Miracle-dz/UniFAR-FTUs huggingface dataset:
Altering the Immunogenicity of Hemagglutinin Immunogens by Hyperglycosylation and Disulfide Stabilization

Influenza virus alters glycosylation patterns on its surface exposed glycoproteins to evade host adaptive immune responses. The viral hemagglutinin (HA), in particular the H3 subtype, has increased its overall surface glycosylation since its introduction in 1968. We previously showed that modulating predicted N-linked glycosylation sites on H3 A/Hong Kong/1/1968 HA identified a conserved epitope at the HA interface. This epitope is occluded on the native HA trimer but is likely exposed during HA “breathing” on the virion surface. Antibodies directed to this site are protective via an ADCC-mediated mechanism. This glycan engineering strategy made an otherwise subdominant epitope dominant in the murine model. Here, we asked whether cysteine stabilization of the hyperglycosylated HA trimer could reverse this immunodominance by preventing access to the interface epitope and focus responses to the HA receptor binding site (RBS). While analysis of serum responses from immunized mice did not show a redirection to the RBS, cysteine stabilization did result in an overall reduction in immunogenicity of the interface epitope. Thus, glycan engineering and cysteine stabilization are two strategies that can be used together to alter immunodominance patterns to HA. These results add to rational immunogen design approaches used to manipulate immune responses for the development of next-generation influenza vaccines.
Detection of Influential Nodes Using Neighbor Closeness in Complex Networks

Identifying the influential nodes is important for understanding and controlling the dynamic processes, such as epidemic spreading, opinion dynamics, and cascading failures. Considering that the influence of nodes is strongly correlated with the degree, the first-order neighbors, and the second-order neighbors, we introduce a new measure, namely neighbor closeness (NC), which determines the influential nodes by calculating the neighbor closeness. To understand the accuracy of different methods and identify the influential nodes by low-complexity, we first study the identification of the influential nodes in the network based on different centrality metrics and take the mutual impact of the first-order and the second-order neighbors as the main factor to measure the influence of nodes. We use the Susceptible-Infected-Recovered (SIR) model and Kendall τ correlation coefficient to evaluate the performance and accuracy of the NC method, respectively. We find that there is a strong similarity between the NC method and the SIR model. Simulations on four real-world networks show that NC is an effective method to detect the influential nodes.
Automatic CNN-Based Enhancement of 360° Video Experience With Multisensorial Effects

High-resolution audio-visual virtual reality (VR) technologies currently offer satisfying experiences for both sight and hearing senses in the world of multimedia. However, the delivery of truly immersive experiences requires the incorporation of other senses such as touch and smell. Multisensorial effects are usually manually synchronized with videos and data is stored in companion files, which contain timestamps for these effects. This manual task becomes very complex for 360° videos, as the scenes triggering effects can occur in different viewpoints. The solution proposed in this paper aims to automatically add extra sensory information to immersive 360° videos. A novel scent prediction scheme using Convolutional Neural Networks (CNN) is proposed to perform scene predictions on 360° videos represented in the Equi-Angular Cubemap format in order to add scents relevant to the detected content. Digital signal processing is used to detect loud sounds in the video with a Root Mean Square (RMS) function, which are then associated with haptic feedback. A prototype was developed, which outputs multisensorial stimuli by using an olfaction dispenser and a haptic mouse. The proposed solution has been tested and it achieved excellent results in terms of accuracy of scene detection, olfaction latency and correct execution of the relevant effects. Different CNN architectures, including AlexNet, ResNet18 and ResNet50, were also assessed comparatively, achieving a labeling accuracy of up to 72.67% for olfaction-enhanced media.
Wireless Sensor Network Localization Using SDP Relaxation

In wireless sensor networks, the problem of sensor node location is a very important indicator. The accuracy of localization depends on the elimination of errors. Based on Euclidean distance, an optimization algorithm for semi- definite programming (SDP) is proposed in this paper. Combining the effective anchor node position selection and ratio range setting, the SDP algorithm was used to relax the non-convex constraints, and effectively reduced the impact of errors and get the actual position of measurement nodes. This algorithm effectively solved the problem of inaccurate estimation of nodes outside the convex hull of the anchor node. The numerical results show that the proposed SDP algorithm achieves high accuracy in the location estimation of unknown nodes.
Sliding Mode Control for Robot Manipulators with Actuator Faults

This paper presents a sliding mode controller for trajectory tracking of robotic manipulators with actuator faults. To achieve finite-time convergence of tracking errors and desirable tracking precision, a fast nonsingular terminal sliding mode controller is designed. The uncertainty caused by actuator faults is transformed into system disturbance which is compensated by introducing the dynamic gain technique. As a result, the prior knowledge about upper bound of system disturbance is not required. Moreover, the sign function is replaced by the saturation function in the control law, so the chattering phenomenon is attenuated. Finally, the designed controller is applied to control a two-link robotic manipulator in simulation, and results validate the effectiveness of the control scheme.
The Effect of Time-Varying Value on Infrastructure Resilience Assessments

