Miracle-dz/UniFAR-FTUs huggingface dataset:
NICT’s Neural and Statistical Machine Translation Systems for the WMT18 News Translation Task

This paper presents the NICT’s participation to the WMT18 shared news translation task. We participated in the eight translation directions of four language pairs: Estonian-English, Finnish-English, Turkish-English and Chinese-English. For each translation direction, we prepared state-of-the-art statistical (SMT) and neural (NMT) machine translation systems. Our NMT systems were trained with the transformer architecture using the provided parallel data enlarged with a large quantity of back-translated monolingual data that we generated with a new incremental training framework. Our primary submissions to the task are the result of a simple combination of our SMT and NMT systems. Our systems are ranked first for the Estonian-English and Finnish-English language pairs (constraint) according to BLEU-cased.
New Constraint in SHE-PWM for Single-Phase Inverter Applications

This paper proposes new angles constraint for selective harmonic elimination-pulsewidth modulation (SHE)-PWM utilized in single-phase inverter applications. The proposed angles constraint naturally eliminates all triplen harmonics, whereas they conventionally appeared in all single-phase output voltages. The harmonic amplitude formula is developed based on the obtained angles constraint, and then modified single-phase SHE equations are defined to eliminate only specified low nontriplen harmonics. Therefore, more harmonics would be eliminated with the same switching frequency in modified SHE-PWM. Five-level voltage waveform has been opted to implement the introduced method, but it can be applied on all multilevel voltage waveforms such as seven-level with even number of angles. Unlike the conventional SHE technique, THD is analyzable in modified SHE to find optimum values for switching angles in order to control noneliminated harmonic amplitudes and minimize THD. Moreover, the cancellation of triplen harmonics empowers a single-phase inverter to supply nonlinear loads beside linear ones. Some experimental tests are accomplished using a single-phase five-level modified packed U-cell (MPUC) inverter. Results are discussed to prove the excellent performance of a single-phase MPUC inverter under both normal and harmonic loads and accurate elimination of determined triplen and nontriplen harmonics by applying the presented angles constraint.
Multifractal analysis of oil slicks on SAR images

In this paper we present a technique for the analysis of low intensity patches on SAR oceanic amplitude images. The proposed technique, which is based on multifractal analysis of the edges of dark areas (here called regions of interest, ROIs), can be used to identify oil slicks generated by moving ships. The core idea is that different physical-chemical interactions of oil slicks and look-alikes with the sea surface imply different multifractal features for the edges of the ROIs on the acquired images. Accordingly, we propose to perform a multifractal analysis on ROIs' edges, which consists in the estimation of their multifractal spectrum and in the evaluation of the "dispersion area" of this spectrum. The proposed procedure is tested on simulated SAR images and methods and results are extensively discussed. First results seem to indicate that the observation of multifractal spectra is useful in order to distinguish between oil slicks generated by moving ships from other kinds of slicks, even when these phenomena have the same degree of irregularity and an estimation of the classical fractal dimension is not suitable for discrimination purposes.
Location Based Garbage Management System with IoT for Smart City

Smart cities integrate multiple ICT and IOT solutions to build a comfortable human habitation. One of these solutions is to provide an environmentally friendly, efficient and effective garbage management system. The current garbage collection system includes routine garbage trucks doing rounds daily or weekly, which not only doesn't cover every zone of the city but is a completely inefficient use of government resources. This paper proposes a cost-effective IOT based system for the government to utilize available resources to efficiently manage the overwhelming amounts of garbage collected each day, while also providing a better solution for the inconvenience of garbage disposal for the citizens. This is done by a network of smart bins which integrates cloud-based techniques to monitor and analyze data collected to provide predictive routes generated through algorithms for garbage trucks. An android app is developed for the workforce and the citizens, which primarily provides the generated routes for the workforce and finds the nearest available smart bin for citizens.
A Peer-to-Peer Interference Analysis for Automotive Chirp Sequence Radars

