Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning-Based Control Compensation for Multi-Axis Gimbal Systems Using Inverse and Forward Dynamics

1 0000-0000 © 2021 IEEE Unmanned aerospace vehicles usually carry sensors (i.e., electro-optical and/or infrared imaging cameras) as their primary payload. These sensors are used for image processing, target tracking, surveillance, mapping, and providing high-resolution imagery for environmental surveys. It is crucial to obtain a steady image in all these applications. This is typically accomplished by using multi-axis gimbal systems. This paper concentrates on the modeling and control of a multi-axis gimbal system. A novel and fully outlined procedure is proposed to derive the nonlinear and highly coupled Equations of Motion of the two-axis gimbal system. Different from the existing literature, Forward Dynamics of the two-axis gimbal system is modeled using multi-body dynamics modeling techniques. In addition to the Forward Dynamics model, the Inverse Dynamics model is developed to estimate the complementary torques associated with the state and mechanism-dependent, complex disturbances acting on the system. A disturbance compensator based on multilayer perceptron (MLP) structure is implemented to cope with external and internal disturbances and parameter uncertainties through the torque input channel. Our initial simulations and experimental work show that the new NN (neural network)-based controller is performs better in the full operational range without requiring any tuning or adjustment when compared with well-known controllers such as cascaded PID, ADRC (Active Disturbance Rejection Control), Inverse Dynamics based controllers. 1
SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is an important technique of autonomous driving. In order to accurately predict the reasonable future trajectory of pedestrians, it is inevitable to consider social interactions among pedestrians and the influence of surrounding scene simultaneously, which can fully represent the complex behavior information and ensure the rationality of predicted trajectories obeyed realistic rules. In this article, we propose one new prediction model named social soft attention graph convolution network (SSAGCN), which aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. In detail, when modeling social interaction, we propose a new social soft attention function, which fully considers various interaction factors among pedestrians. Also, it can distinguish the influence of pedestrians around the agent based on different factors under various situations. For the scene interaction, we propose one new sequential scene sharing mechanism. The influence of the scene on one agent at each moment can be shared with other neighbors through social soft attention; therefore, the influence of the scene is expanded both in spatial and temporal dimensions. With the help of these improvements, we successfully obtain socially and physically acceptable predicted trajectories. The experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results. The project code is available at.
Controllable Animation of Fluid Elements in Still Images

We propose a method to interactively control the animation of fluid elements in still images to generate cinemagraphs. Specifically, we focus on the animation of fluid elements like water, smoke, fire, which have the properties of repeating textures and continuous fluid motion. Taking inspiration from prior works, we represent the motion of such fluid elements in the image in the form of a constant 2D optical flow map. To this end, we allow the user to provide any number of arrow directions and their associated speeds along with a mask of the regions the user wants to animate. The user-provided input arrow directions, their corresponding speed values, and the mask are then converted into a dense flow map representing a constant optical flow map (FD). We observe that FD, obtained using simple exponential operations can closely approximate the plausible motion of elements in the image. We further refine computed dense optical flow map FD using a generative-adversarial network (GAN) to obtain a more realistic flow map. We devise a novel UNet based architecture to autoregressively generate future frames using the refined optical flow map by forward-warping the input image features at different resolutions. We conduct extensive experiments on a publicly available dataset and show that our method is superior to the baselines in terms of qualitative and quantitative metrics. In addition, we show the qualitative animations of the objects in directions that did not exist in the training set and provide a way to synthesize videos that otherwise would not exist in the real world. Project url: https://controllable-cinemagraphs.github. io/
Towards the One Learning Algorithm Hypothesis: A System-theoretic Approach

The existence of a universal learning architecture in human cognition is a widely spread conjecture supported by experimental findings from neuroscience. While no low-level implementation can be specified yet, an abstract outline of human perception and learning is believed to entail three basic properties: (a) hierarchical attention and processing, (b) memory-based knowledge representation, and (c) progressive learning and knowledge compaction. We approach the design of such a learning architecture from a system-theoretic viewpoint, developing a closed-loop system with three main components: (i) a multi-resolution analysis pre-processor, (ii) a group-invariant feature extractor, and (iii) a progressive knowledge-based learning module. Multi-resolution feedback loops are used for learning, i.e., for adapting the system parameters to online observations. To design (i) and (ii), we build upon the established theory of wavelet-based multi-resolution analysis and the properties of group convolution operators. Regarding (iii), we introduce a novel learning algorithm that constructs progressively growing knowledge representations in multiple resolutions. The proposed algorithm is an extension of the Online Deterministic Annealing (ODA) algorithm based on annealing optimization, solved using gradient-free stochastic approximation. ODA has inherent robustness and regularization properties and provides a means to progressively increase the complexity of the learning model i.e. the number of the neurons, as needed, through an intuitive bifurcation phenomenon. The proposed multi-resolution approach is hierarchical, progressive, knowledge-based, and interpretable. We illustrate the properties of the proposed architecture in the context of the state-of-the-art learning algorithms and deep learning methods.
PROPERTIES OF BLEUSTEIN-GULYAEV WAVES

