The Power of Static Pricing for Reusable Resources

We consider the problem of pricing a reusable resource service system. Potential customers arrive according to a Poisson process and purchase the service if their valuation exceeds the current price. If no units are available, customers immediately leave without service. Serving a customer corresponds to using one unit of the reusable resource, where the service time has an exponential distribution. The objective is to maximize the steady-state revenue rate. This system is equivalent to the classical Erlang loss model with price-sensitive customers, which has applications in vehicle sharing, cloud computing, and spare parts management. Although an optimal pricing policy is dynamic, we provide two main results that show a simple static policy is universally near-optimal for any service rate, arrival rate, and number of units in the system. When there is one class of customers who have a monotone hazard rate (MHR) valuation distribution, we prove that a static pricing policy guarantees 90.4\% of the revenue from the optimal dynamic policy. When there are multiple classes of customers that each have their own regular valuation distribution and service rate, we prove that static pricing guarantees 78.9\% of the revenue of the optimal dynamic policy. In this case, the optimal pricing policy is exponentially large in the number of classes while the static policy requires only one price per class. Moreover, we prove that the optimal static policy can be easily computed, resulting in the first polynomial time approximation algorithm for this problem

Neural network encoded variational quantum algorithms

We introduce a general framework called neural network (NN) encoded variational quantum algorithms (VQAs), or NN-VQA for short, to address the challenges of implementing VQAs on noisy intermediate-scale quantum (NISQ) computers. Specifically, NN-VQA feeds input (such as parameters of a Hamiltonian) from a given problem to a neural network and uses its outputs to parameterize an ansatz circuit for the standard VQA. Combining the strengths of NN and parameterized quantum circuits, NN-VQA can dramatically accelerate the training process of VQAs and handle a broad family of related problems with varying input parameters with the pre-trained NN. To concretely illustrate the merits of NN-VQA, we present results on NN-variational quantum eigensolver (VQE) for solving the ground state of parameterized XXZ spin models. Our results demonstrate that NN-VQE is able to estimate the ground-state energies of parameterized Hamiltonians with high precision without fine-tuning, and significantly reduce the overall training cost to estimate ground-state properties across the phases of XXZ Hamiltonian. We also employ an active-learning strategy to further increase the training efficiency while maintaining prediction accuracy. These encouraging results demonstrate that NN-VQAs offer a new hybrid quantum-classical paradigm to utilize NISQ resources for solving more realistic and challenging computational problems.Comment: 4.4 pages, 5 figures, with supplemental material

Diverse biological effects of glycosyltransferase genes from Tartary buckwheat

Background: Tartary buckwheat (Fagopyrum tataricum) is an edible cereal crop whose sprouts have been marketed and commercialized for their higher levels of anti-oxidants, including rutin and anthocyanin. UDP-glucose flavonoid glycosyltransferases (UFGTs) play an important role in the biosynthesis of flavonoids in plants. So far, few studies are available on UFGT genes that may play a role in tartary buckwheat flavonoids biosynthesis. Here, we report on the identification and functional characterization of seven UFGTs from tartary buckwheat that are potentially involved in flavonoid biosynthesis (and have varying effects on plant growth and development when overexpressed in Arabidopsis thaliana.) Results: Phylogenetic analysis indicated that the potential function of the seven FtUFGT proteins, FtUFGT6, FtUFGT7, FtUFGT8, FtUFGT9, FtUFGT15, FtUFGT40, and FtUFGT41, could be divided into three Arabidopsis thaliana functional subgroups that are involved in flavonoid biosynthesis of and anthocyanin accumulation. A significant positive correlation between FtUFGT8 and FtUFGT15 expression and anthocyanin accumulation capacity was observed in the tartary buckwheat seedlings after cold stress. Overexpression in Arabidopsis thaliana showed that FtUFGT8, FtUFGT15, and FtUFGT41 significantly increased the anthocyanin content in transgenic plants. Unexpectedly, overexpression of FtUFGT6, while not leading to enhanced anthocyanin accumulation, significantly enhanced the growth yield of transgenic plants. When wild-type plants have only cotyledons, most of the transgenic plants of FtUFGT6 had grown true leaves. Moreover, the growth speed of the oxFtUFGT6 transgenic plant root was also significantly faster than that of the wild type. At later growth, FtUFGT6 transgenic plants showed larger leaves, earlier twitching times and more tillers than wild type, whereas FtUFGT15 showed opposite results. Conclusions: Seven FtUFGTs were isolated from tartary buckwheat. FtUFGT8, FtUFGT15, and FtUFGT41 can significantly increase the accumulation of total anthocyanins in transgenic plants. Furthermore, overexpression of FtUFGT6 increased the overall yield of Arabidopsis transgenic plants at all growth stages. However, FtUFGT15 shows the opposite trend at later growth stage and delays the growth speed of plants. These results suggested that the biological function of FtUFGT genes in tartary buckwheat is diverse

Intelligent Agents for Negotiation and Recommendation in Mass Customization

Mass customization, as a means to meet individual consumer’s need on a large scale, has recently attracted the attention of both researchers and practitioners. However, as customers and their needs grow increasingly diverse, meeting every consumer’s need has become a surefire way to add unnecessary cost and complexity to operations. Furthermore, consumers are not all really ready for mass customization. They have to face inconveniences such as expensive price, delay delivery and they have to spend time “designing” their product. In order to solve this problem, we proposed a way of intelligent agent assisted negotiation and recommendation. The recommendation is a preference elicitation process, while the negotiation is a communication process based on the situation of manufacturer, such as the inventory level, production cost and lead time. With the aid of intelligent agent of negotiation and recommendation, a good balance between efficiency and customer satisfactions of mass customization can be reached

MuLTI: Efficient Video-and-Language Understanding with MultiWay-Sampler and Multiple Choice Modeling

Video-and-language understanding has a variety of applications in the industry, such as video question answering, text-video retrieval and multi-label classification. Existing video-and-language understanding methods generally adopt heavy multi-modal encoders and feature fusion modules, which consume large amounts of GPU memory. Especially, they have difficulty dealing with dense video frames or long text that are prevalent in industrial applications. In this paper, we propose MuLTI, a highly accurate and memory-efficient video-and-language understanding model that achieves efficient and effective feature fusion through feature sampling and attention modules. Therefore, MuLTI can handle longer sequences with limited GPU memory. Then, we introduce an attention-based adapter to the encoders, which finetunes the shallow features to improve the model's performance with low GPU memory consumption. Finally, to further improve the model's performance, we introduce a new pretraining task named Multiple Choice Modeling to bridge the task gap between pretraining and downstream tasks and enhance the model's ability to align the video and the text. Benefiting from the efficient feature fusion module, the attention-based adapter and the new pretraining task, MuLTI achieves state-of-the-art performance on multiple datasets. Implementation and pretrained models will be released

Durability of Modified Expanded Polystyrene Concrete after Dynamic Cyclic Loading

EPS concrete was produced by mixing the expanded polystyrene spheres (EPS) and polymer emulsion and thickener to the matrix concrete, and this concrete had good vibration energy absorption characteristics. Based on the experimental data obtained on EPS volume ratio of 0%, 20%, 30%, and 40% by replacing matrix or coarse aggregate, the two design styles had nearly the same compressive strength. By applying frequency of 5 Hz, 50000 or 100000 times, 40 KN, 50 KN, and 60 KN cyclic loading, it is shown that the higher the inclusion size was, the lower the compressive strength of the EPS concrete would be; the larger the applying dynamic cyclic load was, the more obvious the compressive strength changing would be. Meanwhile, the strength of EPS concrete had no evident change after durability test. The results of this research had practical significance on using EPS concrete in some long-term cyclic dynamic load engineering

AAEC: An Adversarial Autoencoder-based Classifier for Audio Emotion Recognition

Changzeng Fu, Jiaqi Shi, Chaoran Liu, Carlos Toshinori Ishi, and Hiroshi Ishiguro. 2020. AAEC: An Adversarial Autoencoder-based Classifier for Audio Emotion Recognition. In Proceedings of the 1st International on Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop (MuSe'20). Association for Computing Machinery, New York, NY, USA, 45–51. DOI:https://doi.org/10.1145/3423327.3423669.MM '20: The 28th ACM International Conference on Multimedia [October 16, 2020

Impact of bile acids on the growth of human cholangiocarcinoma via FXR

<p>Abstract</p> <p>Background</p> <p>The objective of the study was to investigate the effect of different types of bile acids on proliferation of cholangiocarcinoma and the potential molecular mechanisms.</p> <p>Methods</p> <p>PCR assay and Western blot were performed to detect the expression of farnesoid × receptor (FXR) in mRNA and protein level. Immunohistochemical analysis was carried out to monitor the expression of FXR in cholangiocarcinoma tissues from 26 patients and 10 normal controls. The effects on in vivo tumor growth were also studied in nude mouse model.</p> <p>Results</p> <p>Free bile acids induced an increased expression of FXR; on the contrary, the conjugated bile acids decreased the expression of FXR. The FXR effect has been illustrated with the use of the FXR agonist GW4064 and the FXR antagonist GS. More specifically, when the use of free bile acids combined with FXR agonist GW4064, the tumor cell inhibitory effect was even more pronounced. But adding FXR antagonist GS into the treatment attenuated the tumor inhibitory effect caused by free bile acids. Combined treatment of GS and CDCA could reverse the regulating effect of CDCA on the expression of FXR. Administration of CDCA and GW 4064 resulted in a significant inhibition of tumor growth. The inhibitory effect in combination group (CDCA plus GW 4064) was even more pronounced. Again, the conjugated bile acid-GDCA promoted the growth of tumor. We also found that FXR agonist GW4064 effectively blocked the stimulatory effect of GDCA on tumor growth. And the characteristic and difference of FXR expressions were in agreement with previous experimental results in mouse cholangiocarcinoma tissues. There was also significant difference in FXR expression between normal and tumor tissues from patients with cholangiocarcinoma.</p> <p>Conclusions</p> <p>The imbalance of ratio of free and conjugated bile acids may play an important role in tumorigenesis of cholangiocarcinoma. FXR, a member of the nuclear receptor superfamily, may mediate the effects induced by the bile acids.</p