Embedding Heterogeneous Information Network in Hyperbolic Spaces

Heterogeneous information network (HIN) embedding, aiming to project HIN into a low-dimensional space, has attracted considerable research attention. Most of the existing HIN embedding methods focus on preserving the inherent network structure and semantic correlations in Euclidean spaces. However, one fundamental problem is whether the Euclidean spaces are the intrinsic spaces of HIN? Recent researches find the complex network with hyperbolic geometry can naturally reflect some properties, e.g., hierarchical and power-law structure. In this article, we make an effort toward embedding HIN in hyperbolic spaces. We analyze the structures of three HINs and discover some properties, e.g., the power-law distribution, also exist in HINs. Therefore, we propose a novel HIN embedding model HHNE. Specifically, to capture the structure and semantic relations between nodes, HHNE employs the meta-path guided random walk to sample the sequences for each node. Then HHNE exploits the hyperbolic distance as the proximity measurement. We also derive an effective optimization strategy to update the hyperbolic embeddings iteratively. Since HHNE optimizes different relations in a single space, we further propose the extended model HHNE++. HHNE++ models different relations in different spaces, which enables it to learn complex interactions in HINs. The optimization strategy of HHNE++ is also derived to update the parameters of HHNE++ in a principle manner. The experimental results demonstrate the effectiveness of our proposed models.

Profile inference revisited

Profile-guided optimization (PGO) is an important component in modern compilers. By allowing the compiler to leverage the program’s dynamic behavior, it can often generate substantially faster binaries. Sampling-based profiling is the state-of-the-art technique for collecting execution profiles in data-center environments. However, the lowered profile accuracy caused by sampling fully optimized binary often hurts the benefits of PGO; thus, an important problem is to overcome the inaccuracy in a profile after it is collected. In this paper we tackle the problem, which is also known as profile inference and profile rectification . We investigate the classical approach for profile inference, based on computing minimum-cost maximum flows in a control-flow graph, and develop an extended model capturing the desired properties of real-world profiles. Next we provide a solid theoretical foundation of the corresponding optimization problem by studying its algorithmic aspects. We then describe a new efficient algorithm for the problem along with its implementation in an open-source compiler. An extensive evaluation of the algorithm and existing profile inference techniques on a variety of applications, including Facebook production workloads and SPEC CPU benchmarks, indicates that the new method outperforms its competitors by significantly improving the accuracy of profile data and the performance of generated binaries.

Examining the influential factors of consumer purchase intentions for blockchain traceable coffee using the theory of planned behaviour

PurposeGiven the increasing industry interest in blockchain technologies for supply chain management and product traceability, this paper aims to investigate consumer purchasing intentions for blockchain traceable coffee and their psychosocial antecedents, utilising an extended model of the theory of planned behaviour (TPB).Design/methodology/approachAn online questionnaire study of 123 participants was deployed, using two traceability systems (one based on blockchain and one on a more established traceability certification) for organic coffee.FindingsAdding variables such as environmental protections, trust and habits significantly increased the predictive power of TPB. The results suggest that attitude, perceived behavioural control and environmental protections drive intentions to purchase blockchain traceable coffee.Research limitations/implicationsApart from establishing the factors affecting consumer intentions for blockchain traceable coffee, this study validates the TPB as a model of explaining coffee purchasing intentions and provides evidence of new variables that can significantly increase the model's predictive power.Practical implicationsThe proposed format of presenting traceability information along with the significant variables revealed in our study can function as a guide for designing product features and marketing strategies for blockchain traceable organic coffee. Increasing consumer awareness on product traceability will also play a crucial role in the success of these products.Originality/valueThis study is the first to explore consumer purchasing intentions for blockchain traceable coffee and establish the psychosocial variables behind them contributing, in that way, to an understudied area in academic literature as well as providing insights for a more consumer-centric design of such products.

