Analytical derivation of the radial distribution function in spherical dark matter halos

The velocity distribution of dark matter near the Earth is important for an accurate analysis of the signals in terrestrial detectors. This distribution is typically extracted from numerical simulations. Here we address the possibility of deriving the velocity distribution function analytically. We derive a differential equation which is a function of radius and the radial component of the velocity. Under various assumptions this can be solved, and we compare the solution with the results from controlled numerical simulations. Our findings complement the previously derived tangential velocity distribution. We hereby demonstrate that the entire distribution function, below 0.7 v_esc, can be derived analytically for spherical and equilibrated dark matter structures.Comment: 6 pages, 5 figures, submitted to MNRA

An Analysis of Chinese English Varieties from the Perspective of Eco-linguistics——A Case Study of Pidgin English

This paper focuses on the nativization of English in China, using Pidgin English as a case study to put Chinese English variants under the theoretical framework of eco-linguistics, and put the ecological environment such as species competition, coexistence and co-evolution, etc. The natural phenomenon is compared with the existence of language phenomenon in the development process of China English represented by Pidgin English.The study found that as the spark of the collision of the two mainstream languages of Chinese and English, the Chinese English varieties play a very important role in the exchange and enrichment of the two languages and cultures. Although academic circles have different attitudes and opinions on Chinese English variants, their existence and development conform to the law of the development of things and are also inevitable in historical development. Blindly ignoring their objective existence will definitely bring adverse effects on the ecological balance of the language. We should face up to the existence of Chinese English variants, comply with the law of language development, and allow it to develop naturally, and make efforts to protect the ecological balance of the world’s languages

Silicon - polymer hybrid integrated microwave photonic devices for optical interconnects and electromagnetic wave detection

textThe accelerating increase in information traffic demands the expansion of optical access network systems that require high-performance optical and photonic components. In short-range communication links, optical interconnects have been widely accepted as a viable approach to solve the problems that copper based electrical interconnects have encountered in keeping up with the surge in the data rate demand over the last decades. Low cost, ease of fabrication, and integration capabilities of low optical-loss polymers make them attractive for integrated photonic applications to support futuristic data communication networks. In addition to passive wave-guiding components, electro-optic (EO) polymers consisting of a polymeric matrix doped with organic nonlinear chromophores have enabled wide-RF-bandwidth and low-power active photonic devices. Beside board level passive and active optical components, on-chip micro- or nano-photonic devices have been made possible by the hybrid integration of EO polymers onto the silicon platform. In recent years, silicon photonics have attracted a significant amount of attentions, because it offers compact device size and the potential of complementary metal–oxide–semiconductor (CMOS) compatible photonic integrated circuits. The combination of silicon photonics and EO polymers can enable miniaturized and high-performance hybrid integrated photonic devices, such as electro-optic modulators, optical interconnects, and microwave photonic sensors. Silicon photonic crystal waveguides (PCWs) exhibit slow-light effects which are beneficial for device miniaturization. Especially, EO polymer filled silicon slotted PCWs further reduce the device size and enhance the device performance by combining the best of these two systems. The potential applications of these silicon-polymer hybrid integrated devices include not only optical interconnects, but also optical sensing and microwave photonics. In this dissertation, the design, fabrication, and characterization of several types of silicon-polymer hybrid photonic devices will be presented, including EO polymer filled silicon PCW modulators for on-chip optical interconnects, antenna-coupled optical modulators for electromagnetic wave detections, and low-loss strip-to-slot PCW mode converters. In addition, some polymer-based devices and silicon-based photonic devices will also be presented, such as traveling wave electro-optic polymer modulators based on domain-inversion directional couplers, and silicon thermo-optic switches based on coupled photonic crystal microcavities. Furthermore, some microwave (or RF) components such as integrated broadband bowtie antennas for microwave photonic applications will be covered. Some on-going work or suggested future work will also be introduced, including in-device pyroelectric poling for EO polymer filled silicon slot PCWs, millimeter- or Terahertz-wave sensors based on EO polymer filled plasmonic slot waveguide, low-loss silicon-polymer hybrid slot photonic crystal waveguides fabricated by CMOS foundry, logic devices based on EO polymer microring resonators, and so on.Electrical and Computer Engineerin

Learning Robust Object Recognition Using Composed Scenes from Generative Models

Recurrent feedback connections in the mammalian visual system have been hypothesized to play a role in synthesizing input in the theoretical framework of analysis by synthesis. The comparison of internally synthesized representation with that of the input provides a validation mechanism during perceptual inference and learning. Inspired by these ideas, we proposed that the synthesis machinery can compose new, unobserved images by imagination to train the network itself so as to increase the robustness of the system in novel scenarios. As a proof of concept, we investigated whether images composed by imagination could help an object recognition system to deal with occlusion, which is challenging for the current state-of-the-art deep convolutional neural networks. We fine-tuned a network on images containing objects in various occlusion scenarios, that are imagined or self-generated through a deep generator network. Trained on imagined occluded scenarios under the object persistence constraint, our network discovered more subtle and localized image features that were neglected by the original network for object classification, obtaining better separability of different object classes in the feature space. This leads to significant improvement of object recognition under occlusion for our network relative to the original network trained only on un-occluded images. In addition to providing practical benefits in object recognition under occlusion, this work demonstrates the use of self-generated composition of visual scenes through the synthesis loop, combined with the object persistence constraint, can provide opportunities for neural networks to discover new relevant patterns in the data, and become more flexible in dealing with novel situations.Comment: Accepted by 14th Conference on Computer and Robot Visio

Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis

Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real EEG data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from ten healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs