Miracle-dz/UniFAR-FTUs huggingface dataset:
A DREB-Like Transcription Factor From Maize (Zea mays), ZmDREB4.1, Plays a Negative Role in Plant Growth and Development

The DREB (dehydration-responsive element binding)-type transcription factors are classified into six subgroups, named A-1 to A-6. The members of DREB A-1 and A-2 subgroups have been reported to be involved in response to various abiotic stresses. However, there were only a few genes belonging to A-3 to A-6 subgroups to be reported. In this study, we cloned a DREB A-4 subgroup gene from maize (Zea mays), ZmDREB4.1, and analyzed its characteristics and functions. ZmDREB4.1 was expressed in roots, stems, and leaves at very low levels. It was not induced by any biotic or abiotic treatment. ZmDREB4.1 was located in the nucleus, could directly bind to the DRE element and functioned as a transcriptional activator. The constitutive expression of ZmDREB4.1 in tobacco (Nicotiana tabacum L.) repressed leaf extension and hypocotyl, petiole and stem elongation. In maize, overexpression of ZmDREB4.1 repressed calli growth and regeneration. Further analysis showed that the smaller leaves of transgenic tobacco resulted from inhibition of cell division. The contents of cytokinin and auxin in transgenic leaves were severely decreased. The shorter hypocotyls, stems and petioles of transgenic tobacco were caused by inhibition of cell elongation. The transgenic hypocotyls, stems and petioles contained reduced gibberellin levels. Application of exogenous GA3 rescued the shorter hypocotyls, stems and petioles, but not the smaller leaves. These results demonstrated that ZmDREB4.1 plays an important role in the negative regulation of plant growth and development.
Neural 1D Barcode Detection Using the Hough Transform

Barcode reading mobile applications to identify products from pictures acquired by mobile devices are widely used by customers from all over the world to perform online price comparisons or to access reviews written by other customers. Most of the currently available 1D barcode reading applications focus on effectively decoding barcodes and treat the underlying detection task as a side problem that needs to be solved using general purpose object detection methods. However, the majority of mobile devices do not meet the minimum working requirements of those complex general purpose object detection algorithms and most of the efficient specifically designed 1D barcode detection algorithms require user interaction to work properly. In this work, we present a novel method for 1D barcode detection in camera captured images, based on a supervised machine learning algorithm that identifies the characteristic visual patterns of 1D barcodes’ parallel bars in the two-dimensional Hough Transform space of the processed images. The method we propose is angle invariant, requires no user interaction and can be effectively executed on a mobile device; it achieves excellent results for two standard 1D barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the performance of a state-of-the-art 1D barcode reading library by coupling it with our detection method.
Fast-LSTM acoustic model for distant speech recognition

The distant-talking automatic speech recognition (ASR) currently becomes an important task in a speech recognition area. Traditionally, hybrid Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) approach are used for ASR. This paper will discuss some deep neural network (DNN) techniques for acoustic modeling, as well as lattice rescoring techniques for ASR. The proposed Fast-long short-term memory neural network (Fast-LSTM) acoustic model combines the time delay neural network (TDNN) and LSTM network to reduce the training time of the standard LSTM acoustic model.
Current and Emerging Innovations for Detection of Food-Borne Salmonella

Salmonella is one of the leading causes of food-borne illnesses worldwide, and one of the main contributors to salmonellosis is the consumption of contaminated egg, poultry, pork, beef, and milk products. Since deleterious effects of Salmonella on public health and the economy continue to occur, improving safety of food products by early detection of food-borne pathogens would be considered an important component for limiting exposure to Salmonella contamination. Therefore, there is an ongoing need to develop more advanced detection methods that can identify Salmonella accurately and rapidly in foods before they reach consumers. In the past three decades, there have been increasing efforts toward developing and improving rapid pathogen detection and characterization methodologies for application to food products. In this chapter, we discuss molecular methods for detection, identification, and genetic characterization of Salmonella in food. In addition, the advantages and disadvantages of the established and emerging rapid detection methods are addressed here. The methods with potential application to the industry are highlighted in this chapter.
Tau Spreading Mechanisms; Implications for Dysfunctional Tauopathies

