Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations

Intelligent personal assistant systems for information-seeking conversations are increasingly popular in real-world applications, especially for e-commerce companies. With the development of research in such conversation systems, the pseudo-relevance feedback (PRF) has demonstrated its effectiveness in incorporating relevance signals from external documents. However, the existing studies are either based on heuristic rules or require heavy manual labeling. In this work, we treat the PRF selection as a learning task and proposed a reinforced learning based method that can be trained in an end-to-end manner without any human annotations. More specifically, we proposed a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT based response ranker to rank the PRF-enhanced responses. The performance of the ranker serves as rewards to guide the selector to extract useful PRF terms, and thus boost the task performance. Extensive experiments on both standard benchmark and commercial datasets show the superiority of our reinforced PRF term selector compared with other potential soft or hard selection methods. Both qualitative case studies and quantitative analysis show that our model can not only select meaningful PRF terms to expand response candidates but also achieve the best results compared with all the baseline methods on a variety of evaluation metrics. We have also deployed our method on online production in an e-commerce company, which shows a significant improvement over the existing online ranking system

A hyper-heuristic based ensemble genetic programming approach for stochastic resource constrained project scheduling problem

In project scheduling studies, to the best of our knowledge, the hyper-heuristic collaborative scheduling is first-time applied to project scheduling with random activity durations. A hyper-heuristic based ensemble genetic programming (HH-EGP) method is proposed for solving stochastic resource constrained project scheduling problem (SRCPSP) by evolving an ensemble of priority rules (PRs). The proposed approach features with (1) integrating the critical path method into the resource-based policy class to generate schedules; (2) improving the existing single hyper-heuristic project scheduling research to construct a suitable solution space for solving SRCPSP; and (3) bettering genetic evolution of each subpopulation from a decision ensemble with three different local searches in corporation with discriminant mutation and discriminant population renewal. In addition, a sequence voting mechanism is designed to deal with collaborative decision-making in the scheduling process for SRCPSP. The benchmark PSPLIB is performed to verify the advantage of the HH-EGP over heuristics, meta-heuristics and the single hyper-heuristic approaches

Research on priority rules for the stochastic resource constrained multi-project scheduling problem with new project arrival

The resource constrained multi-project scheduling problem (RCMPSP) is a general and classic problem, which is usually considered and solved in a deterministic environment. However, in real project management, there are always some unforeseen factors such as one or more new project arrivals that give rise to intermittent changes in the activity duration (or stochastic duration) of the current project in execution by inserting the new project. This study takes two practical factors in terms of stochastic duration of project activities and new project arrivals waiting for insertion into account of the problem space to form a stochastic resource constrained multi-project scheduling problem with new project arrivals (SRCMPSP-NPA). Based on the benchmark of the PSPLIB (Project Scheduling Problem Library), a new data set is built and 20 priority rules (PRs) are applied to solve the problem and their performances are analyzed. In addition, a heuristic hybrid method is designed for solving the problem timely by dividing the entire scheduling process into multi-state scheduling problems solved by the corresponding rules separately. This approach has been verified by experiments and its performance is better than that of a single rule in most situations

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which traditional dynamic scheduling methods cannot solve becomes a new research challenge. Therefore, this paper designs a new bi-level distributed dynamic workshop scheduling architecture, which is based on the workshop digital twin scheduling agent and multiple service unit digital twin scheduling agents. Within this architecture, scheduling a physical workshop is decomposed to the whole workshop scheduling in the first level and its service unit scheduling in the second level. On the first level, the whole workshop scheduling is executed by its virtual workshop coordination (scheduling) agent embedded with the workshop digital twin consisting of multi-service unit digital twins. On the second level, each service unit scheduling coordinated by the first level scheduling is executed in a distributed way by the corresponding service unit scheduling agent associated with its service unit digital twin. The benefits of the new architecture include (1) if a dynamic scheduling only requires a single service unit scheduling, it will then be performed in the corresponding service unit scheduling without involving other service units, which will make the scheduling locally, simply and robustly. (2) when a dynamic scheduling requires changes in multiple service units in a coordinated way, the first level scheduling will be executed and then coordinate the second level service unit scheduling accordingly. This divide-and-then-conquer strategy will make the scheduling easier and practical. The proposed architecture has been tested to illustrate its feasibility and practicality

Grazing weakens competitive interactions between active methanotrophs and nitrifiers modulating greenhouse-gas emissions in grassland soils

This work was financially supported by Natural Science Foundation of China (41977033, 41907026, and 41721001), Fundamental Research Funds for the Central Universities (2019QNA6011), National Key Basic Research Program of China (2014CB138801), Shandong Provincial Natural Science Foundation (ZR2019BD032), China Postdoctoral Science Foundation (2020T130387 and 2019M652448). CG-R was funded by a Royal Society University Research Fellowship (UF150571). Special thanks to ChunMei Meng, Yu Luo, and Yan Zheng for their assistance in laboratory analyses.Peer reviewedPublisher PD

20(S)-Protopanaxadiol Inhibits Titanium Particle-Induced Inflammatory Osteolysis and RANKL-Mediated Osteoclastogenesis via MAPK and NF-κB Signaling Pathways

Osteolysis is a principal reason for arthroplasty failure like aseptic loosening induced by Titanium (Ti) particle. It is a challenge for orthopedic surgeons. Recent researches show that 20(S)-protopanaxadiol can inhibit inflammatory cytokine release in vitro. This study aims to assess the effect of 20(S)-protopanaxadiol on Ti particle-induced osteolysis and RANKL-mediated osteoclastogenesis. Micro-CT and histological analysis in vivo indicated the inhibitory effects of 20(S)-protopanaxadiol on osteoclastogenesis and the excretion of inflammatory cytokines. Next, we demonstrated that 20(S)-protopanaxadiol inhibited osteoclast differentiation, bone resorption area, and F-actin ring formation in a dose-dependent manner. Moreover, mechanistic studies suggested that the suppression of MAPK and NF-κB signaling pathways were found to mediate the inhibitory effects of 20(S)-protopanaxadiol. In conclusion, 20(S)-protopanaxadiol may suppress osteoclastogenesis in a dose- dependent manner and it could be a potential treatment of Ti particle-induced osteolysis

Physical and mechanical behaviors of compacted soils under hydraulic loading of wetting–drying cycles

Exposed geo−infrastructures filled with compacted soils experience cyclic wetting–drying effects due to environment and underground water fluctuations. Soil physical and mechanical behaviors are prone to deterioration to a great extent, e.g., swelling, collapse, or even slope failure, resulting in huge losses to human life, safety, and engineering construction. In this paper, hydraulic loading tests of wetting–drying cycles were carried out on compacted fine soil via a one−dimensional pressure plate apparatus equipped with bender elements. The influences of wetting–drying paths on the soil characteristics of moisture content, void ratio and shear modulus were obtained and analyzed. Results showed that cyclic wetting–drying effects weakened the soil’s water retention capacity. It was observed that it was harder for pore water to approach saturation at a lower matric suction level and to be expelled at a higher matric suction level. Typical swelling and shrinkage deformations occurred during the hydraulic loading processes, and volume expansion was generated after the drying–wetting cycles at a given value of matric suction, which deteriorated the densely compacted soils to a relatively looser state. Then, a unified soil–water characteristic surface was proposed to describe the unique relationships of moisture content, void ratio, and matric suction. Moreover, the small−strain shear modulus of the soil, in terms of shear wave velocity, was reduced by 32.2–35.5% and 13.8–25.8% at the same degree of saturation during the first and second wetting paths, respectively. Therefore, the volume expansion and modulus degradation resulting from the wetting–drying cycles should attract particular attention to avoid further distresses in the practical engineering

On-line optimization of glutamate production based on balanced metabolic control by RQ

In glutamate fermentations by Corynebacterium glutamicum, higher glutamate concentration could be achieved by constantly controlling dissolved oxygen concentration (DO) at a lower level; however, by-product lactate also severely accumulated. The results of analyzing activities changes of the two key enzymes, glutamate and lactate dehydrogenases involved with the fermentation, and the entire metabolic network flux analysis showed that the lactate overproduction was because the metabolic flux in TCA cycle was too low to balance the glucose glycolysis rate. As a result, the respiratory quotient (RQ) adaptive control based “balanced metabolic control” (BMC) strategy was proposed and used to regulate the TCA metabolic flux rate at an appropriate level to achieve the metabolic balance among glycolysis, glutamate synthesis, and TCA metabolic flux. Compared with the best results of various DO constant controls, the BMC strategy increased the maximal glutamate concentration by about 15% and almost completely repressed the lactate accumulation with competitively high glutamate productivity