Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy-Protective-GAN for Face De-identification

Face de-identification has become increasingly important as the image sources are explosively growing and easily accessible. The advance of new face recognition techniques also arises people's concern regarding the privacy leakage. The mainstream pipelines of face de-identification are mostly based on the k-same framework, which bears critiques of low effectiveness and poor visual quality. In this paper, we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input. We evaluate the proposed approach in terms of privacy protection, utility preservation, and structure similarity. Our approach not only outperforms existing face de-identification techniques but also provides a practical framework of adapting GAN with priors of domain knowledge.
Quickly Starting Media Streams Using QUIC

Originally proposed by Google, QUIC is a low-latency transport protocol currently being developed and specified in the IETF. QUIC's low-latency, improved congestion control, multiplexing features are promising and may help improve viewer experience in HTTP adaptive streaming applications. To investigate what issues due to running HTTP over TCP can be alleviated by using HTTP over QUIC, we measured QUIC's streaming performance on wireless and cellular networks. Specifically, we examined QUIC's performance during network interface changes due to viewer's mobility and under unstable network conditions. Results show that QUIC starts media streams more quickly, providing a better streaming and seeking experience, in particular, when there is more congestion in the network, and outperforms TCP when the viewer is mobile and switches between the networks.
Pattern recognition for water flooded layer based on ensemble classifier

In order to establish an effective water flooded layer recognition model to deal with complex chromatogram data and correctly identify the water flooded layer in the oil and gas reservoirs, this paper proposes a modeling approach based on ensemble classifier. First, the proposed approach utilizes the function fitting method to obtain the effective chromatogram characteristic information (CCIs). Moreover, in order to transform the sparse classification problem into a general classification problem, the synthetic minority over-sampling technique (SMOTE) algorithm is used to process the unbalanced training sample as a general training sample. Compared with the traditional classification approach, the robustness and effectiveness of the ensemble classifier model composed of the model-free classification (MFBC) algorithm, the k-nearest neighbor (KNN) algorithm and the support vector machine (SVM) algorithm were validated through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is validated through an application in a complex oil and gas recognition system of China petroleum industry. The CCIs and the prediction results are obtained to provide more reliable water flooded layer information, guide the process of reservoir exploration and development and improve the oil development efficiency.
Initiation of Meiotic Development Is Controlled by Three Post-transcriptional Pathways in Caenorhabditis elegans

A major transition in germ cell development is the switch from mitotic cell cycling to entry into the meiotic developmental pathway. Mohammad et al. report that the SCFPROM-1 substrate-specific E3 ubiquitin ligase complex is a new. A major event in germline development is the transition from stem/progenitor cells to entry into meiosis and gametogenesis. This transition requires downregulation of mitotic cell cycle activity and upregulation of processes associated with meiosis. We identify the Caenorhabditis elegans SCFPROM-1 E3 ubiquitin-ligase complex as functioning to downregulate mitotic cell cycle protein levels including cyclin E, WAPL-1, and KNL-2 at meiotic entry and, independently, promoting homologous chromosome pairing as a positive regulator of the CHK-2 kinase. SCFPROM-1 is thus a novel regulator of meiotic entry, coordinating downregulation of mitotic cell cycle proteins and promoting homolog pairing. We further show that SCFPROM-1 functions redundantly, in parallel to the previously described GLD-1 and GLD-2 meiotic entry pathways, downstream of and inhibited by GLP-1 Notch signaling, which specifies the stem cell fate. Accordingly, C. elegans employs three post-transcriptional pathways, SCFPROM-1-mediated protein degradation, GLD-1-mediated translational repression, and GLD-2-mediated translational activation, to control and coordinate the initiation of meiotic development.
Burnout among rural hospital doctors in the Western Cape: Comparison with previous South African studies

Background Burnout among doctors negatively affects health systems and, ultimately, patient care. Aim To determine the prevalence of burnout among doctors working in the district health system in the Overberg and Cape Winelands districts of the Western Cape Province and to compare the findings with those of previous South African studies. Setting Rural district hospitals. Methods During 2013, a validated questionnaire (Maslach Burnout Inventory) was sent to 42 doctors working in the district health system within the referral area of the Worcester Hospital, consisting of the Overberg health district and the eastern half of the Cape Winelands. Results Response rate was 85.7%. Clinically significant burnout was found among 81% of respondents. High levels of burnout on all three subscales were present in 31% of participants. Burnout rates were similar to those of a previous study conducted among doctors working in the Cape Town Metropolitan Municipality primary health care facilities. Scores for emotional exhaustion (EE) and depersonalisation (DP) were greater than those of a national survey; however, the score for personal accomplishment (PA) was greater. EE and PA scores were similar to that of a study of junior doctors working in the Red Cross Children’s Hospital; however, EE was smaller. Conclusion This study demonstrates high burnout rates among doctors working at district level hospitals, similar to the prevalence thereof in the Cape Town Metropolitan primary health care facilities. Health services planning should include strategies to address and prevent burnout of which adequate staffing and improved work environment are of prime importance.
Voltage-Gated Sodium Channels in Neocortical Pyramidal Neurons Display Cole-Moore Activation Kinetics

