Miracle-dz/UniFAR-FTUs huggingface dataset:
Gallic Acid Potentiates the Antimicrobial Activity of Tulathromycin Against Two Key Bovine Respiratory Disease (BRD) Causing-Pathogens

Bovine respiratory disease (BRD) is the most common infectious disease in dairy and beef cattle. It is associated with significant morbidity and mortality and causes a huge economic loss each year. In western Canada, a one-time injection of tulathromycin is commonly used as a metaphylactic procedure to reduce BRD incidence and eliminate potential BRD outbreak. With increased global concern on antimicrobial usage in dairy and beef products and bacterial resistance to antimicrobials, it is important to develop a novel strategy to eliminate the usage or decrease the dosage of antimicrobials. In this study, we showed that gallic acid was active against both Mannheimia haemolytica and Pasteurella multocida, two key BRD associated-pathogens, with the minimum inhibitory concentration (MIC) measured at 250 and 500 μg/mL, respectively. Co-administration of tulathromycin and gallic acid exhibited a strong additive or weak synergistic effect toward both M. haemolytic and P. multocida. Tulathromycin, gallic acid and their combination were also effective against the mixed culture of M. haemolytic and P. multocida. Furthermore, we showed that pre-exposure to tulathromycin generated bacterial resistance to the antimicrobial in M. haemolytica but not in P. multocida.
The Optimal Chinese Herbal Injections for Use With Radiotherapy to Treat Esophageal Cancer: A Systematic Review and Bayesian Network Meta-Analysis

Ethnopharmacological relevance: Esophageal cancer is one of the most common human cancers, and its incidence is highly endemic in China. The combination of Chinese herbal injections (CHIs) and radiotherapy should be informed by the best available evidence. Aim of the study: To update and expand on previous work in order to compare and rank the efficacy and safety of CHIs in combination with radiotherapy to treat esophageal cancer. Materials and Methods: We searched several electronic databases to identify randomized controlled trials (RCTs) regarding CHIs to treat esophageal cancer from their inception to March 15, 2017. In a network meta-analysis (NMA), the bias of the included trials was assessed by two individuals independently in accordance with the Cochrane Handbook for Systematic Reviews of Interventions. Outcomes such as the clinical effectiveness rate, performance status, adverse reactions (ADRs), and survival rate were evaluated. We performed a random-effects NMA to obtain estimates of efficacy and safety outcomes, and we present these estimates as odds ratios (ORs) and corresponding 95% confidence intervals (CIs) calculated via Stata 13.1 and WinBUGS 1.4 software. Furthermore, the surface under cumulative the ranking curve (SUCRA) was used to rank the efficacy and safety of different CHIs in relation to each outcome. Results: Of 685 identified trials, 55 were eligible for inclusion in the study. These 55 trials included 12 CHIs and 4,114 participants. The cluster analysis results suggested that Compound kushen injection therapy is the optimal CHI treatment for patients with esophageal cancer in terms of improving the clinical effectiveness rate and performance status. Huachansu and Kangai injection are superior in improving 1-year and 2-year survival rates. Lentinan injection may be considered a favorable choice for reliving ADRs, and Compound kushen injection may provide treatment benefits by reducing both gastrointestinal reaction and radiation esophagitis. Conclusions: The current clinical evidence indicated that Compound kushen injection combined with radiotherapy is the most preferable and beneficial option for patients with esophageal cancer in terms of efficacy and safety. However, the results of our study should be interpreted with caution given the limitations of the sample size and the methodological quality of the included trials.
Higher Impact Factor of Neuroimaging Journals Is Associated With Larger Number of Articles Published and Smaller Percentage of Uncited Articles

The relationships among various citation metrics have been probed in multiple scientific research disciplines but not neuroimaging. The aim of the current study was to assess the citation metrics of neuroimaging journals and analyze their relationships. The Journal Citation Reports (JCRs) published by Clarivate Analytics was accessed to extract relevant data for each of the 14 journals from the neuroimaging category. Pearson correlation tests were conducted to test if the citation metrics had significant correlations. Impact factor was positively correlated with citable items (r = 0.717, p = 0.004). Percentage of uncited citable items and percentage of journal self citations were partially negatively correlated with citation distribution, i.e., the percentages of citable items that contributed to 20%, 50% and 80% of total citations. The current study has implied that all the abovementioned metrics should be considered together to provide multi-faceted evaluations instead of using a single metric, at least in the neuroimaging field.
Adaptive Learning Rate Adjustment with Short-Term Pre-Training in Data-Parallel Deep Learning