Infrastructure resilience for a scenario can be assessed quantitatively from resilience curves that plot the evolution of system performance. Summary metrics map these curves to a single value to facilitate comparisons of different systems, scenarios, and policies. Commonly, these metrics are integral-based, e.g., cumulative infrastructure performance. However, since these curves and metrics only examine infrastructure performance, they fail to consider the dynamics of when stakeholders value the performance. For example, a power failure at a hospital during an ordinary day would be of less concern to emergency managers than during a hurricane recovery. This manuscript defines value weighting functions to represent the evolution of stakeholders’ value of performance. Temporal correlation between the performance and value weighting functions is described through a stochastic offset. Together, these concepts are used to define a holistic resilience metric: percent value satisfied. Through analytical and numerical approaches, percent value satisfied is treated as a resilience assessment’s stochastic output, and its distribution is compared to the naïve metric, cumulative infrastructure performance. The naïve summary metric is shown to be misleading in multiple potential scenarios; this work establishes that resilience assessments must consider the impact of time-varying stakeholder value and its correlation with infrastructure performance. These elements also provide new considerations for resilience assessments: opportunities to improve holistic system resilience without directly affecting infrastructure performance; hazard categories to inform value-weighted resilience analysis; and general insights to guide extensions from performance-based to value-weighted assessment.
Electroencephalogram Emotion Recognition Using Variational Modal Decomposition Based Dispersion Entropy Feature Extraction

Electroencephalogram (EEG) emotion recognition has gained considerable attention due to its ability to reflect people’s inner emotional states objectively and naturally. Feature extraction is a critical step in EEG emotion recognition because of non-stationarity and irregularity of EEG signals. A feature extraction method using Variational Modal Decomposition (VMD) to extract Dispersion Entropy (DispEn) is proposed in this paper. Raw EEG signal is decomposed into several components, and DispEn of each component is extracted in eight emotion-related channels. Our method was tested on DEAP dataset in which the EEG emotional states are accessed in Valence-Arousal emotional space. Four emotional states (i.e., HVHA, HVLA, LVHA, LVLA) are classified by Support Vector Machine (SVM). The experimental results show that the accuracy of emotion recognition is 77.87%, which demonstrates its effectiveness.
Pain Assessment Using Facial Action Units and Bayesian Network

Automatic pain assessment systems based on facial videos are consistently studied due to the demand of robust and cost-effective pain management. In order to improve the assessment accuracy under the dynamic and uncertain pain assessment environment, we propose a novel pain assessment method, AUBN, based on facial action units (AUs) and Bayesian network (BN). Firstly, the key feature points of pain expression are extracted through constrained local neural field (CLNF) model, and then AUs with a large amount of pain information are identified. The AUs are labeled to form the sample data set, and the constraints on BN conditional probabilities are constructed from the qualitative expert knowledge. Then, the sample data set is fused with the constraint extended parameter set using the variable weight method. Finally, BN reasoning method is applied to achieve the recognition of facial pain expression. Experimental results show that the proposed method outperforms SLPM+MCSVM, TDSBP+SVM and LBV+CNN methods on recognition accuracy and can significantly improve the recognition performance of facial pain expression.
Building Skill Learning Systems for Robotics

Skill-generating policies have enabled robots to perform a wide range of applications as for example assembly tasks. However, the manual engineering effort for such policies is fairly high and the environment is frequently required to be rather deterministic. For expanding robot deployment to low-volume manufacturing two challenges need to be addressed. First, the robot should acquire the skill-generating policy not from a robot programmer but rather from an expert on the task and second, the robot needs to be able to operate in unstructured environments. In this paper we present a learning approach that combines imitation learning and reinforcement learning to provide a tool for intuitive task teaching followed by self-optimization of the system. The presented approach is applied to a dual-arm assembly task using a real robot and appropriate simulation models. Whereas pure imitation learning does not result in an acceptable success rate for the considered example, after 400 episodes of reinforcement learning the robot can successfully solve the assembly task.
Risk Assessment of Power System Cascading Outages Based on Deep Reinforcement Learning

