Trends in the approval of cancer therapies by the FDA in the twenty-first century
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Biotechnology & Applied Microbiology↗
Engineering topics
Gong, Yutao
Publications and source records attributed to Gong, Yutao.
Selective Uptake of Ethane/Ethylene Mixtures by UTSA-280 is Driven by Reversibly Coordinated Water Defects
The metal–organic framework (MOF) UTSA-280 has been shown previously to separate ethane/ethylene mixtures. We show that this separation is kinetic; if full equilibrium is reached, the MOF is not selective. The time scales associated with this kinetic separation are sufficient to make the material potentially interesting for practical separations. The observation of kinetic separation is surprising because the MOF’s one-dimensional (1D) channels are small enough that molecular motion along the channels would be expected to take place by single-file diffusion, which is not observed. We show using a combination of experimental and modeling techniques that diffusion of molecules between adjacent 1D channels through local defects associated with coordinated water molecules allows net mixing of molecules and, moreover, that this process is potentially responsible for the selective nature of the observed kinetic separation. We show how activation and regeneration conditions play an essential role in controlling the density of these local defects.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Transfer Learning-Based Independent Component Analysis
Understanding the underlying component structure is crucial for multivariate signal analysis. Among all the techniques that try to learn the latent structure, independent component analysis (ICA) is one of the most important and popular methods, which aims to extract independent components from multivariate signals and enables further analysis. For example, in electroencephalogram (EEG) analysis, artifacts filtering and disease detection are conducted based on the independent components of the signals. One critical challenge in existing ICA approaches is that the component extraction accuracy may degrade when the available data of a unit are limited. To address this issue, this paper proposes a transfer learning-based ICA method by innovatively transferring component distribution from a source domain, so that accurate component extraction results can be achieved even when only limited data are available in the target domain. To the best of our knowledge, this is the first work that leverages transfer learning to improve ICA accuracy with limited available data. In particular, we first extract all the independent components from the source domain by maximizing the log-likelihood function with a Newton-like method on a smooth manifold. Then for the target domain, the component with the largest negentropy is extracted in each round. To effectively leverage the knowledge from the source domain and to prevent the negative transfer, we try to find a component in the source domain that matches the component we are extracting. The probability density function of the matched component will then be used to improve the component extraction accuracy if such matched component can be found; otherwise, no knowledge will be transferred. Finally, numerical simulations and a case study with electrocardiogram (ECG) data are conducted, showing the effectiveness of the proposed method in transferring knowledge and reducing negative transfer.
42 ENGINEERING↗