Miracle-dz/UniFAR-FTUs huggingface dataset:
The Simulated Tree-Growth Algorithm Based on T-Invariants for State Reduction of Petri Nets

Petri net is a powerful tool for analyzing and verifying complex systems due to its intuition and inherent concurrency. One major limitation of Petri net approaches is the state explosion problem. Although there are various methods to tackle this problem, the heuristic approaches are used little. In this paper, we propose a heuristic method for state space reduction of Petri nets. The heuristic is derived from state equation and minimal T-invariants, based on which a simulated tree-growth algorithm (STGA) is proposed to generate the state space. The key point of the STGA is the fitness evaluation. Concretely, we design an efficient algorithm to estimate the distance from each state to the target. By this heuristic, the state space can be generated heuristically rather than randomly, then only partial states are generated, which means the state explosion problem can be alleviated. Finally, the empirical study reveals the promising result of our proposal in this paper.
Learning IoT skills in the context of student projects

Extensive use of various types of IoT devices has resulted in high demand for developers with IoT skills. We have identified the challenges facing IoT developers and designed suitable student projects that aim to address these challenges. It is our experience, that it is important to match the IoT platform used for the project to the skills of the students to achieve optimal learning outcomes. In this paper, we describe two case studies that use high-quality modular IoT kits to accelerate the learning process and to allow students with relatively low technical skills to implement working solutions to real-world problems.
Distributed Object Transport of Mobile Manipulators with Optimal Manipulable Coordination

This paper addresses a distributed projected control algorithm for networked mobile manipulators to achieve a task of object transport with optimal manipulable coordination between robot arm and mobile platform. The task is designed as a distributed non-convex optimization problem with time-varying coupled equality constraints. To eliminate the time-varying term in the optimization problem, the task variables are first introduced. The non-convex cost function is then proved convex under a sufficient condition by selecting a proper displacement vector. Moreover, a modified Lagrangian function containing local multipliers and a nonsmooth penalty function is constructed to handle the coupled equality constraints in a distributed manner. Therefore, a fully distributed projected control algorithm is proposed to achieve the object transport task based on the primal-dual subgradient dynamics. We prove the convergence of the algorithm utilizing the Lyapunov stability theory and the invariance principle. Under the proposed control algorithm, the object transport is accomplished with optimal manipulable coordination, which is further validated through numerical simulations.
Iterative Learning Control Design for Uncertain Time-Delay Systems by Generalized KYP Synthesis

This paper deals with the problem of designing an iterative learning control scheme for uncertain time-delay systems using differential repetitive process stability theory. The resulting design produces a stabilizing feedback controller in the time domain and a feedforward controller that guarantees monotonic convergence in the trial-to-trial domain for all admissible uncertainties. The design procedures are delay dependent since the obtained results depend on the size of state delay and are also extended to limited frequency range design specification. A new design procedure is developed in terms of linear matrix inequalities, which guarantee prescribed performance specifications. Finally, a simulation example is given to illustrate the application of the new results.
How To Efficiently Increase Resolution in Neural OCR Models

Modern CRNN OCR models require a fixed line height for all images, and it is known that, up to a point, increasing this input resolution improves recognition performance. However, doing so by simply increasing the line height of input images without changing the CRNN architecture has a large cost in memory and computation (they both scale O(n2) w.r. t. the input line height). We introduce a few very small convolutional and max pooling layers to a CRNN model to rapidly downsample high resolution images to a more manageable resolution before passing off to the "base" CRNN model. Doing this greatly improves recognition performance with a very modest increase in computation and memory requirements. We show a 33% relative improvement in WER, from 8.8% to 5.9% when increasing the input resolution from 30px line height to 240px line height on Open-HART/MADCAT Arabic handwriting data.This is a new state of the art result on Arabic handwriting, and the large improvement from an already strong baseline shows the impact of this technique.
Long‐term follow‐up of ICSI‐conceived offspring compared with spontaneously conceived offspring: a systematic review of health outcomes beyond the neonatal period

A significant increase in the use of intracytoplasmic sperm injection (ICSI) since its introduction in 1992 has been observed worldwide, including beyond its original intended use for severe male factor infertility. Concerns regarding ICSI include the effects of poor quality spermatozoa on offspring health and future fertility, and of the technique itself. The health and development of ICSI‐conceived children beyond early infancy have not been comprehensively assessed.
Accelerated Labeling of Discrete Abstractions for Autonomous Driving Subject to LTL Specifications