Macroeconomic assessment of the environmental quality of economic growth at the regional level

The research featured a macroeconomic assessment of the quality of economic growth. The analysis was based on various environmental factors, obtained in the process of strategic environmental assessment of the developmental priorities of the Kemerovo region in 2002–2020. The research objective was to determine the effect of environmental factors on eco-intensity and economic growth in this resource-based region in the context of global and national environmental challenges. The paper presents an overview of the methods of ecological and economic analysis suitable for strategic environmental assessment. The study featured mathematical methods of calculating the economic eco-intensity and the decoupling effect, as well as the model of economic growth developed by P. Victor. The decoupling effect was rather weak for the main types of negative impact, i.e. pollution, waste generation, disturbed lands, etc. The only green decoupling effect was revealed by the volume of contaminated wastewater. P. Victor's extended model showed the predominance of "brown" economic growth, while the increase in the carbon intensity of the gross domestic product for methane coincided with the significant decrease in the economic development of the region. The article also introduces a forecast of the economic development of the Kemerovo region, based on global and national trends of decarbonization. Transition to the use of the best available technologies should reduce the level of eco-intensity and increase the rate of decarbonization, both in the main industries and in methane processing.

vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding

Background As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria. Results In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool. Conclusions The vCOMBAT online tool is publicly available at https://combat-bacteria.org/.

Green Consumer Behaviour

Environment preservation is a global concern. Textile industry disposes of chemicals which effects environment and human life (water borne diseases). United Nations develops 17 Sustainable Development Goals (UNSDG's) to protect environment. Five SDG's addressing textile industry namely good health and well-being, clean water and sanitation, responsible production and consumption, climate action and life below water. Role of textile industry to achieve SDG's is inevitable. Textile policy of Pakistan 2014-19 confirms that international buyer is concerned about the environment which evidently shows potential of green marketing in textile sector of Pakistan. Green marketing encourages environment friendly marketing practices (product, price, place, promotion). The objective of this study is to integrate the theory of planned behavior and technology acceptance model. Green consumer behavior of textile sector of Pakistan is conceptualized with this extended lens. This study enhances the body of knowledge by conceptualizing green consumer behavior of textile sector through extended model. Practically, this study remains beneficial for marketing professionals and researchers to understand green consumer behavior of textile sector. Success of green marketing is the success of society to curb environmental problems.

How Does Schema Affect Stress and Productivity at the Workplace?

Maintaining mental health has become a great concern not only for one's well-being, but also for companies' management, while one's personality trait has gained its popularity as a cause. In this article, the authors then investigate how worksite productivity loss is accounted for by stress response accompanied with schema. To this end, the conventional stress model is extended so as to include schema. The pivotal idea of the extended model stands in that both stressor and strain are associated with schema. The result of multiple regression analysis showed that workplace productivity loss is most largely affected by irritability, fatigue, and depression. In addition, the result of hierarchical regression analysis revealed that schema affects stress reaction both directly and indirectly, and that there exists a so-called buffering effect between job control and coworkers' support. These findings suggest that work productivity may be improved by intervention regarding schema or by promoting the buffering effect.

Measuring neutrino dynamics in NMSSM with a right-handed sneutrino LSP at the ILC

We study the possibility of measuring neutrino Yukawa couplings in the Next-to-Minimal Supersymmetric Standard Model with right-handed neutrinos (NMSSMr) when the lightest right-handed sneutrino is the Dark Matter (DM) candidate, by exploiting a ‘dijet + dilepton + Missing Transverse Energy’ (MET or "Image missing") signature. We show that, contrary to the miminal realisation of Supersymmetry (SUSY), the MSSM, wherein the DM candidate is typically a much heavier (fermionic) neutralino state, this extended model of SUSY offers one with a much lighter (bosonic) state as DM that can then be produced at the next generation of e+e− colliders with energies up to 500 GeV or so. The ensuing signal, energing from chargino pair production and subsequent decay, is extremely pure so it also affords one with the possibility of extracting the Yukawa parameters of the (s)neutrino sector. Altogether, our results serve the purpose of motivating searches for light DM signals at such machines, where the DM candidate can have a mass around the Electro-Weak (EW) scale.