Reviewerly/exHarmony huggingface dataset:
Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning??

In this paper, we propose to estimate the forward dynamics equations of mechanical systems by learning a model of the inverse dynamics and estimating individual dynamics components from it. We revisit the classical formulation of rigid body dynamics in order to extrapolate the physical dynamical components, such as inertial and gravitational components, from an inverse dynamics model. After estimating the dynamical components, the forward dynamics can be computed in closed form as a function of the learned inverse dynamics. We tested the proposed method with several machine learning models based on Gaussian Process Regression and compared them with the standard approach of learning the forward dynamics directly. Results on two simulated robotic manipulators, a PANDA Franka Emika and a UR10, show the effectiveness of the proposed method in learning the forward dynamics, both in terms of accuracy as well as in opening the possibility of using more structured models.
Accelerating the Performance of Fuzzy-FPGA Based Control in LabVIEW for Trajectory Tracking Problems

Fuzzy Logic (FL) is well-known as an intuitive framework to tackle imprecision and uncertainty while allowing to model expert's knowledge in rule-based control. The objective of this study is to investigate the design of embedded fuzzy control systems combining fast execution and parallel processing capabilities provided by Field Programmable Gate Arrays (FPGAs) and Reconfigurable Inputs/Outputs (RIO) boards. We implemented a suitable fixed-point FL to realize a computationally efficient control framework on an FPGA target. As such, the paper provides a concise method to deploy Fuzzy Logic Control (FLC) using RIO-FPGA technology. We also provide a technique for implementing the three stages that constitute the FLC structures in LabVIEW environment using fixed math operations. The experimental work involves tracking the trajectory of a mobile robot and shows the feasibility and the efficiency of the proposed FLC-FPGA controller in comparison to the traditional PID-FPGA controller, along with the added benefits of fuzzy control frameworks.
Convergence of Policy Gradient Methods for Nash Equilibria in General-sum Stochastic Games

We study Nash equilibria learning of a general-sum stochastic game with an unknown transition probability density function. Agents take actions at the current environment state and their joint action influences the transition of the environment state and their immediate rewards. Each agent only observes the environment state and its own immediate reward and is unknown about the actions or immediate rewards of others. We introduce the concept of weighted asymptotic Nash equilibrium with probability 1 and design a two-loop algorithm by the equivalence of Nash equilibrium and variational inequality problems. In the outer loop, we sequentially update a constructed strongly monotone variational inequality by updating a proximal parameter while employing a single-call extra-gradient algorithm in the inner loop for solving the constructed variational inequality. We show that if the associated Minty variational inequality has a solution, then the designed algorithm converges to the k1/2 -weighted asymptotic Nash equilibrium.
A Semi-Automatic Approach for Asset Administration Shell Creation from Heterogeneous Data

Heterogeneous data sources and inconsistent interfaces between different engineering disciplines pose a tremendous challenge in creating a virtual representation of a real-world product or plant. To enable the interoperability between all partners in the value chain, the Plattform I4.0, consisting of German academia and industry, proposed the Asset Administration Shell (AAS), a standardized concept of digitally representing of the asset. Unfortunately, the creation of a product or process AAS implies huge manual effort in collecting and structuring information pieces from diverse engineering and operation data by hand. This work proposes a semi-automatic approach to extract engineering information from documents of the data types PDF, STP, XML, XML/AML, URDF and a method to execute AAS creation and merging. We analyze the information structure in engineering files and map it to the meta-model of the AAS. We propose a Python implementation that semi-automatically generates AASs from engineering documents and merges them into an integrated AAS representing the asset. By using the demonstrator xPPU and two industrial use cases of gear assembly and robot control, we discuss the possibilities and limitations of our proposed approach.
Ship Manoeuvering Modelling with a Physics-Oriented Neural Network-Based Approach

This paper proposes a novel system identification schema to obtain a model of a ship manouvering process using artificial Neural Networks (ANNs), computational models that have changed the research paradigm, bringing remarkable advantages in several fields. The ANNs capabilities in modelling nonlinear dynamical systems are undeniable and several approaches have been proposed in recent years. In our work, an ordinary black-box approach used to determine the input-output relationship of the system under investigation is initially outlined. Then, a physics-oriented approach to train a recurrent neural network structure is provided and thoroughly explained, investigating also the possible adoption of a simpler network structure. The results obtained with the physics-oriented approach in modelling the ship manouvering process are significantly better than the ones achieved with the pure black-box approach. Consequently, the physics-oriented approach resulted to be an exceptional tool for inferring the physical laws behind a nonlinear system accounting for a limited amount of data. The effectiveness of this method motivates further studies to evaluate its possible implementation in model-based control algorithms.
Distributed Persistent Coverage Control over Importance-based Environments with Quadrotors

This paper proposes a distributed persistent coverage strategy for environments with important regions. The strategy involves using quadrotors at varying altitudes to acquire high-resolution information from these regions. The information is then transmitted through distributed communication protocols that use local coverage maps to retain periodic and high-quality data. To resolve the trade-off between high-resolution data from low altitudes and wider coverage from high altitudes, cost functions based on distance, coverage, and sensor resolution are used to determine three-dimensional waypoints. Further, Voronoi tessellations are used to aid in collision avoidance and efficient computation. To generate feasible trajectories, the quadrotor's dynamics are considered, along with error functions that use state information and desired waypoints. Simulation results demonstrate the efficacy of the proposed approach for multi-agent systems under quadrotor dynamics.
An Active Landing Recovery Method for Quadrotor UAV: Localization, Tracking and Buffering Landing

This paper proposes a principle of fully autonomous ground mobile landing recovery of Unmanned Aerial Vehicles (UAV) for the problems of relatively fixed landing point, passive recovery, poor flexibility, and environmental adaptability, which mainly includes localization, landing point tracking, and buffering landing for quadrotor UAV. Firstly, aiming at the problem that it is difficult to accurately obtain the position of a UAV in dynamic mobile landing recovery, a target location method based on Asynchronous Multisensor Information Fusion(AMIF) and servo turntable focus tracking is proposed. Secondly, to achieve fast and high-precision tracking of UAVs, a tracking control strategy of an independently driven landing recovery system and a Stewart six-degree of freedom platform is proposed. Then, to solve the problems of large impact force and center of gravity instability in the landing process of UAV, a stationarity control algorithm based on model prediction and a compliance control algorithm based on adaptive variable impedance are designed to achieve active compliance control while adjusting the position and attitude of the receiving surface in real-time. Finally, a quadrotor unmanned landing and recovery experimental platform is built to verify the feasibility of the ground mobile landing and recovery strategy proposed in this paper and the effectiveness of the control algorithm.
Test of Homogeneity

The quest for effective tests of homogeneity within mixture models has a long history. An illustrative example provided by Hartigan shows that the likelihood ratio statistic, unlike its counterpart in regular models, tends to diverge to infinity even in the context of an extremely simplified normal mixture model. While imposing compact restrictions on the subpopulation parameter space and a separation condition can prevent this divergence and land on a limiting distribution, it does not lead to a practical testing procedure. In contrast, the C( $$\alpha $$ ) test, which is a modification of the popular score test, exhibits a simple limiting distribution and proves to be an effective tool for homogeneity testing in mixture models with single-parameter subpopulation distributions. Chapter 9 is dedicated to introducing the C( $$\alpha $$ ) test and providing specific expressions for its application within NEF-VEF mixtures.
Order Selection of the Finite Mixture Models

The order selection problem, in contrast to determining whether a lower-ordered finite mixture model should be rejected in favor of a higher-ordered model, seeks to answer the question of what constitutes the most appropriate order for the finite mixture model. The challenge here lies in the fact that there are as many criteria for ”most suitable” as there are statisticians, making it a far more diverse problem than a simple hypothesis test. Moreover, each of these criteria often cannot be directly evaluated but requires approximations using complex asymptotic tools. The already intricate nature of finite mixture models further compounds this challenge. In Chap. 16, we offer a brief overview of several order selection procedures for finite mixture models. These include the transplanted Akaike Information Criterion (AIC), Bayes Information Criterion (BIC), as well as somewhat tailored Widely Applicable Bayesian Information Criterion (WBIC) and Singular Bayesian Information Criterion (sBIC), in addition to regularization-based techniques. This chapter serves as a limited introduction to these various methods without endorsing any particular approach.
Rate of Convergence