Exploring the properties of action potentials is a crucial step towards a better understanding of the computational properties of single neurons and neural networks. The voltage-gated sodium channel is a key player in action potential generation. A comprehensive grasp of the gating mechanism of this channel can shed light on the biophysics of action potential generation. Most models of voltage-gated sodium channels assume it obeys a concerted Hodgkin and Huxley kinetic gating scheme. Here we performed high resolution voltage-clamp experiments from nucleated patches extracted from the soma of layer 5 (L5) cortical pyramidal neurons in rat brain slices. We show that the gating mechanism does not follow traditional Hodgkin and Huxley kinetics and that much of the channel voltage-dependence is probably due to rapid closed-closed transitions that lead to substantial onset latency reminiscent of the Cole-Moore effect observed in voltage-gated potassium conductances. This may have key implications for the role of sodium channels in synaptic integration and action potential generation.
Pre-Anaesthetic Assessment Of Intracranial Pressure Using Optic Nerve Sheath Diameter In Patients Scheduled For Elective Tumour Craniotomy.

Background The objective of study was to determine the pre-anaesthetic status of intracranial pressure (ICP), using ultrasonographic measurement of optic nerve sheath diameter (ONSD) inpatient scheduled for elective tumour craniotomy. The secondary objective was to compare the diagnostic accuracy of ONSD guided rise in ICP with clinical and radiographic parameters. This is prospective observational study, conducted at single neurosurgical theatre of The Aga Khan University over a period of one year. Methods After getting ethical approval and informed consent patient fulfilling inclusion criteria and planned for elective tumour craniotomy were enrolled in study. The clinical and radiographic signs predicting the status of ICP were recorded. The ultrasonographic measurement of ONSD was done using liner array probe. Value more than 5 mm was considered as abnormal. Results Total 26 cases were enrolled. Seventy percent patients showed rise in ICP based on clinical parameters, while 65% diagnosed to have raised ICP on the basis of radiographic findings. The ultrasonographic measurement of ONSD predicted this rise in 61% of cases. The diagnostic accuracy of ONSD in detecting raised ICP in comparison to clinical and radiographic evidence was 87.5% respectively. Conclusions The ultrasonographic-guided ONSD was used successfully for predicting the status of ICP in pre-induction phase of anaesthesia. It also showed good correlation in diagnosing rise in ICP as compared to clinical and radiographic parameters, which indicates that test can be used reliably in preoperative period for patients planned for tumour craniotomy.
Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis

Recent years have witnessed the unprecedented rising of time series from almost all kindes of academic and industrial fields. Various types of deep neural network models have been introduced to time series analysis, but the important frequency information is yet lack of effective modeling. In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis. mWDN preserves the advantage of multilevel discrete wavelet decomposition in frequency learning while enables the fine-tuning of all parameters under a deep neural network framework. Based on mWDN, we further propose two deep learning models called Residual Classification Flow (RCF) and multi-frequecy Long Short-Term Memory (mLSTM) for time series classification and forecasting, respectively. The two models take all or partial mWDN decomposed sub-series in different frequencies as input, and resort to the back propagation algorithm to learn all the parameters globally, which enables seamless embedding of wavelet-based frequency analysis into deep learning frameworks. Extensive experiments on 40 UCR datasets and a real-world user volume dataset demonstrate the excellent performance of our time series models based on mWDN. In particular, we propose an importance analysis method to mWDN based models, which successfully identifies those time-series elements and mWDN layers that are crucially important to time series analysis. This indeed indicates the interpretability advantage of mWDN, and can be viewed as an indepth exploration to interpretable deep learning.
Robust detection for object removal with post-processing by exemplar-based image inpainting

