Direct fiber vector eigenmode multiplexing transmission seeded by integrated optical vortex emitters

Spatial modes have received substantial attention over the last decades and are used in optical communication applications. In fiber-optic communications, the employed linearly polarized modes and phase vortex modes carrying orbital angular momentum can be synthesized by fiber vector eigenmodes. To improve the transmission capacity and miniaturize the communication system, straightforward fiber vector eigenmode multiplexing and generation of fiber-eigenmode-like polarization vortices (vector vortex modes) using photonic integrated devices are of substantial interest. Here, we propose and demonstrate direct fiber vector eigenmode multiplexing transmission seeded by integrated optical vortex emitters. By exploiting vector vortex modes (radially and azimuthally polarized beams) generated from silicon microring resonators etched with angular gratings, we report data-carrying fiber vector eigenmode multiplexing transmission through a 2-km large-core fiber, showing low-level mode crosstalk and favorable link performance. These demonstrations may open up added capacity scaling opportunities by directly accessing multiple vector eigenmodes in the fiber and provide compact solutions to replace bulky diffractive optical elements for generating various optical vector beams

To Mask or Not to Mask

Reluctance to adopt mask-wearing as a preventive measure is widely observed in many Western societies since the beginning of the COVID-19 pandemics. This reluctance toward mask adoption, like any other complex social phenomena, will have multiple causes. Plausible explanations have been identified, including political polarization, skepticism about media reports and the authority of public health agencies, and concerns over liberty, amongst others. In this paper, we propose potential explanations hitherto unnoticed, based on the framework of epistemic injustice. We show how testimonial injustice and hermeneutical injustice may be at work to shape the reluctant mask adoption at both the societal and individual levels. We end by suggesting how overcoming these epistemic injustices can benefit the global community in this challenging situation and in the future

Observation of Majorana fermions with spin selective Andreev reflection in the vortex of topological superconductor

Majorana fermion (MF) whose antiparticle is itself has been predicted in condensed matter systems. Signatures of the MFs have been reported as zero energy modes in various systems. More definitive evidences are highly desired to verify the existence of the MF. Very recently, theory has predicted MFs to induce spin selective Andreev reflection (SSAR), a novel magnetic property which can be used to detect the MFs. Here we report the first observation of the SSAR from MFs inside vortices in Bi2Te3/NbSe2 hetero-structure, in which topological superconductivity was previously established. By using spin-polarized scanning tunneling microscopy/spectroscopy (STM/STS), we show that the zero-bias peak of the tunneling differential conductance at the vortex center is substantially higher when the tip polarization and the external magnetic field are parallel than anti-parallel to each other. Such strong spin dependence of the tunneling is absent away from the vortex center, or in a conventional superconductor. The observed spin dependent tunneling effect is a direct evidence for the SSAR from MFs, fully consistent with theoretical analyses. Our work provides definitive evidences of MFs and will stimulate the MFs research on their novel physical properties, hence a step towards their statistics and application in quantum computing.Comment: 4 figures 15 page

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology; this research solves the problem that the original cutting technology cannot extract certain single teeth; and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN); which can identify caries and restorations from the bitewing images. Moreover; it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image; which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization; (2) a dental image cropping procedure to obtain individually separated tooth samples; and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks; namely; AlexNet; GoogleNet; Vgg19; and ResNet50; experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%; respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion

Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%

The Minimum Variation Timescales of X-ray bursts from SGR J1935+2154

The minimum variation timescale (MVT) of soft gamma-ray repeaters can be an important probe to estimate the emission region in pulsar-like models, as well as the Lorentz factor and radius of the possible relativistic jet in gamma-ray burst (GRB)-like models, thus revealing their progenitors and physical mechanisms. In this work, we systematically study the MVTs of hundreds of X-ray bursts (XRBs) from SGR J1935+2154 observed by {\it Insight}-HXMT, GECAM and Fermi/GBM from July 2014 to Jan 2022 through the Bayesian Block algorithm. We find that the MVTs peak at $\sim$ 2 ms, corresponding to a light travel time size of about 600 km, which supports the magnetospheric origin in pulsar-like models. The shock radius and the Lorentz factor of the jet are also constrained in GRB-like models. Interestingly, the MVT of the XRB associated with FRB 200428 is $\sim$ 70 ms, which is longer than that of most bursts and implies its special radiation mechanism. Besides, the median of MVTs is 7 ms, shorter than the median MVTs of 40 ms and 480 ms for short GRBs or long GRBs, respectively. However, the MVT is independent of duration, similar to GRBs. Finally, we investigate the energy dependence of MVT and suggest that there is a marginal evidence for a power-law relationship like GRBs but the rate of variation is at least about an order of magnitude smaller. These features may provide an approach to identify bursts with a magnetar origin.Comment: accepted for publication in ApJ

Calibration of the Timing Performance of GECAM-C

As a new member of the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) after GECAM-A and GECAM-B, GECAM-C (originally called HEBS), which was launched on board the SATech-01 satellite on July 27, 2022, aims to monitor and localize X-ray and gamma-ray transients from $\sim$ 6 keV to 6 MeV. GECAM-C utilizes a similar design to GECAM but operates in a more complex orbital environment. In this work, we utilize the secondary particles simultaneously produced by the cosmic-ray events on orbit and recorded by multiple detectors, to calibrate the relative timing accuracy between all detectors of GECAM-C. We find the result is 0.1 $\mu \rm s$, which is the highest time resolution among all GRB detectors ever flown and very helpful in timing analyses such as minimum variable timescale and spectral lags, as well as in time delay localization. Besides, we calibrate the absolute time accuracy using the one-year Crab pulsar data observed by GECAM-C and Fermi/GBM, as well as GECAM-C and GECAM-B. The results are $2.02\pm 2.26\ \mu \rm s$ and $5.82\pm 3.59\ \mu \rm s$, respectively. Finally, we investigate the spectral lag between the different energy bands of Crab pulsar observed by GECAM and GBM, which is $\sim -0.2\ {\rm \mu s\ keV^{-1}}$.Comment: submitte

Integration, Launch, and First Results from IDEASSat/INSPIRESat-2 - A 3U CubeSat for Ionospheric Physics and Multi-National Capacity Building

The Ionospheric Dynamics and Attitude Subsystem Satellite (IDEASSat) is a 3U CubeSat carrying a Compact Ionospheric Probe (CIP) to detect ionospheric irregularities that can impact the usability and accuracy of global satellite navigation systems (GNSS), as well as satellite and terrestrial over the horizon communications. The spacecraft was developed by National Central University (NCU) in Taiwan, with additional development and operational support from partners in the International Satellite Program in Science and Education (INSPIRE) consortium. The spacecraft system needed to accommodate these mission objectives required three axis attitude control, dual band communications capable of supporting both tracking, telemetry and command (TT&C) and science data downlink, as well as flight software and ground systems capable of supporting the autonomous operation and short contact times inherent to a low Earth orbit mission developed on a limited university budget with funding agency-imposed constraints. As the first spacecraft developed at NCU, lessons learned during the development, integration, and operation of IDEASSat have proven to be crucial to the objective of developing a sustainable small satellite program. IDEASSat was launched successfully on January 24, 2021 aboard the SpaceX Falcon 9 Transporter 1 flight. and successfully began operations, demonstrating power, thermal, and structural margins, as well as validation of uplink and downlink communications functionality, and autonomous operation. A serious anomaly occurred after 22 days on orbit when communication with the spacecraft were abruptly lost. Communication was re-established after 1.5 months for sufficient time to downlink stored flight data, which allowed the cause of the blackout to be identified to a high level of confidence and precision. In this paper, we will report on experiences and anomalies encountered during the final flight model integration and delivery, commissioning, and operations. The agile support from the international amateur radio community and INSPIRE partners were extremely helpful in this process, especially during the initial commissioning phase following launch. It is hoped that the lessons learned reported here will be helpful for other university teams working to develop spaceflight capacity