Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network

Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the current object detection field, which uses fully convolutional neural network to detect all scaled objects in an image. Deconvolutional Single Shot Detector (DSSD) is an approach which introduces more context information by adding the deconvolution module to SSD. And the mean Average Precision (mAP) of DSSD on PASCAL VOC2007 is improved from SSD's 77.5% to 78.6%. Although DSSD obtains higher mAP than SSD by 1.1%, the frames per second (FPS) decreases from 46 to 11.8. In this paper, we propose a single stage end-to-end image detection model called ESSD to overcome this dilemma. Our solution to this problem is to cleverly extend better context information for the shallow layers of the best single stage (e.g. SSD) detectors. Experimental results show that our model can reach 79.4% mAP, which is higher than DSSD and SSD by 0.8 and 1.9 points respectively. Meanwhile, our testing speed is 25 FPS in Titan X GPU which is more than double the original DSSD.Comment: 7 pages, 3 figures, 3 table

YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018

Music Sequence Prediction with Mixture Hidden Markov Models

Recommendation systems that automatically generate personalized music playlists for users have attracted tremendous attention in recent years. Nowadays, most music recommendation systems rely on item-based or user-based collaborative filtering or content-based approaches. In this paper, we propose a novel mixture hidden Markov model (HMM) for music play sequence prediction. We compare the mixture model with state-of-the-art methods and evaluate the predictions quantitatively and qualitatively on a large-scale real-world dataset in a Kaggle competition. Results show that our model significantly outperforms traditional methods as well as other competitors. We conclude by envisioning a next-generation music recommendation system that integrates our model with recent advances in deep learning, computer vision, and speech techniques, and has promising potential in both academia and industry.Comment: Accepted to the 4th International Conference on Artificial Intelligence and Applications (AI 2018

STUDY OF EXISTING MODES OF REDUCTIONS DURING PRODUCTION OF RAILWAY AXLES

Study of existing modes of reductions during production of railway axles

STUDY OF EXISTING MODES OF REDUCTIONS DURING PRODUCTION OF RAILWAY AXLES

Study of existing modes of reductions during production of railway axles

Correcting soft errors online in fast fourier transform

While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage

The biological and clinical significance of emerging SARS-CoV-2 variants.

The past several months have witnessed the emergence of SARS-CoV-2 variants with novel spike protein mutations that are influencing the epidemiological and clinical aspects of the COVID-19 pandemic. These variants can increase rates of virus transmission and/or increase the risk of reinfection and reduce the protection afforded by neutralizing monoclonal antibodies and vaccination. These variants can therefore enable SARS-CoV-2 to continue its spread in the face of rising population immunity while maintaining or increasing its replication fitness. The identification of four rapidly expanding virus lineages since December 2020, designated variants of concern, has ushered in a new stage of the pandemic. The four variants of concern, the Alpha variant (originally identified in the UK), the Beta variant (originally identified in South Africa), the Gamma variant (originally identified in Brazil) and the Delta variant (originally identified in India), share several mutations with one another as well as with an increasing number of other recently identified SARS-CoV-2 variants. Collectively, these SARS-CoV-2 variants complicate the COVID-19 research agenda and necessitate additional avenues of laboratory, epidemiological and clinical research

Understanding the Eastward Shift and Intensification of the ENSO Teleconnection Over South Pacific and Antarctica Under Greenhouse Warming

The Pacific–South America (PSA) teleconnection pattern triggered by the El Niño/Southern Oscillation (ENSO) is suggested to be moving eastward and intensifying under global warming. However, the underlying mechanism is not completely understood. Previous studies have proposed that the movement of the PSA teleconnection pattern is attributable to the eastward shift of the tropical Pacific ENSO-driven rainfall anomalies in response to the projected El Niño-like sea surface temperature (SST) warming pattern. In this study, we found that with uniform warming, models will also simulate an eastward movement of the PSA teleconnection pattern, without the impact of the uneven SST warming pattern. Further investigation reveals that future changes in the climatology of the atmospheric circulation, particularly the movement of the exit region of the subtropical jet stream, can also contribute to the eastward shift of the PSA teleconnection pattern by modifying the conversion of mean kinetic energy to eddy kinetic energy

Switchable multi-wavelength mode-locked Yb-doped fiber laser using a polarization maintaining 45°-tilted fiber gratings based Lyot filter

We demonstrate a multi-wavelength mode-locked Yb-doped fiber laser by incorporating a pair of polarization maintaining 45° tilted fiber gratings (PM-45°TFG) based Lyot filter. Thanks to the functions of the polarizer and the comb filtering introduced by the Lyot filter, dissipative soliton (DS) pulses centered at 1035.26 nm, 1044.93 nm, 1055.62 nm, 1066.11 nm and 1076.63 nm can be generated respectively by finely tuning the intracavity polarization controllers (PCs). Moreover, the laser also can operate in a multi-wavelength regime via appropriately adjusting the pump power and polarization orientation. The high nonlinearity induced by the long cavity length leads to the generation of h-shaped mode-locked pulse with a repetition rate of 566.27 kHz. In the absence of any disturbance, the laser can operate steadily, that can potentially be used in various fields including wavelength division multiplexing systems etc