Notes

Naphthalimide appended isoquinoline fluorescent probe for specific diagnosis of Al3+ ions and its software in existing mobile or portable photo.
In this paper, we report on a study of visual representations for cyclical data and the effect of interactively wrapping a bar chart `around its boundaries'. Compared to linear bar chart, polar (or radial) visualisations have the advantage that cyclical data can be presented continuously without mentally bridging the visual `cut' across the left-and-right boundaries. To investigate this hypothesis and to assess the effect the cut has on analysis performance, this paper presents results from a crowdsourced, controlled experiment with 72 participants comparing new continuous panning technique to linear bar charts (interactive wrapping). Our results show that bar charts with interactive wrapping lead to less errors compared to standard bar charts or polar charts. Inspired by these results, we generalise the concept of interactive wrapping to other visualisations for cyclical or relational data. We describe a design space based on the concept of one-dimensional wrapping and two-dimensional wrapping, linked to two common 3D topologies; cylinder and torus that can be used to metaphorically explain one- and two-dimensional wrapping. This design space suggests that interactive wrapping is widely applicable to many different data types.Visual Question Answering systems target answering open-ended textual questions given input images. They are a testbed for learning high-level reasoning with a primary use in HCI, for instance assistance for the visually impaired. Recent research has shown that state-of-the-art models tend to produce answers exploiting biases and shortcuts in the training data, and sometimes do not even look at the input image, instead of performing the required reasoning steps. We present VisQA, a visual analytics tool that explores this question of reasoning vs. bias exploitation. It exposes the key element of state-of-the-art neural models - attention maps in transformers. Our working hypothesis is that reasoning steps leading to model predictions are observable from attention distributions, which are particularly useful for visualization. The design process of VisQA was motivated by well-known bias examples from the fields of deep learning and vision-language reasoning and evaluated in two ways. First, as a result of a collaboration of three fields, machine learning, vision and language reasoning, and data analytics, the work lead to a better understanding of bias exploitation of neural models for VQA, which eventually resulted in an impact on its design and training through the proposition of a method for the transfer of reasoning patterns from an oracle model. Second, we also report on the design of VisQA, and a goal-oriented evaluation of VisQA targeting the analysis of a model decision process from multiple experts, providing evidence that it makes the inner workings of models accessible to users.Probabilistic graphs are challenging to visualize using the traditional node-link diagram. Encoding edge probability using visual variables like width or fuzziness makes it difficult for users of static network visualizations to estimate network statistics like densities, isolates, path lengths, or clustering under uncertainty. We introduce Network Hypothetical Outcome Plots (NetHOPs), a visualization technique that animates a sequence of network realizations sampled from a network distribution defined by probabilistic edges. NetHOPs employ an aggregation and anchoring algorithm used in dynamic and longitudinal graph drawing to parameterize layout stability for uncertainty estimation. We present a community matching algorithm to enable visualizing the uncertainty of cluster membership and community occurrence. We describe the results of a study in which 51 network experts used NetHOPs to complete a set of common visual analysis tasks and reported how they perceived network structures and properties subject to uncertainty. Participants' estimates fell, on average, within 11% of the ground truth statistics, suggesting NetHOPs can be a reasonable approach for enabling network analysts to reason about multiple properties under uncertainty. Participants appeared to articulate the distribution of network statistics slightly more accurately when they could manipulate the layout anchoring and the animation speed. Based on these findings, we synthesize design recommendations for developing and using animated visualizations for probabilistic networks.Resolution in deep convolutional neural networks (CNNs) is typically bounded by the receptive field size through filter sizes, and subsampling layers or strided convolutions on feature maps. The optimal resolution may vary significantly depending on the dataset. Modern CNNs hard-code their resolution hyper-parameters in the network architecture which makes tuning such hyper-parameters cumbersome. We propose to do away with hard-coded resolution hyper-parameters and aim to learn the appropriate resolution from data. We use scale-space theory to obtain a self-similar parametrization of filters and make use of the N-Jet a truncated Taylor series to approximate a filter by a learned combination of Gaussian derivative filters. The parameter σ of the Gaussian basis controls both the amount of detail the filter encodes and the spatial extent of the filter. Since σ is a continuous parameter, we can optimize it with respect to the loss. The proposed N-Jet layer achieves comparable performance when used in state-of-the art architectures, while learning the correct resolution in each layer automatically. We evaluate our N-Jet layer on both classification and segmentation, and we show that learning σ is especially beneficial when dealing with inputs at multiple sizes.Multi-view clustering aims to partition objects into potential categories by utilizing cross-view information. One of the core issues is to sufficiently leverage different views to learn a latent subspace, within which the clustering task is performed. Recently, it has been shown that representing the multi-view data by a tensor and then learning a latent self-expressive tensor is effective. However, early works mainly focus on learning essential tensor representation from multi-view data and the resulted affinity matrix is considered as a byproduct or is computed by a simple average in Euclidean space, thereby destroying the intrinsic clustering structure. To that end, here we proposed a novel multi-view clustering method to directly learn a well-structured affinity matrix driven by the clustering task on Grassmann manifold. Specifically, we firstly employed a tensor learning model to unify multiple feature spaces into a latent low-rank tensor space. Then each individual view was merged on Grassmann manifold to obtain both an integrative subspace and a consensus affinity matrix, driven by clustering task. The two parts are modeled by a unified objective function and optimized jointly to mine a decomposable affinity matrix. Extensive experiments on eight real-world datasets show that our method achieves superior performances over other popular methods.Raven's Progressive Matrices (RPM) is highly correlated with human intelligence, and it has been widely used to measure the abstract reasoning ability of humans. In this paper, to study the abstract reasoning capability of deep neural networks, we propose the first unsupervised learning method for solving RPM problems. Since the ground truth labels are not allowed, we design a pseudo target based on the prior constraints of the RPM formulation to approximate the ground-truth label, which effectively converts the unsupervised learning strategy into a supervised one. However, the correct answer is wrongly labelled by the pseudo target, and thus the noisy contrast will lead to inaccurate model training. To alleviate this issue, we propose to improve the model performance with negative answers. Moreover, we develop a decentralization method to adapt the feature representation to different RPM problems. Extensive experiments on three datasets demonstrate that our method even outperforms some of the supervised approaches. EGFR inhibitor Our code is available at https//github.com/visiontao/ncd.Visual surveillance produces a significant amount of raw video data that can be time consuming to browse and analyze. In this work, we present a video synopsis methodology called "scene adaptive online video synopsis via dynamic tube rearrangement using octree (SSOcT)" that can effectively condense input surveillance videos. Our method entailed summarizing the input video by analyzing scene characteristics and determining an effective spatio-temporal 3D structure for video synopsis. For this purpose, we first analyzed the attributes of each extracted tube with respect to scene geometry and complexity. Then, we adaptively grouped the tubes using an online grouping algorithm that exploits these scene characteristics. Finally, the tube groups were dynamically rearranged using the proposed octree-based algorithm that efficiently inserted and refined tubes containing high spatio-temporal movements in real time. Extensive video synopsis experimental results are provided, demonstrating the effectiveness and efficiency of our method in summarizing real-world surveillance videos with diverse scene characteristics.
Functional dyspepsia (FD) is one of the most common conditions in clinical practice. In spite of its prevalence, FD is associated with major uncertainties in terms of its definition, underlying pathophysiology, diagnosis, treatment, and prognosis.