The fundamental requirement in data analysis is the consistent estimation of a parameter. As the sample size increases, the precision of the estimator naturally improves, following a rate of $$n^{-1/2}$$ for parameters under regular statistical models. However, when dealing with finite mixture models, it becomes evident that this rate is strongly influenced by how excessively the order of the mixture is specified. Chapter 8 sheds light on the development of the claimed best possible rate, which was $$n^{-1/4}$$ when the order is over-specified. It is now widely recognized that the minimax rate is $$n^{-1/6}$$ even when the order is merely over-specified by one. The overall scenario is considerably more intricate than initially expected, and this chapter is dedicated to deepening the understanding of the optimal rate of convergence and its implications under finite mixture models.
Geometric Properties of Non-parametric MLE and Numerical Solutions

In Chap. 6, our attention turns to numerical solutions for maximum likelihood estimation within the context of nonparametric mixture models. The challenge arises from the infinite dimension of the nonparametric space, making the resulting optimization problem seem insurmountable. However, the nonparametric maximum likelihood estimator is known to have finite support, meaning it assigns positive probabilities to a finite set of mixing parameter values. Moreover, there exists a well-behaved directional derivative function that is non-positive at the nonparametric maximum likelihood estimate. This characteristic motivates the use of iterative procedures and provides the conceptual tools necessary to verify the maximization of the likelihood function. Throughout this chapter, we present several numerical procedures and engage in discussions regarding their algorithmic convergence. We caution that a successful implementation often demands a higher level of specialized expertise.
em-Test for Higher Order

Chapter 14 serves as a natural extension of the preceding chapter, offering a comprehensive examination of the EM-test when applied to high-order null hypotheses within the finite mixture model. While certain aspects of the results align with those of the modified likelihood ratio test, the unique design of the EM-test enables a clear presentation of the limiting distribution. Importantly, this presentation comes with fewer restrictions on the finite mixture model, making it applicable to a wider range of scenarios, including general orders.
Towards the Complexity Analysis of Programming Language Proof Methods

Numerous proof methods have been proposed to establish language properties such as type soundness, confluence, strong normalization, and others. However, literature does not provide a study of the complexity of carrying out these proof methods. This paper provides an investigation on the complexity of carrying out the “syntactic approach” to type soundness (progress theorem and type preservation theorem) for a class of functional languages, and characterizes the complexity of its proofs as a function of the number of expression constructors, number of typing rules, reduction rules, and other common quantities of operational semantics. Although we do not claim to provide the complexity of this approach, this paper provides the first example of complexity analysis of a programming language proof method.
Modified Likelihood Ratio Test for Higher Order

Chapter 12 centers around the modified likelihood test for high-order null hypotheses within the finite mixture model. While the fundamental concept of testing for high-order null hypotheses remains akin to the modified likelihood ratio test for homogeneity, the endeavor of deriving the limiting distribution is significantly more intricate. This chapter offers a comprehensive exposition of various technical tools and intricate details involved in this challenging task.
Non-Parametric MLE and Its Consistency

In Chapter 2 , our primary focus lies on nonparametric mixture models. In contrast to some existing literature, a nonparametric mixture is defined by having its mixing distribution as a member of a nonparametric family. Given a set of n independent and identically distributed observations, we explore the estimation of the mixing distribution through maximum likelihood. Within this context, we provide three distinct proofs of the consistency of the nonparametric maximum likelihood estimator, delving into their respective advantages and limitations.
A Game-Theoretic Approach to Indistinguishability of Winning Objectives as User Privacy

Game theory on graphs is a basic tool in computer science. In this paper, we propose a new game-theoretic framework for studying the privacy protection of a user who interactively uses a software service. Our framework is based on the idea that an objective of a user using software services should not be known to an adversary because the objective is often closely related to personal information of the user. We propose two new notions, $$\mathcal {O}$$ -indistinguishable strategy ( $$\mathcal {O}$$ -IS) and objective-indistinguishability equilibrium (OIE). For a given game and a subset $$\mathcal {O}$$ of winning objectives (or objectives in short), a strategy of a player is $$\mathcal {O}$$ -indistinguishable if an adversary cannot shrink $$\mathcal {O}$$ by excluding any objective from $$\mathcal {O}$$ as an impossible objective. A strategy profile, which is a tuple of strategies of all players, is an OIE if the profile is locally maximal in the sense that no player can expand her set of objectives indistinguishable from her real objective from the viewpoint of an adversary. We show that for a given multiplayer game with Muller objectives, both of the existence of an $$\mathcal {O}$$ -IS and that of OIE are decidable.
The Availability of Green Transformational Leadership in the General Company for Electrical and Electronic Industries: A Field Study

