Miracle-dz/UniFAR-FTUs huggingface dataset:
Why do fungicide mixtures delay the evolution of resistance? An experimental evolutionary approach

Pesticide resistance poses a critical threat to agriculture, human health and biodiversity. Mixtures of fungicides are recommended and widely used in resistance management strategies. However, the components of the efficiency of such mixtures remain unclear. We performed an experimental evolution study on the fungal pathogen Z. tritici, to determine how mixtures managed resistance. We compared the effect of the continuous use of single active ingredients to that of mixtures, at the minimal dose providing full control of the disease, which we refer to as the “efficient” dose. We found that the performance of efficient-dose mixtures against an initially susceptible population depended strongly on the components of the mixture. Such mixtures were either as durable as the best mixture component used alone, or worse than all components used alone. Moreover, efficient-dose mixture regimes probably select for generalist resistance profiles as a result of the combination of selection pressures exerted by the various components and their lower doses. Our results indicate that mixtures should not be considered a universal strategy. Experimental evaluations of specificities for the pathogens targeted, their interactions with fungicides and the interactions between fungicides are crucial for the design of sustainable resistance management strategies.
Predicting speech intelligibility from a selective attention decoding paradigm in cochlear implant users

Objectives. Electroencephalography (EEG) can be used to decode selective attention in cochlear implant (CI) users. This work investigates if selective attention to an attended speech source in the presence of a concurrent speech source can predict speech understanding in CI users. Approach. CI users were instructed to attend to one out of two speech streams while EEG was recorded. Both speech streams were presented to the same ear and at different signal to interference ratios (SIRs). Speech envelope reconstruction of the to-be-attended speech from EEG was obtained by training decoders using regularized least squares. The correlation coefficient between the reconstructed and the attended ( ρASIR) or the unattended ρUSIR speech stream at each SIR was computed. Additionally, we computed the difference correlation coefficient at the same (ρDiff= ρASIR−ρUSIR) and opposite SIR ( ρDiffOpp= ρASIR−ρU−SIR) . ρDiff compares the attended and unattended correlation coefficient to speech sources presented at different presentation levels depending on SIR. In contrast, ρDiffOpp compares the attended and unattended correlation coefficients to speech sources presented at the same presentation level irrespective of SIR. Main results. Selective attention decoding in CI users is possible even if both speech streams are presented monaurally. A significant effect of SIR on ρASIR , ρDiff and ρ DiffOpp, but not on ρUSIR , was observed. Finally, the results show a significant correlation between speech understanding performance and ρASIR as well as with ρUSIR across subjects. Moreover, ρ DiffOpp which is less affected by the CI artifact, also demonstrated a significant correlation with speech understanding. Significance. Selective attention decoding in CI users is possible, however care needs to be taken with the CI artifact and the speech material used to train the decoders. These results are important for future development of objective speech understanding measures for CI users.
NVNet: An Enhanced Attention Network for Segmenting Neck Vascular from Ultrasound Images

Ultrasound images often contain much noise, and the examination process is easily affected by many factors. Therefore, it is often necessary for ultrasound surgeons to have rich experience in accurately identifying neck vascular from ultrasound images. The NVNet proposed in this paper can accurately segment neck vessels and accurately segment carotid intima-media from ultrasound images. We use an improved full-scale skip connection to obtain richer feature information from the encoder and introduce enhanced attention mechanism, making it possible for NVNet to identify neck vascular from ultrasound images containing much noise accurately. Due to the lack of available datasets, we collate an entirely new carotid longitudinal sectional ultrasound dataset and carry out data annotation under ultrasound surgeons' guidance. The experiment is carried out on the collated dataset and another public dataset of cross-sectional ultrasound images, including carotid artery and internal jugular vein. The final experimental results prove that the segmentation accuracy of NVNet exceeds that of many well-known models in recent years.
A Spatial-Temporal Graph Attention Network for Multi-intersection Traffic Light Control

Traffic light control is an extremely challenging problem in transportation. Recently, an increasing number of studies have employed reinforcement learning approaches to deal with traffic light control problems. However, these studies often observe the target intersection independently and ignore the dynamic effects from the surrounding intersections. Moreover, the dependence of historical information on current traffic conditions is also not fully exploited in multi-intersection traffic light control. In this work, we propose a spatial-temporal graph attention network based multi-agent deep Q-learning model. Our model can capture not only spatial features but also temporal features. Specifically, we first utilize the Graph Attention Network (GAT) to obtain the spatial relationship between the central intersection and surrounding intersections. Then Long Short-Term Memory (LSTM) network and the self-attention mechanism are used to get the time dimension information. Finally, we adopt a deep Q-learning network (DQN) to predict the action-value of each feasible action and choose the signal phase according to the size of the action-value. Experimental results on synthetic and realworld datasets confirm that our method can lead to both the least travel time and the maximum throughput, compared with some existing baselines.
Loss-based Active Learning for Named Entity Recognition

