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Powder metallurgy with hot isostatic pressing (PM-HIP) is an advanced manufacturing process that is envisioned to replace forging for heavy nuclear components, including the reactor pressure vessel (RPV). But PM-HIP products must at least demonstrate comparable irradiation tolerance than forgings in order to be qualified for nuclear applications. The objective of this study is to directly compare PM-HIP to forged SA508 Grade 3 Class 1 low-alloy RPV steel at two neutron irradiation conditions: ~0.5-1.0 displacements per atom (dpa) at ~270C and ~370C. PM-HIP SA508 experiences greater irradiation hardening and embrittlement (total elongation) than forged SA508. However, uniform elongation and approximate toughness are comparable across all irradiated materials, suggesting irradiated PM-HIP SA508 exhibits superior ductility at maximum load-bearing capacity. The irradiation hardening mechanism is linked to composition rather than fabrication method. Since PM-HIP SA508 has higher Mn and Ni concentration, it is more susceptible to irradiation-induced nucleation of Mn-Ni-Si-P (MNSP) nanoprecipitates and dislocation loops, which both contribute to hardening. Conversely, the forged material nucleates fewer MNSPs, causing dislocation loops to control irradiation hardening. These results show promise for the irradiation performance of PM-HIP SA508 and can motivate future nuclear code qualification of PM-HIP fabrication for RPVs.

Training a dialogue policy using deep reinforcement learning requires a lot of exploration of the environment. The amount of wasted invalid exploration makes their learning inefficient. In this paper, we find and define an important reason for the invalid exploration: dead-ends. When a conversation enters a dead-end state, regardless of the actions taken afterward, it will continue in a dead-end trajectory until the agent reaches a termination state or maximum turn. We propose a dead-end resurrection (DDR) algorithm that detects the initial dead-end state in a timely and efficient manner and provides a rescue action to guide and correct the exploration direction. To prevent dialogue policies from repeatedly making the same mistake, DDR also performs dialogue data augmentation by adding relevant experiences containing dead-end states. We first validate the dead-end detection reliability and then demonstrate the effectiveness and generality of the method by reporting experimental results on several dialogue datasets from different domains.

Dialogue policy learning based on reinforcement learning is difficult to be applied to real users to train dialogue agents from scratch because of the high cost. User simulators, which choose random user goals for the dialogue agent to train on, have been considered as an affordable substitute for real users. However, this random sampling method ignores the law of human learning, making the learned dialogue policy inefficient and unstable. We propose a novel framework, Automatic Curriculum Learning-based Deep Q-Network (ACL-DQN), which replaces the traditional random sampling method with a teacher policy model to realize the dialogue policy for automatic curriculum learning. The teacher model arranges a meaningful ordered curriculum and automatically adjusts it by monitoring the learning progress of the dialogue agent and the over-repetition penalty without any requirement of prior knowledge. The learning progress of the dialogue agent reflects the relationship between the dialogue agent's ability and the sampled goals' difficulty for sample efficiency. The over-repetition penalty guarantees the sampled diversity. Experiments show that the ACL-DQN significantly improves the effectiveness and stability of dialogue tasks with a statistically significant margin. Furthermore, the framework can be further improved by equipping with different curriculum schedules, which demonstrates that the framework has strong generalizability.

There exists many complicated $k$-noncrossing pseudoknot RNA structures in nature based on some special conditions. The special characteristic of RNA structures gives us great challenges in researching the enumeration, prediction and the analysis of prediction algorithm. We will study two kinds of typical $k$-noncrossing pseudoknot RNAs with complex structures separately., Comment: 117 pages, 79 figures. The thesis has been defended and published in summer of 2012. This version is updated for correcting several unmeant mistakes and clerical error. It is very thankful for Prof. Christian Reidys and Prof. Qinghu Hou's guidance

Plenty of in-process vulnerabilities are blamed on various out of bound memory accesses. Previous prevention methods are mainly based on software checking associated with performance overhead, while traditional hardware protection mechanisms only work for inter-process memory accesses. In this paper we propose a novel hardware based in-process isolation system called PULP (Protection by User Level Partition). PULP modifies processor core by associating program counter and virtual memory address to achieve in-process data isolation. PULP partitions the program into two distinct parts, one is reliable, called primary functions, and the other is unreliable, called secondary functions, the accessible memory range of which can be configured via APIs. PULP automatically checks the memory bound when executing load/store operations in secondary functions. A RISC-V based FPGA prototype is implementated and functional test shows that PULP can effectively prevent in-process bug, including the Heartbleed and other buffer overflow vulnerabilities, etc. The total runtime overhead of PULP is negligible, as there is no extra runtime overhead besides configuring the API. We run SPEC2006 to evaluate the average performance, considering the LIBC functions as secondary functions. Experimental timing results show that, running bzip2, mcf, and libquantum, PULP bears low runtime overhead (less than 0.1%). Analysis also shows that PULP can be used effectively to prevent the newest "Spectre" bug which threats nearly all out-of-order processors.