Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Fingers Coordination for Robust Grasp and In-Hand Manipulation Under Disturbances and Unknown Dynamics

We present a control framework for achieving a robust object grasp and manipulation in hand. In-hand manipulation remains a demanding task as the object is never stable and task success relies on carefully synchronizing the fingers' dynamics. Indeed, fingers must simultaneously generate motion while maintaining contact with the object and, by staying within the hand's frame, ensuring that the object remains manipulable. These challenges are exacerbated once the hand gets disturbed or when the internal dynamics of the manipulated object are unknown, such as when it is filled with liquid moving during manipulation. We present a control strategy based on coupled dynamical systems (DSs), whereby the fingers move in synchronization using an intermediate dynamics responsible for coordinating fingers. To adapt to changes in forces due to model uncertainties and unexpected disturbances, we employ an adaptive torque-controller combined with a joint impedance regulator that guarantees high tracking accuracy while adapting to dynamic changes. We validate the approach in multiple experiments on 16-degrees-of-freedom robotic hand grasping and manipulating objects with different mass properties, e.g., uneven or varying mass distribution in a glass half-filled with water. We show that the robot can compensate for disturbances generated by internal dynamics and external perturbations. Additionally, we showcase how our controller, in conjunction with learning from human demonstration, provides a robust solution for more complicated manipulations such as finger gaiting.
A Simulation Approach to the Formation of Precipitation Particles Using the Monte Carlo Method

The turbulent effect upon the mechanism of large cloud droplet formation in a warm cloud is discussed. Firstly, the basic data in field works, simultaneous observations of cloud physical elements in a stratiform cloud at a mountainside and observations of the spatial concentration fluctuationof cloud droplets were analysed as regards simulation study and thereby it was suggested that the turbulence arising from the instability of atmospheric layer would exert the influence to the large droplet formation. Secondly, basing upon the above data, the Monte Carlo method was applied to the simulation study for the cases in which cloud droplets distributed spatially at random and in which they distributed spatially in heterogeneous concentration as caused by turbulence, in aiming to obtain the information about the difference in growth of large droplets between the two cases. In the latter case, it was noticed that a few large droplets grew more rapidly than in the former case.
Leveraging the nugget parameter for efficient Gaussian process modeling

Gaussian process (GP) metamodels have been widely used as surrogates for computer simulations or physical experiments. The heart of GP modeling lies in optimizing the log‐likelihood function with respect to the hyperparameters to fit the model to a set of observations. The complexity of the log‐likelihood function, computational expense, and numerical instabilities challenge this process. These issues limit the applicability of GP models more when the size of the training data set and/or problem dimensionality increase. To address these issues, we develop a novel approach for fitting GP models that significantly improves computational expense and prediction accuracy. Our approach leverages the smoothing effect of the nugget parameter on the log‐likelihood profile to track the evolution of the optimal hyperparameter estimates as the nugget parameter is adaptively varied. The new approach is implemented in the R package GPM and compared to a popular GP modeling R package (GPfit) for a set of benchmark problems. The effectiveness of the approach is also demonstrated using an engineering problem to learn the constitutive law of a hyperelastic composite where the required level of accuracy in estimating the response gradient necessitates a large training data set.
A Further Study of Tropical Wave Disturbances by the Use of Spectrum Analysis

The upper wind data of the tropical Pacific stations during the period April through July 1962, which were previously studied by Yanai et al. (1968), are re-examined with respect to both the zonal and meridional wind components covering a wider spectral range from 2 to 25 day periods. A few new samples are added to study the wave disturbances in the western Pacific south of the equator. The results of the power spectrum analysis are presented in height-frequency diagrams. Besides the previously known 4to 5-day periodicity of the meridional wind component, a pronounced zonal wind oscillation with periods longer than 10 days is found throughout the entire troposphere and the lower stratosphere at most of the stations. A periodicity near 7 days in the zonal component is isolated at some stations. All of the major spectral peaks show a maximum concentration of power density in the upper troposphere. Regional differences in the spectral density distribution in the height-frequency diagrams are examined. A preliminary consideration of the interpretation of the time series analysis is also made.
Identifying Interference From Multiple Noise Sources by Magnetic Near Fields Only

As electronic products become more and more complicated, multiple noise sources very likely interfere with the radio frequency receiver simultaneously. This paper proposes a method to identify the interference from different noise sources separately, even when they are radiating at the same time. This method converts magnetic fields to electric fields by the finite-element method (FEM) and employs the decomposition method based on reciprocity theory. In the proposed method, Huygens's surface will be set up for each source. The tangential magnetic near fields on each Huygens's surface are used to solve tangential electric fields correspondingly by the FEM. Then, the sources are removed but their Huygens's surfaces are kept. The victim structure is excited in this case to get the tangential magnetic fields on Huygens's surfaces. A creative FEM processing procedure is applied to obtain tangential electric fields in this situation. Finally, with these two groups of fields, the interference from each noise source can be estimated separately based on reciprocity theory. This method is validated by a numerical example. It is very helpful for engineers to be able to identify the contribution of the coupling from different sources and further solve the electromagnetic interference issues efficiently.
Effective Optimization Algorithms for Blind and Supervised Music Source Separation with Nonnegative Matrix Factorization