A Delphi consensus was initiated with 41 experts from 22 European countries who conducted a literature summary and voting process on 87 statements. Quality of evidence was evaluated using the grading of recommendations, assessment, development, and evaluation (GRADE) criteria. Consensus (defined as >80% agreement) was reached for 36 statements.

The panel agreed with the definition in terms of its cardinal symptoms (early satiation, postprandial fullness, epigastric pain, and epigastric burning), its subdivision into epigastric pain syndrome and postprandial distress syndrome, and the presence of accessory symptoms (upper abdominal bloating, nausea, belching), and overlapping conditions. Also, well accepted are the female predominance of FD, its impact on quon the definition, diagnosis and management of FD.
A multinational group of European experts summarized the current state of consensus on the definition, diagnosis and management of FD.
This study systematically reviewed the literature investigating the relationship between participation in exercise intended to improve fitness or sport and the prevalence of non-specific neck pain in adults. A secondary objective evaluated if exercise characteristics (frequency, and total duration of weekly exercise) impacted any observed relationship between this form of exercise and neck pain prevalence.

Narrative systematic review.

Six databases including Pubmed/Medline, Scopus, EMBASE, SPORTDiscus, CINAHL, and the Cochrane Library were searched from their inception up to April 2021.

Studies were deemed eligible if they investigated the relationship between exercise participation and prevalence of non-specific neck pain. Only full-text, cross-sectional and longitudinal studies in an adult population were included.

Due to heterogeneity of characteristics in the included studies, a meta-analysis was not deemed feasible. Data were synthesised using narrative synthesis with subgroup analysis of exercise themes including frequency, and total weekly duration.
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