Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Puncturing Method for Dimming in Visible Light Communication With Polar Codes

In this letter, an adaptive puncturing method on run-length limited (RLL) codes to achieve dimming control is proposed for visible light communication (VLC) with polar codes. Polar codes can be concatenated with RLL codes to provide 50% dimming in the VLC system, and the dimming value not equal to 50% can be achieved by puncturing and compensation symbols. Conventional puncturing should puncture all bits of RLL symbol, but the proposed method can adaptively puncture almost half bits of the RLL symbol, which corresponds to predetermined frozen bits of polar codes. Therefore, the proposed method can puncture fewer bits of RLL symbol and require fewer CSs in comparison to the conventional method. Simulation results show that the proposed method gives a better performance than the referenced methods.
Convolutional Neural Network for ASR

In Automatic speech recognition paradigm has been shifted from statistical model (GMM-HMM) to deep neural network. Among various types of Deep neural networks architecture, CNNs have been most broadly used and considered. There are many advanced features in CNNs like weight sharing, local filters and pooling etc. An ideal pooling method is to be expected to extract only useful information and discards irrelevant details. Pooling is an important component of CNN to be highlighted. Hence, to understand and select the best pooling technique, this paper aims to provide a broad survey on different types of pooling techniques applied in CNNs architecture. This paper will also summarize the improvements of CNNs, including key properties of CNNs, architecture of CNNs.
Development of Multilevel Inverter Topology with Reduced Device Count Using Smart Control Technique

Multilevel inverter (MLI) topologies are popularly used in medium-voltage, high-power applications in recent years. This is because of effective advantages of multilevel inverter such as less Total Harmonic Distortion (THD) in output voltage, higher efficiency, less stress on semiconductor power switches. In this study, new structure of MLI that uses reduced number of controlled switches, power diodes, and DC voltage sources compared to conventional MLI topology has been simulated. Topology-I is capable of producing seven, nine and eleven levels of output voltage with seven switches and topology-II can produce seventeen levels with eight switches only. A comparison of this topology with the conventional topologies has been made in terms of controlled switches, power diodes, and DC voltage sources. Nearest level control technique has been used to generate the switching pulses. Simulation of the topologies has been carried out using MATLAB/Simulink.
Algorithms to facilitate shared decision-making for the management of mild-to-moderate ulcerative colitis

ABSTRACT Introduction: Nonadherence has been a key barrier to the efficacy of medical treatments in ulcerative colitis (UC). Engaging patients in their IBD care via shared decision-making (SDM) to facilitate self-management may improve adherence to therapy. Areas covered: This review aims to summarize the most recent trial evidence from 2012 to 2017 for mild-to-moderate UC in order to develop clinical algorithms that guide SDM to facilitate self-management. A structured literature search via multiple electronic databases was performed using the search terms ‘ulcerative colitis,’ ‘treatment,’ ‘management,’ ‘medication,’ ‘maintenance,’ ‘remission,’ ‘5-ASA,’ and ‘inflammatory bowel disease. Expert commentary: Novel formulations of existing oral and topical medications have expanded the treatment options available for the induction and maintenance therapy for mild-to-moderate UC. Daily dosing of 5-ASA therapy is equivalent to twice daily dosing. The combination therapies of oral plus topical 5-ASA therapy and 5-ASA plus corticosteroid therapy are more effective than monotherapy. Budesonide MMX now plays a role in the management of mild-to-moderate UC. This review collates the evidence on drug efficacy and safety, adherence and tolerability, and noninvasive monitoring of mild-to-moderate UC into SDM-orientated algorithms to facilitate self-management.
Leptin acts in the carotid bodies to increase minute ventilation during wakefulness and sleep and augment the hypoxic ventilatory response

Leptin is a potent respiratory stimulant. A long functional isoform of leptin receptor, LepRb, was detected in the carotid body (CB), a key peripheral hypoxia sensor. However, the effect of leptin on minute ventilation (VE) and the hypoxic ventilatory response (HVR) has not been sufficiently studied. We report that LepRb is present in approximately 74% of the CB glomus cells. Leptin increased carotid sinus nerve activity at baseline and in response to hypoxia in vivo. Subcutaneous infusion of leptin increased VE and HVR in C57BL/6J mice and this effect was abolished by CB denervation. Expression of LepRb in the carotid bodies of LepRb deficient obese db/db mice increased VE during wakefulness and sleep and augmented the HVR. We conclude that leptin acts on LepRb in the CBs to stimulate breathing and HVR, which may protect against sleep disordered breathing in obesity.
Photosynthesis by marine algae produces sound, contributing to the daytime soundscape on coral reefs

