Reviewerly/exHarmony huggingface dataset:
Inverse problem approach to regularized regression models with application to predicting recovery after stroke

Regression modelling is a powerful statistical tool often used in biomedical and clinical research. It could be formulated as an inverse problem that measures the discrepancy between the target outcome and the data produced by representation of the modelled predictors. This approach could simultaneously perform variable selection and coefficient estimation. We focus particularly on a linear regression issue, Y∼N(Xβ,σIn) , where β∈Rp is the parameter of interest and its components are the regression coefficients. The inverse problem finds an estimate for the parameter β , which is mapped by the linear operator (L:β⟶Xβ) to the observed outcome data Y=Xβ+ε . This problem could be conveyed by finding a solution in the affine subspace L-1(Y) . However, in the presence of collinearity, high-dimensional data and high conditioning number of the related covariance matrix, the solution may not be unique, so the introduction of prior information to reduce the subset L-1(Y) and regularize the inverse problem is needed. Informed by Huber's robust statistics framework, we propose an optimal regularizer to the regression problem. We compare results of the proposed method and other penalized regression regularization methods: ridge, lasso, adaptive-lasso and elastic-net under different strong hypothesis such as high conditioning number of the covariance matrix and high error amplitude, on both simulated and real data from the South London Stroke Register. The proposed approach can be extended to mixed regression models. Our inverse problem framework coupled with robust statistics methodology offer new insights in statistical regression and learning. It could open a new research development for model fitting and learning.
RT-VENet: A Convolutional Network for Real-time Video Enhancement

Real-time video enhancement is in great demand due to the extensive usage of live video applications, but existing approaches are far from satisfying the strict requirements of speed and stability. We present a novel convolutional network that can perform high-quality enhancement on 1080p videos at 45 FPS with a single CPU, which has high potential for real-world deployment. The proposed network is designed based on a light-weight image network and further consolidated for temporal consistency with a temporal feature aggregation (TFA) module. Unlike most image translation networks that use decoders to generate target images, our network discards decoders and employs only an encoder and a small head. The network predicts color mapping functions instead of pixel values in a grid-like container which fits the CNN structure well and also advances the enhancement to be scalable to any video resolution. Furthermore, the temporal consistency of the output will be enforced by the TFA module which utilizes the learned temporal coherence of semantics across frames. We also demonstrate that the mapping representation is general to various enhancement tasks, such as relighting, retouching and dehazing, on benchmark datasets. Our approach achieves the state-of-the-art performance and performs about 10 times faster than the current real-time method on high-resolution videos.
Non-destructive hand vein measurement with self-supervised binocular network

• We designed an end-to-end self-supervised network for measuring hand. • Perceptual loss and Region Strategy are adopted to improve the performance. • We firstly set up a binocular system for non-destructive hand vein measurement. • We create a simulated and a real hand vein dataset to analyze the results.
Deep adversarial domain adaptation network

The advantage of adversarial domain adaptation is that it uses the idea of adversarial adaptation to confuse the feature distribution of two domains and solve the problem of domain transfer in transfer learning. However, although the discriminator completely confuses the two domains, adversarial domain adaptation still cannot guarantee the consistent feature distribution of the two domains, which may further deteriorate the recognition accuracy. Therefore, in this article, we propose a deep adversarial domain adaptation network, which optimises the feature distribution of the two confused domains by adding multi-kernel maximum mean discrepancy to the feature layer and designing a new loss function to ensure good recognition accuracy. In the last part, some simulation results based on the Office-31 and Underwater data sets show that the deep adversarial domain adaptation network can optimise the feature distribution and promote positive transfer, thus improving the classification accuracy.
Patient-Initiated Data: Our Experience with Enabling Patients to Initiate Incorporation of Heart Rate Data into the Electronic Health Record

Provider organizations increasingly allow incorporation of patient-generated data into electronic health records (EHRs). In 2015, we began allowing patients to upload data to our EHR without physician orders, which we henceforth call patient-initiated data (PAIDA). Syncing wearable heart rate monitors to our EHR allows for uploading of thousands of heart rates per patient per week, including many abnormally low and high rates. Physician informaticists expressed concern that physicians and their patients might be unaware of abnormal heart rates, including those caused by treatable pathology.This study aimed to develop a protocol to address millions of unreviewed heart rates.As a quality improvement initiative, we assembled a physician informaticist team to meet monthly for review of abnormally low and high heart rates. By incorporating other data already present in the EHR, lessons learned from reviewing records over time, and from contacting physicians, we iteratively refined our protocol.We developed (1) a heart rate visualization dashboard to identify concerning heart rates; (2) experience regarding which combinations of heart rates and EHR data were most clinically worrisome, as opposed to representing artifact; (3) a protocol whereby only concerning heart rates would trigger a cardiologist review revealing protected health information; and (4) a generalizable framework for addressing other PAIDA.We expect most PAIDA to eventually require systematic integration and oversight. Our governance framework can help guide future efforts, especially for cases with large amounts of data and where abnormal values may represent concerning but treatable pathology.
Amazon Biology: An Augmented Reality-Based E-Book for Biology