Properties of Bleustein-Gulyaev waves (BGW) in Li2BI0, crystal were determined from the measured parameters of a delay line with double electrode interdigital transducers (IDT). From the known IDT parameters, and from the measured center frequency and insertion loss at the third harmonic, the BGW free surface velocity and the square of the electromechanical coupling coefficient were determined as 4921m/s and 0.0027, respectively. The BGW in LizBIO, crystal are very promising high frequency filters and chemical sensor applications. for
Marching with the Pink Parade: Evaluating Visual Search Recommendations for Non-binary Clothing Items

Fashion, a highly subjective topic is interpreted differently by all individuals. E-commerce platforms, despite these diverse requirements, tend to cater to the average buyer instead of focusing on edge cases like non-binary shoppers. This case study1, through participant surveys, shows that visual search on e-commerce platforms like Amazon, Beagle.Vision and Lykdat, is particularly poor for non-binary clothing items. Our comprehensive quantitative analysis shows that these platforms are more robust to binary clothing inputs. The non-binary clothing items are recommended in a haphazard manner, as observed through negative correlation coefficients of the ranking order. The participants also rate the non-binary recommendations lower than the binary ones. Another intriguing observation is that male raters are more inclined to make binary judgements compared to female raters. Thus it is clear that these systems are not inclusive to the minority, disadvantaged communities of society, like LGBTQ+ people. We conclude with a call to action for the e-commerce platforms to take cognizance of our results and be more inclusive.
Identification of Switched Linear Systems: Persistence of Excitation and Numerical Algorithms

This paper investigates two issues on identification of switched linear systems: persistence of excitation and numerical algorithms. The main contribution is a much weaker condition on the regressor to be persistently exciting that guarantees the uniqueness of the parameter sets and also provides new insights in understanding the relation among different subsystems. It is found that for uniquely determining the parameters of switched linear systems, the minimum number of samples needed derived from our condition is much smaller than that reported in the literature. The secondary contribution of the paper concerns the numerical algorithm. Though the algorithm is not new, we show that our surrogate problem, relaxed from an integer optimization to a continuous minimization, has exactly the same solution as the original integer optimization, which is effectively solved by a block-coordinate descent algorithm. Moreover, an algorithm for handling unknown number of subsystems is proposed. Several numerical examples are illustrated to support theoretical analysis.
Analyzing performance of nano-satellite cluster - detector array receiver laser communication

This work presents research in the field of laser satellite communication between a cluster of nano-satellites and a ground station. The scenario under consideration is a cluster of nano-satellites, communicating by means of a laser beam with a detector array receiver, which is located on the Earth's surface and equipped with a common optical system for all incoming beams. A critical parameter, determining the successful receipt of a transmitted signal for a given configuration, is the angular separation between the satellites within the cluster. This separation must be retained to prevent critical overlapping of the spots on the detector. The maximum allowable overlapping is calculated in terms of given BER. The spatial spreading of the beams, caused by scattering from aerosols in different layers of the atmosphere, is calculated for the case of single scattering, as appropriate for the stratified model used. Turbulence influences the beam width especially for the case of short exposure. In this research a new approach is adopted to characterize the atmospheric channel using OTF (Optical Transfer Function) concepts from the field of imaging and remote sensing. We evaluate the effectiveness of this new approach inapplications where spatial spread is very important, and detector array feasibility is currently under investigation.
Hierarchical Neural Data Synthesis for Semantic Parsing

Semantic parsing datasets are expensive to collect. Moreover, even the questions pertinent to a given domain, which are the input of a semantic parsing system, might not be readily available, especially in cross-domain semantic parsing. This makes data augmentation even more challenging. Existing methods to synthesize new data use hand-crafted or induced rules, requiring substantial engineering effort and linguistic expertise to achieve good coverage and precision, which limits the scalability. In this work, we propose a purely neural approach of data augmentation for semantic parsing that completely removes the need for grammar engineering while achieving higher semantic parsing accuracy. Furthermore, our method can synthesize in the zero-shot setting, where only a new domain schema is available without any input-output examples of the new domain. On the Spider cross-domain text-to-SQL semantic parsing benchmark, we achieve the state-of-the-art performance on the development set (77.2% accuracy) using our zero-shot augmentation.
Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks With Base Controllers

