Ultra-broad bandwidth array waveguide grating for high-speed backbone network transmission.

With the rapid development of the backbone network rates, there has been a gradual increase in channel spacing and bandwidth. The C&L band ultra-broad bandwidth array waveguide gratings (AWG) of 60-channel 100 GHz channel spacing are designed and fabricated based on silica waveguide. A new parabolic design is used to achieve ultra-broad bandwidth and good spectrum. For the C band ultra-broad bandwidth AWG, the peak insertion loss, uniformity, 0.5 dB bandwidth, 1 dB bandwidth and 3 dB bandwidth are 2.98 dB, 0.36 dB, 0.614 nm, 0.721 nm and 0.937 nm, respectively. For the L band ultra-broad bandwidth AWG, the peak insertion loss, uniformity, 0.5 dB bandwidth, 1 dB bandwidth and 3 dB bandwidth are 2.91 dB, 0.27 dB, 0.560 nm, 0.665 nm and 0.879 nm, respectively. To ensure ultra-broad bandwidth AWG operation at different temperatures, a temperature control circuit is integrated into the packaging design. It has been observed that the performances remain virtually unchanged within the temperature range of -15 to 65 degree. The ultra-broadband AWGs have been successfully tested to transmit 96 Gbaud signals and can be applied to 600 G/800 G backbone network transmission. By using the C&L ultra-broad bandwidth AWGs of 60-channel 100 GHz channel spacing, the total transmission speed over a single-mode fiber can reach 72Tbps/96Tbps.

Gait can reveal sleep quality with machine learning models.

Sleep quality is an important health indicator, and the current measurements of sleep rely on questionnaires, polysomnography, etc., which are intrusive, expensive or time consuming. Therefore, a more nonintrusive, inexpensive and convenient method needs to be developed. Use of the Kinect sensor to capture one's gait pattern can reveal whether his/her sleep quality meets the requirements. Fifty-nine healthy students without disabilities were recruited as participants. The Pittsburgh Sleep Quality Index (PSQI) and Kinect sensors were used to acquire the sleep quality scores and gait data. After data preprocessing, gait features were extracted for training machine learning models that predicted sleep quality scores based on the data. The t-test indicated that the following joints had stronger weightings in the prediction: the Head, Spine Shoulder, Wrist Left, Hand Right, Thumb Left, Thumb Right, Hand Tip Left, Hip Left, and Foot Left. For sleep quality prediction, the best result was achieved by Gaussian processes, with a correlation of 0.78 (p < 0.001). For the subscales, the best result was 0.51 for daytime dysfunction (p < 0.001) by linear regression. Gait can reveal sleep quality quite well. This method is a good supplement to the existing methods in identifying sleep quality more ecologically and less intrusively.

Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors.

BACKGROUND: Suicide is a great public health challenge. Two hundred million people attempt suicide in China annually. Existing suicide prevention programs require the help-seeking initiative of suicidal individuals, but many of them have a low motivation to seek the required help. We propose that a proactive and targeted suicide prevention strategy can prompt more people with suicidal thoughts and behaviors to seek help. OBJECTIVE: The goal of the research was to test the feasibility and acceptability of Proactive Suicide Prevention Online (PSPO), a new approach based on social media that combines proactive identification of suicide-prone individuals with specialized crisis management. METHODS: We first located a microblog group online. Their comments on a suicide note were analyzed by experts to provide a training set for the machine learning models for suicide identification. The best-performing model was used to automatically identify posts that suggested suicidal thoughts and behaviors. Next, a microblog direct message containing crisis management information, including measures that covered suicide-related issues, depression, help-seeking behavior and an acceptability test, was sent to users who had been identified by the model to be at risk of suicide. For those who replied to the message, trained counselors provided tailored crisis management. The Simplified Chinese Linguistic Inquiry and Word Count was also used to analyze the users' psycholinguistic texts in 1-month time slots prior to and postconsultation. RESULTS: A total of 27,007 comments made in April 2017 were analyzed. Among these, 2786 (10.32%) were classified as indicative of suicidal thoughts and behaviors. The performance of the detection model was good, with high precision (.86), recall (.78), F-measure (.86), and accuracy (.88). Between July 3, 2017, and July 3, 2018, we sent out a total of 24,727 direct messages to 12,486 social media users, and 5542 (44.39%) responded. Over one-third of the users who were contacted completed the questionnaires included in the direct message. Of the valid responses, 89.73% (1259/1403) reported suicidal ideation, but more than half (725/1403, 51.67%) reported that they had not sought help. The 9-Item Patient Health Questionnaire (PHQ-9) mean score was 17.40 (SD 5.98). More than two-thirds of the participants (968/1403, 69.00%) thought the PSPO approach was acceptable. Moreover, 2321 users replied to the direct message. In a comparison of the frequency of word usage in their microblog posts 1-month before and after the consultation, we found that the frequency of death-oriented words significantly declined while the frequency of future-oriented words significantly increased. CONCLUSIONS: The PSPO model is suitable for identifying populations that are at risk of suicide. When followed up with proactive crisis management, it may be a useful supplement to existing prevention programs because it has the potential to increase the accessibility of antisuicide information to people with suicidal thoughts and behaviors but a low motivation to seek help.

Large-capacity and low-loss integrated optical buffer.

Temporarily storing light occupies a pivotal position in all-optical packet switching network and microwave photonics. An integrated optical buffer with large capacity and low loss is demonstrated on a silica wafer. The optical buffer consists of four silica waveguide delay lines connected by five thermo-optic switches. With different switch combinations applied, related delay lines are selected to realize a different storage time in the buffer, and a storage time up to 100 ns with a 10-ns step size is implemented. By optimizing the fabrication process and introducing the offsets between straight and bending waveguides, the propagation loss as low as ~1.08 dB/m is achieved. This large-capacity and low-loss buffer enables broad applications in optical communications and microwave photonics.

Self-esteem recognition based on gait pattern using Kinect.

BACKGROUND: Self-esteem is an important aspect of individual's mental health. When subjects are not able to complete self-report questionnaire, behavioral assessment will be a good supplement. In this paper, we propose to use gait data collected by Kinect as an indicator to recognize self-esteem. METHODS: 178 graduate students without disabilities participate in our study. Firstly, all participants complete the 10-item Rosenberg Self-Esteem Scale (RSS) to acquire self-esteem score. After completing the RRS, each participant walks for two minutes naturally on a rectangular red carpet, and the gait data are recorded using Kinect sensor. After data preprocessing, we extract a few behavioral features to train predicting model by machine learning. Based on these features, we build predicting models to recognize self-esteem. RESULTS: For self-esteem prediction, the best correlation coefficient between predicted score and self-report score is 0.45 (p<0.001). We divide the participants according to gender, and for males, the correlation coefficient is 0.43 (p<0.001), for females, it is 0.59 (p<0.001). CONCLUSION: Using gait data captured by Kinect sensor, we find that the gait pattern could be used to recognize self-esteem with a fairly good criterion validity. The gait predicting model can be taken as a good supplementary method to measure self-esteem.