The effect of correlation between demands on hierarchical forecasting

The forecasting needs for inventory control purposes are hierarchical. For SKUs in a product family or a SKU stored across different depot locations, forecasts can be made from the individual series’ history or derived top-down. Many discussions have been found in the literature, but it is not clear under what conditions one approach is better than the other. Correlation between demands has been identified as a very important factor to affect the performance of the two approaches, but there has been much confusion on whether positive or negative correlation. This paper summarises the conflicting discussions in the literature, argues that it is negative correlation that benefits the top-down or grouping approach, and quantifies the effect of correlation through simulation experiments

Cosmological Constant as a Manifestation of the Hierarchy

There has been the suggestion that the cosmological constant as implied by the dark energy is related to the well-known hierarchy between the Planck scale, $M_{\rm Pl}$, and the Standard Model scale, $M_{\rm SM}$. Here we further propose that the same framework that addresses this hierarchy problem must also address the smallness problem of the cosmological constant. Specifically, we investigate the minimal supersymmetric (SUSY) extension of the Randall-Sundrum model where SUSY-breaking is induced on the TeV brane and transmitted into the bulk. We show that the Casimir energy density of the system indeed conforms with the observed dark energy scale

Registers of supplication and demand in English-as-a-foreign-language technical writing

This project introduces theoretical discussion on five aspects of writing: register, genre, pragmatics, functional writing, and social function the goal of this project aims at improving business peoples\u27 technical writing competence

A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images

In recent years, with the development of aerospace technology, we use more and more images captured by satellites to obtain information. But a large number of useless raw images, limited data storage resource and poor transmission capability on satellites hinder our use of valuable images. Therefore, it is necessary to deploy an on-orbit semantic segmentation model to filter out useless images before data transmission. In this paper, we present a detailed comparison on the recent deep learning models. Considering the computing environment of satellites, we compare methods from accuracy, parameters and resource consumption on the same public dataset. And we also analyze the relation between them. Based on experimental results, we further propose a viable on-orbit semantic segmentation strategy. It will be deployed on the TianZhi-2 satellite which supports deep learning methods and will be lunched soon.Comment: 8 pages, 3 figures, ICNC-FSKD 201