Biology is a conventionally struggling subject to learn from both high school and college students due to its complexity. Students are used to learning Biology from various methods such as reading textbooks, attending lectures. Biology is based on more practical and most of the schools not available proper lab facilities, anatomic structures, and resources to learn the module easily. And teachers who teach the module face a considerable number of issues when delivering the concepts. Some of them face unavailability of teaching aids, time-consuming, lack of lecture materials. Apart from that, the nature of the topic and the teaching style are the main learning problems faced by the students. Therefore, students do not learn the concepts perfectly and interest in the module has been reduced day by day. To overcome these difficulties “Amazon Biology,” mobile application has been proposed. The application consists of three major modules including image processing for the plant classification, augmented reality for human anatomy, and gamification. The proposed application has used the techniques in augmented reality and game-based learning. The developed system delivers nearly 85% level of accuracy and provides more advantages for students. They are effective and efficient learning, teaching via visual materials, and practical.
A Synthetic Haematite Reference Material for LA‐ICP‐MS U‐Pb Geochronology and Application to Iron Oxide‐Cu‐Au Systems

In both nature and synthetic experiments, the common iron oxide haematite (α‐Fe 2 O 3 ) can incorporate significant amounts of U into its crystal structure and retain radiogenic Pb over geological time. Haematite is a ubiquitous component of many ore deposit types and, therefore, represents a valuable hydrothermal mineral geochronometer, allowing direct constraints to be placed on the timing of ore formation and upgrading. However, to date, no suitable natural haematite reference material has been identified. Here, a synthetic haematite U‐Pb reference material (MR‐HFO) is characterised using LA‐ICP‐MS and ID‐TIMS. Centimetre‐scale ‘chips’ of synthesised α‐Fe 2 O 3 were randomly microsampled via laser ablation‐extraction and analysed using ID‐TIMS. Reproducible U/Pb and Pb/Pb measurements were obtained across four separate chips ( n = 13). Subsequently, an evaluation of the suitability MR‐HFO in constraining U‐Pb data via LA‐ICP‐MS is presented using a selection of natural samples ranging from Cenozoic to Proterozoic in age. The MR‐HFO normalised U‐Pb ratios are more concordant and ages more accurate versus the same LA‐ICP‐MS spot analyses normalised to zircon reference material, when compared with independently acquired ID‐TIMS data from the same natural haematite grains. Results establish MR‐HFO as a suitable reference material for LA‐ICP‐MS haematite U‐Pb geochronology.
Learning to Follow Directions in English Through a Virtual Reality Environment

Learning how to give and follow directions in English is one of the key topics in regular English as a Foreign Language (EFL) courses. However, this topic is commonly taught in the classroom with pencil and paper exercises. In this chapter, a scaffolded virtual reality (VR) environment for learning the topic of following directions in English is introduced. An eye tracking study was conducted to determine how students perceive the scaffolds for completing the learning task, and an evaluation of acceptance and usability was conducted to identify the students' perceptions. The results show that scaffolds in the form of text and images are both effective for increasing the students' learning performance. The gaze frequency is higher for the textual scaffold, but the duration of gaze fixations is lower for the scaffolds in the form of images. The acceptance and usability of the VR environment were found to be positive.
Novel Hybrid Neural Network for Dense Depth Estimation using On-Board Monocular Images

