Miracle-dz/UniFAR-FTUs huggingface dataset:
The economic burden of stroke care in England, Wales and Northern Ireland: Using a national stroke register to estimate and report patient-level health economic outcomes in stroke

Introduction Stroke registries are used in many settings to measure stroke treatment and outcomes, but rarely include data on health economic outcomes. We aimed to extend the Sentinel Stroke National Audit Programme registry of England, Wales and Northern Ireland to derive and report patient-level estimates of the cost of stroke care. Methods An individual patient simulation model was built to estimate health and social care costs at one and five years after stroke, and the cost-benefits of thrombolysis and early supported discharge. Costs were stratified according to age, sex, stroke type (ischaemic or primary intracerebral haemorrhage) and stroke severity. The results were illustrated using data on all patients with stroke included in Sentinel Stroke National Audit Programme from April 2015 to March 2016 (n = 84,184). Results The total cost of health and social care for patients with acute stroke each year in England, Wales and Northern Ireland was £3.60 billion in the first five years after admission (mean per patient cost: £46,039). There was fivefold variation in the magnitude of costs between patients, ranging from £19,101 to £107,336. Costs increased with older age, increasing stroke severity and intracerebral hemorrhage stroke. Increasing the proportion of eligible patients receiving thrombolysis or early supported discharge was estimated to save health and social care costs by five years after stroke. Discussion The cost of stroke care is large and varies widely between patients. Increasing the proportion of eligible patients receiving thrombolysis or early supported discharge could contribute to reducing the financial burden of stroke. Conclusion Extending stroke registers to report individualised data on costs may enhance their potential to support quality improvement and research.
Multi-Scale Spatiotemporal Conv-LSTM Network for Video Saliency Detection

Recently, deep neural networks have been crucial techniques for image salient detection. However, two difficulties prevent the development of deep learning in video saliency detection. The first one is that the traditional static network cannot conduct a robust motion estimation in videos. The other is that the data-driven deep learning is in lack of sufficient manually annotated pixel-wise ground truths for video saliency network training. In this paper, we propose a multi-scale spatiotemporal convolutional LSTM network (MSST-ConvLSTM) to incorporate spatial and temporal cues for video salient objects detection. Furthermore, as manually pixel-wised labeling is very time-consuming, we sign lots of coarse labels, which are mixed with fine labels to train a robust saliency prediction model. Experiments on the widely used challenging benchmark datasets (e.g., FBMS and DAVIS) demonstrate that the proposed approach has competitive performance of video saliency detection compared with the state-of-the-art saliency models.
LexNLP: Natural Language Processing and Information Extraction For Legal and Regulatory Texts

LexNLP is an open source Python package focused on natural language processing and machine learning for legal and regulatory text. The package includes functionality to (i) segment documents, (ii) identify key text such as titles and section headings, (iii) extract over eighteen types of structured information like distances and dates, (iv) extract named entities such as companies and geopolitical entities, (v) transform text into features for model training, and (vi) build unsupervised and supervised models such as word embedding or tagging models. LexNLP includes pre-trained models based on thousands of unit tests drawn from real documents available from the SEC EDGAR database as well as various judicial and regulatory proceedings. LexNLP is designed for use in both academic research and industrial applications.
Semantic Correspondence: A Hierarchical Approach

Establishing semantic correspondence across images when the objects in the images have undergone complex deformations remains a challenging task in the field of computer vision. In this paper, we propose a hierarchical method to tackle this problem by first semantically targeting the foreground objects to localize the search space and then looking deeply into multiple levels of the feature representation to search for point-level correspondence. In contrast to existing approaches, which typically penalize large discrepancies, our approach allows for significant displacements, with the aim to accommodate large deformations of the objects in scene. Localizing the search space by semantically matching object-level correspondence, our method robustly handles large deformations of objects. Representing the target region by concatenated hypercolumn features which take into account the hierarchical levels of the surrounding context, helps to clear the ambiguity to further improve the accuracy. By conducting multiple experiments across scenes with non-rigid objects, we validate the proposed approach, and show that it outperforms the state of the art methods for semantic correspondence establishment.
Unsupervised Video-to-Video Translation

Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time. In this paper, we formulate a new task of unsupervised video-to-video translation, which poses its own unique challenges. Translating video implies learning not only the appearance of objects and scenes but also realistic motion and transitions between consecutive frames.We investigate the performance of per-frame video-to-video translation using existing image-to-image translation networks, and propose a spatio-temporal 3D translator as an alternative solution to this problem. We evaluate our 3D method on multiple synthetic datasets, such as moving colorized digits, as well as the realistic segmentation-to-video GTA dataset and a new CT-to-MRI volumetric images translation dataset. Our results show that frame-wise translation produces realistic results on a single frame level but underperforms significantly on the scale of the whole video compared to our three-dimensional translation approach, which is better able to learn the complex structure of video and motion and continuity of object appearance.
Using Gini Impurity to Mine Attribute-based Access Control Policies with Environment Attributes

