On Trading American Put Options with Interactive Volatility

We introduce a simple stochastic volatility model, whose novelty consists in taking into account hitting times of the asset price, and study the optimal stopping problem corresponding to a put option whose time horizon (after the asset price hits a certain level) is exponentially distributed. We obtain explicit optimal stopping rules in various cases one of which is interestingly complex because of an unexpected disconnected continuation region. Finally, we discuss in detail how these stopping rules could be used for trading an American put when the trader expects a market drop in the near future.Comment: improved version (minor corrections), 28 pages, 6 figure

A Multi-robot Coverage Path Planning Algorithm Based on Improved DARP Algorithm

The research on multi-robot coverage path planning (CPP) has been attracting more and more attention. In order to achieve efficient coverage, this paper proposes an improved DARP coverage algorithm. The improved DARP algorithm based on A* algorithm is used to assign tasks to robots and then combined with STC algorithm based on Up-First algorithm to achieve full coverage of the task area. Compared with the initial DARP algorithm, this algorithm has higher efficiency and higher coverage rate

Reinforcement learning design for cancer clinical trials

There has been significant recent research activity in developing therapies that are tailored to each individual. Finding such therapies in treatment settings involving multiple decision times is a major challenge. In this dissertation, we develop reinforcement learning trials for discovering these optimal regimens for life-threatening diseases such as cancer. A temporal-difference learning method called Q-learning is utilized which involves learning an optimal policy from a single training set of finite longitudinal patient trajectories. Approximating the Q-function with time-indexed parameters can be achieved by using support vector regression or extremely randomized trees. Within this framework, we demonstrate that the procedure can extract optimal strategies directly from clinical data without relying on the identification of any accurate mathematical models, unlike approaches based on adaptive design. We show that reinforcement learning has tremendous potential in clinical research because it can select actions that improve outcomes by taking into account delayed effects even when the relationship between actions and outcomes is not fully known. To support our claims, the methodology's practical utility is firstly illustrated in a virtual simulated clinical trial. We then apply this general strategy with significant refinements to studying and discovering optimal treatments for advanced metastatic stage IIIB/IV non-small cell lung cancer (NSCLC). In addition to the complexity of the NSCLC problem of selecting optimal compounds for first and second-line treatments based on prognostic factors, another primary scientific goal is to determine the optimal time to initiate second-line therapy, either immediately or delayed after induction therapy, yielding the longest overall survival time. We show that reinforcement learning not only successfully identifies optimal strategies for two lines of treatment from clinical data, but also reliably selects the best initial time for second-line therapy while taking into account heterogeneities of NSCLC across patients

3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement

With the introduction of spectral-domain optical coherence tomography (SDOCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of 3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of data consisted of 97 OCT images with a resolution of 496 512; experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.Comment: 27 pages, 19 figure

Space and social life : morphological self-evolution of urban villages in Hangzhou

"Village in city" is a unique phenomenon in the process of China's rapid urbanization. As the concentrated embodiment of urban-rural conflicts in the period of urban transformation, urban villages have received extensive attention in the physical space and social security issues. They seem chaotic, but contain rich and colourful social life. They are vibrant communities, which provide a large number of cheap houses for the migrants. Different from modern cities or traditional villages, individuals of different classes and backgrounds constantly compete and cooperate here, forming a unique "community of social life" in urban villages. Based on the perspective of urban morphology, this paper takes Gaotang community, a typical urban village in Hangzhou as the research object, and analyses the relationship between its spatial pattern and social life through field investigation, interview and mapping. Firstly, this paper analyses the texture and public space characteristics of the self-evolution of urban villages in the context of urbanization. Then it insights the life integration of different population based on the environment-behaviour studies, which shows the daily life scenes and neighbourhood relations in a richer spatial level. The research will help to better understand the role of urban village as a social life community carrying a variety of lives. At the same time, the richness and complexity of urban village space and social life provide design strategies and reference for urban organic renewal and future community construction

How Leaders Generate Meanings For Monetary Rewards

Scant research has focused on how to increase the value of monetary rewards when they are delivered by leaders to employees. Drawing upon the perspectives of sensegiving and sensemaking, this study explores how leaders generate meanings of monetary rewards perceived by employee recipients in organizational settings. Using a qualitative method design and analyzing qualitative data from 291 incidents, we found that in the distribution process of monetary rewards, sensemaking of employees included strong and weak instrumental meanings as well as symbolic meanings. The results show that leaders adopted a set of sensegiving strategies in distributing monetary rewards including emphasizing money gain/loss and utility, providing feedback, valuing employees, orienting toward the future, guiding values, and publicizing. In the presence of leader’s sensegiving, employee recipients endorsed more positive symbolic meanings of monetary rewards (i.e., recognition and respect). Our research offers a richer view of the role of leader’s sensegiving in making monetary rewards gain more value through employees’ sensemaking, and enriches understanding of monetary rewards, leadership, sensegiving and sensemaking