Depth information from still 2D images plays an important role in automated driving, driving safety, and robotics. Monocular depth estimation is considered as an ill-posed and inherently ambiguous problem in general, and a tight issue is how to obtain global information efficiently since pure convolutional neural networks (CNNs) merely extract the local information. To end that, some previous works utilized conditional random fields (CRFs) to obtain the global information, but it is notoriously difficult to optimize. In this paper, a novel hybrid neural network is proposed to solve that, and concurrently a dense depth map is predicted from the monocular still image. Specifically: first, the deep residual network is utilized to obtain multi-scale local information and then feature correlation (FCL) blocks are used to correlate these features. Finally, the feature selection attention-based mechanism is adopted to fuse the multi-layer features, and the multi-layer recurrent neural networks (RNNs) are utilized with bidirectional long short-term memory (Bi-LSTM) unit as the output layer. Furthermore, a novel logarithm exponential average error (LEAE) is proposed to overcome over-weighted problem. The multi-scale feature correlation network (MFCN) is evaluated on large-scale KITTI benchmarks (LKT), which is a subset of KITTI raw dataset, and NYU depth v2. The experiments indicate that the proposed unified network outperforms existing methods. This method also updates the state-of-the-art performance on LKT datasets. Importantly, the depth estimation method can be widely used for collision risk assessment and avoidance in driving assistance systems or automated pilot systems to achieve safety in a more economical and convenient way.
Intra- and Inter-Server Smart Task Scheduling for Profit and Energy Optimization of HPC Data Centers

Servers in a data center are underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. Recent research in optimizing the energy consumption of High Performance Computing (HPC) data centers mostly focuses on consolidation of Virtual Machines (VMs) and using dynamic voltage and frequency scaling (DVFS). These approaches are inherently hardware-based, are frequently unique to individual systems, and often use simulation due to lack of access to HPC data centers. Other approaches require profiling information on the jobs in the HPC system to be available before run-time. In this paper, we propose a reinforcement learning based approach, which jointly optimizes profit and energy in the allocation of jobs to available resources, without the need for such prior information. The approach is implemented in a software scheduler used to allocate real applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite to a number of hardware nodes realized with Odroid-XU3 boards. Experiments show that the proposed approach increases the profit earned by 40% while simultaneously reducing energy consumption by 20% when compared to a heuristic-based approach. We also present a network-aware server consolidation algorithm called Bandwidth-Constrained Consolidation (BCC), for HPC data centers which can address the under-utilization problem of the servers. Our experiments show that the BCC consolidation technique can reduce the power consumption of a data center by up-to 37%.
TAPAS-360°: A Tool for the Design and Experimental Evaluation of 360° Video Streaming Systems

Video streaming platforms are required to innovate their delivery pipeline to allow new and more immersive video content to be supported. In particular, Omnidirectional videos enable the user to explore a 360° scene by moving their heads using Head Mounted Display devices. Viewport adaptive streaming allows changing dynamically the quality of the video falling in the user's field of view. In this paper, we present TAPAS-360°, an open-source tool that enables designing and experimenting all the components required to build omnidirectional video streaming systems. The tool can be used by researchers focusing on the design of viewport-adaptive algorithms and also to produce video streams to be employed for subjective and objective Quality of Experience evaluations.
Single Image Shape-from-Silhouettes

Recovering a 3D shape representation from one single image input has been attempted in recent years. Most of the works obtain 3D models from multiple images at different perspectives or ground truth CAD models. However, multiple images from different perspectives or 3D CAD models are not always available in real applications. In this work, we present a novel shape-from-silhouette method based on just a single image, which is an end-to-end learning framework relying on view synthesis and shape-from-silhouette methodology to reconstruct a 3D shape. The reconstructed 3D mesh can approach the real shape of target objects by constraining the silhouettes from both horizontal and vertical directions, especially for those objects with occlusions. Our proposed method achieves state-of-the-art performance on the ShapeNet dataset compared with other recent approaches targeting 3D reconstruction from a single image. Without requiring labor-intensive and time-consuming human annotations, the work has a broad potential to be applied in real-world applications.
SmartShots: Enabling Automatic Generation of Videos with Data Visualizations Embedded

Videos become prevalent for storytellers to inspire viewers' interests. To further enhance narrations, visualizations are integrated into videos to present data-driven insights. However, manually crafting such data-driven videos is difficult and time-consuming. Thus, we present SmartShots, a system that facilitates the automatic integration of in-video visualizations. Specifically, we propose a computational framework that integrates non-verbal video clips, images, a melody, and a data table to create a video with data visualizations embedded. The system automatically translates the multi-media material into shots and then combines the shots into a compelling video. In addition, we develop a set of post-editing interactions to incorporate users' design knowledge and help them re-edit the automatically-generated videos.
A General Defense of Information Fiduciaries

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Effects of the hierarchy in hierarchical, multi-label classification