We have observed that marine macroalgae produce sound during photosynthesis. The resultant soundscapes correlate with benthic macroalgal cover across shallow Hawaiian coral reefs during the day, despite the presence of other biological noise. Likely ubiquitous but previously overlooked, this source of ambient biological noise in the coastal ocean is driven by local supersaturation of oxygen near the surface of macroalgal filaments, and the resultant formation and release of oxygen-containing bubbles into the water column. During release, relaxation of the bubble to a spherical shape creates a monopole sound source that ‘rings’ at the Minnaert frequency. Many such bubbles create a large, distributed sound source over the sea floor. Reef soundscapes contain vast quantities of biological information, making passive acoustic ecosystem evaluation a tantalizing prospect if the sources are known. Our observations introduce the possibility of a general, volumetrically integrative, noninvasive, rapid and remote technique for evaluating algal abundance and rates of primary productivity in littoral aquatic communities. Increased algal cover is one of the strongest indicators for coral reef ecosystem stress. Visually determining variations in algal abundance is a time-consuming and expensive process. This technique could therefore provide a valuable tool for ecosystem management but also for industrial monitoring of primary production, such as in algae-based biofuel synthesis.
Power/Area-Optimized Fault Tolerance for Safety Critical Applications

Increasing the reliability of a system always comes with a high price in performance/power/area overhead. Enabling error detection and correction features can be obtained by employing different kinds of redundancy including hardware, time, information, software or some combination of them. In many cases the imposed overhead is enormous. Fault tolerance is an important requirement for safety critical applications (e.g., automated driving), but significant power/area overhead is not acceptable. This paper summarizes several strategies and methods how to reduce the introduced overhead, while still providing a respectable level of fault tolerance features. Two main methodologies are discussed: static and dynamic. Static methods address the overhead by performing a static trade-off between the achieved level of fault tolerance and the introduced overhead. Dynamic methods on the other hand are based on the actual application requirement, and are dynamically varying the required overhead to fulfill the safety requirements of the application. This paper summarizes practical examples and results in this field.
Feature binding in short-term memory and long-term learning

In everyday experience, we encounter visual feature combinations. Some combinations are learned to support object recognition, and some are arbitrary and rapidly changing, so are retained briefly to complete ongoing tasks before being updated or forgotten. However, the boundary conditions between temporary retention of fleeting feature combinations and learning of feature bindings are unclear. Logie, Brockmole, and Vandenbroucke demonstrated that 60 repetitions of the same feature bindings for change detection resulted in no learning, but clear learning occurred with cued recall of the feature names. We extended those studies in two new experiments with the same array of colour–shape–location combinations repeated for 120 trials. In Experiment 1, change detection was well above chance from Trial 1, but improved only after 40 to 60 trials for participants who subsequently reported becoming aware of the repetition, and after 100 to 120 trials for participants reporting no awareness. Performance improved rapidly in Experiment 2 when participants reconstructed the array by selecting individual features from sets of colours, shapes, and locations. All participants subsequently reported becoming aware of the repetition. We conclude that change detection involves a visual cache memory that functions from the first trial, and retains feature bindings only for the duration of a trial. In addition, a weak residual episodic memory trace accumulates slowly across repetitions, eventually resulting in learning. Reconstructing feature combinations generates a much stronger episodic memory trace from trial to trial, and so learning is faster with performance supported both by the limited capacity visual cache and learning of the array.
Recovery from apraxic deficits and its neural correlate.

BACKGROUND AND OBJECTIVE Apraxia is a deficit of motor cognition leading to difficulties in actual tool use, imitation of gestures, and pantomiming object use. To date, little data exist regarding the recovery from apraxic deficits after stroke, and no statistical lesion mapping study investigated the neural correlate of recovery from apraxia. Accordingly, we here examined recovery from apraxic deficits, differential associations of apraxia task (imitation vs. pantomime) and effector (bucco-facial vs. limb apraxia) with recovery, and the underlying neural correlates. METHODS We assessed apraxia in 39 patients with left hemisphere (LH) stroke both at admission and approximately 11 days later. Furthermore, we collected clinical imaging data to identify brain regions associated with recovery from apraxic deficits using voxel-based lesion-symptom mapping (VLSM). RESULTS Between the two assessments, a significant recovery from apraxic deficits was observed with a tendency of enhanced recovery of limb compared to bucco-facial apraxia. VLSM analyses revealed that within the lesion pattern initially associated with apraxia, lesions of the left insula were associated with remission of apraxic deficits, whereas lesions to the (inferior) parietal lobe (IPL; supramarginal and angular gyrus) and the superior longitudinal fasciculus (SLF) were associated with persistent apraxic deficits. CONCLUSIONS Data suggest that lesions affecting the core regions (and white matter) of the fronto-parietal praxis network cause more persistent apraxic deficits than lesions affecting other regions (here: the left insula) that also contribute to motor cognition and apraxic deficits.
A Quantitative Approach for Determining Lung Cancer Using CT scan Images

