Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-Resistivity ZnO Film-Based Photoconductive X-Ray Detector

High-resistivity ZnO film-based photoconductive detectors have been fabricated on glass substrate by the magnetron sputtering growth method, and tested as X-ray detectors for the first time in this letter. The devices exhibited a low dark current <inline-formula> <tex-math notation="LaTeX">$\sim ~100$ </tex-math></inline-formula> pA at 40 V bias and a fast transient reproducible response to X-ray illumination (generated by bremsstrahlung with a tungsten target with tube voltage 30 kV) <inline-formula> <tex-math notation="LaTeX">$\sim ~200$ </tex-math></inline-formula> ms, which was mainly attributed to the high resistivity <inline-formula> <tex-math notation="LaTeX">$\sim ~10^{9} \Omega $ </tex-math></inline-formula> cm of the ZnO film deposited in the oxygen atmosphere. The high resistivity was due to the zinc vacancy defects’ compensation with the n-type defects in ZnO film as revealed by photoluminescence spectra. Furthermore, the detector achieved an X-ray pulse detection, and the rise time and full width at half maximum of the outputs signal were <inline-formula> <tex-math notation="LaTeX">$139~\mu \text{s}$ </tex-math></inline-formula> and 0.9 ms at 200 V bias, respectively. Associated with material characterizations, it was demonstrated that the high-resistivity ZnO film detector had the potential to be promising for fast X-ray detection application.
Contribution of Stereopsis and Motion Parallax to Fear Responses in the Pit Room Environment

Virtual reality, in contrast to visual stimulation on computer screens, is characterized by the illusion of presence. Stereopsis and motion parallax are two of the main perceptual factors provided by head mounted displays to create a sense of depth in virtual environments. However, the relative contribution of stereopsis and motion parallax to create the sense of presence is not clear and existing results are somewhat controversial. Here, we study the contribution of stereopsis and motion parallax using the classic pit room paradigm. Participants are required to cross a deep abyss between two platforms on a narrow plank under three experimental conditions. Participants responded to a presence questionnaire after each condition and their electrodermal activity (EDA) was recorded as a measure of stress and anxiety in a threatening situation. The EDA results demonstrated the importance of motion parallax over stereopsis, however, the questionnaire scores were not different among three conditions.
Teenagers need to aware their personal responsibility in internet society: our approach in information entrance course

The information literacy that the teenagers should have is widely concerned nowadays. We have been teaching a course entitled Introduction to Computer Science and computer Ethics that is taken by freshmen from computer science, electronic engineering and software engineering. There were more than 400 students in our course annually. The students were divided into groups to participate the in-class discussion. We used to focus on computer professional code of practice in last years. While in the fall semester 2017, we paid more attention on what computer and internet have bring us, and what should we do when we are facing the situations nobody had met before. There were 4 sessions, respectively great changes in society made by computer technology and internet, influences in our traditional culture, responsibility of enterprise in information age, and the artificial intelligent ethics problems. Most topics include new technology or idea. We got over 300 questionnaires after the course finished. From which we found that, compare with the former students, more moral topics have been closely followed. Nowadays, all society members are the contributors of the internet, whatever their major are. Our approach can be spread to similar information entrance course easily.
A bioresponsive hydrogel tuned to chondrogenesis of human mesenchymal stem cells

