Detecting Simultaneous Integer Relations for Several Real Vectors

An algorithm which either finds an nonzero integer vector ${\mathbf m}$ for given $t$ real $n$-dimensional vectors ${\mathbf x}_1,...,{\mathbf x}_t$ such that ${\mathbf x}_i^T{\mathbf m}=0$ or proves that no such integer vector with norm less than a given bound exists is presented in this paper. The cost of the algorithm is at most ${\mathcal O}(n^4 + n^3 \log \lambda(X))$ exact arithmetic operations in dimension $n$ and the least Euclidean norm $\lambda(X)$ of such integer vectors. It matches the best complexity upper bound known for this problem. Experimental data show that the algorithm is better than an already existing algorithm in the literature. In application, the algorithm is used to get a complete method for finding the minimal polynomial of an unknown complex algebraic number from its approximation, which runs even faster than the corresponding \emph{Maple} built-in function.Comment: 10 page

Deep Landscape Forecasting for Real-time Bidding Advertising

The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. Specifically, we utilize a recurrent neural network to flexibly model the conditional winning probability w.r.t. each bid price. Then we conduct the bid landscape forecasting through probability chain rule with strict mathematical derivations. And, in an end-to-end manner, we optimize the model by minimizing two negative likelihood losses with comprehensive motivations. Without any specific assumption for the distribution form of bid landscape, our model shows great advantages over previous works on fitting various sophisticated market price distributions. In the experiments over two large-scale real-world datasets, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: KDD 2019. The reproducible code and dataset link is https://github.com/rk2900/DL

Research of NiMH Battery Modeling and Simulation Based on Linear Regression Analysis Method

 The battery state-of-charge estimation was one of core issues in the development of electric vehicles battery management system, and higher accurate model was needed in state-of-charge estimation correctly. Therefore, accurate battery modeling and simulation was researched here. The Thevenin equivalent circuit model of NiMH battery was established for the poor accuracy of traditional model. Based on the data which were brought from the 6V 6Ah NiMH battery hybrid pulse cycling test experiments, Thevenin model parameters were identified by means of the linear regression analysis method. Then, the battery equivalent circuit simulating model was built in the MATLAB/Simulink environment. The simulation and experimental results showed that the model has better accuracy and can be used to guide the battery state-of-charge estimation

How Does China's Household Portfolio Selection Vary with Financial Inclusion?

Portfolio underdiversification is one of the most costly losses accumulated over a household's life cycle. We provide new evidence on the impact of financial inclusion services on households' portfolio choice and investment efficiency using 2015, 2017, and 2019 survey data for Chinese households. We hypothesize that higher financial inclusion penetration encourages households to participate in the financial market, leading to better portfolio diversification and investment efficiency. The results of the baseline model are consistent with our proposed hypothesis that higher accessibility to financial inclusion encourages households to invest in risky assets and increases investment efficiency. We further estimate a dynamic double machine learning model to quantitatively investigate the non-linear causal effects and track the dynamic change of those effects over time. We observe that the marginal effect increases over time, and those effects are more pronounced among low-asset, less-educated households and those located in non-rural areas, except for investment efficiency for high-asset households

A Genome-Wide Survey on Basic Helix-Loop-Helix Transcription Factors in Giant Panda

The giant panda (Ailuropoda melanoleuca) is a critically endangered mammalian species. Studies on functions of regulatory proteins involved in developmental processes would facilitate understanding of specific behavior in giant panda. The basic helix-loop-helix (bHLH) proteins play essential roles in a wide range of developmental processes in higher organisms. bHLH family members have been identified in over 20 organisms, including fruit fly, zebrafish, mouse and human. Our present study identified 107 bHLH family members being encoded in giant panda genome. Phylogenetic analyses revealed that they belong to 44 bHLH families with 46, 25, 15, 4, 11 and 3 members in group A, B, C, D, E and F, respectively, while the remaining 3 members were assigned into “orphan”. Compared to mouse, the giant panda does not encode seven bHLH proteins namely Beta3a, Mesp2, Sclerax, S-Myc, Hes5 (or Hes6), EBF4 and Orphan 1. These results provide useful background information for future studies on structure and function of bHLH proteins in the regulation of giant panda development

(5-Benzoyl-2-methyl-4-{[1-(pyridin-4-yl)-1H-1,2,3-triazol-4-yl]meth­oxy}-1-benzofuran-7-yl)(phen­yl)methanone

The crystal structure of the title compound, C31H22N4O4, features weak C—H⋯O inter­actions. The dihedral angle between the fused benzene and furan rings is 2.49 (15)°, while that between the triazole and pyridine rings is 10.23(18)°