Block-distributed Gradient Boosted Trees

The Gradient Boosted Tree (GBT) algorithm is one of the most popular machine learning algorithms used in production, for tasks that include Click-Through Rate (CTR) prediction and learning-to-rank. To deal with the massive datasets available today, many distributed GBT methods have been proposed. However, they all assume a row-distributed dataset, addressing scalability only with respect to the number of data points and not the number of features, and increasing communication cost for high-dimensional data. In order to allow for scalability across both the data point and feature dimensions, and reduce communication cost, we propose block-distributed GBTs. We achieve communication efficiency by making full use of the data sparsity and adapting the Quickscorer algorithm to the block-distributed setting. We evaluate our approach using datasets with millions of features, and demonstrate that we are able to achieve multiple orders of magnitude reduction in communication cost for sparse data, with no loss in accuracy, while providing a more scalable design. As a result, we are able to reduce the training time for high-dimensional data, and allow more cost-effective scale-out without the need for expensive network communication.Comment: SIGIR 201

Market Manipulation: A Comprehensive Study of Stock Pools

Using a hand collected new data set, this paper examines in detail a classic account of stock market manipulation—the “stock pools” of the 1920s, which prompted the current anti-manipulation rules in the United States. We examine abnormal turnover and returns and the relationship between them, as well as the long-term performance of the selected stocks. We conclude that the evidence suggests informed trading rather than manipulation. Our findings have implications for regulatory policy as well as the investigation and prosecution of manipulation cases

Культурные аспекты социальных сетей

In my article I raised the topic of social networks, as they appeared in our lives. What has changed since their appearance as they affected the lives of mankind

Effects of antipsychotics on bone mineral density and prolactin levels in patients with schizophrenia: a 12-month prospective study

Objective: Effects of conventional and atypical antipsychotics on bone mineral density (BMD) and serum prolactin levels (PRL) were examined in patients with schizophrenia.Methods: One hundred and sixty-three first-episode inpatients with schizophrenia were recruited, to whom one of three conventional antipsychotics (perphenazine, sulpiride, and chlorpromazine) or one of three atypical antipsychotics (clozapine, quetiapine, and aripiprazole)was prescribed for 12 months as appropriate. BMD and PRL were tested before and after treatment. Same measures were conducted in 90 matched healthy controls.Results Baseline BMD of postero-anterior L1–L4 range from 1.04 ± 0.17 to 1.42 ± 1.23, and there was no significant difference between the patients group and healthy control group. However, post-treatment BMD values in patients (ranging from 1.02 ± 0.15 to 1.23 ± 0.10) were significantly lower than that in healthy controls (ranging from 1.15 ± 0.12 to 1.42 ± 1.36). The BMD values after conventional antipsychotics were significantly lower than that after atypical antipsychotics. The PRL level after conventional antipsychotics (53.05 ± 30.25 ng/ml) was significantly higher than that after atypical antipsychotics (32.81 ± 17.42 ng/ml). Conditioned relevance analysis revealed significant negative correlations between the PRL level and the BMD values after conventional antipsychotics.Conclusion The increase of PRL might be an important risk factor leading to a high prevalence of osteoporosis in patients with schizophrenia on long-term conventional antipsychotic medication.<br/