Mutual interference between automotive radar sensors is becoming a major concern due to the rapid increase of vehicles equipped with such systems. While there has been a plenty of studies on the interference of frequency modulated continuous wave (FMCW) radars, no work on chirp sequence (CS) radars has been reported in the literature in spite of their growing popularity in the automotive field. In this regard, this work presents an investigation of mutual interference for automotive CS radars. We analytically derive formulas describing the probability of ghost target appearance, and the signal-to-interference mitigation gain for interference of different waveforms, including continuous wave, FMCW, and CS waveforms, with comparison to an equivalent FMCW radar model. The derived formulas on the signal-to-interference mitigation gain are also verified by simulation results.
Social-Aware D2D Relay Networks for Stability Enhancement: An Optimal Stopping Approach

Device-to-device (D2D) relay network is regarded as a promising approach to extend the coverage area of cellular networks or improve the service quality for cell-edge users. However, the mobility of relay devices will negatively affect the stability of D2D communications. Aiming to improve the stability in mobile D2D communications and hence deliver more data traffic in D2D relay networks, in this paper, we exploit users’ social behaviors to dynamically detect better relay devices and substitute them for the existing ones. As the mobility of relays pose a critical challenge in achieving the tradeoff between the relay probing time and the optimality of relay selection, we propose the optimal stopping approach for mobile devices conducting the relay re-selection. Specifically, we embed social tie and centrality relationships between a potential relay and the end user to predict the date delivery performance of the potential relay in the D2D relay network. Our optimal stopping approach can be applied to different network topologies for mobile users with different social behaviors. The simulation results show that our proposed scheme can achieve the optimal tradeoff between the time used in relay probing and the optimality of the selected relay. Results also show that both short-term and long-term stability can be obtained and the amount of relayed data traffic can be maximized.
Smart and Robust Speaker Recognition for Context-Aware In-Vehicle Applications

The importance of robust audio speech processing has rapidly increased in the latest years, as the number of smart and connected devices is growing. This effect is strongly related to the Internet of things framework, introducing concepts such as connected vehicles and future smart cities. Context-aware applications are fundamental in this evolving environment, enabling smart and custom-tailored services for a variety of users. The use of on-board speaker recognition (SR) systems can play a key role in enhancing the customization of in-vehicle applications, by identifying the actual users and personalizing services based on their identity. Driven by this motivation, in this paper we present a performance study of an SR system, designed to face typical challenging conditions of an in-vehicle environment. We propose the design of a robust speaker identification algorithm embedding a smart preprocessing method based on voice activity detection, which can effectively reduce the influence of noise and distance on classification. Results show that our solution is able to efficiently improve the correct classification rate, even in the case of distant audio acquisition and in a variety of noisy environments.
Quantitation of Integrated HIV Provirus by Pulsed-Field Gel Electrophoresis and Droplet Digital PCR

We utilized pulsed-field gel electrophoresis (PFGE) to purify high-molecular-weight DNA from HIV-infected cells. This purification, in combination with our previously described droplet digital PCR (ddPCR) assay, was used to develop a method to quantify proviral integrated HIV DNA free of lower-molecular-weight species of HIV DNA. ABSTRACT We utilized pulsed-field gel electrophoresis (PFGE) to purify high-molecular-weight DNA from HIV-infected cells. This purification, in combination with our previously described droplet digital PCR (ddPCR) assay, was used to develop a method to quantify proviral integrated HIV DNA free of lower-molecular-weight species of HIV DNA. Episomal 2-long-terminal-repeat (2-LTR) circles were completely cleared from HIV DNA samples. Technical replicates of the complete assay, starting with the same specimens, resulted in no statistical differences in quantification of integrated HIV gag sequences in cellular DNA from cells from HIV-infected subjects after prolonged treatment with antiretroviral therapy (ART). The PFGE ddPCR assay was compared to the Alu-gag quantitative PCR (qPCR) assay, the most widely used assay to measure proviral integrated HIV DNA. Spearman's rho nonparametric correlation determined PFGE ddPCR results to be positively correlated with Alu-gag qPCR results (r = 0.7052; P = 0.0273). In summary, PFGE ddPCR is a sensitive, reproducible, and robust method to measure proviral integrated HIV DNA and is theoretically more accurate than previously described assays, because it is a direct measure of integrated HIV DNA.
Characterization of the Selective Indoleamine 2,3-Dioxygenase-1 (IDO1) Catalytic Inhibitor EOS200271/PF-06840003 Supports IDO1 as a Critical Resistance Mechanism to PD-(L)1 Blockade Therapy

