Design of an OTN-based Failure/Alarm Propagation Simulator

This thesis presents OTN failure/alarm propagation behavior analysis and an OTN simulator based on failure/alarm propagation behavior on the optical layer of optical transport network (OTN) architecture. The simulator code is implemented by Python. The failure, alarm, and propagation behavior examples discover from the Huawei Optix OSN 8800/6800/3800 V100R009C10 reference book [HW]. The simulator is used to restore the basic failure/alarm propagation behavior and generates valuable results, including alarm, alarm flow, the ground truth matrix of alarm flow, and the active alarm flow dependency graph. The results are the ground truth data for future applications such as root cause analysis and restoring hidden propagation behaviors

A New Species of the Genus Trimeresurus from Southwest China (Squamata: Viperidae)

Species from the Trimeresurus popeiorum complex (Subgenus: Popeia) is a very complex group. T. popeiorum is the only Popeia species known from China. During the past two years, five adult Popeia specimens (4 males, 1 female) were collected from Yingjiang County, Southern Yunnan, China. Molecular, morphological and ecological data show distinct differences from known species, herein we describe these specimens as a new species Trimeresurus yingjiangensis sp. nov Chen, Ding, Shi and Zhang, 2018. Morphologically, the new species distinct from other Popeia species by a combination of following characters: (1) dorsal body olive drab,without cross bands on the scales; (2) a conspicuous bicolor ventrolateral stripe present on each side of males, first row of dorsal scales firebrick with a white ellipse dot on posterior upper part in male, these strips absent in females; (3) eyes firebrick in both gender; (4) suboculars separated from 3rd upper labial by one scale on each side; (5) ventrals 164–168 (n = 5); (6) MSR 21

Automated Runtime Testing to Identify Regression-Causing Features via Method Tracing

Degradation of performance between different release versions of a software is termed as regression. Establishing the specific feature(s) that cause regression requires time-consuming manual work of sifting through numerous code change requests. This disclosure describes techniques to enable testing for regression in a given software version based on dynamic analysis of traces of end-to-end method-level call stacks of the software at runtime. Iterative automated tests can be set up on real devices to mimic critical user journeys to ensure that the regression testing captures the features of interest during runtime tracing of the call stack. Analysis of the granular method-level data can serve to profile every feature in the code and help readily identify the feature(s) causing regression. The described techniques described in this disclosure can be employed for regression testing in a variety of ways, such as feature analysis, daily checking, and release comparison. Implementation of the techniques can avoid or minimize the time-consuming manual work required to identify regression-causing features, and can save substantial time, helping speed up the software development pipeline

Scheduling distributed energy resources and smart buildings of a microgrid via multi-time scale and model predictive control method

To schedule the distributed energy resources (DERs) and smart buildings of a microgrid in an optimal way and consider the uncertainties associated with forecasting data, a two-stage scheduling framework is proposed in this study. In stage I, a day-ahead dynamic optimal economic scheduling method is proposed to minimise the daily operating cost of the microgrid. In stage II, a model predictive control based intra-hour adjustment method is proposed to reschedule the DERs and smart buildings to cope with the uncertainties. A virtual energy storage system is modelled and scheduled as a flexible unit using the inertia of building in both stages. The underlying electric network and the associated power flow and system operational constraints of the microgrid are considered in the proposed scheduling method. Numerical studies demonstrate that the proposed method can reduce the daily operating cost in stage I and smooth the fluctuations of the electric tie-line power of the microgrid caused by the day-ahead forecasting errors in stage II. Meanwhile, the fluctuations of the electric tie-line power with the MPC based strategy are better smoothed compared with the traditional open-loop and single-period based optimisation methods, which demonstrates the better performance of the proposed scheduling method in a time-varying context

Targeted Software Profiling Based on Static Code Analysis to Detect Small Regressions

Degradation of performance between different release versions of a software is termed as regression. Traditional reliability testing and benchmarking tools can detect regressions of large magnitudes much more easily compared to those with smaller regression effects. As code changes accumulate over time, the cumulative impact of undetected micro regressions can add up to noticeable negative impact on performance. This disclosure describes techniques for timely detection of micro regressions based on static analysis of code changes in a change request and by targeted dynamic benchmarking. The static analyses can be performed by comparing the AST and/or call graphs of the software before and after changes connected to a change request. The results of the comparison can be employed to detect any small or large regression resulting from the changes via bytecode injected binaries of the software in a laboratory testing environment. The approach can save substantial time and effort in detecting and addressing small regressions, thus helping speed up the application as well as the software development pipeline and avoiding negative impacts on user engagement

Psychotic Symptom, Mood, and Cognition-associated Multimodal MRI Reveal Shared Links to the Salience Network Within the Psychosis Spectrum Disorders

Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability in discriminating among SZ, SAD, and BP. We observed shared links to functional and structural covariation in prefrontal, medial temporal, anterior cingulate, and insular cortices among SZ, SAD, and BP, although they were linked with different clinical domains. The salience (SAN), default mode (DMN), and fronto-limbic (FLN) networks were the three identified multimodal MRI features within the psychosis spectrum disorders from psychotic symptom, mood, and cognition associations. In addition, using these networks, we can classify patients and controls and distinguish among SZ, SAD, and BP, including their first-degree relatives. The identified multimodal SAN may be informative regarding neural mechanisms of comorbidity for psychosis spectrum disorders, along with DMN and FLN may serve as potential biomarkers in discriminating among SZ, SAD, and BP, which may help investigators better understand the underlying mechanisms of psychotic comorbidity from three different disorders via a multimodal neuroimaging perspective

Voxel-based, brain-wide association study of aberrant functional connectivity in schizophrenia implicates thalamocortical circuitry

Background: Wernicke\u27s concept of \u27sejunction\u27 or aberrant associations among specialized brain regions is one of the earliest hypotheses attempting to explain the myriad of symptoms in psychotic disorders. Unbiased data mining of all possible brain-wide connections in large data sets is an essential first step in localizing these aberrant circuits. Methods: We analyzed functional connectivity using the largest resting-state neuroimaging data set reported to date in the schizophrenia literature (415 patients vs. 405 controls from UK, USA, Taiwan, and China). An exhaustive brain-wide association study at both regional and voxel-based levels enabled a continuous data-driven discovery of the key aberrant circuits in schizophrenia. Results: Results identify the thalamus as the key hub for altered functional networks in patients. Increased thalamus-primary somatosensory cortex connectivity was the most significant aberration in schizophrenia (P=10-18). Overall, a number of thalamic links with motor and sensory cortical regions showed increased connectivity in schizophrenia, whereas thalamo-frontal connectivity was weakened. Network changes were correlated with symptom severity and illness duration, and support vector machine analysis revealed discrimination accuracies of 73.53-80.92%. Conclusions: Widespread alterations in resting-state thalamocortical functional connectivity is likely to be a core feature of schizophrenia that contributes to the extensive sensory, motor, cognitive, and emotional impairments in this disorder. Changes in this schizophrenia-associated network could be a reliable mechanistic index to discriminate patients from healthy controls

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study

BackgroundThe novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models’ generalization ability.MethodsWe retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance.ResultsThe AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05).ConclusionThe nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively

Indoor Positioning System Using Visible Light Communication and Smartphone With Rolling Shutter Camera

Indoor positioning systems provide location based service within buildings. Because the Global Positioning System is usually unavailable in indoor environment, other positioning technologies making use of optical, radio or even acoustic techniques are used for indoor positioning application. In this dissertation, an indoor visible light communication positioning system using a smartphone with rolling shutter camera is proposed. The LED transmits periodical signals with different frequencies high enough to avoid flickering as its optical tags. The camera exploits the rolling shutter effect to detect the fundamental frequency of optical signals. This kind of systems use smartphone camera as the receiver with no requirement of extra hardware. And at the same time, the optical communication link allows a date rate much higher than the frame rates of traditional optical camera communication. For the work of this research topic carried out so far, the roles of camera parameters determining rolling effect performance are studied and a technique to measure the camera readout time per column is presented. Factors limiting the detectable frequency range is explained based on the discussion of rolling shutter mechanism. Followed is the analysis of frequency detection reliability and resolution with Fourier spectrum analysis. After that, we present a background removal algorithm to recover the modulated information from background interference. In our algorithm, only one extra image is used and a one-time image alignment is required. Linear filter performs poorly in this case because the image with rolling shutter effect is a multiplication of modulated signal and the reflectance of the scene. Therefore we use background division rather than background subtraction to remove the background interference. The modulated illumination pattern is analyzed and the performance of background removal is evaluated by experiments. The experiment result shows that the interference is significantly compressed after background removal. After the LED ID being detected successfully, an indoor positioning algorithm is proposed using multi-view imaging geometry with built-in smartphone inertial sensors. Due to the narrow field of view (FOV) of camera and the illumination placement of buildings, there is usually only one LED can be captured within camera FOV at the same time. In our proposed algorithm, only one detected LED with known position and one camera are required. And this system can still work when the VLC link is temporarily blocked or the LED temporarily moves out of the camera FOV. Another advantage of proposed algorithm is that no additional accessory will be needed except those sensors built inside the smartphone. In addition, the position is calculated at the receiver end without the knowledge of transmitter specifications, therefore there is no privacy concerns. The low-cost built in IMU sensor exhibits significant systematic errors, axes misalignment and noisy measurement. Even though a IMU calibration process can eliminate the systematic errors and axes misalignment, the noise remains even after calibration and could magnificently degrade the system performance. To maximize a posterior of obtained measurements, Kalman filter is applied for the sensor fusion by combining the system kinematic prediction and new measurement. Due to the nonlinearity of this system, an extended Kalman filter is used to correct the system model. A simulation is conducted to verify the proposed system. The simulation data of accelerometer and gyroscope are generated based on real world measurement from accelerometer and gyroscope. The simulation results demonstrate that the position error and orientation error are well bounded in the 3 sigma bounds and the maximum position error observed during the 2 minutes simulation is 0.1941 m over 50 averaged Monte Carlo trials