A new solution framework for the task of network dismantling is recently developed, based on a two-scale bipartite factor-graph representation of the original graph where local structures are ...abstracted as factor nodes. This technique leads to advancement of extant dismantling algorithms, among which the belief-propagation decimation (BPD) algorithm has an efficient counterpart (factor BPD, i.e., FBPD) on the factor graph, building upon a mean-field spin-glass theory developed for the underlying long-loop feedback vertex set (FVS) problem. In this paper, I (1) demonstrate the advantage as well as disadvantage of the new factor-graph approach, and investigate the varying choice of factors, (2) show that the method can be supported by an alternative microscopic picture, and the two distinct spin-glass theories derive equivalent outcomes, whose analytical results serve as lower bounds for the FVS size on random regular factor graphs, besides (3) an extra mathematical lower bound from the result on random regular (original) graphs. Performances of graph/factor-graph algorithms are compared on various real networks. It shows empirically and analytically that the factor-graph approach does not interfere with what we could achieve without applying this technique; the new approach does a good job where traditional algorithms may perform poorly.
A
bstract
We study reflected entropy as a mixed state correlation measure in black hole evaporation. As a measure for bipartite mixed states, reflected entropy can be computed between black hole and ...radiation, radiation and radiation, and even black hole and black hole. We compute reflected entropy curves in three different models: 3-side wormhole model, End-of-the-World (EOW) brane model in three dimensions and two-dimensional eternal black hole plus CFT model. For 3-side wormhole model, we find that reflected entropy is dual to island cross section. The reflected entropy between radiation and black hole increases at early time and then decreases to zero, similar to Page curve, but with a later transition time. The reflected entropy between radiation and radiation first increases and then saturates. For the EOW brane model, similar behaviors of reflected entropy are found.
We propose a quantum extremal surface for reflected entropy, which we call quantum extremal cross section. In the eternal black hole plus CFT model, we find a generalized formula for reflected entropy with island cross section as its area term by considering the right half as the canonical purification of the left. Interestingly, the reflected entropy curve between the left black hole and the left radiation is nothing but the Page curve. We also find that reflected entropy between the left black hole and the right black hole decreases and goes to zero at late time. The reflected entropy between radiation and radiation increases at early time and saturates at late time.
We consider joint energy storage management and load scheduling at a residential site with integrated renewable generation. Assuming unknown arbitrary dynamics of renewable source, loads, and ...electricity price, we aim at optimizing the load scheduling and energy storage control simultaneously in order to minimize the overall system cost within a finite time period. Besides incorporating battery operational constraints and costs, we model each individual load task by its requested power intensity and service durations, as well as the maximum and average delay requirements. To tackle this finite time horizon stochastic problem, we propose a real-time scheduling and storage control solution by applying a sequence of modification and transformation to employ Lyapunov optimization that otherwise is not directly applicable. With our proposed algorithm, we show that the joint load scheduling and energy storage control can in fact be separated and sequentially determined. Furthermore, both scheduling and energy control decisions have closed-form solutions for simple implementation. Through analysis, we show that our proposed real-time algorithm has a bounded performance guarantee from the optimal T-slot look-ahead solution, and is asymptotically equivalent to it as the battery capacity and time period goes to infinity. The effectiveness of joint load scheduling and energy storage control by our proposed algorithm is demonstrated through simulation as compared with alternative algorithms.
•Ecosystem health response to LUCC was assessed.•Spatial neighboring effect on ecosystem services was focused.•Ecosystem physical health declined in most towns.•Ecosystem vigor mainly affected the ...physical health.
Quantitative analysis of the response of ecosystem health to rural land use change is required to comprehend the human-nature coupling mechanism and to explore the process of global environmental change, which can interpret the ecological effects of regional land use and land cover change comprehensively. However, the existing regional ecosystem health assessment largely ignored either the internal connection of ecosystem health to land use patterns or the internal representation of ecosystem services to ecosystem health. Using Lijiang City of China as a study area, the average normalized difference vegetation index (NDVI), landscape metrics, and ecosystem elasticity coefficient based on different land use types were used as quantitative indicators. Then the coefficient of spatial neighboring effect was introduced to characterize the adjacency effect on ecosystem services, and to generate the index of integrated ecosystem health. The results showed the change of land use was close to 30% at county level from 1986 to 2006, and forest land was the primary land use type. With respect to the declining physical health of ecosystems in all the four counties, the integrated health experienced a slight increase in Lijiang County. The vast majority of towns’ ecosystem physical health and integrated health declined, while more than 70% of towns did not change distinctly. Ecosystem physical health had distinct influence on the integrated ecosystem health, and ecosystem vitality was the main factor affecting the condition of physical health. Emphasized in the interconnection of pattern and process, this study provided an ecosystem health approach to assessing the integrated ecological effects of regional land use change.
