Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular effects of inorganic and methyl mercury in single exposure and mixture scenarios in the aquatic plant Elodea nuttallii

Mercury (Hg) remains hazardous in aquatic environments because of its biomagnification in food webs. Nonetheless, Hg uptake and impact in primary producers is still poorly understood. Here, we compared the cellular toxicity of inorganic and methyl Hg (IHg and MeHg, respectively) in the aquatic plant Elodea nuttallii. IHg and MeHg regulated contigs involved in similar categories (e.g., energy metabolism, development, transport, secondary metabolism), but MeHg regulated more contigs, supporting a higher molecular impact than IHg. At the organism level, MeHg induced antioxidants, while IHg decreased chlorophyll content. The uptake of Hg and expression of a subset of contigs was subsequently studied in complex media. Measured uptake pointed to a contrasted impact of cell walls and copper (Cu) on IHg and MeHg. Using a speciation modeling, differences in uptake were attributed to the differences in affinities of IHg and MeHg to organic matter in relation to Cu speciation. We also identified a distinct gene expression signature for IHg, MeHg, and Cu, further supporting different molecular toxicity of these trace elements. Our [. ] BEAUVAIS-FLUECK, Rebecca, et al. Molecular Effects, Speciation, and Competition of Inorganic and Methyl Mercury in the Aquatic Plant Elodea nuttallii. Environmental Science & Technology, 2018, vol. 52, no. 15, p. 8876-8884 DOI : 10.1021/acs.est.8b02124
Beam Training Technique for Millimeter-Wave Cellular Systems Using Retrodirective Arrays

Beam training in millimeter-wave (mmWave) cellular systems requires a long processing time that is proportional to the product of the number of transmitting and receiving beams. In this paper, we propose a beam training technique that can significantly reduce the beam training time in mmWave cellular systems, using a retrodirective directional array (RDA). In the proposed technique, the beam sweeping operations required at the base station (BS) and mobile station (MS) are significantly reduced owing to the use of the RDA, which automatically returns a signal in the direction along which it originated. A preamble sequence design technique for beam training is proposed to identify the BS, MS, and beams simultaneously transmitted from the BS/MS, using the Zadoff–Chu sequence. The ambiguity condition and detection algorithms are derived so that we can uniquely identify the parameters for beam alignment in asynchronous environments with symbol timing offset (STO) and carrier frequency offset (CFO). Simulations show that the proposed algorithm can correctly detect the parameters for beam alignment in mmWave cellular systems with RDA in asynchronous environments. Moreover, the proposed technique can significantly reduce the period required for beam training, compared with the conventional techniques.
Modulation and Switching Architecture Performances for Frequency Up-Conversion of Complex-Modulated Data Signals Based on a SOA-MZI Photonic Sampling Mixer

A theoretical and experimental performance analysis of a semiconductor optical amplifier-Mach–Zehnder interferometer (SOA-MZI) photonic sampling mixer used as a frequency up-converter is presented employing Switching and Modulation architectures. An active mode-locked laser, generating 2 ps-width pulses at a repetition rate equal to 10 GHz, is used as a sampling source. An optical carrier intensity modulated by a sinusoidal signal at 1 GHz is up-converted to 9 GHz and 39 GHz. High conversion gains (CGs) of about 15 dB are demonstrated for the frequency conversion to 9 GHz using both architectures, whereas up to 4 dB and 9 dB for the conversion to 39 GHz employing switching and modulation architectures, respectively. Small-signal equations for the up-converted signal in both architectures are formulated and developed, which permit to quantify the CG from closed-form expressions. The numerically calculated CG values are in very good agreement with those obtained experimentally. The validated equations are subsequently employed to explain the performance differences between the two architectures in terms of the CG. Furthermore, signals modulated by QPSK and 16-QAM complex modulation formats at different baud rates are up-converted from 750 MHz to 9.25 GHz and 39.75 GHz and their error vector magnitude is evaluated and compared. The maximum bit rate that meets the forward error correction (FEC) limit is achieved using the Modulation architecture. It is 1 Gbps and 512 Mbps for QPSK and 16-QAM modulations, respectively.
Extraction Algorithm of English Text Information From Color Images Based on Radial Wavelet Transform

