Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Architecture Reasoning Via Deep Deterministic Policy Gradient

In this paper, we introduce global compression learning (GCL) for finding reduced network architecture from a pre-trained network by removing both intra-layer and inter-layer structural redundancy. To accomplish this, we first derive architecture features from a binary representation of the network structure that effectively characterize the relationships between different layers. We then leverage reinforcement learning to iteratively compress the network via deep deterministic policy gradient based on the learned architecture features. To void extensive exploration of the huge space of network architectures, we bound feasible solutions within a small subspace by following a strict accuracy loss tolerance. Benchmarking tests show GCL outperforms the state-of-the-art models. On CIFAR-10 dataset, our model reduces 60.5% FLOPs and 93.3% parameters on VGG-16 without hurting the network accuracy, and yields a significantly compressed architecture for ResNet-110 by reductions of 71.92% FLOPs and 79.62% parameters with the cost of only 0.11% accuracy loss.
A 4.3fJ/Conversion-Step 6440μm2 All-Dynamic Capacitance-to-Digital Converter with Energy-Efficient Charge Reuse

An ultra-low power all-dynamic capacitance-to-digital converter (CDC) that exploits a novel charge reuse technique is proposed, achieving a FoM as low as 4.3fJ/conv-step, which is >3× better than the state-of-the-art. It supports an inherent scaling of power vs. speed with a minimum power of only 44pW and a compact chip area of 6440μm2.
Enhanced Cu Partitioning Search Method for Intra Coding in HEVC

Most encoders in High Efficiency Video Coding (HEVC) standard determine the coding unit (CU) partitions in a depth-first full search manner. For intra coding, this method ignores the impact of partitioning on the subsequent blocks, which incurs less accurate prediction and worsens encoding efficiency performance. In this paper, we present an enhanced CU partitioning search method, where the various partitioning types are examined to effectively mitigate such impact. To reduce the additional complexity, we further refine the search candidates, early terminate the search process, and reuse encoding information. Experimental results show that compared with the unmodified HM software, the proposed method achieves a bit rate saving of 0.34% on average and 0.67% at most. Since our method doesn’t modify the original definition of HEVC, the encoded bitstreams can be uncompressed by a standard HEVC decoder with no complexity increase.
Root and shoot growth of newly-transplanted apple trees as affected by rootstock cultivar, defoliation and time after transplanting

SummaryAn experiment with Malus demonstrated that a large proportion of the transplanted root system was lost through death and decomposition soon after transplanting in the open ground. Mortality of the roots was not influenced by the rootstock cultivars or by defoliation but increased significantly with time. In the first month, shoots of maiden trees of Malus transplanted in June when in-leaf grew, but roots did not. Subsequently, most of the new roots on the rootstock M.9 regenerated from the rootstock stem, whereas with MM.106 the old coarse roots (>2.0 mm diameter) initially present at planting were most important. Root growth occurred in concert with shoot growth such that a functional balance was maintained as shown by the existence of a constant root length:leaf area ratio over a large part of the growing season. Following transplanting, the trees appear to re-establish their optimal ‘functional’ ratio by way of a co-ordinating pattern of growth tending to correct any disturbance to the ratio res.
Residual Attention Based Network for Automatic Classification of Phonation Modes

Phonation mode is an essential characteristic of singing style as well as an important expression of performance. It can be classified into four categories, called neutral, breathy, pressed and flow. Previous studies used voice quality features and feature engineering for classification. While deep learning has achieved significant progress in other fields of music information retrieval (MIR), there are few attempts in the classification of phonation modes. In this study, a Residual Attention based network is proposed for automatic classification of phonation modes. The network consists of a convolutional network performing feature processing and a soft mask branch enabling the network focus on a specific area. In comparison experiments, the models with proposed network achieve better results in three of the four datasets than previous works, among which the highest classification accuracy is 94.58%, 2.29% higher than the baseline.
Airport Congestion Propagation Model for Temporal Airline Network of China

In this paper, given the initial congested airport vs and the time intervals T of congestion, we propose an efficient traffic flows re-allocate algorithm for a congested airport on temporal airline networks $G_{AN}^t,t = 1 \ldots T$, such that the status of other airports caused by an initial congested airport in different time intervals TIt, t = 1 … T, can be analyzed at the macroscopic level. In the proposed model, considering the allocation results of traffic flows on $G_{AN}^{t - 1}$ of the previous time intervals TIt−1, the traffic flows re-allocate algorithm for congested airport on $G_{AN}^t,t = 1 \ldots T$, would be sequentially performed to analyze the propagation results. Experimental results show that the proposed model can reduce 21.2% of the total congested airports propagated by an initial congested airport when compared with the Rate-based model.
Semi-NMF-Based Reconstruction for Hyperspectral Compressed Sensing

