Local Trajectory Research Articles

Hyporheic exchange in recirculating flumes under heterogeneous bacterial and morphological conditions

Hyporheic exchange (HE) contributes to the biogeochemical turnover of macro- and micro-pollutants in rivers. However, the spatiotemporal complexity and variability of HE hinder understanding of its role in the overall functioning of riverine ecosystems. The present study focuses on investigating the role of bacterial diversity and sediment morphology on HE using a multi-flume experiment. A fully coupled surface–subsurface numerical model was used to highlight complex exchange patterns between surface water and the underlying flow field in the sediments. Under the experimental conditions, the surface water flow induced by bedforms has a prominent effect on both local trajectories and residence time distributions of hyporheic flow paths, whereas mean hyporheic retention times are mainly modulated by average surface flowrates. In case of complex bedform morphologies, the numerical model successfully reproduces the HE estimated by means of salt dilution tests. However, the 2D numerical representation of the system falls short in predicting HE in absence of bedforms, highlighting the intrinsic complexity of water circulation patterns in real scenarios. Finally, results show that higher bacterial diversities in the stream sediments can significantly reduce hyporheic fluxes. This work provides a framework to interpret micropollutants turnover in light of the underlying physical transport processes in the hyporheic zone. The study emphasizes the importance of better understanding the tradeoff between physically driven transport processes and bacterial dynamics in the hyporheic zone to quantify the fate of pollutants in streams and rivers.

RGB+2D skeleton: local hand-crafted and 3D convolution feature coding for action recognition

Most 3D skeleton feature-based human action recognition methods are sensitive to changes in viewpoints, motion scales, and human scales. In addition, acquiring depth information from a real scene in outdoor environments results in poor precision or high computational costs. To address these drawbacks, in this study, we propose a new RGB video and 2D skeleton-based action recognition method including local joint trajectory volume representation and feature coding. First, a video is transferred to a set of volumes, which are called the local joint trajectory volumes. Then, hand-crafted and convolutional networks are used to calculate the features of each volume-based RGB image sequence. Different from most works that use convolutional networks to learn global video features, in this paper, the problem of using a convolutional network to represent local video regions is discussed. Finally, the feature set of each joint is encoded into the Fisher vector as an action feature. The classifier is trained by a linear SVM. The experimental results show that skeleton joint-based features result in a more compact and effective action representation approach than other approaches.

Application of Dislocation Theory to Minimize Defects in Artificial Solids Built with Nanocrystal Building Blocks.

ConspectusOriented atomic attachment of colloidal inorganic nanocrystals represents a powerful synthetic method for preparing complex inorganic superstructures. Examples include fusion of nanocrystals into dimer and superlattice structures. If the attachment were perfect throughout, then the resulting materials would have single crystal-like alignment of the individual nanocrystals' atomic lattices. While individual colloidal nanocrystals typically are free of many defects, there are a multitude of pathways that can generate defects upon nanocrystal attachment. These attachment generated defects are typically undesirable, and thus developing strategies to favor defect-free attachment or heal defective interfaces are essential. There may also be some cases where attachment-derived defects are desirable. In this Account, we summarize our current understanding of how these defects arise, in order to offer guidance to those who are designing nanocrystal derived solids.The small size of inorganic nanocrystals means short diffusion lengths to the surface, which favor the formation of nanocrystal building blocks with pristine atomic structures. Upon attachment, however, there are numerous pathways that can lead to atomic scale defects, and bulk crystal dislocation theory provides an invaluable guide to understanding these phenomena. As an example, an atomic step edge can be incorporated into the interface leading to an extra half-plane of atoms, known as an edge dislocation. These dislocations can be well described by the Burgers vector description of dislocations, which geometrically identifies planes in which a dislocation can move. Our in situ measurements have verified that bulk dislocation theory predictions for 1D defects hold true at few-nanometer length scales in PbTe and CdSe nanocrystal interfaces. Ultimately, the applicability of dislocation theory to nanocrystal attachment enables the predictive design of attachment to prevent or facilitate healing of defects upon nanocrystal attachment. We applied similar logic to understand formation of planar (2D) defects such as stacking faults upon nanocrystal attachment. Again concepts from bulk crystal defect crystallography can identify attachment pathways that can prevent or deterministically form planar defects upon nanocrystal attachment. The concepts we discuss work well for identifying favorable attachment geometries for nanocrystal pairs; however it is currently unclear how to translate these ideas to near-simultaneous multiparticle attachment. Geometric frustration, which prevents nanocrystal rotation, and yet-to-be considered defect generation pathways unique to multiparticle attachment complicate defect-free superlattice attachment. New imaging methods now allow for the direct observation of local attachment trajectories and may enable improved understanding of such multiparticle phenomena. With further refinement, a unified framework for understanding and ultimately eliminating structural defects in fused nanocrystal superstructures may well be achievable in coming years.

