REAL EFFECTIVE EXCHANGE RATE AND UNEMPLOYMENT RATE: THE DIFFERENCE BETWEEN RE-EXPORTING AND NON-RE-EXPORTING COUNTRIES

In this paper, I examine the relationship between unemployment rate and real effective exchange rate in several countries from 1994 to 2009. The analysis shows that most of countries have a negative relationship between those two factors, which implies increase of exchange rate can improve employment rate in an economy. However, re-exporting countries like the Netherlands, Singapore and Hong Kong has a less negative relationship than other countries. I hypothesize that those results are caused by the different elasticity of demand for imports

Non-local Neural Networks

Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our non-local models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code is available at https://github.com/facebookresearch/video-nonlocal-net .Comment: CVPR 2018, code is available at: https://github.com/facebookresearch/video-nonlocal-ne

Quantum Noise of Kramers-Kronig Receiver

Abstrac--Kramers-Kronig (KK) receiver, which is equivalent to heterodyne detection with one single photodetector, provides an efficient method to reconstruct the complex-valued optical field by means of intensity detection given a minimum-phase signal. In this paper, quantum noise of the KK receiver is derived analytically and compared with that of the balanced heterodyne detection. We show that the quantum noise of the KK receiver keeps the radical fluctuation of the measured signal the same as that of the balanced heterodyne detection, while compressing the tangential noise to 1/3 times the radical one using the information provided by the Hilbert transform. In consequence, the KK receiver has 3/2 times the signal-to-noise ratio of balanced heterodyne detection while presenting an asymmetric distribution of fluctuations, which is also different from that of the latter. More interestingly, the projected in-phase and quadrature field operators of the retrieved signal after down conversion have a time dependent quantum noise distribution depending on the time-varying phase. This property provides a feasible scheme for controlling the fluctuation distribution according to the requirements of measurement accuracy in the specific direction. Under the condition of strong carrier wave, the fluctuations of the component requiring to be measured more accurately can be compressed to 1 / 6, which is even lower than 1/4 by measuring a coherent state. Finally, we prove the analytic conclusions by simulation results

Microstructural analysis of skeletal muscle force generation during aging.

Human aging results in a progressive decline in the active force generation capability of skeletal muscle. While many factors related to the changes of morphological and structural properties in muscle fibers and the extracellular matrix (ECM) have been considered as possible reasons for causing age-related force reduction, it is still not fully understood why the decrease in force generation under eccentric contraction (lengthening) is much less than that under concentric contraction (shortening). Biomechanically, it was observed that connective tissues (endomysium) stiffen as ages, and the volume ratio of connective tissues exhibits an age-related increase. However, limited skeletal muscle models take into account the microstructural characteristics as well as the volume fraction of tissue material. This study aims to provide a numerical investigation in which the muscle fibers and the ECM are explicitly represented to allow quantitative assessment of the age-related force reduction mechanism. To this end, a fiber-level honeycomb-like microstructure is constructed and modeled by a pixel-based Reproducing Kernel Particle Method (RKPM), which allows modeling of smooth transition in biomaterial properties across material interfaces. The numerical investigation reveals that the increased stiffness of the passive materials of muscle tissue reduces the force generation capability under concentric contraction while maintains the force generation capability under eccentric contraction. The proposed RKPM microscopic model provides effective means for the cellular-scale numerical investigation of skeletal muscle physiology. NOVELTY STATEMENT: A cellular-scale honeycomb-like microstructural muscle model constructed from a histological cross-sectional image of muscle is employed to study the causal relations between age-associated microstructural changes and age-related force loss using Reproducing Kernel Particle Method (RKPM). The employed RKPM offers an effective means for modeling biological materials based on pixel points in the medical images and allow modeling of smooth transition in the material properties across interfaces. The proposed microstructure-informed muscle model enables quantitative evaluation on how cellular-scale compositions contribute to muscle functionality and explain differences in age-related force changes during concentric, isometric and eccentric contractions

FoVR: Attention-based VR Streaming through Bandwidth-limited Wireless Networks

Consumer Virtual Reality (VR) has been widely used in various application areas, such as entertainment and medicine. In spite of the superb immersion experience, to enable high-quality VR on untethered mobile devices remains an extremely challenging task. The high bandwidth demands of VR streaming generally overburden a conventional wireless connection, which affects the user experience and in turn limits the usability of VR in practice. In this paper, we propose FoVR, attention-based hierarchical VR streaming through bandwidth-limited wireless networks. The design of FoVR stems from the insight that human's vision is hierarchical, so that different areas in the field of view (FoV) can be served with VR content of different qualities. By exploiting the gaze tracking capacity of the VR devices, FoVR is able to accurately predict the user's attention so that the streaming of hierarchical VR can be appropriately scheduled. In this way, FoVR significantly reduces the bandwidth cost and computing cost while keeping high quality of user experience. We implement FoVR on a commercial VR device and evaluate its performance in various scenarios. The experiment results show that FoVR reduces the bandwidth cost by 88.9% and 76.2%, respectively compared to the original VR streaming and the state-of-the-art approach

Large Steklov eigenvalues on hyperbolic surfaces

In this paper, we first construct a sequence of hyperbolic surfaces with connected geodesic boundary such that the first normalized Steklov eigenvalue $\tilde{\sigma}_1$ tends to infinity. We then prove that as $g\rightarrow \infty$, a generic $\Sigma\in \mathcal{M}_{g,n}(L_g)$ satisfies $\tilde{\sigma}_1(\Sigma)>C\cdot \|L_g\|_1$ where $C$ is a positive universal constant. Here $\mathcal{M}_{g,n}(L_g)$ is the moduli space of hyperbolic surfaces of genus $g$ and $n$ boundary components of length $L_g=(L_g^1,\cdots, L_g^n)$ endowed with the Weil-Petersson metric where $\|L_g\|_1\rightarrow\infty$ satisfies certain conditions.Comment: 20pages, new results added, second theorem is improve

Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data

The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area