Aerosol size information is important to the understanding of aerosol dynamics, which change rapidly during wildfire, dust transport, and volcanic eruption events over Asia. In this study, a deep neural network model was trained using Advanced Meteorological Imager (AMI) level 1b observations, AMI Yonsei aerosol retrieval aerosol products, and observation geometries to retrieve the aerosol optical depth (AOD), Ångström exponent (AE), and spectral derivatives of AE (AE′). The fine-mode fraction (FMF) was calculated with a spectral deconvolution algorithm using retrieved AE and AE′ when AOD > 0.2. The retrieved aerosol products were validated using AERONET (AOD at 550 nm: R = 0.837, RMSE = 0.219, MBE = –0.066; AE: R = 0.726; RMSE = 0.231; MBE = –0.007; FMF: R = 0.875; RMSE = 0.072; MBE = 0.007). Case studies of dust transport, wildfire, and haze events in Asia revealed that the retrieved aerosol size products may be used for analysis of sudden pollution events. Results of this study indicate the potential for comprehensive analysis of aerosol properties in Asia using continuous aerosol size data from geostationary Earth orbit satellite observations.

Simultaneously clustering rows and columns of a matrix, co-clustering can exploit the complex relationships between two different domains and identify groups of distinct nature. In this work, we introduce CI-GAN, a novel GAN-based approach for co-clustering. The model exploits two distinct GAN that cluster each domain independently and combines them intelligently by maximizing the mutual information between the input data and the generated co-clusters. From the experiments constructed with image, audio, and text datasets, it is found that such a systematic way of sharing information between the networks can improve co-clustering performance substantially; when CI-GAN is compared against five standard algorithms, it consistently reports the highest accuracy on both synthetic and real datasets.

Result diversification is an important aspect in web-based search, document summarization, facility location, portfolio management, and other applications. Given a set of ranked results for a set of objects (e.g., web documents, facilities, etc.) with a distance between any pair, the goal is to select a subset S satisfying the following three criteria: (a) the subset S satisfies some constraint (e.g., bounded cardinality), (b) the subset contains results of high “quality,” and (c) the subset contains results that are “diverse” relative to the distance measure. The goal of result diversification is to produce a diversified subset while maintaining high quality as much as possible. We study a broad class of problems where the distances are a metric, where the constraint is given by independence in a matroid, where quality is determined by a monotone submodular function and diversity is defined as the sum of distances between objects in S . Our problem is a generalization of the max-sum diversification problem studied in Gollapudi and Sharma [2009], which in turn is a generalization of the max-sum p-dispersion problem studied extensively in location theory. It is NP-hard even with the triangle inequality. We propose two simple and natural algorithms: a greedy algorithm for a cardinality constraint and a local search algorithm for an arbitrary matroid constraint. We prove that both algorithms achieve constant approximation ratios.

Result diversification has many important applications in databases, operations research, information retrieval, and finance. In this paper, we study and extend a particular version of result diversification, known as max-sum diversification. More specifically, we consider the setting where we are given a set of elements in a metric space and a set valuation function f defined on every subset. For any given subset S, the overall objective is a linear combination of f(S) and the sum of the distances induced by S. The goal is to find a subset S satisfying some constraints that maximizes the overall objective.

In this paper we focus on the problem of ranking news stories within their historical context by exploiting their content similarity. We observe that news stories evolve and thus have to be ranked in a time and query dependent manner. We do this in two steps. First, the mining step discovers metastories, which constitute meaningful groups of similar stories that occur at arbitrary points in time. Second, the ranking step uses well known measures of content similarity to construct implicit links among all metastories, and uses them to rank those metastories that overlap the time interval provided in a user query. We use real data from conventional and social media sources (weblogs) to study the impact of different meta-aggregation techniques and similarity measures in the final ranking. We evaluate the framework using both objective and subjective criteria, and discuss the selection of clustering method and similarity measure that lead to the best ranking results.

The presence of duplicate records is a major data quality concern in large databases. To detect duplicates, entity resolution also known as duplication detection or record linkage is used as a part of the data cleaning process to identify records that potentially refer to the same real-world entity. We present the Stringer system that provides an evaluation framework for understanding what barriers remain towards the goal of truly scalable and general purpose duplication detection algorithms. In this paper, we use Stringer to evaluate the quality of the clusters (groups of potential duplicates) obtained from several unconstrained clustering algorithms used in concert with approximate join techniques. Our work is motivated by the recent significant advancements that have made approximate join algorithms highly scalable. Our extensive evaluation reveals that some clustering algorithms that have never been considered for duplicate detection, perform extremely well in terms of both accuracy and scalability.

Many web pages and resources are primarily relevant to certain geographic locations. For example, in many queries web pages on restaurants, hotels, or movie theaters are mostly relevant to those users who are in geographic proximity to these locations. Moreover, as the number of queries with a local component increases, searching for web pages which are relevant to geographic locations is becoming increasingly important. The performance of geographically-oriented search is greatly affected by how we use geographic information to rank web pages. In this paper, we study the issue of ranking web pages using geographically-sensitive link analysis algorithms. More precisely, we study the question of whether geographic information can improve search performance. We propose several geographically-sensitive link analysis algorithms which exploit the geographic linkage between pages. We empirically analyze the performance of our algorithms.

A collaborative crawler is a group of crawling nodes, in which each crawling node is responsible for a specific portion of the web. We study the problem of collecting geographi-cally-aware pages using collaborative crawling strategies. We first propose several collaborative crawling strategies for the geographically focused crawling, whose goal is to collect web pages about specified geographic locations, by considering features like URL address of page, content of page, extended anchor text of link, and others. Later, we propose various evaluation criteria to qualify the performance of such crawling strategies. Finally, we experimentally study our crawling strategies by crawling the real web data showing that some of our crawling strategies greatly outperform the simple URL-hash based partition collaborative crawling, in which the crawling assignments are determined according to the hash-value computation over URLs. More precisely, features like URL address of page and extended anchor text of link are shown to yield the best overall performance for the geographically focused crawling.

A geometric kinematics modeling of a three wheeled autonomous mobile robot is proposed. Three types of basic motions are proposed for the path generation of the developed mobile robot. All paths of the mobile robot are achieved by the combination of proposed basic motions. The proposed scheme is verified through computer simulations and a practical model.