Session Types in a Linearly Typed Multi-Threaded Lambda-Calculus

We present a formalization of session types in a multi-threaded lambda-calculus (MTLC) equipped with a linear type system, establishing for the MTLC both type preservation and global progress. The latter (global progress) implies that the evaluation of a well-typed program in the MTLC can never reach a deadlock. As this formulated MTLC can be readily embedded into ATS, a full-fledged language with a functional programming core that supports both dependent types (of DML-style) and linear types, we obtain a direct implementation of session types in ATS. In addition, we gain immediate support for a form of dependent session types based on this embedding into ATS. Compared to various existing formalizations of session types, we see the one given in this paper is unique in its closeness to concrete implementation. In particular, we report such an implementation ready for practical use that generates Erlang code from well-typed ATS source (making use of session types), thus taking great advantage of the infrastructural support for distributed computing in Erlang.Comment: This is the original version of the paper on supporting programming with dyadic session types in AT

Hydrodeoxygenation of p-cresol on unsupported Ni–P catalysts prepared by thermal decomposition method

AbstractUnsupported Ni–P catalysts were prepared from the mixed precursor of NiCl2 and NaH2PO2 by thermal decomposition method, and their catalytic activities were measured using the hydrodeoxygenation (HDO) of p-cresol as probe. The effects of the H2PO2−/Ni2+ molar ratio in the precursor and the thermal decomposition temperature on the catalyst purity, crystallite size and HDO activity were studied. The HDO of p-cresol on these Ni–P catalysts proceeded with two parallel pathways yielding methylbenzene and methylcyclohexane as final products. The higher HDO catalytic activity of the catalyst was attributed to its bigger crystallite size and purer phase of Ni2P

Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective

Unsupervised video domain adaptation is a practical yet challenging task. In this work, for the first time, we tackle it from a disentanglement view. Our key idea is to handle the spatial and temporal domain divergence separately through disentanglement. Specifically, we consider the generation of cross-domain videos from two sets of latent factors, one encoding the static information and another encoding the dynamic information. A Transfer Sequential VAE (TranSVAE) framework is then developed to model such generation. To better serve for adaptation, we propose several objectives to constrain the latent factors. With these constraints, the spatial divergence can be readily removed by disentangling the static domain-specific information out, and the temporal divergence is further reduced from both frame- and video-levels through adversarial learning. Extensive experiments on the UCF-HMDB, Jester, and Epic-Kitchens datasets verify the effectiveness and superiority of TranSVAE compared with several state-of-the-art methods. The code with reproducible results is publicly accessible.Comment: 18 pages, 9 figures, 7 tables. Code at https://github.com/ldkong1205/TranSVA