This paper addresses the practical issue of lacking training data when building named entity recognition (NER) systems. To this aim, we introduce a new active learning method for reducing the number of training samples required by the underlying NER system. Different from prior work that only focuses on training data, we define a new loss function that when estimating loss and uncertainty scores of training samples for selection, it takes also into account the uncertainty of the $K$ unlabelled test instances most similar to the unlabelled training instances. Experimental results on both general domain and clinical benchmark datasets show that the proposed active learning method allows to train the NER system with between 5% to 7% less training data compared to state of the art uncertainty sampling methods, while retaining high NER effectiveness.
Development of an Airborne-Based GPR System for Landmine and IED Detection: Antenna Analysis and Intercomparison

Airborne-based Ground Penetrating Radar (GPR) systems have proved to be an efficient solution for safe and accurate detection of buried threats such as Improvised Explosive Devices (IEDs) and anti-personnel and anti-tank landmines. The design of these prototypes is influenced by several parameters such as the working frequency band or the maximum weight and size of the payload to be placed on board the Unmanned Aerial Vehicle (UAV). In this sense, one of the main bottlenecks found in the design of these systems is the proper selection of the GPR antenna. This contribution focuses on the analysis of different Ultra Wideband (UWB) Vivaldi antennas and their performance in the context of an airborne-based GPR system. First, the Vivaldi antennas are characterized in terms of $S_{11}$ , radiation pattern, directivity, and phase center. Next, they are placed on board the implemented airborne-based GPR prototype to assess their impact on the detection capabilities of the system. In addition, other criteria such as the weight and size of the antennas are considered to make the final selection. Finally, the selected UWB Vivaldi antennas are tested in a realistic scenario.
Joint Precoding and Power Allocation for Multiuser MIMO System With Nonlinear Power Amplifiers

As the demand to alleviate the high cost and energy consumption problem in cellular communication systems grows, research on ways to compensate the hardware impairments for introducing low-cost devices into multi-input multi-output (MIMO) systems is becoming important. In particular, the nonlinearity of power amplifiers may severely degrade the performance of MIMO systems by distorting the precoded signals. In this work, we study the performance and precoding strategy of downlink multiuser MIMO systems with nonlinear power amplifiers. First, we analyze the signal-to-interference-plus-noise ratio (SINR) of nonlinear systems to establish the relationship between the transmit power and SINR. We find that the condition for maximizing the SINR depends on the average channel gain. Specifically, the maximum SINR is determined by the uncorrelated distortion when the average channel gain is large and by the distortion of precoding vector when the average channel gain is small. Motivated by the analysis, we propose an iterative algorithm, which alternately updates the precoding and power allocation by resolving the complicated coupling factors in the SINR expressions of nonlinear system. Furthermore, the proposed algorithm implicitly controls the total transmit power so that the desired signal, interference, and distortion are properly balanced. By the simulation results, we verify the analysis results and show that the proposed scheme outperforms the conventional schemes and achieves a near-optimal sum rate.
Ejected Droplet-Directed Transportation and Self-Alignment of Microfibers to Micro Trenches

Microfibers are important components in the fabrication of fiber-reinforced materials, and the distribution and alignment of microfibers can affect the mechanical, electrical, and thermal properties of the fiber-reinforced materials. However, it remains a challenge to precisely align and distribute microfibers on a large scale at a high throughput. This paper reports a facile droplet-directed method to transport and self-align microfibers to micro trenches using droplets ejected from a non-contact dispenser. Employing super hydrophilic-super hydrophobic trenches with sizes matching the microfibers, the method can align and transport randomly dispersed fibers around trenches into the trenches as desired. We studied this alignment and transport mechanism and the results suggest that liquid volume is critical to allow the capillary force to be dominant leading to the success of the process. We demonstrated that microfibers having an aspect ratio of 200, a diameter of $15~\mu \text{m}$ and a length of 3 mm, can reliably self-align to super hydrophilic-super hydrophobic trenches and transport to the target location, achieving linear alignment accuracy of $0.5~\mu \text{m}$ and angular accuracy of 0.1°. The high accuracy and fast process of the proposed technique may greatly impact larger scale and high throughput construction of complex microstructures using microfibers. [2021-0108]
Remote Sensing of Turbidity for Lakes in Northeast China Using Sentinel-2 Images With Machine Learning Algorithms

