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Obtaining crowd flow distribution with recognized human intention is extremely valuable for a series of applications for metropolitan cities. Previous solutions look at spatial correlation and temporal periodicity based on historical crowd flow information to calculate future crowd flow distribution. However, these mechanisms cannot recognize the intention behind crowd flow. We address this problem by leveraging a key insight – people's intention behind their movement is highly correlated with the point-of-interest (POI) distribution of the corresponding regions and adjacent regions. Therefore, we propose DeepFlowGen to model the complicated relationship between crowd flow, POI, check-ins, and time to generate intention-aware crowd flow. Specifically, we solve the conflict between dynamic crowd flow and static POI distribution by fusing the information in both time and POI domains. Besides, we employ a sequence of residual blocks in DeepFlowGen to address the challenges of modeling the diverse temporal rhythms and heterogeneous influence of POI. Furthermore, we examine the generated intention-aware crowd flow from two aspects to substantiate the reasonability of DeepFlowGen. Extensive experiments demonstrate that our model outperforms the state-of-the-art solutions by at most 30 percent in terms of NRMSE of total crowd flow. Moreover, the correlation between the generated intention-aware crowd flow and the check-in distribution across different categories of POIs is as high as 0.90 and 0.80 in Beijing and Shanghai. Combined with extensive case studies, we demonstrate the strong ability of our model in generating intention-aware crowd flow. [ABSTRACT FROM AUTHOR]

As a fundamental component in map service, map matching is of great importance for many trajectory-based applications, e.g., route optimization, traffic scheduling, and fleet management. In practice, Hidden Markov Model and its variants are widely used to provide accurate and efficient map matching service. However, HMM-based methods fail to utilize the knowledge (e.g., the mobility pattern) of enormous trajectory big data, which are useful for intelligent map matching. Furthermore, with many following-up works, they are still easily influenced by the common noisy and sparse records in the reality. In this paper, we revisit the map matching task from the data perspective and propose to utilize the great power of massive data and deep learning to solve these problems. Based on the seq2seq learning framework, we build a trajectory2road model with attention mechanism to map the sparse and noisy trajectory into the accurate road network. Different from previous algorithms, our deep learning based model complete the map matching in the latent space, which provides the high tolerance to the noisy trajectory and also enhances the matching with the knowledge of mobility pattern. Extensive experiments demonstrate that the proposed model outperforms the widely used HMM-based methods by more than 10 percent (absolute accuracy) in various situations especially the noisy and sparse settings. [ABSTRACT FROM AUTHOR]

With the prevalence of smartphones, people have left abundant behavior records in cyberspace. Discovering and understanding individuals’ cyber activities can provide useful implications for policymakers, service providers, and app developers. In this paper, we propose a framework to discover daily cyber activity patterns across people's mobile app usage. The framework first segments app usage traces into short time windows and then applies a probabilistic topic model to infer users’ cyber activities in each window. By constructing and exploring the coherence of users’ activity sequences, the framework can identify individuals’ daily patterns. Next, the framework uses a hierarchical clustering algorithm to recognize the common patterns across diverse groups of individuals. We apply the framework on a large-scale and real-world dataset, consisting of 653,092 users with 971,818,946 usage records of 2,000 popular mobile apps. Our analysis shows that people usually follow yesterday's activity patterns, but the patterns tend to deviate as the time-lapse increases. We also discover five common daily cyber activity patterns, including afternoon reading, nightly entertainment, pervasive socializing, commuting, and nightly socializing. Our findings have profound implications on identifying the demographics of users and their lifestyles, habits, service requirements, and further detecting other disrupting trends such as working overtime and addiction to the game and social media. [ABSTRACT FROM AUTHOR]