Tumors use indoleamine 2,3-dioxygenase-1 (IDO1) as a major mechanism to induce an immunosuppressive microenvironment. IDO1 expression is upregulated in many cancers and considered to be a resistance mechanism to immune checkpoint therapies. IDO1 is induced in response to inflammatory stimuli such as IFNγ and promotes immune tolerance by depleting tryptophan and producing tryptophan catabolites, including kynurenine, in the tumor microenvironment. This leads to effector T-cell anergy and enhanced Treg function through upregulation of FoxP3. As a nexus for the induction of key immunosuppressive mechanisms, IDO1 represents an important immunotherapeutic target in oncology. Here, we report the identification and characterization of the novel selective, orally bioavailable IDO1 inhibitor EOS200271/PF-06840003. It reversed IDO1-induced T-cell anergy in vitro. In mice carrying syngeneic tumor grafts, PF-06840003 reduced intratumoral kynurenine levels by over 80% and inhibited tumor growth both in monotherapy and, with an increased efficacy, in combination with antibodies blocking the immune checkpoint ligand PD-L1. We demonstrate that anti–PD-L1 therapy results in increased IDO1 metabolic activity thereby providing additional mechanistic rationale for combining PD-(L)1 blockade with IDO1 inhibition in cancer immunotherapies. Supported by these preclinical data and favorable predicted human pharmacokinetic properties of PF-06840003, a phase I open-label, multicenter clinical study (NCT02764151) has been initiated.
Health Risks Associated with Occupational Exposure to Ambient Air Pollution in Commercial Drivers: A Systematic Review

Ambient air pollution is a major global health problem and commercial drivers are particularly exposed to it. As no systematic assessment of the health risks associated with occupational exposure to ambient air pollution in this population had yet been carried out, we conducted a systematic review using a protocol-driven strategy. Papers published from inception to April 20, 2018 in MEDLINE, EMBASE, CINAHL, African journals online, the Cochrane library, ISRCTN WHO ICTRP, and the Web of Science and Scopus databases were screened for inclusion by two independent reviewers. Original articles with at least an available abstract in English or French were included. The initial search retrieved 1454 published articles of which 20 articles were included. Three studies reported a significant difference in white blood cells (106/L) among commercial motorcyclists compared to rural inhabitants (5.041 ± 1.209 vs. 5.900 ± 1.213, p = 0.001), an increased risk of lung cancer (RR = 1.6, 95%CI 1.5–1.8) in bus drivers and an increased standardized mortality ratio (SMR) in bus drivers from Hodgkin’s lymphoma (SMR 2.17, 95%CI 1.19–3.87) compared to white-collar workers. Other studies also found that drivers had more oxidative DNA damage and chromosome breaks. Four papers failed to demonstrate that the drivers were more exposed to air pollution than the controls. Three other studies also reported no significant difference in lung function parameters and respiratory symptoms. The genetic polymorphisms of detoxifying enzymes were also not homogeneously distributed compared to the controls. There is some evidence that occupational exposure to ambient air pollution among commercial drivers is associated with adverse health outcomes, but the existing literature is limited, with few studies on small sample size, methodological weaknesses, and contradictory findings—thus, further research is recommended.
Performance Evaluation Strategies for Eye Gaze Estimation Systems with Quantitative Metrics and Visualizations