In this dissertation, to address a music source separation problem, several optimization algorithms are proposed. Music source separation is a technique to extract or separate specific music sources from an observed mixture signal that contains multiple music instrumental and vocal sounds. There are many feasible applications for this technique, for example, audio remixing by users, automatic music transcription, and musical instrument education. A general audio source separation problem has been investigated for a long time, particularly in the speech signal processing field to reduce background noise and enhance only the speech signal in the observation. Many techniques have been proposed for various recording conditions in the past, and they can roughly be divided into two situations: determined (or overdetermined) and underdetermined cases. In the determined situation, sufficient number of observations (microphones used in the recording) can be utilized for solving the separation problem, whereas the underdetermined situation, which includes monaural observation, basically lacks such multi-dimensional information. Also, presence of external prior information (supervision) such as music scores, source locations, or sound examples of each source in the mixture is another important issue. The source separation techniques without any prior information is often called blind source separation, which is the most difficult but a practical technique. The objective of this dissertation is to develop an effective optimization algorithm for the music source separation and to achieve satisfactory separation performance. Two main topics are here addressed: determined (and overdetermined) blind source separation and single-channel (underdetermined) semi-supervised source separation. The semi-supervised source separation
Hybrid approach using deep learning and graph comparison for building change detection

ABSTRACT Existing methods of detecting building changes from very-high-resolution (VHR) images are limited by positional displacement. Although various change detection (CD) methods including deep learning methods have been proposed, they are incapable of overcoming the aforementioned limitation. Therefore, this study proposes a two-step hybrid approach using deep learning and graph comparison to detect building changes in VHR temporal images. First, the building objects were detected using mask regional-convolutional neural networks (Mask R-CNN), wherein the centroid of the bounding box was extracted as the building node. Second, for each image, graphs were generated using the extracted building nodes. Accordingly, the changed nodes were identified based on iterative graph comparison, which could be voluntarily halted without setting thresholds by examining the changes in the proposed index while sequentially eliminating the building changes. To demonstrate the effectiveness of the proposed method, we experimentally tested the simulated images with synthetic changes and positional displacements. The results verified that the proposed method effectively reduced the false detections originating from positional inconsistencies. Consequently, the proposed method could overcome the limitations of conventional CD methods by employing a graph model based on the connectivity between adjacent buildings.
Four Consecutive Samples Based Sensorless Control for PMSM Drives

Phase-locked loop (PLL) and frequency-locked loop (FLL) synchronization technology in permanent magnet synchronous motor (PMSM) has been paid attention to by many researchers. However, in the actual operation of PMSM drivers, harmonics and dc offsets are inevitable, which brings great challenges to the synchronization technology. To tackle this issue, this article proposes a novel four consecutive samples (4CS) based sensorless control algorithm for PMSM drives. The 4CS algorithm establishes the relationship between flux and its consecutive sampling signals, and it has inherent ability to suppress the influence of harmonics and dc offsets. Furthermore, the adaptability of 4CS is improved for better PMSM drive performance. The stability of the proposed 4CS algorithm is proven. Also, experimental results are provided to verify the efficacy of the proposed method.
Expected Squared Estimation Error of Stochastic Approximation in Finite Samples

Stochastic approximation is a recursive procedure to seek for a solution of an unknown nonlinear equation based on random noise corrupted residuals. This paper presents an upper bound of the expected squared estimation error of stochastic approximation in ﬁnite samples of the measurements. The bound is given as an a ﬃ ne function of the squared error of the initial candidate of the solution with parameters. Once the parameters are speciﬁed, the necessary number of the measurements can readily be computed in advance of execution of the procedure, which establishes a rigorous stopping rule for stochastic approximation.
Counterfactual Visual Dialog: Robust Commonsense Knowledge Learning From Unbiased Training

