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关键词： Mnemonic discrimination   Pattern separation   Fear conditioning   Fear learning   Skin conductance responses  

摘要： Background and objectives: It is important to be able to learn which stimuli in our surroundings predict aversive outcomes. To maintain emotional well-being, it is similarly important to be able to learn which stimuli predict safety. The ability to discriminate between stimuli that predict danger and safety has been suggested to not only have an emotional component, but also a cognitive one. One such candidate mechanism is mnemonic discrimination (MD), the ability to differentiate between two memories that are similar but not identical. In the present study, we wanted to examine if MD performance helps to explain inter-individual differences in the ability to acquire a differentiated fear response during fear conditioning. Methods: Participants performed a task assessing MD ability, and then underwent a fear conditioning procedure. Fear responses were measured using skin conductance responses (SCRs). Results: Results revealed no support for MD ability being associated with to which degree a differentiated fear response was acquired, or with the time needed to acquire such a response. Limitations: Our only outcome measurement was SCRs. Future studies need to include fear ratings, expectancy ratings and neural responses. Future studies also need to examine this using a stimulus material where the conditioned stimulus and the safety stimulus are more difficult to distinguish from each other. Conclusions: If MD ability has a role in inhibiting overgeneralization of fear learning, this does not seem to be driven by MD already during the initial learning.

关键词： brain-eye-ear   migraine   multiple sclerosis   retinocochleocerebral vasculopathy   Susac syndrome  

摘要： Background and purpose Susac syndrome (SuS) is an inflammatory condition of the brain, eye and ear. Diagnosis can be challenging, and misdiagnosis is common. Methods This is a retrospective review of the medical records of 32 adult patients from an Australasian cohort of SuS patients. Results An alternative diagnosis prior to SuS was made in 30 patients (94%) with seven patients receiving two or more diagnoses. The median time to diagnosis of SuS was 3 months (range 0.5-100 months). The commonest misdiagnoses were migraine in 10 patients (31%), cerebral vasculitis in six (19%), multiple sclerosis in five (16%) and stroke in five (16%). Twenty-two patients were treated for alternative diagnoses, 10 of whom had further clinical manifestations prior to SuS diagnosis. At presentation seven patients (22%) met criteria for definite SuS, 19 (59%) for probable SuS and six (19%) for possible SuS. Six patients (19%) presented with brain-eye-ear involvement, 14 with brain-ear (44%), six with brain-eye (19%) and six (19%) with only brain involvement. In patients with the complete triad of symptoms the median delay to diagnosis was 3 months (range 1-9 months) compared to 5.25 months (range 0.5-100 months) for patients with encephalopathy and ocular symptoms at presentation. Conclusions Susac syndrome patients are frequently misdiagnosed at initial presentation, despite many having symptoms or radiological features that are red flags for the diagnosis. Delayed diagnosis can lead to patient morbidity. The varied ways in which SuS can present, and clinician failure to consider or recognize SuS, appear to be the main factors leading to misdiagnosis.

关键词： Industrial Internet of Things   Collaborative work   Data models   Computational modeling   Distributed databases   Security   Blockchain   Blockchain   differential privacy   federated learning   homomorphic encryption   privacy protection  

摘要： With rapid growth in data volume generated from different industrial devices in IoT, the protection for sensitive and private data in data sharing has become crucial. At present, federated learning for data security has arisen, and it can solve the security concerns on data sharing by model sharing on Internet of mutual distrust. However, the hackers still launch attack aiming at the security vulnerabilities (e.g., model extraction attack and model reverse attack) in federated learning. In this article, to address the above problems, we first design an application model of blockchain-enabled federated learning in Industrial Internet of Things (IIoT), and formulate our data protection aggregation scheme based on the above model. Then, we give the distributed K-means clustering based on differential privacy and homomorphic encryption, and the distributed random forest with differential privacy and the distributed AdaBoost with homomorphic encryption methods, which enable multiple data protection in data sharing and model sharing. Finally, we integrate the methods with blockchain and federated learning, and provide the complete security analysis. Extensive experimental results show that our aggregation scheme and working mechanism have the better performance in the selected indicators.

关键词： 三角测量  

ISBN: (纸本)9787115584243

摘要： 中国科学院院士林群用一个例子讲透微分方程在测量树高、山高的思维转换中，领略数学的自由与魅力一个微分方程所做的，不过是一系列三角测量的总和以基础测量诠释微积分真谛用初等算术直观讲解微分方程本书使用中学生熟悉的三角测量知识，通过测量树高、山高的实际例子，直观地推导出了微积分的基本定理“牛顿-莱布尼茨公式”，并逐步讲解了微分方程的基本特征，从初等三角学的角度呈现了微分方程的意义。本书行文简洁、图例丰富、启发性强，可作为了解微分方程的科普读物，也适合相关专业的学生阅读和参考。

关键词： auxiliary differential equation   convolutional perfectly matched layer   finite-difference time-domain   weakly conditionally stable  

摘要： On basis of the auxiliary differential equation, an improved convolutional perfectly matched layer (CPML) scheme has been proposed and applied to the three-dimensional weakly conditionally stable (WCS) finite-difference time-domain (FDTD) method. With this method, the maximum error is reduced effectively, and the absorption effect is more obvious. The simulation results show the proposed improvement scheme with more calculation accuracy than initial CPML under various Courant-Friedrich-Levy number (CFLN) conditions while ensuring similar calculation time and memory cost. Moreover, when CFLN is increased to eight, the calculation time is <30% of the traditional method, which proved the proposed method with more calculation efficiency.

关键词： 常微分方程  

ISBN: (纸本)9787121435430

摘要： 本书主要介绍常微分方程的一些常用解析方法和数值方法，对于一阶常微分方程，介绍了4种常用的解析方法，即变量分离法、常数变易法、积分因子法、参数表示法；对于高阶常微分方程，重点讨论了特征根法、比较系数法、拉普拉斯变换法、降阶法和幂级数法；对于线性常微分方程组，介绍了其一般理论及基解矩阵的计算等。此外，本书还介绍了常微分方程初值问题和边值问题的数值求解方法，这些数值方法不仅包括经典的欧拉方法、Runge-Kutta方法、有限差分方法、有限元方法等，还涉及近年来数值计算中流行的配点方法。解析方法与数值方法并驾齐驱，相互促进，是求解常微分方程的两种重要手段。本书以各类方法为切入点，通过引入大量的经典常微分方程模型，深入浅出地阐述了各种模型问题的求解。本书可供数学专业高年级本科生或研究生阅读，也可作为从事数学建模、数学实验、科学工程计算等方面工作的理工类专业人员的参考书。

摘要： We show that local deformations, near closed subsets, of solutions to open partial differential relations can be extended to global deformations, provided all but the highest derivatives stay constant along the subset. The applicability of this general result is illustrated by a number of examples, dealing with convex embeddings of hypersurfaces, differential forms, and lapse functions in Lorentzian geometry. The main application is a general approximation result by sections that have very restrictive local properties on open dense subsets. This shows, for instance, that given any K is an element of Double-struck capital R every manifold of dimension at least 2 carries a complete C-1,C- 1-metric which, on a dense open subset, is smooth with constant sectional curvature K. Of course, this is impossible for C-2-metrics in general. (c) 2021 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.

关键词： cardiovascular disease   diabetic nephropathy   randomized trial   SGLT2 inhibitor   type 2 diabetes  