Tauopathies comprise a group of progressive age-associated neurodegenerative diseases where tau protein deposits are found as the predominant pathological signature (primary tauopathies) or in combination with the presence of other toxic aggregates (secondary tauopathies). In recent years, emerging evidence suggests that abnormal tau accumulation is mediated through spreading of seeds of the protein from cell to cell, favouring the hypothesis of a prion-like transmission of tau to explain the propagation of the pathology. This would also support the concept that the pathology initiates in a very small part of the brain before becoming symptomatic and spreads across the brain over time. To date, many key questions still remain unclear, such as the nature of the tau species involved in the spreading, the precise seeding/template and uptaking mechanisms or the selectivity explaining why certain neurons are affected and some others are not. A better understanding of the tau spreading machinery will contribute to the development of new therapeutic approaches focused on halting the abnormal propagation, offering also new perspectives for early diagnosis and preventive therapies. In this review, we will cover the most recent advances in tau spreading mechanisms as well as the implications of these findings for dysfunctional tauopathies.
The Computing of Optimized Clustering Threshold Values Based on Quasi-Classes Space for the Merchandise Recommendation

The merchandise recommendation is an important part of electronic commerce. In view of the difficulty in obtaining user private information and modeling user interest, this paper is based on the relationship between goods for commodity recommendation. We use fuzzy clustering learning to construct quasi-classes space. Through the intersection of quasi-class and the collection of goods that are being ordered by users, we can know the customers appetites for merchandise, and then recommend the goods. In the construction of quasi-classes space, the value of the threshold Λ must be appropriate, because the threshold Λ determines the size of the quasi-class. It will affect the recommendation of the goods that the size of the quasi-class is too large or too small. The influence of threshold Λ on commodity recommendation is discussed by numerical example, and we finally find the best value of Λ in this paper.
A MISSPECIFICATION TEST FOR SIMULATION METAMODELS

In this paper we propose a novel misspecification test for simulation metamodels. It is a consistent test that helps to assess the adequacy of simulation metamodels. The test statistic we construct is shown to be asymptotically normally distributed under the null hypothesis that the metamodel is correct, while diverging to infinity at a rate of √ n, where n is the test sample size if the given metamodel is inadequate. Furthermore, as a by-product, we construct confidence intervals for mean squared errors of the metamodels. Preliminary numerical studies show that the test works quite well and has good finite-sample properties.
Market Segmentation with Cluster Analysis based on Video Streaming Data

Over the last years, the growth and development of video on demand (VOD) services has given new possibilities of performing machine learning on large amounts of video history data. A common usage of machine learning for businesses is market segmentation, which is usually addressed with cluster analysis. Market segmentation with cluster analysis has been performed for the video streaming service company Viaplay. It was found that K-means with cosine measure performed best of the attempted methods and has been shown to facilitate a useful and interpretable market segmentation based on a set of segment criteria: understandability, homogeneity, independence, stability and actionability. The thesis also shows an example of how to evaluate clustering of video streaming users. A version of term frequency-inverse document frequency (tf-idf) was introduced, which is called video importance score (VIS). VIS is used to find videos specifically important to a cluster, and has proven to be helpful in interpreting the resulting clusters. The results were evaluated within a commonly used market segmentation evaluation framework, which was adapted to the problem at hand. Although the market segmentation strongly indicates to be useful, it still has to be in real-word scenario evaluated by the company before any definitive conclusions can be drawn.
A 0.6 mW 1.6 dB Noise Figure Inductorless Shunt Feedback Wideband LNA With Gm Enhancement and Current Reuse in 65 nm CMOS

This paper presents the design of an inductorless low power differential Low Noise Amplier (LNA) for multi-standard radio applications between 0.2-3.2 GHz. Conventionally, the low power shunt-feedback based LNA noise performance suffers due to the low intrinsic gm of MOS transistors. A single stage differential shunt-feedback LNA which incorporates both, Gm boosting and current-reuse technique is proposed to overcome the noise performance degradation in low power designs. A detailed analysis of the conventional inductorless shunt-feedback based LNA along with the proposed technique is provided. It provides a good trade-off between different performance parameters after sizing and biasing optimization under ultra low power design constraint. The proposed technique is implemented in 65 nm CMOS technology and occupies an active area of 0.25 mm2. It exhibits a power gain of 13.5 dB with 1.6 dB NF while dissipating only 0.6 mW power.
Leveraging Edge Computing through Collaborative Machine Learning

The Internet of Things (IoT) offers the ability to analyze and predict our surroundings through sensor networks at the network edge. To facilitate this predictive functionality, Edge Computing (EC) applications are developed by considering: power consumption, network lifetime and quality of context inference. Humongous contextual data from sensors provide data scientists better knowledge extraction, albeit coming at the expense of holistic data transfer that threatens the network feasibility and lifetime. To cope with this, collaborative machine learning is applied to EC devices to (i) extract the statistical relationships and (ii) construct regression (predictive) models to maximize communication efficiency. In this paper, we propose a learning methodology that improves the prediction accuracy by quantizing the input space and leveraging the local knowledge of the EC devices.
White list-based ransomware real-time detection and prevention for user device protection

