A Method for Geometry Optimization in a Simple Model of Two-Dimensional Heat Transfer

This investigation is motivated by the problem of optimal design of cooling elements in modern battery systems. We consider a simple model of two-dimensional steady-state heat conduction described by elliptic partial differential equations and involving a one-dimensional cooling element represented by a contour on which interface boundary conditions are specified. The problem consists in finding an optimal shape of the cooling element which will ensure that the solution in a given region is close (in the least squares sense) to some prescribed target distribution. We formulate this problem as PDE-constrained optimization and the locally optimal contour shapes are found using a gradient-based descent algorithm in which the Sobolev shape gradients are obtained using methods of the shape-differential calculus. The main novelty of this work is an accurate and efficient approach to the evaluation of the shape gradients based on a boundary-integral formulation which exploits certain analytical properties of the solution and does not require grids adapted to the contour. This approach is thoroughly validated and optimization results obtained in different test problems exhibit nontrivial shapes of the computed optimal contours.Comment: Accepted for publication in "SIAM Journal on Scientific Computing" (31 pages, 9 figures

The Grandmothers' Farewell to Childcare Provision under China's Two-Child Policy: Evidence from Guangzhou Middle-Class Families

As China’s one-child policy is replaced by the two-child policy, young Chinese women and their spouses are increasingly concerned about who will take care of the ‘second child.’ Due to the absence of public childcare services and the rising cost of privatised care services in China, childcare provision mainly relies on families, such that working women’s choices of childbirth, childcare and employment are heavily constrained. To deal with structural barriers, young urban mothers mobilise grandmothers as joint caregivers. Based on interviews with Guangzhou middle-class families, this study examines the impact of childcare policy reform since 1978 on childbirth and childcare choices of women. It illustrates the longstanding contributions and struggles of women, particularly grandmothers, engaged in childcare. It also shows that intergenerational parenting involves a set of practices of intergenerational intimacy embedded in material conditions, practical acts of care, moral values and power dynamics. We argue that the liberation, to some extent, of young Chinese mothers from childcare is at the expense of considerable unpaid care work from grandmothers rather than being driven by increased public care services and improved gender equality in domestic labour. Given the significant stress and seriously constrained choices in later life that childcare imposes, grandmothers now become reluctant to help rear a second grandchild. This situation calls for changes in family policies to increase the supply of affordable and good-quality childcare services, enhance job security in the labour market, provide supportive services to grandmothers and, most importantly, prioritise the wellbeing of women and families over national goals

Stratified Transfer Learning for Cross-domain Activity Recognition

In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready version

Joint Object and Part Segmentation using Deep Learned Potentials

Segmenting semantic objects from images and parsing them into their respective semantic parts are fundamental steps towards detailed object understanding in computer vision. In this paper, we propose a joint solution that tackles semantic object and part segmentation simultaneously, in which higher object-level context is provided to guide part segmentation, and more detailed part-level localization is utilized to refine object segmentation. Specifically, we first introduce the concept of semantic compositional parts (SCP) in which similar semantic parts are grouped and shared among different objects. A two-channel fully convolutional network (FCN) is then trained to provide the SCP and object potentials at each pixel. At the same time, a compact set of segments can also be obtained from the SCP predictions of the network. Given the potentials and the generated segments, in order to explore long-range context, we finally construct an efficient fully connected conditional random field (FCRF) to jointly predict the final object and part labels. Extensive evaluation on three different datasets shows that our approach can mutually enhance the performance of object and part segmentation, and outperforms the current state-of-the-art on both tasks

Governor Celebrates Funding for Mattapan Community Health Center

BACKGROUND:There is no single standard chemotherapy regimen for elderly patients with advanced gastric cancer (AGC). A phase III trial has confirmed that both capecitabine monotherapy and capecitabine plus oxaliplatin are well tolerated for elderly patients with AGC, but their economic influence in China is unknown. OBJECTIVE:The purpose of this cost-effectiveness analysis was to estimate the effects of capecitabine monotherapy and capecitabine plus oxaliplatin in elderly patients with AGC on health and economic outcomes in China. METHODS:We created a Markov model based on data from a Korean clinical phase III trial to analyze the cost-effectiveness of the treatment of elderly patients in the capecitabine monotherapy (X) group and capecitabine plus oxaliplatin (XELOX) group. The costs were obtained from published reports and the local health system. The utilities were assumed on the basis of the published literature. Costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICER) were estimated. One-way and probabilistic sensitivity analyses (Monte Carlo simulations) were performed. RESULTS:In the cost-effectiveness analysis, X had a lower total cost ($45,731.68) and cost-effectiveness ratio ($65,918.93/QALY). The one-way sensitivity analysis suggested that the most influential parameter was the risk of requiring second-line chemotherapy in XELOX group. The probabilistic sensitivity analysis predicted that the X regimen was cost-effective 100% of the time, given a willingness-to-pay threshold of $26,598. CONCLUSIONS:Our findings show that the XELOX regimen is less cost-effective compared to the X regimen for elderly patients with AGC in China from a Chinese healthcare perspective

A Multi-Criteria Group Decision-Making Method with Possibility Degree and Power Aggregation Operators of Single Trapezoidal Neutrosophic Numbers

Single valued trapezoidal neutrosophic numbers (SVTNNs) are very useful tools for describing complex information, because of their advantage in describing the information completely, accurately and comprehensively for decision-making problems. In the paper, a method based on SVTNNs is proposed for dealing with multi-criteria group decision-making (MCGDM) problems. Firstly, the new operations SVTNNs are developed for avoiding evaluation information aggregation loss and distortion

A Novel Clustering Algorithm Inspired by Membrane Computing

P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature