Miracle-dz/UniFAR-FTUs huggingface dataset:
An Overview of Perception Methods for Horticultural Robots: From Pollination to Harvest

Horticultural enterprises are becoming more sophisticated as the range of the crops they target expands. Requirements for enhanced efficiency and productivity have driven the demand for automating on-field operations. However, various problems remain yet to be solved for their reliable, safe deployment in real-world scenarios. This paper examines major research trends and current challenges in horticultural robotics. Specifically, our work focuses on sensing and perception in the three main horticultural procedures: pollination, yield estimation, and harvesting. For each task, we expose major issues arising from the unstructured, cluttered, and rugged nature of field environments, including variable lighting conditions and difficulties in fruit-specific detection, and highlight promising contemporary studies.
Personality and Cognition in Gamers: Avoidance Expectancies Mediate the Relationship Between Maladaptive Personality Traits and Symptoms of Internet-Gaming Disorder

Internet-gaming disorder (IGD) has become a clinically relevant phenomenon worth investigating with respect to its mechanisms of development and maintenance. Considering theoretical models of specific Internet-use disorders, we assumed an interaction of maladaptive personality traits as unspecific predisposing factors and experience-based, gaming-related Internet-use expectancies in predicting symptoms of IGD. Therefore, 103 male and female regular Internet gamers were investigated with questionnaires assessing maladaptive personality traits in accordance to DSM-5, gaming-related positive and avoidance Internet-use expectancies, and symptoms of IGD. The results demonstrated that negative affectivity, detachment, antagonism, disinhibition, psychoticism as well as gaming-related positive and avoidance expectancies were related to symptoms of IGD. Moreover, the relationship between maladaptive personality traits as represented by negative affectivity, detachment, and psychoticism with symptoms of IGD was mediated by avoidance expectancies. Positive gaming-related use expectancies were related to detachment, and were not a significant mediator in the hypothesized model. The findings give reason to assume that maladaptive personality traits in combination with gaming-related positive expectancies and avoidance expectancies are important factors for the development of IGD, but that positive expectancies and avoidance expectancies play a differential role regarding there mediating role between personality characteristics and symptoms of IGD.
Bilateral Vestibulopathy: Vestibular Function, Dynamic Visual Acuity and Functional Impact

Introduction: Patients suffering from bilateral vestibular hypofunction (BVH) often experience ataxia as well as visual instability. Even though progress has been made in vestibular testing, insights regarding vestibular deficit in BVH remain incomplete since no method allows evaluation of frequency ranges of vestibular sensors in a continuous way. The aim of our study was to give a detailed description of the level of vestibular deficit in different ranges of vestibular stimulation and an exhaustive evaluation of the functional impact including dynamic visual acuity (DVA) in a cohort of BVH patients in different etiologies. Methods: We prospectively included 20 patients with chronic BVH. All patients underwent clinical evaluation and functional assessment including evaluation of their symptoms related to BVH, quality of life questionnaire and DVA in the horizontal and vertical plane. Patients underwent vestibulo-ocular reflex (VOR) testing using rotatory chair, caloric stimulation and video head impulse (vHIT) in the plane of the 6 canals, and cervical and ocular Vestibular evoked myogenic potentials. Results: Mean rotatory VOR gain was 0.07 (SD = 0.07). Mean rotatory VOR gain during vHIT for the lateral, anterior and posterior canals was respectively < 0.28, < 0.34, and < 0.20. Mean loss of DVA in the 4 directions was >0.30 LogMAR. In our population fall frequency was significantly higher in patients with lower UniPedal Stance Test (UPST), higher Dizziness Handicap Inventory and Ataxia Numeric Scale (ANS) scores, as well as greater loss of upwards DVA. Patients with ototoxic BVH had a significantly higher residual VOR gain during vHIT in the anterior canal plane and lower DHI than other patients. In the general population anterior canal function was significantly higher than lateral or posterior canal function. Conclusions: This study gives extensive descriptive results of residual vestibular function, DVA and quality of life in a population of patients suffering from severe BVH. UPST and ANS are good indicators for fall risk in case of BVH. Gentamicin induced BVH seems to have a lesser impact on quality of life than other etiologies.Anterior semi-circular canal function seems less deteriorated than lateral and posterior function.
Why does the magnitude of genotype‐by‐environment interaction vary?