Linear temporal logic and automaton-based run-time verification provide a powerful framework for designing task and motion planning algorithms for autonomous agents. The drawback to this approach is the computational cost of operating on high resolution discrete abstractions of continuous dynamical systems. In particular, the computational bottleneck that arises is converting perceived environment variables into a labeling function on the states of a Kripke structure or analogously the transitions of a labeled transition system. This paper presents the design and empirical evaluation of an approach to constructing the labeling function that exposes a large degree of parallelism in the operation as well as efficient memory access patterns. The approach is implemented on a commodity GPU and empirical results demonstrate the efficacy of the labeling technique for real-time planning and decision-making.
Towards Systematic Benchmarking of Activity Recognition Algorithms

In this paper we propose a benchmarking frame-work for evaluating activity recognition methods. We use an ontology for describing activity recognition datasets on the meta-level and propose a fine-grained annotation scheme for activity recognition datasets. Given a method that implements a defined interface, an evaluation client can be used to automatically run experiments on annotated datasets. Our framework helps to find relevant datasets and makes results reproducible by fixing concrete experimental settings. We show how to use the framework and report about a preliminary evaluation experiment.
Design of an Indoor Exploration and Multi-Objective Navigation System

In this paper, we propose an indoor robot autonomous navigation system. The robot firstly explores in an unknown environment, and then navigates autonomously by using the explored map. The robot is equipped a 2D laser scanner as the main sensor. The laser scanner is used for path planning and frontier-based exploration. A 2D global occupancy map is built for path planning, frontier-based exploration and multi-objective autonomous navigation. Laser scans are transmitted into Simultaneous Localization and Mapping (SLAM) process in the exploration phase. In indoor environment, the exploration efficiency is improved by merging a heuristic algorithm. By using multi-threading technology and a 3D perception approach proposed in this paper, the robot equipped with a low-cost RGBD sensor can detect all kinds of obstacles to achieve highly reliable navigation in complicated 3D environment. Meanwhile, we develop a multi-objective navigation application to make human-robot interaction more convenient and satisfy multi-task deployment. Our approaches are demonstrated by experimental results.
Network Models in Neuroimaging: A Survey of Multimodal Applications

Mapping the brain structure and function is one of the hardest problems in science. Different image modalities, in particular the ones based on magnetic resonance imaging (MRI) can shed more light on how it is organised and how its functions unfold, but a theoretical framework is needed. In the last years, using network models and graph theory to represent the brain structure and function has become a major trend in neuroscience. In this review, we outline how network modelling has been used in neuroimaging, clarifying what are the underlying mathematical concepts and the consequent methodological choices. The major findings are then presented for structural, functional and multimodal applications. We conclude outlining what are still the current issues and the perspective for the immediate future.
Population Size Control for Efficiency and Efficacy Optimization in Population Based Metaheuristics

This paper proposes a mechanism of dynamic adjustment of the population size of population based metaheuristics in order to balance its efficacy and efficiency. In this approach, an external trajectory based metaheuristic (MH) is used to dynamically adjust the population size of an inner population based metaheuristic. A Particle Swarm Optmization (PSO) implemented for a Compute Unified Device Architecture platform (CUDA), called CUDA-PSO, is used as inner MH, while a sequential Simulated Annealing (SA) is used as an external one. The main objective of this paper is to evaluate the SA capabilities of finding a good balance between efficiency and efficacy during the CUDA-PSO execution and to assess its adaptability to different hardwares without any prior information about the computing platform. The results show that the new approach was able to find a good balance in most cases. Also, it was observed that this approach is able to adapt its operation to different hardwares.
A Novel R Peak Detection Method for Mobile Environments

With the development of the portable electrocardiogram (ECG) sensor, R peaks can be monitored during various physical activities in mobile environment. However, such ECG signals contain real-world noise, complicating the accurate detection of R peak. In this paper, we propose a novel approach for R peak detection in ECG signals with real-world noise by using signal envelop filtering (SEF) and Shannon energy envelope with the Savitzky–Golay filter (SEE-SG). SEF was designed to diminish abrupt peaks due to movement by adjusting its amplitude based on an automatically determined threshold. SEE-SG was used to enhance the QRS-complex. We first designed the experimental protocol for physical activity (sit, walk, and ascend), namely, normal ECG in a physical activity database, and validated our method by using the ECG data of 23 subjects. Moreover, to avoid overtuning on our database, we validated the proposed method using two public databases: the MIT-BIH QT database (QTDB) and Noise Stress Test Database (NSTDB). The experimental results show promising performance on QTDB (SE and P+: 99.96% and ER: 0.08%), NSTDB (SE: 94.07%, P+: 93.45, and ER: 12.57%), and NPADB (SE: 99.95%, P+: 99.78%, and ER: 0.27%). We also assessed our method by comparing the absolute differences of R–R intervals and heart rates between actual and detected R peaks. In conclusion, as the proposed method achieved noise robustness and reliable performance on R peak detection during physical activities, it can be used to monitor a user’s ECG and cardiac health information accurately in daily life.
Thermal Analysis on the Degradation of GaN HEMTs

