Miracle-dz/UniFAR-FTUs huggingface dataset:
Revolutionizing Biomechanics: Integrating AI, Collaborative Coding Environments, and Simulation Software for Enhanced Model Development and Analysis

Biomechanics is a crucial field that investigates human movement and performance. This paper presents a comprehensive framework that combines ChatGPT, Colab, and OpenSim to streamline the creation and analysis of biomechanical models. By integrating ChatGPT and Colab, the framework offers a user-friendly and accessible approach to model development, while OpenSim provides a versatile platform for simulations. The framework's advantages include intuitive model development, online accessibility, and simplified setup. However, considerations such as the accuracy of ChatGPT responses and data security in Colab should be addressed. The paper demonstrates the framework's potential through practical examples, including muscle strength enhancement, center of mass determination, and extracting model details, showcasing its efficacy in advancing biomechanical research.
Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks

Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities govern various cellular processes. The limitations in genomics data and the complexity of the interactions between components often pose huge uncertainties in the models of these biological systems. Meanwhile, inferring/estimating the interactions between components of the GRNs using data acquired from the normal condition of these biological systems is a challenging or, in some cases, an impossible task. Perturbation is a well-known genomics approach that aims to excite targeted components to gather useful data from these systems. This paper models GRNs using the Boolean network with perturbation, where the network uncertainty appears in terms of unknown interactions between genes. Unlike the existing heuristics and greedy data-acquiring methods, this paper provides an optimal Bayesian formulation of the data-acquiring process in the reinforcement learning context, where the actions are perturbations, and the reward measures step-wise improvement in the inference accuracy. We develop a semi-gradient reinforcement learning method with function approximation for learning near-optimal data-acquiring policy. The obtained policy yields near-exact Bayesian optimality with respect to the entire uncertainty in the regulatory network model, and allows learning the policy offline through planning. We demonstrate the performance of the proposed framework using the well-known p53-Mdm2 negative feedback loop gene regulatory network.
Controlling the Shape of Soft Robots Using the Koopman Operator

In nature, animals with soft body parts demonstrate remarkable control over their shape, such as an elephant trunk wrapping around a tree branch to pick it up. However, most research on robotic manipulators focuses on controlling the end effector, partly because the manipulator’s arm is rigidly articulated. With recent advances in soft robotics research, controlling a soft manipulator into many different shapes will significantly improve the robot’s functionality, such as medical robots morphing their shape to navigate the digestive system and deliver drugs to specific locations. However, controlling the shape of soft robots is challenging due to their highly nonlinear dynamics that are computationally intensive. In this paper, we leverage a physics-informed, data-driven approach using the Koopman operator to realize the shape control of soft robots. We simulate the dynamics of a soft manipulator using a physics-based simulator (PyElastica) to generate the input-output data, which is then used to identify an approximated linear model based on the Koopman operator. We then formulate the shapecontrol problem as a convex optimization problem that is computationally efficient. Our linear model is over 12 times faster than the physics-based model in simulating the manipulator’s motion. Further, we can control a soft manipulator into different shapes using model predictive control. We envision that the proposed method can be effectively used to control the shapes of soft robots to interact with uncertain environments or enable shape-morphing robots to fulfill diverse tasks. This paper is complemented with a video.
Iterative Planner/Controller Design to Satisfy Signal Temporal Logic Specifications

This paper considers the design of a planner/tracker for a dynamical system with complex mission specifications expressed as a Signal Temporal Logic (STL) formula. The design consists of two parts: (i) a high-level planner to generate a reference trajectory to satisfy the desired STL formula, and (ii) a low-level controller to generate the control inputs to track the given reference trajectory. Traditionally, these two parts are often designed in a decoupled fashion. Moreover, the planner is often designed using an open-loop plant model that neglects (or only loosely accounts for) the low-level controller. We propose a control synthesis framework in which the high-level planner and the low-level controller are designed simultaneously in an iterative process. We demonstrate our results using a quadcopter scenario and benchmark our results with existing methods in the literature.
Accuracy and Coverage Analysis of IP Geolocation Databases

