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In this paper we continue investigating the optimal dividend and investment problems under the Sparre Andersen model. More precisely, we assume that the claim frequency is a renewal process instead of a standard compound Poisson process, whence semi-Markovian. Building on our previous work \cite{BaiMa17}, where we established the dynamic programming principle via a {\it backward Markovization} procedure and proved that the value function is the unique {\it constrained} viscosity solution of the HJB equation, in this paper we focus on the construction of the optimal strategy. The main difficulties in this effort is two fold: the regularity of the viscosity solution to a non-local, nonlinear, and degenerate parabolic PDE on an unbounded domain, which seems to be new in its own right; and the well-posedness of the closed-loop stochastic system. By introducing an auxiliary PDE, we construct an $\e$-optimal strategy, and prove the well-posedness of the corresponding closed-loop system, via a "bootstrap" technique with the help of a Krylov estimate.

Inter-personal relationship is the basis of human society. In order to automatically identify the relations between persons from texts, we need annotated data for training systems. However, there is a lack of a massive amount of such data so far. To address this situation, we introduce IPRE, a new dataset for inter-personal relationship extraction which aims to facilitate information extraction and knowledge graph construction research. In total, IPRE has over 41,000 labeled sentences for 34 types of relations, including about 9,000 sentences annotated by workers. Our data is the first dataset for inter-personal relationship extraction. Additionally, we define three evaluation tasks based on IPRE and provide the baseline systems for further comparison in future work.

In this paper we study grasp problem in dense cluster, a challenging task in warehouse logistics scenario. By introducing a two-step robust suction affordance detection method, we focus on using vacuum suction pad to clear up a box filled with seen and unseen objects. Two CNN based neural networks are proposed. A Fast Region Estimation Network (FRE-Net) predicts which region contains pickable objects, and a Suction Grasp Point Affordance network (SGPA-Net) determines which point in that region is pickable. So as to enable such two networks, we design a self-supervised learning pipeline to accumulate data, train and test the performance of our method. In both virtual and real environment, within 1500 picks (~5 hours), we reach a picking accuracy of 95% for known objects and 90% for unseen objects with similar geometry features.

Robotic grasp detection is a fundamental capability for intelligent manipulation in unstructured environments. Previous work mainly employed visual and tactile fusion to achieve stable grasp, while, the whole process depending heavily on regrasping, which wastes much time to regulate and evaluate. We propose a novel way to improve robotic grasping: by using learned tactile knowledge, a robot can achieve a stable grasp from an image. First, we construct a prior tactile knowledge learning framework with novel grasp quality metric which is determined by measuring its resistance to external perturbations. Second, we propose a multi-phases Bayesian Grasp architecture to generate stable grasp configurations through a single RGB image based on prior tactile knowledge. Results show that this framework can classify the outcome of grasps with an average accuracy of 86% on known objects and 79% on novel objects. The prior tactile knowledge improves the successful rate of 55% over traditional vision-based strategies., Comment: submitted to ICRA2020

Though neural networks have achieved much progress in various applications, it is still highly challenging for them to learn from a continuous stream of tasks without forgetting. Continual learning, a new learning paradigm, aims to solve this issue. In this work, we propose a new model for continual learning, called Bayesian Optimized Continual Learning with Attention Mechanism (BOCL) that dynamically expands the network capacity upon the arrival of new tasks by Bayesian optimization and selectively utilizes previous knowledge (e.g. feature maps of previous tasks) via attention mechanism. Our experiments on variants of MNIST and CIFAR-100 demonstrate that our methods outperform the state-of-the-art in preventing catastrophic forgetting and fitting new tasks better., Comment: 8 pages

This paper focuses on robotic picking tasks in cluttered scenario. Because of the diversity of poses, types of stack and complicated background in bin picking situation, it is much difficult to recognize and estimate their pose before grasping them. Here, this paper combines Resnet with U-net structure, a special framework of Convolution Neural Networks (CNN), to predict picking region without recognition and pose estimation. And it makes robotic picking system learn picking skills from scratch. At the same time, we train the network end to end with online samples. In the end of this paper, several experiments are conducted to demonstrate the performance of our methods., Comment: 6 pages, 7 figures, conference

We introduce a new notion of conditional nonlinear expectation under probability distortion. Such a distorted nonlinear expectation is not sub-additive in general, so it is beyond the scope of Peng's framework of nonlinear expectations. A more fundamental problem when extending the distorted expectation to a dynamic setting is time-inconsistency, that is, the usual "tower property" fails. By localizing the probability distortion and restricting to a smaller class of random variables, we introduce a so-called distorted probability and construct a conditional expectation in such a way that it coincides with the original nonlinear expectation at time zero, but has a time-consistent dynamics in the sense that the tower property remains valid. Furthermore, we show that in the continuous time model this conditional expectation corresponds to a parabolic differential equation whose coefficient involves the law of the underlying diffusion. This work is the first step towards a new understanding of nonlinear expectations under probability distortion, and will potentially be a helpful tool for solving time-inconsistent stochastic optimization problems., Comment: 38 pages, 4 figures. To appear in Mathematics of Operations Research

Exciton spectra of monolayer transition metal dichalcogenides (TMDs) in various dielectric environments are studied. The screened hydrogen model (SHM) [Phys. Rev. Lett. 116, 056401 (2016)] is examined by comparing its exciton spectra with the radial equation (RE) solutions. While the SHM is found to describe the nonhydrogenic exciton Rydberg series reasonably well, it fails to account for the linear decrease of the exciton energy with the orbital quantum number $m$. The exciton Bohr orbit shrinks as $\lvert m\rvert$ becomes larger resulting in increased strength of the electron-hole interaction and a decrease of the exciton energy. The exciton effective radius expression of the SHM can characterize the exciton radius's dependence on $n$, but it cannot properly describe the exciton radius's dependence on $m$, which is the cause of the SHM's poor description of the exciton energy's $m$-dependence. For monolayer WS$_2$ on the SiO$_2$ substrate, our calculated $s$ exciton Rydberg series agrees closely with that measured by optical reflection spectroscopy [Phys. Rev. Lett. 113, 076802 (2014)], while the calculated $p$ excitons offer an explanation for the two broad features of a two-photon absorption spectrum [Nature 513, 214 (2014)]. Our calculated exciton energies for monolayer TMDs in various dielectric environments compare favourably with experimental data. Variational wave functions are obtained for a number of strongly bound exciton states and further used to study the Stark effects in monolayer TMDs, an analytical expression being deduced which yields a redshift of the ground state energy to a good accuracy. The numerical solution of the RE combined with the variational method provides a simple and effective approach for the study of excitons in monolayer TMDs., Comment: 27 pages, 11 figures