Cartilage tissue engineering aims to replace damaged or diseased tissue with a functional regenerate that restores joint function. Scaffolds are used to deliver cells and facilitate tissue development, but they can also interfere with the structural assembly of the cartilage matrix. Biodegradable scaffolds have been proposed as a means to improve matrix deposition and the biomechanical properties of neocartilage. The challenge is designing scaffolds with appropriate degradation rates, ideally such that scaffold degradation is proportional to matrix deposition. In this study, we developed a bioresponsive hydrogel with cell‐mediated degradation aligned to the chondrogenic differentiation of human mesenchy‐mal stem cells (hMSCs). We identified matrix metallopro‐teinase 7 (MMP7) as an enzyme with a temporal expression pattern that corresponded with cartilage development. By embedding MMP7 peptide substrates within a poly(ethylene glycol) diacrylate backbone, we built MMP7‐sensitive hydrogels with distinct degradation rates. When MMP7‐sensitive scaffolds were compared with nondegradable scaffolds in vitro, photoencapsulated hMSCs produced neocartilage constructs with more extensive collagenous matrices, as demonstrated through immunohistochemistry and biochemical quantification of matrix molecules. Furthermore, these changes translated into an increased dynamic compressive modulus. This work presents a practical strategy for designing biomaterials uniquely tuned to individual biological processes. —Bahney, C. S. , Hsu, C. ‐W. , Yoo, J. U. , West, J. L. , Johnstone, B. A bioresponsive hydrogel tuned to chondrogenesis of human mesenchymal stem cells. FASEB J. 25, 1486–1496 (2011). www.fasebj. org
Pedestrian Detection Using an Extended Fast RCNN based on a Secure Margin in RoI Feature Maps

Pedestrian Detection based on Deep Convolutional Neural Network (DCNN) has recently gained a great deal of attention. Most of the proposed CNN based methods train networks employing either of the well-known Region-based CNN (RCNN) or Fast Region-based CNN (FRCNN) approaches. In this paper, we present a novel method to train Deep CNN. This method is based on an extended and improved FRCNN for pedestrian detection. It performs both classification and bounding-box regression more accurately. The proposed approach takes the advantage of a Secure Margin in Region of Interest (SM-RoI) to create multi-RoIs. Then based on some criteria, it chooses one of the RoIs with the highest score. The bounding-box extracted from the proposed FRCNN-SM approach is more effective than that of FRCNN approach in fitting and covering pedestrian. Evaluated on Caltech dataset, our proposed approach detects pedestrian more accurately than RCNN and FRCNN approaches.
No-Reference Image Quality Assessment: An Attention Driven Approach

In this paper, we tackle no-reference image quality assessment (NR-IQA), which aims to predict the perceptual quality of a distorted image without referencing its pristine-quality counterpart. Inspired by the free-energy principle, we assume that, while perceiving a distorted image, the human visual system (HVS) tends to predict the pristine image then estimates the perceptual quality based on the distorted-restored pair. Furthermore, the perceptual quality depends heavily on the way how human beings attend to distorted images, namely, the cooperation of foveal vision and the eye movement mechanism. Inspired by these properties of the HVS, given the distorted-restored pair, we implement an attention-driven NR-IQA method with reinforcement learning (RL). The model learns a policy to attend to several regions parallelly. The observations of the fixation regions are aggregated in a weighted average way, which is inspired by the robust averaging strategy. For policy learning, the rewards are derived from two tasks—classifying the distortion type and estimating the perceptual score. The goal of policy learning is to maximize the expectation of the accumulated rewards. Extensive experiments on LIVE, TID2008, TID2013 and CSIQ demonstrate the superiority of our methods.
Transforming Query Sequences for High-Throughput B+ Tree Processing on Many-Core Processors

The throughput of B+ tree query processing is critical to many databases, file systems, and cloud applications. Based on bulk synchronous parallel (BSP), latch-free B+ tree query processing has shown promise by processing queries in small batches and avoiding the use of locks. As the number of cores on CPUs increases, it becomes possible to process larger batches in parallel without adding any extra delays. In this work, we argue that as the batch size increases, there will be more optimization opportunities exposed beyond parallelism, especially when the query distributions are highly skewed. These include the opportunities of avoiding the evaluations of a large ratio of redundant or unnecessary queries. To rigorously exploit the new opportunities, this work introduces a query sequence analysis and transformation framework – QTrans. QTrans can systematically reason about the redundancies at a deep level and automatically remove them from the query sequence. QTrans has interesting resemblances with the classic data-flow analysis and transformation that have been widely used in compilers. To confirm its benefits, this work integrates QTrans into an existing BSP-based B+ tree query processing system, PALM tree, to automatically eliminate redundant and unnecessary queries1. Evaluation shows that, by transforming the query sequence, QTrans can substantially improve the throughput of query processing on both real-world and synthesized datasets, up to 16X.1Artifact available at: https://doi.org/10.5281/zenodo.1486393
Switchable Abnormal THz Wave Reflector Based on Molybdenum Disulfide (MoS2)

