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Since the beginning of the 21st century, China’s Internet has developed rapidly, and the rapid development of the network has driven the creation and rapid spread of online transactions across the country. It is in this context that a new kind of intermediate trading platform has been developed, which has rapidly swept through the lives of the public in the past decade. From the perspective of finance, supplemented by personal research, the author begins the article by clarifying the meaning of China’s network payment and other related concepts and briefly introduces the development of network payment at home and abroad in recent years, analyzing and listing the risks and their causes in the development of network payment; then introduces the relevant documents and measures taken by China for the regulation of network payment and analyzes the current Then, we introduce the relevant documents and measures taken by China’s online payment regulation and analyze the dilemma faced by China in preventing the risk of online payment under the current regulatory policy, and further conclude the risk prevention countermeasures to improve the regulation of online payment in China.

This article studies the optimal mean-variance reinsurance-investment selection for insurers with mispricing. Assuming that insurers wish to purchase proportional/excess-of-loss reinsurance and exc...

In this paper, we adopt a Poisson approach to find Laplace transforms of joint occupation times over n disjoint intervals for pre-exit diffusion processes. Then we generalize previous result for the 2-dimensional case and the 3-dimensional case.

In this paper, we analyze a robust optimal investment-reinsurance problem involving a defaultable security for an ambiguity-averse insurer(AAI), who worries about uncertainty in model parameters. T...

In this paper, we apply the martingale approach to investigate the optimal investment and risk control problem for an insurer in an incomplete market. The claim risk of per policy is characterized ...

Artificial light at night has been proposed as a global threat to biodiversity. Insectivorous bats are strictly nocturnal animals that are vulnerable to disruption from artificial light. Given that many light-sensitive bats tend to avoid night light during roost departure, it is often assumed that nighttime light pollution reduces their foraging opportunities, albeit empirical evidence in support of this hypothesis remains elusive. Here, we used least horseshoe bats, Rhinolophus pusillus, to assess whether white artificial light is detrimental for the opportunities of foraging. We manipulated the levels of ambient illumination and perceived predation risk inside the bat roost. We monitored bats' emergence activity using high-speed video and audio recording systems. DNA-based faecal dietary analysis and insect survey were applied to determine activity time of prey in foraging areas. Following experimentally manipulation of white light-emitting diode (LED) lighting 0–15 min after sunset, bat pass, flight duration, and echolocation pulse emission decreased. The mean emergence time of bats flying out was delayed by 14 min under lit treatment compared with the dark control. Only 10% of bats left for foraging during 40 min of light exposure. Aversive effects of LED light on bat emergence were robust regardless of the presence of a potential predator. Insect prey reached a peak of abundance between 30 and 60 min after sunset. These results demonstrate that white artificial light hinders evening emergence behavior in least horseshoe bats, leading to a mismatch between foraging onset and peak food availability. Our findings highlight that light pollution overrides foraging onset, suggesting the importance of improving artificial lighting scheme near the roosts of light-sensitive bats.

In this paper, we consider a robust optimal investment-reinsurance problem with a default risk. The ambiguity-averse insurer (AAI) may carry out transactions on a risk-free asset, a stock, and a defaultable corporate bond. The stock’s price is described by a jump-diffusion process, and both the jump intensity and the distribution of jump amplitude are uncertain, i.e., the jump is ambiguous. The AAI’s surplus process is assumed to follow an approximate diffusion process. In particular, the reinsurance premium is calculated according to the generalized mean-variance premium principle, and the reinsurance type has to follow a self-reinsurance function. In performing dynamic programming, both the predefault case and the postdefault case are analyzed, and the optimal strategies and the corresponding value functions are derived under the worst-case scenario. Moreover, we give a detailed proof of the verification theorem and give some special cases and numerical examples to illustrate our theoretical results.

The taboo rate is first defined, which satisfies with the Chapman–Kolmogorov equation. Then the differentials of hitting time distribution are expressed by many different taboo rates, which deeply reveal the intrinsic relationship between the transition rate matrix and the hitting time distribution in continuous-time reversible Markov chains. As an example, the explicit expressions of the differentials of the hitting time distribution at a single state are provided for the birth and death chain, hence the transition rate matrix can be identified. Such differentials improve the theory of statistical identification of continuous-time reversible Markov chains.