The Chinese and English text information of color image reflects some important contents of color image to a certain extent, and these texts are extracted automatically, which has important application value in the fields of information retrieval, digital library, web page retrieval and intelligent transportation. In this paper, various signal extraction techniques based on radial wavelet transform modulus Maxima are analyzed, and it is found that these techniques have poor ability to extract weak signals, and the higher requirements for directionality lead to pseudo-boundary phenomena in two-dimensional image extraction results. Based on the correlation denoising method of radial wavelet coefficients, the radial wavelet entropy is introduced into the field of signal extraction, and the complex Morlet radial wavelet is selected as the basis function. A complex Morlet radial wavelet entropy extraction algorithm suitable for extracting the English text characteristics of weak signal color images is proposed. In addition, a method of scene text recognition is proposed. Based on D-SIFT local features and spatio-temporal histogram, this method vectorizes text samples in the framework of Bo Ws model. Because the spatio-temporal histogram can flexibly model the structural information of the text, this method can effectively describe the scene text. On this basis, the selective ensemble learning method is used to improve the performance of the text extractor. In order to expand the application range of the algorithm and improve the running efficiency of the integrated extractor, a model compression method based on color image English text samples and integrated extractor is proposed. The integrated extractor which takes up more space and runs slowly is compressed into an equivalent and more efficient one. In order to reduce the number of pseudo-samples needed in the process of model compression, a method of training local extractor based on color image English text samples is proposed, which greatly reduces the number of pseudo-samples needed and improves the efficiency of the model compression method.
On the Effectiveness of AI-Assisted Anomaly Detection Methods in Maritime Navigation

The automatic identification system (AIS) has become an essential tool for maritime security. Nevertheless, how to effectively use the static and dynamic voyage information of the AIS data in maritime traffic situation awareness is still a challenge. This paper presents a comparative study of artificial intelligence (AI) techniques on their effectiveness in dealing with various anomalies in maritime domain using the AIS data. The AIS on-off switching (OOS) anomaly is critical in maritime security, since AIS technology is susceptible to manipulation and it can be switched on and off to hide illegal activities. Thus, we try to detect and distinguish between intentional and non-intentional AIS OOS anomalies through our AI -assisted anomaly detection framework. We use AIS data, in particular positional and navigational status of vessels, to study the effectiveness of seven AI techniques, such as artificial neural network, support vector machine, logistic regression, k-nearest neighbors, decision tree, random forest and naive Bayes, in detecting the AIS OOS anomalies. Our experimental results show that ANN and SVM are the most suitable techniques in detecting the AIS OOS anomalies with 99.9% accuracy. Interestingly, the ANN model outperforms others when trained with a balanced (i.e., same order of samples per class) dataset, and SVM, on the other hand, is suitable when training dataset is unbalanced.
DPSO and Inverse Jacobian-Based Real-Time Inverse Kinematics With Trajectory Tracking Using Integral SMC for Teleoperation

A six-degree-of-freedom robotic manipulator inverse kinematics (IK) for position control is proposed for the bilateral teleoperation process that is implemented through a joystick for nuclear power plant dismantling operations. The control strategy of the manipulator includes the use of the joystick to generate the Cartesian space trajectory followed by the IK to yield the joint space trajectory for implementing position control. In this paper, a novel technique for the IK is proposed. It involves the use of the particle swarm optimization (PSO) algorithm with the inverse Jacobian (IJ). The special case of the dual PSO is based on dividing the PSO algorithm into two such that the trajectory position and orientation are separately optimized by the algorithms, resulting in a faster convergence. In contrast, the inverse Jacobian aids in generating a smooth joint trajectory. The integral sliding mode control (ISMC) is proposed for position control because it does not require information on system dynamics. The ISMC improves the system trajectory tracking performance by using a switching gain to compensate for system dynamics and perturbations (disturbance and unmatched uncertainties), ultimately reducing the time delay. The effectiveness of the PSO combined with the IJ and the robustness of the ISMC in the teleoperation process are confirmed by the experimental results.
Development of the UFRJ Nautilus' AUV: A Multisensor Data Fusion case study