Recently Controlling the electromagnetic wave, especially in terahertz range is highly demanded due to the development of practical devices and application systems. In this paper, we propose a scheme to design tunable abnormal reflection structure consisting of molybdenum disulfide (MoS2) array on a grounded SiO2 and a gold layer beneath them to reflect the terahertz wave. In this structure, MoS2 can be controlled by intrinsic carrier density or voltage bias, to control the electromagnetic reflected beam at terahertz frequency. By adjusting the geometric dimensions of the MoS2 elements, the reflection phase can cover the range of 0–2π with linear phase shift, thus abrupt phase shifts can be introduced to design the reflected wavefront. Furthermore, the reflective phase gradient over the structure can be switched by controlling the intrinsic carrier density or voltage bias of the MoS2, hence reflected beam directions can be switched dynamically. The proposed scheme will bring much freedom in the design of beam manipulation devices and may be applied to terahertz radiation control. Numerical simulations verify the Snell’s law generalized at working frequency and far field scattering illustrate the abnormal reflection.
Simultaneous PET Image Reconstruction and Feature Extraction Method using Non-negative, Smooth, and Sparse Matrix Factorization

Positron emission tomography (PET) is an important imaging technique to visualize a number of functions in the brain or human body. For reconstructing PET images from the sinogram data, an inverse problem has to be solved using numerical optimizations such as expectation-maximization (EM)-based methods. However, the standard EM method suffers from measurement noise added in the sinogram data. In this paper, we propose a new simultaneous PET image reconstruction and parts extraction method using constrained non-negative matrix factorization. In contrast that the many existing methods reconstruct a single PET image independently, we reconstruct the time-series of PET images simultaneously from the time-series of sinograms using non-negative matrix factorization. Furthermore, we impose the smoothness constraint for the temporal feature, and the exclusive LASSO-based sparseness constraint for the spatial feature for robust image reconstruction and physically meaningful feature extraction.
Genome‐wide gene expression profiling and a forward genetic screen show that differential expression of the sodium ion transporter Ena21 contributes to the differential tolerance of Candida albicans and Candida dubliniensis to osmotic stress

Candida albicans is more pathogenic than Candida dubliniensis. However, this disparity in virulence is surprising given the high level of sequence conservation and the wide range of phenotypic traits shared by these two species. Increased sensitivity to environmental stresses has been suggested to be a possible contributory factor to the lower virulence of C. dubliniensis. In this study, we investigated, in the first comparison of C. albicans and C. dubliniensis by transcriptional profiling, global gene expression in each species when grown under conditions in which the two species exhibit differential stress tolerance. The profiles revealed similar core responses to stresses in both species, but differences in the amplitude of the general transcriptional responses to thermal, salt and oxidative stress. Differences in the regulation of specific stress genes were observed between the two species. In particular, ENA21, encoding a sodium ion transporter, was strongly induced in C. albicans but not in C. dubliniensis. In addition, ENA21 was identified in a forward genetic screen for C. albicans genomic sequences that increase salt tolerance in C. dubliniensis. Introduction of a single copy of CaENA21 was subsequently shown to be sufficient to confer salt tolerance upon C. dubliniensis.
Singing Voice Conversion Using Posted Waveform Data on Music Social Media