摘要： Aims This prespecified exploratory analyses from VERTIS CV (NCT01986881) aimed to assess the effects of the sodium-glucose cotransporter-2 (SGLT2) inhibitor ertugliflozin on glucosuria-related (glycated haemoglobin [HbA1c], uric acid, body weight) and natriuresis-related (blood pressure, haemoglobin, haematocrit, serum albumin) biomarkers according to kidney function risk category. Materials and methods Patients with type 2 diabetes and atherosclerotic cardiovascular disease were randomized to placebo, ertugliflozin 5 mg, or ertugliflozin 15 mg (1:1:1). Analyses compared placebo (n = 2747) versus ertugliflozin (pooled;n = 5499) on glucosuria- and natriuresis-related biomarkers according to baseline estimated glomerular filtration rate (eGFR) subgroup and Kidney Disease: Improving Global Outcomes in Chronic Kidney Disease (KDIGO CKD) risk category. Results Patients were classified according to KDIGO CKD low- (49%), moderate- (32%) and high-/very-high-risk categories (19%), and eGFR groups 1 (25%), 2 (53%) and 3 (19%). At Week 18, the high-/very-high-risk category had a smaller placebo-subtracted least squares mean (LSM) change from baseline (95% confidence interval) in HbA1c (-0.34 [-0.43, -0.25]) compared with the low- and moderate-risk categories (-0.54 [-0.60, -0.49] and - 0.47 [-0.54, -0.40], respectively). This pattern was maintained throughout the study (P-interaction = 0.0001). Similar patterns based on baseline eGFR G stage were observed. Placebo-subtracted LSM changes from baseline in uric acid were lowest in the high-/very-high-risk category at Weeks 6 and 18, but the pattern was not maintained after Week 156 (P-interaction = 0.15). Effects of ertugliflozin on body weight and natriuresis-related biomarkers did not differ across KDIGO CKD categories. Conclusions In VERTIS CV, ertugliflozin was associated with physiologically favourable changes in glucosuria- and natriuresis-related biomarkers. Glycaemic efficacy of ertugliflozin was attenuated in patients wi

关键词： Privacy   Volume measurement   Differential privacy   Transportation   Estimation   Cyber-physical systems   Servers   Differential privacy   traffic estimation   vehicular cyber-physical system  

摘要： Intelligent vehicular cyber-physical systems can perform automatic traffic measurement, which provides critical information for transportation engineering. However, one of the biggest challenges in traffic measurement is to protect the vehicles' location and trajectory privacy, which may be revealed from the recorded traffic data. Prior studies in traffic measurement only offer heuristic privacy protection but lack a precisely defined privacy model. This article proposes an efficient traffic estimator with differential privacy protection. In our design, each road-side unit communicates with the passing vehicles and records their presence in a privacy-preserving data structure. By performing probabilistic analysis on the anonymized records, the proposed method can precisely estimate the number of common vehicles passed by multiple given locations during the given measurement period. Through theoretical analysis, we prove that the proposed method can protect the trajectory privacy of the vehicles with epsilon-differential privacy even when the point privacy has been leaked. We also evaluated our traffic estimator based on a real-world transportation traffic dataset. The evaluation results demonstrate that the proposed estimator can achieve high estimation accuracy and high-level privacy protection through controllable tradeoffs.

关键词： Differential Evolution   Harris Hawk optimization algorithm   localization error   localization time   node localization   optimization   wireless sensor network  

摘要： Despite the close of a tumultuous 2020 and the start of 2021, connected devices will continue to shape the future of numerous industries, and businesses are confident that the Internet of Things (IoT) will play a key role in the future success of their trade. The growing Internet of Things (IoT) is connecting devices to a variety of sensors, applications, and other IoT elements to automate business processes and support human efficiencies in business and the home. WSN along with node localization algorithms can play a critical role in IoT applications. Nevertheless, in IoT applications, the context of real-time location-based services is gaining an overwhelming interest. To do this, several approaches are proposed in the recent literature based mainly on computational intelligence algorithms. This paper proposes a node localization algorithm based on swarm intelligence algorithms, that is, a hybrid Harris Hawks optimization based on differential evolution (HHODE).HHODE algorithm relies on Euclidian Distance as objective function to evaluate best-fit coordinates of sensor nodes in a wireless sensor network. Moreover, several experimentations are performed depending on the network size, communication range of sensors, geographical distribution, and the beacon nodes' density to demonstrate the efficiency of the HHODE algorithm. Compared to Standard DE, HOO, PSO, and Bat Algorithm, HHODE shows higher performance with regard to node localization.