Monitoring water quality of inland lakes and reservoirs is a great concern for the public and government in China. Water turbidity is a reliable and direct indicator that can reflect the water quality. Remote sensing has become an efficient technology for monitoring large-scale water turbidity. This study aims to search an optimal regression model to accurately predict water turbidity using remote sensing data. To achieve this goal, 187 water samples were collected from field campaigns across Northeast China, in 2018, of which the samples were gathered within ±6 days of Sentinel-2 overpasses. The spectral reflectance data was used as independent variables for modeling. The simple regression, partial least squares regression, support vector regression, extreme learning machine, back-propagation neural network, classification and regression tree, gradient boosting decision tree (GBDT), random forest (RF), and K-nearest neighbor were used to compare. From model validation, we identified GBDT as the best regression model (R2 = 0.88, RMSE = 9.90 NTU, MAE = 6.71 NTU). We applied GBDT to retrieve the water turbidity and obtained a satisfactory result. Feature selection technique from tree-based ensemble method was also tested. We selected B2, B3, B4, and B5 as the important variables because of their high ability to explain the variation of turbidity. These results demonstrated the significance of using a promising method to retrieve water turbidity using Sentinel-2 imagery at the regional scale. It is beneficial to monitor the spatial-temporal distribution of water turbidity; support water quality management and inland water environment protection.
Thermal Behaviour of Natural Ester Based Oil Used in Distribution Transformers

This work compares the thermal behavior of a distribution transformer when using as dielectric liquid a mineral oil or natural esters. These cases have been analyzed using Finite Elements Method (FEM) at the software COMSOL Multiphysics® with a 3D-symmetrical model through the Heat Transfer in Solid module. The results of simulations show a higher values of maximum temperature in mineral oil submerged transformer than in natural ester, for the same operational conditions.
Investigating Refractoriness in Collision Perception Neuronal Model

Currently, collision detection methods based on visual cues are still challenged by several factors including ultrafast approaching velocity and noisy signal. Taking inspiration from nature, though the computational models of lobula giant movement detectors (LGMDs) in locust's visual pathways have demonstrated positive impacts on addressing these problems, there remains potential for improvement. In this paper, we propose a novel method mimicking neuronal refractoriness, i.e. the refractory period (RP), and further investigate its functionality and efficacy in the classic LGMD neural network model for collision perception. Compared with previous works, the two phases constructing RP, namely the absolute refractory period (ARP) and relative refractory period (RRP) are computationally implemented through a ‘link (L) layer’ located between the photoreceptor and the excitation layers to realise the dynamic characteristic of RP in discrete time domain. The L layer, consisting of local time-varying thresholds, represents a sort of mechanism that allows photoreceptors to be activated individually and selectively by comparing the intensity of each photoreceptor to its corresponding local threshold established by its last output. More specifically, while the local threshold can merely be augmented by larger output, it shrinks exponentially over time. Our experimental outcomes show that, to some extent, the investigated mechanism not only enhances the LGMD model in terms of reliability and stability when faced with ultra-fast approaching objects, but also improves its performance against visual stimuli polluted by Gaussian or Salt-Pepper noise. This research demonstrates the modelling of refractoriness is effective in collision perception neuronal models, and promising to address the aforementioned collision detection challenges.
Towards Authorship Attribution in Arabic Short-Microblog Text

Authorship attribution is the study to identify individuals by their writing styles without knowing their actual identities. This is a challenging task in natural language processing. Most work on authorship attribution focused on English, whereas, the problem is understudied in Arabic language. However, due to the complex and distinct morphological nature of the Arabic language, techniques developed for English are not directly applicable to Arabic. This paper explored the possibility of using state-of-the-art classifiers, Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Random Forest, to predict authorship in Arabic short-microblog text. We employed three commonly used linguistic features, character-, lexical- and syntactic-based, in an incremental manner to predict the accuracy of the selected classifiers. The results elucidate that a systematic combination of linguistic features improves authorship classification. However, an inverse correlation was observed in authorship classification accuracy and the number of authors. Overall, SVM and Random Forest classifier are comparable and attained ~65% accuracy, whereas KNN hardly attained ~35% accuracy. In addition, lexical features offer more discriminatory power as compared to the character and syntactic features.
Power-Rating Balance Control and Reliability Enhancement in Mismatched Photovoltaic Differential Power Processing Systems

