Online Influence Maximization (Extended Version)

Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or "seed nodes"), with the hope that they will convince their friends to buy it. One way to formalize marketers' objective is through influence maximization (or IM), whose goal is to find the best seed nodes to activate under a fixed budget, so that the number of people who get influenced in the end is maximized. Recent solutions to IM rely on the influence probability that a user influences another one. However, this probability information may be unavailable or incomplete. In this paper, we study IM in the absence of complete information on influence probability. We call this problem Online Influence Maximization (OIM) since we learn influence probabilities at the same time we run influence campaigns. To solve OIM, we propose a multiple-trial approach, where (1) some seed nodes are selected based on existing influence information; (2) an influence campaign is started with these seed nodes; and (3) users' feedback is used to update influence information. We adopt the Explore-Exploit strategy, which can select seed nodes using either the current influence probability estimation (exploit), or the confidence bound on the estimation (explore). Any existing IM algorithm can be used in this framework. We also develop an incremental algorithm that can significantly reduce the overhead of handling users' feedback information. Our experiments show that our solution is more effective than traditional IM methods on the partial information.Comment: 13 pages. To appear in KDD 2015. Extended versio

Updated insights into 3D architecture electrodes for micropower sources

Microbatteries (MBs) and microsupercapacitors (MSCs) are primary on-chip micropower sources that drive autonomous and stand-alone microelectronic devices for implementation of the Internet of Things (IoT). However, the performance of conventional MBs and MSCs is restricted by their 2D thin-film electrode design, and these devices struggle to satisfy the increasing IoT energy demands for high energy density, high power density, and long lifespan. The energy densities of MBs and MSCs can be improved significantly through adoption of a 2D thick-film electrode design; however, their power densities and lifespans deteriorate with increased electrode thickness. In contrast, 3D architecture electrodes offer remarkable opportunities to simultaneously improve MB and MSC energy density, power density, and lifespan. To date, various 3D architecture electrodes have been designed, fabricated, and investigated for MBs and MSCs. This review provides an update on the principal superiorities of 3D architecture electrodes over 2D thick-film electrodes in the context of improved MB and MSC energy density, power density, and lifespan. In addition, the most recent and representative progress in 3D architecture electrode development for MBs and MSCs is highlighted. Finally, present challenges are discussed and key perspectives for future research in this field are outlined

Alignments of galaxies within cosmic filaments from SDSS DR7

Using a sample of galaxy groups selected from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7), we examine the alignment between the orientation of galaxies and their surrounding large scale structure in the context of the cosmic web. The latter is quantified using the large-scale tidal field, reconstructed from the data using galaxy groups above a certain mass threshold. We find that the major axes of galaxies in filaments tend to be preferentially aligned with the directions of the filaments, while galaxies in sheets have their major axes preferentially aligned parallel to the plane of the sheets. The strength of this alignment signal is strongest for red, central galaxies, and in good agreement with that of dark matter halos in N-body simulations. This suggests that red, central galaxies are well aligned with their host halos, in quantitative agreement with previous studies based on the spatial distribution of satellite galaxies. There is a luminosity and mass dependence that brighter and more massive galaxies in filaments and sheets have stronger alignment signals. We also find that the orientation of galaxies is aligned with the eigenvector associated with the smallest eigenvalue of the tidal tensor. These observational results indicate that galaxy formation is affected by large-scale environments, and strongly suggests that galaxies are aligned with each other over scales comparable to those of sheets and filaments in the cosmic web.Comment: 11 pages, 10 figures, accepted for publication in Ap

Spin alignments of spiral galaxies within the large-scale structure from SDSS DR7