In Attribute-based Access Control (ABAC) systems, utilizing environment attributes along with the subject and object attributes introduces a dynamic nature to the access decisions. The inclusion of environment attributes helps in achieving a more fine-grained access control. In this paper, we present an ABAC policy mining algorithm that considers the environment attributes and their associated values while forming the rules. Furthermore, we use gini impurity to form the rules. This helps to minimize the number of rules in the generated policy. The experimental evaluation shows that our approach is quite effective in practice.
Improved Reconstruction Technique for Moiré Imaging Using an X-Ray Phase-Contrast Talbot-Lau Interferometer

X-ray phase-contrast imaging is a promising method for medical imaging and non-destructive testing. Information about the attenuation, small-angle scattering and phase-shifting properties of an object can be gained simultaneously in three image modalities using a Talbot–Lau interferometer. This is a highly sensitive approach for retrieving this information. Nevertheless, until now, Talbot–Lau interferometry has been a time-consuming process due to image acquisition by phase-stepping procedures. Thus, methods to accelerate the image acquisition process in Talbot–Lau interferometry would be desirable. This is especially important for medical applications to avoid motion artifacts. In this work, the Talbot–Lau interferometry is combined with the moire imaging approach. Firstly, the reconstruction algorithm of moire imaging is improved compared to the standard reconstruction methods in moire imaging that have been published until now. Thus, blurring artifacts resulting from the reconstruction in the frequency domain can be reduced. Secondly, the improved reconstruction algorithm allows for reducing artifacts in the reconstructed images resulting from inhomogeneities of the moire pattern in large fields of view. Hence, the feasibility of differential phase-contrast imaging with regard to the integration into workflows in medical imaging and non-destructive testing is improved considerably. New fields of applications can be gained due to the accelerated imaging process—for example, live imaging in medical applications.
Enterocytozoon bieneusi in Minks (Neovison vison) in Northern China: A Public Health Concern

Enterocytozoon bieneusi is the most important causative agent of microsporidiosis and can infect almost all vertebrate and invertebrate hosts, including minks (Neovison vison). In the present study, a total of 298 feces samples (including 79 from Heilongjiang province, 31 from Hebei province, 67 from Jilin province, 90 from Liaoning province, and 31 from Shandong province, Northern China) were examined by nested PCR amplification of the internal transcribed spacer (ITS) region of the rRNA gene. The overall prevalence of E. bieneusi in minks was 10.1%, with 10.5% in Jilin province, 32.3% in Hebei province, 8.9% in Liaoning province, 0% in Shandong province, and 6.3% in Heilongjiang province. Furthermore, multiple logistic regression analysis revealed that region was only risk factors associated with E. bieneusi infection in the investigated minks. Five E. bieneusi ITS genotypes (three known genotypes, namely D, Peru11, and EbpC; two novel genotypes, namely, NCM-1 and NCM-2) were found in the current study. Importantly, genotypes D, Peru11 and EbpC, previously identified in humans, were also found in minks, which suggested that minks are the potential sources of human microsporidiosis. To the best of our knowledge, this is the first report of E. bieneusi infection in minks worldwide. The results of the present survey have implications for the controlling E. bieneusi infection in minks, other animals and humans.
Distributed Algorithms for Minimum Degree Spanning Trees

The minimum degree spanning tree (MDST) problem requires the construction of a spanning tree $T$ for graph $G=(V,E)$ with $n$ vertices, such that the maximum degree $d$ of $T$ is the smallest among all spanning trees of $G$. In this paper, we present two new distributed approximation algorithms for the MDST problem. Our first result is a randomized distributed algorithm that constructs a spanning tree of maximum degree $\hat d = O(d\log{n})$. It requires $O((D + \sqrt{n}) \log^2 n)$ rounds (w.h.p.), where $D$ is the graph diameter, which matches (within log factors) the optimal round complexity for the related minimum spanning tree problem. Our second result refines this approximation factor by constructing a tree with maximum degree $\hat d = O(d + \log{n})$, though at the cost of additional polylogarithmic factors in the round complexity. Although efficient approximation algorithms for the MDST problem have been known in the sequential setting since the 1990's, our results are first efficient distributed solutions for this problem.
Interpretable Partitioned Embedding for Customized Multi-item Fashion Outfit Composition