Local Trajectory Occurrence Patterns for Partial Action and Gesture Recognition

Action and gesture recognition is essential in computer vision because of their multiple and potential applications. Nowadays, in the literature, dramatic advances have been reported regarding recognizing gestures and actions under uncontrolled scenarios with significant appearance and motion variations. Nevertheless, much of these approaches still require manual segmentation of temporal action boundaries and complete processing of whole sequences to obtain a prediction. This work introduces a novel motion description that can recognize actions and gestures over partial sequences. The approach starts by representing video sequences as a set of key-point trajectories. Such trajectories are then hierarchically represented from a local and regional perspective, following a statistical counting process. Firstly, each trajectory is defined as a binary occurrence pattern that allows for standing out critical motions by neighborhood densities from a local perspective. Such occurrence patterns are involved in a regional bag-of-words representation of actions. Both representations could be obtained for any interval inside the video, achieving a partial recognition of motion, and regional representation is mapped to a support vector machine to obtain a prediction. The proposed approach was evaluated on academic action recognition datasets and a large gesture dataset used for sign recognition. Regarding partial video sequence recognition, the proposed approach achieves an accuracy rate of 63% using only 20% of frames. The proposed strategy achieved a very compact description, with only 400 scalar values, which ideal for online applications.

A trajectory data publishing algorithm satisfying local suppression

Suppressing the trajectory data to be released can effectively reduce the risk of user privacy leakage. However, the global suppression of the data set to meet the traditional privacy model method reduces the availability of trajectory data. Therefore, we propose a trajectory data differential privacy protection algorithm based on local suppression Trajectory privacy protection based on local suppression (TPLS) to provide the user with the ability and flexibility of protecting data through local suppression. The main contributions of this article include as follows: (1) introducing privacy protection method in trajectory data release, (2) performing effective local suppression judgment on the points in the minimum violation sequence of the trajectory data set, and (3) proposing a differential privacy protection algorithm based on local suppression. In the algorithm, we achieve the purpose Maximal frequent sequence (MFS) sequence loss rate in the trajectory data set by effective local inhibition judgment and updating the minimum violation sequence set, and then establish a classification tree and add noise to the leaf nodes to improve the security of the data to be published. Simulation results show that the proposed algorithm is effective, which can reduce the data loss rate and improve data availability while reducing the risk of user privacy leakage.

Study on the reliability evaluation method for sun-synchronous orbit satellite power subsystem

As an important component to provide satellite energy, the power subsystem in-service operation state is of great significance to satellite safety. In this paper, the power subsystem reliability evaluation is carried out on the basis of the actual telemetry data from satellites. Firstly, the influence factors of the power subsystem telemetry data are analyzed, and the modified method to calculate the output parameters is established; Furthermore, the degradation trajectory of the power subsystem output power is analyzed under different in-service time, and then the difference between the global and the local degradation is discussed. Finally, using the performance degradation model, the reliability evaluation method for the power subsystem is set up. The result shows that the local degradation trajectory in the end of lifetime is critical for the remaining useful life prediction, and the method can provide references for the in-service reliability analysis of the on-orbit satellites.

