This paper reviews advanced optical burst switching (OBS) and optical packet switching (OPS) technologies and discusses their roles in the future photonic Internet. Discussions include optoelectronic ...and optical systems technologies as well as systems integration into viable network elements (OBS and OPS routers). Optical label switching (OLS) offers a unified multiple-service platform with effective and agile utilization of the available optical bandwidth in support of voice, data, and multimedia services on the Internet Protocol. In particular, OLS routers with wavelength routing switching fabrics and parallel optical labeling allow forwarding of asynchronously arriving variable-length packets, bursts, and circuits. By exploiting contention resolution in wavelength, time, and space domains, the OLS routers can achieve high throughput without resorting to a store-and-forward method associated with large buffer requirements. Testbed demonstrations employing OLS edge routers show high-performance networking in support of multimedia and data communications applications over the photonic Internet with optical packets and bursts switched directly at the optical layer
Background
A virtual reality (VR) tour of the operating theatre before anaesthesia could provide a realistic experience for children. This study was designed to determine whether a preoperative VR ...tour could reduce preoperative anxiety in children.
Methods
Children scheduled for elective surgery under general anaesthesia were randomized into a control or VR group. The control group received conventional information regarding anaesthesia and surgery. The VR group watched a 4‐min video showing Pororo, the famous little penguin, visiting the operating theatre and explaining what is in it. The main outcome was preoperative anxiety, assessed using the modified Yale Preoperative Anxiety Scale (m‐YPAS) before entering the operating theatre. Secondary outcomes included induction compliance checklist (ICC) and procedural behaviour rating scale (PBRS) scores during anaesthesia.
Results
A total of 69 children were included in the analysis, 35 in the control group and 34 in the VR group. Demographic data and induction time were similar in the two groups. Children in the VR group had a significantly lower m‐YPAS score than those in the control group (median 31·7 (i.q.r. 23·3–37·9) and 51·7 (28·3–63·3) respectively; P < 0·001). During anaesthesia, the VR group had lower ICC and PBRS scores than the control group.
Conclusion
This preoperative VR tour of the operating theatre was effective in alleviating preoperative anxiety and increasing compliance during induction of anaesthesia in children undergoing elective surgery. Registration number: UMIN000025232 (http://www.umin.ac.jp/ctr).
Reduces anxiety
This Tutorial will discuss the motivation, benefits, and challenges of photonic switching in data centers and cover prospects of future data centers involving emerging new technologies and ...cross-layer solutions. The primary motivation for considering photonic switching in data centers rises from the need for energy-efficient and scalable intra-data center networks to meet rapid increases in data traffic driven by emerging applications, including machine learning. The data traffic inside the data centers (East-West traffic) is typically significantly greater than that of the traffic coming in and out of the data centers (North-South traffic) (Benson et al. 2010). To accommodate such traffic, today's large-scale data centers employ cascaded stages of many power-hungry electronic packet switches interconnected across the data center network in fixed hierarchical communication topologies. These electronic switches add significant latency and energy consumption while limiting the communication bandwidth. On the other hand, photonic switches can, in principle, support interconnections at very high data rates on many parallel wavelengths while keeping their energy consumption nearly independent of the switch port bandwidth. Numerous research papers have predicted significant benefits in scalability, throughput, and power efficiency from deploying photonic switches in data centers. However, photonic switching is not yet widely deployed in commercial warehouse-scale data centers at the time of writing this Tutorial due to significant challenges. They are related to 1) cross-layer issues involving control and management planes together with data integrity during switching, 2) scalability to >5000 racks (>a quarter-million servers), 3) performance monitoring required for reliable operation, 4) currently existing standards allowing limited power margin (3 dB), and 5) other practical (technology-dependent) issues relating to polarization sensitivity, temperature sensitivity, cost, etc. In telecom, commercial deployments of reconfigurable optical add-drop multiplexers (ROADMs) (Lightwave, 2003)-(Perrin, 2015) also had faced similar challenges and took place nearly ten years after the first research network testbed demonstrations in 1997 (Garrett, 2015) with fully implemented network control and management planes. In data centers, the challenges are far more significant due to the scale of the network and the dynamicity of the traffic. We will discuss possible solutions for future data centers involving cross-layer methods, new topologies, and innovative photonic switching technologies. In particular, the Tutorial broadly surveys state-of-the-art photonic switching technologies, architectures, and experimental results, and further covers the details of arrayed-waveguide-grating-router-based switch fabrics offering hybrid switching methods with distributed control planes towards scalable data center networking.