The UFRJ Nautilus is a student-driven engineering project team at Federal University of Rio de Janeiro, focused on building and designing AUVs to compete in the AUSVI RoboSub Competition. There are several challenges on developing an AUV: location, computer vision, filters, collect and evaluate data from several sensors. The priority of the team was deliver a robot capable of localizing it self on a pool, with more reliability from all hardware and mechanical systems. We have developed a echo-localization algorithm based on the traditional beamforming that considers both time and frequency in order to have a faster and less power intensive procedure, Simulation showed that our algorithm achieved those objectives.
Learning Salient Features for Flower Classification Using Convolutional Neural Network

Image features can be utilized for image classification. Convolutional Neural Network (CNN) is able to extract features automatically from images rather than collecting features by hand. There are more and more flower images on the internet, and it is necessary to develop a system for identification of flower type. In this paper, we collect flower images from the internet and label them according to the species, then by using a deep CNN, we learn salient features of the flower images and achieve a significant performance of 78% in term of classification accuracy.
DynRP- Non-Intrusive Profiler for Dynamic Reconfigurability

Emerging technological areas such as machine learning, speech recognition, computer vision, autonomous robots, AI, bioinformatics involving big data, require implementation in complex heterogeneous accelerator platforms, to be able to handle data explosion with higher efficiency, lower power, and better performance. Dynamic reconfiguration in such platforms can help in run-time optimization to meet the design goals. The required optimal platform configuration can be achieved by a flexible design space exploration and appropriate task partitioning obtained through profiling computation and communication of processes in application code. This paper focuses on profiling, it being the key to the success of obtaining optimal platform configurations. It points to existing profiling techniques, their pros and cons vis-à-vis dynamic reconfigurable architectures, and the challenges in their design for obtaining optimal profiling performance. It further outlines desirable specifications for a profiler to allow dynamic real-time profiling for effective use of dynamic reconfiguration. DynRP, a non-intrusive hardware profiler for dynamic reconfiguration is proposed based on the desirable specifications, followed by its design and implementation details.
Analysis of Azimuthal Partition Periodic Disk-Loaded Coaxial Structure for Bifrequency MILO Using Equivalent Circuit Approach

In this article, an equivalent circuit approach has been used to study the bifrequency generation by the azimuthally partitioned axially periodic metal disk-loaded coaxial structure. Considering the effect of all harmonics present within the structure and using an equivalent circuit approach, the expression of the equivalent series inductance per unit length and equivalent shunt capacitance per unit length has been obtained for this equivalent transmission line. In addition, with the help of computed equivalent series induction and equivalent shunt capacitance, expressions for the dispersion relation, phase velocity, and characteristic impedance have been obtained. The maximum possible frequency difference (i.e., bandwidth) between the modes (i.e., resonating frequency) and its region of operation have also been analytically computed. Finally, the parametric studies have been performed to find out the effect of structural parameter variation on dispersion behavior. In order to validate the numerical analysis, the structure has been first simulated with the help of CST microwave studio, and then, the analytically obtained results (such as dispersion behavior and phase velocity) have been compared with the results obtained from simulation. The comparison of results shows that the relative error is below than 5%, which shows a good agreement between them.
EVC-TDMA: An enhanced TDMA based cooperative MAC protocol for vehicular networks

In order to optimize the communication mechanism in the vehicular adHoc networks (VANETs), this paper presents an enhanced TDMA based cooperative MAC protocol called EVC-TDMA. In EVC-TDMA, when the vehicles need to transmit multi-hop messages, they choose relay nodes dynamically according to the relative speeds and buffer lengths. By monitoring the broadcasting messages, the other nodes can know the buffer lengths of the relay nodes, they will help transmit the relay messages when their buffer are idle or less. Considering the data arrive follows a Poison Point Process, this paper analyzes the nodes' buffer length based on a two-dimension Markov process. The simulation results show that EVC-TDMA can reduce the dropping rate and improve the throughput of the network.
A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition

Recently, it has attracted much attention to build reliable named entity recognition (NER) systems using limited annotated data. Nearly all existing works heavily rely on domain-specific resources, such as external lexicons and knowledge bases. However, such domain-specific resources are often not available, meanwhile it’s difficult and expensive to construct the resources, which has become a key obstacle to wider adoption. To tackle the problem, in this work, we propose a novel robust and domain-adaptive approach RDANER for low-resource NER, which only uses cheap and easily obtainable resources. Extensive experiments on three benchmark datasets demonstrate that our approach achieves the best performance when only using cheap and easily obtainable resources, and delivers competitive results against state-of-the-art methods which use difficultly obtainable domainspecific resources. All our code and corpora can be found on https://github.com/houking-can/RDANER.
The effect of edible coating with combined Thymus Vulgaris extract and glycerol monoestearate on oyster mushroom ҆s shelf life

Shelf-life of mushrooms is very low, because of several characteristics, such as their thin epidermal structure and high respiration rates. They tend to lose their quality after harvest. Hence, mushrooms need supportive care to keep freshness. Several protective methods have been recommended. In the current study, the effect of lipid-edible coating with different doses of glycerol monoestearate and thyme extract, for the extending of edible mushroom’s shelf life was evaluated. After, preparation of aqueous thyme extract (TE) by the Clevenger method, the mushroom treatments were prepared with different concentrations of the glycerol monoestearate (GMS) and thyme extract. The chemical composition of the extract was performed using GC-MS method. The texture tightness, color and weight loss were respectively, measured using the texture analyzer, HunterLab and digital balance. The Sensory and antimicrobial evaluations were also performed during the 15 days. Analysis of the extract has detected the 23 chemical compositions with the different structures and functional groups. The high texture tightness and the low weight loss determined for the mixture of GMS and 150 mg/kg TE, and the color indices (a*b*L*) have less significant change by adding the GMS with doses of 100 and 150 mg/kg of TE. Furthermore, the high antimicrobial activities resolute for the GMS+TE150 mg/kg. In conclusion, the GMS+EO150 mg/kg coating could be used significantly for preserving the quality of oyster mushrooms throughout long-term storage
Conversational Music Recommendation based on Bandits

Music is one of the most popular products in the recommender system, and there have been many various methods of exploring music recommendations. Traditional music recommendations commonly collect users’ feedbacks in limited ways for preference analysis. The text dialogue is a direct and natural interactive mode, providing diversified information. In this paper, we discuss the music recommendation in an innovative scenario – a conversational music recommendation model, which integrates the advantages both from the recommender system and dialog system. This paper adopts a ”user ask, system respond” interactive way to obtain users’ real-time requirements, and users are allowed to express their requirements on music in free text. In order to face the fast-changing music preferences, this paper adopts the bandit-based algorithm to absorb users’ attitudes to the current recommendation, and the results show these methods achieve better performance than baselines. Besides, it also constructs a music-domain knowledge graph to support the richer users’ musical expressions with millions of music items and tens of millions of relations.
MM-GAN: 3D MRI Data Augmentation for Medical Image Segmentation via Generative Adversarial Networks

Due to the limited amount of the labelled dataset, which hampers the training of deep architecture in medical imaging. The data augmentation is an effective way to extend the training dataset for medical image processing. However, subjective intervention is inevitable during this process, not only in the pertinent augmentation but also the non-pertinent augmentation. In this paper, to simulate the distribution of real data and sample new data from the distribution of limited data to populate the training set, we propose a generative adversarial network based architecture for the MRI augmentation and segmentation (MM-GAN), which can translate the label maps to 3D MR images without worrying about violating the pathology. Through a series of experiments of the tumor segmentation on BRATS17 dataset, we validate the effectiveness of MM-GAN in data augmentation and anonymization. Our approach improves the dice scores of the whole tumor and the tumor core by 0.17 and 0.16 respectively. With our method, only 29 samples are used for fine-tuning the model trained with the pure fake data and achieve comparable performance to the real data, which demonstrates the ability for the patient privacy protection. Furthermore, to verify the expandability of MM-GAN model, the dataset LIVER100 is collected. Experiment results on the LIVER100 illustrate similar outcome as on BRATS17, which validates the performance of our model.
The Epidemiologic Investigation and Characterization of Hantavirus Carried by Rattus in Yunnan

