Peaceman-Rachford splitting for a class of nonconvex optimization problems

We study the applicability of the Peaceman-Rachford (PR) splitting method for solving nonconvex optimization problems. When applied to minimizing the sum of a strongly convex Lipschitz differentiable function and a proper closed function, we show that if the strongly convex function has a large enough strong convexity modulus and the step-size parameter is chosen below a threshold that is computable, then any cluster point of the sequence generated, if exists, will give a stationary point of the optimization problem. We also give sufficient conditions guaranteeing boundedness of the sequence generated. We then discuss one way to split the objective so that the proposed method can be suitably applied to solving optimization problems with a coercive objective that is the sum of a (not necessarily strongly) convex Lipschitz differentiable function and a proper closed function; this setting covers a large class of nonconvex feasibility problems and constrained least squares problems. Finally, we illustrate the proposed algorithm numerically

Online Shopping Behavior in Cross-cultural Context: An Empirical Research in China

As a newly evolved emergence from e-business, social commerce has attracted increasingly attention from both researchers and practitioners. Distinguished from the majority of extant research paradigm, the current empirical study extends social commerce research into cross-cultural context and unveils the underlying mechanism through which two dimensions of social media usage (informational and socializing) impact user’s intention to purchase on social commerce websites, thereby facilitating online shopping behaviors. In addition, the research demonstrates the role of cultural distance as a boundary condition attenuating the positive effects of social media usage in cross-cultural social commerce application. Research implications and limitations for future venues are also discussed

Behavior Analysis and Recognition of Hidden Populations in Online Social Network Based on Big Data Method

Hidden populations refer to the minority groups that not well-known to the public. Traditional statistical survey methods are difficult to apply in the study of hidden populations because of that the hidden populations individuals are very troublesome to be found and they are not willing to share the inner opinion with the others. On the other hand, with the development of the Web 2.0, the hidden populations gather and share their views in online social networks due to the openness and anonymity of the Internet. So, this paper analyzes the behavioral characteristics of the hidden populations based on their data in online social networks. This paper uses the lesbian population as an example and analyzes the behavioral characteristics of lesbian by analyzing the data of the lesbian population in Douban Group. First, the activity data on lesbian are collected from Douban Group. Second, behavior characteristics of lesbian are analysed, the regional characteristic, temporal characteristic and text characteristic are mined out by big data method. Third, a lesbian recognition model is proposed based on the above analytical characteristics, and the effectiveness of the recognition model is varified by experiment study. The research of this paper is helpful to understand the behavioral characteristics of hidden populations deeply, and provides decision-making basis of management and service for hidden populations

HEAT SINK FIXTURE DESIGNS FOR ROW-BASED COMPONENTS PROVIDING ENHANCED THERMAL PERFORMANCE

The installation of components in a row-wise arrangement on a printed circuit board (PCB) is a common occurrence in current switch design. However, with the increasing power dissipation of such components the use of heat sinks becomes necessary. Techniques are presented herein that support a new heat sink fixture design which handles components that are installed in a row. Aspects of the presented techniques consume a minimal amount of PCB area, support the use of a phase-change material (PCM), and are circuit trace friendly

A Causal Disentangled Multi-Granularity Graph Classification Method

Graph data widely exists in real life, with large amounts of data and complex structures. It is necessary to map graph data to low-dimensional embedding. Graph classification, a critical graph task, mainly relies on identifying the important substructures within the graph. At present, some graph classification methods do not combine the multi-granularity characteristics of graph data. This lack of granularity distinction in modeling leads to a conflation of key information and false correlations within the model. So, achieving the desired goal of a credible and interpretable model becomes challenging. This paper proposes a causal disentangled multi-granularity graph representation learning method (CDM-GNN) to solve this challenge. The CDM-GNN model disentangles the important substructures and bias parts within the graph from a multi-granularity perspective. The disentanglement of the CDM-GNN model reveals important and bias parts, forming the foundation for its classification task, specifically, model interpretations. The CDM-GNN model exhibits strong classification performance and generates explanatory outcomes aligning with human cognitive patterns. In order to verify the effectiveness of the model, this paper compares the three real-world datasets MUTAG, PTC, and IMDM-M. Six state-of-the-art models, namely GCN, GAT, Top-k, ASAPool, SUGAR, and SAT are employed for comparison purposes. Additionally, a qualitative analysis of the interpretation results is conducted

Polymorphisms of the _ENPP1_ gene are not associated with type 2 diabetes or obesity in the Chinese Han population

*Objective:* Type 2 Diabetes mellitus is a metabolic disorder characterized by chronic hyperglycemia and with a major feature of insulin resistance. Genetic association studies have suggested that _ENPP1_ might play a potential role in susceptibility to type 2 diabetes and obesity. Our study aimed to examine the association between _ENPP1_ and type 2 diabetes and obesity.

*Design:* Association study between two SNPs, rs1044498 (K121Q) and rs7754561 of ENPP1 and diabetes and obesity in the Chinese Han population.

*Subjects:* 1912 unrelated patients (785 male and 1127 female with a mean age 63.8 ± 9 years), 236 IFG/IGT subjects (83 male and 153 female with a mean age 64 ± 9 years) and 2041 controls (635 male and 1406 female with a mean age 58 ± 9 years).
 
*Measurements:* Subjects were genotyped for two SNPs using TaqMan technology on an ABI7900 system and tested by regression analysis.