An eye tracker’s accuracy and system behavior play critical roles in determining the reliability and usability of eye gaze data obtained from them. However, in contemporary eye gaze research, there exists a lot of ambiguity in the definitions of gaze estimation accuracy parameters and lack of well-defined methods for evaluating the performance of eye tracking systems. In this paper, a set of fully defined evaluation metrics are therefore developed and presented for complete performance characterization of generic commercial eye trackers, when they operate under varying conditions on desktop or mobile platforms. In addition, some useful visualization methods are implemented, which will help in studying the performance and data quality of eye trackers irrespective of their design principles and application areas. Also the concept of a graphical user interface software named GazeVisual v1.1 is proposed that would integrate all these methods and enable general users to effortlessly access the described metrics, generate visualizations and extract valuable information from their own gaze datasets. We intend to present these tools as open resources in future to the eye gaze research community for use and further advancement, as a contribution towards standardization of gaze research outputs and analysis.
Identification of Icing Thickness of Transmission Line Based on Strongly Generalized Convolutional Neural Network

Due to various problems such as weather or light changing, icing images have the characteristics of low resolution and multi-morphological. How to find the image-based recognition method with strong generalization performance has become the research hotspots. In this paper, an IBP-CNN method based on strong generalized convolutional neural network is proposed to identify the icing thickness of the transmission line. Firstly, the number of each layer filter is determined by GAP mechanism. Secondly, the optimal model layer is obtained by bidirectional greedy algorithm. Finally, the parameters of each layer are trained by back propagation algorithm. Compared with mechanical model monitoring method and image edge detection method, the experimental results show that the proposed method can maintain high accuracy and low computational complexity under different resolution and different position angles, it has strong natural scene generalization ability and can be put into practice in the future.
Recent Statistical Contributions to Medical Device Development

Clinical trials in the development of new medical device products are in many ways analogous to clinical trials in the development of new drug or biologic products. However, the differences are important and not always intuitive to a statistician with only experience supporting development of drug and biologic products. In this paper we discuss some of the interesting differences with focus on the statistical innovation that is coming out of the medical device area. We discuss examples of the differences in clinical trial design and effects of these differences on clinical development programs.
A Search of Verilog Code Plagiarism Detection Method

In order to detect the plagiarism in Verilog codes of CPU design experiment, the existing code detection technologies are studied, and a Verilog code plagiarism detection combining the MOSS system and abstract grammar tree model (AST) is proposed. This method first verifies the Verilog code's executable and correctness, then filters the suspected plagiarism code through the MOSS system, then filters the suspected plagiarism code by the AST-based code detection method, the sum of the two filtered files is the final result. This method can detect both similarity of texts and similarity of structure. And the latter can make progress for detecting the program plagiarism that changes the code structure.
Model Predictive Control for Hybrid Energy StorageSystem using Single-InductorDual-Input Single-Output Converter

This paper proposes a single-inductor dual-input single-output (SI-DISO) converter for the hybrid energy storage system (HESS) which consists of two energy storages with different characteristics. A model predictive control (MPC) based controller is developed to distribute the variable load power demand and renewable power generation efficiently to the two energy storages based on their characteristics. Ultimately, the DC bus voltage of the DC microgrid is maintained at a constant voltage value, without being affected much by the fluctuation of renewable power generation and the variable load power demand. By sharing a single-inductor converter for the energy storages, the number of circuit components, topology complexity and cost can be reduced. The workability and effectiveness of the proposed method is validated by conducting hardware in-loop (HIL) experiments, and the HIL experimental results are analyzed in details.
Cell type-specific DNA methylation in neonatal cord tissue and cord blood: a 850K-reference panel and comparison of cell types