A dispersion-managed, figure-of-9, mode-locked ultrafast Yb-doped fiber laser is presented in this letter. The laser cavity employs a chirped fiber Bragg grating with a proper anomalous dispersion for intra-cavity dispersion compensation. The laser with large normal net dispersion delivers an output with maximum average power of 20 mW and pulse duration of 5.50 ps at the repetition rate of 39.1 MHz, and the pulse can be dechirped to 228 fs. While net dispersion of cavity is very close to zero, the laser delivers an output with an average power of 12 mW at the repetition rate of 47.3 MHz, and the pulse duration is 1.86 ps, which can be dechirped to 175 fs. The whole cavity is very compact and environmentally stable, for its all-polarization-maintaining and all-fiber structure. [ABSTRACT FROM AUTHOR]

With the ever-increasing urbanization process, systematically modeling people's activities in the urban space is being recognized as a crucial socioeconomic task. It is extremely challenging due to the lack of reliable data and suitable methods, yet the emergence of population-scale urban mobility data sheds new light on it. However, recent works on discovering activity patterns from urban mobility data are still limited in terms of concisely and specifically modeling the temporal dynamics of people's urban activities. To bridge the gap, we present a State-sharing Hidden Markov Model (SSHMM), a novel time-series modeling method that uncovers urban dynamics with massive urban mobility data. SSHMM models the urban dynamics from two aspects. First, it extracts the urban states from the whole city, which captures the volume of population flows as well as the frequency of each type of Point of Interests (PoIs) visited. Second, it characterizes the urban dynamics of each urban region as the state transition on the shared-states, which reveals distinct daily rhythms of urban activities. We evaluate our method via large-scale real-life mobility dataset. The results demonstrate that SSHMM learns semantics-rich urban dynamics, which are highly correlated with the functions of the region. Besides, it recovers the urban dynamics in different time slots with RMSE of 0.0793 when only learn limited states for the whole city, which outperforms the general HMM by 54.2 percent. [ABSTRACT FROM AUTHOR]

Due to explosive growth of mobile data over the overburdened cellular network, opportunistic offloading is proposed to transmit the traffic originally transmitted through infrastructure network to delay-tolerant device-to-device networks at the edge of mobile networks. Existing offloading schemes mainly focus on selecting a subset of mobile nodes to work as offloadees, who first download the contents through the infrastructure, and then transmit them to subscriber nodes. However, when there is large amount of contents to be offloaded, and if all contents are disseminated through only one fixed set of offloadees every time, it may result in the risk of extreme cases that only extremely few nodes received the content, even if the expectation is large. This is like putting all of your eggs in one basket. To deal with this problem, we introduce an economic concept, conditional value at risk (CVaR) to measure the risk in offloadee set selection. CVaR is a widely used risk assessment measure for investments, which provides a unified and comprehensive risk evaluation framework for complex investment portfolio. We propose an algorithm to compute a portfolio over multiple offloadee sets with a guarantee on CVaR. Moreover, we prove the feasibility of our methodology from the mathematical point of view. Extensive evaluations with real-world traces show that our proposed offloading scheme can obtain a portfolio with significantly better CVaR, i.e., 91.53 percent performance gain at most, compared with the state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

The explosive growth of traffic-consumption by mobile devices is leading to severe cellular network congestion, which is posing challenges for Internet Service Providers (ISPs) to provide good quality services with limited cellular capacity and impacting the user’s experience. Data pricing has been proven to be an effective way to enhance both the service quality and ISP’s profit. However, traditional data pricing schemes do not consider the real Mobile Application (App) Usage Patterns (MAUPs) among large scale cellular networks. In this paper, MAUPs aware smart data pricing scheme is proposed. In our work, we firstly extract and model the users’ app usage behaviors of approximately 9,600 cellular towers as two-dimensional MAUPs (time, app category). Then 7 distinct derived MAUPs are considered to be incorporated into the user satisfaction model and ISP’s profit model. The performance of our proposal is evaluated and verified by numerical experiments from the aspects of ISP’s profit, consumption surplus, capacity utilization and traffic efficiency. The MAUPs based pricing scheme can be periodically updated according to the operational conditions and therefore significantly instructive for ISPs. [ABSTRACT FROM AUTHOR]