This paper introduces a method to adaptively choose a learning rate (LR) with short-term pre-training (STPT). This is useful for quick model prototyping in data-parallel deep learning. For unknown models, it is necessary to tune numerous hyperparameters. The proposed method reduces computational time and increases efficiency in finding an appropriate LR; multiple LRs are evaluated by STPT in data-parallel deep learning. STPT means training only with the beginning iterations in an epoch. When eight LRs are evaluated using eight parallel workers, the proposed method can easily reduce the computational time by 87.5% in comparison with the conventional method. The accuracy is also improved by 4.8% in comparison with the conventional method with a reference LR of 0.1; thus, no deterioration in accuracy is observed. For an unknown model, this method shows a better training curve trend than other cases with fixed LRs.
E-Polarized Diffraction by a Lossy Dielectric Wedge

E-polarized diffraction by a lossy dielectric wedge is analysed using the hidden rays of diffraction (HRD) technique. After the ordinary ray-tracing is terminated in the physical region, the hidden ray-tracing is added in the complementary region. The one-to-one correspondence with the geometrical rays renders the diffraction coefficients to be expressed by the finite sum of cotangent functions, of which angular period is adjusted to satisfy the edge condition at the wedge tip. Performing the uniform asymptotic integration to the diffraction coefficients, one may obtain the edge-diffracted fields. In comparison with the conventional physical optics (PO) solution, the HRD edge-diffracted fields approach gradually to the exact solution to the perfectly conducting wedge as the imaginary part of relative dielectric constant increases from 1 to 100.
Design of Skiplist Based Key-Value Store on Non-Volatile Memory

Non-volatile Memory is promising to persistent data storage, which has outstanding advantages against traditional storage devices such as HDD and SSD. One of its hugest advantages is its DRAM-like read latency and micro second level write latency, which is several hundred times faster than the original block device. However, one of the issues on using non-volatile memory as storage device is designing a suitable indexing system for data stores there, in which the characteristics of non-volatile memory can be able to make full use of. The state of the art indexing systems of non-volatile key-value stores are usually based on B+-tree or its variant, which are originally designed for mechanic hard disk and volatile memory. The semantics of B+-tree requires inside data being sorted and frequent split and merge for keeping its balance. However, both of sorting and splitting will cause extra write to non-volatile memory, which will downgrade the performance. As a result, B+-tree and its variant may not be naturally suitable for non-volatile memory. In this paper, we proposed a skiplist based indexing system for non-volatile memory key-value store(NV-Skiplist), which can take fully use of the features of both the non-volatile memory and DRAM. NV-Skiplist constructs its bottom layer in the non-volatile memory for data persistence and supporting range scan, it also builds its upper layers in the DRAM to retain fast index searching and prevent large consistent overhead. We also introduced a multi-ranged variant to increase the search performance. We evaluate the performance of NV-Skiplist on a non-volatile memory emulator with a server that has Intel Xeon E5-2620 v2 processor. The experimental results show that our design outperforms the original tree-based non-volatile key-value store on both insertion and search performance by both 27% and 12% in each case.
Bandwidth Efficient Architectures for Convolutional Neural Network

In recent years, Convolutional Neural Network (CNN) has been rapidly evolving and the real-time CNN implementations in embedded systems are becoming highly demanding. It is necessary that high performance and real time CNN based implementations be realized in local processors. Conventional approaches designing CNN accelerators focus on reducing the computational workload of CNNs. However, the limited external memory bandwidth has become the main bottleneck of CNN acceleration in embedded systems. Because in deep and large CNN models the feature map pixels and weights, which are numerous and must be stored in external memories, need to be exchanged between off-chip and on-chip memories frequently. Hence the performance is constrained by the limited external memory bandwidth. In this paper, bandwidth efficient architectures for CNN implementation are proposed. The intermediate pixel data are stored on chip and kernel weights are transferred in an efficient way. Compared to mainstream CNN implementation methods, the proposed architectures can efficiently utilize external memory bandwidth while preserving the original throughput.
Comparison between Generalized Integrated Interleaved Codes and Generalized Error Location Codes

