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

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.

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

Consolidation and permeability of flocculated kaolinite sediment

posterVast oil sand resources are located in the province of Alberta, Canada, where water-based oil sands extraction operations are found including extraction and separation of the bitumen from the clay, sand, and water. The production of each barrel of synthetic crude oil (SCO) requires 2 m3 of processed water and generates 1.8 tonnes of solid tailings[1]. While coarse solids (sands) settle quickly to form beaches along the tailings pond, the fines (mainly silts and clays) take a much longer time to settle. Kaolinite is a major clay mineral found in oil sand tailings and organic polymers have been used to flocculate kaolinite to enhance the dewatering rate and sediment compaction

Beyond Text: Unveiling Multimodal Proficiency of Large Language Models with MultiAPI Benchmark

The proliferation of Large Language Models like ChatGPT has significantly advanced language understanding and generation, impacting a broad spectrum of applications. However, these models predominantly excel in text-based tasks, overlooking the complexity of real-world multimodal information. This study introduces MultiAPI, a pioneering comprehensive large-scale API benchmark dataset aimed at expanding LLMs' proficiency in multimodal contexts. Developed collaboratively through ChatGPT, MultiAPI consists of 235 diverse API calls and 2,038 contextual prompts, offering a unique platform evaluation of tool-augmented LLMs handling multimodal tasks. Through comprehensive experiments, our findings reveal that while LLMs demonstrate proficiency in API call decision-making, they face challenges in domain identification, function selection, and argument generation. What's more, we surprisingly notice that auxiliary context can actually impair the performance. An in-depth error analysis paves the way for a new paradigm to address these challenges, suggesting a potential direction for future LLM research.Comment: Work in Progres