Miracle-dz/UniFAR-FTUs huggingface dataset:
VISTA as a ligand downregulates LPS-mediated inflammation in macrophages and neutrophils

V-domain immunoglobulin suppressor of T-cell activation (VISTA) has emerged as a unique immunoregulatory receptor on cells of the myeloid lineage. Agonizing VISTA on myeloid cells has recently been demonstrated to have a profound effect on dampening inflammatory responses. VISTA has been proposed to function both as a ligand and as a receptor. In this context, the role of VISTA as a ligand has been largely ignored. Using a high-avidity agonist of the VISTA receptor (VISTA-COMP), we investigated the effect of exogenous VISTA, as a ligand, on macrophages and neutrophil cellular pathways in an acute inflammatory setting. RNA sequencing analysis demonstrated that VISTA-COMP downregulates pro-inflammatory cytokines and chemokines and upregulates immunoregulatory genes in both LPS-stimulated macrophages and neutrophils ex vivo. Interestingly, unlike VISTA itself, the receptor is only expressed following LPS stimulation of these cell populations. Furthermore, the administration of VISTA-COMP attenuated the rise in circulating TNFα levels in LPS-treated mice in vivo. These results suggest that VISTA serves a redundant role on macrophages and neutrophils acting as both a ligand and a receptor in the context of an acute inflammatory event.
Neural dynamics of retrieval suppression in abolishing item-specific cortical pattern of unwanted emotional memories

When reminded of an unpleasant experience, people often try to exclude the unwanted memory from awareness in an effort to forget it, a process known as retrieval suppression. Yet, how fast can individual memories be targeted and controlled, and the neural dynamics in modulating item-specific cortical traces, remain elusive. Here, using multivariate decoding analyses on time-domain and time-frequency-domain EEGs, we found that retrieval suppression of aversive memories was distinct from retrieval and passive viewing, when given a reminder. Specifically, early elevation of mid-frontal theta power during the first 500 ms distinguished retrieval suppression from passive viewing, suggesting that suppression recruited early active control processes. On an item-level, we could discern activities relating to individual memories during active retrieval - initially, based on perceptual responses to reminders (0-500 ms) and later, via the reinstatement and maintenance of the target aversive scenes (500-3000 ms). Critically, suppressing retrieval significantly weakened (during 420-600 ms) and eventually abolished these item-specific cortical patterns till cue disappeared (1200-3000 ms), suggesting the successful exclusion of the unwelcome memory from awareness. Suppression of item-specific cortical patterns, particularly during an early 300-680 ms time window, bore behavioral consequences in predicting subsequent episodic forgetting. These findings present a precise timeline on the suppression of specific unwelcome memories: upon perceiving an unwelcome reminder, people rapidly deploy inhibitory control to truncate retrieval within 500 ms, which likely terminate the reminder-to-memory conversion at around 500 ms that would ordinarily arise through hippocampal pattern completion. We concluded that both rapid and sustained control are critical in abolishing cortical patterns of individual memories, limiting unwelcome awareness, and precipitating later forgetting.
Extendable Navigation Network based Reinforcement Learning for Indoor Robot Exploration

This paper presents a navigation network based deep reinforcement learning framework for autonomous indoor robot exploration. The presented method features a pattern cognitive non-myopic exploration strategy that can better reflect universal preferences for structure. We propose the Extendable Navigation Network (ENN) to encode the partially observed high-dimensional indoor Euclidean space to a sparse graph representation. The robot’s motion is generated by a learned Q-network whose input is the ENN. The proposed framework is applied to a robot equipped with a 2D LIDAR sensor in the GAZEBO simulation where floor plans of real buildings are implemented. The experiments demonstrate the efficiency of the framework in terms of exploration time.
ULT-model: Towards a one-legged unified locomotion template model for forward hopping with an upright trunk

While many advancements have been made in the development of template models for describing upright-trunk locomotion, the majority of the effort has been focused on the stance phase. In this paper, we develop a new compact dynamic model as a first step toward a fully unified locomotion template model (ULT-model) of an upright-trunk forward hopping system, which will also require a unified control law in the next step. We demonstrate that all locomotion subfunctions are enabled by adding just a point foot mass and a parallel leg actuator to the well-known trunk SLIP model and that a stable limit cycle can be achieved. This brings us closer toward the ultimate goal of enabling closed-loop dynamics for anchor matching and thus achieving simple, efficient, robust and stable upright-trunk gait control, as observed in biological systems.
Information-Theoretic Analysis of OFDM With Subcarrier Number Modulation

