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Fanatic ingestion, bodyweight, along with adiposity within sufferers together with diabetes type 2 symptoms: a planned out evaluate as well as meta-analysis involving randomized governed trial offers.
3D point clouds have found a wide variety of applications in multimedia processing, remote sensing, and scientific computing. Although most point cloud processing systems are developed to improve viewer experiences, little work has been dedicated to perceptual quality assessment of 3D point clouds. In this work, we build a new 3D point cloud database, namely the Waterloo Point Cloud (WPC) database. In contrast to existing datasets consisting of small-scale and low-quality source content of constrained viewing angles, the WPC database contains 20 high quality, realistic, and omni-directional source point clouds and 740 diversely distorted point clouds. We carry out a subjective quality assessment experiment over the database in a controlled lab environment. Our statistical analysis suggests that existing objective point cloud quality assessment (PCQA) models only achieve limited success in predicting subjective quality ratings. We propose a novel objective PCQA model based on an attention mechanism and a variant of information content-weighted structural similarity, which significantly outperforms existing PCQA models. The database has been made publicly available at https//github.com/qdushl/Waterloo-Point-Cloud-Database.Given a degraded image, image restoration aims to recover the missing high-quality image content. Numerous applications demand effective image restoration, e.g., computational photography, surveillance, autonomous vehicles, and remote sensing. Significant advances in image restoration have been made in recent years, dominated by convolutional neural networks (CNNs). The widely-used CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatial details are preserved but the contextual information cannot be precisely encoded. In the latter case, generated outputs are semantically reliable but spatially less accurate. This paper presents a new architecture with a holistic goal of maintaining spatially-precise high-resolution representations through the entire network, and receiving complementary contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing the following key elements (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) non-local attention mechanism for capturing contextual information, and (d) attention based multi-scale feature aggregation. Extensive experiments on six real image benchmark datasets demonstrate that our method, named as MIRNet-v2, achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes. To alleviate this problem, off-the-shelf image generation techniques based on Generative Adversarial Networks (GANs) can potentially create additional training images. However, these GAN-generated images are often not helpful for actually improving the accuracy of one-shot fine-grained recognition. In this paper, we proposes a meta-learning framework to combine generated images with original images, so that the resulting hybrid training images can improve one-shot learning. Specifically, the generic image generator is updated by a few training instances of novel classes, and a Meta Image Reinforcing Network (MetaIRNet) is proposed to conduct one-shot fine-grained recognition as well as image reinforcement. Our experiments demonstrate consistent improvement over baselines on one-shot fine-grained image classification benchmarks. Furthermore, our analysis shows that the reinforced images have more diversity compared to the original and GAN-generated images.Despite their impressive performance under the single-domain setup, current fully-supervised re-ID models degrade significantly when transplanted to an unseen domain. D609 research buy According to the characteristics of the re-ID task, such degradation is mainly attributed to the dramatic variation within the target domain and the severe shift between the source and target domain, which we call dual discrepancy in this paper. To achieve a model that generalizes well to the target domain, it is desirable to take such dual discrepancy into account. In terms of the former issue, a prevailing solution is to enforce consistency between nearest-neighbors in the embedding space. However, we find that the search of neighbors is highly biased in our case due to the discrepancy across cameras. For this reason, we equip the vanilla neighborhood invariance approach with a camera-aware learning scheme. As for the latter issue, we propose a novel cross-domain mixup scheme. It works in conjunction with virtual prototypes which are employed to handle the disjoint label space between the two domains. In this way, we can realize the smooth transfer by introducing the interpolation between the two domains as a transition state. Extensive experiments on four public benchmarks demonstrate the superiority of our method.In Machine Learning, a supervised model's performance is measured using the evaluation metrics. In this study, we first present our motivation by revisiting the major limitations of these metrics, namely one-dimensionality, lack of context, lack of intuitiveness, uncomparability, binary restriction, and uncustomizability of metrics. In response, we propose Contingency Space, a bounded semimetric space that provides a generic representation for any performance evaluation metric. Then we showcase how this space addresses the limitations. In this space, each metric forms a surface using which we visually compare different evaluation metrics. Taking advantage of the fact that a metric's surface warps proportionally to the degree of which it is sensitive to the class-imbalance ratio of data, we introduce Imbalance Sensitivity that quantifies the skew-sensitivity. Since an arbitrary model is represented in this space by a single point, we introduce Learning Path for qualitative and quantitative analyses of the training process. Using the semimetric that contingency space is endowed with, we introduce Tau as a new cost sensitive and Imbalance Agnostic metric. Lastly, we show that contingency space addresses multi-class problems as well. Throughout this work, we define each concept through stipulated definitions and present every application with practical examples and visualizations.Encoding data visually is at the heart of visualization. We usually assume that encodings are read as specified (i.e., if a bar chart is drawn by the length of the bars based on the data, that is also how we read them). In this paper, we question this assumption and demonstrate that observed encodings often differ from the ones used to specify the visualization. The value of a chart also often comes from higher level derived encodings, and which encodings end up getting used also depends on the user's task.Classical algorithms typically contain domain-specific insights. This makes them often more robust, interpretable, and efficient. On the other hand, deep-learning models must learn domain-specific insight from scratch from a large amount of data using gradient-based optimization techniques. To have the best of both worlds, we should make classical visual computing algorithms differentiable to enable gradient-based optimization. Computing derivatives of classical visual computing algorithms is challenging there can be discontinuities, and the computation pattern is often irregular compared to high-arithmetic intensity neural networks. In this article, we discuss the benefits and challenges of combining classical visual computing algorithms and modern data-driven methods, with particular emphasis to my thesis, which took one of the first steps toward addressing these challenges.A description is given of the milieu, including people, events, and research activities, surrounding the development of the author's doctoral thesis, "Applications of B-spline Approximation to Geometric Problems of Computer-Aided Design." Although initially slow to become adopted, today nonuniform B-splines have become the international de facto standard representation in the CAD industry.We have developed a system called "Podiy" that supports handicraft production of pouch-style bags using three-dimensional (3-D) computer graphics. We divide the making of a pouch into four steps pouch design, fabric design, pattern design, and production, each supported by different panels. In pouch design mode, the user can design both the size and pattern. In fabric design mode, the user can design two types of checkered patterns. In cloth cutout mode, the user can choose from three ways of printing the pattern onto the fabric. In production mode, the system shows the user a 3-D illustration view using the user-designed fabrics. Using Podiy, even a handicraft novice can design an original pouch and produce it successfully.
Advances in computational models of biological systems and artificial neural networks enable rapid virtual prototyping of neuroprosthetics, accelerating innovation in the field. Here, we present an end-to-end computational model for predicting speech perception with cochlear implants (CI), the most widely-used neuroprosthetic.