Exemplar-based image inpainting can be maliciously used for object removal forgery without leaving any perceptual clues. Especially, post-processing might further brings challenges for its blind forensics. In the paper, a robust forensics approach is presented to detect object removal tamper by exemplar-based image inpainting with post-processing, such as JEPG compression, blurring, imnoise, and so on. Object removal changes local texture and gradient smoothness, which destroys the inherent properties of nature images. Two local texture descriptors including LBP (Local Binary Patterns) and GLCM (Gray-level Co-occurrence Matrix) are exploited to measure texture variation, and image gradient is used to describe the structure change. Fourteen statistical features including Zernike zero-order moment, min, max, mean, variance and standard deviation are extracted from them. Then, support vector machine (SVM) is exploited as pattern classifier to determine whether an image has been suffered from object removal or not. Experimental results show the detection robustness for object-removal forgery with post-processing.
Brief integrated sleep-focused treatment for persistent sleep disturbance in residual depression: an assessor-blind, parallel group, randomised controlled study.

Major depressive disorder (MDD) is one of the most prevalent major medical conditions with a lifetime and 12-month prevalence of 2%-16% and 8%, respectively, in the United States and local Chinese populations. It is a debilitating and recurrent illness associated with tremendous personal distress, considerable impairment of psychosocial functioning, as well as increased morbidity and mortality. The treatment goal is sustained resolution of symptomatology with remission of MDD. About 15% to 90% of patients continue to experience residual symptoms, particularly sleep disturbances, despite optimised antidepressant treatment. There is an imperative need to explore specific sleep-focused treatments in addition to usual antidepressant prescription in the routine clinical management of depression. Growing evidence suggests a close interplay between sleep disruption and psychopathology, in which sleep disturbance is associated with susceptibility to the subsequent development of depression, increased risk of non-remission, and exaggeration of other clinical symptoms, and prognostic implication in predicting suicidality. Although empirical support for cognitive behavioural therapy for insomnia has been well documented, especially for primary insomniacs, there is a paucity of clinical studies to evaluate an integrated sleepsymptom-specific treatment strategy in relation to clinical management of MDD. This study aimed to develop and evaluate a Hong Kong Med J 2018;24(Suppl 2):S8-11 HHSRF project number: 10110341
Age of Information: A New Concept, Metric, and Tool

In an increasingly networked world in which information is becoming increasingly actual, how can a system know with certainty just how fresh the information on a remote system is? Much work has been done on delay or latency in systems, but only recently has the concept of the freshness of information in or about a system become quantifiable. This is termed the Age of Information. Age of Information provides a whole new set of metrics and tools that can be used to characterize the freshness of information in communications, control and, indeed, information systems. In its early development, this monograph provides the reader with an easy-to-read tutorial-like introduction into this novel approach of dealing with information within systems. A critical summary of the work to date details the concept for the reader. A description of the fundamentals of the performance metrics is then presented before showing how the approach can be used as a tool in improving metrics in other contexts. The survey and tutorial nature of this monograph will save any researcher or student considerable time in understanding the basics of Age of Information, which is destined to become an important research topic in networked systems.
A novel campus navigation APP with augmented reality and deep learning

Augmented reality has been widely used in many applications because of its ability to offer an amazing way to overlay computer-generated images over the user's real-world view, creating a composite view rooted in real and virtual worlds. Augmented Reality is a realistic, direct or indirect view of the physical reality environment whose elements are “enhanced” through computer-generated or sensory input such as sound, video, graphics, tactile, or GPS data. In this paper, we present a novel campus navigation APP that uses augmented reality to provide users with a new and interesting way to meet our campus. With advanced augmented reality technologies such as computer vision and object recognition, the information about the campus environment and its objects is overlaid on the real world and becomes interactive. In order to improve the APP efficiency, this paper presents a virtual terrain modeling interface with deep learning to improve the object recognition ability.
SYNTHETIC OFF-AXIS LIGHT CURVES FOR LOW-ENERGY GAMMA-RAY BURSTS

We present results for a large number of gamma-ray burst (GRB) afterglow light curve calculations, done by combining high-resolution two-dimensional relativistic hydrodynamics simulations using ram with a synchrotron radiation code. Results were obtained for jet energies, circumburst medium densities, and jet angles typical for short and underluminous GRBs, different observer angles, and observer frequencies from low radio (75 MHz) to X-ray (1.5 keV). We summarize the light curves through smooth power-law fits with up to three breaks, covering jet breaks for small observer angles, the rising phase for large observer angles, and the rise and decay of the counterjet. All light curve data are publicly available on the Web. The data can be used for model fits to observational data and as an aid for predicting observations by future telescopes such as LOFAR or the Square Kilometer Array and will benefit the study of neutron star mergers using different channels, such as gravitational-wave observations with LIGO or Virgo. For small observer angles, we find jet break times that vary significantly between frequencies, with the break time in the radio substantially postponed. Increasing the observer angle also postpones the measured jet break time. The rising phase of the light curve for large observer angle has a complex shape that cannot always be summarized by a simple power law. Except for very large observer angles, the counterjet is a distinct feature in the light curve, although in practice the signal will be exceedingly difficult to observe by then.
Predicting Mobile-Captured Document Images Sharpness Quality