High temperature operation(HTO) experiments were performed on industrial GaN HEMTs devices. Several degradation failure modes of DC parameters such as transconductance reduction, threshold voltage shift, and gate leakage current increase were identified. The vertical failure localization and analysis of the devices were carried out by electrical method. The results show that the increase of the degradation of the die attach layer is the main reason for the increase of the junction temperature and thermal resistance of the devices. Furthermore, the results are analyzed by three-dimensional X-ray imaging confirming the degradation of the die attach layer. The voids in the die attach layer expanded during the high temperature operation stress experiments, led to a worse heat dissipation performance, which resulting in increased junction temperature, and further accelerate the performance degradation of the devices.
A  $G$ -Band Broadband Balanced Power Amplifier Module Based on Cascode mHEMTs

The first full <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>-band power amplifier module demonstrated in GaAs metamorphic high electron mobility transistor (mHEMT) technology has been developed for use as a driver amplifier in instrumentation. The circuit integrated in this module uses a compact balanced cascode topology and is based on a 35-nm mHEMT technology in a grounded coplanar waveguide environment. High compactness is achieved due to the use of a small ground-to-ground spacing of <inline-formula> <tex-math notation="LaTeX">$14~\mu {\mathrm{ m}}$ </tex-math></inline-formula> and an advanced process with three metallization layers. The millimeter-wave integrated circuit exhibits a linear gain higher than 15.3 dB and return losses better than 10 dB from 118 to 236 GHz, which represents an ultrabroad relative bandwidth (RBW) of 67%. A peak output power of 10 dBm is achieved at 200 GHz. The WR-5 module uses broadband transitions, which show insertion losses of less than 1.5 dB. It demonstrates a small-signal gain that exceeds 13.4 dB in the whole <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>-band (RBW ≥ 54%). Large-signal characterization exhibits power levels higher than 3.6 dBm from 130 to 210 GHz (RBW = 47%). This module achieves the highest bandwidth in <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>-band and the highest output power when comparing it with modules based on similar technologies.
Vibration Energy Harvesting from Multi-Directional Motion Sources

Vibration energy harvesting can be used as a sustainable power source for various applications. Usually, the generators are designed as devices with a single degree of freedom (SDoF) along the direction of the driving motion. In this research, harvesting from multi-directional (translational) motion sources will be investigated. Three strategies are assessed: a reference SDoF generator, a SDoF generator using an orientation strategy, and a Multi Degree of Freedom (MDoF) system. This led to the development of a design metric by which any 2D design problem can be described by two dimensionless parameters: the relative strength of vibrations, $p_{v}$, and the relative dimension of the design space, $p_{l}$. It was shown that the relative power density (RPD) of a 2DoF system compared to a reference SDoF system only depends on the product $p^{\ast}=p_{v}p_{l}$, and has a maximum of 1.185 for $p^{\ast}=1$. The application of powering a hearing aid is investigated as a case study. It was found that the vibrations in the area of the human head while walking can be represented by a two-directional vibration source with $p_{v}=0.55$. Three different design spaces are assessed for a miniaturized generator and three different optimal embodiments are found. For one of the considered situations where $p^{\ast}=1.1$, a 2DoF system was found to have a 16% higher power output compared to a SDoF reference. The aim of future work will be the validation of the developed metric.
Hierarchical Control Strategy Design for A 6WD Unmanned Skid-steering Vehicle