This work investigates an optimal financing and dividend problem for an insurer whose surplus process is modulated by an observable continuous-time and finite-state Markov chain. We assume that the insurer should never go bankrupt by issuing new equity. The goal of the insurer is to maximize the expected present value of the dividends payout minus the discounted cost of equity issuance. We obtain the optimal policies and explicit expressions for the value functions when the risk reserve process is modeled by both upward jump model and its diffusion approximation. Numerical illustrations of the sensitivities of the model parameters are provided.

This article studies the optimal proportional reinsurance and investment problem under a constant elasticity of variance (CEV) model. Assume that the insurer's surplus process follows a jump-diffusion process, the insurer can purchase proportional reinsurance from the reinsurer via the variance principle and invest in a risk-free asset and a risky asset whose price is modeled by a CEV model. The diffusion term can explain the uncertainty associated with the surplus of the insurer or the additional small claims. The objective of the insurer is to maximize the expected exponential utility of terminal wealth. This optimization problem is studied in two cases depending on the diffusion term's explanation. In all cases, by using techniques of stochastic control theory, closed-form expressions for the value functions and optimal strategies are obtained.

This paper analyzes the optimal proportional reinsurance and investment problem for an insurer in a defaultable market. We assume that the reinsurance premium is calculated via the exponential premium principle. The insurer can allocate his/her wealth among the following securities: a bank account, a risky stock asset and a corporate bond. We decompose the original optimization problem into two sub-problems: a pre-default case and a post-default case. The optimal reinsurance and investment policies that maximize the expected CARA utility of the terminal wealth are explicitly derived. Numerical examples are given to illustrate our results, and we discuss relevant economic insights obtained from these results.

This paper considers a d-dimensional stochastic optimization problem in neuroscience. Suppose the arm’s movement trajectory is modeled by high-order linear stochastic differential dynamic system in d-dimensional space, the optimal trajectory, velocity, and variance are explicitly obtained by using stochastic control method, which allows us to analytically establish exact relationships between various quantities. Moreover, the optimal trajectory is almost a straight line for a reaching movement; the optimal velocity bell-shaped and the optimal variance are consistent with the experimental Fitts law; that is, the longer the time of a reaching movement, the higher the accuracy of arriving at the target position, and the results can be directly applied to designing a reaching movement performed by a robotic arm in a more general environment.

A class of delayed renewal risk processes with multi-layer dividend strategy is addressed here. Under the assumption that the premium rate is a step function depending on the current surplus level, a piecewise integro-differential equation for the Gerber–Shiu discounted penalty function in the delayed renewal risk model is derived, as an analogue of that in the ordinary renewal model, and the relationship between this function and the one in the ordinary renewal model is investigated. Subsequently, this relationship is detailed as regards both the stationary renewal risk model and the ruin probability. Finally, explicit expressions for some ruin-related quantities are included to illustrate the procedure, where the inter-claim times are generalized Erlang(2) distributed and the claims are exponentially distributed.

Based on the usual approximation scheme, we lay a so-called second order spiking network with the renewal process inputs, which employ both first and second order statistical representation, i.e., the means, variances and correlations of the synaptic input. Then we apply an error minimization technique to train the network and derive the corresponding backpropagation learning rule to present a more biologically plausible so-called Second Order Spiking Perceptron. It shows that such perceptron, even a single neuron, is able to perform various complex non-linear tasks like the XOR problem that classical single-layer perceptrons are unable to perform. Among including the second order statistics in computations, such perceptron offers the most important advantage over their predecessors, in that it can train not only the output means but the output variances by introducing the variance term in the error presentation. As a result, we can not only obtain the desired output means but decrease the output noises (variances) by training the networks, and also can reach the trade-off between the output mean error and output variance by the adjustment of the penalty factor in error function, due to a specific learning task.

We consider how to determine all transition rates of an ionic channel when it can be conformationally described by a star-graph branch Markov chain with continuous time. It is found that all transition rates are uniquely determined by the distributions of their lifetime and death-time at the end state of each branch. An algorithm to exactly calculate all transition rates is developed. Numerical examples are included to demonstrate the application of our approach to data.

The stability of synchronized states (including equilibrium point, periodic orbit or chaotic attractor) in stochastic coupled dynamical systems (ordinary differential equations) is considered. A general approach is presented, based on the master stability function, Gershg¨ orin disc theory and the extreme value theory in statistics, to yield constraints on the distribution of coupling to ensure the stability of synchronized dynamics. Three types of different behaviour: global-stable, exponential-stable and power-stable, are found, depending on the nature of the distribution of the interactions between units. Systems with specific coupling schemes are used as examples to illustrate our general method.

We explore neuronal mechanisms of discriminating between masked signals. It is found that when the correlation between input signals is zero, the output signals are separable if and only if input signals are separable. With positively (negatively) correlated signals, the output signals are separable (mixed) even when input signals are mixed (separable). Exact values of discrimination capacity are obtained for two most interesting cases: the exactly balanced inhibitory and excitatory input case and the uncorrelated input case. Interestingly, the discrimination capacity obtained in these cases is independent of model parameters, input distribution and is universal. Our results also suggest a functional role of inhibitory inputs and correlated inputs or, more generally, the large variability of efferent spike trains observed in in vivo experiments: the larger the variability of efferent spike trains, the easier it is to discriminate between masked input signals.

We consider how to determine all transition rates of an ion channel when it can be described by a birth–death chain or a Markov chain on a star-graph with continuous time. It is found that all transition rates are uniquely determined by the distribution of its lifetime and death-time histograms at a single state. An algorithm to calculate the transition rates exactly, based on the statistics of the lifetime and death-time of the Markov chain at the state, is provided. Examples to illustrate how an ion channel activity is fully determined by the observation of a single state of the ion channel are included.

In terms of the informax principle, and the input-output relationship of the integrate-and-fire (IF) model, IF neuron learning rules are developed. For supervised learning and with uniform weight of synapses (the theoretically tractable case), we show that the derived learning rule is stable and the stable state is unique. For unsupervised learning, within physiologically reasonable parameter regions, both long-term potentiation (LTP) and long-term depression (LTD) could happen when the inhibitory input is weak, but LTD cannot be observed when inhibitory input is strong enough. When both LTP and LTD occur, LTD is observable when the output of the postsynaptic neuron is faster than pre-synaptic inputs, otherwise LTP is observable, as observed in recent experiments. Learning rules of general cases are also studied and numerical examples show that the derived learning rule tends to equalize the contribution of different inputs to the output firing rates.

We have developed a totally different approach from Maximum likelihood widely employed to estimate the kinetic constants of single-ion channels of hierarchical mechanism. It is found that all kinetic constants are uniquely determined by the probability density functions of their lifetime and death-time of the middle states. An algorithm to calculate all kinetic constants is provided, which employs the intrinsic properties of the Markov process. Once we have them, all subsequent calculations are then automatic and exact.

NOK (Novel Oncogene with Kinase-domain) is a newly identified receptor protein-tyrosine kinases (RPTKs) subfamily, which possesses strong oncogenic potential including enhancing cell transformation, tumorigenesis, invasion and metastasis. However, NOK protein lacks extracellular domain, and how the NOK is activated by the membrane receptor and the expression of NOK in non small cell lung cancer (NSCLC) are not clear. Our aim of this study was to investigate the expressions of NOK, EGFR proteins in NSCLC.The expression rates of NOK, EGFR proteins in 155 cases with NSCLC was deteced by immunohistochemical stain; The clinical correlations between two proteins were analyzed by statistics.The NOK positive expression was mainly located in cytoplasm and EGFR was mainly located in the plasma membrane and cytoplasm. NOK, EGFR proteins were high expressed in NSCLC and there were extremely differences comparing the expression of NOK, EGFR proteins in squamous cell lung cancer, lung adenocarcinoma with those in corresponding normal tissue respectively (P0.001); and there were no significant differences between the two kinds of the cancers for their comparison of NOK, EGFR expression (P=0.099; P=0.23); however, there were significant differences for NOK and EGFR expression among histological grades and TNM stages in squamous cell lung cancers, lung adenocarcinoma (P0.01). There were correlation between positive and intensity of NOK and EGFR expression in whatever total NSCLC or squamous cell lung cancer or lung adenocarcinoma (P0.001), while the correlation coefficients(rs) were 0742, 0.722, 0.756 respectively.The NOK proteins are highly expressed in the NSCLC, and the expression rates of NOK proteins are correlated with the cancers' histological grades and TNM stages, which may be concern with tumorgenesis and progression; the positive expression rate and intensity of NOK protein correlate well with the expression of EGFR protein in NSCLC, which the activation of NOK probably owe to EGFR activation, all that would provide evidence for further study on the effective mechanism of NOK in the tumorigenesis or tumor progression.

Multidrug resistance (MDR) is the main reason of the failure of chemotherapy in lung cancer. The MDR mechanism in lung cancer is complex, which relates to sorts of drug resistant genes. ABCC10 is a recently identified member of a subset of the C family of ATP-binding cassette (ABC) transporters. The drug resistance capability of ABCC10 has been partly determined. The aim of the study is to investigate the expression of ABCC10 in non small cell lung cancer (NSCLC) and normal tissues, and analyze the relationship between ABCC10 expression level and clinical features, such as TNM stages, histological types and pathological grades.The expression of ABCC10 was detected in squamous cell carcinoma, adenocarcinoma and normal lung tissues by immunohistochemistry. Then the statistical analysis was performed.The expression of ABCC10 was mainly located in cell membrane and cytoplasm. There were significant differences between the expression of ABCC10 in NSCLC and normal tissues (P0.01). The positive rates in squamous cell carcinoma and adenocarcinoma were 73.26% and 81.16%, respectively with significant differences (P0.05). There were significant differences among histological grades in squamous cell carcinoma (P0.01). There were significant differences among histological grades and TNM stages respectively in adenocarcinoma (P0.01).ABCC10 is overexpressed in NSCLC. The expression of ABCC10 in adenocarcinoma is higher than that in squamous cell carcinoma. The expression in adenocarcinoma is correlated to pathological grades and TNM stages. The results of this study would be benefit for further study of ABCC10 in tumor and its chemoresistance.

We present the non-linear properties of Spike-Rate Perceptron with super-Poisson inputs, which employs both first and second statistical representation, i.e. the means, variances and correlations of the synaptic input. It shows that such perceptron, even a single neuron, is able to perform various complex non-linear tasks like the XOR problem. Here such perceptron offers a significant advantage over classical models, in that they include both the mean and the variance of the input signal.

Spike trains recorded in cortical neurons in vivo can be approximated by renewal processes, but are generally not Poisson. Besides, the spiking activity of neighboring neurons display small yet not negligible correlations. The Artificial Neuronal Network theory has traditionally neglected such observations, assuming that neurons could simply be described by their mean firing rate. Here we present a theoretical framework in which the dynamics of a system of neurons is specified in terms of higher-order moments of their spiking activity beyond the mean firing rate.

A theoretical framework is developed for moment neuronal networks (MNNs). Within this framework, the behavior of the system of spiking neurons is specified in terms of the first- and second-order statistics of their interspike intervals, i.e., the mean, the variance, and the cross correlations of spike activity. Since neurons emit and receive spike trains which can be described by renewal--but generally non-Poisson--processes, we first derive a suitable diffusion-type approximation of such processes. Two approximation schemes are introduced: the usual approximation scheme (UAS) and the Ornstein-Uhlenbeck scheme. It is found that both schemes approximate well the input-output characteristics of spiking models such as the IF and the Hodgkin-Huxley models. The MNN framework is then developed according to the UAS scheme, and its predictions are tested on a few examples.