Abstract Genotype‐by‐environment interaction (G × E), that is, genetic variation in phenotypic plasticity, is a central concept in ecology and evolutionary biology. G×E has wide‐ranging implications for trait development and for understanding how organisms will respond to environmental change. Although G × E has been extensively documented, its presence and magnitude vary dramatically across populations and traits. Despite this, we still know little about why G × E is so evident in some traits and populations, but minimal or absent in others. To encourage synthetic research in this area, we review diverse hypotheses for the underlying biological causes of variation in G × E. We extract common themes from these hypotheses to develop a more synthetic understanding of variation in G × E and suggest some important next steps.
Bias dependence of the access resistance in GaN HEMTs

Based on TCAD simulations and s-parameter measurement of GaN HEMTs, it is shown that the source and drain resistance are bias dependent, even though gate-forward resistance extraction still yields a constant value. If the extrinsic resistance variation is ignored in parameter extraction, the inaccuracy is consumed by the transconductance, while s-parameter prediction still agrees with the measurements.
Novel feedforward controller for straight-fiber-type artificial muscle based on an experimental identification model

This paper reports on an improvement to a feedforward controller for a straight-fiber-type artificial muscle that can control the amount of contraction, stiffness, and contraction force by use of an experimental identification model. This straight-fiber-type artificial muscle has a higher contraction force and a higher contraction rate than a McKibben artificial muscle. In a previous study, we developed a feedforward controller for a straight-fiber-type artificial muscle based on a mechanical model. However, this controller could not accurately control the stiffness or the contraction force. A feedback controller was necessary to compensate for the lack of feedforward control accuracy, which increased the system complexity. In addition, the calculations of the previous controller were so complex that the microcontroller could not keep up with the sequential calculations. This is not practical when the controller is used in devices such as an assist suit. In this paper, to solve these problems, we propose a novel feedforward controller based on an experimental identification model whose calculations are simpler than the previous ones. An experimental identification model enables the feedforward controller to improve the accuracy by identifying the parameters used in the model. Also, we compare the accuracy of the proposed controller with the previous one.
Detection of rotating stall of axial compressors based on mixed pattern library

In this paper, a mixed pattern library including normal patterns, spike stall inception patterns and modal stall inception patterns is built to detect the rotating stall in axial compressors. Firstly, the Mansoux-C2 model and the Mansoux-C3 model are used to generate the modal stall and the spike stall, respectively. In order to facilitate modeling and detection of rotating stall, system dynamics of the compressor are divided into the dominant and secondary dynamics. Then, the dominant dynamics are accurately identified and stored in constant radial basis function (RBF) networks based on deterministic learning. The stored knowledge is used to build a mixed pattern library. Based on the library, the rotating stall inception of the test compressor can be rapidly detected by the dynamic pattern recognition method and the smallest residual principle. Simulation studies are given to show the validity of the proposed approach. Simulation results show that the test rotating stall pattern can be rapidly detected without false alarm.
Physiological and Biological Responses to Short-Term Intermittent Hypobaric Hypoxia Exposure: From Sports and Mountain Medicine to New Biomedical Applications

In recent years, the altitude acclimatization responses elicited by short-term intermittent exposure to hypoxia have been subject to renewed attention. The main goal of short-term intermittent hypobaric hypoxia exposure programs was originally to improve the aerobic capacity of athletes or to accelerate the altitude acclimatization response in alpinists, since such programs induce an increase in erythrocyte mass. Several model programs of intermittent exposure to hypoxia have presented efficiency with respect to this goal, without any of the inconveniences or negative consequences associated with permanent stays at moderate or high altitudes. Artificial intermittent exposure to normobaric hypoxia systems have seen a rapid rise in popularity among recreational and professional athletes, not only due to their unbeatable cost/efficiency ratio, but also because they help prevent common inconveniences associated with high-altitude stays such as social isolation, nutritional limitations, and other minor health and comfort-related annoyances. Today, intermittent exposure to hypobaric hypoxia is known to elicit other physiological response types in several organs and body systems. These responses range from alterations in the ventilatory pattern to modulation of the mitochondrial function. The central role played by hypoxia-inducible factor (HIF) in activating a signaling molecular cascade after hypoxia exposure is well known. Among these targets, several growth factors that upregulate the capillary bed by inducing angiogenesis and promoting oxidative metabolism merit special attention. Applying intermittent hypobaric hypoxia to promote the action of some molecules, such as angiogenic factors, could improve repair and recovery in many tissue types. This article uses a comprehensive approach to examine data obtained in recent years. We consider evidence collected from different tissues, including myocardial capillarization, skeletal muscle fiber types and fiber size changes induced by intermittent hypoxia exposure, and discuss the evidence that points to beneficial interventions in applied fields such as sport science. Short-term intermittent hypoxia may not only be useful for healthy people, but could also be considered a promising tool to be applied, with due caution, to some pathophysiological states.
Functional Mapping of Quantitative Trait Loci (QTLs) Associated With Plant Performance in a Wheat MAGIC Mapping Population