Hyperspectral compressed sensing (HCS) is a new imaging method that effectively reduces the power consumption of data acquisition. In this article, we present a novel HCS algorithm by incorporating spatial–spectral hybrid compressed sensing, followed by a reconstruction based on spectral unmixing. At the sampling stage, the measurements are acquired by a spatial–spectral hybrid compressive sampling scheme to preserve the necessary information for the following spectral unmixing, where spatial compressive sampling mainly retains the endmember information, and spectral compressive sampling mainly retains the abundance information. Due to the limitations of the traditional linear mixed model, an improved mixed model is proposed for HCS reconstruction, which considers spectral variability, nonlinear mixing, and other factors. At the reconstruction stage, based on the improved mixed model, semi-nonnegative matrix factorization is introduced to realize spectral unmixing on the measurements to achieve the final reconstruction by using an alternate iteration manner. The proposed algorithm is tested on real hyperspectral data, and the selection of parameters is fully analyzed. Experimental results demonstrate that the proposed algorithm can significantly outperform state-of-the-art HCS algorithms in terms of reconstruction performance.
Transient Fault Diagnosis Algorithm based on Feature Classification and Its Application in High-Power Laser facility

High-power laser facility plays an import role both in scientific research and military application, and the occurred faults are usually transient. In this paper, a transient fault diagnosis algorithm based on feature classification is proposed, and the algorithm has been applied in the high-power laser facility. The method extracts the features of input data with an autoencoder network. And based on the extracted features, a multi-layer full connection neural network is adopted for fault diagnosis. By comparing the results with those obtained by traditional fault diagnosis methods, the performance of the algorithm has been verified.
H∞ control for a class of switched fuzzy systems via output feedback

The H∞ control problem is investigated for a class of switched fuzzy systems via output feedback. The solvability for the H∞ control problem of each fuzzy system is not assumed. A static output feedback control law and a switching law are designed such that the H∞ control problem of the closed loop switched fuzzy system is solvable based on the multiple Lyapunov functions method. Finally, an algorithm is provided to solve the matrix inequalities.
Learn A Robust Representation For Cover Song Identification Via Aggregating Local And Global Music Temporal Context

Recently, deep learning models have been proposed for cover song identification and designed to learn fixed-length feature vectors for music recordings. However, the aspect of the temporal progression of music, which is important for measuring the melody similarity between two recordings, is not well exploited in those models. In this paper, we propose a new Siamese architecture to learn deep representations for cover song identification where Dilated Temporal Pyramid Convolution is used to exploit the local temporal context and Temporal Self-Attention to exploit the global temporal context in music recordings. In addition to the traditional block which calculates the similarity between a pair of recordings, we add a classification block to classify the recordings to their respective cliques. By combining the regression loss and the classification loss, our model can leam more robust and discriminative latent representations. The representations extracted by our model show substantial superiority to existing hand-crafted features and learned deep features. Experimental results show that our approach far outperforms the state-of the-art methods on several public datasets.
Output-feedback Quadratic Tracking Control of Continuous-time Systems by Using Off-policy Reinforcement Learning with Neural Networks Observer

In this paper, an improved off-policy reinforcement learning (RL) algorithm with neural networks(NN) observer is proposed to solve the linear quadratic tracking (LQT) problem for continuous-time (CT) systems without any knowledge of the system dynamics. The offline algorithm solves a Lyapunov equation to find a optimal solution which requires complete knowledge of the system dynamics. Later the off-policy RL algorithm was used to solve the state-feedback control which does not require any knowledge of the system dynamics by using the same input and state information repeatedly in previous research. The proposed output-feedback (OPFB) control algorithm solves Bellman equation which demands the system state information by using an adaptive NN state observer to estimate the system state with the input and output information of CT systems. Simulation results provide the efficiency of the proposed approach. Key Words: Off-policy, Reinforcement Learning (RL), Linear Quadratic Tracking (LQT), Output-feedback (OPFB)
Preliminary Analysis For Automatic Tidal Inlets Mapping Using Google Earth Engine

This work aims to define the basic parameters for the automatic mapping of the channel between the Lagoa do Peixe and the Atlantic Ocean, which is located in the municipalities of Tavares and Mostardas, Rio Grande do Sul state, Brazil. The automatic mapping is based on an unsupervised classification of Landsat 8 satellite images at the Google Earth Engine cloud computing platform. The images used were selected to present both channel situations (opened and closed). Three images were selected with acquisition dates that presented the open channel and three that presented the closed channel. Each image was classified using the K-means clustering method, using separately band 6, band 7 (both located at shortwave infrared - SWIR) and the Normalized Difference Water Index (NDWI). Once the number of clusters must be defined a priori by the analyst, as well as the training sample area, these parameters were tested over the dataset and clustering results were compared. All of the generated clusters maps were analyzed over 10 random points, identifying the clustering hits and errors. Due to the absence of reference maps, all the final clustering maps for each date were compared with the composite true color image from the same acquisition date. The NDWI cluster maps showed the best results in separating water and non-water pixels.
Multi-Agent Formation Design Considering Minimum Fuel Consumption

