Assessing Counter-Terrorism field training with multiple behavioral measures

Development of behavioral pattern recognition and analysis skills is an essential element of Counter-Terrorism training, particularly in the field. Three classes of behavioral measures were collected in an assessment of skill acquisition during a US Joint Forces Command-sponsored course consisting of Combat Tracking and Combat Profiling segments. Measures included situational judgment tests, structured behavioral observation checklists, and qualitative assessments of the emergence of specific knowledge–skills–attitudes over the course of the training. The paper describes statistical evidence across the three types of measures that indicate that behavioral pattern recognition and analysis skills were successfully acquired by most students (a mix of Army and civilian law enforcement personnel) during the field training exercises. Implications for broader training of these critical skills are also discussed.     

Assessing accessibility with local coefficients for the LUTI model MARS

Accessibility-based land use and transport interaction (LUTI) models are tools for policy assessments that facilitate coherent implementation of sustainable strategic urban plans. This study aims to improve one of those LUTI models introducing the different impact of factors influencing residential and workplace choice by computing local coefficients. In particular, this research explores the methodology of integrating the public choice model into the MARS (Metropolitan Activity Relocation Simulator) model using a complex accessibility indicator. We established a new approach to input the variation of the influence of each public service across space with the use of Geographically Weighted Regression (GWR). The model update and extension of MARS are all based on the Region of Madrid, Spain. Using the new accessibility indicator yielded better results and corrected some under estimation and overestimations in the number of workplaces. The correlation using the new accessibility indicator is significantly higher than the one using the old one. The prediction using the new indicator achieves better results for the whole area whatever the zone is small or large. The analysis evidences the convenience of GIS and LUTI combination to improve model accuracy and precision. Using the new accessibility indicator based on local coefficients, MARS model fits better with the real data in respect of the distribution of workplaces and residents, which are the key representatives of the land use sub-model.     

融合全局与局部视角的光场超分辨率重建

针对光场相机结构和像素传感器分辨率的限制导致光场图像空间分辨率和角度分辨率都较低的问题,提出一种融合全局与局部视角的光场超分辨率重建算法,同时提高光场图像的空间分辨率和角度分辨率。首先根据待重建新视角的位置,自适应选择局部视角,利用空间超分辨率卷积神经网络提高全局视角和局部视角的空间分辨率,然后提取并融合全局视角和局部视角在新视角处映射图像的深度特征和颜色特征,通过角度分辨率卷积神经网络重建获得新视角图像。实验结果表明,与现有方法相比,峰值信噪比(PSNR)提高约3 dB,结构相似性指数(SSIM)提高约0.02,有效地解决了遮挡情况下重建新视角局部目标丢失现象,同时更好地保持新视角的边缘信息,获得更优的重建效果。     

On coherence, random-self-reducibility, and self-correction

We study three types of self‐reducibility that are motivated by the theory of program verification. A set A is random‐self‐reducible if one can determine whether an input x is in A by making random queries to an A‐oracle. The distribution of each query may depend only on the length of x. A set B is self‐correctable over a distribution if one can convert a program that is correct on most of the probability mass of to a probabilistic program that is correct everywhere. A set C is coherent if one can determine whether an input x is in C by asking questions to an oracle for C–{x}.?We first show that adaptive coherence is more powerful than nonadaptive coherence, even if the nonadaptive querier is nonuniform. Blum et al.(1993) showed that every random‐self‐reducible function is self‐correctable. It is unknown, however, whether self‐correctability implies random‐self‐reducibility. We show, assuming a reasonable complexity‐theoretic hypothesis, that certain hard, sparse, tally sets exist, and that there is a self‐correctable function which is not random‐self‐reducible. For easily samplable distributions, however, we show that constructing a self‐correctable function that is not random‐self‐reducible is as hard as proving that P is different from PP. Received: 14 June, 1996     

Dispersion in electroosmotic flow generated by oscillatory electric field interacting with oscillatory wall potentials

An analytical study is presented in this article on the dispersion of a neutral solute released in an oscillatory electroosmotic flow (EOF) through a two-dimensional microchannel. The flow is driven by the nonlinear interaction between oscillatory axial electric field and oscillatory wall potentials. These fields have the same oscillation frequency, but with disparate phases. An asymptotic method of averaging is employed to derive the analytical expressions for the steady-flow-induced and oscillatory-flow-induced components of the dispersion coefficient. Dispersion coefficients are functions of various parameters representing the effects of electric double-layer thickness (Debye length), oscillation parameter, and phases of the oscillating fields. The time–harmonic interaction between the wall potentials and electric field generates steady as well as time-oscillatory components of electroosmotic flow, each of which will contribute to a steady component of the dispersion coefficient. It is found that, for a thin electric double layer, the phases of the oscillating wall potentials will play an important role in determining the magnitude of the dispersion coefficient. When both phases are zero (i.e., full synchronization of the wall potentials with the electric field), the flow is nearly a plug flow leading to very small dispersion. When one phase is zero and the other phase is π,?the flow will be sheared to the largest possible extent at the center of the channel, and such a sharp velocity gradient will lead to the maximum possible dispersion coefficient.     

An integrated path-planning and control approach for nonholonomic unicycles using switched local potentials

In this paper, navigation and control of an autonomous mobile unicycle robot in an obstacle-ridden environment is considered. The unicycle dynamic model used has two differentially driven wheels, with the motor torques as the system input. Two novel potential-field-based controllers are derived, which stabilize the robot within a surrounding circular area (henceforth called a bubble) of arbitrary size. The first controller takes the unicycle to the center of its bubble, while the second corrects its orientation. The designed potentials also work with a kinematic model. Explicit bounds for permissible initial speeds are derived, such that maximum torque limits and/or maximum speed limits are not violated once the controller is activated. These controllers are then embedded in a navigation framework. An existing global planner is used to first create a string of variable-sized bubbles which connect the start point to the goal point, with each bubble's size indicative of the radial obstacle clearance available from its center. The robot then keeps itself within a fixed-sized bubble, which it then moves in discrete steps, according to the direction provided by the global plan, while repulsively avoiding unexpected obstacles. Hence, the gross movement is created by switching local potential-field-based controllers. This scheme is first verified in computer simulation of a single robot moving in a maze. It is then implemented on an experimental setup of robots equipped with proximity sensors. Results are presented to illustrate the effectiveness of the system.     

Extraction of microsaccade-related signal from single-trial local field potential by ICA with reference

During visual fixation, we unconsciously make tiny, involuntary eye movements or ‘microsaccades’, which have been shown to have a crucial influence on analysis and perception of our visual environment. Given the small size and high irregularity of microsaccades, it is a significant challenge to detect and extract the microsaccade-related neural activities. In this work, we present a novel application of the independent component analysis with reference algorithm to extract microsaccade-related neural activity from single-trial local field potential (LFP). We showed via extensive computer simulations that our approach can be used to reliably extract microsaccade-related activity. We then applied our method to real cortical LFP data collected from multiple visual areas of monkeys performing a generalized flash suppression task and demonstrated that our approach has excellent performance in extracting microsaccade-related signal from single-trial LFP data.     

Synchronization, coherence, and event ordering in multiprocessors

The problems addressed apply to both throughput-oriented and speedup-oriented multiprocessor systems, either at the user level or the operating-system level. basic definitions are provided. Communication and synchronization are briefly explained, and hardware-level and software-level synchronization mechanisms are discussed. The cache coherence problem is examined, and solutions are described. Strong and weak ordering of events is considered. The user interface is discussed.<>     

Video dehazing with spatial and temporal coherence

This paper describes a new framework for video dehazing, the process of restoring the visibility of the videos taken under foggy scenes. The framework builds upon techniques in single image dehazing, optical flow estimation and Markov random field. It aims at improving the temporal and spatial coherence of the dehazed video. In this framework, we first extract the transmission map frame-by-frame using guided filter, then estimate the forward and backward optical flow between two neighboring frames to find the matched pixels. The flow fields are used to help us building an MRF model on the transmission map to improve the spatial and temporal coherence of the transmission. The proposed algorithm is verified in both real and synthetic videos. The results demonstrate that our algorithm can preserve the spatial and temporal coherence well. With more coherent transmission map, we get better refocusing effect. We also apply our framework on improving the video coherence on the application of video denoising.     

Elimination of end effects in local mean decomposition using spectral coherence and applications for rotating machinery

Local mean decomposition (LMD) is widely used in signal processing and fault diagnosis of rotating machinery as an adaptive signal processing method. It is developed from the popular empirical mode decomposition (EMD). Both of them have an open problem of end effects, which influences the performance of the signal decomposition and distort the results. Using the cyclostationary property of a vibration signal generated by rotating machinery, a novel signal waveform extension method is proposed to solve this problem. The method mainly includes three steps: waveform segmentation, spectral coherence comparison, and waveform extension. Its main idea is to automatically search the inside segment having similar frequency spectrum to one end of the analyzed signal, and then use its successive segment to extend the waveform, so that the extended signal can maintain temporal continuity in time domain and spectral coherence in frequency domain. A simulated signal is used to illustrate the proposed extension method and the comparison with the popular mirror extension and neural-network-based extension methods demonstrates its better performance on waveform extension. After that, combining the proposed extension method with normal LMD, the improved LMD method is applied to three experimental vibration signals collected from different rotating machines. The results demonstrate that the proposed waveform extension method based on spectral coherence can well extend the vibration signal, accordingly, errors caused by end effects would not distort the signal as well as its decomposition results.     