In males the most common reason for the death nowadays is lung cancer. From the past few years computed tomography has gained its popularity in detection of lung cancer. The application of image processing has increased day by day. This results in new inventions in diagnosing disease accurately. Accuracy obtained by radiologist in classification of lung cancer may tend to decrease when dealing with large volume of images. This paper proposes a method for classification of lung cancer based on two categories, four shapes and four margins. This study shows the outcome of applying image processing operation like, preprocessing, segmentation and feature extraction. Proposed work uses the support vector machine classifier as classification technique and proven to achieve high accuracy.
Clinical Feasibility of Gadoxetic Acid–Enhanced Isotropic High-Resolution 3-Dimensional Magnetic Resonance Cholangiography Using an Iterative Denoising Algorithm for Evaluation of the Biliary Anatomy of Living Liver Donors

ObjectivesThe aim of this study was to evaluate the clinical feasibility of gadoxetic acid–enhanced isotropic high-resolution (IHR) 3-dimensional (3D) T1-weighted (T1W) magnetic resonance cholangiography (MRC) using an iterative denoising (ID) algorithm for evaluation of the biliary anatomy of living liver donors in comparison with conventional 3D multislice T2-weighted (T2W) MRC. Materials and MethodsIn this institutional review board–approved retrospective study, a total of 75 living liver donors who underwent conventional 3D multislice T2W-MRC and IHR-3D-T1W-MRC on a 3 T scanner and subsequent right hepatectomy for liver donation were included. Isotropic high-resolution T1W-MRCs were obtained in both axial and coronal planes using the 3D VIBE Dixon sequence and an ID algorithm implemented with wavelet thresholding of 3D complex-valued data of the noise level, g-factor, and k-space filtering. Thereafter, 3 board-certified radiologists independently reviewed the examinations for visibility and sharpness of the bile ducts (BDs), as well as overall image quality on a 5-point scale. For diagnostic performance, anatomic variations of the BD, length of right hepatic duct, and the expected number of BD openings at right hepatectomy were also recorded. As the reference standard, BD variation was determined by surgeons in consensus using intraoperative real-time fluorescent cholangiography. ResultsMean acquisition times of 3D-T2W-MRC and IHR-T1W-MRC were 367 seconds and 17 seconds (P < 0.001), respectively. Compared with 3D-T2W-MRCs, IHR-T1W-MRCs yielded significantly improved visibility and sharpness of all evaluated intrahepatic bile ducts (all Ps < 0.05), and higher overall image quality (P < 0.01). The IHR-T1W-MRCs also demonstrated significantly higher agreement in BD variation (87.6% vs 81.3%, P = 0.03) and expected BD openings (76.9% vs 70.2%, P = 0.006) than 3D-T2W-MRC compared with the reference standard. Interobserver agreement in estimating the length of right hepatic duct, IHR-T1W-MRC showed excellent interobserver agreement (intraclass correlation coefficient, 0.94), whereas 3D-T2W-MRC showed good interobserver agreement (intraclass correlation coefficient, 0.78). ConclusionsIsotropic high-resolution T1W-MRCs with ID provided significantly improved BD image quality and more accurate depiction of the BD anatomy and BD openings at right donor hemihepatetomy than 3D-T2W-MRC.
R2BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification

Over the past decade, keystroke-based pattern recognition techniques, as a forensic tool for behavioral biometrics, have gained increasing attention. Although a number of machine learning-based approaches have been proposed, they are limited in terms of their capability to recognize and profile a set of an individual’s characteristics. In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the randomized radial basis function network, which recognizes and profiles the educational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using the empirical data obtained by recording volunteers’ keystrokes during their daily usage of a computer. Its performance is also compared with other well-referenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on the keystroke dynamics information only.
Change in Food and Beverage Availability and Marketing Following the Introduction of a Healthy Food Financing Initiative–Supported Supermarket