Generalized Integrated Interleaved (GII) codes and Generalized Error Location (GEL) codes are both multi-level codes. , whose redundancy of each layer is shared by other layers. Original GII codes are defined with a specific conversion matrix. , which is studied in detail in this paper. We generalize the conversion matrix for GII codes and make it possible to employ a matrix in row echelon form to reduce the computational complexity of matrix multiplications. It is also shown that with inner codes and outer codes over the same Galois Field. , a GEL code degenerates into a GII code. Moreover. , the decoding performance is analyzed and some GEL and GII codes are constructed to compare from the aspects of overhead and error performance.
A Very Deep One Dimensional Convolutional Neural Network (VDOCNN) for Appliance Power Signature Classification

Estimating appliance specific power consumption using a single measuring device, known as Non-Intrusive Load Monitoring (NILM), is a challenging Blind Signal source Separation (BSS) problem. For the past two decades, numerous mathematical and pattern recognition techniques, including Fractional Hidden Markov Model (FHMM), Gaussian Mixture Model (GMM) and Mean Shift Based Clustering Techniques (MSBCT) have been proposed to decompose the total power consumption of a household into appliance specific power signals. The measurement sampling rate, operating characteristic of individual appliances and an unknown number of mixed signals create a big challenge in separating them. The main challenge is to design an algorithm that can learn appliance features accurately, before applying the algorithm to disaggregate the main power signals. To address this problem, A Very Deep One dimensional Convolutional Neural Network (VDOCNN) for appliance power signature classification is proposed in this research. As a first step, we have applied VDOCNN in learning appliance features from a given set of labeled training data. VDOCNN has achieved accuracy up to 98% in detecting appliance from its power signature using a UK Domestic Appliance-Level Electricity (UK-DALE) dataset. Using this algorithm, we are working towards disaggregation of power signatures for different appliances from a single power signal in future research.
Fast Quantized Arithmetic on x86: Trading Compute for Data Movement

We introduce Clover, a new library for efficient computation using low-precision data, providing mathematical routines required by fundamental methods in optimization and sparse recovery. Our library faithfully implements variants of stochastic quantization that guarantee convergence at low precision, and supports data formats from 4-bit quantized to 32-bit IEEE-754 on current Intel processors. In particular, we show that 4-bit can be implemented efficiently using Intel AVX despite the lack of native support for this data format. Experimental results with dot product, matrix-vector multiplication (MVM), gradient descent (GD), and iterative hard thresholding (IHT) demonstrate that the attainable speedups are in many cases close to linear with respect to the reduction of precision due to reduced data movement. Finally, for GD and IHT, we show examples of absolute speedup achieved by 4-bit versus 32-bit, by iterating until a given target error is achieved.
Development of a Battery Monitoring and Control Unit Aiding Utilities in Demand Side Management

As the need for energy is increasing these days, the energy generated by the conventional grid is not sufficient to meet the demand and this results in supply-demand mismatch. As a solution, in distributed storage, batteries are used as dispatchable loads in demand side for balancing the supply-demand mismatch. This article proposes design and development of a Battery Monitoring and Control Unit (BMACU) for the batteries used in distributed storage. The BMACU facilitates monitoring of State of Charge (SoC) and updating the computed value in the utility server. The control unit allows various modes of charging of battery based on SoC and the current scenario. BMACU enables fast charging if there is excess power generation and discharge the battery during deficit, thus improving system reliability.
Detection and Recognition of Security Detection Object Based on Yolo9000

