More ferroelectrics discovered by switching spectroscopy piezoresponse force microscopy?

The local hysteresis loop obtained by switching spectroscopy piezoresponse force microscopy (SS-PFM) is usually regarded as a typical signature of ferroelectric switching. However, such hysteresis loops were also observed in a broad variety of non-ferroelectric materials in the past several years, which casts doubts on the viewpoint that the local hysteresis loops in SS-PFM originate from ferroelectricity. Therefore, it is crucial to explore the mechanism of local hysteresis loops obtained in SS-PFM testing. Here we proposed that non-ferroelectric materials can also exhibit amplitude butterfly loops and phase hysteresis loops in SS-PFM testing due to the Maxwell force as long as the material can show macroscopic D-E hysteresis loops under cyclic electric field loading, no matter what the inherent physical mechanism is. To verify our viewpoint, both the macroscopic D-E and microscopic SS-PFM testing are conducted on a soda-lime glass and a non-ferroelectric dielectric material Ba0.4Sr0.6TiO3. Results show that both materials can exhibit D-E hysteresis loops and SS-PFM phase hysteresis loops, which can well support our viewpoint.Comment: 12 pages,4 figure

Exact Single-Source SimRank Computation on Large Graphs

SimRank is a popular measurement for evaluating the node-to-node similarities based on the graph topology. In recent years, single-source and top-$k$ SimRank queries have received increasing attention due to their applications in web mining, social network analysis, and spam detection. However, a fundamental obstacle in studying SimRank has been the lack of ground truths. The only exact algorithm, Power Method, is computationally infeasible on graphs with more than $10^6$ nodes. Consequently, no existing work has evaluated the actual trade-offs between query time and accuracy on large real-world graphs. In this paper, we present ExactSim, the first algorithm that computes the exact single-source and top-$k$ SimRank results on large graphs. With high probability, this algorithm produces ground truths with a rigorous theoretical guarantee. We conduct extensive experiments on real-world datasets to demonstrate the efficiency of ExactSim. The results show that ExactSim provides the ground truth for any single-source SimRank query with a precision up to 7 decimal places within a reasonable query time.Comment: ACM SIGMOD 202

A Comparative Study of Heat Transfer Coefficients for Film Condensation

Film condensation heat transfer has wide applications in a variety of industrial systems. A number of film condensation heat transfer correlations (FCHTCs) have been proposed. However, their predictions are often inconsistent. This paper presents a comparative study of existing FCHTCs. Totally 1214 experimental data points are obtained from 10 published papers, and 14 FCHTCs are reviewed, among which four correlations are used for horizontal flow outside smooth tubes, three for flow on vertical surfaces of plates or tubes, two for flow inside smooth tubes either vertically or horizontally, and five for horizontal flow inside smooth tubes. 13 FCHTCs are compared with the experimental data. There are three FCHTCs for horizontal flow inside smooth tubes having a mean absolute relative deviation (MARD) less than 26%, among which the best one has an MARD of 22.2%. More efforts should be made to develop better correlations.Key words: Correlation; Heat transfer; Film; Condensatio

PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs

{\it SimRank} is a classic measure of the similarities of nodes in a graph. Given a node $u$ in graph $G =(V, E)$, a {\em single-source SimRank query} returns the SimRank similarities $s(u, v)$ between node $u$ and each node $v \in V$. This type of queries has numerous applications in web search and social networks analysis, such as link prediction, web mining, and spam detection. Existing methods for single-source SimRank queries, however, incur query cost at least linear to the number of nodes $n$, which renders them inapplicable for real-time and interactive analysis. { This paper proposes \prsim, an algorithm that exploits the structure of graphs to efficiently answer single-source SimRank queries. \prsim uses an index of size $O(m)$, where $m$ is the number of edges in the graph, and guarantees a query time that depends on the {\em reverse PageRank} distribution of the input graph. In particular, we prove that \prsim runs in sub-linear time if the degree distribution of the input graph follows the power-law distribution, a property possessed by many real-world graphs. Based on the theoretical analysis, we show that the empirical query time of all existing SimRank algorithms also depends on the reverse PageRank distribution of the graph.} Finally, we present the first experimental study that evaluates the absolute errors of various SimRank algorithms on large graphs, and we show that \prsim outperforms the state of the art in terms of query time, accuracy, index size, and scalability.Comment: ACM SIGMOD 201

Scene-Aware Feature Matching

Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene. This results in significant performance degradation when handling challenging scenes such as scenes with large viewpoint and illumination changes. To tackle this problem, we propose a novel model named SAM, which applies attentional grouping to guide Scene-Aware feature Matching. SAM handles multi-level features, i.e., image tokens and group tokens, with attention layers, and groups the image tokens with the proposed token grouping module. Our model can be trained by ground-truth matches only and produce reasonable grouping results. With the sense-aware grouping guidance, SAM is not only more accurate and robust but also more interpretable than conventional feature matching models. Sufficient experiments on various applications, including homography estimation, pose estimation, and image matching, demonstrate that our model achieves state-of-the-art performance.Comment: Accepted to ICCV 202

Ultrafast pump-probe spectroscopic signatures of superconducting and pseudogap phases in YBa2Cu3O7-{\delta} films

Femtosecond pump-probe spectroscopy is applied to identify transient optical signatures of phase transitions in optimally doped YBa2Cu3O7-{\delta} films. To elucidate the dynamics of superconducting and pseudogap phases, the slow thermal component is removed from the time-domain traces of photo-induced reflectivity in a high-flux regime with low frequency pulse rate. The rescaled data exhibit distinct signatures of the phase separation with abrupt changes at the onsets of TSC and TPG in excellent agreement with transport data. Compared to the superconducting phase, the response of the pseudogap phase is characterized by the strongly reduced reflectivity change accompanied by a faster recovery time.Comment: 14 pages, 3 figure

An Efficient Built-in Temporal Support in MVCC-based Graph Databases

Real-world graphs are often dynamic and evolve over time. To trace the evolving properties of graphs, it is necessary to maintain every change of both vertices and edges in graph databases with the support of temporal features. Existing works either maintain all changes in a single graph or periodically materialize snapshots to maintain the historical states of each vertex and edge and process queries over proper snapshots. The former approach presents poor query performance due to the ever-growing graph size as time goes by, while the latter one suffers from prohibitively high storage overheads due to large redundant copies of graph data across different snapshots. In this paper, we propose a hybrid data storage engine, which is based on the MVCC mechanism, to separately manage current and historical data, which keeps the current graph as small as possible. In our design, changes in each vertex or edge are stored once. To further reduce the storage overhead, we simply store the changes as opposed to storing the complete snapshot. To boost the query performance, we place a few anchors as snapshots to avoid deep historical version traversals. Based on the storage engine, a temporal query engine is proposed to reconstruct subgraphs as needed on the fly. Therefore, our alternative approach can provide fast querying capabilities over subgraphs at a past time point or range with small storage overheads. To provide native support of temporal features, we integrate our approach into Memgraph, and call the extended database system TGDB(Temporal Graph Database). Extensive experiments are conducted on four real and synthetic datasets. The results show TGDB performs better in terms of both storage and performance against state-of-the-art methods and has almost no performance overheads by introducing the temporal features