Identifying the geographical location of Internet hosts is crucial for researchers, governments, and commercial entities. While public and commercial geolocation services are commonly employed for this task, their accuracy in locating Internet hosts remains questionable. This paper studies the accuracy and coverage of four popular geolocation databases; MaxMind, DBIP, IP2Location, and IPGeolocationIO. We assess the consistency and comprehensiveness of these services by analyzing the entire IPv4 space. Furthermore, we investigate the issue at the prefix level since geolocation databases typically provide location data at that level. Finally, we create a ground truth dataset by employing a DNS-based approach and publicly available vendor locations to evaluate the accuracy of the databases. Our findings indicate that these databases provide comprehensive coverage, whereas their accuracy is far from satisfactory. Therefore, it is essential to use the information obtained from these databases cautiously and verify its accuracy before making any decisions based on it.
An Intelligent Hybrid Neural Collaborative Filtering Approach for True Recommendations

Recommendation services become a critical and hot research topic for researchers. A recommendation agent that automatically suggests products to users according to their tastes or preferences instead of wandering in a huge corpus for a product. Social data such as Reviews play an important role in the recommendation of products. Improvement was achieved by neural network methods for capturing user and product information from a short text. However, such approaches do not fairly and efficiently incorporate users’ preferences and product characteristics. We are proposing the novel Hybrid Neural Collaborative Filtering (HNCF) model that combines deep learning capabilities and deep interaction modelling for recommender systems with a rating matrix. To overcome the cold start problem, we use the new overall rating by aggregating the Deep Multivariate Rating DMR (votes, likes, stars and sentiment scores of reviews) from different external data sources because different sites have different rating scores about the same product. The propose novel model consists of four major modules HUAPA-DCF+NSC+DMR (Hierarchical User Attention and Hierarchical Product Attention, Deep Collaborative Filtering, Neural Sentiment Classifier, and Deep Multivariant Rating) to solve the addressed problems. Initially, the HUAPA module is based on BiLSTM’s hierarchical user attention (HUA) and hierarchical product attention (HPA) to embed the user preferences and product characteristics respectively. Further, these are combined nonlinear representations and fed as input to the interaction module. Secondly, the deep collaborative filtering module is implemented to find the explicit interaction between user and product. Thirdly, NSC module will extract the user’s semantic about products by incorporating the user’s preferences and product characteristics. Finally, the module uses explicit information (Deep Multivariant Rating) to the maximum extent for final classification. Experimental results demonstration that our novel model is outperforming than state-of-the-art at IMDb, Yelp2013 and Yelp2014 datasets for the true recommendation of top-n products using HNCF (HUAPA+DCF+NSC+DMR) to increase the accuracy, confidence, and trust on recommendation services.
Learning Based Optimal Sensor Selection for Linear Quadratic Control with Unknown Sensor Noise Covariance *

In this article, an optimal sensor selection problem is considered under the framework of linear quadratic control. The objective is to find the best strategy of selecting one sensor among a set of sensors at each time step so that the expected system performance is minimized over multiple time steps. This problem is formulated as a multi-armed bandit problem. Uncertainties are captured through noisy sensor measurements, which account for the performance deterioration caused by unknown sensor noise covariance. In this context, several action-value based reinforcement learning methods are proposed to evaluate the performance of different sensor selection strategies. Moreover, a statistical method is developed to estimate the unknown sensor noise covariance as a byproduct. The almost sure convergence to the true sensor noise covariance is guaranteed as the number of times a sensor being selected goes to infinity. A linear quadratic control example is presented to illustrate the proposed approaches and to demonstrate their effectiveness.
Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://sites.google. com/view/pre-grasp-sliding.
Robust Filtering based on Complex Cell Networks from the Visual Cortex

We propose a novel approach for filtering that is inspired by Complex Cell Networks (CCN) in the primary visual cortex of mammals; our aim is to emulate the robustness of the biological system, showing graceful degradation in face of gross deterioration of the input. Instead of relying on energy minimization as in frequency-based filter design, or on Bayes’ theorem as in statistical filtering, our formulation is founded on three principles that have been observed in real neural responses: 1) winner-take-all, where perceptual ambiguity is solved by focusing on the strongest signal; 2) persistence, where information is fused across time to lessen the impact of noise, outliers, and temporary cancellations in the input data; and 3) boundedness, where the responses in the filter are bounded to be non-negative and below a maximum value. In neuroscience, the typical goal is to find models that match and explain measurements from a biological system. In this paper, we take an engineering approach, where we encode the three properties above as mathematical constraints, and find filter parameters that guarantee convergence of the filter (for constant, bounded inputs), optimize bounds on the convergence rate, and improve sparsity of the filter kernel; overall, the filter is obtained from the solution to a Linear Program (LP). As a proof-of-concept, we integrated the proposed filter architecture with a neural network to estimate the vehicle speed solely based on camera images in extremely noisy environments.
A Tactile Feedback Insertion Strategy for Peg-in-Hole Tasks

