Multitasking Correlation Network for Depth Information Reconstruction

In this paper, we propose a novel multi-tasking network for stereo matching. The proposed network is trained to approximate similarity functions in statistics and linear algebra such as correlation coefficient, distance correlation and cosine similarity. By doing this, the proposed method decreases the amount of time needed to calculate the disparity map by using CNN's ability to calculate multiple pairs of image patches at the same time. We then compare the execution time and overall accuracy between the traditional method using functions and our method. The results show the model's ability to mimic the traditional method's performance while taking considerably less time to perform the task

A DOUBLE-SHRINK AUTOENCODER FOR NETWORK ANOMALY DETECTION

The rapid development of the Internet and the wide spread of its applications has affected many aspects of our life. However, this development also makes the cyberspace more vulnerable to various attacks. Thus, detecting and preventing these attacks are crucial for the next development of the Internet and its services. Recently, machine learning methods have been widely adopted in detecting network attacks. Among many machine learning methods, AutoEncoders (AEs) are known as the state-of-the-art techniques for network anomaly detection. Although, AEs have been successfully applied to detect many types of attacks, it is often unable to detect some difficult attacks that attempt to mimic the normal network traffic. In order to handle this issue, we propose a new model based on AutoEncoder called Double-Shrink AutoEncoder (DSAE). DSAE put more shrinkage on the normal data in the middle hidden layer. This helps to pull out some anomalies that are very similar to normal data. DSAE are evaluated on six well-known network attacks datasets. The experimental results show that our model performs competitively to the state-of-the-art model, and often out-performs this model on the attacks group that is difficult for the previous methods

Simulation Study of Mid-infrared Supercontinuum Generation at Normal Dispersion Regime in Chalcogenide Suspended-core Fiber Infiltrated with Water

We report simulation results of supercontinuum generation in the suspended-core optical fibers made of chalcogenide (As2S3) infiltrated with water at mid-infrared wavelength range. Applying water-hole instead of the air-hole in fibers allows improving the dispersion characteristics, hence, contributing to supercontinuum generations. As a result, the broadband supercontinuum generation ranging from 1177 nm to 2629 nm was achieved in a 10 cm fiber by utilizing very low input pulse energy of 0.01 nJ and pulse duration of 100 fs at 1920 nm wavelength

MULTI-PIXEL PHOTON COUNTER FOR OPERATING A TABLETOP COSMIC RAY DETECTOR UNDER LOOSELY CONTROLLED CONDITIONS

The multi-pixel photon counter (MPPC) has recently emerged as a great type of silicon photomultiplier to replace or compensate for conventional vacuum-based photomultiplier tubes. An MPPC provides many advantageous features, such as high electrical gain, outstanding photon detection efficiency, fast timing response, immunity to magnetic fields, low-voltage operation, compactness, portability, and cost-effectiveness. This article examines the electrical and optical characteristics of an MPPC under loosely controlled environmental conditions. We also report a measurement of the light yield captured by the MPPC when a cosmic ray passes through the plastic scintillator, demonstrating that such a setup is suitable as a simple, cost-effective tabletop cosmic ray detector for educational and research purposes

Wigner-Seitz cells in neutron star crust with finite range interactions

The structure of Wigner-Seitz cells in the inner crust of neutron stars is investigated using a microcospic Hartree-Fock-BCS approach with finite range D1S and M3Y-P4 interactions. Large effects on the densities are found compared to previous predictions using Skyrme interactions. Pairing effects are found to be small, and they are attenuated by the use of finite range interactions in the mean field.Comment: 11 pages, 5 figure

The Influence of the Self-focusing Effect on the Optical Force Acting on Dielectric Particle Embedded in Kerr Medium

The influence of the self-focusing effect arised from Kerr effect on the optical force acting on the dielecric particle embedded in the Kerr medium, which is irradiated by the Gaussian beam, is proposed to concern. The expressions of the optical forces with the nonlinear refractive index and nonlinear focal length are derived. Using them, the distribution of the optical forces in the trapping region of the optical tweezer is simulated and discussed for same distinguished case of the Kerr medium with different nonlinear coefficients. The results show that the stabe region of the optical tweezer depends on the nonlinear coefficient of refractive index. Moreover, the stable region could be brokendown with a critical value of the nonlinear coefficient of refractive index of the surrounding medium irradiated by Gaussian laser pulse described by given parameters as  intensity, duration and radius of beam waist.

