients. An empirical Bayesian procedure for the estimation of hyperparameters is considered; two fast inference algorithms extending the relevance vector machine (RVM) are developed. Example results on several data sets demonstrate the effectiveness and robustness of the proposed algorithms.

Bovine tuberculosis: an old disease but a new threat to Africa.

Abstract: Bovine tuberculosis (TB) is a disease characterised by progressive development of specific granulomatous lesions or tubercles in lung tissue, lymph nodes or other organs. Mycobacterium bovis is the causative agent of the disease. Bovine species, including bison and buffaloes, are susceptible to the disease, but nearly all warm-blooded animals can be affected. All species are not equally susceptible to the disease; some are spill-over (end) hosts and others maintenance hosts. In Africa, bovine TB primarily affects cattle; however, infection in other farm and domestic animals, such as sheep, goats, pigs, dogs and cats, is not uncommon. Wild ruminants and carnivores are also affected and are the natural reservoirs of the infectious agent in the wild. Man is also susceptible to the disease, the highest risk groups being individuals with concomitant HIV/AIDS infection. In Africa, human TB is widely known to be caused by M. tuberculosis; however, an unknown proportion of cases are due to M. bovis. This infection in humans is under-reported as a result of the diagnostic limitations of many laboratories in distinguishing M. bovis from M. tuberculosis. None of the national reports submitted to the OIE and WHO by African member states mention the importance of M. bovis in human TB cases. Consumption of unpasteurised milk and poorly heat-treated meat and close contact with infected animals represent the main sources of infection for humans. This review attempts to examine the impact of bovine TB on the health of animals and humans.

Learning from Noisy Labels with Distillation

Abstract: The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally, label noise has been treated as statistical outliers, and techniques such as importance re-weighting and bootstrapping have been proposed to alleviate the problem. According to our observation, the real-world noisy labels exhibit multimode characteristics as the true labels, rather than behaving like independent random outliers. In this work, we propose a unified distillation framework to use “side” information, including a small clean dataset and label relations in knowledge graph, to “hedge the risk” of learning from noisy labels. Unlike the traditional approaches evaluated based on simulated label noises, we propose a suite of new benchmark datasets, in Sports, Species and Artifacts domains, to evaluate the task of learning from noisy labels in the practical setting. The empirical study demonstrates the effectiveness of our proposed method in all the domains.

Valuable Nutrients and Functional Bioactives in Different Parts of Olive (Olea europaea L.)—A Review

Abstract: The Olive tree (Olea europaea L), a native of the Mediterranean basin and parts of Asia, is now widely cultivated in many other parts of the world for production of olive oil and table olives Olive is a rich source of valuable nutrients and bioactives of medicinal and therapeutic interest Olive fruit contains appreciable concentration, 1–3% of fresh pulp weight, of hydrophilic (phenolic acids, phenolic alchohols, flavonoids and secoiridoids) and lipophilic (cresols) phenolic compounds that are known to possess multiple biological activities such as antioxidant, anticarcinogenic, antiinflammatory, antimicrobial, antihypertensive, antidyslipidemic, cardiotonic, laxative, and antiplatelet Other important compounds present in olive fruit are pectin, organic acids, and pigments Virgin olive oil (VOO), extracted mechanically from the fruit, is also very popular for its nutritive and health-promoting potential, especially against cardiovascular disorders due to the presence of high levels of monounsaturates and other valuable minor components such as phenolics, phytosterols, tocopherols, carotenoids, chlorophyll and squalene The cultivar, area of production, harvest time, and the processing techniques employed are some of the factors shown to influence the composition of olive fruit and olive oil This review focuses comprehensively on the nutrients and high-value bioactives profile as well as medicinal and functional aspects of different parts of olives and its byproducts Various factors affecting the composition of this food commodity of medicinal value are also discussed

The community-search problem and how to plan a successful cocktail party

Abstract: A lot of research in graph mining has been devoted in the discovery of communities. Most of the work has focused in the scenario where communities need to be discovered with only reference to the input graph. However, for many interesting applications one is interested in finding the community formed by a given set of nodes. In this paper we study a query-dependent variant of the community-detection problem, which we call the community-search problem: given a graph G, and a set of query nodes in the graph, we seek to find a subgraph of G that contains the query nodes and it is densely connected. We motivate a measure of density based on minimum degree and distance constraints, and we develop an optimum greedy algorithm for this measure. We proceed by characterizing a class of monotone constraints and we generalize our algorithm to compute optimum solutions satisfying any set of monotone constraints. Finally we modify the greedy algorithm and we present two heuristic algorithms that find communities of size no greater than a specified upper bound. Our experimental evaluation on real datasets demonstrates the efficiency of the proposed algorithms and the quality of the solutions we obtain.