Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomized Controlled Trials of Pediatric Massage: A Review

The existing reviews of massage therapy (MT) research are either limited to infants, adults, or were conducted prior to the publication of the most recent studies using pediatric samples. Randomized controlled trials (RCTs) of pediatric MT are reviewed. A literature search yielded 24 RCTs of pediatric MT, defined as the manual manipulation of soft tissue intended to promote health and well-being in recipients between 2 and 19 years of age. Because RCTs of pediatric MT varied considerably in the amount and types of data reported, quantitative and narrative review methods were both used. Single-dose and multiple-dose effects were examined separately. Among single-dose effects, significant reductions of state anxiety were observed at the first session (g = 0.59, P < 0.05) and the last session (g = 1.10, P < 0.01) of a course of treatment. Effects for salivary cortisol (g = 0.28), negative mood (g = 0.52) and behavior (g = 0.37) were non-significant. Three of eleven multiple-dose effects were statistically significant. These were trait anxiety (g = 0.94, P < 0.05), muscle tone (g = 0.90, P < 0.01) and arthritis pain (g = 1.33, P < 0.01). Results of studies not permitting effect size calculation were judged to be generally consistent with quantitative results. MT benefits pediatric recipients, though not as universally as sometimes reported. Numerous weaknesses endemic to MT research (e.g. low statistical power, frequent failure to report basic descriptive statistics) are identified, and recommendations for future pediatric MT research are discussed.
Multi-fingered robotic hand employing strings transmission named “Twist Drive” — Video contribution

A goal of our research is to produce a light-weight, low-cost five fingered robotic hand that has similar degrees of freedom as a human hand. The joints in the fingers of the developed robotic hand are powered by a newly proposed strings transmission named “Twist Drive”. The transmission converts torque into a pulling force by using a pair of strings that twist on each other. The basic characteristics of the transmission are given in the paper. A robotic hand prototype with 18 joints of which 14 are independently powered by Twist Drives was produced. The size of the hand is equal to the size of an adult human's hand and its weight including the power circuits is approximately 800 grams. The mechanical and the control systems of the hand are presented in the paper. Fingers position control and simple pinching and grasping tasks by using open loop force control are presented in the video.
Differential Characteristics of Viral siRNAs between Leaves and Roots of Wheat Plants Naturally Infected with Wheat Yellow Mosaic Virus, a Soil-Borne Virus

RNA silencing is an important innate antiviral defense in plants. Soil-borne plant viruses naturally infect roots via soil-inhabiting vectors, but it is unclear how antiviral RNA silencing responds to virus infection in this particular tissue. In this study, viral small interfering RNA (siRNA) profiles from leaves and roots of wheat plants naturally infected with a soil-borne virus, wheat yellow mosaic virus (WYMV, genus Bymovirus), were analyzed by deep sequencing. WYMV siRNAs were much more abundant in roots than leaves, which was positively correlated with the accumulation of viral RNA. WYMV siRNAs in leaves and roots were predominantly 21- and 22-nt long and equally derived from the positive- and negative-strands of the viral genome. WYMV siRNAs from leaves and roots differed in distribution pattern along the viral genome. Interestingly, compared to siRNAs from leaves (and most other reports), those from roots obviously had a lower A/U bias at the 5′-terminal nucleotide. Moreover, the expression of Dicer-like genes upon WYMV infection were differently regulated between leaves and roots. Our data suggest that RNA silencing in roots may operate differently than in leaves against soil-borne virus invasion.
Power flow control and islanding detection of a local generation system with induction generator

In this paper a local generation system (LGS) based on an induction generator (IG) is presented. The IG supplies the active power to the system with the support of a voltage source converter (VSC) for power conditioning. With the LGS connected to the grid through coupling inductors the deficit or the excess of power production of the IG is imported or exported to the grid at unity power factor. In autonomous operation of the LGS a buck converter, bi-directional boost converter and battery set are connected at the DC terminals of the VSC to control the DC bus voltage and supplying power to the local load at constant voltage frequency and amplitude. The LGS propose include an active islanding detection method, battery charge function and a procedure to reconnect the LGS with the grid after the voltage is reestablished.
Gaussian Interference Channel Capacity to Within One Bit

The capacity of the two-user Gaussian interference channel has been open for 30 years. The understanding on this problem has been limited. The best known achievable region is due to Han and Kobayashi but its characterization is very complicated. It is also not known how tight the existing outer bounds are. In this work, we show that the existing outer bounds can in fact be arbitrarily loose in some parameter ranges, and by deriving new outer bounds, we show that a very simple and explicit Han-Kobayashi type scheme can achieve to within a single bit per second per hertz (bit/s/Hz) of the capacity for all values of the channel parameters. We also show that the scheme is asymptotically optimal at certain high signal-to-noise ratio (SNR) regimes. Using our results, we provide a natural generalization of the point-to-point classical notion of degrees of freedom to interference-limited scenarios.
Joint Species Distribution Modeling: Dimension Reduction Using Dirichlet Processes