When assessing the risk of cascading outages of power system, a common method is to search the fault chains with risk continuously through cascading outage simulation. However, in large systems, large amount of fault chains bring difficulties to risk assessment. In this paper, a cascading outage risk assessment strategy based on deep reinforcement learning is proposed. This method aims to improve the efficiency of risk assessment by using an agent to guide the search of high risk fault chains. A cascading outage is abstracted into a two-process Markov process, and a risk indicator considering outage probability is proposed, then a tree search framework based on Markov decision process and DQN is constructed. Compared with the classical Q-learning method, the deep Q network avoids "dimensional disaster" and has potential of large system application. The feasibility and accuracy of the method were verified by comparing with the classical Q-learning results on the IEEE 9-Bus System, and the simulation was carried out on the IEEE 39-Bus New England System to further demonstrate the efficiency of the method.
A Novel Stability Criterion for Nonlinear Networked Control Systems using an Improved Triple-integral Inequality

This paper studied the stability problem of a class of nonlinear networked control systems (NCSs). First, it is assumed that the network-induced delay is differential and bounded on the interval. Second, by constructing a new Lyapunov-Krassovskii functional (LKF), and combining an improved triple-integral inequality with a condition of quadratic matrix inequality which is necessary and sufficient, a new criterion of system stability is obtained. Last, a numerical example is given to show the effectiveness of the proposed approach.
Bilinear MobileNets for Multi-class Brain Disease Classification Based on Magnetic Resonance Images

Early detection of brain diseases is necessary to deliver further and suitable treatment to save the patient life. Automated brain disease diagnosis is possible to be carried out with the availability of imaging techniques and the Deep Learning method. In recent years, many researchers have been interested in medical image classification problems, including brain disease detection based on Magnetic Resonance Images (MRI) using the Convolutional Neural Network (CNN) algorithm of Deep Learning. CNN has a unique advantage compared with traditional Machine Learning to do automated image feature extraction. However, CNN will perform better if numerous datasets are provided. Unfortunately, the lack of data due to privacy is still a problem in the medical image analysis topic. In order to solve that problem, many researchers have implemented a transfer learning technique to train the CNN models with small data. This study has proposed bilinear models based on CNN to distinguish brain MR images into five classes. In this study, MobileNetV1 and MobileNetV2 are employed as backbone networks to extract features via transfer learning, and the bilinear method is implemented to integrate the features from both networks. The proposed method improved the classification performance of the CNN model with a testing accuracy of 98.03%.
Robust Energy Trading for Interconnected Microgrids under Uncertain Electricity Price

This paper studies the energy trading between the interconnected microgrid, considering the uncertain electricity price of the microgrid and main grid for energy trading, we design a robust energy trading strategy, which not only considers the individual interests of the microgrid, but also enables the microgrid participating in the energy trading to get a fair share of the benefits from the cooperation. The uncertain electricity price is described by discrete scenarios. In order to give consideration to the optimization and conservatism of the solution, a robust optimization method based on the threshold of bad scenario set is used to consider more bad scenarios. To solve the robust energy trading model, an alternating direction method of multipliers based on the update of bad scenario set is proposed. The final experimental results show that the robust energy trading strategy based on the threshold of bad scenario set can reduce the total operating cost of the society by 11.11%, and the cost of a single microgrid can be reduced by 15.36% at most.
A Self-Organized Line Marching Formation Control Algorithm

Most of the existing exact formation control algorithms for multiagent systems are label-specified, i.e., the desired position for each agent in the formation is pre-specified by its label. In this paper, a self-organized control algorithm is proposed for line marching formation of a team of planar kinematic robots. The desired positions for the robots in the formation are not label-specified, but determined dynamically. By constantly forming a chain of leader-follower pairs, exact formation can be achieved by pairwise leader-follower tracking. Comprehensive simulation results are provided to evaluate the performance of the proposed control algorithm.
Black Hole Search in Dynamic Rings

In this paper, we start the investigation of distributed computing by mobile agents in dangerous dynamic networks. The danger is posed by the presence in the network of a black hole (BH), a harmful site that destroys all incoming agents without leaving any trace. The problem of determining the location of the black hole in a network, known as black hole search (BHS), has been extensively studied in the literature, but always and only assuming that the network is static. At the same time, the existing results on mobile agents computing in dynamic networks never consider the presence of harmful sites. In this paper we start filling this research gap by studying black hole search in temporal rings, specifically focusing on 1-interval connectivity adversarial dynamics. The main complexity parameter of BHS is the number of agents (called size) needed to solve the problem; other parameters are the number of moves (called cost) performed by the agents, and the time until termination. Feasibility and complexity depend on many factors; the size n of the ring, whether or not n is known, and the type of inter-agent communication (whiteboards, tokens, face-to-face, visual). In this paper, we provide a complete feasibility characterization presenting size optimal algorithms. Furthermore, we establish lower bounds on the cost and time of size-optimal solutions and show that our algorithms achieve those bounds.
Real-time Steel Surface Defect Recognition Based on CNN