Nowadays the number of mobile applications is fast growing. Among them, mobile applications based on Optical Character Recognition (OCR) play an important role. One of the main challenge of such applications to overcome is that the image acquisition procedure is in a manner unreliable and may contain many distortions. As a consequence, a suitable OCR output requires efforts to enhance the quality of the captured image. This leads to an increase of computation time and cost. In this perspective, we focus on the prediction of image's sharpness quality. We choose to concentrate on image's sharpness quality because blur distortions seriously alter readability for both human and computer. Our contribution consists of a method combining focus and sharpness measures with a Support Vector Machine to classify image's sharpness quality. This approach is fast, reliable, and can be easily implemented on mobile devices. Experimental results show that our method is efficient for OCR based mobile-captured document images.
Human activity recognition based on random forests

Human activity recognition is a hot topic in the field of pervasive computing and context aware computing, and may support lots of potential applications, such as healthcare, smart home, etc. In this paper, a novel human activity recognition method is proposed, where accelerometer data are collected from wearable devices and used as input of random forests (RF) model to classify human activity. Random forests model for human activity recognition is established, and the algorithm is designed and analyzed as well. Experiment results proved our method valid, which achieved an overall accuracy about 90%.
Hand Written Characters Recognition via Deep Metric Learning

Deep metric learning plays an important role in measuring similarity through distance metrics among arbitrary group of data. MNIST dataset is typically used to measure similarity however this dataset has few seemingly similar classes, making it less effective for deep metric learning methods. In this paper, we created a new handwritten dataset named Urdu-Characters with set of classes suitable for deep metric learning. With this work, we compare the performance of two state-of-the-art deep metric learning methods i.e. Siamese and Triplet network. We show that a Triplet network is more powerful than a Siamese network. In addition, we show that the performance of a Triplet or Siamese network can be improved using most powerful underlying Convolutional Neural Network architectures.
Specificity of process analytical tools in the monitoring of multicomponent pharmaceutical powders

Abstract The application of Process Analytical Technologies in pharmaceutical manufacturing has been the subject of many studies. Active pharmaceutical ingredient monitoring in real time throughout the manufacturing process is commonly the target of many such implementations. The tools in place must be sensitive to, and selective of, the parameter(s) to be monitored, i.e. in the case of component quantification, they must respond to the component in question and be robust against all others. In this study, four different ingredients (riboflavin, ferrous fumarate, ginseng, and ascorbic acid) in a multi-component blend were monitored by three different tools (near infrared spectroscopy, laser-induced fluorescence and red-green-blue camera) using a full factorial design. The goal was to develop efficient and robust concentration-reading/prediction models able to assess and monitor component interference. Despite relatively high complexity of the blend studied, the three tools demonstrated reasonable specificity for the tracked ingredients (and showed advantages when combined), taking into account larger acceptance criteria typical of dietary products. In certain cases, some interference might lead to biased predictions, highlighting the importance of good calibration. The tools tested and the methodology proposed has divulged their potential in monitoring these components, despite the complexity of the 31-component blend.
Detection of Marijuana in blood using GaAs substrate based photonic sensor

Marijuana apart from its slight usage in certain medical departments is a very harmful drug if consumed on a regular basis. Detection of Marijuana in blood is a must to identify the addicted person and provide treatment. In current study, we have proposed detection and analysis of drugs in blood by using the Photonic Crystal (PHC) based sensor. A two-dimensional PHC based double ring resonator structure is thereby used to detect its presence. Here, we have used GaAs (Gallium Arsenide) substrate. The objective is to provide fabricator an ideal basis for the production ready design. While designing our emphasis was not compromise on efficiency and sensitivity of the device. MEEP Simulation tool was used for implementation of Finite Difference Time Domain (FDTD) method for modelling the double ring resonator structure. Output transmitted spectrum shows shift in frequency because of change in refractive index due to presence of Marijuana in blood (analyte). The sensor can differentiate between the signature of normal and drug infected blood. A high quality factor obtained of 27,692 for normal blood and 17,149 for marijuana contaminated blood. The novelty of the work is use of GaAs and double ring resonator structure.