Species distribution models are used to evaluate the variables that affect the distribution and abundance of species and to predict biodiversity. Historically, such models have been fitted to each species independently. While independent models can provide useful information regarding distribution and abundance, they ignore the fact that, after accounting for environmental covariates, residual interspecies dependence persists. With stacking of individual models, misleading behaviors, may arise. In particular, individual models often imply too many species per location. Recently developed joint species distribution models have application to presence–absence, continuous or discrete abundance, abundance with large numbers of zeros, and discrete, ordinal, and compositional data. Here, we deal with the challenge of joint modeling for a large number of species. To appreciate the challenge in the simplest way, with just presence/absence (binary) response and say, S species, we have an S-way contingency table with 2 cell probabilities. Even if S is as small as 100 this is an enormous table, infeasible to work with without some structure to reduce dimension. We develop a computationally feasible approach to accommodate a large number of species (say order 10) that allows us to: 1) assess the dependence structure across species; 2) identify clusters of species that have similar dependence patterns; and 3) jointly predict species distributions. To do so, we build hierarchical models capturing dependence between species at the first or “data” stage rather than at a second or “mean” stage. We employ the Dirichlet process for clustering in a novel way to reduce dimension in the joint covariance structure. This last step makes computation tractable. We use Forest Inventory Analysis (FIA) data in the eastern region of the United States to demonstrate our method. It consists of presence–absence measurements for 112 tree species, observed east of the Mississippi. As a proof of concept for our dimension reduction approach, we also include simulations using continuous and binary data. ∗Postdoctoral Associate, SAMSI/Duke University, Research Triangle Park, NC 27709, taylor-rodriguez@samsi.info †Postdoctoral Researcher, SAMSI/North Carolina State University, Research Triangle Park, NC 27709, kkaufeld@samsi.info ‡Assistant Professor, Department of Statistics, University of Missouri, Columbia, MO 65211, schliepe@missouri.edu §Professor, Nicholas School of the Environment, Department of Statistical Science, Duke University, Durham, NC 27708, jimclark@duke.edu ¶Professor, Department of Statistical Science, Duke University, Durham, NC 27708, alan@stat.duke.edu c © 2017 International Society for Bayesian Analysis DOI: 10.1214/16-BA1031 940 Joint Species Distribution Modeling with Dirichlet Processes
Comparative Study of PMSG Controllers for Variable Wind Turbine Power Optimization

With a large increase in wind power generation, the direct driven Permanent Magnet Synchronous Generator is the most promising technology for variable speed operation and it also fulfills the grid requirements with high efficiency. This paper studies and compares conventional based on PI controller and proposed control technique for a direct driven PMSG wind turbine. The generator model is established in the Park synchronous rotating d-q reference frame. To achieve maximum power capture, the aeroturbine is controlled through Maximum Power Point Tracking (MPPT) while the PMSG control is treated through field orientation where the two currents control loops are regulated. A proposed direct-current based d-q vector control design is designed by the integration of the Internal Model Controller. Then an optimal control is developed for integrated control of PMSG power optimization and Voltage Source Converter control. The design system was done using SimWindFarm Matlab/Simulink toolbox to evaluate the performance of conventional and proposed technique control of PMSG wind turbine. The analysis, simulation results prove the effectiveness and robustness of the proposed control strategy.
Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches

This paper reports on how intelligent Greedy-Dual approaches based on supervised machine learning were used to improve the web proxy caching performance. The proposed intelligent Greedy-Dual approaches predict the significant web objects’ demand for web proxy caching using Naive Bayes (NB), decision tree (C4.5), or support vector machine (SVM) classifiers. Accordingly, the proposed intelligent Greedy-Dual approaches effectively make the cache replacement decision based on the trained classifiers. The trace-driven simulation results indicated that in terms of byte hit ratio and/or hit ratio, the performance of each of the conventional Greedy-Dual-Size-Frequency (GDSF) and Greedy-Dual-Size (GDS) was noticeably enhanced by applying the proposed Greedy-Dual approaches on five real datasets.
Fast End-to-End Trainable Guided Filter Supplementary Material

L0 smoothing. L0 smoothing [18] is effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures. Such an operator makes use of L0 gradient minimization, which can identify the most important edges by penalizing the number of non-zero gradients in the image. For generating the ground truth images, we use the official implementation of [18] with the default parameters, which could be downloaded from http://www.cse. cuhk.edu. hk/ ̃leojia/ projects/L0smoothing.
Time-Resolved Light Transport Decomposition for Thermal Photometric Stereo

We present a novel time-resolved light transport decomposition method using thermal imaging. Because the speed of heat propagation is much slower than the speed of light propagation, transient transport of far infrared light can be observed at a video frame rate. A key observation is that the thermal image looks similar to the visible light image in an appropriately controlled environment. This implies that conventional computer vision techniques can be straightforwardly applied to the thermal image. We show that the diffuse component in the thermal image can be separated and, therefore, the surface normals of objects can be estimated by the Lambertian photometric stereo. The effectiveness of our method is evaluated by conducting real-world experiments, and its applicability to black body, transparent, and translucent objects is shown.
Attribute Augmented Convolutional Neural Network for Face Hallucination

Though existing face hallucination methods achieve great performance on the global region evaluation, most of them cannot recover local attributes accurately, especially when super-resolving a very low-resolution face image from 14 × 12 pixels to its 8 × larger one. In this paper, we propose a brand new Attribute Augmented Convolutional Neural Network (AACNN) to assist face hallucination by exploiting facial attributes. The goal is to augment face hallucination, particularly the local regions, with informative attribute description. More specifically, our method fuses the advantages of both image domain and attribute domain, which significantly assists facial attributes recovery. Extensive experiments demonstrate that our proposed method achieves superior visual quality of hallucination on both local region and global region against the state-of-the-art methods. In addition, our AACNN still improves the performance of hallucination adaptively with partial attribute input.
Kernelized Subspace Pooling for Deep Local Descriptors

Representing local image patches in an invariant and discriminative manner is an active research topic in computer vision. It has recently been demonstrated that local feature learning based on deep Convolutional Neural Network (CNN) can significantly improve the matching performance. Previous works on learning such descriptors have focused on developing various loss functions, regularizations and data mining strategies to learn discriminative CNN representations. Such methods, however, have little analysis on how to increase geometric invariance of their generated descriptors. In this paper, we propose a descriptor that has both highly invariant and discriminative power. The abilities come from a novel pooling method, dubbed Subspace Pooling (SP) which is invariant to a range of geometric deformations. To further increase the discriminative power of our descriptor, we propose a simple distance kernel integrated to the marginal triplet loss that helps to focus on hard examples in CNN training. Finally, we show that by combining SP with the projection distance metric [13], the generated feature descriptor is equivalent to that of the Bilinear CNN model [22], but outperforms the latter with much lower memory and computation consumptions. The proposed method is simple, easy to understand and achieves good performance. Experimental results on several patch matching benchmarks show that our method outperforms the state-of-the-arts significantly.
Deeply Learned Filter Response Functions for Hyperspectral Reconstruction

Hyperspectral reconstruction from RGB imaging has recently achieved significant progress via sparse coding and deep learning. However, a largely ignored fact is that existing RGB cameras are tuned to mimic human trichromatic perception, thus their spectral responses are not necessarily optimal for hyperspectral reconstruction. In this paper, rather than use RGB spectral responses, we simultaneously learn optimized camera spectral response functions (to be implemented in hardware) and a mapping for spectral reconstruction by using an end-to-end network. Our core idea is that since camera spectral filters act in effect like the convolution layer, their response functions could be optimized by training standard neural networks. We propose two types of designed filters: a three-chip setup without spatial mosaicing and a single-chip setup with a Bayer-style 2x2 filter array. Numerical simulations verify the advantages of deeply learned spectral responses compared to existing RGB cameras. More interestingly, by considering physical restrictions in the design process, we are able to realize the deeply learned spectral response functions by using modern film filter production technologies, and thus construct data-inspired multispectral cameras for snapshot hyperspectral imaging.
Interpreting New England subduction complex rocks using deep-sea drilling results from the Nankai Trough (offshore southwest Japan)

Two distinct structural styles are considered likely to have affected parts of the subduction complex of the New England Orogen. Imbricate thrusting, as occurs by duplexing at a ramp along the decollement at the base of the subduction complex, accounts for the structure of units such as the Gundahl Complex (also the Wandilla Formation of central Queensland and the Sandon beds of the Armidale district). These units have relatively straight-forward imbricate structure although in the case of the Gundahl Complex the thickness of units is condensed and reflects factors controlling deposition on the ocean plate and in the trench. In comparison units such as the Coramba beds contain excessively thick sections of turbidite wedge deposits that only seem explicable by complicated internal imbricate thrust repetition perhaps requiring earlier thrusts being cut by out-of-sequence thrusts. Much more age control than presently exists is required to improve our understanding of the sedimentary and structural processes that have developed these units. Deep-sea drilling and study of modern subduction zones, such as the Nankai Trough, provides an essential starting point for the interpretation of ancient subduction complexes.
Persistent Memory Residual Network for Single Image Super Resolution

Progresses has been witnessed in single image superresolution in which the low-resolution images are simulated by bicubic downsampling. However, for the complex image degradation in the wild such as downsampling, blurring, noises, and geometric deformation, the existing superresolution methods do not work well. Inspired by a persistent memory network which has been proven to be effective in image restoration, we implement the core idea of human memory on the deep residual convolutional neural network. Two types of memory blocks are designed for the NTIRE2018 challenge. We embed the two types of memory blocks in the framework of enhanced super resolution network (EDSR), which is the NTIRE2017 champion method. The residual blocks of EDSR is replaced by two types of memory blocks. The first type of memory block is a residual module, and one memory block contains four residual modules with four residual blocks followed by a gate unit, which adaptively selects the features needed to store. The second type of memory block is a residual dilated convolutional block, which contains seven dilated convolution layers linked to a gate unit. The two proposed models not only improve the super-resolution performance but also mitigate the image degradation of noises and blurring. Experimental results on the DIV2K dataset demonstrate our models achieve better performance than EDSR.
On optimal channel training for uplink network MIMO systems

We study a multi-cell frequency-selective fading uplink channel from K user terminals (UTs) to B base stations (BSs). The BSs, assumed to be oblivious of the applied encoding scheme, compress and forward their observations to a central station (CS) via capacity limited backhaul links. The CS jointly decodes the messages from all UTs. Since we assume no prior channel state information, the channel needs to be estimated during its coherence time. Based on a lower bound of the ergodic mutual information, we determine the optimal fraction of the coherence time used for channel training. We then study how the optimal training length is impacted by the backhaul capacity. Our analysis is based on large random matrix theory but shown by simulations to be tight for even small system dimensions.
Multilevel multiphase space vector PWM algorithm with switching state redundancy applied to three-phase converters

This paper presents the particularization of a recent multilevel multiphase space vector pulse-width modulation algorithm for three-phase converters. This modulation technique takes advantage of the switching state redundancy, which permits to achieve different goals like extending the modulation index range and reducing the number of switchings. The particularized algorithm is then compared with an existing multilevel space vector modulation technique showing many similarities. Finally, the algorithm is implemented in a low cost FPGA and it is tested in laboratory with a real prototype by using a neutral point clamped inverter.
Coordinated path following control for a group of car-like vehicles

This paper presents a new control law for the problem of coordinated path following. The paper deals with a group of car like vehicles, where each vehicle is represented by the bicycle kinematic model. A nonlinear control law has been designed by the Lyapunov approach. The Lyapunov function includes the position errors of all vehicles with respect to the desired paths, and the coordination errors that describe deviations from the reference geometric formation. The time derivative of the path parameter is utilized as an additional control input that synchronizes between vehicles. The main contribution of this paper stems from the inclusion of the bicycle kinematic model in the development (and not only the unicycle model as in earlier works) that is more complex due to its two nonholonomic constraints. The stability of the coordinated path following controller is proven and its effectiveness is demonstrated using numerical simulations.
Design and Performance Comparison of Fractional Slot Concentrated Winding Spoke Type Synchronous Motors With Different Slot-Pole Combinations

Fractional slot concentrated winding (FSCW) interior permanent magnet (IPM) synchronous motors are nowadays an attractive solution in automotive applications due to their advantages in terms of high performance and manufacturing simplicity. Among the different topologies, the spoke type is an effective configuration when high torque density is required. On the other hand, the motor performance are heavily related to the selected slot-pole combination. This choice is critical in demanding automotive applications, such as electric power steering (EPS) systems, where high constraints in terms of torque density, torque quality, flux weakening performance, and noise/vibration/harshness are requested. This paper deals with the design and analysis of eight optimal slot/pole combinations FSCW IPM spoke type EPS motors. Two different low cost heavy rare earths free PM typologies, ferrite and hot-pressed NdFeB, are considered. A design and optimization procedure based upon the finite element analysis is presented. The impact of the magnetic properties and the slot/pole combination on the optimal motor dimensions, torque density, and motor cost is discussed. Cogging torque, torque ripple, electromechanical performance, and the stator deformation of the machines are evaluated, and the results are compared, highlighting the advantages and the differences among the solutions.
Research on Counting Method of Bus Passenger Flow Based on Kinematics of Human Body and SVM

According to the current research on the bus passengers flow counting, this paper proposes a new counting method of bus passenger flow based on kinematics of human body and SVM. This method solves the problem that low cost device canpsilat count the number of passengers accurately and canpsilat distinguish the direction of passengerspsila movement. Finally, this paper proves the feasibility and effectivity of the new method through experimental results.