This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs ...an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to <inline-formula><tex-math notation="LaTeX">99\%</tex-math></inline-formula> anomaly detection accuracy can be achieved with a false positive rate below <inline-formula><tex-math notation="LaTeX">1\%</tex-math></inline-formula> .
The human body characteristics as a signal transmission medium are studied for the application to intrabody communication. The measurements of the body channel cover the frequency range from 100 kHz ...to 150 MHz and the distance on the body up to 1.2 m. A distributed RC model is developed to analyze the large variation of the channel properties according to the frequency and channel length. The simulation results using the channel model match well with the measurements in both the frequency and time domains. The effect of the ground plane to the body channel transceivers is also investigated and an empirical formula for the minimum ground size is obtained. Finally, the amount of the electromagnetic radiation due to the body antenna effect is measured. With regards to the Federal Communications Commission regulations, the proper frequency range for the intrabody communication is determined to satisfy given bit error rate requirements
Despite ionizing radiation (IR) is being widely used as a standard treatment for lung cancer, many evidences suggest that IR paradoxically promotes cancer malignancy. However, its molecular ...mechanisms underlying radiation-induced cancer progression remain obscure. Here, we report that exposure to fractionated radiation (2 Gy per day for 3 days) induces the secretion of granulocyte-colony-stimulating factor (G-CSF) that has been commonly used in cancer therapies to ameliorate neutropenia. Intriguingly, radiation-induced G-CSF promoted the migratory and invasive properties by triggering the epithelial-mesenchymal cell transition (EMT) in non-small-cell lung cancer cells (NSCLCs). By irradiation, G-CSF was upregulated transcriptionally by β-catenin/TCF4 complex that binds to the promoter region of G-CSF as a transcription factor. Importantly, irradiation increased the stability of β-catenin through the activation of PI3K/AKT (phosphatidylinositol 3-kinase/AKT), thereby upregulating the expression of G-CSF. Radiation-induced G-CSF is recognized by G-CSFR and transduced its intracellular signaling JAK/STAT3 (Janus kinase/signal transducers and activators of transcription), thereby triggering EMT program in NSCLCs. Taken together, our findings suggest that the application of G-CSF in cancer therapies to ameliorate neutropenia should be reconsidered owing to its effect on cancer progression, and G-CSF could be a novel therapeutic target to mitigate the harmful effect of radiotherapy for the treatment of NSCLC.
This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online ...RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.
Summary
The incidence of atypical femoral fractures (AFFs) was 2.95% among 6644 hip and femoral fractures. Independent risk factors included the use of bisphosphonates (BPs), osteopenia or ...osteoporosis, rheumatoid arthritis, increased femoral curvatures, and thicker femoral cortices. Patients with AFFs and BP treatment were more likely to have problematic healing than those with typical femoral fractures (TFFs) and no BP treatment.
Introduction
To determine the incidence and risk factors of atypical femoral fractures (AFFs), we performed a multicenter case-control study. We also investigated the effects of bisphosphonates (BPs) on AFF healing.
Methods
We retrospectively reviewed the medical records and radiographs of 6644 hip and femoral fractures of patients from eight tertiary referral hospitals. All the radiographs were reviewed to distinguish AFFs from TFFs. Univariate and multivariate logistic regression analyses were performed to identify risk factors, and interaction analyses were used to investigate the effects of BPs on fracture healing.
