Crop yield estimation at the field scale is essential for farmers, crop insurance companies to make informed decisions. Methodologies based on assimilating remote sensing LAI into crop growth models have shown advantages in crop yield estimates. Compared with MODIS and Landsat, Sentinel-2 satellites provide higher spatial and temporal resolution data, which brings revolutionary opportunities for crop monitoring. This study is to evaluate the performance of assimilating Sentinel-2 LAI into the WOFOST model for winter wheat yield estimation using the Ensemble Kalman Filter algorithm. The results showed that assimilating Sentinel-2 LAI improved the yield estimation (R2= 0.45; RMSE = 512 kg/ha) compared to the situation without data assimilation (R2= 0.27; RMSE = 818 kg/ha), which demonstrated the potential usage of the Sentinel-2 LAI for yield estimation at the field scale.

Social networks have significantly altered the behavior patterns of netizens all around the world. Therefore, accurate and expressive model of social users is increasingly demanded as it pose great value in a variety of scenarios, such as e-commerce, cyber security, and entertainment to name a few. In this paper, we propose the Pop Culture Attention Writing Model (PAWM) to explore the writing patterns of social users by explicitly capturing the influence of Internet pop culture ingredients with an attention mechanism. The writing pattern representations are learned by a memory network through storing and updating historical latent patterns. We then develop the Deep Social User Model via jointly modeling basic properties of social users, temporal contents, and the learned writing patterns based on PAWM. This paper is the first trial, to the best of our knowledge, which captures Internet pop culture information and applies deep neural network to model user writing pattern. A series of experiments conducted on social bot detection and social user identification demonstrate and validate the effectiveness of the proposed models.

Caching query results is an efficient technique for Web search engines. A state-of-the-art approach named Static-Dynamic Cache (SDC) is widely used in practice. Replacement policy is the key factor on the performance of cache system, and has been widely studied such as LIRS, ARC, CLOCK, SKLRU and RANDOM in different research areas. In this paper, we discussed replacement policies for static-dynamic cache and conducted the experiments on real large scale query logs from two famous commercial Web search engine companies. The experimental results show that ARC replacement policy could work well with static-dynamic cache, especially for large scale query results cache.

Monitoring the evolution phases of real-time event including occurrence, development, climax, decline and ending is crucial for management department to intuitively and comprehensively understand the event and then make better decisions. However, there have been very few studies on performing phase evolution analysis of event using the number of posts at the specific time unit. The challenge of this problem is how to identify temporal pattern and mine topic of different phases automatically. In this paper, we propose a unified phase evolution mining model, it firstly identifies the temporal patterns of phases based on k-means and empirical rules, then, burst detection algorithm is adopted to discover peak interval of all phases, finally, we use a summarization technique TextRank to extract keywords from contents to summarize the topics in each phase. In addition, we perform experiments on two real-world datasets collected from different social media platform to understand the event evolution in a more comprehensive way. Experimental results show the characteristics of event evolution on different social media platforms and verify the efficacy of the proposed model.

Social behaviors such as retweetings, comments or likes are valuable information for human activities analysis. We focus here on user's retweeting behavior which has been considered as a key mechanism of information diffusion in social networks. Since we can only observe on which messages user retweet. It is a typically one-class setting which only positive examples or implicit feedback can be observed. However, few research works on retweeting prediction consider one-class setting. In this paper, we analyze and study the fundamental factors that might affect retweetability of a tweet, and then employ one-class collaborative filtering method by quantitatively measure the user personal preference and social influence between users and messages to predict user's retweeting behavior. Experimental results on a real-world dataset from social network show that the proposed method is effective and can improve the performance of the one-class collaborative filtering over baseline methods through leveraging weighted negative examples information.

