Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning-Based Model Predictive Control Under Signal Temporal Logic Specifications

This paper presents a control strategy synthesis method for dynamical systems with differential constraints while satisfying a set of given rules in consideration of their importances. A special attention is given to situations where all rules cannot be met in order to fulfill a given task. Such dilemmas compel us to make a decision on the degree of satisfaction of each rule including which rule should be maintained or not. In this work, we propose a learning-based model predictive control method in order to solve this problem, where a key insight is to combine a learning method and traditional control scheme so that the designed controller behaves close to human experts. A rule is represented as a signal temporal logic (STL) formula. A robustness slackness, a margin to the satisfaction of the rule, is learned from expert's demonstrations using Gaussian process regression. The learned margin is used in a model predictive control procedure, which helps to decide how much to obey each rule, even ignoring specific rules. In track driving simulation, we show that the proposed method generates human-like behavior and efficiently handles dilemmas as human teachers do.
Detection of Cyclostationarity Using Generalized Coherence

A class of detectors for cyclostationarity is introduced. These detectors are based on the use of generalized coherence to measure correlation among two or more collections of random vectors. The generalized coherence framework allows any finite collection of pertinent samples of the cyclic auto-correlation function estimates formed from the measured signal data to be combined into the detection statistic. The performance of this approach is demonstrated and compared against other established cyclostationarity detectors in both a cognitive radio scenario and a multi-channel passive surveillance scenario.
Emergency Laparotomy in the Critically Ill: Futility at the Bedside

Background Critically ill patients are often evaluated for an intra-abdominal catastrophe. In the absence of a preoperative diagnosis, abdominal exploration may be offered despite desperate circumstances. We hypothesize that (1) abdominal exploration for such patients is associated with a high mortality and (2) commonly obtained physiologic measures at laparotomy anticipate mortality. Methods All acute care surgery (ACS) patients undergoing emergency laparotomy at a quaternary referral center during a 3-year period were reviewed. Inclusion was defined by emergency laparotomy in the operating room (OR) in a patient with an American Society of Anesthesiologists (ASA) score ≥4 or bedside laparotomy in the ICU (BSL). Mortality was the primary endpoint and was stratified by demographics, admitting service, surgical findings, and physiology. Comparisons between OR and BSL were by Fisher's exact and Mann–Whitney tests. Results 144 patients underwent emergency laparotomy (45 BSL vs. 99 OR). Overall mortality was 55.6% (77.8% BSL vs. 45.5% OR; p < 0.001). Mortality by admitting service was cardiac 71.4% (n=42), medical 70% (n=30), ACS 42% (n=50), and other 36.4% (n=22) services. Preoperative lactate levels were higher in nonsurvivors (2.7 vs. 8.5 mmol/L, p < 0.001), as was vasopressor use (62.5% vs. 97.5%, p < 0.001), acute kidney injury (51.6% vs. 72.5%, p < 0.01), leukocytosis (53.1% vs. 71.3%, p < 0.04), and anemia (45.3% vs. 71.3%, p < 0.01). The presence of any identifiable abdominal pathology established a 90% mortality rate. Conclusions The need for BSL portends an extremely high mortality rate and is likely useful in preintervention counselling. Emergency OR laparotomy leads to mortality in nearly half of such patients and is anticipatable based on concurrent abnormal physiology.
Ambient air quality and subjective stress level using Community Health Survey data in Korea

OBJECTIVES Air pollution causes various disease in exposed populations, and can lead to premorbid health effects manifested as both physical and psychological functional impairment. The present study investigated the subjective stress level in daily life in relation to the level of air pollution. METHODS Data from the Community Health Survey (2013), comprising 99,162 men, and 121,273 women residing in 253 healthcare administrative districts, were combined with air pollutant concentration modelling data from the Korean Air Quality Forecasting System, and were stratified by subjective stress levels into five strata for multiple logistic regression. Levels of exposure were divided into five quintiles according to the annual concentration of nitrogen dioxide (NO2), and were analyzed using a single-pollutant model using NO2 concentration only, and a multi-pollutant model adjusted for the concentration of particulate matter <10 μm in diameter. RESULTS Analysis of men and women in various age groups showed the highest odds ratio (OR) for subjective stress level at the highest NO2 concentration quintile in men and women aged 30–64 years (men: 2.91; 95% confidence interval [CI], 2.12 to 4.01; women: 1.82; 95% CI, 1.32 to 2.51). As the NO2 concentration quintile increased, the OR increased. Men showed higher ORs than women in all strata. CONCLUSIONS In the present study, annual NO2 concentrations were found to be associated with subjective stress levels. This association was especially clear among socioeconomically active men and women aged 30-64 years.
Diagnostic and prognostic value of serum miR-99a expression in oral squamous cell carcinoma.