In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Trait data from single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding.
An adaptive pulse-width modulation for limited molecule production and storage

The nature of molecular transmitter imposes some limitations on the molecule production process and its storage. As these molecules act the role of the information carriers, their limitation affect the transmission process and the system performance considerably. In this paper, we focus on these limitations at the transmitter, the limited production rate and finite storage capacity. We consider a time-slotted communication where the transmitter opens its outlet and releases the stored molecules for a time duration to send bit “1” and remains silent to send bit “0”. By changing the duration molecules are released, we propose an adaptive pulse-width modulation. The objective is to find the optimal transmission pulse-widths to minimize the probability of error. We characterize the properties of the optimal pulse-widths and use them to derive upper and lower bounds on the performance.
A multiple criteria evaluation technique for missing values imputation

Missing values are a common problem in most data quality research. Most existing imputation methods of missing values are often evaluated using some distance measure computed between the reference data and the imputed one. Another alternative to evaluate the quality of the imputation is to assess the classification accuracy. In this paper, we address the problem of the evaluation of missing values imputation methods showing that the “best” imputation method according to one criterion is not necessary the “best” according to other criteria. Unfortunately, evaluating an imputation method according to a single aspect is not sufficient. Nevertheless, it is possible that we are interested in the “best” method in terms of two or three aspects simultaneously. This paper proposes different criteria aggregation based on the idea of preserving the characteristics of the original data and considers the evaluation technique of the missing values imputation methods as a Multiple Criteria Decision Making (MCDM) problem. We use the TOPSIS method to implement this multiple criteria evaluation technique. Carried out experiments on benchmark datasets confirm the soundness of our approach.
Wheel classification using convolutional neural networks

With the fast development of automobile wheel industry, many methods of wheel classification have been proposed. The appearance of wheel changes a lot during the process of production, but few of these conventional methods is designed to handle the many challenges of wheel classification for different appearances. This paper studies on wheel classification for different appearances during the process of production, and proposes a wheel classification method using convolutional neural network. Firstly, we implement circle detection, gray value analysis and size normalization on wheel images and collect ten common types of wheel to build a dataset. Based on the features of the wheel images, a convolutional neural network is proposed. Then data augmentation is implemented on the dataset and local response normalization layer is added in the convolutional neural network. The experiment results show that the presented method has a better performance both in classification accuracy and real-time requirement than conventional methods and achieves 95.6% accuracy on wheel classification in the dataset.
Concurrent MAC unit design using VHDL for deep learning networks on FPGA

Deep neural network algorithms have proven their enormous capabilities in wide range of artificial intelligence applications, specially in Printed/Handwritten text recognition, Multimedia processing, Robotics and many other high end technological trends. The most challenging aspect nowadays is to overcome the extremely computational processing demands in applying such algorithms, especially in real-time systems. Recently, the Field Programmable Gate Array (FPGA) has been considered as one of the optimum hardware accelerator platform for accelerating the deep neural network architectures due to its large adaptability and the high degree of parallelism it offers. In this paper, the proposed 8-bits fixed-point parallel multiply-accumulate (MAC) unit architecture aimed to create a fully-customize MAC unit for the Convolutional Neural Networks (CNN) instead of depending on the conventional DSP blocks and embedded memories units on the FPGAs architecture silicon fabrics. The proposed 8-bits fixed-point parallel multiply-accumulate (MAC) unit architecture is designed using VHDL language and can performs a computational speed up to 4.17 Giga Operation per Second (GOPS) using high-density FPGAs.
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