Ameliorated Frenet Trajectory Optimization Method Based on Artificial Emotion and Equilibrium Optimizer

An ameliorated Frenet trajectory optimization (AFTO) method based on artificial emotion (AE) and an equilibrium optimizer (EO) is proposed for the local trajectory planning of an unmanned ground vehicle (UGV). An artificial emotional potential field (AEPF) model is established to simulate AE. To realize a humanoid driving mode with emotional intelligence, AE is introduced into the Frenet trajectory optimization (FTO) method to determine the optimal trajectory. Based on the optimal discrete goal state of the FTO method, a first-sampling-then-optimization (FSTO) framework combining the FTO method with the EO is designed to obtain the optimal trajectory in a continuous goal state space. With different AEPF levels corresponding to different types of obstacles, simulation results show that the AEPF effectively adjusts the trajectory into different levels of safe distance between the UGV and obstacles corresponding to the humanoid driving mode. From the results of 30 independent experiments based on the AEPF, the FSTO framework in the AFTO method is effective for optimizing the trajectory of the FTO method at a lower cost. Moreover, the effectiveness of the proposed method for different types of roads is verified on a straight road and a curved road with obstacles in simulation. The improvement based on emotional intelligence and trajectory optimization in the AFTO method provides a humanoid driving mode for the UGV in the continuous goal state space.

Globular Gaussian Essential Part in Meager Bayesian Knowledge Structure for Nonlinear Deterioration

Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

A compact and recursive Riemannian motion descriptor for untrimmed activity recognition

A very low dimension frame-level motion descriptor is herein proposed with the capability to represent incomplete dynamics, thus allowing online action prediction. At each frame, a set of local trajectory kinematic cues are spatially pooled using a covariance matrix. The set of frame-level covariance matrices forms a Riemannian manifold that describes motion patterns. A set of statistic measures are computed over this manifold to characterize the sequence dynamics, either globally, or instantaneously from a motion history. Regarding the Riemannian metrics, two different versions are proposed: (1) by considering tangent projections with respect to updated recursive statistics, and (2) by mapping the covariance onto a linear matrix using as reference the identity matrix. The proposed approach was evaluated for two different tasks: (1) for action classification on complete video sequences and (2) for online action recognition, in which the activity is predicted at each frame. The method was evaluated using two public datasets: KTH and UT-interaction. For action classification, the method achieved an average accuracy of 92.27 and 81.67%, for KTH and UT-interaction, respectively. In partial recognition task, the proposed method achieved similar classification rate as for the whole sequence using only the 40 and 70% on KTH and UT sequences, respectively. The code of this work is available at [code].

Asian Children's Literature and Film in a Global Age ed. by Bernard Wilson and Sharmani Patricia Gabriel

Reviewed by: Asian Children's Literature and Film in a Global Age ed. by Bernard Wilson and Sharmani Patricia Gabriel Lucia Obi Translated by Nikola von Merveldt ASIAN CHILDREN'S LITERATURE AND FILM IN A GLOBAL AGE. Local, National, and Transnational Trajectories. Edited by Bernard Wilson and Sharmani Patricia Gabriel. Series: Asia-Pacific and Literature in English. Palgrave Macmillan / Springer Nature Singapore, 2020, 398 pages. ISBN: 978-981-15-2630-5 While postcolonial studies have long addressed the marginalization of non-Western literatures, the book market, which obeys the rules of global capitalism, still causes unequal circulation of children's literatures. There are more children's books on the Western market about Asia that perpetuate notions of "backwardness" and "exoti cism" than there are books from Asia itself that reflect the reality of contemporary Asian societies. Conversely, neocolonial practices, such as cultural appropriation and reinterpretation of "cultur ally authentic" non-Western sources, determine the flow of the commodity of "children's literature" from Asia. Research on Asian children's literature is also dominated by Western discourses and aesthetic concepts. It was high time for this book, whose authors present Asian children's literature and films as expressions of their specific cultures of origin, but also within the complex symbiosis of their local, national, transnational, global, and glocalized networks. The methodologically varied contributions cover diverse children's media from the Asian American diaspora, China, India, Indonesia, Iran, Japan, Malaysia, the Philippines, South Korea, Sri Lanka, and Taiwan. The concept of "children's literature" rooted in national contexts and transporting collective ideological interests can certainly be observed in Asia. Even though "Asia" is not conceived here as a static, homogeneous entity, but rather as a heterogeneous space with many centers and peripheries, complex local cultures, and constant historical change in the wake of globalization, regional thematic emphases can be identified in the discussion of children's literatures. The book is organized to reflect this. The first chapter deals with East Asian literature from Taiwan, China, South Korea, and Japan, which focuses on changes in family structures and a redefinition of gender roles. Andrea Mei-Ying Wu looks at transnational cultural dynamics and the local appropriation of the term "child heart" (tongxin), which was adapted from romantic Western notions of childhood into Chinese literary theory and was influential in the development of Taiwanese children's literature in the postwar period. Xiangshu Fang and Lijun Bi reflect on the redefinition of the family shaped by Confucianism or nationalist political movements evident in Chinese children's books from 1978 to 2014. Three South Korean films presented by Sung-Ae Lee manifest similar social pressures through family structures. They are considered a metaphor for society as a whole, showing the negative consequences of nonconformity, but also the possibilities for change it can bring. A social transformation—a redefinition of family, away from heteronormative, monocultural, and patriarchal structures and binary gender roles—becomes more evident in contemporary Japanese children's literature presented by Yasuko Doi. Bernard Wilson sees in the Japanese anime version of Andersen's "Mermaid" a challenge to social norms with their social construction of gender. [End Page 97] The literature selections from South and West Asia, India, Sri Lanka, and Iran grouped together in the second chapter are presented by region, yet they focus on the globally significant children's literature theme of inequality and marginalization of young people due to their age, gender, ethnicity, or sexual orientation. Sonia Ghalian traces concepts of childhood in Hindi films since the 1950s that reveal fundamental changes in Indian society. Suchismita Banerjee explores the question of representation of queer lifestyles in Indian heteronormative society through the new phenomenon of LGBTQIA+ literature for children and young adults. Neluka Silva addresses the demonization of alterity along ethnic and ideological lines in Sri Lankan society through two popular children's novels by author Prashani Rambukwella. Amir Ali Nojoumian and Amir Hadi Nojoumian analyze the aesthetic approach to the representation of childhood and child protagonists in films by Iranian director Abbas Kiarostami. The third section of the book, with contributions on Malaysia, Indonesia, and the Philippines, focuses on apparently completely different Southeast Asian literatures that draw on local oral traditions or...

Cyclic Path Planning of Hyper-redundant Manipulator using Whale Optimization Algorithm

This paper develops a path planning algorithm of hyper-redundant manipulators to achieve a cyclic property. The basic idea is based on a geometrical analysis of a 3-link planar series manipulator in which there is an orientation angle boundary of a prescribed path. To achieve the repetitive behavior, for hyper-redundant manipulators consisting of 3-link components, an additional path is chosen in such away so that it is a repetitive curve which has the same curve frequency with the prescribed end-effector path. To solve the redundancy resolution, meta-heuristic optimizations, namely Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA), are applied to search optimal trajectories inside local orientation angle boundaries. Results show that using constant of the local orientation angle trajectories for the 3-link component, the cyclic properties can be achieved. The performance of the WOA shows very promising result where generally it obtains the lowest fitness value as compare with the GA. Depending on the complexity of the path planning, dividing the path into several stages via intermediate points may be necessary to achieve the good posture. The performance of the swarm based meta-heuristic optimization, namely the WOA, shows very promising result where generally it obtains the lowest fitness value as compare with the GA. Using the developed approach, not only the cyclic property is obtained but also the optimal movement of the hyper-redundant manipulator is achieved.

