Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Condition Training of Denoising Autoencoder by Augmenting Simulated Reverberant Speech Data

When speech recognition task is done by an automatic speech recognition system (ASR), it always has to process the noise and reverberation mixed with the speech in natural environment, which is a challenge. Recently, DNN-based ASR systems are showing better performance than other methods. Also, increasing training data for the DNN has been proven effective. But in real world, there can be many exceptions when it comes to collect data for training the DNN. In that case, if the characteristics of reverberant data for which the performance increases can be found, it will be a great help in the field of speech recognition. In this research, we analyze the effect of artificial reverberation condition on the data those are used for training DNN-based ASR front-end. There are possibilities to enhance the dataset by using suitable artificial data created by adding simulated reverberation to clean speech instead of using just real noisy recordings for training dataset. In case of simulated reverberation, the range of room impulse response (RIR) and direct-to-reverberant energy ratio (DRR) play important roles in training the system affecting the accuracy of recognition. Our analysis in this paper discusses the way of finding optimum RIR conditions to simulate the data without increasing training data amount.
A decision analysis comparing 3 active surveillance protocols for the treatment of patients with low‐risk prostate cancer

Active surveillance (AS) is a viable management option for approximately 50% of men who are newly diagnosed with prostate cancer. To the authors’ knowledge, no direct comparisons between the different variants of AS protocols have been conducted to date. The authors developed a microsimulation decision model to evaluate which of 3 alternative AS protocols is optimal for men with low‐risk prostate cancer, and compared each of these with immediate treatment.
Design of a Closed Loop Direct Torque Control for an Induction Motor Drive

This paper investigates three – phase induction motor dynamics based on direct torque control (DTC). The DTC scheme is less complex because coordinate transform is not required, voltage or current decoupling network is not required and the torque response time is faster than that of vector control. Simulation (using MATLAB/Simulink) has been done to verify the feasibility of this control.
Throughput Prediction Using Recurrent Neural Network Model

To ensure good quality of experience for user when transmitting video content, throughput prediction can contribute to the selection of proper bitrate. In this paper, we propose a throughput prediction method with recurrent neural network (RNN) model. Experiments are conducted to evaluate the methods, and the results indicate that proposed method can decrease the prediction error by a maximum of 29.39% compared with traditional methods.
Natural Killer Cell Education Is Associated With a Distinct Glycolytic Profile

NK cells expressing self-inhibitory receptors display increased functionality compared to NK cells lacking those receptors. The acquisition of functional competence in these particular NK-cell subsets is termed education. Little is known about the underlying mechanisms that lead to the functional differences between educated and uneducated NK cells. An increasing number of studies suggest that cellular metabolism is a determinant of immune cell functions. Thus, alterations in cellular metabolic pathways may play a role in the process of NK-cell education. Here, we compared the glycolytic profile of educated and uneducated primary human NK cells. KIR-educated NK cells showed significantly increased expression levels of the glucose transporter Glut1 in comparison to NKG2A-educated or uneducated NK cells with and without exposure to target cells. Subsequently, the metabolic profile of NK-cell subsets was determined using a Seahorse XF Analyzer. Educated NK cells displayed significantly higher rates of cellular glycolysis than uneducated NK cells even in a resting state. Our results indicate that educated and uneducated NK cells reside in different metabolic states prior to activation. These differences in the ability to utilize glucose may represent an underlying mechanism for the superior functionality of educated NK cells expressing self-inhibitory receptors.
Power Optimization Based on Successive Convex Approximation in Smart Grid Communications

In this paper, we introduce the successive convex approximation algorithm (SCAA)to solve the non-linear and non-convex power optimization problem in smart grid communications. The SCAA can effectively converge to the convex solution of power optimization and reduce the co-tier interferences among demand-side communication networks. Simulation results demonstrate that the SCAA has a fast convergence speed and improve the transmission rate of the whole network.
A causal role for right temporo-parietal junction in signaling moral conflict