Review rating prediction is of much importance for sentiment analysis and business intelligence. Existing methods work well when aspect-opinion pairs can be accurately extracted from review texts and aspect ratings are complete. The challenges of improving prediction accuracy are how to capture the semantics of review content and how to fill in the missing values of aspect ratings. In this paper, we propose a novel review rating prediction method, which improves the prediction accuracy by capturing deep semantics of review content and alleviating data missing problem of aspect ratings. The method firstly learns the latent vector representation of review content using skip-thought vectors, a state-of-the-art deep learning method, then, the missing values of aspect ratings are filled in based on users? history reviewing behaviors, finally, a novel optimization framework is proposed to predict the review rating. Experimental results on two real-world datasets demonstrate the efficacy of the proposed method.

The sociological theory of entrainment accounts for the synchronization of human rhythmic modalities through social interactions: they coordinate in a variety of dimensions including linguistic styles, facial expressions, music pace, applause, and so on. Though highly relevant, emotion entrainment has received little attention to date. In addition, most previous studies on entrainment are done through small scale or controlled laboratory studies. In this paper, we investigate emotion entrainment in the context of online social media. To the best of our knowledge, this is the first time that emotion entrainment has been examined on a large scale, real world setting. For this purpose, we propose a framework that can model entrainment phenomenon and measure its effect. Our framework differentiates from previous research by its model-free essential and discerning in entrainment directions. These traits enable us to model entrainment dynamics under few assumptions, and distinguish emotion flow of entrainment. In our studies, we investigate entrainment patterns under different emotion states, i.e. positive, neutral and negative. We discover that entrainments under different emotions all follow a power law distribution. Besides, people are willing to entrain to others under positive emotion, and users with positive emotion are more likely to be entrained. By inspecting the interactions between entrainment and emotion, we reveal that entrainment has an effect of negotiating different emotion types toward an even distribution.

With the development of microblog services, tens of thousands of messages are produced every day and recommending useful messages according to users' interest is recognized as an effective way to overcome the information overload problem. Collaborative filtering which rooted from recommender system has been utilized for microblog recommendation, where social relationship information can help improve the recommendation performance. However, most of existing methods only consider the static relationship, i.e. the following relationship, which totally ignores the relationship conveyed by users' repost behaviors. To explore the effects of behavior based relationship on recommendation, we propose an Interaction Based Collaborative Filtering (IBCF) approach. Specifically, we first use topic model to analyze users' interactive behaviors and measure the topic-specific relationship strength, then we incorporate the relationship factor into the matrix factorization framework. Experimental results show that compared to the current popular social recommendation methods, IBCF can achieve better performance on the MAP and NDCG evaluation measures, and have better interpretability for the recommended results.

Caching query results is an efficient technique for Web search engines. Admission policy can prevent infrequent queries from taking space of more frequent queries in the cache. In this paper we present two novel admission policies tailored for query results cache. These policies are based on query results prefetching information. We also propose a demote operation for the query results cache to improve the cache hit ratio. We then use a trace of over 5 million queries to evaluate our admission policies, as well as traditional policies. Experimental results show that our prefetch-aware admission policies can achieve hit ratios better than state-of-the-art admission policies.

Query results caching is an efficient technique for Web search engines. In this paper we present User-Aware Cache, a novel approach tailored for query results caching, that is based on user characteristics. We then use a trace of around 30 million queries to evaluate User-Aware Cache, as well as traditional methods and theoretical upper bounds. Experimental results show that this approach can achieve hit ratios better than state-of-the-art methods.

In order to meet the challenges of significant storage and application growth, as well as shortened backup windows and limited IT resources, more and more organizations embrace Hierarchical Storage Management (HSM). Parts of SAN file systems provide the ability to support HSM, but they didn't provide more details in design considerations and how to implement. In this paper, we present design considerations in Blue Whale File System (BWFS) which is a high performance SAN file system and implement the system. Event, migration and attribute constitute the three aspects of data management mechanism in BWFS. As for READ/WRITE/TRUNCATE event, the problem is that a client directly accesses to storage devices containing file data, rather than through a metadata server. We monitor LayoutGet operation instead of real I/O path. BWFS is a multi-volume file system which can support single name space across multiple data volumes. When different storage devices are managed by BWFS, migration is between volumes in BWFS. We also implement migration between BWFS and external repository. We believe the solutions to the problem we encountered are useful to file system developers.