Research on EPQ Model Based on Random Defective Rate

In the real economic life, it is inevitable that a lot of phenomena will happen, such as damage in transportation and machine failure, which may generate a certain percentage of defective products in the process of logistics and production. Especially in the production process, the stoppage on the production line often brings about defective products. To provide mathematical models that more closely conform to actual inventories and respond to the factors that contribute to inventory costs, based on the classical EPQ model, this paper develops an EPQ model for defective items with a certain price relative to the defective level. And this paper also considers the issue that defective items are sold at a lower price which depends on the degree of product defects. A mathematical model is developed and numerical examples are provided to illustrate the solution procedure. The research will enrich researches and it has important practical significance

CPMLHO:Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimization

The hyperparameter optimization of neural network can be expressed as a bilevel optimization problem. The bilevel optimization is used to automatically update the hyperparameter, and the gradient of the hyperparameter is the approximate gradient based on the best response function. Finding the best response function is very time consuming. In this paper we propose CPMLHO, a new hyperparameter optimization method using cutting plane method and mixed-level objective function.The cutting plane is added to the inner layer to constrain the space of the response function. To obtain more accurate hypergradient,the mixed-level can flexibly adjust the loss function by using the loss of the training set and the verification set. Compared to existing methods, the experimental results show that our method can automatically update the hyperparameters in the training process, and can find more superior hyperparameters with higher accuracy and faster convergence

Draft Genome of the Leopard Gecko, \u3cem\u3eEublepharis Macularius\u3c/em\u3e

Background Geckos are among the most species-rich reptile groups and the sister clade to all other lizards and snakes. Geckos possess a suite of distinctive characteristics, including adhesive digits, nocturnal activity, hard, calcareous eggshells, and a lack of eyelids. However, one gecko clade, the Eublepharidae, appears to be the exception to most of these ‘rules’ and lacks adhesive toe pads, has eyelids, and lays eggs with soft, leathery eggshells. These differences make eublepharids an important component of any investigation into the underlying genomic innovations contributing to the distinctive phenotypes in ‘typical’ geckos. Findings We report high-depth genome sequencing, assembly, and annotation for a male leopard gecko, Eublepharis macularius (Eublepharidae). Illumina sequence data were generated from seven insert libraries (ranging from 170 to 20 kb), representing a raw sequencing depth of 136X from 303 Gb of data, reduced to 84X and 187 Gb after filtering. The assembled genome of 2.02 Gb was close to the 2.23 Gb estimated by k-mer analysis. Scaffold and contig N50 sizes of 664 and 20 kb, respectively, were compble to the previously published Gekko japonicus genome. Repetitive elements accounted for 42 % of the genome. Gene annotation yielded 24,755 protein-coding genes, of which 93 % were functionally annotated. CEGMA and BUSCO assessment showed that our assembly captured 91 % (225 of 248) of the core eukaryotic genes, and 76 % of vertebrate universal single-copy orthologs. Conclusions Assembly of the leopard gecko genome provides a valuable resource for future comptive genomic studies of geckos and other squamate reptiles

Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification

© 2018 IEEE. Person re-identification (re-ID) models trained on one domain often fail to generalize well to another. In our attempt, we present a 'learning via translation' framework. In the baseline, we translate the labeled images from source to target domain in an unsupervised manner. We then train re-ID models with the translated images by supervised methods. Yet, being an essential part of this framework, unsupervised image-image translation suffers from the information loss of source-domain labels during translation. Our motivation is two-fold. First, for each image, the discriminative cues contained in its ID label should be maintained after translation. Second, given the fact that two domains have entirely different persons, a translated image should be dissimilar to any of the target IDs. To this end, we propose to preserve two types of unsupervised similarities, 1) self-similarity of an image before and after translation, and 2) domain-dissimilarity of a translated source image and a target image. Both constraints are implemented in the similarity preserving generative adversarial network (SPGAN) which consists of an Siamese network and a CycleGAN. Through domain adaptation experiment, we show that images generated by SPGAN are more suitable for domain adaptation and yield consistent and competitive re-ID accuracy on two large-scale datasets