The right temporo-parietal junction (rTPJ) has been proposed to play a key role in guiding human altruistic behavior, but its precise functional contribution to altruism remains unclear. We aimed to disentangle three possible functions of the rTPJ for human altruism, namely: implementing the motivation to help, signaling conflicts between moral and material values, or representing social reputation concerns. Our novel donation-decision task consisted of decisions requiring trade-offs of either positive moral values and monetary cost when donating to a good cause, or negative moral values and monetary benefits when sending money to a bad cause. Disrupting the rTPJ using transcranial magnetic stimulation did not change the general motivation to give or to react to social reputation cues, but specifically reduced the behavioral impact of moral-material conflicts. These findings reveal that signaling moral-material conflict is a core rTPJ mechanism that may contribute to a variety of human moral behaviors.
Grid Supporting Inverter for Transient Load Relief in Islanded Microgrids

In islanded Microgrids the load step capability of gas engine driven Combined Heat and Power plants may be exceeded in case of a severe load step. As a result, the engine could be damaged or the generator protection system could be triggered causing a loss of generation. Such a limited robustness of system operation might render backup power supply islands useless under real world conditions. This paper presents an approach for integrating a grid supporting inverter of a battery system into a Microgrid. An existing transient droop approach is modified and then applied to achieve an adjustable transient load relief for a Combined Heat and Power plant. In case of a severe load step it can be demonstrated via simulations that the inverter is able to reduce maximum overshoot and settling time and thus to enhance the Microgrid’s robustness.
Severity level detection of diabetic retinopathy using ELM classifier

An eye disease which destroys the normal vision ability of diabetic patients is known as diabetic retinopathy. Early diagnosis of this disease is necessary because, it is severe in the later stages. The presence exudates, micro aneurysms(MAs) and hemorrhages are the first clinical symptoms of this disease. Exudates are red dots formed by swelling of the weak part of the capillary wall. The detection of exudate in retinal fundus images and the severity level is an important task in applications such as diabetic retinopathy screening and early treatment. Diabetic retinopathy is identified by pouring chemical solution to the eye and then capturing the dilated image of the patient's eye. This process causes irritation to the patients. This paper proposes a method to find the severity level of diabetic retinopathy. It uses non-dilated retinal fundus image to help ophthalmologists diagnose the disease. The exudates from the low contrast images are detected. A neighborhood based segmentation technique is used for localizing the exudates from the images. A support vector machine (SVM) and Extreme learning Machine (ELM) are used as the classifiers. The method assess the severity of the disease. The average classification accuracy for the ELM is 94.76%.
Power Optimization of a 0.5V 0.286-to-18MHz ADPLL in 65nm CMOS Process

A clock generator is an important part of most systems as it is used for synchronization and data processing. For low-power operations, an all-digital phase-locked loop (ADPLL) is a suitable implementation of a clock generator for wireless sensing applications. Design decisions in different levels of abstraction were done to further reduce the power of an implemented ADPLL. It was shown that its power consumption can be minimized by at most 70%. Moreover, the output frequency of the ADPLL ranges from 0.286 - 18MHz with a power consumption of 4. 606µW at 18MHz.
The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