Using a sample of spiral galaxies selected from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7) and Galaxy Zoo 2 (GZ2), we investigate the alignment of spin axes of spiral galaxies with their surrounding large scale structure, which is characterized by the large-scale tidal field reconstructed from the data using galaxy groups above a certain mass threshold. We find that the spin axes of only have weak tendency to be aligned with (or perpendicular to) the intermediate (or minor) axis of the local tidal tensor. The signal is the strongest in a \cluster environment where all the three eigenvalues of the local tidal tensor are positive. Compared to the alignments between halo spins and local tidal field obtained in N-body simulations, the above observational results are in best agreement with those for the spins of inner regions of halos, suggesting that the disk material traces the angular momentum of dark matter halos in the inner regions.Comment: 8 pages, 7 figures, accepted for publication in Ap

Compromiso con el trabajo y rendimiento en el trabajo: el papel moderador del apoyo organizacional percibido.

The present research was aim to examine whether the relationship between work engagement and objective task performance is moderated by perceived organizational support (POS). Based on the existing literature, perceived organizational support is hypothesized to strengthen the positive association between employees’ work engagement and their objective task performance. The hypotheses were tested on a sample of 1049 employees. Results of hierarchical regression analysis show that: (1) work engagement is positively related to objective task performance, and (2) the relationship between work engagement and objective task performance is moderated by POS, such that the positive relationship is more significant when POS higher than lower. In the end, theoretical and practical implications, and suggestions for future research are discussed.La presente investigación tuvo el objetivo de examinar si la relación entre compromiso con el trabajo y el rendimiento en los objetivos de las tareas está moderada por el apoyo perceptivo de la organización (APO). En base a la literatura existente, el apoyo percibido de la organización se hipotetiza que fortalece la asociación positiva entre el compromiso laboral de los empleados y su rendimiento en los objetivos de las tareas. Las hipótesis fueron comprobadas en una muestra de 1049 empleados. Los resultados del análisis de regresión jerárquico muestran que: (1) el compromiso en el trabajo está positivamente relacionado con el rendimiento en los objetivos de las tareas, y (2) la relación entre compromiso en el trabajo y el rendimiento en los objetivos de las tareas está moderado por el APO, de modo que la relación positiva es más significativa cuando el APO es mayor. Al final se discuten las implicaciones teóricas y prácticas, y las sugerencias para futuras investigaciones

iTag: Incentive-Based Tagging

In social tagging systems, such as Delicious1 and Flickr2, users are allowed to annotate resources (e.g., Web URLs and images) with textual descriptions called tags. Tags have proven to be invaluable building blocks in algorithms for searching, mining and recommending resources. In practice, however, not all resources receive the same attention from users, and as a result, most tags are added to the few highly-popular resources, while most of the resources receive few tags. Crucially, this incomplete tagging on resources can severely affect the effectiveness of all tagging applications. We present iTag, an incentive-based tagging system, which aims at improving tagging quality of resources, by incentivizing taggers under budget constraints. Our system is built upon traditional crowdsourcing systems such as Amazon Mechanical Turk (MTurk). In our demonstration, we will show how our system allows users to use simple but powerful strategies to significantly improve the tagging quality of resources.published_or_final_versio

Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance

Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recently, deep learning based terrain generation has emerged, notably the ones based on generative adversarial networks (GAN). However, these methods often struggle to fulfill the requirements of flexible user control and maintain generative diversity for realistic terrain. Therefore, we propose a novel diffusion-based method, namely terrain diffusion network (TDN), which actively incorporates user guidance for enhanced controllability, taking into account terrain features like rivers, ridges, basins, and peaks. Instead of adhering to a conventional monolithic denoising process, which often compromises the fidelity of terrain details or the alignment with user control, a multi-level denoising scheme is proposed to generate more realistic terrains by taking into account fine-grained details, particularly those related to climatic patterns influenced by erosion and tectonic activities. Specifically, three terrain synthesisers are designed for structural, intermediate, and fine-grained level denoising purposes, which allow each synthesiser concentrate on a distinct terrain aspect. Moreover, to maximise the efficiency of our TDN, we further introduce terrain and sketch latent spaces for the synthesizers with pre-trained terrain autoencoders. Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. Our code and dataset will be publicly available