LATTE: Application Oriented Social Network Embedding

In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node is represented as a feature vector. However, due to the detachment of embedding process with external tasks, the learned embedding results by most existing embedding models can be ineffective for application tasks with specific objectives, e.g., community detection or information diffusion. In this paper, we propose study the application oriented heterogeneous social network embedding problem. Significantly different from the existing works, besides the network structure preservation, the problem should also incorporate the objectives of external applications in the objective function. To resolve the problem, in this paper, we propose a novel network embedding framework, namely the "appLicAtion orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network structure can be applied to compute the node "diffusive proximity" scores, which capture both local and global network structures. Based on these computed scores, Latte learns the network representation feature vectors by extending the autoencoder model model to the heterogeneous network scenario, which can also effectively unite the objectives of network embedding and external application tasks. Extensive experiments have been done on real-world heterogeneous social network datasets, and the experimental results have demonstrated the outstanding performance of Latte in learning the representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl

A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound

In this work, we develop a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from both labeled and unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptions about the integral operator, this approach is able to achieve an improved regression error bound better than existing bounds of supervised learning. We also verify the effectiveness of the proposed algorithm by an empirical study.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

The thermal SZ tomography

The thermal Sunyaev-Zel'dovich (tSZ) effect directly measures the thermal pressure of free electrons integrated along the line of sight and thus contains valuable information on the thermal history of the universe. However, the redshift information is entangled in the projection along the line of sight. This projection effect severely degrades the power of the tSZ effect to reconstruct the thermal history. We investigate the tSZ tomography technique to recover this otherwise lost redshift information by cross correlating the tSZ effect with galaxies of known redshifts, or alternatively with matter distribution reconstructed from weak lensing tomography. We investigate in detail the 3D distribution of the gas thermal pressure and its relation with the matter distribution, through our adiabatic hydrodynamic simulation and the one with additional gastrophysics including radiative cooling, star formation and supernova feedback. (1) We find a strong correlation between the gas pressure and matter distribution, with a typical cross correlation coefficient r ~ 0.7 at k . 3h/Mpc and z < 2. This tight correlation will enable robust cross correlation measurement between SZ surveys such as Planck, ACT and SPT and lensing surveys such as DES and LSST, at ~20-100{\sigma} level. (2) We propose a tomography technique to convert the measured cross correlation into the contribution from gas in each redshift bin to the tSZ power spectrum. Uncertainties in gastrophysics may affect the reconstruction at ~ 2% level, due to the ~ 1% impact of gastrophysics on r, found in our simulations. However, we find that the same gastrophysics affects the tSZ power spectrum at ~ 40% level, so it is robust to infer the gastrophysics from the reconstructed redshift resolved contribution.Comment: 10 pages, 7 figures, 2 appendices, accepted by Ap

Benefits of Viewing Nature: A Review of Landscape Health Research

Nowadays, several studies demonstrate that viewing nature has positive effects on human health and well-being. This essay discusses about the essential methods of viewing natural environment and their impacts on human well-being by clarifying four important theoretical models: reducing stress, lowering heart rate, improving outcome of surgery, and increasing attention. In addition, some important research results in this field are taken as examples to introduce research methods. By collecting and organizing existing studies and theories about the relationship between viewing nature and human well-being, the methods of viewing nature can be divided into two parts: viewing nature through specific media (e.g., through a window, a book, a painting or a videotape) and being with the presence of nature. This study aims to clarify the research significance of viewing nature and find deficiency in this field to maximize the role of landscapes in human health and well-being.

Beyond Profit: A Multi-Objective Framework for Electric Vehicle Charging Station Operations

This paper explores the pricing and scheduling strategies of the electric vehicle charging stations in response to the rising demand for cleaner transportation. Most of the existing methods focus on maximizing the energy efficiency or the charging station profit, however, the reputation of EVs is also a key factor for the long-term charging station operations. To address these gaps, we propose a novel framework for jointly optimizing pricing and continuous-multiple charging rates. Our approach aims to maximize both charging station profit and reputation, considering multi-objective optimization and continuous rate control within physical constraints. Introducing a pricing fluctuating penalty for reputation modeling and a linear programming-based safe layer for constraints, we confront the complexity of continuous charging rates' action space. To enhance convergence, we explore a soft action critic framework with novel entropy temperature tunning technique. The experiments conducted with real data demonstrate that the proposed method can provide extra 25.45\%-52.20\% average JPR than the representative baselines.Comment: Accepted By VTC24-Sprin