In this paper, a convolutional neural network model based on YOLO9000 is introduced to meet the need of real-time engineering computing. This network model can study and classify the targets in depth, aiming at the characteristics of scissors and aerosols. The characteristics have various kinds such as overlap, cover and multiscale. At the present stage, the average speed is 68 FPS on the windows platform with GPU (Geforce GTX Titan X) acceleration. In addition, the average precision and recall rate are 94. 5%, 92. 6%, respectively.
POLYBiNN: A Scalable and Efficient Combinatorial Inference Engine for Neural Networks on FPGA

Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) have gained significant popularity in several classification and regression applications. The massive computation and memory requirements of deep NN architectures pose particular challenges for their FPGA implementation. Moreover, programming FPGAs requires hardware-specific knowledge that many machine-learning researchers do not possess. To make the power and versatility of FPGAs available to a wider deep learning user community and to improve DNN design efficiency, we introduce POLYBiNN, a scalable and efficient combinatorial inference engine for DNNs and CNNs. POLYBiNN is composed of a stack of decision trees, which are binary classifiers by nature, and utilizes AND-OR gates instead of multipliers and accumulators. POLYBiNN drastically cuts the hardware consumption down while maintaining high accuracy, and it is a memory free inference engine. We also propose a tool that generates automatically a low-level hardware description of the trained POLYBiNN for a given application. We evaluate POLYBiNN for the MNIST dataset when implemented in a ZYNQ-7000 ZC706 FPGA platform. The system achieves a throughput of up to 100 million image classifications per second with 90 ns latency on the MNIST dataset with 97.18% accuracy. The power consumption of PLOYBiNN is less than 1.2 W. We also show how POLYBiNN can be used in the fully connected layers of a CNN and apply this approach to the CIFAR-10 dataset.
The Dynamics of an HTLV-1 System with Cell-Mediated Immune Response

In this paper, we formulated a four dimensional system to explore the interaction between Tax-expression and CTL immune response in HTLV-1 infected individuals. Two parameters ($R_{0}$ and $R_{1}$) are derived to study the system dynamics. We analyzed the existence and stability of the disease-free equilibrium, the immune-free equilibrium and the infected equilibrium theoretically. By numerical simulations, the impact of Tax-expression and immune response on the proviral load were demonstrated.
Sparsity Aware Hybrid Adaptive Algorithms for Modeling Acoustic Paths

Modeling of acoustic paths may be considered as a sparse system identification problem, owing to the sparse nature of the many acoustic impulse responses. Adaptive algorithms based on the concept of proportionate filtering as well as the ones based on zero attraction has been widely used for sparse system identification. A re-weighted zero attraction least mean square (RZA-LMS) algorithm is a popular sparse adaptive algorithm and is effective for modeling sparse as well as non-sparse systems. However, it offers a slow initial convergence, which will slow down the system identification process. The improved proportionate normalized least mean square (IPNLMS) algorithm offers a fast initial convergence and is effective for sparse system identification. But the steady state mean square error offered by the algorithm is not as effective as that offered by RZA-LMS algorithm. In an endeavour to achieve sparse system identification with improved initial convergence speed and enhanced steady state mean square error, this paper proposes a modeling scheme based on a convex combination of IPNLMS and RZA-LMS algorithms. The new scheme has been shown to be effective in modeling sparse systems including an acoustic feedback path in a behind the ear digital hearing aid.
Spectrally Constrained MIMO Radar Waveform Design Based on Mutual Information

We address the waveform design problem for multiple-input multiple-output (MIMO) radar in spectrally crowded environments. We exploit the mutual information between the target reflections and the target responses as the design metric. To tackle the associated nonconvex optimization problem, we propose an algorithm based on first-order Taylor series approximation as well as minorization–maximization (MM) based algorithms to design the spectrally constrained waveforms. Interestingly, for some scenarios, we can synthesize the globally optimal (spectrally constrained) waveforms with the maximum mutual information. We also show that, through intelligent waveform design, MIMO radar can coexist more efficiently with other communication systems occupying the same spectrum, while suffering from insignificant mutual information losses.
Clonagem in vitro de tamareira (Phoenix dactylifera L.)