Skid-steering vehicle has strong maneuverability. There are a variety of skid-steering wheeled vehicle designed for military or civilian at home and abroad. In this paper, based on the need of skid-steering vehicle dynamic control, a hierarchical controller is designed for a 6×6 skid-steering vehicle. The vehicle speed and steering is controlled through driving torque of motor to following the desired vehicle speed and yaw rate. Contrary to the common approaches by designing a two-tier control strategy, the reference wheel speed generator is used to calculate the wheel speed and the wheel speed follower is used to reach the target wheel speed. To perform small radius cornering maneuvers, a dynamic control strategy for steering when vehicle speed is zero is also designed. The Trucksim/Simulink co-simulation model is developed and simulated in the model. Compared with the data of hierarchical control strategy and traditional control strategy through simulation, it is proved that the hierarchical control strategy can make the vehicle follow the target value quickly and accurately. And setting the simulation in different peak value of friction coefficient, it is proved that the hierarchical control strategy has strong robustness.
Gamification Design in a History E-Learning Context

Engagement is one of the main reasons for introducing gamification to learning and thus serves as an important measurement of its effectiveness. Gamification of learning aims to improve the learning process by making use of the motivating effects of digital game elements and techniques. However, summarizing gamification into points, badges and leaderboards is a very common misunderstanding of gamification hence greatly reducing purposed effect on the target (students). In this paper, we will explore the key elements that can lead to a good gamification in “History” as a learning context, driven by target (students) motivation and, based on the Octalysis framework.
Maternal Depressive Symptoms During the Pre- and Postnatal Periods and Infant Attention to Emotional Faces.

We examined how infants' attentional disengagement from happy, fearful, neutral, and phase-scrambled faces at 8 months, as assessed by eye tracking, is associated with trajectories of maternal depressive symptoms from early pregnancy to 6 months postpartum (decreasing n = 48, increasing n = 34, and consistently low symptom levels n = 280). The sample (mother-infant dyads belonging to a larger FinnBrain Birth Cohort Study) was collected between 5/2013-6/2016. The overall disengagement probability from faces to distractors was not related to maternal depressive symptoms, but fear bias was heightened in infants whose mothers reported decreasing or increasing depressive symptoms. Exacerbated attention to fearful faces in infants of mothers with depressive symptoms may be independent of the timing of the symptoms in the pre- and postnatal stages.
Estimating contribution of rare non‐coding variants to neuropsychiatric disorders

Owing to recent advances in DNA sequencing technology, a number of large‐scale comprehensive analyses of genetic variations in protein‐coding regions (i.e., whole‐exome sequencing studies), have been conducted for neuropsychiatric and neurodevelopmental disorders, such as autism spectrum disorders, intellectual disability, and schizophrenia. These studies, especially those focusing on de novo (newly arising) mutations and extremely rare variants, have successfully identified previously unrecognized disease genes/mutations with a large effect size and deepen our understanding of the biology of neuropsychiatric diseases. Along with the continuously dropping sequencing cost, now the target of sequencing studies is expanding from the exome to the whole human genome. Several pioneering works have provided important insights into the contribution of rare non‐coding variants to neuropsychiatric diseases. At the same time, these studies highlight need for further larger sample sizes and improvement in annotation of non‐coding regulatory variants. In this review, key findings from recent studies as well as likely future directions are overviewed.
SiC MOSFET Threshold-Voltage Instability Under High Temperature Aging

A systematic study of discrete SiC MOSFETs’ reliability under High Temperature stress has been carried out. High Temperature stress is performed in this work to characterize the threshold voltage instability. To investigate the degradation mechanism of devices, simulation according to the structure of MOSFET cell has been performed. The result shows that the threshold voltages change trends of both MOSFETs are the same, including drop-down period at very early time due to a decrease of doping concentration at channel region and gradual raise-up period at in the rest of time resulting from decline of interface charge.
Further Modification on CRM-Based Model Reference Adaptive Control

In order to achieve better transient performance of model reference adaptive control (MRAC), a new idea, named closed-loop reference model (CRM), has been recently proposed. However, there exists a potential peaking phenomenon in the CRM-based MRAC systems due to the introduced feedback gain in the CRM, which may deteriorate the original tracking control response. In this paper, we revisit the CRM-based MRAC design, and provide a new prospective to analyze the bound of peaking value by using $\text{L}_{2}$ norm and Cauchy-Schwartz inequality. Following the analysis, we further provide a potential solution to alleviate the peaking phenomenon by reducing the impact of the unknown system dynamics. This has been achieved by introducing a modification term on the control action. This new framework allows to use large feedback gains in the CRM to improve transient control performance, while the peaking phenomenon can be reduced. A wing rock aircraft model is used as the numerical example to validate the effectiveness of the proposed method and the improved performance over the traditional CRM-based MRAC.