Results
The incidence of AFFs among 6644 hip and femoral fractures was 2.95% (90 subtrochanter and 106 femoral shaft fractures). All patients were females with a mean age of 72 years, and 75.5% were exposed to BPs for an average duration of 5.2 years (range, 1–17 years). The use of BPs was significantly associated with AFFs (
p
< 0.001, odds ratio = 25.65; 95% confidence interval = 10.74–61.28). Other independent risk factors for AFFs included osteopenia or osteoporosis, rheumatoid arthritis, increased anterior and lateral femoral curvatures, and thicker lateral femoral cortex at the shaft level. Interaction analyses showed that patients with AFFs using BPs had a significantly higher risk of problematic fracture healing than those with TFFs and no BP treatment.
Conclusions
The incidence of AFFs among 6644 hip and femoral fractures was 2.95%. Osteopenia or osteoporosis, use of BPs, rheumatoid arthritis, increased anterior and lateral femoral curvatures, and thicker lateral femoral cortex were independent risk factors for the development of AFFs. Patients with AFFs and BP treatment were more likely to have problematic fracture healing than those with TFFs and no BP treatment.
The RENO experiment reports more precisely measured values of θ_{13} and |Δm_{ee}^{2}| using ∼2200 live days of data. The amplitude and frequency of reactor electron antineutrino (νover ¯_{e}) ...oscillation are measured by comparing the prompt signal spectra obtained from two identical near and far detectors. In the period between August 2011 and February 2018, the far (near) detector observed 103 212 (850 666) νover ¯_{e} candidate events with a background fraction of 4.8% (2.0%). A clear energy and baseline dependent disappearance of reactor νover ¯_{e} is observed in the deficit of the measured number of νover ¯_{e}. Based on the measured far-to-near ratio of prompt spectra, we obtain sin^{2}2θ_{13}=0.0896±0.0048(stat)±0.0047(syst) and |Δm_{ee}^{2}|=2.68±0.12(stat)±0.07(syst)×10^{-3} eV^{2}.
Photonic spiking neural networks (PSNNs) potentially offer exceptionally high throughput and energy efficiency compared to their electronic neuromorphic counterparts while maintaining their benefits ...in terms of event-driven computing capability. While state-of-the-art PSNN designs require a continuous laser pump, this paper presents a monolithic optoelectronic PSNN hardware design consisting of an MZI mesh incoherent network and event-driven laser spiking neurons. We designed, prototyped, and experimentally demonstrated this event-driven neuron inspired by the Izhikevich model incorporating both excitatory and inhibitory optical spiking inputs and producing optical spiking outputs accordingly. The optoelectronic neurons consist of two photodetectors for excitatory and inhibitory optical spiking inputs, electrical transistors’ circuits providing spiking nonlinearity, and a laser for optical spiking outputs. Additional inclusion of capacitors and resistors complete the Izhikevich-inspired optoelectronic neurons, which receive excitatory and inhibitory optical spikes as inputs from other optoelectronic neurons. We developed a detailed optoelectronic neuron model in Verilog-A and simulated the circuit-level operation of various cases with excitatory input and inhibitory input signals. The experimental results closely resemble the simulated results and demonstrate how the excitatory inputs trigger the optical spiking outputs while the inhibitory inputs suppress the outputs. The nanoscale neuron designed in our monolithic PSNN utilizes quantum impedance conversion. It shows that estimated 21.09 fJ/spike input can trigger the output from on-chip nanolasers running at a maximum of 10 Gspike/second in the neural network. Utilizing the simulated neuron model, we conducted simulations on MNIST handwritten digits recognition using fully connected (FC) and convolutional neural networks (CNN). The simulation results show 90% accuracy on unsupervised learning and 97% accuracy on a supervised modified FC neural network. The benchmark shows our PSNN can achieve 50 TOP/J energy efficiency, which corresponds to 100 × throughputs and 1000 × energy-efficiency improvements compared to state-of-art electrical neuromorphic hardware such as Loihi and NeuroGrid.
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