BACKGROUND Despite progress in the treatment of oral squamous cell carcinoma (OSCC) over past years, the prognosis for OSCC patients remains dismal. MicroRNA-99a (miR-99a) has been found to involve in the development of many cancer types, but its clinical role in OSCC is unclear. OBJECTIVE The aim of this study was to explore the clinical implications of serum miR-99a in OSCC. METHODS This study detected serum miR-99a levels in 121 OSCC cases and 55 healthy controls by using quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis. RESULTS The data showed that serum miR-99a expression was significantly decreased in OSCC patients compared with normal controls. OSCC patients with low miR-99a expression experienced more frequent poor differentiation and advanced clinical stage. Furthermore, in screening OSCC cases from normal controls, miR-99a could yield a receiver-operating characteristic (ROC) area under the curve (AUC) of 0.911 with 83.6% specificity and 80.2% sensitivity. Notably, patients with high miR-99a expression had longer overall survival and recurrence free survival. Finally, serum miR-99a was identified to be an independent prognostic indicator for OSCC. CONCLUSIONS These results suggested that miR-99a might be a valuable marker for the prediction of early detection and prognosis in OSCC.
Deep active learning for object detection

Object detection methods like Single Shot Multibox Detector (SSD) provide highly accurate object detection that run in real-time. However, these approaches require a large number of annotated training images. Evidently, not all of these images are equally useful for training the algorithms. Moreover, obtaining annotations in terms of bounding boxes for each image is costly and tedious. In this paper, we aim to obtain a highly accurate object detector using only a fraction of the training images. We do this by adopting active learning that uses ‘human in the loop’ paradigm to select the set of images that would be useful if annotated. Towards this goal, we make the following contributions: 1. We develop a novel active learning method which poses the layered architecture used in object detection as a ‘query by committee’ paradigm to choose the set of images to be queried. 2.Weintroduce a framework to use the exploration/exploitation trade-off in our methods. 3. We analyze the results on standard object detection datasets which show that with only a third of the training data, we can obtain more than 95% of the localization accuracy of full supervision. Further our methods outperform classical uncertainty-based active learning algorithms like maximum entropy.
Frequency Domain Zadoff-Chu Sounding Technique for USRPs

This paper proposes a technique for a wireless channel sounding and shows its real-world measurement results. The proposed technique is exploited to characterize different indoor channels for a 800–900 MHz band. We utilize an Universal Software Radio Peripheral (USRP) and GNU Radio software. At the transmitter side, Zadoff-Chu sequence is generated and sent across the channel, while the carrier frequency of the transmitted sequence is swept across the measured bandwidth. The achieved response is also compared with the frequency domain technique, where continuous wave is used as a sounding signal. Using the proposed method, the basic channel characteristics like RMS delay spread, mean excess delay, path-loss and coherence bandwidth are extracted in shorter time period compared to the continuous wave method.
Stateful Distributed Firewall as a Service in SDN

Software-defined networking (SDN) is a newly emerging approach in computer networking which abstracts network control functionalities and enables its direct programmability at the management plane. A new framework of communication between the control-plane and the data-plane is gaining a lot of attraction recently, which combines the advantages of the proactive approach, in pre-installing the flow rules in the data-plane, and the advantages of the reactive approach, in its ability to dynamically react to network events. This hybrid approach utilizes the potential of the SDN switches to recognize and host state machines. While the trending success of SDN is set to continue, this evolving network paradigm requires a new set of tools and strategies to secure the network elements against intrusions and at the same time maintain its efficiency and reliability. In this paper, we take advantage of the hybrid approach of network controllability and management to offload the processing of stateful applications from the control-plane to the data-plane and propose our framework, SDFS, which optimizes a distributed stateful application in the data-plane to transform the SDN network into one big firewall. While maintaining modularity of the framework, SDFS offers an optimized processing burden distribution of the stateful application in the data-plane among the switches in the network with inherent fault-tolerance mechanisms that eliminate the need for immediate controller intervention even in cases of network failure or attacks.
Towards an accurate and efficient heuristic for species/gene tree co‐estimation

