Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Safety Profiles between Non-operating Room Anesthesia and Operating Room Anesthesia: a Study of 199,764 Cases at a Korean Tertiary Hospital

Background Despite expanding demands for non-operating room anesthesia (NORA) worldwide, studies in this field are scarce. We compared the overall characteristics and the nature of deaths occurring within 48 hours after surgery between NORA and operating room anesthesia (ORA) cases at a Korean tertiary hospital. Methods We retrospectively analyzed the medical records of patients who underwent surgical procedures under anesthesia services in and outside the operating room from January 2013 to November 2017. All of the mortalities were categorized by principal cause into groups such as patient disease or condition, surgery, anesthesia, and others. Results Overall, 16,383 NORA cases and 183,381 ORA cases were analyzed. Eighty-six deaths were identified. The mortality rate of NORA cases was similar to that of ORA cases (4.9 per 10,000 cases [95% confidence interval (CI), 2.1–9.6] vs. 4.3 per 10,000 cases [95% CI, 3.4–5.3], respectively). Similar to ORA cases, higher American Society of Anesthesiologists physical status and very young age (< 2 years) were significantly associated with mortality in NORA cases. A patient's disease or condition was the most important cause of mortality (65/86, 75.6%), followed by surgery-related causes (16/86, 18.6%). Two cases of anesthesia-related mortality were only identified in the ORA cases, resulting in an overall anesthesia-related mortality of 0.1 per 10,000 cases (95% CI, 0.0–0.4). Conclusion Although NORA cases showed an equivalent perioperative mortality rate compared to ORA cases, there may be more room for improving patient safety when considering their favorable characteristics (healthier patients, less invasive and shorter procedures). Trial Registration Clinical Research Information Service Identifier: KCT0002719
Stochastic Block Model for Hypergraphs: Statistical limits and a semidefinite programming approach

We study the problem of community detection in a random hypergraph model which we call the stochastic block model for $k$-uniform hypergraphs ($k$-SBM). We investigate the exact recovery problem in $k$-SBM and show that a sharp phase transition occurs around a threshold: below the threshold it is impossible to recover the communities with non-vanishing probability, yet above the threshold there is an estimator which recovers the communities almost asymptotically surely. We also consider a simple, efficient algorithm for the exact recovery problem which is based on a semidefinite relaxation technique.
SmartSeed: Smart Seed Generation for Efficient Fuzzing

Fuzzing is an automated application vulnerability detection method. For genetic algorithm-based fuzzing, it can mutate the seed files provided by users to obtain a number of inputs, which are then used to test the objective application in order to trigger potential crashes. As shown in existing literature, the seed file selection is crucial for the efficiency of fuzzing. However, current seed selection strategies do not seem to be better than randomly picking seed files. Therefore, in this paper, we propose a novel and generic system, named SmartSeed, to generate seed files towards efficient fuzzing. Specifically, SmartSeed is designed based on a machine learning model to learn and generate high-value binary seeds. We evaluate SmartSeed along with American Fuzzy Lop (AFL) on 12 open-source applications with the input formats of mp3, bmp or flv. We also combine SmartSeed with different fuzzing tools to examine its compatibility. From extensive experiments, we find that SmartSeed has the following advantages: First, it only requires tens of seconds to generate sufficient high-value seeds. Second, it can generate seeds with multiple kinds of input formats and significantly improves the fuzzing performance for most applications with the same input format. Third, SmartSeed is compatible to different fuzzing tools. In total, our system discovers more than twice unique crashes and 5,040 extra unique paths than the existing best seed selection strategy for the evaluated 12 applications. From the crashes found by SmartSeed, we discover 16 new vulnerabilities and have received their CVE IDs.
Direct Microscopy of Stool Samples for Determining the Prevalence of Soil-Transmitted Helminthic Infections among Primary School Children in Kaduwela MOH Area of Sri Lanka following Floods in 2016

