Learning Dynamical Demand Response Model in Real-Time Pricing Program

Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the aggregate energy consumption as the output. This, however, neglects the inherent temporal correlation of the EUC behaviors, and may result in large errors when predicting the actual responses of EUCs in real-time pricing (RTP) programs. In this paper, we propose a dynamical DR model so as to capture the temporal behavior of the EUCs. The states in the proposed dynamical DR model can be explicitly chosen, in which case the model can be represented by a linear function or a multi-layer feedforward neural network, or implicitly chosen, in which case the model can be represented by a recurrent neural network or a long short-term memory unit network. In both cases, the dynamical DR model can be learned from historical price and energy consumption data. Numerical simulation illustrated how the states are chosen and also showed the proposed dynamical DR model significantly outperforms the static ones.Comment: Accepted to IEEE ISGT NA 201

Neutrophil Spontaneous Death Is Mediated by Down-Regulation of Autocrine Signaling through GPCR, PI3Kγ\gamma, ROS, and actin

Neutrophil spontaneous apoptosis plays a crucial role in neutrophil homeostasis and the resolution of inflammation. We previously established Akt deactivation as a key mediator of this tightly regulated cellular death program. Nevertheless, the molecular mechanisms governing the diminished Akt activation were not characterized. Here, we report that Akt deactivation during the course of neutrophil spontaneous death was a result of reduced PtdIns(3,4,5)P3 level. The phosphatidylinositol lipid kinase activity of $PI3K\gamma$, but not class IA PI3Ks, was significantly reduced during neutrophil death. The production of PtdIns(3,4,5)P3 in apoptotic neutrophils was mainly maintained by autocrinely released chemokines that elicited $PI3K\gamma$ activation via G protein–coupled receptors. Unlike in other cell types, serum-derived growth factors did not provide any survival advantage in neutrophils. $PI3K\gamma$, but not class IA PI3Ks, was negatively regulated by gradually accumulated ROS in apoptotic neutrophils, which suppressed $PI3K\gamma$ activity by inhibiting an actin-mediated positive feedback loop. Taken together, these results provide insight into the mechanism of neutrophil spontaneous death and reveal a cellular pathway that regulates PtdIns(3,4,5)P3/Akt in neutrophils

Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem

The 0-1 multidimensional knapsack problem(MKP) is a classical NP-hard combinatorial optimization problem. In this paper, we propose a novel heuristic algorithm simulating evolutionary computation and large neighbourhood search for the MKP. It maintains a set of solutions and abstracts information from the solution set to generate good partial assignments. To find high-quality solutions, integer programming is employed to explore the promising search space specified by the good partial assignments. Extensive experimentation with commonly used benchmark sets shows that our approach outperforms the state of the art heuristic algorithms, TPTEA and DQPSO, in solution quality. It finds new lower bound for 8 large and hard instance

View suggestion for interactive segmentation of indoor scenes

Point cloud segmentation is a fundamental problem. Due to the complexity of real-world scenes and the limitations of 3D scanners, interactive segmentation is currently the only way to cope with all kinds of point clouds. However, interactively segmenting complex and large-scale scenes is very time-consuming. In this paper, we present a novel interactive system for segmenting point cloud scenes. Our system automatically suggests a series of camera views, in which users can conveniently specify segmentation guidance. In this way, users may focus on specifying segmentation hints instead of manually searching for desirable views of unsegmented objects, thus significantly reducing user effort. To achieve this, we introduce a novel view preference model, which is based on a set of dedicated view attributes, with weights learned from a user study. We also introduce support relations for both graph-cut-based segmentation and finding similar objects. Our experiments show that our segmentation technique helps users quickly segment various types of scenes, outperforming alternative methods