Motivation Species and gene trees represent how species and individual loci within their genomes evolve from their most recent common ancestors. These trees are central to addressing several questions in biology relating to, among other issues, species conservation, trait evolution and gene function. Consequently, their accurate inference from genomic data is a major endeavor. One approach to their inference is to co‐estimate species and gene trees from genome‐wide data. Indeed, Bayesian methods based on this approach already exist. However, these methods are very slow, limiting their applicability to datasets with small numbers of taxa. The more commonly used approach is to first infer gene trees individually, and then use gene tree estimates to infer the species tree. Methods in this category rely significantly on the accuracy of the gene trees which is often not high when the dataset includes closely related species. Results In this work, we introduce a simple, yet effective, iterative method for co‐estimating gene and species trees from sequence data of multiple, unlinked loci. In every iteration, the method estimates a species tree, uses it as a generative process to simulate a collection of gene trees, and then selects gene trees for the individual loci from among the simulated gene trees by making use of the sequence data. We demonstrate the accuracy and efficiency of our method on simulated as well as biological data, and compare them to those of existing competing methods. Availability and implementation The method has been implemented in PhyloNet, which is publicly available at http://bioinfocs.rice. edu/phylonet.
Listening Effort: Order Effects and Core Executive Functions

Abstract Background: Listening effort seems to depend on input-related listening demands and several factors internal to the individual listener. Input-related demands may be listening in noise compared with listening in quiet, and internal factors may be cognitive functions. Purpose: The purpose was to apply measures of listening effort and perceived listening effort in participants with normal hearing, to determine if there are any presentation order effects, and to explore the relationship between listening effort measured as accuracy, response times, efficiency of information encoding into long-term memory, perceived listening effort, and core executive functions. Research Design: A within-subject design with repeated measures was used and a study of relationships between variables was made. Study Sample: Thirty-two healthy adults with normal hearing. Data Collection and Analysis: Participants were tested individually by a listening task using a dual-task paradigm. The listening task was performed in quiet and in multitalker babble noise at 10 dB signal-to-noise ratio (SNR). Perceived listening effort and core executive functions (working memory, inhibitory control, and cognitive flexibility) were also assessed. Results: The measures of listening effort (correct responses, response times, and immediate and delayed listening comprehension) failed to demonstrate increased listening effort in multitalker babble noise (10 dB SNR) compared with quiet, although a significant test order effect was seen for correct responses indicating that participants who first listened in noise did not improve in quiet. Perceived listening effort increased significantly in noise compared with quiet. No relationship was found between measures of listening effort and ratings of perceived listening effort. Working memory and cognitive flexibility were not related to ratings of perceived listening effort. In contrast, better inhibitory control was related to higher ratings in both quiet and in noise. Conclusions: It is possible that the SNR and measures used were not as sensitive as required to measure listening effort behaviorally. In the present experimental setup, prior noise exposure impedes the beneficial effects of performing a task in quiet. Self-reports seem to provide a valid measure of perceived listening effort that is related to the individual’s inhibitory control. The present findings suggest that participants with better inhibitory control are more susceptible to the task demand level both in quiet and in noise.
Gender differences on benefits and risks associated with oral antithrombotic medications for coronary artery disease

ABSTRACT Introduction: Despite the significant advances in the pharmacological and interventional management of patients with cardiovascular disease (CVD) over the last decades, cardiovascular mortality remains the leading cause of death. Large randomized clinical trials have investigated the efficacy and safety of different antithrombotic medications in patients with CVD. Although female gender is generally underrepresented in these clinical trials, most analyses clearly indicate that both men and women may accrue therapeutic benefits from antithrombotic management strategies. Areas covered: This review aims to provide a comprehensive and focused update on gender-related comparative clinical studies of antithrombotic therapies in patients suffering from CVD. Expert opinion: Current evidence supports the understanding of a similar therapeutic effect between genders with signals of an increased risk of bleeding in women. However, important gaps in evidence exist due to the overall limited percentage of women that have been enrolled in randomized controlled trials. A greater awareness of gender-related issues in antithrombotic therapy should be promoted among physicians and further evidence from large clinical trials looking at the safety and efficacy balance of different antithrombotic strategies in women is warranted.
Performance Analysis of Accurate End Point Identification Method of Static Hand Gesture Recognition

Static Hand Gesture Recognition is an important area of research as it is used for developing a variety of useful applications in the domains like robotics, artificial intelligence, mobile games etc. Variety of useful methods is implemented to detect static hand gesture. In our previous study presented in [1] describes the implementation of Accurate End Point Identification (AEPI) Method for static hand gesture recognition. The AEPI method has been implemented to address the problems of varying background, luminance, blurring etc. Five different phases of AEPI method includes preprocessing, centroid detection, removal of unwanted objects, thinning and recognition which are already discussed in [1], [2]. In this paper, we present the result and performance analysis of AEPI method for all the possible input patterns of static hand gesture recognition.
Optimal Pooling of Covariance Matrix Estimates Across Multiple Classes