A descriptive cross-sectional school based study was carried out to investigate the prevalence of soil-transmitted intestinal helminths and the associated factors among school children in Kaduwela Medical Officer of Health (MOH) area in the Colombo district, which was affected by floods in 2016. The study was conducted in 9 selected schools in Kaduwela MOH area from September 2016 to March 2017. Permission was obtained from the relevant authorities. Grade 1 students were enrolled in the study after obtaining informed written consent from their parents/guardian. Interviewer based questionnaire was administered to gather demographic data and other relevant information. Stool samples were collected and examined by direct wet saline smear. Study population comprised 53.4% male students. None of stool samples were positive for soil-transmitted helminths but 17.4% of students complained of nocturnal itching and parents of 23% of them had seen worms passing out from their children's anus at night. Fourteen stool samples were found to be positive for cyst of Entamoeba coli. Majority of parents (69%) stated that their children always wash their hands with soap and water before meals, whereas 26% stated that their children practice this sometimes. Majority of students (88%) washed their hands after going to the toilet. Almost all students (86%) used water sealed toilets and very few used pit latrines (14%). In this study, 67% of students had received anti-helminthic drugs after the floods. These findings suggest that zero prevalence of helminthic infections could be due to anti-helminthic prophylaxis and good health practices. Further studies should be done in this area with a large sample size to investigate the true prevalence of helminthic infections. Students and parents should be educated on Enterobius vermicularis infection. The source of water supply should be tested for fecal contamination.
Defining the gut microbiota in individuals with periodontal diseases: an exploratory study

ABSTRACT Background: This exploratory study aimed to characterize the gut microbiome of individuals with different periodontal conditions, and correlate it with periodontal inflammation and tissue destruction. Methods: Stool samples were obtained from individuals presenting periodontal health (PH = 7), gingivitis (G = 14) and chronic periodontitis (CP = 23). The intestinal microbiome composition was determined by Illumina MiSeq sequencing. Results: A lower alpha-diversity in the gut microbiome of individuals with CP was observed, although no significant difference among groups was found (p > 0.01). Firmicutes, Proteobacteria, Verrucomicrobia and Euryarchaeota were increased, whereas Bacteroidetes were decreased in abundance in patients with periodontitis compared to PH. Prevotella (genus), Comamonadaceae (family) and Lactobacillales (order) were detected in higher numbers in G, while Bacteroidales (order) was predominant in PH (p < 0.01). Significant correlations (rho = 0.337–0.468, p < 0.01) were found between OTUs representative of periodontal pathogens and attachment loss. Mogibacteriaceae, Ruminococcaceae and Prevotella were able to discriminate individuals with periodontal diseases from PH (overall accuracy = 84%). Oral taxa were detected in high numbers in all stool samples. Conclusions: Individuals with periodontal diseases present a less diverse gut microbiome consistent with other systemic inflammatory diseases. High numbers of oral taxa related to periodontal destruction and inflammation were detected in the gut microbiome of individuals regardless of periodontal status.
A novel method for fast arbitrary-oriented scene text detection

In this paper, we propose a novel method for fast arbitrary-oriented text detection in scene images. Our proposed method is simple and effective which can predict word-level bounding boxes via a single fully convolutional network. Our method extracts features from the input images by residual network and apply multi-level fusion over the extracted features. It has two outputs, pixel-wise classification between text and non-text and word-level bounding boxes. Our method achieves an F-measure of 83.46% and 56.39% on ICDAR2015 Incidental Scene Text benchmark and COCO-Text dataset respectively, outperforming the previous methods by a large margin. Also, it can run at over 11 FPS on 704 × 1280 images, which is much faster than the previous works.
Fast and Accurate Point Cloud Registration using Trees of Gaussian Mixtures

Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, SLAM, object/scene recognition, and augmented reality. In this paper, we present a new registration algorithm that is able to achieve state-of-the-art speed and accuracy through its use of a hierarchical Gaussian Mixture Model (GMM) representation. Our method constructs a top-down multi-scale representation of point cloud data by recursively running many small-scale data likelihood segmentations in parallel on a GPU. We leverage the resulting representation using a novel PCA-based optimization criterion that adaptively finds the best scale to perform data association between spatial subsets of point cloud data. Compared to previous Iterative Closest Point and GMM-based techniques, our tree-based point association algorithm performs data association in logarithmic-time while dynamically adjusting the level of detail to best match the complexity and spatial distribution characteristics of local scene geometry. In addition, unlike other GMM methods that restrict covariances to be isotropic, our new PCA-based optimization criterion well-approximates the true MLE solution even when fully anisotropic Gaussian covariances are used. Efficient data association, multi-scale adaptability, and a robust MLE approximation produce an algorithm that is up to an order of magnitude both faster and more accurate than current state-of-the-art on a wide variety of 3D datasets captured from LiDAR to structured light.
Using Symmetries in Solving Minimal Problems in Computer Vision submitted by Viktor Korotynskiy

The topic of Viktor Korotynskiy’s bachelor thesis is symmetries in polynomial systems. This is an interesting problem since finding the symmetries of a given polynomial system may significantly simplify the solution of the system. Many problems in computer vision require solving systems of polynomial equations. Therefore, using symmetries may result in the improved efficiency of existing solutions to many computer vision problems or may lead to efficient solutions of unsolved problems. Symmetries are well studied in algebraic geometry but have only recently been used in computer vision [9]. Their obscurity in computer vision applications is likely due to the deep knowledge of algebraic geometry that is required to understand the concept of symmetries.
Neural Document Summarization by Jointly Learning to Score and Select Sentences

Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences. It first reads the document sentences with a hierarchical encoder to obtain the representation of sentences. Then it builds the output summary by extracting sentences one by one. Different from previous methods, our approach integrates the selection strategy into the scoring model, which directly predicts the relative importance given previously selected sentences. Experiments on the CNN/Daily Mail dataset show that the proposed framework significantly outperforms the state-of-the-art extractive summarization models.
Contraction and Robustness of Continuous Time Primal-Dual Dynamics

The Primal-dual (PD) algorithm is widely used in convex optimization to determine saddle points. While the stability of the PD algorithm can be easily guaranteed, strict contraction is nontrivial to establish in most cases. This letter focuses on continuous, possibly non-autonomous PD dynamics arising in a network context, in distributed optimization, or in systems with multiple time-scales. We show that the PD algorithm is indeed strictly contracting in specific metrics and analyze its robustness establishing stability and performance guarantees for different approximate PD systems. We derive estimates for the performance of multiple time-scale multi-layer optimization systems, and illustrate our results on a PD representation of the Automatic Generation Control of power systems.
The role of FGF21 in type 1 diabetes and its complications

Data from the International Diabetes Federation show that 347 million people worldwide have diabetes, and the incidence is still rising. Although the treatment of diabetes has been advanced, the current therapeutic options and outcomes, e.g. complications, are yet far from ideal. Therefore, an urgent need exists for the development of more effective therapies. Numerous studies have been conducted to establish and confirm whether FGF21 exerts beneficial effects on obesity and diabetes along with its complications. However, most of the studies associated with FGF21 were conducted in the patients with type 2 diabetes. Subsequently, the effect of FGF21 in the prevention or treatment of type 1 diabetes and its complications were also increasingly reported. In this review, we summarize the findings available on the function of FGF21 and the status of FGF21's treatment for type 1 diabetes. Based on the available information, we found that FGF21 exerts a hypoglycemic effect, restores the function of brown fat, and inhibits various complications in type 1 diabetes patients. Although these features are predominantly similar to those observed in the studies that showed the beneficial impact of FGF21 on type 2 diabetes and its complications, there are also certain distinct features and findings that may be of provide important and instructive for us to understand mechanistic insights and further promote the prevention and treatment of type 1 diabetes.
Integrative analysis of genome‐wide lncRNA and mRNA expression in newly synthesized Brassica hexaploids

Abstract Polyploidization, as a significant evolution force, has been considered to facilitate plant diversity. The expression levels of lncRNAs and how they control the expression of protein‐coding genes in allopolyploids remain largely unknown. In this study, lncRNA expression profiles were compared between Brassica hexaploid and its parents using a high‐throughput sequencing approach. A total of 2,725, 1,672, and 2,810 lncRNAs were discovered in Brassica rapa, Brassica carinata, and Brassica hexaploid, respectively. It was also discovered that 725 lncRNAs were differentially expressed between Brassica hexaploid and its parents, and 379 lncRNAs were nonadditively expressed in this hexaploid. LncRNAs have multiple expression patterns between Brassica hexaploid and its parents and show paternal parent‐biased expression. These lncRNAs were found to implement regulatory functions directly in the long‐chain form, and acted as precursors or targets of miRNAs. According to the prediction of the targets of differentially expressed lncRNAs, 109 lncRNAs were annotated, and their target genes were involved in the metabolic process, pigmentation, reproduction, exposure to stimulus, biological regulation, and so on. Compared with the paternal parent, differentially expressed lncRNAs between Brassica hexaploid and its maternal parent participated in more regulation pathways. Additionally, 61 lncRNAs were identified as putative targets of known miRNAs, and 15 other lncRNAs worked as precursors of miRNAs. Some conservative motifs of lncRNAs from different groups were detected, which indicated that these motifs could be responsible for their regulatory roles. Our findings may provide a reference for the further study of the function and action mechanisms of lncRNAs during plant evolution.
Stability analysis of continuous-time linear system with sampled-data observer

In this paper, the stability problem of continuous-time linear system via sampled output is investigated. By assumption that only sampled output can be available, a sampled-data based state observer is provided, by which a controller is constructed for the continuous-time linear system. By using the Lyapunov approach, the stability problem is addressed via our provided control law. Finally, a simulation example is provided to illustrate the efficiency of the theoretical analysis.
Recurrent neural networks based online learning algorithms for distributed systems