Deep reinforcement learning (DRL) enables robots to perform some intelligent tasks end-to-end. However, there are still many challenges for long-horizon sparse-reward robotic manipulator tasks. On the one hand, a sparse-reward setting causes exploration inefficient. On the other hand, exploration using physical robots is of high cost and unsafe. In this article, we propose a method of learning long-horizon sparse-reward tasks utilizing one or more existing traditional controllers named base controllers in this article. Built upon deep deterministic policy gradients (DDPGs), our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Furthermore, we present a straightforward way of synthesizing different base controllers to integrate their strengths. Through experiments ranging from stacking blocks to cups, it is demonstrated that the learned state-based or image-based policies steadily outperform base controllers. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and improves performance. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers.
VPP-ART: An Efficient Implementation of Fixed-Size-Candidate-Set Adaptive Random Testing Using Vantage Point Partitioning

Adaptive random testing (ART) is an enhancement of random testing (RT), and aims to improve the RT failure-detection effectiveness by distributing test cases more evenly in the input domain. Many ART algorithms have been proposed, with fixed-size-candidate-set ART (FSCS-ART) being one of the most effective and popular. FSCS-ART ensures high failure-detection effectiveness by selecting as the next test case the candidate farthest from previously executed test cases. Although FSCS-ART has good failure-detection effectiveness, it also faces some challenges, including heavy computational overheads. In this article, we propose an enhanced version of FSCS-ART, vantage point partitioning ART (VPP-ART). VPP-ART addresses the FSCS-ART computational overhead problem using VPP, while maintaining the failure-detection effectiveness. VPP-ART partitions the input domain space using a modified vantage point tree (VP-tree) and finds the approximate nearest executed test cases of a candidate test case in the partitioned subdomains—thereby significantly reducing the time overheads compared with the searches required for FSCS-ART. To enable the FSCS-ART dynamic insertion process, we modify the traditional VP-tree to support dynamic data. The simulation results show that VPP-ART has a much lower time overhead compared to FSCS-ART, but also delivers similar (or better) failure-detection effectiveness, especially in the higher dimensional input domains. According to statistical analyses, VPP-ART can improve on the FSCS-ART failure-detection effectiveness by approximately 50–58%. VPP-ART also compares favorably with the KD-tree-enhanced fixed-size-candidate-set ART (KDFC-ART) algorithms (a series of enhanced ART algorithms based on the KD-tree). Our experiments also show that VPP-ART is more cost-effective than FSCS-ART and KDFC-ART.
Anaerobic treatment of pulp and paper mill wastewaters

Secondary treatment of pulp and paper mill wastewaters for biochemical oxygen demand (BOD) removal worldwide has traditionally been accomplished, and particularly in North America, by aerobic treatment processes, including aerated stabilization basin and activated sludge. In recent years, however, interest in applying anaerobic processes to treat pulp and paper mill effluents has increased, with at least 25 full-scale installations currently operating or under construction.
SLE is going Operational

CCSDS has developed a set of Recommendations for Space Link Extension (SLE) Services with the aim to provide a uniform cross support capability for agencies who develop interfaces to their facilities in accordance to these Recommendations. SLE services are specified in terms of operations at application level and associated data structures for exchanging space data units between crosssupporting Agencies. ESOC and JPL, initially in the context of cross support of the ESA Integral mission using NASA DSN stations, have implemented a basic set of these recommendations. This consists of the Return All Frame (RAF), and the Return Channel Frame (RCF) services for the return link and the Command Link Transfer Unit (CLTU) service for the forward link. Beyond the Integral project, these services will also be used for the cross support between ESA and NASA/JPL of future missions, and particularly, in 2003, of Rosetta and Mars Express. Although during the parallel ESOC and JPL development, the two Agencies have adopted respectively the role of “service user” (ESA) and “service provider” (NASA), the development concept adopted is based on the potential interchangeability of the kernel software (the API element) for the implementation of both “user”/”provider” interfaces. Thus for example, ESA also developed a “provider” capability for installation in its simulator, essential for test purposes. The CCSDS SLE management Recommendations required for the operation of the SLE services is not yet available and so ESOC and JPL have devised a private (ad hoc) management scheme to support the necessary SLE operational set-up. This is based on the definition, for each support period, of a Sequence Of Events (SoE) and set of defined “Service Instances” (SIs) derived from it. The definition of the latter is supported by a management tool called the Service Instance Configuration Manager (SICM). The implementation of the above services and service management has been completed and is undergoing an intensive testing and operational validation campaign. During the operational validation process, certain system engineering issues arose. These issues and their consequent design considerations are discussed in this paper with the aim of providing a useful guidance to future implementations of the SLE services. The SLE services will soon support Integral, Rosetta and Mars Express operations. This paper presents the status as of July 2002.
Thermal expansion mismatch resonant infrared sensor and array

Abstract. A resonant infrared thermal sensor with high sensitivity, whose sensing element is a bimaterial structure with thermal expansion mismatch effect, is presented. The sensor detects infrared radiation by means of tracking the change in resonance frequency of the bimaterial structure with temperature attributed to the infrared radiation from targets. The bimaterial structure is able to amplify the change in resonance frequency compared with a single material structure for a certain mode of vibration. In accordance with the vibration theory and the design principle of an infrared thermal detector, the resonant sensor, which can be arranged in an array, is designed. The simulation results, by using finite element analysis, demonstrate that the dependence of resonance frequency on temperature of the designed structure achieves 1 Hz/10 mK. An array of 6×6 resonant thermal sensors is fabricated by using microelectronics processes that are compatible with integrated circuit fabrication technology. The frequency variation corresponding to the temperature shift is obtained by electrical measurement.
Events of Importance in External Auditing in Malaysia, 1957-1997 a

The enlarged exogenous analytical framework supported by the conduct of qualitative case study method is used to investigate the role and contribution of external auditing in Malaysian society. The interpretive framework provides guidance in identifying significant historical events categorized into three groups: intrusive events, central response events and the rest of response events. Following the identification of these events, there is the analysis of available patterns of transformation decomposed into source, diffusion and reaction phases. The source phase giving significance to the influence brought about by the environmental attributes surrounding audit and other systems has the intrusive events coming into the picture. As for the diffusion phase, there is the presence of the central response events and the rest of response events to the exclusion of the intrusive events. Finally, when it concerns the reaction phase, there is the involvement of all three types of events: intrusive events, central response events and the rest of response events. The study provides understanding of the operational form of contemporary audit practice in Malaysia and the historical, social, economic and political determinants of that form.
Plasma Membrane Anchoring and Gag:Gag Multimerization on Viral RNA Are Critical Properties of HIV-1 Gag Required To Mediate Efficient Genome Packaging

To generate infectious virions, HIV-1 must package its full-length RNA as the genome during particle assembly. HIV-1 Gag:RNA interactions mediate genome packaging, but the mechanism remains unclear. ABSTRACT Human immunodeficiency virus type 1 (HIV-1) Gag selects and packages the HIV RNA genome during virus assembly. However, HIV-1 RNA constitutes only a small fraction of the cellular RNA. Although Gag exhibits a slight preference to viral RNA, most of the cytoplasmic Gag proteins are associated with cellular RNAs. Thus, it is not understood how HIV-1 achieves highly efficient genome packaging. We hypothesize that besides RNA binding, other properties of Gag are important for genome packaging. Many Gag mutants have assembly defects that preclude analysis of their effects on genome packaging. To bypass this challenge, we established complementation systems that separate the particle-assembling and RNA-binding functions of Gag: we used a set of Gag proteins to drive particle assembly and an RNA-binding Gag to package HIV-1 RNA. We have developed two types of RNA-binding Gag in which packaging is mediated by the authentic nucleocapsid (NC) domain or by a nonviral RNA-binding domain. We found that in both cases, mutations that affect the multimerization or plasma membrane anchoring properties of Gag reduce or abolish RNA packaging. These mutant Gag can coassemble into particles but cannot package the RNA genome efficiently. Our findings indicate that HIV-1 RNA packaging occurs at the plasma membrane and RNA-binding Gag needs to multimerize on RNA to encapsidate the viral genome. IMPORTANCE To generate infectious virions, HIV-1 must package its full-length RNA as the genome during particle assembly. HIV-1 Gag:RNA interactions mediate genome packaging, but the mechanism remains unclear. Only a minor portion of the cellular RNA is HIV-1 RNA, and most of the RNAs associated with cytoplasmic Gag are cellular RNAs. However, >94% of the HIV-1 virions contain viral RNA genome. We posited that, besides RNA binding, other properties of Gag contribute to genome packaging. Using two complementation systems, we examined features of Gag that are important for genome packaging. We found that the capacities for Gag to multimerize and to anchor at the plasma membrane are critical for genome packaging. Our results revealed that Gag needs to multimerize on viral RNA at the plasma membrane in order to package RNA genome.