Visual Dialog (VD) requires an agent to answer the current question by engaging in a conversation with humans referring to an image. Despite the recent progress, it is beneficial to introduce external commonsense knowledge to fully understand the given image and dialog history. However, the existing knowledge-based VD models are inclined to rely on severe learning bias brought by commonsense, e.g., the retrieved $< {\mathtt{{bus}}}, {\mathtt{capable\;of}}, {\mathtt{transport\;people}}>$, $< {\mathtt{{bus}}}, {\mathtt{is\;a}}, {\mathtt{public\;transport}}>$, and $< {\mathtt{{bus}}}, {\mathtt{is\;a}}, {\mathtt{car}}>$ can induce a spurious correlation between the question “What is the bus used for? ” and the false answer “City bus”. There are two challenges to make commonsense learning more robust against spurious correlations: 1) how to disentangle the true effect of “good” commonsense knowledge from the whole, and 2) how to estimate and remove the effect of “bad” commonsense bias on answers. In this article, we propose a novel CounterFactual Commonsense learning scheme for the Visual Dialog task (CFC-VD). First, comparing with the causal graph of existing VD models, we add one new commonsense node and one new link to multi-modal information from history, question, and image. Since the retrieved knowledge prior is subtle and uncontrollable, we consider it as an unobserved confounder in the commonsense node, which leads to spurious correlations for the answer inference. Then, to remove the effect of the confounder, we formulate it as the direct causal effect of commonsense on answers and remove the direct language effect by subtracting it from the total causal effect via counterfactual reasoning. Experimental results certify the effectiveness of our method on the prevailing Visdial v0.9 and Visdial v1.0 datasets.
Short-term Power Prediction Method of Photovoltaic Based on Output Clustering in Smart Grid

Accurate photovoltaic power prediction can ensure the smart grid's safe and stable operation, as well as reasonable energy scheduling. Weather influences photovoltaic output, which is irregular and unstable, and photovoltaic output is similar under similar meteorological conditions. The paper proposes a solar power forecast model based on Mean-Shift clustering, support vector machine (SVM), and residual neural network (ResNet) in this regard. Firstly, the Mean-Shift algorithm is used to cluster similar days. Then, the SVM model is constructed to learn the similarity between the data of each meteorological type, and the similar day matching is performed on the forecast day. Finally, the short-term photovoltaic output prediction based on ResNet algorithm is carried out for the corresponding weather type. The suggested method's high forecast accuracy and stability are confirmed by experimental examination of a commercial photovoltaic power station.
The Potential of Jamblang Root or Java Plum (Syzygium cumini) in Medicinal Uses: A Systematic Review

Jamblang root has long been used in traditional medicine as a natural remedy for various health conditions. This study aimed to present a systematic review to explore the potential of jamblang roots in the health sector. This study is a systematic literature review in which exploration was carried out literature from various reputable scientific publication databases. This study follows the preferred reporting items for systematic reviews and meta-analysis (PRISMA) recommendations. Several studies have shown that jamblang root has anti-inflammatory properties that can help reduce inflammation in the body. In conclusion, jamblang root or java plum (Syzygium cumini) has the potential for anti-diabetic, anti-inflammatory, antioxidant, hepatoprotective, and astringent effects.
Computational Mining of Meso-Scale Physics From High-Energy X-Ray Data Sets

Migration of grain boundaries in an idealized, well-ordered polycrystalline sample is an often-studied phenomenon in microstructural materials science. It is the subject of many imaging experiments in two dimensions and simulations in two and three dimensions, the collective knowledge of which has given us many insights into behavior of specific materials. This thesis describes the characterization of the grain boundaries in a specially prepared polycrystalline aggregate of high-purity iron with a bodycentered cubic lattice whose microstructure was imaged non-destructively in three dimensions with near-field high-energy diffraction microscopy (nf-HEDM) which uses synchrotron X-rays as a probe. The sample was imaged using nf-HEDM before and after a cycle of annealing in order to activate boundary migration for a short interval of time. Also described are the development of computational techniques for denoising grain boundary images in three dimensions as well as a scheme to solve the inverse problem of computing the dynamical parameters influencing boundary migration from the observed boundary geometries and transport. The two snapshots of the full three-dimensional grain boundary network were used to quantify the geometry and transport of individual grains and track their progress through the annealing. A study of the influence of grain boundary curvature on boundary velocity revealed a weak correlation for a large fraction of the boundaries.
Controlled Quantum Teleportation via Four Particle Asymmetric Entangled State

A general scheme for controlled teleportation of an arbitrary single-qubit state with four-qubit asymmetric state is proposed. In this scheme, the sender performs Bell measurement on his particle, the two controllers and the receiver perform joint unitary operation on the rest particles. Finally, the receiver can reconstruct the single particle by introducing an auxiliary particle, he first does unitary transformation on his particle and the auxiliary particle, then performs a Von Neumann measurement on the auxiliary particle. Thus the scheme can be realized in certain probability.
MINDFULNESS TRAINING – EFFECT ON ADULTS WITH ADHD AND DEPRESSIVE SYMPTOMS AND THE ROLE OF DIGITAL TECHNOLOGIES

