A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years since it is matched with the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms existing methods in terms of solution quality and improves best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to analysis further their sensitivity to the performance of our method.Comment: Technical Report. 34 pages, 5 figure

A Local Search Modeling for Constrained Optimum Paths Problems (Extended Abstract)

Constrained Optimum Path (COP) problems appear in many real-life applications, especially on communication networks. Some of these problems have been considered and solved by specific techniques which are usually difficult to extend. In this paper, we introduce a novel local search modeling for solving some COPs by local search. The modeling features the compositionality, modularity, reuse and strengthens the benefits of Constrained-Based Local Search. We also apply the modeling to the edge-disjoint paths problem (EDP). We show that side constraints can easily be added in the model. Computational results show the significance of the approach

Enhancing Few-shot Image Classification with Cosine Transformer

This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only. One major challenge of the few-shot learning problem is the large variety of object visual appearances that prevents the support samples to represent that object comprehensively. This might result in a significant difference between support and query samples, therefore undermining the performance of few-shot algorithms. In this paper, we tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the relational map between supports and queries is effectively obtained for the few-shot tasks. The FS-CT consists of two parts, a learnable prototypical embedding network to obtain categorical representations from support samples with hard cases, and a transformer encoder to effectively achieve the relational map from two different support and query samples. We introduce Cosine Attention, a more robust and stable attention module that enhances the transformer module significantly and therefore improves FS-CT performance from 5% to over 20% in accuracy compared to the default scaled dot-product mechanism. Our method performs competitive results in mini-ImageNet, CUB-200, and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and few-shot configurations. We also developed a custom few-shot dataset for Yoga pose recognition to demonstrate the potential of our algorithm for practical application. Our FS-CT with cosine attention is a lightweight, simple few-shot algorithm that can be applied for a wide range of applications, such as healthcare, medical, and security surveillance. The official implementation code of our Few-shot Cosine Transformer is available at https://github.com/vinuni-vishc/Few-Shot-Cosine-Transforme

An Online Distributed Boundary Detection and Classification Algorithm for Mobile Sensor Networks

We present a novel online distributed boundary detection and classification algorithm in order to improve accuracy of boundary detection and classification for mobile sensor networks. This algorithm is developed by incorporating a boundary detection algorithm and our newly proposed boundary error correction algorithm. It is a fully distributed algorithm based on the geometric approach allowing to remove boundary errors without recursive process and global synchronization. Moreover, the algorithm allows mobile nodes to identify their states corresponding to their positions in network topologies, leading to self-classification of interior and exterior boundaries of network topologies. We have demonstrated effectiveness ofthis algorithm in both simulation and real-world experiments and proved that the accuracy of the ratio of correctly identified nodes over the total number of nodes is 100%

Simultaneous Effect of Ownership and Economic Sector on the Performance of Enterprises in Vietnam

This paper examines the differences in the impact of ownership types and economic sectors on the business efficiency of 4,733 enterprises in Vietnam by the year of 2015. By the method of analysis of variance (ANOVA), it is shown that while types of ownership, foreign, state and private ownership, have a significant and different impact on the performance of businesses, the difference in economic sectors does not affect the enterprise efficiency. In addition, when testing simultaneous effect of these factors, some findings are as follows: private-owned enterprises’ efficiency in the manufacturing and service sectors is better than those in agriculture, forestry and fishing sector; conversely, foreign invested enterprises operating in agriculture, forestry and fishing sector own better performance than theirs in manufacturing and service sectors; state-owned enterprises in manufacturing and service sectors is very less efficient than theirs in agriculture, forestry and fishing sector

Igf2 Gene Expression Levels in Wild-Type and Mutant Mice

Genomic imprinting occurs where only one allele of a gene is expressed depending on its parental origin. The imprinted Igf2 gene (Insulin-like growth factor 2) is encoding a growth factor, which play an important role in embryonic development and formation of the placenta. The regulation and expression of Igf2 is carried out by different promoters. Promoter expression is extremely complex in wild-type mice during development and is altered in several mutant mice bearing deletions at the Igf2/H19 locus. In this work, we analyzed the Igf2 RNA expression of the placenta-specific P0 promoter in placental tissue (embryonic day 17) of both wild-type and mutant mice. For all the other promoters, we used RNA extracted from liver tissues (postnatal day 7.5). All these RNAs were reverse transcribed to cDNA before quantifying expression levels of the promoters by quantitative PCR (qPCR).Our results show that transcriptions of Igf2 P2 and P3 promoters are the highest in all mice analyzed, except in ΔU2/Dom mutant mice where P0 and P1 promoters were highly expressed, while they display low expression in all the other mice strains analyzed. Furthermore, all promoters were stably expressed at high levels in wild-type and ΔU2/Dom mutation, but at a low level in Δ3/Do

Gravity terrain correction for mainland territory of Vietnam

Terrain corrections for gravity data are a critical concern in rugged topography, because the magnitude of the corrections may be largely relative to the anomalies of interest. That is also important to determine the inner and outer radii beyond which the terrain effect can be neglected. Classical methods such as Lucaptrenco, Beriozkin and Prisivanco are indeed too slow with radius correction and are not extended while methods based on the Nagy’s and Kane’s are usually too approximate for the required accuracy. In order to achieve 0.1 mGal accuracy in terrain correction for mainland territory of Vietnam and reduce the computing time, the best inner and outer radii for terrain correction computation are 2 km and 70 km respectively. The results show that in nearly a half of the Vietnam territory, the terrain correction values ≥ 10 mGal, the corrections are smaller in the plain areas (less than 2 mGal) and higher in the mountainous region, in particular the correction reaches approximately 21 mGal in some locations of northern mountainous region. The complete Bouguer gravity map of mainland territory of Vietnam is reproduced based on the full terrain correction introduced in this paper