In this paper, we investigate online parameter learning for Long Short Term Memory (LSTM) architectures in distributed networks. Here, we first introduce an LSTM based structure for regression. Then, we provide the equations of this structure in a state space form for each node in our network. Using this form, we then learn the parameters via our Distributed Particle Filtering based (DPF) training method. Our training method asymptotically converges to the optimal parameter set provided that we satisfy certain trivial requirements. While achieving this performance, our training method only causes a computational load that is similar to the efficient first order gradient based training methods. Through real life experiments, we show substantial performance gains compared to the conventional methods.
Study on the relationship between investor sentiment and stock bubble

The change of investors' sentiment is one of the important factors which can affect the volatility of stock price. However, few studies are related to stock bubble from the perspective of investor sentiment. Based on behavioral finance theory, this paper builds the investor sentiment index by principal component analysis. And, it uses the quarter panel data of listed companies of our country in 2006-2015, explores the relationship of investor sentiment and stock bubble. It indicates: 1) it finds that stock bubble is positively correlated with investor sentiment, new stock accounts, market price earning ratio, the amount of IPO. Meanwhile, the stock bubble is negative correlation with volume of market transactions, market turnover rate. 2) it calculates the fundamental value of the stock by using residual income model, and finds that there are lots of bubbles among the listed companies of our country. The results improve the system of behavioral finance theory, and provides some suggestions for government.
Position-aware Self-attention with Relative Positional Encodings for Slot Filling

This paper describes how to apply self-attention with relative positional encodings to the task of relation extraction. We propose to use the self-attention encoder layer together with an additional position-aware attention layer that takes into account positions of the query and the object in the sentence. The self-attention encoder also uses a custom implementation of relative positional encodings which allow each word in the sentence to take into account its left and right context. The evaluation of the model is done on the TACRED dataset. The proposed model relies only on attention (no recurrent or convolutional layers are used), while improving performance w.r. t. the previous state of the art.
Can musical intervention improve memory in Alzheimer’s patients? Evidence from a systematic review

ABSTRACT Treatment with music has shown effectiveness in the treatment of general behavioural and cognitive symptoms of patients with various types of dementia. Objective: To assess the effectiveness of treatment with music on the memory of patients with Alzheimer’s disease (AD). Methods: A systematic search was performed on PubMed (Medline), Cochrane Library, PsycINFO and Lilacs databases up to June 2017 and included all randomized controlled trials that assessed memory using musical interventions in patients with AD. Results: Forty-two studies were identified, and 24 studies were selected. After applying the exclusion criteria, four studies involving 179 patients were included. These studies showed the benefits of using music to treat memory deficit in patients with AD. Conclusion: To the best of our knowledge, this is the first systematic review focusing on randomized trials found in the literature that analysed the role of musical interventions specifically in the memory of patients with AD. Despite the positive outcome of this review, the available evidence remains inconsistent due to the small number of randomized controlled trials.
Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Novelty detection is the problem of identifying whether a new data point is considered to be an inlier or an outlier. We assume that training data is available to describe only the inlier distribution. Recent approaches primarily leverage deep encoder-decoder network architectures to compute a reconstruction error that is used to either compute a novelty score or to train a one-class classifier. While we too leverage a novel network of that kind, we take a probabilistic approach and effectively compute how likely it is that a sample was generated by the inlier distribution. We achieve this with two main contributions. First, we make the computation of the novelty probability feasible because we linearize the parameterized manifold capturing the underlying structure of the inlier distribution, and show how the probability factorizes and can be computed with respect to local coordinates of the manifold tangent space. Second, we improve the training of the autoencoder network. An extensive set of results show that the approach achieves state-of-the-art performance on several benchmark datasets.
SensePods: A ZigBee-Based Tangible Smart Home Interface

Low-cost sensors and ubiquitous wireless networking is enabling novel ways in which homeowners can interact with their smart homes. Many complementary approaches like using voice commands, direct interaction by using touch or weight, or by using body gestures are emerging. This paper shows the design and implementation of a novel low-power, low-cost, hand-held wireless device called a SensePod. SensePods can be used by a consumer to interact with a smart home using simple gestures like rubbing, taping or rolling the device on any home surface like a dining table. The device is only 4.5 cm long, forms an ad-hoc wireless network using the ZigBee protocol, and can be easily interfaced to existing home management systems using a universal serial bus port. The gestures in each device can be programmed to control various objects of a smart home like smart curtains, for example. Hidden Markov models were used to train the device to recognize various gestures. The device was tested with a variety of gestures and has a recognition rate of over 99.7%, and a response time of less than two milliseconds.