ABSTRACT Accounting for cellular heterogeneity is essential in neonatal epigenome-wide association studies (EWAS) performed on heterogeneous tissues, such as umbilical cord tissue (CT) or cord blood (CB). Using a reference-panel-based statistical approach, the cell type composition of heterogeneous tissues can be estimated by comparison of whole tissue DNA methylation profiles with cell type-specific DNA methylation signatures. Currently, there is no adequate DNA methylation reference panel for CT, and existing CB panels have been generated on lower coverage Infinium HumanMethylation450 arrays. In this study, we generate a reference panel for CT and improve available CB panels by using the higher coverage Infinium MethylationEPIC arrays. We performed DNA methylation profiling of 9 cell types isolated from CT and CB samples from 14 neonates. In addition to these cell types, we profiled DNA methylation of unfractionated CT and CB. Cell type composition of these unfractionated tissue samples, as estimated by our reference panels, was in agreement with that obtained by flow cytometry. Expectedly, DNA methylation profiles from CT and CB were distinct, reflecting their mesenchymal and hematopoietic stem cell origins. Variable CpGs from both unfractionated CT and its isolated cell types were more likely to be located in open seas and intronic regions than those in CB. Cell type specific CpGs in CT were enriched in intercellular matrix pathways, while those from CB were enriched in immune-related pathways. This study provides an open source reference panel for estimation and adjustment of cellular heterogeneity in CT and CB, and broadens the scope of tissue utilization assessed in future neonatal EWAS studies.
Excellent Degradation Performance of a Versatile Phthalic Acid Esters-Degrading Bacterium and Catalytic Mechanism of Monoalkyl Phthalate Hydrolase

Despites lots of characterized microorganisms that are capable of degrading phthalic acid esters (PAEs), there are few isolated strains with high activity towards PAEs under a broad range of environmental conditions. In this study, Gordonia sp. YC-JH1 had advantages over its counterparts in terms of di(2-ethylhexyl) phthalate (DEHP) degradation performance. It possessed an excellent degradation ability in the range of 20–50 °C, pH 5.0–12.0, or 0–8% NaCl with the optimal degradation condition 40 °C and pH 10.0. Therefore, strain YC-JH1 appeared suitable for bioremediation application at various conditions. Metabolites analysis revealed that DEHP was sequentially hydrolyzed by strain YC-JH1 to mono(2-ethylhexyl) phthalate (MEHP) and phthalic acid (PA). The hydrolase MphG1 from strain YC-JH1 hydrolyzed monoethyl phthalate (MEP), mono-n-butyl phthalate (MBP), mono-n-hexyl phthalate (MHP), and MEHP to PA. According to molecular docking and molecular dynamics simulation between MphG1 and monoalkyl phthalates (MAPs), some key residues were detected, including the catalytic triad (S125-H291-D259) and the residues R126 and F54 potentially binding substrates. The mutation of these residues accounted for the reduced activity. Together, the mechanism of MphG1 catalyzing MAPs was elucidated, and would shed insights into catalytic mechanism of more hydrolases.
Analyzing the Effect of High-k Dielectric-Mediated Doping on Contact Resistance in Top-Gated Monolayer MoS2 Transistors

A scalable process that can yield low-resistance contacts to transition metal dichalcogenides is crucial for realizing a viable device technology from these materials. Here, we systematically examine the effect of high-k dielectric-mediated doping on key device metrics including contact resistance and carrier mobility. Specifically, we use top-gated transistors from monolayer MoS2 as a test vehicle and vary the MoS2 doping level by adjusting the amount of oxygen vacancies in the HfOx gate dielectric. To understand the effect of doping on the contact resistance, from a fundamental standpoint, we first estimate the doping level in monolayer MoS2. The results of our device studies quantitatively show that the reduction in contact resistance with an increase in doping is due to the doping-induced lowering of the Schottky barrier height (SBH) at the metal–semiconductor interface. Furthermore, our temperature-dependent measurements reveal that a mixture of thermionic and field emissions, even at high carrier densities, dominates carrier conduction at the contact. While our study reveals the effectiveness of dielectric-induced doping in lowering SBH, it suggests that a further reduction of SBH using alternative methods is necessary for achieving an ohmic-like contact to monolayer MoS2.