The paper considers the problem of estimating the covariance matrices of multiple classes in a low sample support condition, where the data dimensionality is comparable to, or larger than, the sample sizes of the available data sets. In such conditions' a common approach is to shrink the class sample covariance matrices (SCMs) towards the pooled SCM. The success of this approach hinges upon the ability to choose the optimal regularization parameter. Typically, a common regularization level is shared among the classes and determined via a procedure based on cross-validation. We use class-specific regularization levels since this enables the derivation of the optimal regularization parameter for each class in terms of the minimum mean squared error (MMSE). The optimal parameters depend on the true unknown class population covariances. Consistent estimators of the parameters can, however, be easily constructed under the assumption that the class populations follow (unspecified) elliptically symmetric distributions. We demonstrate the performance of the proposed method via a simulation study as well as via an application to discriminant analysis using both synthetic and real data sets.
Complement System and Age-Related Macular Degeneration: Implications of Gene-Environment Interaction for Preventive and Personalized Medicine

Age-related macular degeneration (AMD) is the most common cause of visual loss in developed countries, with a significant economic and social burden on public health. Although genome-wide and gene-candidate studies have been enabled to identify genetic variants in the complement system associated with AMD pathogenesis, the effect of gene-environment interaction is still under debate. In this review we provide an overview of the role of complement system and its genetic variants in AMD, summarizing the consequences of the interaction between genetic and environmental risk factors on AMD onset, progression, and therapeutic response. Finally, we discuss the perspectives of current evidence in the field of genomics driven personalized medicine and public health.
Annotating Traversable Gaps in Walkable Environments

Autonomous agents typically need a navigation mesh of a 3D virtual environment to allow efficient path planning. This mesh needs as input a continuous representation of the walkable areas. However, the walkable environment (WE), i.e. the parts of the 3D environment that an agent can walk on, may contain gaps. These may be due to the filtering steps performed to compute the WE, because of modelling errors in the 3D model, or simply be part of the geometry of the environment. We provide an algorithm that identifies and fills these gaps. We detect gaps, up to a given distance, between pairs of boundary edges of the walkable environment, and fill them with polygons. We employ a heuristic for choosing which pairs of edges should be connected. We compare our algorithm to Recast [10], a voxel-based method for navigation mesh generation. We find that our method gives more accurate results in many environments: it retains the exact representation of the walkable environment, semantically separates the gaps from the walkable areas, and requires no tweaking of parameters to obtain good results. However, our method is currently slower than Recast, and requires more memory.
Spatial Ensemble Kernel Learning for Scene Classification

Scene recognition is one of the most important tasks in computer vision. Apart from appearance, spatial layout carries the crucial cue for discriminative representation. In this paper, we propose spatial ensemble kernel (SEK) learning, which enables fusion of multi-scale spatial information to achieve compact while discriminative representation of scenes. Based on the spatial pyramid, SEK combines the CNN features in each level of the pyramid in an ensemble and fuse them by kernels. By kernel approximation, we achieve Fourier feature embedding of CNN features in each scale, which establishes a nonlinear layer of the neural network with a cosine activation function. The parameters of the nonlinear layer can be learned jointly in one single optimization framework by supervised learning, which enables compact and discriminative feature representations. We show the effectiveness of the proposed SEK on two recent scene benchmark datasets, i.e., MIT indoor and SUN 397. The propose SEK produces high performance on two datasets which are competitive to state-of-the-art algorithms.
Facial Feature Extraction Using Hierarchical MAX(HMAX) Method

In the digital revolution identifying the attributes of human population has become necessity for social economic benefit distribution, security and surveillance. Age estimation is one of the interesting and challenging research problem from last several years. Estimation of Age is defined as determine particular person age or age group from given face image. Feature extraction is most important focusing area, were pixel level feature, global feature, local feature are extracted from face image. Person's age is determine based on biometric features. In this paper focus is given on feature extraction. We first perform Pre-processing using HSV Color space model and Gaussian filter from given face image then Hierarchical MAX(HMAX) model are used to extract Biologically Inspired features(BIF). This paper compares our feature extraction approach with standard HMAX algorithm for better age estimation. The experimental result shows that our proposed feature extraction method extract more feature and increase response time compared to standard HMAX method.