With the prevalence of orthogonal frequency-division multiplexing (OFDM) in many standards, e.g., IEEE 802.11, IEEE 802.16, DVB-T, and DVB-T2, a number of variant modulation schemes based on OFDM have been proposed, which resort to signal sparsity to further enhance spectral efficiency and mitigate the high peak-to-average ratio (PAPR) problem. Among these variants, OFDM with subcarrier number modulation (OFDM-SNM) has been proven to be efficient for simple communication systems with low constellation modulation orders and limited decoding capability. To rigorously verify the performance advantages of OFDM-SNM, we present the study of OFDM-SNM in this paper from the information-theoretic perspective. In particular, we determine an upper bound on the mutual information of OFDM-SNM in closed form by using the log sum inequality. Also, we analyze the optimal pattern utilization probabilities (PUPs) for OFDM-SNM by channel-dependent coding and propose an easy-to-implement iterative algorithm to approach the optimal PUPs. Moreover, considering the practical achievability, we propose a Huffman coding based achievable PUP vector construction scheme to obtain the achievable PUPs and the corresponding achievable rate. We carry out numerical simulations to verify the effectiveness of this study and illustrate the efficiency of the obtained PUPs in comparison with several benchmarks.
A Novel Correntropy Analysis Method with Application to Multi-view Feature Representation

In this paper, a novel correntropy analysis (CORA) method is proposed for multi-view feature representation. By joint utilization the correntropy and nonlinear kernel transformation tools, the presented CORA method is able to measure the localized similarity between two random variables and further reveal the intrinsic relation between them effectively, leading to a high quality feature representation. Unlike many existing techniques for feature representation such as canonical correlation analysis (CCA) and kernel CCA (KCCA), CORA indicates and explores the mutual relation of two random variables according to the probability density. In addition, different from the kernel entropy component analysis (KECA) method revealing the structural information only from a single data space, CORA is able to explore the mutual structural information between two data spaces jointly instead. The effectiveness of the proposed method is evaluated through experiments on audio emotion recognition and face recognition examples. Comparisons are conducted on the statistics machine learning (SML) and deep neural network (DNN) based algorithms. The results show that the proposed CORA method outperforms other methods.
A Comparative Study of In-Domain vs Cross-Domain Learning for Porn Cartoon Classification

Detection of adult contents such as pornography, sex, and nudity has been investigated extensively in the literature. Recently, content moderator is a significant component for social platforms to be integrated in their software applications and services. Cartoon content moderator is a specific kind of moderators that should be highly accurate to reduce the classification error and increase the model’s sensitivity to adult contents. This paper aims to compare the models pre-trained on natural adult images and called cross-domain learning models with ones pre-trained on cartoon images and called in-domain learning models for adult content detection in cartoons. The paper utilized pre-trained convolutional neural networks such as ResNet and EfficientNet to extract features that were applied to support vector machine for porn/normal classification. It was found that in-domain models outperformed cross-domain model in terms of performance metrics to improve the accuracy by 13 %, recall by 2 %, precision by 18 %, F1 score by 14 %, false negative rate by 2 %, and false positive rate by 16 %.
Feasible and Adaptive Multimodal Trajectory Prediction with Semantic Maneuver Fusion

Predicting trajectories of participating vehicles is a crucial task towards full and safe autonomous driving. General unconstrained machine learning methods often report unrealistic predictions, and need to be combined with different motion constraints. Existing work either defines some shallow maneuvers and modes to regulate the output, or uses vehicle dynamics as the main source of constraints, for instance via kinematic models. In contrast, we present a new approach that guides the learning models by complex semantic maneuvers, constructing from both vehicle states and the surrounding objects. We propose a novel Maneuver Fusion layer to incorporate the logic-based semantic maneuvers into deep neural networks. We also incorporate and refine the different loss functions to account for the feasibility of the trajectories, adapting to different maneuver types. Finally, we design a hierarchical multi-task learning framework with adaptive loss to provide a multimodal trajectory prediction. Our method was evaluated on a large-scale real world data set for urban driving and was shown to give promising improvement over the states of the art.
Design and Analysis of a Novel Lightweight, Versatile Soft-rigid Robot

Soft robots are compliant to wrap large objects and adaptive to unstructured environments, while rigid robots can bend discretely at joints and pinch small objects easily. In this paper, we aim to create a novel robot that can behave like a rigid or soft robot adaptively, without additional stiffness-tunable mechanism. This approach makes the robot lightweight and practical. First, we present the fabricating process of the soft-rigid robot developed in the work. A steel flexure is especially optimized and embedded in the flexible body of the robot, which enhances the bending stiffness in the axial direction dramatically while keeping the body in other directions flexible and adaptive. Second, the kinematic model and analysis are developed to characterize the different configurations of the soft-rigid robot influenced by various parameters. Third, experiments are conducted to evaluate the performance of the flexible body and validate the accuracy of the kinematic model. Finally, a two-fingered gripper is built based on the soft-rigid robots, and grasping experiments are conducted to demonstrate how the soft-rigid robots could enhance the capability of the gripper in real-word.
Developing of A Rigid-Compliant Finger Joint Exoskeleton Using Topology Optimization Method