A ransomware encrypts valuable user/system files found on a victim's device, and then asks for a ransom to release a decryption key needed to recover the plaintext files. Recently, the damage caused by such ransomwares is sharply increasing. So far, ransomware countermeasures are very similar to existing malware countermeasures. For example, after analyzing ransomware code itself, some methods detect/delete ransomwares from a device/system based on the analyzed result. Also, some methods try to find/block ransomware distribution roots. A common point of these countermeasures is that a trust party should first analyze ransomware codes and/or behaviors based on black lists (i.e., specific signatures of ransomwares). In this case, users should patch ransomware information from the trust party to prevent ransomwares installed/executed on their devices. However, since such approaches cannot prevent known/new ransomwares, the trust party can get/analyze a ransomware only after the ransomware has attacked some users. It means that an unspecified, large number of users can inevitably become victims of new ransomwares. Another approach is detect abnormal behavior of software executed on users' devices. It is one of such approaches to point a folder as a secure folder and then to control software accessing files in the folder. However, such an approach may cause inconvenience to users and limit users' behavior. Hence, to effectively as well as securely detect/control ransomwares, this paper proposes a new method to detect/block ransomwares by analyzing the file operation procedure of the operating system on user's device and applying an access control scheme to the file operation procedure. The proposed scheme can in real-time detect ransomwares to encrypt valuable files in users' devices without the patch of a ransomware information published by a trust party. So, it can prevent both new and variant ransomwares in real time.
The application of transient thermography for the thermal characterisation of carbon fibre/epoxy composites

Purpose: Primary purpose of the present experimental study was to evaluate the local fibre content in carbon fibre/epoxy composites using transient thermography. Design/methodology/approach: The experiments have been performed using transient thermography to obtain the thermograms for carbon/epoxy specimens with different carbon fibre content. From obtained thermograms the thermal diffusivity values were determined and compared for each specimen and correlated with carbon content. The composites were two times tested using two different heating conditions to check the conformity of determined diffusivity values. Findings: It was found from obtained results that composites with different carbon fibre content had different values of thermal diffusivity, indicating that transient thermography can be considered as a non-destructive testing method for fiber content evaluation in CFRP composites. Research limitations/implications: Developed empirical formula is not universal for any other fibre reinforced polymer composite, so different relationships should be determined for different composites. Practical implications: The results obtained from present experiment would be of great importance in the industrial applications to obtain first estimate of carbon fibre content in fibre reinforced composite materials. Originality/value: The originality of present investigation is in application of transient thermography for local fibre content evaluation in polymer composite materials. The method should be of interest for the industrial quality
Incidence of sexually transmitted infections before and after preexposure prophylaxis for HIV

Objective: Use of preexposure prophylaxis (PrEP) for HIV raises concerns about sexually transmitted infection (STI) incidence because of decreased condom use among MSM. This study examines whether PrEP is associated with STIs in the 12 months following PrEP prescription relative to the 12 months prior to PrEP and if STI rates are higher among PrEP users relative to individuals receiving postexposure prophylaxis (PEP). Design: Retrospective cohort study including PrEP users with more than 12 months of follow-up before PrEP prescription and individuals receiving PEP from 2010 to 2015 at Clinique l’Actuel (Montréal, Canada). Methods: Incidence of chlamydia, gonorrhoea, syphilis and hepatitis C virus over 12 months was compared before and after PrEP; and for PrEP versus PEP users using Poisson models to generate incidence rate ratios (IRRs) with 95% confidence intervals (CIs) and adjusted IRRs (aIRRs) controlling for frequency of STI-screening visits. Models comparing PrEP and PEP users were further adjusted for age and education. Results: One hundred and nine PrEP and 86 PEP users were included. Increased rates of STIs were observed in the 12 months after PrEP relative to the 12 months prior (IRR: 1.72, CI: 1.22–2.41; aIRR: 1.39, CI 0.98–1.96). PrEP users were also at higher STI risk relative to PEP users (IRR: 2.18, CI: 1.46–3.24; aIRR: 1.76, CI: 1.14–2.71). Conclusion: Increased rates of STIs among individuals after initiation of PrEP may suggest greater risk behaviours during the first year on PrEP. Further studies are needed to measure long-term trends in STI acquisition following PrEP initiation.
Tree Morphology for Phenotyping from Semantics-Based Mapping in Orchard Environments