High efficiency video coding (HEVC) significantly reduces bit rates over the preceding H.264 standard but at the expense of extremely high encoding complexity. In HEVC, the quad-tree partition of the ...coding unit (CU) consumes a large proportion of the HEVC encoding complexity, due to the brute-force search for rate-distortion optimization (RDO). Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra-and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network. First, we establish a large-scale database including substantial CU partition data for the HEVC intra- and inter-modes. This enables deep learning on the CU partition. Second, we represent the CU partition of an entire coding tree unit in the form of a hierarchical CU partition map (HCPM). Then, we propose an early terminated hierarchical CNN (ETH-CNN) for learning to predict the HCPM. Consequently, the encoding complexity of intra-mode HEVC can be drastically reduced by replacing the brute-force search with ETH-CNN to decide the CU partition. Third, an ETH-LSTM is proposed to learn the temporal correlation of the CU partition. Then, we combine the ETH-LSTM and the ETH-CNN to predict the CU partition for reducing the HEVC complexity at inter-mode. Finally, experimental results show that our approach outperforms the other state-of-the-art approaches in reducing the HEVC complexity at both intra- and inter-modes.
Versatile Video Coding (VVC), as the latest standard, significantly improves the coding efficiency over its predecessor standard High Efficiency Video Coding (HEVC), but at the expense of sharply ...increased complexity. In VVC, the quad-tree plus multi-type tree (QTMT) structure of the coding unit (CU) partition accounts for over 97% of the encoding time, due to the brute-force search for recursive rate-distortion (RD) optimization. Instead of the brute-force QTMT search, this paper proposes a deep learning approach to predict the QTMT-based CU partition, for drastically accelerating the encoding process of intra-mode VVC. First, we establish a large-scale database containing sufficient CU partition patterns with diverse video content, which can facilitate the data-driven VVC complexity reduction. Next, we propose a multi-stage exit CNN (MSE-CNN) model with an early-exit mechanism to determine the CU partition, in accord with the flexible QTMT structure at multiple stages. Then, we design an adaptive loss function for training the MSE-CNN model, synthesizing both the uncertain number of split modes and the target on minimized RD cost. Finally, a multi-threshold decision scheme is developed, achieving a desirable trade-off between complexity and RD performance. The experimental results demonstrate that our approach can reduce the encoding time of VVC by 44.65%~66.88% with a negligible Bjøntegaard delta bit-rate (BD-BR) of 1.322%~3.188%, significantly outperforming other state-of-the-art approaches.
An extensive study on the in-loop filter has been proposed for a high efficiency video coding (HEVC) standard to reduce compression artifacts, thus improving coding efficiency. However, in the ...existing approaches, the in-loop filter is always applied to each single frame, without exploiting the content correlation among multiple frames. In this paper, we propose a multi-frame in-loop filter (MIF) for HEVC, which enhances the visual quality of each encoded frame by leveraging its adjacent frames. Specifically, we first construct a large-scale database containing encoded frames and their corresponding raw frames of a variety of content, which can be used to learn the in-loop filter in HEVC. Furthermore, we find that there usually exist a number of reference frames of higher quality and of similar content for an encoded frame. Accordingly, a reference frame selector (RFS) is designed to identify these frames. Then, a deep neural network for MIF (known as MIF-Net) is developed to enhance the quality of each encoded frame by utilizing the spatial information of this frame and the temporal information of its neighboring higher-quality frames. The MIF-Net is built on the recently developed DenseNet, benefiting from its improved generalization capacity and computational efficiency. In addition, a novel block-adaptive convolutional layer is designed and applied in the MIF-Net, for handling the artifacts influenced by coding tree unit (CTU) structure in HEVC. Extensive experiments show that our MIF approach achieves on average 11.621% saving of the Bjøntegaard delta bit-rate (BD-BR) on the standard test set, significantly outperforming the standard in-loop filter in HEVC and other state-of-the-art approaches.
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bstract
Defect extremal surface is defined by extremizing the Ryu-Takayanagi formula corrected by the quantum defect theory. This is interesting when the AdS bulk contains a defect brane (or ...string). We introduce a defect extremal surface formula for reflected entropy, which is a mixed state generalization of entanglement entropy measure. Based on a decomposition procedure of an AdS bulk with a brane, we demonstrate the equivalence between defect extremal surface formula and island formula for reflected entropy in AdS
3
/BCFT
2
. We also compute the evolution of reflected entropy in evaporating black hole model and find that defect extremal surface formula agrees with island formula.
No community detection algorithm can be optimal for all possible networks, thus it is important to identify whether the algorithm is suitable for a given network. We propose a multi-step algorithmic ...solution scheme for overlapping community detection based on an advanced label propagation process, which imitates the community formation process on social networks. Our algorithm is parameter-free and is able to reveal the hierarchical order of communities in the graph. The unique property of our solution scheme is self-falsifiability; an automatic quality check of the results is conducted after the detection, and the fitness of the algorithm for the specific network is reported. Extensive experiments show that our algorithm is self-consistent, reliable on networks of a wide range of size and different sorts, and is more robust than existing algorithms on both sparse and large-scale social networks. Results further suggest that our solution scheme may uncover features of networks' intrinsic community structures.
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