This paper proposes a method of selecting training data for many-to-one singing voice conversion (VC) from waveform data on the social media music app “nana. ” On this social media app, users can share sounds such as speaking, singing, and instrumental music recorded by their smartphones. The number of hours of accumulated waveform data has exceeded one million, and it is regarded as “big data. ” It is widely known that big data can create huge values by advanced deep learning technology. A lot of post data of multiple users having sung the same song is contained in nana's database. This data is considered suitable training data for VC. This is because VC frameworks based on statistical approaches often require parallel data sets that consist of pairs of waveform data of source and target singers who sing the same phrases. The proposed method can compose parallel data sets that can be used for many-to-one statistical VCs from nana's database by extracting frames that have small differences in the timing of utterances, based on the results of dynamic programming (DP) matching. Experimental results indicate that a system that uses training data composed by our method can convert acoustic features more accurately than a system that does not use the method.
Sound Source Localization Using Time Differences of Arrival; Euclidean Distance Matrices Based Approach

In the source localization problem, time differences of arrival (TDOA) and intensity level differences (ILD) of microphones can be employed to estimate the source location. Due to existing additive noise in real applications the ILD measurement provides less reliable information compared to the TDOA. Therefore, this study is focused on developing algorithms employing the TDOA information only. In the past studies, TDOA were used mostly for estimation of direction of the arrival (DOA) parameter. To find the source location from TDOA of different microphones, the intersection of several equations must be calculated which this solving process requires complex numerical analysis. The solving processes, which generally ignore the noise existence, are not robust to noise and might not converge to the true answer in real-world applications. This paper tackles the source localization problem by converting the numerical analysis approach to an iterative minimization one in order to improve localization accuracy in noisy conditions. The performance of the proposed iterative minimization algorithm is seen to be sensitive to the initial values. To address this issue, another algorithm, based on Euclidean Distance theory, is developed to obtain stable and accurate results. The proposed framework works properly in different SNR conditions. The results show that the proposed methods are more accurate than the existing numerical analysis based methods in different noisy conditions even in very low SNR conditions.
An asymptotic solution to KIJIMA’S equation with Weibull-type distributions

The Kijima maintenance model based on the virtual age of components of repairable systems, is one of the most important models in the reliability theory. However, due to the complexity of the resulting integral equation, it has not yet been possible to obtain an adequate expression for calculating the hazard rate for wide class of distributions. Previously, an asymptotic solution of Kijima’s equation has been obtained only for a reduced number of distribution functions, that are not appropriate for describing the system aging. In this paper, Kijima’s equation is asymptotically solved for the Weibull-type distributions and an arbitrary value of the so-called depth of repair $0 \lt a \lt 1$. Based on this solution, the optimal period of equipment replacement and respective optimal repair-cost, are estimated through minimizing the cost rate. Several numerical examples are presented in order to show the efficiency of the obtained asymptotic solution.
An Optimization-Driven Incremental Inline Substitution Algorithm for Just-in-Time Compilers

Inlining is one of the most important compiler optimizations. It reduces call overheads and widens the scope of other optimizations. But, inlining is somewhat of a black art of an optimizing compiler, and was characterized as a computationally intractable problem. Intricate heuristics, tuned during countless hours of compiler engineering, are often at the core of an inliner implementation. And despite decades of research, well- established inlining heuristics are still missing. In this paper, we describe a novel inlining algorithm for JIT compilers that incrementally explores a program’s call graph, and alternates between inlining and optimizations. We devise three novel heuristics that guide our inliner: adaptive decision thresholds, callsite clustering, and deep inlining trials. We implement the algorithm inside Graal, a dynamic JIT compiler for the HotSpot JVM. We evaluate our algorithm on a set of industry-standard benchmarks, including Java DaCapo, Scalabench, Spark-Perf, STMBench7 and other benchmarks, and we conclude that it significantly improves performance, surpassing state-of-the-art inlining approaches with speedups ranging from 5% up to $3\times. $
Handling OOV Words in NMT Using Unsupervised Bilingual Embedding

