Explore the vast range of titles available.

The visual analysis of complex geo-spatial data is a challenging task. Typically, different views are used to communicate different aspects. With changing topics of interest, however, novel views are required. This leads to dynamically changing presentations of multiple views. This paper introduces a novel approach to support such scenarios. It allows for a spontaneous incorporation of views from different sources and to automatically layout these views in a multi-display environment. Furthermore, we introduce an enhanced undo/redo mechanism for this setting, which records user interactions and, in this way, enables swift reconfigurations of displayed views. Hence, users can fluently switch the focus of visual analysis without extensive manual interactions. We demonstrate our approach by the particular use case of discussing geo-spatial climate data.

Sixteenth-century Venetians decorated the walls of their homes with maps as well as pictures of all kinds. A large corpus of inventories of household goods records the location of these wall decorations and, together with books offering advice on the display of maps, provides evidence that maps were intentionally placed in the most public spaces in the house. The manuals also confirm the impression gained from the inventories that the maps were valued for their ability to construct a public identity for the owner. They were versatile objects that could demonstrate that the owner was a cultured, cosmopolitan man educated about the world, reinforce his professional or trade standing, or enhance a military persona, all to the glorification of the family name.

It has been known that categorical interpretations of dependent type theory with Σ- and Id -types induce weak factorization systems. When one has a weak factorization system$({\\cal L},{\\cal R})$on a category$\\mathbb{C}$in hand, it is then natural to ask whether or not$({\\cal L},{\\cal R})$harbors an interpretation of dependent type theory with Σ- and Id - (and possibly Π-) types. Using the framework of display map categories to phrase this question more precisely, one would ask whether or not there exists a class${\\cal D}$of morphisms of$\\mathbb{C}$such that the retract closure of${\\cal D}$is the class${\\cal R}$and the pair$(\\mathbb{C},{\\cal D})$forms a display map category modeling Σ- and Id - (and possibly Π-) types. In this paper, we show, with the hypothesis that$\\cal{C}$is Cauchy complete, that there exists such a class$\\cal{D}$if and only if$(\\mathbb{C},\\cal{R})$itself forms a display map category modeling Σ- and Id - (and possibly Π-) types. Thus, we reduce the search space of our original question from a potentially proper class to a singleton.

China has abundant wild bird resources. Many bird lovers collect a large quantity of bird images, audio and video data. However, there is no such comprehensive platform that can be used to publicize, share and apply these data resources. In view of this, the paper attempts to design and implement a Chinese bird data resources platform, which not only aims to offer a channel of uploading bird data, but also to review the distribution area and time of bird species on the map and to search the image data of bird species and the relationship between species in a dynamic and flexible way. Also, various identification methods like DNA barcoding, morphological features and image analysis are offered based on in-depth data analysis and research to identify the exact species. This system tends to provide a simple but convenient integrated application platform for those bird research workers, bird lovers and the public as a whole.

The aim of this research is to examine how children use visual variables to represent nominal and ordinal data on thematic maps. Greek students from first (six- to seven-year old), second (seven to eight-year-old), and third grades (eight- to nine-year-old), without any systematic cartographic experience, were invited to participate in the composition of thematic maps at the stage of symbolization of various lists of items/attributes, using a specially designed software tool. The results revealed that many students, when making cartographic decisions, discovered basic principles of the application of visual variables in symbolization of nominal and ordinal data and expressed their preferences.

In medical visualization, there are a number of mapping techniques that are employed to reduce 3D data, either by projection or reformation, to effective 2D visual representations. Straightforward projection, as employed in the standard 3D graphics pipeline, also serves to reduce 3D data to a 2D representation. However, in this chapter we discuss examples that go a step further in terms of complexity and effectiveness. We have classified these reductive techniques broadly into two groups: projections and reformations. In projections, 3D information is accumulated, or flattened, along one or more directions to obtain a 2D image. In reformations, 3D information is sampled along some geometry that can later be flattened, and the sampled information along with it. There are examples that have characteristics of more than one class. The Bull’s Eye Plot, that is frequently used in cardiology, is an example of a projection (data is aggregated through the heart wall), whereas multiplanar reformatting (MPR), is a straightforward example of reformation.

The focus of this article is to develop a map display for information retrieval. Through an examination of relationships among visual displays, information retrieval, and browsing, advantages of visual displays for information retrieval are characterized as (1) the ability to convey a large amount of information in a limited space, (2) the potential to reveal semantic relationships of terms and documents, and (3) the facilitation of browsing and perceptual inferences on retrieval interfaces. These advantages are further demonstrated through a map display generated by a neural network's self‐organizing algorithm. The map display detects complex relationships among given documents, and reveals the relationships through a spatial arrangement of terms ed from the documents. The map display also provides interactive tools to allow the user to interact with the underlying information. Examples of the map displays show that such map displays can be used both as an overview tool and an access or exploration tool, and the map displays will likely increase the amount of information that the user is willing to browse.

Monoclonal antibodies targeting a variety of epitopes have been isolated from individuals previously infected with SARS-CoV-2, but the relative contributions of these different antibody classes to the polyclonal response remains unclear. Here we use a yeast-display system to map all mutations to the viral spike receptor-binding domain (RBD) that escape binding by representatives of three potently neutralizing classes of anti-RBD antibodies with high-resolution structures. We compare the antibody-escape maps to similar maps for convalescent polyclonal plasmas, including plasmas from individuals from whom some of the antibodies were isolated. While the binding of polyclonal plasma antibodies are affected by mutations across multiple RBD epitopes, the plasma-escape maps most resemble those of a single class of antibodies that target an epitope on the RBD that includes site E484. Therefore, although the human immune system can produce antibodies that target diverse RBD epitopes, in practice the polyclonal response to infection is skewed towards a single class of antibodies targeting an epitope that is already undergoing rapid evolution. Emerging SARS-CoV-2 mutants may escape neutralization by antibodies. Here, the authors use deep mutational scanning to identify mutations in the RBD that escape human monoclonal antibodies or convalescent plasmas.

Both global dependency and local correlation are crucial for solving the scale variation of crowd. However, most of previous methods fail to take two factors into consideration simultaneously. Against the aforementioned issue, a deformable channel non‐local network, abbreviated as DCNLNet for crowd counting, which can simultaneously learn global context information and adaptive local receptive field is proposed. Specifically, the proposed DCNLNet consists of two well‐crafted designed modules: deformable channel non‐local block (DCNL) and spatial attention feature fusion block (SAFF). The DCNL encodes long‐range dependencies between pixels and the adaptive local correlation with channel non‐local and deformable convolution, respectively, benefiting for improving the spatial discrimination of features. While the SAFF aims to aggregate the cross‐level information, which interacts these features from different depths and learns specific weights for the feature maps with spatial attention. Extensive experiments are performed on three crowd counting benchmark datasets and experimental results indicate that the proposed DCNLNet achieves compelling performance compared to other representative counting models. In the letter, a deformable channel non‐local network (DCNLNet) has been proposed for crowd counting. In order to explore global and local information, we develop a deformable channel non‐local module, which contains two branches, deformable convolution branch and channel non‐local branch, to learn adaptive local correlation and long‐range dependency. Moreover, we introduce a spatial attention feature fusion module to aggregate cross‐level features obtained from the encoder and the decoder.