Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite-time consensus for heterogeneous dynamic agents networks with unavailable velocity information

The purpose of this paper is to solve the finite-time consensus problem for heterogeneous dynamic agents networks with unavailable velocity information. The considered system is composed of first-order agents and second- order integrator agents in which the velocity information is unavailable over fixed topologies. Two classes of new consensus protocols are presented by feat of auxiliary systems. By constructing new Lyapunov functionals and applying homogeneity theory and Lasalle's invariance principle, the proposed protocols are proved to make all states of the systems tend to be consistent. Some simulation examples are given to verify the efficiency of the obtained results.
Dissimilatory Sulfate Reduction Under High Pressure by Desulfovibrio alaskensis G20

Biosouring results from production of H2S by sulfate-reducing microorganisms (SRMs) in oil reservoirs. H2S is toxic, corrosive, and explosive, and as such, represents a significant threat to personnel, production facilities, and transportation pipelines. Since typical oil reservoir pressures can range from 10 to 50 MPa, understanding the role that pressure plays in SRM metabolism is important to improving souring containment strategies. To explore the impact of pressure, we grew an oil-field SRM isolate, Desulfovibrio alaskensis G20, under a range of pressures (0.1–14 MPa) at 30°C. The observed microbial growth rate was an inverse function of pressure with an associated slight reduction in sulfate and lactate consumption rate. Competitive fitness experiments with randomly bar-coded transposon mutant library sequencing (RB-TnSeq) identified several genes associated with flagellar biosynthesis and assembly that were important at high pressure. The fitness impact of specific genes was confirmed using individual transposon mutants. Confocal microscopy revealed that enhanced cell aggregation occurs at later stages of growth under pressure. We also assessed the effect of pressure on SRM inhibitor potency. Dose-response experiments showed a twofold decrease in the sensitivity of D. alaskensis to the antibiotic chloramphenicol at 14 MPa. Fortuitously, pressure had no significant influence on the inhibitory potency of the common souring controlling agent nitrate, or the emerging SRM inhibitors perchlorate, monofluorophosphate, or zinc pyrithione. Our findings improve the conceptual model of microbial sulfate reduction in high-pressure environments and the influence of pressure on souring inhibitor efficacy.
Spatially consistent LOS/NLOS model for time-varying MIMO channels

A cluster scattering MIMO channel model is proposed combining advantages of map-based models such as spatial consistency as well as stochastic properties of more general statistical models. The model is constructed by generating clusters based on LOS/NLOS visibility regions and generating stochastic MIMO channel coefficients according to temporal, spatial and spectral correlations between large and small scale fading parameters. Time and frequency sampled version of the channel coefficients are proposed which can be used as computationally effective discrete model for time-varying MIMO channels. The application of the model is illustrated by high-speed train scenario and Manhattan-like grid urban scenario.
Performance Comparison of a SDN Network between Cloud-Based and Locally Hosted SDN Controllers

In a SDN network model, the robustness, scalability and reliability requirement of the control plane makes it an ideal candidate for being hosted on a cloud infrastructure. In addition, the control plane performs large volume of data processing from packet headers to network monitoring data in order to provide adequate level of QoS to the traffic. The realization of a cloud based SDN networking approach is predominantly dependent on the performance of the SDN controllers on the cloud environment. This paper presents a comparative study of the performance of a SDN network between a locally hosted SDN controller within the enterprise with a cloud based remote controller. Since a wide range of SDN controllers are available in the market with different levels of functionalities, performance and complexities, the analysis is validated by comparing the results across three different types of controllers. Furthermore, the impact of the network topology on the performance of the controllers is further validated by comparing the performance across two different topologies. In addition, a comparative performance analysis of the throughput and a theoretical evaluation of the controllers are also presented.
A deep learning approach for understanding natural language commands for mobile service robots

Using natural language to give instructions to robots is challenging, since natural language understanding is still largely an open problem. In this paper we address this problem by restricting our attention to commands modeled as one action, plus arguments (also known as slots). For action detection (also called intent detection) and slot filling various architectures of Recurrent Neural Networks and Long Short Term Memory (LSTM) networks were evaluated, having LSTMs achieved a superior accuracy. As the action requested may not fall within the robots capabilities, a Support Vector Machine(SVM) is used to determine whether it is or not. For the input of the neural networks, several word embedding algorithms were compared. Finally, to implement the system in a robot, a ROS package is created using a SMACH state machine. The proposed system is then evaluated both using well-known datasets and benchmarks in the context of domestic service robots.
An improved condition for set invariance for neutral time-delay systems considering input saturation and fault