Congestive heart failure (CHF) is a cardiovascular disease associated with autonomic dysfunction, where sympathovagal imbalance was reported in many studies using heart rate variability (HRV). To learn more about the dynamic interaction in the autonomic nervous system (ANS), we explored the directed interaction between the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) with the help of transfer entropy (TE). This article included 24-h RR interval signals of 54 healthy subjects (31 males and 23 females, 61.38 ± 11.63 years old) and 44 CHF subjects (8 males and 2 females, 19 subjects’ gender were unknown, 55.51 ± 11.44 years old, 4 in class I, 8 in class II and 32 in class III~IV, according to the New York Heart Association Function Classification), obtained from the PhysioNet database and then segmented into 5-min non-overlapping epochs using cubic spline interpolation. For each segment in the normal group and CHF group, frequency-domain features included low-frequency (LF) power, high-frequency (HF) power and LF/HF ratio were extracted as classical estimators of autonomic activity. In the nonlinear domain, TE between LF and HF were calculated to quantify the information exchanging between SNS and PNS. Compared with the normal group, an extreme decrease in LF/HF ratio (p = 0.000) and extreme increases in both TE(LF→HF) (p = 0.000) and TE(HF→LF) (p = 0.000) in the CHF group were observed. Moreover, both in normal and CHF groups, TE(LF→HF) was a lot greater than TE(HF→LF) (p = 0.000), revealing that TE was able to distinguish the difference in the amount of directed information transfer among ANS. Extracted features were further applied in discriminating CHF using IBM SPSS Statistics discriminant analysis. The combination of the LF/HF ratio, TE(LF→HF) and TE(HF→LF) reached the highest screening accuracy (83.7%). Our results suggested that TE could serve as a complement to traditional index LF/HF in CHF screening.
Motion Recognition System for Worker Safety in Manufacturing Work Cell

With a paradigm shift of the manufacturing process into an active module process system, an increasing demand for a lightweight and safe manufacturing environment has developed. To share a workspace between a worker and robot in a manufacturing work cell, technical methods ensuring the safety of the worker must be provided. In this paper, we propose a vision-based worker safety system for enabling a safe operation even when the worker of a manufacturing process shares a workspace with a robot. The proposed system monitors the position of the worker based on a vision system and supports the worker safety by avoiding collisions with the robot according to the workspace classification. The system also recognizes dangerous movements of the worker, and provides safety services through interactions with the manufacturing robot by matching predefined motion models.
Dynamic Guardband Selection: Thermal-Aware Optimization for Unreliable Multi-Core Systems

Circuit aging has become the major reliability concern in current and upcoming technology nodes. For instance, Bias Temperature Instability (BTI) leads to an increase in the threshold voltage of a transistor. That, in turn, may prolong the critical path delay of the processor and eventually may lead to timing errors. In order to avoid aging-induced timing errors, designers employ guardbands either with respect to voltage or frequency. State-of-the-art techniques determine a guardband type at the circuit level at design time irrespective from the running workload at the system level. Our investigation revealed that generated temperatures by a running workload have the potential to play a key role in determining the appropriate guardband type with respect to system performance. Therefore, we propose a paradigm shift in designing guardbands: to select the guardband types on-the-fly with respect to the workload-induced temperatures aiming at optimizing for performance under temperature and reliability constraints. Moreover, different guardband types for different cores can be selected simultaneously when multiple applications with diverse properties suggest this to be useful. Our dynamic guardband selection allows for a higher performance compared to techniques that employ a fixed (at design time) guardband type throughout.
Enhancing Performance of Lexical Entailment Recognition for Vietnamese based on Exploiting Lexical Structure Features

The lexical entailment recognition problem aims to identify the is-a relation between words. The problem has recently been receiving research attention in the natural language processing field. In this study, we propose a novel method (VLER) for this problem on Vietnamese. For this purpose, we first exploit such lexical structure information of words as a feature, then combine this feature with vectors representation of words such as a unique feature for recognizing the relation. Moreover, we applied a number of methods based on word embedding and supervised learning, experimental results showed that our method achieves the best performance in the hypernymy detection task than other methods in terms of accuracy.
Button Filtering for Converting Image to Coordinates of Swarming Drones

In this paper, we propose Button Filtering which is a method of non-linear filtering that creates a set of points from an image. This set of points can be used as coordinates of drones. Button filtering is considered nonlinear filtering because it is not applied as convolution, which is used on typical filtering. Nevertheless, it is easy and fast owing to use of a simple button mask, and it has the advantage of immense extendibility. To date, it is only available for the 8-bit grayscale image, which is generally the edge of the original image or masked image. However, it can be applied to a color image, two-dimensional images in a 3D space, and even three-dimensional objects. In this paper, we present the use of button filtering for any object in any image. To this end, we must perform pre-processing before button filtering. We thus present six methods that can handle almost every image type and identify the specific methods that are effective for certain image types.
Persistent arterial wall inflammation in patients with elevated lipoprotein(a) despite strong low-density lipoprotein cholesterol reduction by proprotein convertase subtilisin/kexin type 9 antibody treatment.