The consequences of the choice of a hierarchy in hierarchical multi-label classification (HMLC) have previously not been considered in any detail. Three hierarchy-related factors in HMLC are examined here: hierarchy structure, class location in the hierarchy, and class distribution in feature space. Four general model groups are found to exist in HMLC modeling: "non-informative", "semi-informative", "comparable", and "hierarchical". Studies of synthetic and real data show that the choice of hierarchy used in the modeling is important in setting the relative error rates of false positives and false negatives. The choice of hierarchy depends upon the relative consequences of false positive and false negative errors produced by the resulting model. A low false negative error rate results from use of a "comparable" HMLC model with a hierarchy designed to maximize intergroup separation. A low false positive error rate results from use of a "hierarchical" HMLC model using any hierarchy. Modest differences in accuracy and F1 measure occur between the best-performing HMLC models built on several external and internal hierarchies for a complex, multiclass dataset. HMLC methods using "comparable" and "hierarchical" HMLC models and phylogenetic hierarchies examined slightly outperform a conventional classification using the same classifier on the Dalbergia data. Studies of synthetic and real data show that the structure of a multi-label hierarchy in hierarchical, multi-label classification (HMLC) is important in setting the relative error rate of false positives and false negatives. A low false negative error rate results from use of a "comparable" HMLC model with a hierarchy designed to maximize inter-class separation. A low false positive error rate results from use of a "hierarchical" HMLC model with any hierarchy.
DroidCloud

Cloud rendering is an emerging technology in which rendering-heavy applications run on the cloud server and then stream the rendered contents to the end-user device. High density and high scalability of the cloud rendering services are crucial to support millions of users concurrently and cost-effectively. However, it is still challenging to run Android OS in cloud smoothly with high density and high scalability without compromising user experience. This paper presents DroidCloud, the first open-source Android\footnoteAndroid is a trademark of Google LLC. cloud rendering solution focusing on the scalable design and density aspect optimization to the best of our knowledge. To cloudify Android OS, DroidCloud utilizes thevHAL technology in order to support remote devices and keep transparent to Android applications. And aFlexible rendering scheduling policy is introduced to break the boundary of GPU physical locations. Thus, both remote GPUs and local GPUs can accommodate render tasks by forwarding rendering tasks and making it possible to support multiple Android OSes with GPU acceleration. Besides, to further improve the density, DroidCloud optimizes the resource cost both in a single instance and across instances. We show that DroidCloud can run hundreds of Android OSes on a single Intel Xeon server with GPU acceleration simultaneously, increasing the density at the scale of one order of magnitude compared to current cloud gaming systems. Further experimental results demonstrate that DroidCloud can transparently run Android applications at native speed with lower CPU, memory, and storage utilization.
Food Computing for Multimedia

Food computing applies computational approaches for acquiring and analyzing heterogeneous food data from disparate sources for perception, recognition, retrieval, recommendation, prediction and monitoring of food to address food-related issues in multimedia and beyond. It has received more attention from both academia and industry as one emerging interdiscipline for its various applications, such as improving human health and understanding the culinary culture. Recently, there are more studies on food computing in the multimedia, such as food recognition and multimodal recipe analysis. This tutorial will provide a basic understanding of food computing, and discuss its use in various multimedia tasks, ranging from food recognition, retrieval, recommendation, recipe analysis to cooking behavior understanding. Specifically, we will first introduce food computing, including its method, task and applications. Then we will discuss several typical tasks of food computing in the multimedia including food image recognition, food retrieval and recommendation, multimodal recipe analysis and cooking action anticipation. Finally, we will point out future research directions on food computing in the multimedia.
Patch-Based Non-local Bayesian Networks for Blind Confocal Microscopy Denoising

Confocal microscopy is essential for histopathologic cell visualization and quantification. Despite its significant role in biology, fluorescence confocal microscopy suffers from the presence of inherent noise during image acquisition. Non-local patch-wise Bayesian mean filtering (NLB) was until recently the state-of-the-art denoising approach. However, classic denoising methods have been outperformed by neural networks in recent years. In this work, we propose to exploit the strengths of NLB in the framework of Bayesian deep learning. We do so by designing a convolutional neural network and training it to learn parameters of a Gaussian model approximating the prior on noise-free patches given their nearest, similar yet non-local, neighbors. We then apply Bayesian reasoning to leverage the prior and information from the noisy patch in the process of approximating the noise-free patch. Specifically, we use the closed-form analytic maximum a posteriori (MAP) estimate in the NLB algorithm to obtain the noise-free patch that maximizes the posterior distribution. The performance of our proposed method is evaluated on confocal microscopy images with real noise Poisson-Gaussian noise. Our experiments reveal the competitive performance of our approach compared to the state-of-the-art.