Tatejonassen0803

In various domains, there are abundant streams or sequences of multi-item data of various kinds, e.g. streams of news and social media texts, sequences of genes and sports events, etc. Comparison is an important and general task in data analysis. For comparing data streams involving multiple items (e.g., words in texts, actors or action types in action sequences, visited places in itineraries, etc.), we propose Co-Bridges, a visual design involving connection and comparison techniques that reveal similarities and differences between two streams. Co-Bridges use river and bridge metaphors, where two sides of a river represent data streams, and bridges connect temporally or sequentially aligned segments of streams. Commonalities and differences between these segments in terms of involvement of various items are shown on the bridges. Interactive query tools support the selection of particular stream subsets for focused exploration. The visualization supports both qualitative (common and distinct items) and quantitative (stream volume, amount of item involvement) comparisons. We further propose Comparison-of-Comparisons, in which two or more Co-Bridges corresponding to different selections are juxtaposed. We test the applicability of the Co-Bridges in different domains, including social media text streams and sports event sequences. We perform an evaluation of the users' capability to understand and use Co-Bridges. The results confirm that Co-Bridges is effective for supporting pair-wise visual comparisons in a wide range of applications.Many data abstraction types, such as networks or set relationships, remain unfamiliar to data workers beyond the visualization research community. We conduct a survey and series of interviews about how people describe their data, either directly or indirectly. We refer to the latter as latent data abstractions. We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas. We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects. To continue the discussion, we make our dataset open along with a visual interface for further exploration.Street Scene Change Detection (SSCD) aims to locate the changed regions between a given street-view image pair captured at different times, which is an important yet challenging task in the computer vision community. The intuitive way to solve the SSCD task is to fuse the extracted image feature pairs, and then directly measure the dissimilarity parts for producing a change map. Therefore, the key for the SSCD task is to design an effective feature fusion method that can improve the accuracy of the corresponding change maps. #link# To this end, we present a novel Hierarchical Paired Channel Fusion Network (HPCFNet), which utilizes the adaptive fusion of paired feature channels. Specifically, the features of a given image pair are jointly extracted by a Siamese Convolutional Neural Network (SCNN) and hierarchically combined by exploring the fusion of channel pairs at multiple feature levels. In addition, based on the observation that the distribution of scene changes is diverse, we further propose a Multi-Part Feature Learning (MPFL) strategy to detect diverse changes. Based on the MPFL strategy, our framework achieves a novel approach to adapt to the scale and location diversities of the scene change regions. Extensive experiments on three public datasets (i.e., PCD, VL-CMU-CD and CDnet2014) demonstrate that the proposed framework achieves superior performance which outperforms other state-of-the-art methods with a considerable margin.This article presents the design approach and the first demonstration of a wideband hybrid monolithic acoustic filter in the K -band, which exceeds the limitation of electromechanical coupling on the fractional bandwidth (FBW) of acoustic filters. The hybrid filter utilizes the codesign of electromagnetic (EM) and acoustic to attain wide bandwidth while keeping the advantages of small sizes and high Q in the acoustic domain. The performance trade space and design flow of the hybrid filter are also presented in this article, which allows this technology to be applied for filters with different center frequencies and FBWs. The hybrid filter is simulated by hybridizing the EM and acoustic finite element analysis, which are carried out separately and combined at a system level. The fabricated filter built with resonators having an electromechanical coupling of 0.7% based on the seventh-order antisymmetric Lamb wave mode (A7) has a 3-dB FBW of 2.4% at 19 GHz and a compact footprint of 1.4 mm2.A typical approach to reduce speckle in coherent imaging systems is to average same-target images with different speckle realizations. We study settings where such realizations come from applying different transducer-array element weights at reception, referred to here as receive compounding. An effect of such compounding is reduced spatial resolution, causing smearing of point-like image structures, filling of cysts, and expansion of hyperechoic regions. In this article, we study how these unwanted effects can be mitigated by combining the compounding with a small, phase-based, adaptive steering of the array at reception. The adaptivity is based on a criterion akin to that of the Capon beamformer; a minimum-output distortionless response. Here, the distortionless part ensures that however we steer, we have a uniform at-focus response. selleckchem have applied this adaptive steering in combination with several receive compounding techniques on simulated Field II, phantom, and in vivo data. The results show that all the studied compounding techniques respond to this positively in light of the mentioned unwanted effects. The technique based on Thomson's multitaper method even surpassed the noncompounded equivalent in reproducing the geometry of structures. The speckle reduction, as measured by the change in the pixel mean to standard deviation ratio, is indeed lower, and there are subtle changes in the spatial speckle patterns when applying steering; however, we believe that in most cases, the negative effects are tolerable in light of the benefits gained. The suggested approach is intuitive and easily implemented.