General Deep Multinomial Logit Model

Multinomial logit model (MNL) is by far the most widely used discrete choice model that is widely used to explain or predict a choice from a set of two or more discrete alternatives. MNL operates within a framework of the random utility model (RUM) in which the utility of an alternative perceived by an individual consists of two components: systematic component and random component. The systematic component is usually defined as a linear function. However, practical decision processes involve complex considerations regarding various aspects of the alternatives and individual which cannot be adequately represented by simple linear models. To overcome the weakness of linear utility model and improve the performance of MNL, in this paper, we propose a general deep multinomial logit model (GDMNL) that takes advantage of both traditional MNL and deep learning. In this model, deep neural networks are applied to extend MNL by learning different nonlinear utility functions of various alternatives. The empirical study in the domain of transit route choice analysis demonstrates the validity and superiority of the proposed model

Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine Learning Algorithms

Lobster eye telescopes are ideal monitors to detect X-ray transients, because they could observe celestial objects over a wide field of view in X-ray band. However, images obtained by lobster eye telescopes are modified by their unique point spread functions, making it hard to design a high efficiency target detection algorithm. In this paper, we integrate several machine learning algorithms to build a target detection framework for data obtained by lobster eye telescopes. Our framework would firstly generate two 2D images with different pixel scales according to positions of photons on the detector. Then an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images. At last, a random forest algorithm will be used to pick up final detection results from candidates obtained by previous steps. Tested with simulated data of the Wide-field X-ray Telescope onboard the Einstein Probe, our detection framework could achieve over 94% purity and over 90% completeness for targets with flux more than 3 mCrab (9.6 * 10-11 erg/cm2/s) and more than 94% purity and moderate completeness for targets with lower flux at acceptable time cost. The framework proposed in this paper could be used as references for data processing methods developed for other lobster eye X-ray telescopes.Comment: Accepted by the APJS Journal. Full source code could be downloaded from the China VO with DOI of https://doi.org/10.12149/101175. Docker version of the code could be obtained under request to the corresponding autho