A sub-micron resolution, bunch-by-bunch beam trajectory feedback system and its application to reducing wakefield effects in single-pass beamlines

A high-precision intra-bunch-train beam orbit feedback correction system has been developed and tested in the ATF2 beamline of the Accelerator Test Facility at the High Energy Accelerator Research Organization in Japan. The system uses the vertical position of the bunch measured at two beam position monitors (BPMs) to calculate a pair of kicks which are applied to the next bunch using two upstream kickers, thereby correcting both the vertical position and trajectory angle. Using trains of two electron bunches separated in time by 187.6 ns, the system was optimised so as to stabilize the beam offset at the feedback BPMs to better than 350 nm, yielding a local trajectory angle correction to within 250 nrad. The quality of the correction was verified using three downstream witness BPMs and the results were found to be in agreement with the predictions of a linear lattice model used to propagate the beam trajectory from the feedback region. This same model predicts a corrected beam jitter of c. 1 nm at the focal point of the accelerator. Measurements with a beam size monitor at this location demonstrate that reducing the trajectory jitter of the beam by a factor of 4 also reduces the increase in the measured beam size as a function of beam charge by a factor of c. 1.6.

Comment on “Analyses of Mössbauer Experiments in a Rotating System: Proper and Improper Approaches” [Annals of Physics Volume 418, July 2020, 168191

The notion of classical well localized trajectories of a single photon in Minkowski spacetime does not make any rigorous sense by the well-known existence of a proof that single photons cannot be well localized. This leads to principal difficultness when photodetection probability on relativistic non inertial frame of reference is considered. In order to resolve this tension, we extend canonical Minkowski geometry up to relevant point-free Minkowski geometry [Ann. Physics 423 (2020) 168329]. The photodetection probability density on uniformly rotating frame endrowed with point-free Lorentzian geometry is obtained. The result of S. A. Podosenov et al. [Ann. Physics 413 (2020) 168047] is obtained without any reference to unphysical notion of the classical trajectories of photon. The paper again shows the correctness of the remarkable result of Prof. C. Corda concerning the Mossbauer rotor experiment as new proof of general relativity, which has been awarded by the Gravity Research Foundation. In addition, the paper also shows various very elementary mistakes, misunderstandings and flaws by the self-called “YARK group”, which is a group of fringe researchers who attempts to promote wrong science, in particular, against the relativity theory.

The Debate on student evaluations of teaching: global convergence confronts higher education traditions

ABSTRACT Through co-occurrence analysis of 1139 documents (1964–2018) we identified discussions about the implementation of student teaching evaluation (SET). We found that: (1) Attention to SET originated in the US in the 1970s, spreading to German-speaking countries in the mid-1990s and continuing in China and Latin America in the early 2000s. (2) SET is commonly viewed as a control tool deserving methodological improvement, while bias is debated in the US. We also found local trajectories: (3) Whereas in the US and Latin America SET is primarily seen as a management tool, German-speaking and Chinese authors reflect more on improving teaching. Chinese scholars consider SET a valid instrument for state control associated with artificial intelligence. Also, (4) SET is commonly used in medical education in the US and the German-speaking region and in physical education in China. We conclude that SET is discussed cross-nationally but affected by regional path dependencies.

A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization

Unmanned aerial vehicles (UAVs) play a key role in modern surveillance-related missions. A major task for performing these missions is to find the precise location of a moving target in real-time, for which the main challenge is to estimate the target position to high precision using the noisy measurements from the airborne sensors. In this paper, we present a closed-form on-line simultaneous target localization and UAV trajectory optimization method based on the visual platform, which can effectively minimize the localization uncertainty to the target. The proposed method can be elucidated explicitly using two phases, of which, in the target localization phase, the expended information filtering (EIF) is exploited, which can express the predicted Fisher information matrix (FIM) of the target explicitly and iteratively, and in the UAV trajectory optimization phase, the property of the predicted FIM is exploited to establish the UAV waypoint optimization objective by taking account of the UAV motion limit. Compared with existing methods of the same class, the proposed method not only estimates the next target position more correctly, but also takes the error of the target motion into consideration, thus improving the effectiveness of the optimized UAV trajectory. Both simulations and field experiments were conducted, which show that the proposed method outperformed the existing methods.