Aims Subjects with lipoprotein(a) [Lp(a)] elevation have increased arterial wall inflammation and cardiovascular risk. In patients at increased cardiovascular risk, arterial wall inflammation is reduced following lipid-lowering therapy by statin treatment or lipoprotein apheresis. However, it is unknown whether lipid-lowering treatment in elevated Lp(a) subjects alters arterial wall inflammation. We evaluated whether evolocumab, which lowers both low-density lipoprotein cholesterol (LDL-C) and Lp(a), attenuates arterial wall inflammation in patients with elevated Lp(a). Methods and results In this multicentre, randomized, double-blind, placebo-controlled study, 129 patients {median [interquartile range (IQR)]: age 60.0 [54.0-67.0] years, Lp(a) 200.0 [155.5-301.5] nmol/L [80.0 (62.5-121.0) mg/dL]; mean [standard deviation (SD)] LDL-C 3.7 [1.0] mmol/L [144.0 (39.7) mg/dL]; National Cholesterol Education Program high risk, 25.6%} were randomized to monthly subcutaneous evolocumab 420 mg or placebo. Compared with placebo, evolocumab reduced LDL-C by 60.7% [95% confidence interval (CI) 65.8-55.5] and Lp(a) by 13.9% (95% CI 19.3-8.5). Among evolocumab-treated patients, the Week 16 mean (SD) LDL-C level was 1.6 (0.7) mmol/L [60.1 (28.1) mg/dL], and the median (IQR) Lp(a) level was 188.0 (140.0-268.0) nmol/L [75.2 (56.0-107.2) mg/dL]. Arterial wall inflammation [most diseased segment target-to-background ratio (MDS TBR)] in the index vessel (left carotid, right carotid, or thoracic aorta) was assessed by 18F-fluoro-deoxyglucose positron-emission tomography/computed tomography. Week 16 index vessel MDS TBR was not significantly altered with evolocumab (-8.3%) vs. placebo (-5.3%) [treatment difference -3.0% (95% CI -7.4% to 1.4%); P = 0.18]. Conclusion Evolocumab treatment in patients with median baseline Lp(a) 200.0 nmol/L led to a large reduction in LDL-C and a small reduction in Lp(a), resulting in persistent elevated Lp(a) levels. The latter may have contributed to the unaltered arterial wall inflammation.
Zigzagging Strategies for Temporal Induction

Model Checking is at the heart of formal methods for software and hardware verification. In this area of active research, Bounded Model Checking (BMC) and k-induction have reached very impressive results, especially when both methods are working together. They are based on a common approach that unrolls the transition relation, but each method serves a different purpose in practice. BMC is usually used for bugs findings, while k-induction aims at building inductive invariants. The ZigZag approach, proposed 15 years ago, takes benefit from both strategies by successively calling each one of them, while trying to share a lot of information between calls thanks to the mechanism of SAT clauses learning. Despite the practical importance of the ZigZag algorithm, it was mainly used forwardly until last year. The transition relation was unrolled by increasing depths only. However, as stated by the authors of ZigZag themselves, it was possible to consider the ZigZag approach backwardly. The experimental study of backward zigzag performances was only proposed one year ago. In this paper, we propose to extend the idea of the ZigZag algorithm by allowing to unroll the transitions from the middle. This has the nice property of allowing the SAT solver to keep learnt clauses that are both close to the initial state and to the bad state in the search. Our experimental study however shows that the best option for ZigZag is still to perform it backward, as stated in a previous work. However, we also show that our hybrid approach offers the same performances as forward ZigZag, while allowing more flexible strategies to be developed in the future, for example by choosing the right transition to expand.
High‐permittivity pad design tool for 7T neuroimaging and 3T body imaging