Objective To investigate the types and their distribution of the hantavirus carried by Rattus norvegicus and R.flabipectus in Yunnan.Methods Rodents were captured in the main epidemic areas,and hantavirus antigens in rats lungs were detected by indirect immunofluorescence assay.Partial M and S segments were amplified in antigen-positive samples by RT-PCR,and then sequenced and analyzed.Results 307 rats were trapped in the main epidemic areas,and Hantavirus specific-antigens were identified in 14 out of the 307 R.norvegicus and R.flabipectus((4.56%)). The genetic analysis of the partial S(600-999nt),G1(140-640nt) and G2(2003-2302nt) genes indicated that all hantaviruses in the 14 hantavirus-positive lung tissues are Seoul virus.The further genetic and phylogenetic analysis indicated that 14 viruses were grouped into 2 genetic subtypes,Seoul 1 and Seoul 3.Conclusion Yunnan was a Rattus-type HFRS epidemic areas.There are two subtypes of Seoul virus,ie.1 and 3 of Seoul virus,and they distribute widely in Yunnan.
Robust and Accurate Representation Learning for High-dimensional and Sparse Matrices in Recommender Systems

How to accurately represent a high-dimensional and sparse (HiDS) user-item rating matrix is a crucial issue in implementing a recommender system. A latent factor (LF) model is one of the most popular and successful approaches to address this issue. It is developed by minimizing the errors between the observed entries and the estimated ones on an HiDS matrix. Current studies commonly employ L2-norm to minimize the errors because it has a smooth gradient, making a resultant LF model can accurately represent an HiDS matrix. As is well known, however, L2-norm is very sensitive to the outlier data or called unreliable ratings in the context of the recommender system. Unfortunately, the unreliable ratings often exist in an HiDS matrix due to some malicious users. To address this issue, this paper proposes a Smooth L1-norm-oriented Latent Factor (SL1-LF) model. Its main idea is to employ smooth L1-norm rather than L2-norm to minimize the errors, making it have both high robustness and accuracy in representing an HiDS matrix. Experimental results on four HiDS matrices generated by industrial recommender systems demonstrate that the proposed SL1-LF model is robust to the outlier data and has significantly higher prediction accuracy than state-of-the-art models for the missing data of an HiDS matrix.
A Novel Diagnostic Algorithm for Heart Disease in ECG Monitoring System

Healthcare Internet of Things (HIoT) can connect mobile and wearable devices in the medical field, making disease monitoring and diagnosis possible anytime and anywhere. Most of these mobile and wearable devices can collect physiological signals of the human body in real time. Among them, ECG signal as a non-invasively collected signal that can effectively reflect the physiological changes of the heart plays a vital role in clinical and HIoT. We firstly propose a practical ECG monitoring system based on Humeds Portable ECG Monitor. Secondly, based on wavelet transform (WT) and deep convolutional neural network (DCNN), we propose a new algorithm suitable for the diagnosis of atrial fibrillation (AF) and arrhythmia. The sensitivity of AF is 0.978 and the accuracy rate of arrhythmia diagnosis is 0.991. Thirdly, we collected ECG data from 17 volunteers and verified the AF algorithm, the final average accuracy is 0.852. The ECG monitoring system designed in this paper can be used as a complete and effective application of HIoT. The algorithm designed in this paper is not only applicable to the ECG monitoring system proposed but also can be integrated as a potential algorithm in other ECG mobile and wearable devices.