With the increase of the component number, the power stress distribution among differential power processing (DPP) converters, control implementation, system cost, and reliability become the most challenging issues for a practical photovoltaic (PV) DPP system. This article introduces an improved power-rating balance (IPRB) control for the PV-to-bus based DPP architecture that ensures each PV submodule operate at its true maximum power point (MPP) while achieving more balanced power stress distribution and higher reliability. Specifically, a submodule-level finite-state-machine-based MPP tracking is implemented to guarantee always maximum power yield, whereas a string-level power-rating balancing (PRB) control is adopted to balance the unit-maximum proceeded power by DPP converters based on the built power flow model with respect to the string current. A comprehensive comparison of advanced control strategies for PV-to-bus DPP architectures, including least power point tracking, voltage equalization (VE) based PRB control, and the proposed IPRB, has been carried out with the mission-profile-based reliability assessment under different partial shading scenarios. Component-failure-rate-based reliability analysis shows that the PV-to-bus DPP architecture with the proposed IPRB control can significantly improve the system reliability. Main simulation and experimental evaluations are carried out to verify the effectiveness of the proposed control.
Learning to Optimise Routing Problems using Policy Optimisation

Deep reinforcement learning (DRL) has demonstrated promising performance to learn effective heuristics to solve complex combinatorial optimisation problems via policy networks. However, traditional reinforcement learning (RL) suffers from insufficient exploration, which often results in pre-convergence to poor policies and many challenges the performance of DRL. To prevent this, we propose an Entropy Regularised Reinforcement Learning (ERRL) method that supports exploration by providing more stochastic policies, improving optimisation. The ERRL method incorporates an entropy term, defined over the policy network's outputs, into the loss function of the policy network. Hence, policy exploration can be explicitly advocated subjected to a balance to maximise the reward. As a result, the risk of pre-convergence to inferior policies can be reduced. We implement the ERRL method based on two existing DRL algorithms. We have compared the performances of our implementations with the two DRL algorithms along with several state-of-the-art heuristic-based non-RL approaches for three categories of routing problems, i.e., travelling salesman problem (TSP), capacitated vehicle routing problem (CVRP) and multiple routing with fixed fleet problems (MRPFF). Experimental results show that the proposed method can find better and faster solutions in most test cases than the state-of-the-art algorithms.
A Modular Multilevel Converter Topology With Novel Middle Submodules to Reduce Capacitor Voltage Fluctuations

This letter proposes a modified topology of modular multilevel converter for medium-voltage applications, which can reduce the ac-voltage fluctuation in the submodule (SM) capacitor at low-frequency operation. A novel structure of middle SM is inserted between the upper and lower arms, which has lesser count of components compared with the existing ones, leading to the reduction in cost and power losses. At the low operating frequency, furthermore, the middle SM can attenuate the power fluctuation in arms by distributing the high-frequency power components. Therefore, the SM capacitor voltage fluctuation is significantly alleviated without imposing the common-mode voltage on the ac side. The effectiveness of the proposed topology has been verified by experimental results.
Analysis of Student Learning Pattern in Learning Management System (LMS) using Heuristic Mining a Process Mining Approach

The COVID-19 pandemic has had a significant impact on educational organizations, one of which is the Telkom University educational institute. Telkom University using Learning Management System (LMS) during pandemic covid-19. The use of an LMS will have a huge number of data records stored in the form of event logs. Opportunities to process data recorded in event logs must be maximized to improve Telkom University's LMS continuously. In this study, the process mining method conducted using event logs from Website Application Development (WAD) and Enterprise System (ES) courses to compare student learning patterns between programming courses and non-programming courses. Process mining conducted using PROM 5.2 and the Heuristic Miner plugins. The modeling process uses a heuristic miner algorithm because of its ability to present the main behavior recorded in the event log well by analyzing conformance checking. The result of conformance checking shows a fitness value of 0.887 for WAD and 0.847 for ES. It means the process model can represent the event log well. WAD's value of advanced behavioral appropriateness and degree of model flexibility are 0.594 and 0.411, whereas ES 0.181 and 0.155. The value of the structure shows 1.0 for the two subjects. After obtaining a process model, the next step is to analyze student learning patterns based on the frequency of access to the LMS. With this research, it is hoped that this research can contribute to adding new insights regarding the use of event logs in the field of education and knowing the activities carried out by students through LMS learning media, as well as providing visualization of student behavior patterns in the LMS (Learning Management System) of Telkom University.