Intelligent fashion outfit composition becomes more and more popular in these years. Some deep learning based approaches reveal competitive composition recently. However, the uninterpretable characteristic makes such deep learning based approach cannot meet the designers, businesses and consumers' urge to comprehend the importance of different attributes in an outfit composition. To realize interpretable and customized multi-item fashion outfit compositions, we propose a partitioned embedding network to learn interpretable embeddings from clothing items. The network consists of two vital components: attribute partition module and partition adversarial module. In the attribute partition module, multiple attribute labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the partition adversarial module, adversarial operations are adopted to achieve the independence of different parts. With the interpretable and partitioned embedding, we then construct an outfit composition graph and an attribute matching map. Extensive experiments demonstrate that 1) the partitioned embedding have unmingled parts which corresponding to different attributes and 2) outfits recommended by our model are more desirable in comparison with the existing methods.
Thyroid Hormone Enhances Neurite Outgrowth in Neuroscreen 1 Cells

Objectives: Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of individuals. Moreover, hypothyroidism has been identified as one of the risk factors that may contribute to the development of AD. Here, we investigated whether there was a correlation among expression levels of proteins involved in the formation of AD lesions, neurite outgrowth, and thyroid hormone levels. Methods: Cells were grown in media supplemented with different levels of 3,5,3’-triiodothyronine (T3) and then processed for neurite outgrowth and to prepare RNA samples. RNA samples were analysed using quantitative real-time PCR. Protein levels were measured using in cell-Western blotting analysis. Results: By using neurite outgrowth studies, it was demonstrated that T3 treatment enhanced neurite outgrowth in NS-1 cells in a time- and dose-dependent manner. Quantitative real-time PCR studies further confirmed that NS-1 cells expressed substantial levels of TRα and significantly less TRβ, either of which could be responsible for the T3-dependent effects on neurite outgrowth. Although the overall tau protein expression was not affected in response to T3 treatment, the splicing of tau exon 10 was impacted in the direction of producing more tau molecules that excluded the exon (tau 3R). Conclusion: The results of this study are critical not only to understand the probable link between hypothyroidism and AD but also in providing the basis for future prevention and treatment of AD in hypothyroid patients.
Rapid Smart Environment Prototyping for Early Conceptual Design

Smart environments involving various IoT devices and people in large spaces have become popular yet challenging subject matters of design. Prototyping smart environments reflecting user experiences is expensive and time consuming. Few methods and tools have been investigated to address the needs of the iterative rapid prototyping of the design subjects involving complex interactions in the early design phases. This doctoral research involves investigating methods and tools for rapid smart environment prototyping in the early phase of a design process. Experimental prototyping systems based on conceptual frameworks for dealing with people's interactions in smart environments are proposed and evaluated with professionals in the design field. This research will add to the knowledge on prototyping tools and methods for designers and will contribute to the professional practice of designing inter-connected interactive systems that multiple users use in spaces.
Spatial: a language and compiler for application accelerators

Industry is increasingly turning to reconfigurable architectures like FPGAs and CGRAs for improved performance and energy efficiency. Unfortunately, adoption of these architectures has been limited by their programming models. HDLs lack abstractions for productivity and are difficult to target from higher level languages. HLS tools are more productive, but offer an ad-hoc mix of software and hardware abstractions which make performance optimizations difficult. In this work, we describe a new domain-specific language and compiler called Spatial for higher level descriptions of application accelerators. We describe Spatial's hardware-centric abstractions for both programmer productivity and design performance, and summarize the compiler passes required to support these abstractions, including pipeline scheduling, automatic memory banking, and automated design tuning driven by active machine learning. We demonstrate the language's ability to target FPGAs and CGRAs from common source code. We show that applications written in Spatial are, on average, 42% shorter and achieve a mean speedup of 2.9x over SDAccel HLS when targeting a Xilinx UltraScale+ VU9P FPGA on an Amazon EC2 F1 instance.
Parental history of type 2 diabetes is associated with lower resting energy expenditure in normoglycemic subjects

Objective Resting energy expenditure (REE) is linked to obesity, insulin resistance and type 2 diabetes (T2DM). REE and T2DM are inherited traits. Therefore, we investigated the effect of parental T2DM on REE in normoglycemic subjects. Methods Eighty-seven subjects with parental T2DM and 83 subjects without parental T2DM were matched in age, gender, race, BMI, weight and waist circumference. Subjects underwent a 75 g oral glucose tolerance test; REE was determined by indirect calorimetry and body composition was assessed by dual energy X-ray absorptiometry. Statistical analysis was performed using Student’s t-test, analysis of variance and regression analysis. Results The mean age was 38.8±11.3 years, 57% were females and 53% were African-Americans. The mean BMI was 28.5±6.1 kg/m2, waist circumference 91.8±15.1 cm, weight 83.9±20.3 kg, fat mass 31.0%±10.0%, mean fat-free mass (FFM) 54.4±12.9 kg. REE was significantly lower in subjects with parental diabetes, normalized REE 1364.4±263.4Kcal/day vs 1489.4±323.2 Kcal/day, p=0.006 and 29.2±5.3Kcal/kg FFM/day vs 31.9±6.0 Kcal/kg FFM/day, p=0.002. African-Americans had a lower REE compared with Caucasians 28.6±5.4Kcal/kg FFM/day vs 32.6±5.5 Kcal/kg FFM/day, p<0.0001. In a multiple regression model, ethnicity (p<0.0001), parental history of T2DM (p=0.006) and FFM (p=0.021) were independent predictors of REE. Conclusion Compared with subjects without parental diabetes, offspring with parental T2DM had lower REE, which was more pronounced in African-Americans. This metabolic alteration could increase the risk of obesity, insulin resistance and dysglycemia.
Predicting Image Aesthetics using Objects in the Scene

