Instance-based Deep Transfer Learning

Deep transfer learning recently has acquired significant research interest. It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain. Model-based deep transfer learning is probably the most frequently used method. However, very little research work has been devoted to enhancing deep transfer learning by focusing on the influence of data. In this paper, we propose an instance-based approach to improve deep transfer learning in a target domain. Specifically, we choose a pre-trained model from a source domain and apply this model to estimate the influence of training samples in a target domain. Then we optimize the training data of the target domain by removing the training samples that will lower the performance of the pre-trained model. We later either fine-tune the pre-trained model with the optimized training data in the target domain, or build a new model which is initialized partially based on the pre-trained model, and fine-tune it with the optimized training data in the target domain. Using this approach, transfer learning can help deep learning models to capture more useful features. Extensive experiments demonstrate the effectiveness of our approach on boosting the quality of deep learning models for some common computer vision tasks, such as image classification.Comment: Accepted to WACV 2019. This is a preprint versio

A Comparative Study on Human Embryonic Stem Cell Patent Law in the United States, the European Patent Organization, and China

With the recent developments in biotechnology, associated patent law issues have been a growing concern since the 1980s. Among all the subcategories within the general field of biotechnology, human embryonic stem cell research, as one of the most controversial, is receiving different patent system treatment in different countries. China explicitly opposes the patentability of hESCs in its patent regulations on the basis that patenting hESCs is contrary to morality and the public interest. Similarly, the EPO, relying on ambiguous language in the European Patent Convention [EPC], excludes hESCs from patentability by broadly interpreting the morality clause of the EPC. In contrast, the United States has become the main progenitor of hESC patents. By analyzing the reasons to grant or deny patents on hESCs, and considering patent law doctrines and justifications, this dissertation reaches two conclusions. First, patent law should not include a morality clause and should only take into consideration technical concerns. Moral issues should be left to other mechanisms such as administrative law. This is an approach deeply rooted in the American patent system, but not in China or the EPO. Second, by reviewing the requirements of patentability such as novelty, non-obviousness and utility, it can be concluded that hESCs themselves are not patentable because they lack a specific concrete utility and, since they already exist in nature, they lack novelty as well. However, hESC production processes and derivative products are patentable

Nernst effect and dimensionality in the quantum limit

Nernst effect, the transverse voltage generated by a longitudinal thermal gradient in presence of magnetic field has recently emerged as a very sensitive, yet poorly understood, probe of electron organization in solids. Here we report on an experiment on graphite, a macroscopic stack of graphene layers, which establishes a fundamental link between dimensionality of an electronic system and its Nernst response. In sharp contrast with single-layer graphene, the Nernst signal sharply peaks whenever a Landau level meets the Fermi level. This points to the degrees of freedom provided by finite interlayer coupling as a source of enhanced thermoelectric response in the vicinity of the quantum limit. Since Landau quantization slices a three-dimensional Fermi surface, each intersection of a Landau level with the Fermi level modifies the Fermi surface topology. According to our results, the most prominent signature of such a topological phase transition emerges in the transverse thermoelectric response.Comment: 13 pages, 4 figures and supplementary information; To appear in Nature Physic