Sand Spit Morphology at an Inlet on Phu Quoc Island, Vietnam

Tidal inlets with attached sand spits are a very common coastal landform. Since the evolution of sand spits along coastlines influence the social-economic development of local coastal areas, sand spits have become the objects of numerous studies. However, previous studies have mainly focused on sand spits that are usually in the scale of hundreds of meters in width, whilst knowledge about the evolution of smaller-scale sand spits still remains limited. Therefore, in this study, the morphological change of a small and unexplored sand spit in front of Song Tranh Inlet on the west coast of Phu Quoc Island, Vietnam is investigated. Satellite images are first used to observe the morphological change of the sand spit and calculate the longshore sediment transport rates (LSTR) along the sand spit. Waves and beach sediments are collected at the study site to calculate the longshore sediment transport rate using the CERC formula. It is found that there is a seasonal variation in the evolution of the sand spit at Song Tranh Inlet. The longshore sediment transport rates along the spit calculated by image analysis are 39,000 m$^3$/year, 66,000 m$^3$/year, and 40,000 m$^3$/year, whilst the longshore sediment transport rate calculated by the CERC formula is 72,000 m$^3$/year. This study aims to contribute to the methodology for investigating the evolutions of small sand spits and, specifically, sustainable coastal management for Phu Quoc Island, which is well-known as the Pearl Island of Vietnam

On the Impact of Dataset Size: A Twitter Classification Case Study

The recent advent and evolution of deep learning models and pre-trained embedding techniques have created a breakthrough in supervised learning. Typically, we expect that adding more labeled data improves the predictive performance of supervised models. On the other hand, collecting more labeled data is not an easy task due to several difficulties, such as manual labor costs, data privacy, and computational constraint. Hence, a comprehensive study on the relation between training set size and the classification performance of different methods could be essentially useful in the selection of a learning model for a specific task. However, the literature lacks such a thorough and systematic study. In this paper, we concentrate on this relationship in the context of short, noisy texts from Twitter. We design a systematic mechanism to comprehensively observe the performance improvement of supervised learning models with the increase of data sizes on three well-known Twitter tasks: sentiment analysis, informativeness detection, and information relevance. Besides, we study how significantly better the recent deep learning models are compared to traditional machine learning approaches in the case of various data sizes. Our extensive experiments show (a) recent pre-trained models have overcome big data requirements, (b) a good choice of text representation has more impact than adding more data, and (c) adding more data is not always beneficial in supervised learning

Contribution of forest to rural households’ livelihood: evidences from Da river basin in the northwest mountainous region of Vietnam

This paper examined how forest has contributed to rural households’ livelihood in Da river basin, the northwest mountainous region of Vietnam. The results revealed that forest predominantly contributes to the total income of rural residents in the region. Specifically, forestry land area, access to non-timber forest products, and payment for forest environmental services significantly affected household’s income in the region. However, rural people in the region have still faced several difficulties that constrain household’s livelihood. Of these difficulties, lack of financial capital, epidemic diseases in animal husbandry, limited access to market information and natural disaster are popular barriers to livelihood of people in the region. This paper also recommended several policies to improve rural livelihood in Da river basin. These includes: (i) integrating issues regarding payment for forest environmental services and REDD+ into socioeconomic development plan; (ii) improving awareness of local people on sustainable natural capital use through ecosystem conservation policy; (iii) providing preferential credit and training on agricultural production techniques; and (iv) encouraging market-oriented agriculture.