Due to the absence of previous studies discussing the availability of transformational leadership in the General Company for Electrical and Electronic Industries / Baghdad - Al-Waziriyah, this study develops a research framework to fill the research gap. This study explores the effects of green transformational leadership on the company under consideration. Green transformational leadership, which can successfully stimulate the innovation performance of organizations, contributes to the development of new ideas in the innovation process. Through its four dimensions (ideal influence, inspirational motivation, intellectual stimulation, individual considerations). The results of the current study generally indicate that the company's leadership must possess sufficient capabilities that enable it to motivate human resources and that the leader behaves in an exemplary manner that human resources seek to emulate and gain Through it the trust, respect and appreciation of human resources and encourages them all to discover solutions to the complex problems facing the company. The results of the statistical analysis of the current study indicate The general arithmetic mean is average for the variable green transformational leadership, which indicates that the data is homogeneous, this means that the level of green transformational leadership is within a somewhat average level in the company under study according to the respondents' point of view. We recommend that the management of the company under study possess a green transformational leadership that enables it to motivate its human resources.
The Landscape of Computing Symmetric n-Variable Functions with 2n Cards

Secure multi-party computation using a physical deck of cards, often called card-based cryptography, has been extensively studied during the past decade. Card-based protocols to compute various Boolean functions have been developed. As each input bit is typically encoded by two cards, computing an n-variable Boolean function requires at least 2n cards. We are interested in optimal protocols that use exactly 2n cards. In particular, we focus on symmetric functions. In this paper, we formulate the problem of developing 2n-card protocols to compute n-variable symmetric Boolean functions by classifying all such functions into several NPN-equivalence classes. We then summarize existing protocols that can compute some representative functions from these classes, and also solve some open problems in the cases $$n=4$$ , 5, 6, and 7. In particular, we develop a protocol to compute a function kMod3, which determines whether the sum of all inputs is congruent to k modulo 3 ( $$k \in \{0,1,2\}$$ ).
Interactive Matching Logic Proofs in Coq

Matching logic (ML) is a formalism for specifying and reasoning about mathematical structures by means of patterns and pattern matching. Previously, it has been used to capture a number of other logics, e.g., separation logic with recursive definitions and linear temporal logic. ML has also been formalized in the Coq Proof Assistant, and the soundness of its Hilbert-style proof system has been mechanized. However, using a Hilbert-style system for interactive reasoning is challenging—even more so in ML, which lacks a general deduction theorem. Therefore, we propose a single-conclusion sequent calculus for ML that is more amenable to interactive proving. Based on this sequent calculus, we implement a proof mode for interactive reasoning in ML, which significantly simplifies the construction of ML proofs in Coq. The proof mode is a mechanism for displaying intermediate proof states and an extensible set of proof tactics that implement the rules of the sequent calculus. We evaluate our proof mode on a collection of examples, showing a substantial improvement in proof script size and readability.
Modelling and Verifying Robotic Software that Uses Neural Networks

Verifying learning robotic systems is challenging. Existing techniques and tools for verification of an artificial neural network (ANN) are concerned with component-level properties. Here, we deal with robotic systems whose control software uses ANN components, and with properties of that software that may depend on all components. Our focus is on trained fully connected ReLU neural networks for control. We present an approach to (1) modelling ANN components as part of behavioural models for control software and (2) verification using traditional and ANN-specific verification tools. We describe our results in the context of RoboChart, a domain-specific modelling language for robotics with support for formal verification. We describe our modelling notation and a strategy for automated proof using Isabelle and Marabou, for example.
Integration of a physics-based direct normal irradiance (DNI) model to enhance the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.