Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.Comment: Accepted at Thirty-First AAAI Conference on Artificial Intelligence (AAAI17

The Performance Evaluation of the Listed Security Companies in China Based on the DEA Model

Abstract With the continuous development of domestic stock market, the listed security companies have stepped into a stable growth period, the study of their performance becomes also deeper. This paper firstly introduces the methods of evaluating the performance of the listed security companies, and discusses the DEA method in detail. Then we evaluate the corporate performance of 16 domestic security companies by the DEA method. Finally we analyses the empirical results from three aspects, which are technical efficiency, pure technical efficiency and scale efficiency. This paper evaluates the performance of the listed security companies with a more scientific method

Poly[[diaqua­[μ4-4,4′-carbonyl­bis(benzene-1,2-dicarboxyl­ato)]bis­(dipyrido[3,2-a:2′,3′-c]phenazine)dicadmium(II)] monohydrate]

In the title compound, {[Cd2(C17H6O9)(C18H10N4)2(H2O)2]·H2O}n, the CdII atom is seven-coordinated by five O atoms from two different 4,4′-carbonyl­bis(benzene-1,2-dicarboxyl­ate) (BPTC) anions and one water mol­ecule, and by two N atoms from one chelating dipyrido[3,2-a:2′,3′-c]phenazine (L) ligand in a distorted penta­gonal-bipyramidal geometry. The BPTC anions link the CdII atoms, forming a one-dimensional chain structure. The L ligands are attached on both sides of the chain. A twofold rotation axis passes through the complex molecule. The crystal structure involves O—H⋯O hydrogen bonds

A New Method to Prove and Find Analytic Inequalities

We present a new method to study analytic inequalities. As for its applications, we prove the well-known Hölder inequality and establish several new analytic inequalities

Metronidazolium perchlorate

In the crystal structure of the title compound [systematic name: 1-(2-hy­droxy­eth­yl)-2-methyl-5-nitro-1H-imidazol-3-ium perchlorate], C6H10N3O3 +·ClO4 −, the cations are linked by inter­molecular N—H⋯O hydrogen bonds into zigzag chains along the c axis. The cations and anions are connected by O—H⋯O and C—H⋯O hydrogen bonds. A weak intra­molecular C—H⋯O hydrogen bond is also observed