No abstract

Internet data traffic capacity is rapidly reaching limits imposed by optical fiber nonlinear effects. Having almost exhausted available degrees of freedom to orthogonally multiplex data, the possibility is now being explored of using spatial modes of fibers to enhance data capacity. We demonstrate the viability of using the orbital angular momentum (OAM) of light to create orthogonal, spatially distinct streams of data-transmitting channels that are multiplexed in a single fiber. Over 1.1 kilometers of a specially designed optical fiber that minimizes mode coupling, we achieved 400-gigabits-per-second data transmission using four angular momentum modes at a single wavelength, and 1.6 terabits per second using two OAM modes over 10 wavelengths. These demonstrations suggest that OAM could provide an additional degree of freedom for data multiplexing in future fiber networks.

We investigate the orthogonality of orbital angular momentum (OAM) with other multiplexing domains and present a free-space data link that uniquely combines OAM-, polarization-, and wavelength-division multiplexing. Specifically, we demonstrate the multiplexing/demultiplexing of 1008 data channels carried on 12 OAM beams, 2 polarizations, and 42 wavelengths. Each channel is encoded with 100 Gbit/s quadrature phase-shift keying data, providing an aggregate capacity of 100.8 Tbit/s (12×2×42×100 Gbit/s).

We propose a novel silicon waveguide that exhibits four zero-dispersion wavelengths for the first time, to the best of our knowledge, with a flattened dispersion over a 670-nm bandwidth. This holds a great potential for exploration of new nonlinear effects and achievement of ultra-broadband signal processing on a silicon chip. As an example, we show that an octave-spanning supercontinuum assisted by dispersive wave generation can be obtained in silicon, over a wavelength range from 1217 to 2451 nm, almost from bandgap wavelength to half-bandgap wavelength. Input pulse is greatly compressed to 10 fs.

Abstract. We show that, over the base theory RCA 0 , Stable Ramsey's Theorem for Pairs implies neither Ramsey's Theorem for Pairs nor Σ 0 2 -induction.

We propose a silicon strip/slot hybrid waveguide that produces flattened dispersion of 0 ± 16 ps/(nm∙km), over a 553-nm wavelength range, which is 20 times flatter than previous results. Different from previously reported slot waveguides, the strip/slot hybrid waveguide employs the mode transition from a strip mode to a slot mode to introduce unique waveguide dispersion. The flat dispersion profile is featured by three zero-dispersion wavelengths, which is obtained for the first time in on-chip silicon waveguides, to the best of our knowledge. The waveguide exhibits flattened dispersion from 1562-nm to 2115-nm wavelength, which is potentially useful for both telecom and mid-infrared applications.

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To facilitate the research and development of medical dialogue systems, we build large-scale medical dialogue datasets -MedDialog, which contain 1) a Chinese dataset with 3.4 million conversations between patients and doctors, 11.3 million utterances, 660.2 million tokens, covering 172 specialties of diseases, and 2) an English dataset with 0.26 million conversations, 0.51 million utterances, 44.53 million tokens, covering 96 specialties of diseases. To our best knowledge, MedDialog is the largest medical dialogue dataset to date. We pretrain several dialogue generation models on the Chinese MedDialog dataset, including Transformer, GPT, BERT-GPT, and compare their performance. It is shown that models trained on MedDialog are able to generate clinically correct and human-like medical dialogues. We also study the transferability of models trained on MedDialog to lowresource medical dialogue generation tasks. It is shown that via transfer learning which finetunes the models pretrained on MedDialog, the performance on medical dialogue generation tasks with small datasets can be greatly improved, as shown in human evaluation and automatic evaluation.

The phishing email is one of the significant threats in the world today and has caused tremendous financial losses. Although the methods of confrontation are continually being updated, the results of those methods are not very satisfactory at present. Moreover, phishing emails are growing at an alarming rate in recent years. Therefore, more effective phishing detection technology is needed to curb the threat of phishing emails. In this paper, we first analyzed the email structure. Then, based on an improved recurrent convolutional neural networks (RCNN) model with multilevel vectors and attention mechanism, we proposed a new phishing email detection model named THEMIS, which is used to model emails at the email header, the email body, the character level, and the word level simultaneously. To evaluate the effectiveness of THEMIS, we use an unbalanced dataset that has realistic ratios of phishing and legitimate emails. The experimental results show that the overall accuracy of THEMIS reaches 99.848%. Meanwhile, the false positive rate (FPR) is 0.043%. High accuracy and low FPR ensure that the filter can identify phishing emails with high probability and filter out legitimate emails as little as possible. This promising result is superior to the existing detection methods and verifies the effectiveness of THEMIS in detecting phishing emails.