In this paper, authors gave an improved condition for set invariance for neutral time-delay systems with actuator saturation and fault. Methods for designing indirect adaptive fault-tolerant controllers are given for actuator fault compensations. Based on the on-line estimation of eventual faults, the adaptive fault-tolerant controller parameters are updating automatically to compensate the fault effects on systems. The improved condition for set invariance are developed in the framework of linear matrix inequality (LMI) approach, which can guarantee that the domain of asymp­totic stability of closed-loop systems is enlarged as possible in the cases of actuator saturation, time-delay and actuator failures.
Monitoring of functional and psycho-emotional status of the person during daily activities

The principles of functional and psycho-emotional status of a person control on the basis of continuous electrocardio signal recording are described. The results of a number of works related to various aspects of automation of such monitoring are summarized. Provides information processing steps, taking into account not only the use of the HRV parameters but parameters associated with the predictors of disturbances in the cardiovascular system and the respiratory signal parameters. Methods of formation of informative parameters of electrocardio signal, cardiorhythmogram, respiratory signal and informative signs of disease on their basis are stated. Comprehensive indicators of the functional and psycho-emotional status of a person are proposed.
A Sporadic Four-Year Hospital Outbreak of a ST97-IVa MRSA With Half of the Patients First Identified in the Community

This study describes a sporadically occurring 4-year outbreak of methicillin-resistant Staphylococcus aureus (MRSA) originating from a surgical ward. Whole-genome sequencing (WGS) identified the outbreak clone as spa type t267, sequence type ST97, and SCCmec IVa. Prompted by the finding of four patients within 6 months in the same ward with this unusual MRSA type, an outbreak was suspected. Subsequent MRSA screening in the ward in February 2017 identified three-additional patients and two health care workers (HCWs) with t267/ST97-IVa. WGS linked these 9 isolates to 16 previous isolates in our WGS database and the outbreak thus included 23 patients and two HCWs. Twenty-one patients had a connection to the surgery ward during the period 2013–2017, but half of them had MRSA diagnosed in the community long after discharge. The community debut of several patients MRSA infections weeks to months after hospital discharge made the identification of a hospital source difficult and it was the SNP relatedness of the isolates that led us to identify the common denominator of hospitalization. An index patient was not identified, but our hypothesis is that HCWs with unrecognized long-term MRSA colonization could have caused sporadic nosocomial transmission due to intermittent breaches in infection prevention and control practice.
Feature Assisted bi-directional LSTM Model for Protein-Protein Interaction Identification from Biomedical Texts

Knowledge about protein-protein interactions is essential in understanding the biological processes such as metabolic pathways, DNA replication, and transcription etc. However, a majority of the existing Protein-Protein Interaction (PPI) systems are dependent primarily on the scientific literature, which is yet not accessible as a structured database. Thus, efficient information extraction systems are required for identifying PPI information from the large collection of biomedical texts. Most of the existing systems model the PPI extraction task as a classification problem and are tailored to the handcrafted feature set including domain dependent features. In this paper, we present a novel method based on deep bidirectional long short-term memory (B-LSTM) technique that exploits word sequences and dependency path related information to identify PPI information from text. This model leverages joint modeling of proteins and relations in a single unified framework, which we name as Shortest Dependency Path B-LSTM (sdpLSTM) model. We perform experiments on two popular benchmark PPI datasets, namely AiMed & BioInfer. The evaluation shows the F1-score values of 86.45% and 77.35% on AiMed and BioInfer, respectively. Comparisons with the existing systems show that our proposed approach attains state-of-the-art performance.
Formation shape control of multi-UAV with collision avoidance

This paper presents an area-based shape controller for a swarm of unmanned aerial vehicles (UAVs). In this control method, the UAVs move as a group inside a desired area while maintaining a minimum distance among themselves. Various shapes of the desired area can be formed by choosing the appropriate objective functions. In multi-UAV formation flight control, each UAV only needs to communicate with neighboring UAVs rather than the entire community. The UAVs do not have specific identities within the group. Therefore, the proposed method does not require specific positions or attitude information of the UAVs inside the area and yet different formations can be formed for a swarm of UAVs. A Lyapunov function is presented for convergence analysis of the multi-UAV systems. Simulation results illustrate the performance of the proposed controller.
Research on the task assignment of heterogeneous UAV formation in the anti-radar combat

In this paper, task assignment about the anti-radar cooperative combat with heterogeneous unmanned aerial vehicle (UAV) formation is analyzed, and an optimal task assignment is proposed based on mixed integer linear program (MILP). Specifically, the variable definition and reasonable coordination constraints between UAVs and the tasks are presented in detail. The simulation and analysis show the proposed algorithm is available for the anti-radar cooperative combat, and the related advantages and applicability of the proposed algorithm are analyzed too.
Design of bidirectional DC-DC converter based on single chip microcomputer