Few ideas have enjoyed as large an impact on deep learning as convolution. For any problem involving pixels or spatial representations, common intuition holds that convolutional neural networks may be appropriate. In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate transform problem, which simply requires learning a mapping between coordinates in (x,y) Cartesian space and one-hot pixel space. Although convolutional networks would seem appropriate for this task, we show that they fail spectacularly. We demonstrate and carefully analyze the failure first on a toy problem, at which point a simple fix becomes obvious. We call this solution CoordConv, which works by giving convolution access to its own input coordinates through the use of extra coordinate channels. Without sacrificing the computational and parametric efficiency of ordinary convolution, CoordConv allows networks to learn either complete translation invariance or varying degrees of translation dependence, as required by the end task. CoordConv solves the coordinate transform problem with perfect generalization and 150 times faster with 10--100 times fewer parameters than convolution. This stark contrast raises the question: to what extent has this inability of convolution persisted insidiously inside other tasks, subtly hampering performance from within? A complete answer to this question will require further investigation, but we show preliminary evidence that swapping convolution for CoordConv can improve models on a diverse set of tasks. Using CoordConv in a GAN produced less mode collapse as the transform between high-level spatial latents and pixels becomes easier to learn. A Faster R-CNN detection model trained on MNIST showed 24% better IOU when using CoordConv, and in the RL domain agents playing Atari games benefit significantly from the use of CoordConv layers.
Topology optimized design, fabrication and evaluation of a multimaterial soft gripper

In this paper, a systemic approach to design and fabricate a multimaterial soft gripper is proposed. Driven by pneumatic pressure, the soft material inside the gripper acts as integrated actuator and the relatively hard material provides support for its soft body. Because of a large design space, it's hardly to design a multimaterial structure by intuitive or biomimetic approaches. Herein, this structural design problem is tackled by topology optimization approach, where each gripper finger is modeled as a compliant mechanism to achieve its maximum bending deflection. Considering the fabrication process, the soft material structure is preserved unchangeable during the optimization process. Thereafter, the optimized hard material is fabricated through 3D printing and the soft material is created by molding. Characterization experiments show that each gripper finger can undergo 32° bending deformation and exert 0.54N grasping force under 0.05MPa actuation pressure. Moreover, the multimaterial soft gripper can sustain more than 1000 working cycles and grasping a variety of objects ranging from tiny regular skews to large and delicate sunglasses. The proposed design and fabrication approach is freely extendable to soft robots by forming the corresponding optimization model, and stands as a gateway toward high-performance multi-material soft robots.
Multi-directional crawling robot with soft actuators and electroadhesive grippers

This paper presents the design of a planar, low profile, multi-directional soft crawling robot. The robot combines soft electroactive polymer actuators with compliant electroadhesive feet. A theoretical model of a multi-sector dielectric elastomer actuator is presented. The relation between actuator stroke and blocking force is experimentally validated. Electrostatic adhesion is employed to provide traction between the feet of the robot and the crawling surface. Shear force is experimentally determined and forces up to 3N have been achieved with the current pad design. A 2D multi-directional gait is demonstrated with the robot prototype. Speeds up to 12mm/s (0.1 body-lengths/s) have been observed. The robot has the potential to move on a variety of surfaces and across gradients, a useful ability in scenarios involving exploration.
Separability is not the best goal for machine learning

Neural networks use their hidden layers to transform input data into linearly separable data clusters, with a linear or a perceptron type output layer making the final projection on the line perpendicular to the discriminating hyperplane. For complex data with multimodal distributions this transformation is difficult to learn. Projection on $k\geq 2$ line segments is the simplest extension of linear separability, defining much easier goal for the learning process. Simple problems are 2-separable, but problems with inherent complex logic may be solved in a simple way by $k$-separable projections. The difficulty of learning non-linear data distributions is shifted to separation of line intervals, simplifying the transformation of data by hidden network layers. For classification of difficult Boolean problems, such as the parity problem, linear projection combined with \ksep is sufficient and provides a powerful new target for learning. More complex targets may also be defined, changing the goal of learning from linear discrimination to creation of data distributions that can easily be handled by specialized models selected to analyze output distributions. This approach can replace many layers of transformation required by deep learning models.
Analysis of symbolic models of biometrie data and their use for action and user identification

Smart devices are becoming an extension of ourselves that contain sensitive information and are often targeted for theft. The development of an intelligent and reliable means of user identification and authentication is critical. Not only can the development of user models performing tasks be used for user and task identification, but systems can also notify individuals if there is a potential health concern. The construction of an idealized model of human locomotion may give medical care providers a better understanding of individual differences and guide therapy and treatment. Data was gathered from a smartwatch worn by six subjects performing five different tasks and Genetic Programming was used to perform symbolic regression — a model free, nonlinear type of regression analysis. Symbolic regression was applied to smartwatch data and a collection of nonlinear closed form symbolic mathematical models were generated. Not only did these models fit the data well, but they provided insight into the underlying system. With only 5 seconds of unseen data, the models could classify which subjects were performing which task with 83.9% accuracy when chance was only 3.33%.