The Peg-In-Hole (PiH) task performed under un-certain conditions still represents a challenge for autonomous robots. When the peg is not rigidly connected to the robot end-effector, the external forces generated by peg-environment interactions can change the in-hand pose of the peg. This aspect must be taken into account when performing the insertion. This paper deals with this problem and proposes an insertion strategy driven by tactile feedback. In particular, we consider holding the peg using a parallel gripper equipped with tactile sensors, whose measurements are processed to capture in-hand rotations of the peg pose. This information is fed back to the robot controller and used to compensate for changes in the peg orientation and end-point position occurring during the task execution. The approach is validated on a real robot using a two-finger gripper equipped with two capacitive-based tactile sensor arrays hosting 20 tactile elements each. We show that the proposed method achieves an insertion success rate of 38/40 with a 0.1 mm clearance between the peg and hole.
Sharp Performance Bounds for PASTA

LiDAR is a standard sensor choice for self-localization and SLAM on indoor autonomous robots. While there are many methods to estimate a robot’s location using LiDAR measurements, most rely on algorithms that solve a generic LiDAR scan matching problem. When safety is a concern, these algorithms must provide a bound on the localization error to enable safety enforcing controllers, such as those based on Control Barrier Functions. Unfortunately, most existing scan matching algorithms offer no formal guarantees and are tailored to structured, high-resolution 3D point clouds. In this letter, we present an improved theoretical analysis for a low-cost alternative to these methods named PASTA (Provably Accurate Simple Transformation Alignment), originally introduced in (Marchi et al., 2022). We provide a formal worst-case guarantee on the localization error and show, experimentally, that it is tight. This characterization of the localization error simplifies the interface between high-dimensional perception data and safety-critical control.
Gallium Nitride Depth Porosity Using Image Processing Method

Porous Gallium Nitride (GaN) structure is commonly prepared by using direct current photo-assisted electrochemical etching (DC-PECE) technique to generate porous structure. However, to reach a generalized pores depth porosity measured using conventional methods is impossible due to the complex and very thin layer of the GaN structure. Image processing has been widely used in validating the measurement and may be significant for this application. This study is aimed to propose a recent method to measure pores depth porosity using image processing techniques. Porosity of the structures obtained by calculating the areas occupied by the pores. To validate the method, the evaluation of porous GaN quality is performed through a non-destructive investigation of its nanostructures using adapting image analysis techniques. The quantitative results showed good agreement between measurement and calculation (percentage porosity and pore depth) based on the image-processing data with 91 % and 78 % correlation coefficient.
From Concept to Field Tests: Accelerated Development of Multi-AUV Missions Using a High-Fidelity Faster-than-Real-Time Simulator

We designed and validated a novel simulator for efficient development of multi-robot marine missions. To accelerate development of cooperative behaviors, the simulator models the robots' operating conditions with moderately high fidelity and runs significantly faster than real time, including acoustic communications, dynamic environmental data, and high-resolution bathymetry in large worlds. The simulator's ability to exceed a real-time factor (RTF) of 100 has been stress-tested with a robust continuous integration suite and was used to develop a multi-robot field experiment.
Distributed Formation Keeping Control for Multiple Hypersonic Gliding Vehicles Based on Fixed-time Consensus Theory

In this study, with an undirected communication topology, the control problem of formation keeping for the multiple hypersonic gliding vehicles (HGVs) is investigated. On the basis of the theory of fixed-time consensus, virtual control signals are designed to reduce the complexity for formation keeping problem of multiple HGVs. The formation keeping problem is addressed by control laws that makes the altitude and path angle of each vehicle consensus. Therefore, the virtual control signals for the altitude and path angle keeping controller are designed by using first-order fixed-time and second-order fixed-time consensus theory respectively. Meanwhile, upper bound of the settling time for virtual signals can be estimated ahead of time with the information of communication topology and the given control parameters. Simulation result is carried out to demonstrated the formation keeping control laws.
Design Evolution of MADMAX Conductor to a Nb–Ti Cable in Copper Conduit