Analyzing aesthetic quality of images is a highly challenging task because of its subjectiveness. With the exponential rise of digital images in social media, it is of great demand to assess the aesthetics of images for several multimedia applications such as increasing social popularity etc. Previous approaches to address this problem have used hand-designed features or automated features extracted by deep convolutional neural network architectures. In this paper we predict the aesthetics of images by using the inferential information depending on the visual content found in an image. To the best of our knowledge, this is the first attempt to address such problem by using tags predicted. Experimental results show that our proposed method outperforms the traditional machine learning methods and demonstrate competitive performance compared to the state-of-the-art methods of image aesthetics prediction.
Teaching Your Wireless Card New Tricks: Smartphone Performance and Security Enhancements Through Wi-Fi Firmware Modifications

Smartphones come with a variety of sensors and communication interfaces, which make them perfect candidates for mobile communication testbeds. Nevertheless, proprietary firmwares hinder us from accessing the full capabilities of the underlying hardware platform which impedes innovation. Focusing on FullMAC Wi-Fi chips, we present Nexmon, a C-based firmware modification framework. It gives access to raw Wi-Fi frames and advanced capabilities that we found by reverse engineering chips and their firmware. As firmware modifications pose security risks, we discuss how to secure firmware handling without impeding experimentation on Wi-Fi chips. To present and evaluate our findings in the field, we developed the following applications. We start by presenting a ping-offloading application that handles ping requests in the firmware instead of the operating system. It significantly reduces energy consumption and processing delays. Then, we present a software-defined wireless networking application that enhances scalable video streaming by setting flow-based requirements on physical-layer parameters. As security application, we present a reactive Wi-Fi jammer that analyses incoming frames during reception and transmits arbitrary jamming waveforms by operating Wi-Fi chips as software-defined radios (SDRs). We further introduce an acknowledging jammer to ensure the flow of non-targeted frames and an adaptive power-control jammer to adjust transmission powers based on measured jamming successes. Additionally, we discovered how to extract channel state information (CSI) on a per-frame basis. Using both SDR and CSI-extraction capabilities, we present a physical-layer covert channel. It hides covert symbols in phase changes of selected OFDM subcarriers. Those manipulations can be extracted from CSI measurements at a receiver. To ease the analysis of firmware binaries, we created a debugging application that supports single stepping and runs as firmware patch on the Wi-Fi chip. We published the source code of our framework and our applications to ensure reproducibility of our results and to enable other researchers to extend our work. Our framework and the applications emphasize the need for freely modifiable firmware and detailed hardware documentation to create novel and exciting applications on commercial off-the-shelf devices.
Supervised topic models with weighted words: multi-label document classification

Supervised topic modeling algorithms have been successfully applied to multi-label document classification tasks. Representative models include labeled latent Dirichlet allocation (L-LDA) and dependency-LDA. However, these models neglect the class frequency information of words (i.e., the number of classes where a word has occurred in the training data), which is significant for classification. To address this, we propose a method, namely the class frequency weight (CF-weight), to weight words by considering the class frequency knowledge. This CF-weight is based on the intuition that a word with higher (lower) class frequency will be less (more) discriminative. In this study, the CF-weight is used to improve L-LDA and dependency-LDA. A number of experiments have been conducted on real-world multi-label datasets. Experimental results demonstrate that CF-weight based algorithms are competitive with the existing supervised topic models.
Feature Reconstruction by Laplacian Eigenmaps for Efficient Instance Search

Instance search aims at retrieving images containing a particular query instance. Recently, image features derived from pre-trained convolutional neural networks (CNNs) have been shown to provide promising performance for image retrieval. However, the robustness of these features is still limited by hard positives and hard negatives. To address this issue, this work focuses on reconstructing a new representation based on conventional CNN features to capture the intrinsic image manifold in the original feature space. After the feature reconstruction, the Euclidean distance can be applied in the new space to measure the pairwise distance among feature points. The proposed method is highly efficient, which benefits from the linear search complexity and a further optimization for speedup. Experiments demonstrate that our method achieves promising efficiency with highly competitive accuracy. This work succeeds in capturing implicit embedding information in images as well as reducing the computational complexity significantly.