Neural machine translation has recently become the premier approach in Machine Translation however, it still has some unsolved issues. In this paper we have focused on handling the out-of-vocabulary (OOV) words as an open problem in neural machine translation. The method we introduce in this paper chooses appropriate alternative words inside the vocabulary for the OOV words by considering the word embeddings trained on monolingual corpora. Both monolingual and bilingual embeddings are used in finding the proper substitute for each OOV word. Using this technique we have improved the quality of translation up to 2.3 BLEU without using any additional annotated data.
A Comparative Study of Convolutional Neural Network and Twin SVM for Automatic Glaucoma Diagnosis

The number of people who suffer from tension problem is growing around the world, which is capable of causing other very serious illness like glaucoma. This last is a chronic and irreversible disease which can lead to vision loss and blindness, that's why doctors recommend continuous follow-up all the time. The development of computer aided diagnosis systems (CAD) in the medical field has been viewed with great interest by doctors to have a second diagnosis. In our previous study concerning classical methods, a Twin Support Vector Machine (TWSVM) classifier was proposed to glaucoma images classification by using handcrafted features. To overcome the main limits of classical approach, we explore a deep learning algorithm based on convolutional neural network (CNN) for the automatic generation of features depending on any used dataset. To highlight the impact of the CNN classifier in medical diagnosis, several empirical studies was performed based on convolution layers numbers and activation function. Obtained results will be compared and discussed with previous work based on TWSVM, by applying the RimOne dataset.
An ontological network to identify accessibility metadata in learning objects: an approach based on Web Content Accessibility Guidelines, schemas, and disabilities analysis

According to the guidelines provided in the UNESCO program “Learning for All,” the stakeholders (governments, institutions, teachers, instructional designers and quality assurance/qualification recognition bodies) are responsible on the development of contribution to guarantee the quality access learning for persons with disabilities or special needs. For this reason, in this paper, we present a proposal for an ontological network to model the knowledge of accessibility guidelines related to Learning Objects (LO). Our approach relies on the Web Content Accessibility Guidelines (WCAG 2.0), the Schema accessibility metadata, and a novel proposal to include the preference according to disability characteristics.
Full wave Modeling and Analysis of Plasmonic HEMT performance

The full wave analysis of 2-D plasmon propagation in a high electron mobility transistor (HEMT) channel is presented. The Maxwell’s equations and the hydrodynamic transport equations are solved simultaneously to model the structure, using the finite difference time domain (FDTD) numerical technique. By applying different bias voltages to the drain and the gate terminals, the properties of the 2-D plasmons along the channel are investigated. The analysis results show that the wavelength and the decay length of the propagation are significantly influenced by varying the bias voltages. So, it is possible to control the characteristics of the 2-D plasmon propagation along the channel and produce a reconfigurable structure for terahertz applications.
Traffic Sign Detection and Recognition using a CNN Ensemble

In today’s world, almost everything we do has been simplified by automated tasks. In an attempt to focus on the road while driving, drivers often miss out on signs on the side of the road, which could be dangerous for them and for the people around them. This problem can be avoided if there was an efficient way to notify the driver without having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays an important role here by detecting and recognizing a sign, thus notifying the driver of any upcoming signs. This not only ensures road safety, but also allows the driver to be at little more ease while driving on tricky or new roads. Another commonly faced problem is not being able to understand the meaning of the sign. With the help of this Advanced Driver Assistance Systems (ADAS) application, drivers will no longer face the problem of understanding what the sign says. In this paper, we propose a method for Traffic Sign Detection and Recognition using image processing for the detection of a sign and an ensemble of Convolutional Neural Networks (CNN) for the recognition of the sign. CNNs have a high recognition rate, thus making it desirable to use for implementing various computer vision tasks. TensorFlow is used for the implementation of the CNN. We have achieved higher than 99% recognition accuracies for circular signs on the Belgium and German data sets.