Abstract
Residential customers account for an indispensable part in the demand response (DR) program for their capability to provide flexibility when the system required. However, their available DR capacity has not been fully comprehended by the aggregator, who needs the information to bid accurately on behalf of the residential customers in the market transaction. To this end, this paper devised an optimal bidding strategy for the aggregator considering the bottom-up responsiveness of residential customers. Firstly, we attempt to establish the customers' responsiveness function in relation to different incentives, during which a home energy management system (HEMS) is introduced to implement load adjustment for electrical appliances. Secondly, the function is applied to the aggregator's decision-making process to formulate the optimal bidding strategy in the day-ahead (DA) market and the optimal scheduling scheme for the energy storage system (ESS) with the aim to maximize its own revenue. Finally, the validity of the proposed method is verified using the dataset from the Pecan Street experiment in Austin. The obtained outcome demonstrates the practical rationality of the proposed method.

Abstract
An approach to analyzing Streaming Big Data as it comes in while maintaining the proper context of past events is to employ contiguous visualizations with an increasingly aggressive aggregation degree. This allows for the most recent data to be displayed in detail, while older data is shown in an aggregated form according to how long ago it was received. However, the transitions applied between visualizations with different aggregations must not compromise the understandability of the data flow. Particularly, new data should be perceived considering the context established by older data, and the visualizations should not be perceived as independent or unconnected. In this paper, we present the first study on transitions between two contiguous visualizations, focusing on time series data. We developed several animated transitions between a scatter plot, where all data points are individually represented as they arrive, and other visualizations where data is displayed in an aggregated form. We then conducted a user evaluation to assess the most appealing and effective transitions that allow for the best comprehension of the displayed data for each visualization pair.

Abstract
We provide a framework for analyzing geographical influence networks that have impacts on visit event sequences for a set of point-of-interests (POIs) in a city. Since mutually-exciting Hawkes processes can naturally model temporal event data and capture interactions between those events, previous work presented a probabilistic model based on Hawkes processes, called CHP model, for finding cooperative structure among online items from their share event sequences. In this paper, based on Hawkes processes, we propose a novel probabilistic model, called RH model, for detecting geographical competitive structure in the set of POIs, and present a method of inferring it from the POI visit event history. We mathematically derive an analytical approximation formula for predicting the popularity of each of the POIs for the RH model, and also extend the CHP model so as to extract geographical cooperative structure. Using synthetic data, we first confirm the effectiveness of the inference method and the validity of the approximation formula. Using real data of Location-Based Social Networks (LBSNs), we demonstrate the significance of the RH model in terms of predicting the future events, and uncover the latent geographical influence networks from the perspective of geographical competitive and cooperative structures.

Abstract
We present near-infrared interferometric data on the Seyfert 2 galaxy NGC 1068, obtained with the GRAVITY instrument on the European Southern Observatory Very Large Telescope Interferometer. The extensive baseline coverage from 5 to 60 M lambda allowed us to reconstruct a continuum image of the nucleus with an unrivaled 0.2 pc resolution in the K-band. We find a thin ring-like structure of emission with a radius r=0.24 +/- 0.03 pc, inclination i=70 +/- 5 degrees, position angle PA=-50 +/- 4 degrees, and h/r< 0.14, which we associate with the dust sublimation region. The observed morphology is inconsistent with the expected signatures of a geometrically and optically thick torus. Instead, the infrared emission shows a striking resemblance to the 22 GHz maser disc, which suggests they share a common region of origin. The near-infrared spectral energy distribution indicates a bolometric luminosity of (0.4-4.7) x 10(45) erg s(-1), behind a large A(K)5.5 (A(V)approximate to 90) screen of extinction that also appears to contribute significantly to obscuring the broad line region.

Abstract

Abstract
Chronic wound assessment and wound healing are important for diagnostic, follow up and wound treatment. However, this growing disease affecting nearly 2 thousand million and 5.7 million people in the USA and Europe, costing around $20 billion and $8 thousand million USD per year, still relies on subjective human assessment of wounds. A scoping review allowed us to identify 109 articles that map the literature on the topic of computer vision for chronic wound assessment and healing. These results were carefully analyzed and mapped into relevant clinical challenges associated with this field, identifying the maturity of each different computer vision challenge that needs addressing. Results show that wound size and tissue type classification already have interesting work, but various other clinical areas are in need of larger datasets and computer vision research efforts for achieving a relevant impact in today's clinical routine. © 2020 IEEE.