Nowadays, more and more people are faced with symptoms of depression. This literature review focuses on adults with ADHD and depressive symptoms and presents the therapeutic technique of mindfulness and its benefits. The purpose of the paper was to add another method of reducing the symptoms of people who suffer from such difficulties in their daily lives. At the same time, the role of new technologies in the solution of mindfulness was also sought because developers and researchers work every day to provide solutions through technology to people who face developmental disorders such as ADHD or medical diseases such as depression. The research question of the work was presented and the contribution of mindfulness training is attributed as an alternative practice with the main axis of shifting the attention of individuals to the situation they are faced with and consequently to the fullest possible ideal awareness and cultivation of positive emotions. The findings identified were very interesting as mechanisms were found that were favored by mindfulness training. The concerns and research data led to the main conclusion that indeed the mindfulness technique can be administered by therapists as a way to reduce the symptoms experienced by adults with ADHD and depression.
Open HD map service model: an interoperable high-Definition map data model for autonomous driving

ABSTRACT With the growth in the vehicle industry, autonomous driving has become a hot topic worldwide and has attracted increasing attention from both industrial and academic sectors. Maps, as pivotal geospatial information carriers, play a vital role in route planning and navigation service. Compared with conventional maps, high-definition (HD) maps possesses higher precision, richer information, and various services and are regarded as critical infrastructure for autonomous driving. However, heterogeneous HD map data standards and models have different characteristics and advantages, and thus they rarely meet all autonomous driving requirements for different driving objectives. This research presents an interoperable map data model, the Open HD Map Service Model (OHDMSM), to provide a reference for HD map development. The designed OHDMSM, which contains three data layers and a set of corresponding interfaces, demonstrates high interoperability for HD map data fusion and application. As a proof of concept, an HD map data system is implemented with all functions following the designed data model and interfaces of OHDMSM. The design and development of OHDMSM data structures, interfaces and systems will benefit data requesting, updating, and interoperation for HD map data worldwide, which can be helpful for developing autonomous driving and intelligent transportation in the Digital Earth.
Individualization in Spatial Behaviour and Map Reading

Abstract. For a long time, research has investigated spatial behaviour of people in navigation research and identified different wayfinding strategies. Researchers agree, that the environment with its structure and features influences the wayfinding strategies. The present paper presents a tool to investigate people’s strategies during navigation tasks. GeoGami, an educational geogame fostering navigational map reading skills, can be used to collect trajectories of people navigating through an environment. We developed a semantically annotated representation of the environment and clustered the resulting trajectories according to their spatio-temporal and semantic similarity. The result shows the existence of groups of common spatial patterns along with individual spatial behaviours that cannot be grouped into a cluster.
Finite‐time adaptive event‐triggered control for nonstrict‐feedback stochastic nonlinear systems with full state time‐varying constraints

This article is concerned with the finite‐time adaptive event‐triggered control for stochastic nonstrict‐feedback nonlinear systems, which are under asymmetric full‐state time‐varying constraints. By utilizing the hyperbolic tangent function as a nonlinear mapping approach, the system with time‐varying constraints is transformed into an unconstrained stochastic nonlinear system. To reduce the communication burden and energy consumption, adaptive event‐triggered control (ETC) is extended to the converted stochastic nonstrict‐feedback system combining the dynamic surface control (DSC) technology and the characteristic of Gaussian function. The designed controller can make the system semi‐globally finite‐time stable in probability (SGFSP), which ensures a fast convergence speed. Moreover, to approximate the unknown nonlinear functions, radial basis function neural networks (RBF NNs) are adopted. The proposed control scheme can be confirmed in the fact that all signals in the closed‐loop system are semi‐globally uniformly bounded in probability, and the tracking error converges rapidly to a small neighborhood of zero in finite time. Two simulation results are given to demonstrate the effectiveness of the proposed controller.
State-of-the-art technologies of gallium oxide power devices

Gallium oxide (Ga2O3) has gained increased attention for power devices due to its superior material properties and the availability of economical device-quality native substrates. This review illustrates recent advances in Ga2O3 device technologies, beginning with an overview of the social circumstances that motivate the development of new-generation switching devices. Following an introduction to the material properties of Ga2O3 from the viewpoint of power electronics, growth technologies of Ga2O3 bulk single crystals and epitaxial thin films are discussed. The fabrication and performance of state-of-the-art Ga2O3 transistors and diodes are then described. We conclude by identifying the directions and challenges of Ga2O3 power device development in the near future.
Sur quelques extensions de la notion de distribution

© Gauthier-Villars (Éditions scientifiques et médicales Elsevier), 1960, tous droits réservés. L’accès aux archives de la revue « Annales scientifiques de l’É. N.S. » (http://www. elsevier.com/locate/ansens) implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/legal.php). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright.