Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis

In this work, we develop a novel framework to measure the similarity between dynamic financial networks, i.e., time-varying financial networks. Particularly, we explore whether the proposed similarity measure can be employed to understand the structural evolution of the financial networks with time. For a set of time-varying financial networks with each vertex representing the individual time series of a different stock and each edge between a pair of time series representing the absolute value of their Pearson correlation, our start point is to compute the commute time matrix associated with the weighted adjacency matrix of the network structures, where each element of the matrix can be seen as the enhanced correlation value between pairwise stocks. For each network, we show how the commute time matrix allows us to identify a reliable set of dominant correlated time series as well as an associated dominant probability distribution of the stock belonging to this set. Furthermore, we represent each original network as a discrete dominant Shannon entropy time series computed from the dominant probability distribution. With the dominant entropy time series for each pair of financial networks to hand, we develop a similarity measure based on the classical dynamic time warping framework, for analyzing the financial time-varying networks. We show that the proposed similarity measure is positive definite and thus corresponds to a kernel measure on graphs. The proposed kernel bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks extracted through New York Stock Exchange (NYSE) database demonstrate the effectiveness of the proposed approach.Comment: Previously, the original version of this manuscript appeared as arXiv:1902.09947v2, that was submitted as a replacement by a mistake. Now, that article has been replaced to correct the error, and this manuscript is distinct from that articl

Quantum kernels for unattributed graphs using discrete-time quantum walks

In this paper, we develop a new family of graph kernels where the graph structure is probed by means of a discrete-time quantum walk. Given a pair of graphs, we let a quantum walk evolve on each graph and compute a density matrix with each walk. With the density matrices for the pair of graphs to hand, the kernel between the graphs is defined as the negative exponential of the quantum Jensen–Shannon divergence between their density matrices. In order to cope with large graph structures, we propose to construct a sparser version of the original graphs using the simplification method introduced in Qiu and Hancock (2007). To this end, we compute the minimum spanning tree over the commute time matrix of a graph. This spanning tree representation minimizes the number of edges of the original graph while preserving most of its structural information. The kernel between two graphs is then computed on their respective minimum spanning trees. We evaluate the performance of the proposed kernels on several standard graph datasets and we demonstrate their effectiveness and efficiency

Localization Accuracy of Ultrasound-Actuated Needle with Color Doppler Imaging

An ultrasonic needle-actuating device for tissue biopsy and regional anaesthesia offers enhanced needle visibility with color Doppler imaging. However, its specific performance is not yet fully determined. This work investigated the influence on needle visibility of the insertion angle and drive voltage, as well as determined the accuracy and agreement of needle tip localization by comparing color Doppler measurements with paired photographic and B-mode ultrasound measurements. Needle tip accuracy measurements in a gelatin phantom gave a regression trend, where the slope of trend is 0.8808; coefficient of determination (R2) is 0.8877; bias is −0.50 mm; and the 95% limits of agreement are from −1.31 to 0.31 mm when comparing color Doppler with photographic measurements. When comparing the color Doppler with B-mode ultrasound measurements, the slope of the regression trend is 1.0179; R2 is 0.9651; bias is −0.16 mm; and the 95% limits of agreement are from −1.935 to 1.605 mm. The results demonstrate the accuracy of this technique and its potential for application to biopsy and ultrasound guided regional anaesthesia

Geochemical characteristics and hydrocarbon generation potential of main source rocks in the Upper Triassic Xujiahe Formation, Sichuan Basin, China

In order to have a comprehensive understanding of the characterization and hydrocarbon generation potential of the source rock in the Upper Triassic Xujiahe Formation, Sichuan Basin, the geochemical data of more than 1,500 cuttings and 106 core samples were collected and analyzed. The T3x5 member of Xujiahe formation show the highest average TOC content (3.63%) followed by the T3x1+2 and T3x3 members. The TOC contents of different members show a general decreasing trend from the bottom to the top in Xujiahe formation. From the rock pyrolysis and kerogen δ13C values, the source rock trend to be kerogen type III with minor amounts of type Ⅱ2. According to the Ro values, the Xujiahe source rock shows high maturity in the northwest and low maturity in the southeast. Most of the source rock in T3x1+2 members are in high to overmature stage, while most of the source rock in the T3x3 and T3x5 member are in the mature to high mature stage. By comparing the burial history and hydrocarbon generation evolution history of source rocks in central and western Sichuan basin, it can be found that the sedimentation rate differences during the Cretaceous period is the main cause of the thermal evolution difference of the source rock. The gas generation intensity and quantity of different members are also compared. The T3x5 member show the highest gas generation potential followed by the T3x31 and the T3x1+2 members. In general, horizontally, the source rock of Xujiahe formation in Sichuan Basin is characterized by great thickness, high maturity, and high gas generation intensity in the northwest, which are gradually decrease to the southeast. Vertically, the T3x5 member show the highest gas generation content, which account for 39.6% of the total amount

Coexistence of Cu(ii) and Cu(i) in Cu ion-doped zeolitic imidazolate frameworks (ZIF-8) for the dehydrogenative coupling of silanes with alcohols.

Recently, metal-ion-doped zeolitic imidazolate frameworks have gained considerable attention for their structure tailorability and potential catalytic applications. Herein, Cu ion-doped ZIF-8 nanocrystals were successfully prepared by the mechanical grinding of Cu(NO3)2, ZnO and 2-methylimidazole (HMeIM) using ethanol as an additive. In contrast to the general view that only Cu(ii) is present in Cu-doped ZIF-8, we found the coexistence of Cu(ii) and Cu(i) in this material, which was supported by XPS and X-ray induced Auger electron spectroscopy (XAES) characterizations. Moreover, ethanol might have acted as a reducer to induce the reduction of Cu(ii) during synthesis. Due to the mixed valency of Cu ions, the Cu ion-doped ZIF-8 nanocrystals showed excellent catalytic performance in the dehydrogenative coupling of silanes with alcohols

Identifying the most informative features using a structurally interacting elastic net

Feature selection can efficiently identify the most informative features with respect to the target feature used in training. However, state-of-the-art vector-based methods are unable to encapsulate the relationships between feature samples into the feature selection process, thus leading to significant information loss. To address this problem, we propose a new graph-based structurally interacting elastic net method for feature selection. Specifically, we commence by constructing feature graphs that can incorporate pairwise relationship between samples. With the feature graphs to hand, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature graph representation. This measure is used to obtain a structural interaction matrix where the elements represent the proposed information theoretic measure between feature pairs. We then formulate a new optimization model through the combination of the structural interaction matrix and an elastic net regression model for the feature subset selection problem. This allows us to (a) preserve the information of the original vectorial space, (b) remedy the information loss of the original feature space caused by using graph representation, and (c) promote a sparse solution and also encourage correlated features to be selected. Because the proposed optimization problem is non-convex, we develop an efficient alternating direction multiplier method (ADMM) to locate the optimal solutions. Extensive experiments on various datasets demonstrate the effectiveness of the proposed method

Structures, properties, and applications of CNT-graphene heterostructures

Both carbon nanotube (CNT) and graphene exhibit excellent properties and have many potential applications in integrated circuits, composite materials, thermal management, sensors, energy storage, and flexible electronics. However, their superior properties are confined to one or two dimensions, thus limiting their utility in interconnects or thermal interface materials that require a 3D structure for efficient electron and/or phonon transport. It is conceivable that a combined CNT-graphene structure would provide new opportunities for realizable applications in these and other fields. In recent years, numerous results on synthesis, structural analyses, theoretical modeling, and potential applications of various CNT-graphene heterostructures have been reported. In this review, we summarize the possible structures that can be formed by connecting CNT and graphene. We then report existing experimental efforts to synthesize the heterostructures based on growth method, catalyst design, and the resulting properties. Also, theoretical studies on various heterostructures are reviewed, with the focus on electron and thermal transport within the heterostructure and across the CNT-graphene interface. Several potential applications are briefly discussed, and a combined theoretical and experimental approach is proposed with the objective of enhancing the understanding of the CNT-graphene heterostructure and attaining a realistic assessment of its feasibility in practical applications