Purpose: The Healthy Food Financing Initiative (HFFI) aims to expand access to healthy foods in low-resourced communities across the United States. This study examined changes in food and beverage availability and marketing in nearby small food stores after the opening of an HFFI-supported supermarket in a predominately low-income and African American community. Design: Natural experiment. Setting: Rockford, Illinois. Participants: A full audit was conducted of the small grocery and limited service stores located in a 1-mile radius around the new supermarket (N = 22) and a 1-square mile area within a nearby demographically matched comparison community (N = 18). Stores were audited in 2015 (1 month preopening) and 2016 (1 year afterward). Measures: Store characteristics, item availability, and interior and exterior promotions/advertisements were examined. Analysis: Difference-in-difference (DID) regression models assessed pre- and postintervention changes in availability and marketing between small food stores in the intervention and comparison communities. Results: The DID regression models indicated no difference between intervention and comparison communities with respect to changes in availability and marketing of all food items with the exception of frozen vegetables which had higher availability postintervention in the comparison community versus intervention (β for interaction term = .67; standard error: 0.33; P = .04). Conclusion: After the opening of the HFFI-supported supermarket, food and beverage availability and marketing in nearby small food stores did not change significantly. However, the wide range of staple foods offered by the supermarket contributed to the expansion of healthy food retail in the intervention community.
Contribution to the Management of Microgrids by the Application of Short Term Photovoltaic Power Forecasting

Currently, smart MICROGRIDs or MICROGRIDs also called mini Smart grids permeate the whole world by their advantages which reside in a high degree of intelligence, autonomy and controllability. Therefore they require effective management within the different architectures of MICROGRIDs. Otherwise, these intelligent MICROGRIDS are made up of several microsources, namely one to several micro-turbines, a mini-hydraulic, one to several generating sets and renewable sources, namely wind and solar photovoltaic. The latter two sources are random and stochastic in nature and conditioned by meteorological parameters and are also trapped in climate changes during the days of the year. Indeed, the variability of meteorological parameters actually impacts the forecasting of the energy produced by natural resources (sun and wind). In short, the forecasting of solar energy becomes crucial to ensure the stability of the network and to allow an optimal unit commitment and an economic distribution. The objective of this study is to contribute to the modeling and improvement of the quality of short-term forecasting of solar photovoltaic energy in MICROGRIDs through the application of classification techniques. The classification technique envisaged for this work is the KNN model. To analyze results we compare the last technique with the persistence model. In order to test the performance of the different forecasting models, we used nMRE and nRMSE to analyze the performance aid criteria.
Reliability Improvements for Multiprocessor Systems by Health-Aware Task Scheduling

The probability that a particular device is operational for a given duration, or reliability, is a dependability attribute and key metric for systems in critical applications. For example, systems for long-term autonomous exploration missions have to be operational during their complete mission. Other critical applications like banking, medical automotive or aerospace face similar reliability requirements that are only met by dependable systems. Traditional dependable systems, compared to their non-dependable counterparts, have three key issues: They are more expensive, consume more power, and provide less performance.
Efficient Message Authentication Scheme with Conditional Privacy-Preserving and Signature Aggregation for Vehicular Cloud Network

Vehicular cloud network (VCN) is deemed as the most promising platform for providing transportation safety, road optimization, and valued-added application services. Because VCN is of distinguishing feature with super-large scale and unstable communication, it is a challenging task to study efficient authentication scheme for VCN without losing security and conditional privacy-preserving. To meet the challenge, a new efficient message authentication scheme is proposed in this paper. A batch message verification and signature aggregation are included in the proposed scheme to improve the authentication efficiency and decrease the communication cost. Compared with the similar conditional privacy-preserving authentication schemes, the proposed scheme has superior performance in computation and communication cost. Simulation analysis further proves that the proposed scheme has better advantages in reducing the verification loss rate and message delay in the application of VCN.
HealthLog Monitor: A Flexible System-Monitoring Linux Service

Error monitoring is a critical procedure for most computing systems, varying from HPC to embedded systems domains. Several generic architectures have been proposed and employed in modern processors, offering the capability of hardware-level error detection. This critical information is required to isolate and/or mitigate failures. However, research has revealed many cases where indications of upcoming failures can be identified early and before the actual fail occurrence, known as symptoms. Such cases become more frequent as technology trends try to exploit the conservative worst-case voltage guardbands and push computing systems towards more aggressive and often hazardous regions. In this paper we present HealthLog monitor, a flexible system monitoring service that offers a generic abstraction layer to combine both error and symptom monitoring. HealthLog is capable of monitoring hardware measurements (performance, sensor and errors) as well as external health-related data, allowing combined symptom description and reaction features supported by an API. The scope of the monitor is to offer a universal standard for error reporting and system monitoring mechanisms in all system layers. The current version of HealthLog was developed and tested on AppliedMicro’s X-Gene 2 micro-server, but it is a cross-platform solution as it does not depend on a specific architecture. This work demonstrates how platform events, software metrics and external peripheral mechanisms can be combined to deliver early warnings of upcoming failures and trigger evading reactions.