The Duality of Subtyping

Subtyping is a concept frequently encountered in many programming languages and calculi. Various forms of subtyping exist for different type system features, including intersection types, union types or bounded quantification. Normally these features are designed independently of each other, without exploiting obvious similarities (or dualities) between features. This paper proposes a novel methodology for designing subtyping relations that exploits duality between features. At the core of our methodology is a generalization of subtyping relations, which we call Duotyping. Duotyping is parameterized by the mode of the relation. One of these modes is the usual subtyping, while another mode is supertyping (the dual of subtyping). Using the mode it is possible to generalize the usual rules of subtyping to account not only for the intended behaviour of one particular language construct, but also of its dual. Duotyping brings multiple benefits, including: shorter specifications and implementations, dual features that come essentially for free, as well as new proof techniques for various properties of subtyping. To evaluate a design based on Duotyping against traditional designs, we formalized various calculi with common OOP features (including union types, intersection types and bounded quantification) in Coq in both styles. Our results show that the metatheory when using Duotyping does not come at a significant cost: the metatheory with Duotyping has similar complexity and size compared to the metatheory for traditional designs. However, we discover new features as duals to well-known features. Furthermore, we also show that Duotyping can significantly simplify transitivity proofs for many of the calculi studied by us

The Duality of Subtyping (Artifact)

This artifact contains the Coq formalization associated with the paper The Duality of Subtyping submitted in ECOOP 2020. This document explains how to run the Coq formalization. Artifact can either be compiled in the pre-built docker image with all the dependencies installed or it could be built from the scratch. Sections 1-7 explain the basic information about the artifact. Section A explains how to get the docker image for the artifact. Section B explains the prerequisites and the steps to run coq files from scratch. Section C explains coq files briefly. Section D shows the correspondence between important lemmas discussed in paper and their respective Coq formalization. The term MonoTyping used in artifact corresponds to the standard subtyping systems

Magneto hydrodynamic simulations of the supernova remnant G1.9+0.3

The youngest Galactic supernova remnant G1.9+0.3 shows a discrete feature between its radio and X-ray morphologies. The observed radio morphology features a single maximum in the north, while the X-ray observation shows two opposite 'ears' on the east and west sides. Using 3D magneto hydrodynamical simulations, we investigate the formation of the discrete feature of the remnant. We have tested different parameters for better simulation and reproduced similar discrete features under an environment with density gradient and an environment with clump, which provides a possible explanation of the observation

Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation

Existing neural methods have shown great potentials towards generating informative text from structured tabular data as well as maintaining high content fidelity. However, few of them shed light on generating personalized expressions, which often requires well-aligned persona-table-text datasets that are difficult to obtain. To overcome these obstacles, we explore personalized table-to-text generation under a zero-shot setting, by assuming no well-aligned persona-table-text triples are required during training. To this end, we firstly collect a set of unpaired persona information and then propose a semi-supervised approach with contrastive persona distillation (S2P-CPD) to generate personalized context. Specifically, tabular data and persona information are firstly represented as latent variables separately. Then, we devise a latent space fusion technique to distill persona information into the table representation. Besides, a contrastive-based discriminator is employed to guarantee the style consistency between the generated context and its corresponding persona. Experimental results on two benchmarks demonstrate S2P-CPD's ability on keeping both content fidelity and personalized expressions.Comment: Accepted by ICASSP 202

MTSS: Learn from Multiple Domain Teachers and Become a Multi-domain Dialogue Expert

How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits the quality of dialogue policy, and further affects the generated response. In this paper, we propose a novel method to acquire a satisfying policy and subtly circumvent the knotty dialogue state representation problem in the multi-domain setting. Inspired by real school teaching scenarios, our method is composed of multiple domain-specific teachers and a universal student. Each individual teacher only focuses on one specific domain and learns its corresponding domain knowledge and dialogue policy based on a precisely extracted single domain dialogue state representation. Then, these domain-specific teachers impart their domain knowledge and policies to a universal student model and collectively make this student model a multi-domain dialogue expert. Experiment results show that our method reaches competitive results with SOTAs in both multi-domain and single domain setting.Comment: AAAI 2020, Spotlight Pape

Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types. All our models and datasets in this study are open sourced and can be downloaded from https://mekhub.cn/as/fault_diagnosis_with_few-shot_learning/

Gamma-tocotrienol stimulates the proliferation, differentiation, and mineralization in osteoblastic MC3T3-E1 cells

Gamma-tocotrienol, a major component of tocotrienol-rich fraction of palm oil, has been suggested to exhibit bone protective effects in vivo. However, the effects of γ-tocotrienol on osteoblast cells are still unclear. In this study, the effects of γ-tocotrienol on the proliferation, differentiation, and mineralization in osteoblastic MC3T3-E1 cells were investigated. Our results showed that γ-tocotrienol (2–8 μmol/L) significantly improved the cell proliferation (), but it did not affect cell cycle progression. γ-Tocotrienol significantly increased alkaline phosphatase (ALP) activity (), secretion levels of osteocalcin (OC) and osteonectin (ON), and mRNA levels of collagen type I (Col I) of MC3T3-E1 cells. Meanwhile, we found that γ-tocotrienol is promoted in differentiation MC3T3-E1 cells by upregulation of the expression of Runx2 protein. Moreover, the number of bone nodules increased over 2.5-fold in cells treated with γ-tocotrienol (2–8 μmol/L) for 24 d compared to control group. These results indicated that γ-tocotrienol at low dose levels, especially 4 μmol/L, could markedly enhance the osteoblastic function by increasing the proliferation, differentiation, and mineralization of osteoblastic MC3T3-E1 cells. Moreover, our data also indicated that Runx2 protein may be involved in these effects. Further studies are needed to determine the potential of γ-tocotrienol as an antiosteoporotic agent