Robotic hand exoskeletons can provide assistance to people who suffer from hand functional disability or spinal cord injury (SCI). However, the current hand exoskeletons remain challenging with respect to having a user-friendly design that satisfies human motion with a lightweight structure. Here we propose a method of using topology optimization in the design of finger exoskeletons, which is a lightweight and integrate manufactured exoskeleton. The exoskeleton is designed by generating the topology configuration according to the bending state of the finger. The objective function in the optimization is set to be the maximization of output displacement of the design domain. After obtaining the topology configuration, a conjugate surface flexure hinge is developed as an elastic joint to replace the compliant part of the topologies. Finally, a rigid-compliant parallel exoskeleton is fabricated by a 3D printer and the performance of the mechanism is verified.
Encapsulation Research of Microfiber Mach-Zehnder Interferometer Temperature and Salinity Sensor in Seawater

Encapsulation of microfiber Mach-Zehnder interferometer (MMZI) sensor for temperature and salinity sensing in seawater is developed. It includes a C-shape metal tube with a slit on the bottom and polymer adhesive with large elasticity modulus, by which a more robust sensor with relatively fast response can be obtained. To evaluate the performance of sensor after encapsulation, effects of different polymer adhesives on tension withstanding, temperature and salinity sensitivities are evaluated, and dependence of response time on shape of metal tube is investigated. Results show that sensitivities of the sensor in temperature and salinity sensing can reach to be about −1.77 nm/°C and 1.18 nm/ ‰, and response times are about 219.0 ms and 12.5 ms, respectively. By comparing temperatures and salinities measured by sensor with those measured by commercial device, accuracy of the sensor after encapsulation is proved. In addition, cross-sensitivities of strain/pressure and short/long-term stability after encapsulation are also evaluated. Finally, based on the basic structure, further improvements are attempted, such as double encapsulation with stainless steel mesh and complex encapsulation with two kinds of polymer. Encapsulations of MMZI demonstrated here show advantages of low cost, improved robustness, relatively fast response, long-term stability and easy expandability, which will not only afford a critical support for optical sensors operated in the ocean, but also offer some useful references for microfiber sensors used in other sensing applications.
An Encoder-Free Joint Velocity Estimation Method for Serial Manipulators Using Inertial Sensors

This paper focuses on developing a real-time and flexible velocity estimation approach for serial revolute manipulator using only one inertial measurement unit (IMU) mounted on each link side of the manipulator. Particularly, the proposed approach has no requirement for the installation position and orientation of the IMU, which improves the flexibility of the implementation procedure. A joint velocity model is established based on the proposed principle of constructing coordinate system according to the robotic geometric information. The general solutions are derived in detail, thereby the proposed algorithm can be generalized into any other robots with the same geometric configuration. With the method, the joint rotational velocity measurements of static and dynamic robotic motion are provided compared to encoders. Experimental results based on the six degrees of freedom (DOF) collaborative manipulator have validated the feasibility and effectiveness of the proposed approach. The proposed method has the benefits of low cost and flexibility, which could work as a redundant velocity monitor criterion to provide assistant joint velocity measurements.
Reaching Pruning Locations in a Vine Using a Deep Reinforcement Learning Policy

We outline a neural network-based pipeline for perception, control and planning of a 7 DoF robot for tasks that involve reaching into a dormant grapevine canopy. The proposed system consists of a 6 DoF industrial robot arm and a linear slider that can actuate on an entire grape vine. Our approach uses Convolutional Neural Networks to detect buds in dormant grape vines and a Reinforcement Learning based control strategy to reach desired cut-point locations for pruning tasks. Within this framework, three methodologies are developed and compared to reach the desired locations: the learned policy-based approach (RL), a hybrid method that uses the learned policy and an inverse kinematics solver (RL+IK), and lastly a classical approach commonly used in robotics. We first tested and validated the suitability of the proposed learning methodology in a simulated environment that resembled laboratory conditions. A reaching accuracy of up to 61.90% and 85.71% for the RL and RL+IK approaches respectively was obtained for a vine that the agent observed while learning. When testing in a new vine, the accuracy was up to 66.66% and 76.19% for RL and RL+IK, respectively. The same methods were then deployed on a real system in an end to end procedure: autonomously scan the vine using a vision system, create its model and finally use the learned policy to reach cutting points. The reaching accuracy obtained in these tests was 73.08%.
Deep Reinforcement Learning Framework for Underwater Locomotion of Soft Robot