The model integrates CI signal processing, a finite element model of the electrically-stimulated cochlea, and an auditory nerve model to predict neural responses to speech stimuli. An automatic speech recognition neural network is then used to extract phoneme-level speech perception from these neural response patterns.

Compared to human CI listener data, the model predicts similar patterns of speech perception and misperception, captures between-phoneme differences in perceptibility, and replicates effects of stimulation parameters and noise on speech recognition. Information transmission analysis at different stages along the CI processing chain indicates that the bottleneck of information flow occurs at the electrode-neural interface, corroborating studies in CI listeners.

An end-to-end model of CI speech perception replicated phoneme-level CI speech perception patterns, and was used to quantify information degradation through the CI processing chain.

This type of model shows great promise for developing and optimizing new and existing neuroprosthetics.
This type of model shows great promise for developing and optimizing new and existing neuroprosthetics.Introduction Primary care prevention strategies that support and provide tools for general practice have the potential to slow and reverse rates of overweight and obesity. Aim To test the effectiveness of a novel 12-week, online, structured, evidence-based weight management and lifestyle modification programme in general practices. Methods Between August 2018 and March 2020, participants with a body mass index (BMI)  ≥ 25 were recruited from general practices in the Hunter New England and Central Coast Primary Health Network region of Australia. Practices were randomly assigned to deliver a 'low-intensity' (LI) or 'high-intensity' (HI) variant of the programme. Practitioners were trained in programme delivery. The intervention involved weekly progress and accountability checks and scripted education sessions on evidenced-based nutrition, physical activity and lifestyle modification. The trial included follow-up evaluations at 6 and 12 months. Results In total, 695 participants were recruited from 26 practices.
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