Probing quantum coherence,uncertainty, steerability of quantum coherence and quantum phase transition in the spin model

In this paper, we study the relation among quantum coherence, uncertainty, steerability of quantum coherence based on skew information and quantum phase transition in the spin model by employing quantum renormalization-group method. Interestingly, the results show that the value of the local quantum uncertainty is equal to the local quantum coherence corresponding to local observable \(\sigma _z\) in XXZ model, and unlikely in XY model, local quantum uncertainty is minimal optimization of the local quantum coherence over local observable \(\sigma _x\) and this proposition can be generalized to a multipartite system. Therefore, one can directly achieve quantum correlation measured by local quantum uncertainty and coherence by choosing different local observables \(\sigma _x\), \(\sigma _z\), corresponding to the XY model and XXZ model separately. Meanwhile, steerability of quantum coherence in XY and XXZ model is investigated systematically, and our results reveal that no matter what times the QRG iterations are carried out, the quantum coherence of the state of subsystem cannot be steerable, which can also be suitable for block–block steerability of local quantum coherence in both XY and XXZ models. On the other hand, we have illustrated that the quantum coherence and uncertainty measure can efficiently detect the quantum critical points associated with quantum phase transitions after several iterations of the renormalization. Moreover, the nonanalytic and scaling behaviors of steerability of local quantum coherence have been also taken into consideration.     

Random field model for integration of local information and global information

This paper presents a proposal of a general framework that explicitly models local information and global information in a conditional random field. The proposed method extracts global image features as well as local ones and uses them to predict the scene of the input image. Scene-based top-down information is generated based on the predicted scene. It represents a global spatial configuration of labels and category compatibility over an image. Incorporation of the global information helps to resolve local ambiguities and achieves locally and globally consistent image recognition. In spite of the model's simplicity, the proposed method demonstrates good performance in image labeling of two datasets.     

静止和旋转孤立车轮局部流场的评价

为研究车轮周围流场特征,分别对某孤立车轮静止和旋转工况下周围流场进行数值研究,并给予试验验证.计算采用定常雷诺时均纳维斯托克斯方程.在1∶15的模型风洞中进行试验.数值模拟结果与试验数据吻合.针对数值模拟结果,详细分析静止和旋转孤立车轮周围流场的流动情况、表面压力因数、气动阻力因数和气动升力因数等,得到孤立车轮旋转对车轮附近局部流场的影响以及形成机理.车轮的转动使总体压差减小,降低气动阻力和升力,改善气动性能。     

Assessing the discomfort of the whole-body multi-axis vibration: laboratory and field experiments

Laboratory and field experiments were conducted to determine the best procedure for predicting the discomfort caused by multi-axis vibration. In the laboratory experiment, 11 seated subjects compared single-axis vibration in one axis to single-axis vibration in another axis, and compared dual-axis vibration to single-axis vibration. In the field experiment, 22 lorry drivers rated the discomfort of 16 different rides. The results show that the best procedure for predicting the discomfort is to combine the vibration inputs by taking the square root of the sum of squares of the weighted r.m.s. values of the vibration in each axis.     

Assessing the discomfort of the whole-body multi-axis vibration: laboratory and field experiments

Laboratory and field experiments were conducted to determine the best procedure for predicting the discomfort caused by multi-axis vibration. In the laboratory experiment, 11 seated subjects compared single-axis vibration in one axis to single-axis vibration in another axis, and compared dual-axis vibration to single-axis vibration. In the field experiment, 22 lorry drivers rated the discomfort of 16 different rides. The results show that the best procedure for predicting the discomfort is to combine the vibration inputs by taking the square root of the sum of squares of the weighted r.m.s. values of the vibration in each axis.     

Global and local clustering with kNN and local PCA

Multimedia Tools and Applications - This paper proposes a new clustering method that combines the k Near Neighbor (k NN) method and the local Principal Component Analysis (PCA) to consider the...     

解决势场法路径规划中局部极小问题的角度累积法

通过势场法进行路径规划而导致的局部极小问题备受关注.针对该问题,分析机器人陷入局部极小时势力场与速度方向间的关系,提出一种基于运动累积角的避障法.该方法利用机器人行进过程中自身内外角及其累计变化量,分析自身与目标间的方向角度关系,定义了基于角度累积量的“关键重置点”;通过自身位置刷新、累积角度重置,简化了复杂环境,实现在未知环境中的路径规划.仿真实验表明,该方法可实现状态间的转换条件的合理设计、状态间的有序过渡与平稳运行,提高状态变换决策的灵活性与可靠性;与已有相关算法相比,该方法具有规划路程更短、运行效率更高的优点.利用自制的移动机器人实验来验证该方法的避障可行性.上述结果表明:该方法可解决机器人路径规划中的局部最小问题,适用于初次通过未知复杂环境且无需建图的路径规划场景.     

Analysis and neuronal modeling of the nonlinear characteristics of a local cardiac reflex in the rat

Previous experimental results have suggested the existence of a local cardiac reflex in the rat. In this study, the putative role of such a local reflex in cardiovascular regulation is quantitatively analyzed. A model for the local reflex is developed from anatomical experimental results and physiological data in the literature. Using this model, a systems-level analysis is conducted. Simulation results indicate that the neuromodulatory mechanism of the local reflex attenuates the nonlinearity of the relationship between cardiac vagal drive and arterial pressure. This behavior is characterized through coherence analysis. Furthermore, the modulation of phase-related characteristics of the cardiovascular system is suggested as a plausible mechanism for the nonlinear attenuation. Based on these results, it is plausible that the functional role of the local reflex is highly robust nonlinear compensation at the heart, which results in less complex dynamics in other parts of the reflex.