Formation control of multiple autonomous robots is an important part of autonomous robot research. And theoretical research should be based on reality. Therefore, thinking of each robot as an agent, this article considers the problems in actual situation of multi-agent formation control, namely energy consumption. The formation is accompanied by energy consumption. If fuel conservation is not considered, it will lead to three problems: 1. Environmental pollution; 2. The formation task cannot be carried out for a long time; 3. Carrying a large amount of fuel will definitely affect the flexibility of autonomous robots. Therefore, how to reduce energy consumption while implementing formation tasks is the focus of this paper. In this paper, the directed graph in graph theory is used to simulate the communication links between multi-agents for continuous systems and discrete systems in general linear systems. According to the linear quadratic method, optimal controllers are designed respectively to realize the control of target formation. At the same time, fuel consumption is reduced as much as possible to achieve the minimum performance index.
Low-Overhead Near-Real-Time Flow Statistics Collection in SDN

In Software-Defined Networking, near-real-time collection of flow-level statistics provided by OpenFlow (e.g. byte count) is needed for control and management applications like traffic engineering, heavy hitters detection, attack detection, etc. The practical way to do this near-real-time collection is a periodic collection at high frequency. However, periodic polling may generate a lot of overheads expressed by the number of OpenFlow request and reply messages on the control network. To handle these overheads, adaptive techniques based on the pull model were proposed. But we can do better by detaching from the classical OpenFlow request-reply model for the particular case of periodic statistics collection. In light of this, we propose a push and prediction based adaptive collection to handle efficiently periodic OpenFlow statistics collection while maintaining good accuracy. We utilize the Ryu Controller and Mininet to implement our solution and then we carry out intensive experiments using real-world traces. The results show that our proposed approach can reduce the number of pushed messages up to 75% compared to a fixed periodic collection with a very good accuracy represented by a collection error of less than 0.5%.
Design of Two-Elements Subarray with Parasitic Patch for 5G Application

Two Elements antenna with parasitic patch are presented for fifth generation (5G) wireless standards. Each antenna consists of a single element antenna with parasitic patch for the bandwidth enhancement. The primary patch is fed by the coaxial probe. Antennas are designed and simulated, which are single antenna, single antenna with parasitic patch, two-elements antenna arrayed in the H-plane and the E-plane with varying element spacing. Simulation results show wide bandwidth of 17% can be achieved using parasitic patch techniques. The mutual coupling of the two-elements antenna arrayed in the H-plane for all elements spacing is less than -20 dB, which means the effect of mutual coupling can be ignored. Whereas the mutual coupling of two-elements antenna arrayed in the E-plane with elements spacing λ and 3/4 λ are based on criteria. But the element spacing of ½ λ does not match criteria because there is a peak greater than -20 dB, so it is necessary to apply the method of mutual coupling reduction. The bandwidth of the two-elements subarray in the H-plane and E-plane is 609.9 – 616.4 MHz on all variations of the element spacing, so it can be used for 5G applications.
Distributed Adaptive Neural Network Stabilization Control for a Class of Uncertain Multi-agent Systems with Different Dimensions

A novel distributed feedback adaptive control firstly is developed for the stabilization tracking for a class of multi-agent robot manipulator system with unstructured model. In this study, the dynamical behavior of the leader system can be tracked perfectly by the states of each multi-agent robot manipulator system with 2 or 3 degrees of freedom (DOF) through the controller with the similar characters of its own structure, the similar elements such as matrices or vectors that are used to design a distributed feedback control with coupling weights adaptive laws and neural network weights online, in which these relative similar information of the follower agents and leader system can be calculated by the proposed algorithm according to the characters of each agent robot manipulator and the reference leader system. This method extends the existing works with identical multi-agent systems and leader system to nonidentical multi-agent systems with different dimensions contrast to other results in many literatures. Finally, the good stabilization performances are demonstrated to multi-agent robot manipulator system.
β-Myrcene, a Potent Attractant Component of Pine Wood for the Pine Wood Nematode, Bursaphelenchus xylophilus