Soft robotics is an emerging technology with excellent application prospects. However, due to the inherent compliance of the materials used to build soft robots, it is extremely complicated to control soft robots accurately. In this paper, we introduce a data-based control framework for solving the soft robot underwater locomotion problem using deep reinforcement learning (DRL). We first built a soft robot that can swim based on the dielectric elastomer actuator (DEA). We then modeled it in a simulation for the purpose of training the neural network and tested the performance of the control framework through real experiments on the robot. The framework includes the following: a simulation method for the soft robot that can be used to collect data for training the neural network, the neural network controller of the swimming robot trained in the simulation environment, and the computer vision method to collect the observation space from the real robot using a camera. We confirmed the effectiveness of the learning method for the soft swimming robot in the simulation environment by allowing the robot to learn how to move from a random initial state to a specific direction. After obtaining the trained neural network through the simulation, we deployed it on the real robot and tested the performance of the control framework. The soft robot successfully achieved the goal of moving in a straight line in disturbed water. The experimental results suggest the potential of using deep reinforcement learning to improve the locomotion ability of mobile soft robots.
Tactual Recognition of Soft Objects From Deformation Cues

Recognition of soft objects is a critical step to tailor policy for dexterous manipulation. Deformation property is one of the valuable tactile cues for inferring the identities of soft objects, especially those having similar appearance features. In this work, we implemented tactual grasping for discriminating soft objects from the response curves of indentation displacement in relation to grasping force. Unlike some existing methods defining certain local features from the force-displacement observations, we viewed the force-displacement curve as continuous function rather than discrete observations. We proposed to use functional data analysis for classifying the kind of force-displacement curve data. Functional principal component analysis (FPCA) was used to extract multivariate features from force-displacement curves. Different traditional machine learning models were trained for classifying the multivariate features and the best one was selected based on cross-validation. Case study demonstrated that our method could accurately distinguish eight different soft objects. The FPCA based features outperformed those manually defined features. We indicated that the force had crucial influence on the classification accuracy and proposed how to select the proper force for the classification task. Overall, this work provides practical guidelines on tactual recognition of soft objects.
Automated Sleep Stage Analysis and Classification Based on Different Age Specified Subjects From a Single-Channel of EEG Signal

The present work mainly focused on automatic classification of the sleep stages classification from the different medical-conditioned subjects under American Academy of Sleep Medicine (AASM). The present research study was used single individual channel for classifying two-state sleep stage classification problem in between wake versus Sleep. During first part, we have conducted experiments such as acquisition of data from participated subjects, preprocessing the raw signal to remove the irrelevant artefacts and muscle movements from recorded sleep data, extract quantitative features obtained from EEG signal and also used feature selection techniques to choose the suitable features which is most useful for characterizing the abnormality pattern. In second step, we have used two machine learning classifiers such as support vector machine (SVM) and K-nearest neighbor (KNN). The proposed scheme was conducted through six different medical conditions subjects with considering both affected sleep diseases subjects and healthy subjects and their one session and two session recordings. The obtained results demonstrated that the proposed methodologies support to sleep experts for accurately measure the irregularities occurred during sleep and also helps the clinicians to evaluate the presence and criticality of sleep related disorders.
JCS: An Explainable Surface Defects Detection Method for Steel Sheet by Joint Classification and Segmentation

For surface defect images that captured from a practical steel production line, different shape, size, location and texture of defect object may cause inter-class similarity and intra-class difference of defect images. Despite attractive results have been achieved in some surface methods for defect classification and segmentation, it is still far from meeting the needs of real-world applications due to lack of adaptiveness of these methods. Considering the surface defect image can be decomposed into defect foreground image and defect-free background image, the paper develops a novel joint classification and segmentation (JCS) approach to perform surface defects detection for steel sheet. It comprises of the classification method based on a class-specific and shared discriminative dictionary learning (CASDDL) and the segmentation method based on a double low-rank based matrix decomposition (DLMD), respectively. For the proposed CASDDL method, we learn a shared sub-dictionary as well as several class-specific sub-dictionaries to explicitly capture common information shared by all classes and class-specific information belonging to corresponding class. We adopt a mutual incoherence constrain for each sub-dictionary, a Fisher-like discriminative criterion and low-rank constrain on coding vector to improve the discriminative ability of learned dictionary. For the proposed DLMD method, we formulate the segmentation task as a double low-rank based matrix factorization problem, and the Laplacian and sparse regularization terms are introduced into the matrix decomposition framework. Experimental results demonstrate that our proposed JCS method achieve a comparable or better performance than the state-of- the-art methods in classifying and segmenting surface defects of steel sheet.