Abstract Social recommendation often incorporates trusted social links with user-item interactions to enhance rating prediction. Although methods that aggregate explicit social links have shown promising prospects, they are often constrained by the absence of explicit social data and the assumption of homogeneity, thus overlooking variations in social influence and consistency. These limitations hinder semantic expression and recommendation performance. Therefore, we propose a novel framework for social recommendation. First, we design a bipartite network embedding scheme, which learns vertex representations in the embedding space by modeling 1st-order explicit relations and higher-order implicit relations between vertices. Then, the similarity of the embedding vectors is used to extract top-k semantically consistent friends for each user. Next, we design an algorithm to assign a specific influence value to each user. Finally, we combine the top-k friends of the user and their influence values into an ensemble and add it as a regularization term to the rating prediction process of the user to correct the bias. Experiments on three real benchmark datasets show significant improvements in EISF over state-of-the-art methods.

Increasing the throughput by balancing the traffic load through the whole network is important for the transportation system. User optimal routing service usually results in congestions in the bottleneck links. On the other hand, individual travelers typically have delay constraints to satisfy, which might be ignored in the system optimum. In this work, we look into the traffic assignment problem in the stochastic network, considering the maximum delay constraint, so that any route suggested by the system optimal traffic assignment does not incur a delay longer than the constraint in most cases. We formulate the Stochastic Delay Constrained Maximum Flow problem (SDCMF), and prove that it is NP- Complete. The delay aware algorithms are proposed to solve the SDCMF problem, which not only find a set of paths with maximum flow, but also consider the delay of each path.

Multicore processors are increasingly adopted in resource-constrained real-time embedded applications. In the development of such applications, efficient use of RAM memory is as important as the effective scheduling of software tasks. Preemption Threshold Scheduling (PTS) is a well-known technique for controlling the degree of preemption, possibly improving system schedulability, and to reduce system stack usage. In this paper, we consider partitioned multi-processor scheduling on a multicore processor with either Fixed-Priority or Earliest Deadline First scheduling algorithms with PTS and address the design optimization problem of mapping tasks to processor cores and assignment of task priorities and preemption thresholds with the optimization objective of minimizing system stack usage. We present both optimal solution techniques based on Mixed Integer Linear Programming and efficient heuristic algorithms that can achieve high-quality results. We perform extensive performance evaluations using both synthetic tasksets and industrial case studies.

Idling, or running the engine when the vehicle is not moving, accounts for 13%-23% of vehicle driving time and costs billions of gallons of fuel each year. In this paper, we consider the problem of idling reduction under the uncertainty of vehicle stop time. We abstract it as a classic ski rental problem, and propose a constrained version with two statistics μB- and qB+, the expected length of short stops and the probability of long stops. We develop two online algorithms, a suboptimal closed-form algorithm and an optimal numerical solution, that combine the best of the well-known deterministic and randomized schemes to minimize the worst case competitive ratio. We demonstrate the algorithms perform better than existing solutions in terms of both worst case guarantee and average case performance using simulation and real-world driving data.

Idling, or running the engine when the vehicle is not moving, accounts for 13% - 23% of vehicle driving time and costs billions of gallons of fuel each year. In this paper, we consider the problem of idling reduction under the uncertainty of vehicle stop time. We abstract it as a classic ski rental problem, and propose a constrained version with two statistics μB− and qB+, the expectation of short stops' lengths and the probability of long stops. We develop an online algorithm that combines the best of the well-known deterministic and randomized schemes to minimize the worst case competitive ratio. We demonstrate the robustness of the algorithm in terms of both worst case guarantee and average case performance using simulation and real-world driving data.

Multicore platforms are increasingly used in realtime embedded applications. In the development of such applications, an efficient use of RAM memory is as important as the effective scheduling of software tasks. Preemption Threshold Scheduling is a well-known technique for controlling the degree of preemption, possibly improving system schedulability, and allowing savings in stack space. In this paper, we target at the optimal mapping of tasks to cores and the assignment of the scheduling parameters for systems scheduled with preemption thresholds. We formulate the optimization problems using Mixed Integer Linear Programming framework, and propose an efficient heuristic as an alternative. We demonstrate the efficiency and quality of both approaches with extensive experiments using random systems as well as two industrial case studies.

The growing awareness about global climate change has boosted the need to mitigate greenhouse gas emissions from existing power systems and spurred efforts to accelerate the integration of renewable energy sources (e.g. wind and solar power) into the electrical grid. A fundamental difficulty here is that renewable energy sources are usually of high variability. The electrical grid must absorb this variability through employing many additional operations (e.g., operating reserves, energy storage), which will largely raise the cost of electricity from renewable energy sources. To make it affordable, numerous advancements in technologies and methods for the smart grid are required. In this paper, we will confine ourselves to one of them: how to plan the construction of wind farms with high capacity and low variability locally and distributedly. We first study the characteristics of both wind resources and wind turbines and present a more accurate wind power evaluation method based on Gaussian Regression. Then, we analyze a trade-off between wind power's quantity and quality and propose an approach to optimally combine different types of wind turbines to balance the trade-off for a specific site. Finally, we explore geographical diversity among different sites and develop an extended approach that jointly optimizes the combination of sites and turbine types. Extensive experiments using the realistic historical wind resource data are conducted for either of the local and distributed case. Encouraging results are shown for the proposed approaches and some interesting insights are also provided.

The need to reduce greenhouse gases from our current power systems accelerates the integration of renewable energy sources (for example, wind and solar power). A fundamental difficulty is that renewable energy is usually of high variability. Numerous advancements in technologies and methods for the smart grid are required to mitigate and absorb this variability. In this paper, we focus on one of them: how to plan wind farms with high capacity and low variability locally and distributedly. First, we study the characteristics of both wind resource and wind turbines and propose a novel wind power estimation method based on Gaussian regression. The experimental result shows that our method achieves a more accurate estimation compared to other ones and has a nearly zero error for most of the turbine types. Then, we analyze a tradeoff between wind power's quantity and quality for large-scale wind farms, and find that there is an optimal turbine type for each location as to either the quantity or the quality. We propose an approach to optimally combine different types of wind turbines to balance the tradeoff. Finally, we explore geographical diversity among different locations and develop an extended approach that jointly optimizes the combination of locations and turbine types. Besides applying to plan new wind farms, we also discuss how to adapt the two approaches to decide an upgrade plan for a wind farm and a network of wind farms, respectively. We conduct extensive experiments using two different wind resource data traces for both local and distributed cases. The result shows that the proposed approaches significantly outperform those approaches using a single turbine type and those separately optimizing locations and turbine types. We also provide interesting insights about the quantity-quality balancing.

A low-noise triple band transmitter for GSM triple-band and WCDMA is presented. By programming parameters, analog baseband and RF frontend can handle signals of different protocols and frequency bands. Novel linearization method is used in LPF and driver amplifier to meet the demand of 3G systems. Noise optimization is also adopted so that our transmitter achieves −157 dBc/Hz noise at 190 MHz frequency offset in WCDMA band, which eliminates external SAW filter.