A new convenient assay method for nematode attractants was developed. Using this method, the attracting activity of volatile components in a pine, Pinus densiflora, was examined for the pine wood nematode, Bur saphelenchus xylophilus, which causes serious damage to pine trees in Japan. Among the volatile components, β-myrcene showed the strongest attracting potency. This compound was assumed to play an important role in the transmigration of the nematode from the sawyer to the pine tree and for the movement of the nematode inside the pine wood.
Walking Decision of Hydraulic Quadruped Robot in Complex Environment

To make the hydraulic quadruped robot walk through the complex unstructured environment, a whole-body motion planning algorithm is proposed. The layered motion planning algorithm is the main contribution of the algorithm. A bilateral time logic game between the hydraulic quadruped robot and external environment is built. The given task planning based on time logic is satisfied by a high-level walking decision strategy. Moreover, a low-level gait transition strategy is able to complete the generated command. The gait transition strategy is designed based on the Bezier curve, ADRC and the optimal objectives of minimum switching time. The gait of the Quadruped robot is chosen autonomously under complicated environment by the whole-body motion planning algorithm, which achieves high smoothness and stability of pitch angle. The effectiveness of gait transition strategy and walking decision strategy is confirmed by the dynamic simulation of hydraulic quadruped robot under complex environment.
A Reflectivity-Based GPR Signal Processing Methodology for Mapping Tree Root Systems of Street Trees

Street trees are broadly acknowledged to be an essential asset of cities. Nonetheless, the growth of tree roots can lead to critical damage, such as the uplifting or cracking of road pavements and curbs, with severe implications on the infrastructure’s safety. For this reason, the assessment and control of tree root systems’ development have become crucial task in forestry and urban management. Within this framework, Ground Penetrating Radar (GPR) is widely recognised as an effective non-destructive testing (NDT) method for the monitoring and assessment of road infrastructures. This study aims to demonstrate the capability of GPR in mapping the root system’s architecture of street trees. To this end, a GPR system equipped with a 700 MHz central frequency antenna was employed to investigate the area around a street tree, located in the vicinity of a flexible pavement structure. A novel processing framework based on the analysis of the signal reflectivity was implemented, in order to automatically identify the reflections from the pavement layers and apply dedicated advanced signal processing techniques. A multistage data processing methodology was employed to map the tree root system architecture. Finally, information on the mass density of roots at different depths was also provided. Results have proven the potential of the proposed methodology to achieve an automatic detection and mapping of roots under road pavements.
The German-Speaking Twitter Community Reference Data Set

News providers and politicians increasingly publish and disseminate their content on online social media to reach broader audiences effectively. Directed by ubiquitous mobile use, the majority of individuals reportedly consume daily news directly on these platforms, mainly in an incidental manner. This bears many risks of misconceptions and misinformation: Social media users tend to extend unwarranted trust in posts that are distributed by contacts on the platform and therefore have difficulties evaluating the credibility and trustworthiness of information and its sources. Reduced political proficiency and social understanding have been reported as directed results, as well as the risk of succumbing to partisan echo chambers, user groups amplify and reinforce their own beliefs due to almost exclusive exposition. Measuring and understanding these phenomena requires analysis of the user behavior on these platforms, and a virtually complete data set of one representative community. We focus on Twitter and present collection techniques to obtain a complete data set of specified sub-groups of its users, with the example of the German-tweeting community, in this paper. We show how to collect a representative snapshot of all tweets pertaining to this community over the period of two months. The resulting sample includes 77 million tweets and 6.9 million users. We validate the sample with exhaustive evaluations, and identify the notable impact of political events, such as the 2019 European Parliament election.
Detection of Temporal Communities in Mobile Social Networks

In recent years, community detection in Mobile Social Networks (MSNs) has attracted a lot of attentions from both academia and industry areas. The existing methods of community detection usually focus on static networks. However, MSNs in essence are dynamically intermittently connected networks with a time-varying topology. Therefore, this paper proposes a novel temporal community detection method based on nodes' temporal closeness. In order to evaluate the performance of the proposed method and study the evolution law of the temporal community, we conduct extensive simulations using two real mobility traces, the MIT Reality trace and the Infocom 06 trace. The results show that many temporal communities share similar members and appear at different times. Furthermore, we find the periodic appearance of temporal community.
Modeling and Quantitative Assessment of Environment Complexity for Autonomous Vehicles

The paper investigates a novel modeling and quantitative assessment approach for environment complexity of autonomous vehicles (AVs), whose purpose is to establish a systematic, logical and quantitative framework for evaluating the intelligence of AVs in complex scenarios. By reducing features of AVs’ driving environment, analyzing the properties and relations of environment factors, a topology structure and scheme of fundamental and additional factors is established. Based on an integrated evaluation way of expert evaluation and the analytic hierarchy process (AHP), fundamental and additional environment complexity models are established to realize the subjective and objective evaluation of AVs. The effectiveness of the proposed approach is testified by experimental data and tests.