Measuring tree morphology for phenotyping is an essential but labor-intensive activity in horticulture. Researchers often rely on manual measurements which may not be accurate for example when measuring tree volume. Recent approaches on automating the measurement process rely on LIDAR measurements coupled with high-accuracy GPS. Usually each side of a row is reconstructed independently and then merged using GPS information. Such approaches have two disadvantages: (1) they rely on specialized and expensive equipment, and (2) since the reconstruction process does not simultaneously use information from both sides, side reconstructions may not be accurate. We also show that standard loop closure methods do not necessarily align tree trunks well. In this paper, we present a novel vision system that employs only an RGB-D camera to estimate morphological parameters. A semantics-based mapping algorithm merges the two-sides 3D models of tree rows, where integrated semantic information is obtained and refined by robust fitting algorithms. We focus on measuring tree height, canopy volume and trunk diameter from the optimized 3D model. Experiments conducted in real orchards quantitatively demonstrate the accuracy of our method.
n-3 Fatty acids and mood: the devil is in the detail

Increasing evidence suggests that a low dietary intake of the n-3 long-chain PUFA EPA and DHA may contribute not only to the risks for various physical illnesses (particularly cardiovascular and immune system disorders), but also to many disorders of mental health and performance1,2. From their recent comprehensive review of the evidence for these n-3 fatty acids in the prevention and treatment of psychiatric disorders3, the American Psychiatric Association (APA) made the following recommendations:
A 7-Nm Dual Port 8T SRAM with Duplicated Inter-Port Write Data to Mitigate Write Disturbance

Two read-write 8T dual port static random accessmemories (SRAMs) suffer write disturb issue when both of its ports are accessed simultaneously. Write disturb is detrimental at low voltages in deep submicron technologies due to increased variations. This paper proposes a duplicated inter-port write data to mitigate write disturb in dual port SRAM design targeted on 7-nm FinFET TSMC technology. We have implemented duplicated inter-port write by employing two NMOS switches per bit line. We choose moderate sizes for these switches to have abalanced trade-off between area and duplication of inter-port write data. With NMOS switch size of nfin=4, we significantly suppress the write disturb and improve the write time by 2.16x at low voltage of 0.63V. Moreover, at further low voltages of 0.585V and 0.54V, our design achieves write time improvement over single port access.
Deep neural network compression via knowledge distillation for embedded applications

Deep neural networks have shown significant success across various applications. To solve the complex problems, the increasing depth and complexity pose the challenges of large computation and storage requirements when deploying such networks on embedded devices with limited storage and power. Many techniques have been developed by researchers to compress the deep neural networks to make them deployable on portable devices by reducing the storage requirements. This paper describes the implementation of deep neural network with teacher student model. A comparatively smaller student model learns the information passed from the larger teacher model without losing the accuracy and its learning/inference rate is also improved. So this kind of framework is suitable for embedded applications deployment where real-time performance is required.
Improving TCP Performance over Wireless WANs Using TCP/IP-Friendly Link Layer

In this paper we propose the use of a TCP/IP-friendly link level error recovery mechanism with novel design in conjunction with state-of-the-art Transmission Control Protocol (TCP) enhancements to improve TCP performance on network paths involving Wireless Wide-Area Network (W-WAN) links. We show that by combining a selected set of TCP enhancements TCP performance is significantly improved over W-WAN links. In addition, we employ a TCP/IP-friendly link layer protocol which minimizes the additional delay due to the Automatic Repeat reQuests (ARQ) by limiting the number of retransmission attempts and by adding redundancy in the retransmitted frames in a novel way. We perform experiments in an emulated satellite environment with a real implementation of the TCP/IP-friendly link layer and TCP enhancements in Linux. The results show that both TCP enhancements and link-level ARQ significantly improve TCP performance over W-WAN links, and combining the approaches yields the best performance.
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection

To better detect pedestrians of various scales, deep multi-scale methods usually detect pedestrians of different scales by different in-network layers. However, the semantic levels of features from different layers are usually inconsistent. In this paper, we propose a multi-branch and high-level semantic network by gradually splitting a base network into multiple different branches. As a result, the different branches have the same depth and the output features of different branches have similarly high-level semantics. Due to the difference of receptive fields, the different branches are suitable to detect pedestrians of different scales. Meanwhile, the multi-branch network does not introduce additional parameters by sharing convolutional weights of different branches. To further improve detection performance, skip-layer connections among different branches are used to add context to the branch of relatively small receptive filed, and dilated convolution is incorporated into part branches to enlarge the resolutions of output feature maps. When they are embedded into Faster RCNN architecture, the weighted scores of proposal generation network and proposal classification network are further proposed. Experiments on KITTI dataset, Caltech pedestrian dataset, and Citypersons dataset demonstrate the effectiveness of proposed method. On these pedestrian datasets, the proposed method achieves state-of-the-art detection performance. Moreover, experiments on COCO benchmark show the proposed method is also suitable for general object detection.