Session-based recommender systems have attracted much attention recently. To capture the sequential dependencies, existing methods resort either to data augmentation techniques or left-to-right style autoregressive training. Since these methods are aimed to model the sequential nature of user behaviors, they ignore the future data of a target interaction when constructing the prediction model for it. However, we argue that the future interactions after a target interaction, which are also available during training, provide valuable signal on user preference and can be used to enhance the recommendation quality.

Capacitance extraction or simulation has become a challenging problem in the computer-aided design of integrated circuits (ICs), flat panel display, etc. Due to its scalability and reliability, the parallel floating random walk (FRW) based capacitance solver is widely used. In practice, the parallel FRW algorithms involve an issue of reproducibility and may consume a lot of time in the scenario requesting high accuracy. To relieve these issues, techniques are developed in this paper to enhance the reproducibility and reusability of the parallel FRW based simulation. With them we ensure that same result is reproduced while rerunning the parallel FRW solver with same setting. A "jump start" feature is also implemented to reduce the total runtime of simulating same structure with multiple accuracy criteria. Experiments on shared-memory and distributed-memory platforms have validated the effectiveness of the presented techniques. Compared with a synchronization based approach ensuring the reproducibility, the proposed technique with static workload allocation can brings 4.8X more parallel speedup while sacrificing nothing.

Due to the advantages on scalability and reliability, the floating random walk (FRW) algorithm has been widely adopted for calculating the capacitances among three-dimensional (3-D) conductors. This is evidenced by the industrial practice of interconnect capacitance extraction during the design of high-performance very large-scale integrated (VLSI) circuits. In this work, the FRW algorithm is enhanced through the distributed parallel computing. With an efficient and adaptive task allocation scheme, the communication among different computer nodes is largely reduced. A distributed algorithm for accelerating the space management is also proposed. They have been implemented with Message Passing Interface (MPI) and applied to the high-precision capacitance simulation for touchscreen design and the interconnect capacitance extraction of VLSI circuits. Experiments on a computer cluster show that the proposed techniques achieve up to 114X speedup while using 120 cores, and build up the space management structure for a VLSI case including two million conductor blocks in just 22 seconds (37X parallel speedup on 60 cores).

Accurate capacitance calculation for structures including floating metals is of great interest to both the modeling of interconnect wires and the verification of on-chip capacitors in the design of integrated circuit. The former problem involves regular-shape or cuboid floating dummy fills, and has been addressed with an existing fast algorithm based on floating random walk (FRW) method. The latter problem involves floating metals in more general and complex shape, and is crucial for the design of high-density metal-insulator-metal (MIM) capacitor which endures higher voltage. How to efficiently handle these general-shape floating metals becomes a challenge. In this paper, we first investigate the mechanism of the existing FRW-based approach for handling floating dummies, and then propose an approach based on the central difference formula for handling the general-shape floating metals. The proposed approach has comparable cost to the existing work, but is much more reliable and accurate. Experiments on the structures with floating dummies and MIM capacitor structures have validated the effectiveness and advantage of the proposed approach.

In this paper, two techniques based on Q -learning and action dependent heuristic dynamic programming (ADHDP) are demonstrated for the demand-side management of domestic electric water heaters (DEWHs). The problem is modeled as a dynamic programming problem, with the state space defined by the temperature of output water, the instantaneous hot water consumption rate, and the estimated grid load. According to simulation, Q-learning and ADHDP reduce the cost of energy consumed by DEWHs by approximately 26% and 21%, respectively. The simulation results also indicate that these techniques will minimize the energy consumed during load peak periods. As a result, the customers saved about $466 and $367 annually by using Q-learning and ADHDP techniques to control their DEWHs (100 gallons tank size) operation, which is better than the cost reduction that resulted from using the state-of-the-art ($246) control technique under the same simulation parameters. To the best of the authors' knowledge, this is the first work that uses the approximate dynamic programming techniques to solve the DEWH's load management problem.

The non-Manhattan conductor geometry existing in some capacitance extraction problems brings difficulty to the floating random walk (FRW) method using cubic transition domains. In this paper, techniques are proposed to enhance the FRW method for handling the structures with non-Manhattan conductors. Based on the aligned-box distances and corresponding calculating approaches, the techniques for generating the Gaussian surface and constructing axis-aligned transition cubes are proposed. A practical strategy is then proposed to judge the domination relationship of non-Manhattan conductor blocks for building the space management structure with candidate list. Finally, the strategy using rotated transition cube and related space management technique are proposed to make further acceleration. Experiments on 3-D interconnect structures including from 8 to 1000 non-Manhattan blocks show that the proposed method is from 2.9× to 96× faster than a simple extension of the original FRW method. The proposed method is also up to 39× faster than a boundary element method-based solver. Additional experiments are carried out to further validate the accuracy and efficiency of the proposed techniques, and to demonstrate their suitability for large and multi-dielectric structures.

In this paper, a random walk based solver is presented which calculates the capacitances for verifying the touchscreen design. To suit the complicated conductor geometries in touchscreen structures, we extend the floating random walk (FRW) method for handling non-Manhattan conductors. A unified dielectric pre-characterization scheme is proposed to suit arbitrary dielectric profiles while keeping high accuracy. The algorithm is finally implemented on a computer cluster, which enables massively parallel computing. Numerical experiments validate the accuracy of proposed techniques and the up to 67X parallel speedup. While comparing with other schemes, the unified dielectric pre-characterization scheme achieves the highest accuracy while costing the least memory usage.