Cycles in Stone Mining and Copper Circulation in Europe 5500–2000bc: A View from Space

The authors of this article consider the relationship in European prehistory between the procurement of high-quality stones (for axeheads, daggers, and other tools) on the one hand, and the early mining, crafting, and deposition of copper on the other. The data consist of radiocarbon dates for the exploitation of stone quarries, flint mines, and copper mines, and of information regarding the frequency through time of jade axeheads and copper artefacts. By adopting a broad perspective, spanning much of central-western Europe from 5500 to 2000bc, they identify a general pattern in which the circulation of the first copper artefacts was associated with a decline in specialized stone quarrying. The latter re-emerged in certain regions when copper use decreased, before declining more permanently in the Bell Beaker phase, once copper became more generally available. Regional variations reflect the degrees of connectivity among overlapping copper exchange networks. The patterns revealed are in keeping with previous understandings, refine them through quantification and demonstrate their cyclical nature, with additional reference to likely local demographic trajectories.

Planning for a world beyond COVID-19: Five pillars for post-neoliberal development.

COVID-19 has shaken the world. Countless lives have been lost or devastated, and as of November 2020, the pandemic shows little sign of abatement. Emergency responses across the world have led to drastic changes in local and global development trajectories within a very short period of time. Yet precisely how these changes will take shape depends on underlying historical and socio-economic forces that must become part of our understanding of, and plans for, a world beyond COVID-19.

A Dynamically Feasible Fast Replanning Strategy with Deep Reinforcement Learning

In this work, we aim to develop a fast trajectory replanning methodology enabling highly agile aerial vehicles to navigate in cluttered environments. By focusing on reducing complexity and accelerating the replanning problem under strict dynamical constraints, we employ the b-spline theory with local support property for defining the high dimensional agile flight trajectories. We utilize the differential flatness model of an aerial vehicle, allowing us to directly map the desired output trajectory into input states to track a high dimensional trajectory. Dynamically feasible replanning problem is addressed through regenerating the local b-splines with control point reallocation. As the geometric form of the trajectory based on the location of the control points and the knot intervals, the control point reallocation for fast replanning with dynamical constraints is turned into a constrained optimization problem and solved through deep reinforcement learning. The proposed methodology enables generating dynamically feasible local trajectory segments, which are continuous to the existing, hence provides fast local replanning for collision avoidance. The DRL agent is trained with different environmental complexities, and through the batch simulations, it is shown that the proposed methodology allows to solve fast trajectory replanning problem under given or hard dynamical constraints and provide real-time applicability for such collision avoidance applications in agile unmanned aerial vehicles. Hardware implementation tests of the algorithm with the agile trajectory tracker to a small UAV can bee seen in the following video link: https://youtu.be/8IiLQFQ3V0E .

Lane‐changing decision method based Nash Q‐learning with considering the interaction of surrounding vehicles

In order to ensure a safer and more reliable trajectory during the lane change process, the motion decision algorithm needs to predict the possibility of different interaction behaviours with surrounding vehicles and then makes an advantageous decision. For this purpose, a motion decision method of considering the interaction of surrounding vehicles is proposed. Firstly, this study builds the payoff functions to determine the driving revenue of autonomous driving vehicles. Then, an interactive motion prediction method based on game theory is established to predict the interaction behaviours possibility and future local trajectories of surrounding vehicles. Based on this, a motion decision algorithm based on Nash Q-learning for an autonomous driving vehicle is established. With externalising the main behaviours predicted by the interactive game and the greedy optimisation method, the autonomous vehicle can determine the optimal sequence of actions and take into account the interaction of the surrounding vehicles. Finally, the motion decision in this study is validated by MATLAB in the merging lane scene, and compared with the existing rule-based lane change decision algorithm. The results show that the decision method in this study not only has superiority in safety and efficiency but also can effectively predict the interaction of surrounding vehicles.

Changing settlement patterns and subsistence strategies in Northeast China: Results of the Yueliang regional survey

Between 2012 and 2016, a systematic regional survey was conducted around the Xinhuang and Yueliang Lakes in northwest Jilin, Northeast China. This paper investigates the results of that fieldwork. We are especially interested in the degree to which the settlements are integrated with one another. The spatial analysis of the results shed new light on the local trajectory of settlement patterns and subsistence strategies from pre-Neolithic to the historical era, and provides data about the current and ancient environment and socio-economic changes in this region and how they differ from other nearby regions.