Steel is one of the most important building materials of our time, and the process of producing flat plates is complex. Before steel is shipped or delivered, the sheets must undergo a thorough inspection procedure to avoid defects. Identification and classification of rolled metal surface defects are one of the main tasks for the correct evaluation of product quality. This work aims to develop a method for recognizing and classifying defects of metal surfaces by their images in real-time. The algorithm is aimed at improving production standards and process efficiency. In this paper, Deep Learning (DL) and Computer Vision (CV) techniques are used to solve the problem of defect detection on the steel surface sheets. Convolutional Neural Network (CNN) architectures are compared, and various steel defects are detected and recognized. The result of this work is a comparative analysis of DL models and the choice of an algorithm designed to search and classify defects in real-time. The use of one CNN model can make it possible to create a tool that greatly facilitates the work of a person.
Impact of Circuit Intake Power on the Spectral Energy Efficiency of Massive MIMO With Channel Estimation Errors

The influence of circuit intake power on Spectral Energy Efficiency (S.E.E) of massive Multiple-Input-Multiple-Output (MIMO) under the existence of channel estimation errors is examined using Zero-Forcing (ZF) linear pre-coding scheme. The system model which includes new defined total intake power involving the transmit power of amplifiers and the circuit power intake of the analog devices is developed. To analyze the impact of circuit intake power on S.E. E, the S.E. E with and without circuit intake power is investigated and presented after formulating a closed-form expression of S.E. E that incorporates new defined power model. Our investigation reveals that, the impact of circuit intake power is very significant when Base Station (BS) is arrayed with large number of antennas. Using the new S.E. E closed-form formula, derived from our new defined power intake model, the S.E. E is evaluated when the number of BS antennas (M) rises and it is observed that, S.E. E behaves like a concave function. The same results for S.E. E are also observed when transmit power increases. We conclude that, it is easy to get maximum S.E. E using a small number of BS antennas and optimal transmit power, when circuit power intake is included in the power consumption model. When transmit power totally dominates the circuit intake power, the maximum S.E. E is obtained only when all antennas are used (meaning maximum S.E.E is obtained when the number of BS antennas becomes very large). The numerical results reveal the importance of circuit intake power on optimizing S.E. E of massive MIMO under the presence of channel estimation errors.
Investigation of Influence from Variation in Color on LiDAR Sensor for Perception of Environment in Autonomous Vehicles

Perception of the vehicle surrounding is one of the most vital tasks in achieving a vision of fully autonomous driving. To achieve this task, forward-looking sensors like RADAR, LiDAR, and camera are widely used for object detection and classification. Accurate range information at several closely distributed points from the object’s outer surface gives the LiDAR sensor an advantage for estimating the shape and size of the opponent object. Though the LiDAR sensor can provide accurate range information in point cloud data, there are some variations in intensity values and no. of points reflected from objects with different colors. In this paper, we analyze the differences in the LiDAR intensities and no. of point clouds received from different colored objects by recording LiDAR data of three different colored cars at varied orientation angles and distances from the LiDAR sensor in similar environmental conditions and the impact it might have in perception of vehicle surrounding. The results show that the color of the object has a considerable influence on the LiDAR data and can affect the output of the pre-crash estimation algorithms. This highlights the need to further investigate this effect to improve the pre-crash estimation algorithms by considering the behavior of the variation in the LiDAR data based on the object color.
Flexible Direction-of-Arrival Simulation for Automotive Radar Target Simulators

In order to simulate realistic traffic scenarios, a radar target simulator must be able to generate multiple radar targets with different directions of arrival. The presented concept is able to generate an arbitrary amount of targets with individual directions of arrival for the radar under test. By measuring the radar channel, the novel approach enables target simulators to simulate arbitrary directions of arrival, while minimizing the required hardware. The optimum setup is derived for radars with a uniform linear receive antenna array. The compensation of placement errors for automotive chirp-sequence frequency modulated continuous wave radars is demonstrated. Finally, the calibration for the setup is provided, and the performance of the presented approach is validated.
Finite-time Attitude Coordinated Control for Spacecraft Formation Flying Using Consensus Algorithm

For the spacecraft formation flying system with unknown external disturbances and model uncertainties, a finite-time attitude coordinated control law is proposed based on consensus theory. Firstly, a disturbance observer is developed to estimate generalized disturbances consisting of external disturbances and model uncertainties, which can guarantee the estimation errors to converge to zero in finite time. Secondly, a novel nonsingular fast terminal sliding mode attitude cooperative controller based on the finite-time disturbance observer is designed to ensure that the attitude errors of the system converge to zero in finite time. Finally, simulation results are come out to demonstrate the effectiveness of the proposed control law.