Cultivares; Potencial da especie para o Nordeste brasileiro; Tecnicas de propagacao; Clonagem in vitro da tamareira adulta; Escolha da matriz; Coleta das espatas; Desinfestacao das espatas e preparo dos explantes; Composicao dos meios de cultura; Conducao das culturas.
Efecto de un Recubrimiento Comestible y Diferentes Tipos de Empaque en los Atributos Físico-Químicos y Sensoriales de Piña Manzana' Mínimamente Procesada

Resumen Se evaluo el efecto de recubrimiento comestible y varios tipos de empaque en Pina `Manzana´ minimamente procesada bajo refrigeracion a 5±1°C y 90±2% de humedad relativa. Se efectuo un seguimiento de los atributos de calidad cada cuatro dias hasta el dia 24. Se utilizo el delineamiento experimental completamente al azar con tres repeticiones totalizando ocho tratamientos derivados de cuatro condiciones de empaques y dos de recubrimiento. Se aplico estadistica descriptiva, ANOVA y comparacion de medias segun Tukey. Se utilizo el software SAS 9.3 (p<0.05). El recubrimiento comestible tuvo un efecto significativo en la conservacion de acuerdo a los valores de porcentaje de acidez titulable AT y de pH. Sin embargo, la pina minimamente procesada acondicionada al vacio, tratada con acido ascorbico (1% v/v), acido citrico (1% v/v) y CaCl 2 (1% v/v), sin recubrimiento comestible y almacenada bajo refrigeracion permitio conservar los atributos de calidad por un periodo 16 dias.
Image Denoising Algorithm Based on Improved Wavelet Threshold Function and Median Filter

An improved wavelet threshold function is designed in this paper. The wavelet decomposing detail coefficients of the image mixed with Gaussian noise are denoised by this improved threshold function, then reconstructed together with the wavelet decomposing approximation coefficients to get the denoised image. The experimental results show that the denoising effect of the improved threshold function is superior to hard threshold and soft threshold. Different thresholds will be set for wavelet details of each level when denoising. Considering the advantage of median filter, this paper proposes to combine wavelet improved threshold denoising with median filtering, namely combined denoising method. MATLAB simulation results show that, this method has better effect on removing Gaussian noise, speckle noise and salt- and-pepper noise mixed in images. This paper also does the denoising experiments on fingerprint images and printed circuit board images contaminated by various noises, and the better denoising effect demonstrates the adaptability of the combined denoising method.
Doubly-Selective Channel Estimation for Continuous Phase Modulation

In this paper, we present two Data-Aided channel estimators for Continuous Phase Modulation (CPM) in the case of transmissions over doubly -selective channels. They both capitalize on the Basis Expansion Model (BEM), widely used for OFDM systems and for Single Carrier transmission with linear modulation. However, in the case of CPM signals, we need to work on a over-sampled received signal (fractionally-spaced representation) as the equalization techniques are also working on the over-sampled received signal. The first one is a classical Least Squares (LS) estimation of the BEM parameters whereas the second channel estimator introduces first a parametric dependence on the paths delays. Indeed, in the case where those delays are known (by estimation or by geometrical consideration as for the aeronautical channel by satellite), the second LS estimation on the BEM parameters is improved and less computationally demanding. Simulations results are provided and show good performance of our parametric LS estimation.
The Effects of a UAV on a Terrestrial MANET

Technological advances on Unmanned Aerial Vehicles (UAVs) and autonomous control could make UAVs useful for communication purposes, extending the range and increasing the performance of Mobile Ad-Hoc Networks (MANETs). However, how and whether the UAV should be deployed in an otherwise terrestrial MANET depends on multiple parameters and objectives. In this paper, we analyze the effect of a UAV as a traffic relay compared to terrestrial-bound forwarding in a connected CSMA/CA MANET with omnidirectional antennas. A UAV is superior to terrestrial nodes in terms of connectivity, due to the larger Line-of-Sight coverage. This also means that a UAV can reduce the average number of hops in a MANET, thus reducing the self-interference problem and improve the network resource use. However, there are also negative effects of a UAV. The potential for spatial reuse is reduced, and problems due to hidden nodes increase. The results show that the UAV's impact on throughput and fairness depends on the traffic patterns, the topology, and the traffic load.