High‐permittivity materials in the form of flexible “dielectric pads” have proved very useful for addressing RF inhomogeneities in high field MRI systems. Finding the optimal design of such pads is, however, a tedious task, reducing the impact of this technique. We present an easy‐to‐use software tool which allows researchers and clinicians to design dielectric pads efficiently on standard computer systems, for 7T neuroimaging and 3T body imaging applications.
Optimal Sensor Formation for 3D Cooperative Localization of AUVs Using Time Difference of Arrival (TDOA) Method

The cooperative localization of submerged autonomous underwater vehicles (AUVs) using the Time Difference of Arrival (TDOA) measurements of surface AUV sensors is an effective method for many applications of AUVs. Proper positioning of the sensors to maximize the observability of the AUVs is very critical for cooperative localization. In this paper, a novel method for obtaining the optimal formation of sensor AUVs has been presented for the three-dimensional (3D) cooperative localization of targets using the TDOA technique. An evaluation function for estimating the optimal formation has been derived based on Fisher Information Matrix (FIM) theory for a single target as well as multiple-target cooperative localization systems. An iterative stepping algorithm has been followed to solve the evaluation function and obtain the optimal positions of the sensors. The algorithm ensured that the computation complexity should remain limited, even when the number of sensor AUVs is increased. Various simulation examples are then presented to calculate the optimal formation for different systems/situations. The effect of the position of the reference sensor and operating depth of the target AUVs on the optimal formation of the sensors has also been studied, and conclusions are drawn. For implementation of the proposed method for more practical scenarios, a simulation example is also presented for cases when the target’s position is only known with uncertainty.
Inhospital Exercise Training in Children With Cancer: Does It Work for All?

Purpose: Physical exercise training might counteract the weakening effects of both pediatric cancer and anti-cancer treatment. We aimed to analyze the prevalence of “responders” and “non-responders” to inhospital exercise training in children with cancer and to identify the factors that could influence responsiveness, which might help personalize exercise interventions for this patient population. Methods: We performed an ancillary analysis of the randomized controlled trial “Physical activity in Pediatric Cancer” (NCT01645436), in which 49 children with solid tumors were allocated to an inhospital exercise intervention or control group. The present study focused on the children in the former group (n = 24, 10 ± 4 years), who performed 3 weekly training sessions (aerobic + strength exercises). The intervention lasted 19 ± 8 weeks (i.e., from the start to the end of neoadjuvant chemotherapy treatment). A responder-vs-non-responder analysis was performed for physical capacity-related endpoints (five-repetition maximum strength, functional mobility tests, and cardiorespiratory fitness [CRF]). Only those participants showing improvements in a given test of a magnitude greater than both the random error and the threshold for clinically meaningful changes were considered responders. Results: Most participants improved their performance in the strength tests, with 80, 88, and 93% of total showing a positive response for seated bench press, lateral row, and leg press, respectively (p < 0.001). No significant improvements were observed for the functional mobility tests or CRF (p > 0.05, rate of responsiveness ≤ 50%). No differences between responders and non-responders were observed for sex, age, type of cancer, or treatment (i.e., including or not anthracyclines/radiotherapy). However, significant differences (p < 0.05) were observed between responders and non-responders for baseline performance in all the tests, and a significant (p < 0.05) inverse relationship was found between baseline performance and relative improvement for most endpoints. Conclusions: Although most children improved their muscle strength after the exercise intervention, a considerable individual variability was observed for the training responsiveness of functional mobility and CRF. A lower baseline performance was associated with a higher responsiveness for all the study endpoints, with the fittest children at the start of treatment showing the lowest responses. Efforts to individualize exercise prescription are needed to maximize responsiveness in pediatric cancer patients.