The MAgnetized Disc and Mirror Axion eXperiment (MADMAX) project aims at detecting axion dark matter in the mass range around 100 μeV. Prerequisite for success is a very large dipole (1.35-m warm bore) with a high magnetic field up to 10 T. A first conductor design made of Nb–Ti Rutherford cable on conduit conductor with a hole in the copper conduit (RCOCC type) was already presented. The coolant is static bath of superfluid helium at 1.8 K. Unfortunately, no conductor manufacturer with an available production line was found to take in charge the cable insertion/soldering process. Actually, such a production line should be compatible with large conductor section (∼400 mm2), including a solder wave device. The type of conductor was changed to use a line developed for ITER cable-in-conduit conductor. Nevertheless, the jacket part was kept in copper. The main evolution of the MADMAX conductor design is presently reviewed. The final outer dimensions of the conductor, the void fraction, and the required cold work of the copper induce a reverse engineering challenge on the initial shape of the copper profile. Then, the cable geometry modification involves a lots of differences in terms of self-field, strand diameters, transposition paths, joints technique, and conductance between the strands and the stabilizer. The mechanical aspects are subject to evolution for the project due to the magnetic design optimization and conductor-type change. A submodeling approach is used to detail the stress level in the stabilizer jacket. The possibility of wire motion and wire deformation during the conductor compaction in the production line is also discussed. Concerning the thermal analysis, the major difference is the thermohydraulic quench back that induces a much higher pressure drop of the helium in the conduit. So, higher pressure-induced higher helium velocity and a fast detection after a quench event in superfluid environment become possible.
Dimensional Optimization and Anti-Disturbance Analysis of an Upgraded Feed Mechanism in FAST

Five-hundred-meter aperture spherical radio telescope (FAST) is a very famous large-scale scientific facility with excellent performance for astronomical observation in the world, but it currently fails to observe the center of the Milky Way Galaxy due to the limited observation angle that is affected by the heavy weight of the feed cabin. To improve this problem, an upgraded feed mechanism (UFM) with a lighter cable structure is designed and employed to replace the existing heavy rigid A-B rotator and Stewart platform in the feed cabin of FAST. The structural dimension of the UFM is analyzed and optimized under cable tension constraints to meet the requirements of the observation angle. Then, a novel disturbance increment method is proposed to analyze the anti-disturbance ability of the UFM, where a gradually increased disturbance wrench is applied to the UFM with the stiffness matrix iteratively updated. Through the dimensional optimization and further anti-disturbance analysis, the newly-designed UFM can indeed meet the higher demand for astronomical observation with the larger observation angle, which benefits from the lightweight cable structure. Besides, the UFM also has the appreciable anti-disturbance ability for long-term stable operation of FAST.
SDWAN with IDPS Efficient Network Solution

The Wide Area Network and firewall are the current networking technologies that are widely used in most enterprises. But due to the rapid development of new technologies, there is a rising demand for a new efficient networking solution that could overcome the current technologies’ limitations and cater to the end users’ new requirements. This paper focuses on investigating and finding out the contrast and comparison between old and new intranet and extranet technologies in which Software Defined-Wide Area Network with Intrusion Detection and Prevention System solution is proposed. An extensive systematic review was conducted of peer referred literature. The proposed solution enables network performance and security improvements, resulting in an efficient network solution.
Improving latency performance trade-off in keyword spotting applications at the edge

Keyword Spotting (KWS) is handy in many innovative ambient intelligence applications, such as smart cities and home automation. While solving KWS on GP/GPUs has become a trivial task in recent years, many benefits arise when KWS applications run at the edge (e.g., privacy by design and infrastructure sustainability), where resources are limited. Hardware-aware scaling (HAS) is a novel paradigm that brings neural architectures to low-resource platforms. With HAS, it is possible to optimize neural architectures to fit on embedded platforms (e.g., microcontrollers) while maximizing the performance-complexity tradeoff and the performance-latency tradeoff. This paper shows how HAS, coupled with a neural network with appropriate scaling capabilities, can outperform architectures designed with neural architecture search techniques, such as MCUNet. Our method achieves 94.5% accuracy when classifying the 35 keywords in Google Speech Commands v2, with only 70 ms of latency and overall power consumption of less than 10 mJ.