*Results:* By logistic regression analysis, rs1044498 (K121Q) and rs7754561 showed no statistical association with type 2 diabetes, obesity under additive, dominant and recessive models either before or after adjusting for sex and age. Haplotype analysis found a marginal association of haplotype C-G (p=0.05) which was reported in the previous study.

*Conclusion:* Our investigation did not replicated the positive association found previously and suggested that the polymorphisms of _ENPP1_ might not play a major role in the susceptibility to type 2 diabetes or obesity in the Chinese Han population

Estudio de la construcción que hacen las madres del soporte necesario para la instauración y mantenimiento de la lactancia materna

Programa Oficial de Doutoramento en Ciencias Sociosanitarias. 512V01[Resumen] La lactancia materna (LM) ha sido históricamente y continúa siendo motivo de interés por parte de numerosas disciplinas, entre ellas, la enfermería. En los humanos, como mamíferos que son, la LM forma parte de su ciclo reproductivo. Sin embargo, a diferencia de éstos, en las mujeres la LM no es un acto exclusivamente biológico. En el ser humano la lactancia es una construcción social y, por lo tanto, depende del aprendizaje, creencias, valores, normas y condicionantes socioculturales que cambian a lo largo de los tiempos y de los individuos que en ellos viven. Es por ello que se hace necesario incorporar en el estudio de la LM la perspectiva de los sujetos implicados, en este caso las madres. La investigación en salud que se presenta en esta tesis se ha realizado como un Estudio Mix- Method Transformativo con una estructura secuencial cuantitativa-cualitativa. Los resultados de esta investigación establecen que la decisión que han de tomar las mujeres sobre la alimentación del RN está condicionada por los beneficios que ellas atribuyen a la LM y a la LA; la estigmatización de la LM en público; y la presión que ejercen los profesionales sanitarios, la sociedad y las propias mujeres, influidos por las normas sociales y culturales del momento. Al tratarse de un estudio transformativo se finaliza con una propuesta de agenda para cambiar o reformar los aspectos que se han desarrollado como resultado de la investigación. Esta agenda transformadora implica, además de a los profesionales sanitarios responsables del cuidado de la mujer gestante, a los gestores de la Xerencia de Xestión Integrada de Ferrol (XXIF).[Resumo] A lactación materna (LM) foi historicamente e segue sendo motivo de interese por parte de numerosas disciplinas, entre elas, a enfermaría. Nos humanos, coma mamíferos que son, a LM forma parte do seu ciclo reprodutivo. Con todo, a diferencia destes, nas mulleres a LM non é un acto exclusivamente biolóxico. A diferenza doutros animais, no ser humano a lactación é unha construción social e, polo tanto, depende da aprendizaxe, crenzas, valores, normas e condicionantes socioculturais que mudan ó longo dos tempos e dos individuos que neles viven. É por iso que se fai necesario incorporar no estudo da LM a perspectiva dos suxeitos implicados, neste caso as nais. A investigación en saúde que se presenta nesta tese realizouse coma un Estudo Mix-Method Transformativo cunha estructura secuencial cuantitativa-cualitativa. Os resultados desta investigación establecen que a decisión que teñen que tomar as mulleres sobre a alimentación do recén nado (RN) está condicionada polos beneficios que elas atribúen á LM e á LA; a estigmatización da LM en público; e a presión que exercen os profesionais sanitarios, a sociedade e as propias mulleres, influídos polas normas sociais e culturais do momento. Ó se tratar dun estudo transformativo finalízase cunha proposta de axenda para cambiar ou reformar os aspectos que se desenvolveron como resultado da investigación. Esta axenda transformadora implica, ademáis dos profesionais sanitarios do coidado da muller xestante, ós xestores da Xerencia de Xestión Integrada de Ferrol (XXIF).[Abstract] Historically and to this day, breastfeeding has been a topic of great interest in many disciplines, among them in the field of nursing. In humans, as mammals that they are, breastfeeding is part of their reproductive cycle. However, unlike the latter, in women breastfeeding is not exclusively and merely a biological act. Differing from other animals, for a human being breastfeeding is a social construct that depends on learning, beliefs, values, norms and sociocultural variables that change in time as well as the individuals that live through them. For this reason, it is necessary to include in the breastfeeding research the perspective of the involved subjects, the mothers. The health research presented in this thesis has been completed as a transformative Mix- Method study with a quantitative-qualitative sequential structure. The results obtained from this research establish that the decision women make in regards to feeding their newborn is conditioned by the benefits they attribute to breastfeeding and to formula; the stigmatization of breastfeeding in public, and pressure from health professionals, society and women as a whole, influenced by the social and cultural norms in the moment. Complying with the research methodology, at the end of the study there is a scheduled proposal to change or improve the aspects developed as a result of the research. Besides the health professionals responsible for the well being during pregnancy, the proposal also involves the management team of the Xerencia de Xestión Integrada de Ferrol (XXIF)

D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization

Recently, many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these detection methods focus on the local patches or local objects. In fact, image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. However, some specific image contents are hardly retained by CNN-based detection networks, but if included, would improve the detection accuracy of the networks. To resolve these issues, we propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network. This dual-encoder is followed by a spatial pyramid global-feature extraction module that expands the global insight of D-Unet for classifying the tampered and non-tampered regions more accurately. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection, without requiring pre-training or training on a large number of forgery images. Moreover, it was stably robust to different attacks.Comment: 13 pages, 13 figure