A bidirectional DC-DC converter based on single chip microcomputer (SCM) is designed by adopting bidirectional buck-boost topology instead of the traditional method of separately designing boost circuit and buck circuit. The converter is designed to achieve charging and discharging functions of five 18650 lithium-ion batteries. The main control chip of the converter is SCM STC12C5A60S2. The IR2104 half-bridge driver with bootstrap function is selected to drive MOSFET for stable turn-on and turn-off. The converter includes sampling circuit formed by the sampling resistor and the op amp op07 and display circuit formed by LCD1602, which can sample and display current and voltage in real time in both charging mode and discharging mode of the converter. In addition, the converter can control the current through keys. The overall system structure is simple, easy to control and low cost. The conversion efficiency is greatly improved using synchronous rectification. In the prototype test, the current fluctuation is small and the voltage stability is high. The designed converter is applied to a small DC motor drive circuit in a laboratory, the motor works well.
Mobility aware partition of MEC regions in wireless metropolitan area networks

Along with the rapid proliferation of mobile applications, Mobile Edge Computing (MEC) attracts growing interests in recent years. The number of service handovers due to users' movements between different MEC regions impacts heavily on QoE and service operation cost. In this poster, we propose a randomized algorithm to minimize the number of possible handovers between different MEC regions by carefully dividing a metropolitan area into disjoint clusters. Preliminary experiments exhibit that the proposed algorithm could find sub-optimal partitions with reduced total number of handovers.
Human gait recognition based on deterministic learning and Kinect sensor

Model construction, fitting and feature extraction remain the main difficult tasks in model-based gait recognition methods. To develop an easy practical model-based gait recognition system, this paper invetigates the combined use of deterministic learning and Microsoft Kinect sensor for human gait recognition. First, real-time data stream of human joints position are acquired by using Microsoft Kinect and MATLAB toolbox. Second, four different time-varying gait feature are selected to represent human walking. Third, gait variability underlying different individuals' time-varying gait features is effectively modeled by using deterministic learning algorithm. This kind of variability reflects the change of the walking information while preserving temporal dynamics information. Gait patterns are represented as the gait variability underlying time-varying gait features and a rapid recognition scheme is presented. Experimental results show that the combination of deterministic learning and Microsoft Kinect sensor can provide an encouraging recognition accuracy.
Secrecy Energy Efficiency Optimization in AN-Aided Distributed Antenna Systems With Energy Harvesting

In this paper, the artificial-noise (AN)-aided downlink distributed antenna system with simultaneous wireless information and power transfer is considered, in which the legitimate user uses the power-splitting (PS) scheme to simultaneously decode information and harvest energy. To prevent eavesdroppers from wiretapping the legitimate user, AN is transmitted with the confidential signal at the transmitter. The secrecy energy efficiency maximization (SEEM) problem is studied under the constraints of the minimum secrecy rate requirement, minimum required harvested energy, total radio frequency transmission power, and range of the PS ratio. Because of the coupling of variables, the formulated SEEM problem is non-convex and intractable. Then, a double-layer iterative optimization algorithm is proposed. The outer layer is a 1-D search algorithm for the introduced tight relaxation variable, whereas the inner one is based on the Dinkelbach method to address the fractional optimization problem. The closed-form expressions for the power of the transmitted confidential signal and AN power are further derived. The simulation results reveal the efficiency of the proposed algorithm and the advantage of AN in improving the system secrecy energy efficiency.
Scaling-up reasoning and advanced analytics on BigData

Abstract BigDatalog is an extension of Datalog that achieves performance and scalability on both Apache Spark and multicore systems to the point that its graph analytics outperform those written in GraphX. Looking back, we see how this realizes the ambitious goal pursued by deductive database researchers beginning 40 years ago: this is the goal of combining the rigor and power of logic in expressing queries and reasoning with the performance and scalability by which relational databases managed BigData. This goal led to Datalog which is based on Horn Clauses like Prolog but employs implementation techniques, such as semi-naïve fixpoint and magic sets, that extend the bottom-up computation model of relational systems, and thus obtain the performance and scalability that relational systems had achieved, as far back as the 80s, using data-parallelization on shared-nothing architectures. But this goal proved difficult to achieve because of major issues at (i) the language level and (ii) at the system level. The paper describes how (i) was addressed by simple rules under which the fixpoint semantics extends to programs using count, sum and extrema in recursion, and (ii) was tamed by parallel compilation techniques that achieve scalability on multicore systems and Apache Spark. This paper is under consideration for acceptance in Theory and Practice of Logic Programming.
Generalized circle agent for geometry friends using deep reinforcement learning

Reinforcement learning began to perform at human-level success in game intelligence after deep learning revolution. Geometry Friends is a puzzle game, where we can benefit from deep learning and expect to have successful game playing agents. In the game, agents are collecting targets in two dimensional environment and they try to overcome obstacles in the way. In this paper, Q-learning approach is applied to this game and a generalized circle agent for different types of environment is implemented. Agent is trained by giving only screen pixels as input via a Convolutional Neural Network. Experimental results show that with the proposed method game completion rate and completion times are improved compared to random agent.
Human behavior recognition method based on double-branch deep convolution neural network

Aiming at the poor robustness and the low accuracy of 2D image recognition, this paper presents a method of human behaviors recognition based on double-branch deep convolution neural network. Firstly, the features of the input image are extracted, and the feature maps are input into the double-branch deep convolution neural network to obtain the joints information of human body and the joints connection information of human body respectively. And we used the Hopcroft-Karp algorithm to optimally match skeletal joints to obtain the human skeletal sequence diagram. The human behaviors are identified by the multi-classification support vector machines. Finally, through the training and testing of standing, walking, running, waving, bending, squatting and sitting on seven kinds of general human behaviors, the experimental results show that the proposed approach has good accuracy and robustness.
Angioplasty and Stenting of Intracranial Arterial Stenosis in Perforator-Bearing Segments: A Comparison Between the Anterior and the Posterior Circulation

Background and Purpose: Subgroup analysis of the SAMMPRIS trial showed a higher rate of periprocedural perforator strokes with the Wingspan stent in the basilar artery in patients with symptomatic intracranial atherosclerotic stenosis (ICAS). It remains unclear whether angioplasty (PTA) alone or in combination with other stent types (PTAS) will yield similar results in perforator-bearing segments of the anterior and posterior circulation. Methods: We retrospectively analyzed the periprocedural complication rate, long term outcome and stroke etiology in 59 consecutive patients with ICAS of the middle cerebral artery (79 treatments) and 67 patients with ICAS of the intracranial vertebral and basilar artery (76 treatments) treated with PTA or PTAS from 2007 to 2015 in a high-volume neuro-interventional center. Results: Periprocedural symptomatic ischemic strokes occurred significantly more often in patients with posterior vs. anterior ICAS treatment (14.5 vs. 5.1%, p = 0.048). During a mean follow-up period of 19 (±23.7) months, 5 recurrent ischemic and 2 hemorrhagic strokes (10.4%) occurred in the territory of the treated artery in posterior circulation compared to 2 ischemic strokes in the anterior circulation (3.4%, p = 0.549). Overall, significantly more patients treated for a posterior ICAS suffered a periprocedural or follow-up stroke [25% vs. 11.4%, p = 0.024]. Periprocedural ischemic strokes were predominantly perforator strokes (73.3%), while all ischemic strokes during follow-up were caused by distal embolization (57.1%) or delayed stent occlusion (42.9%). There was no difference between PTA alone and PTAS. Conclusion: The periprocedural and long-term symptomatic stroke rate was significantly higher in the treatment of perforator-bearing arteries in the posterior circulation. There was no difference between PTA alone or PTAS.
Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts

The goal of our industrial ticketing system is to retrieve a relevant solution for an input query, by matching with historical tickets stored in knowledge base. A query is comprised of subject and description, while a historical ticket consists of subject, description and solution. To retrieve a relevant solution, we use textual similarity paradigm to learn similarity in the query and historical tickets. The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts. We present a novel Replicated Siamese LSTM model to learn similarity in asymmetric text pairs, that gives 22% and 7% gain (Accuracy@10) for retrieval task, respectively over unsupervised and supervised baselines. We also show that the topic and distributed semantic features for short and long texts improved both similarity learning and retrieval.
A Generative Approach to Question Answering

Question Answering has come a long way from answer sentence selection, relational QA to reading and comprehension. We shift our attention to generative question answering (gQA) by which we facilitate machine to read passages and answer questions by learning to generate the answers. We frame the problem as a generative task where the encoder being a network that models the relationship between question and passage and encoding them to a vector thus facilitating the decoder to directly form an abstraction of the answer. Not being able to retain facts and making repetitions are common mistakes that affect the overall legibility of answers. To counter these issues, we employ copying mechanism and maintenance of coverage vector in our model respectively. Our results on MS-MARCO demonstrate it's superiority over baselines and we also show qualitative examples where we improved in terms of correctness and readability
ctrlTCP: Reducing latency through coupled, heterogeneous multi-flow TCP congestion control

We present ctrlTCP, a method to combine the congestion controls of multiple TCP connections. In contrast to the previous methods such as the Congestion Manager, ctrlTCP can couple all TCP flows that leave one sender, traverse a common bottleneck (e.g., a home user's thin uplink) and arrive at different destinations. Using ns-2 simulations and an implementation in the FreeBSD kernel, we show that our mechanism reduces queuing delay, packet loss, and short flow completion times while enabling precise allocation of the share of the available bandwidth between the connections according to the needs of the applications.