3,334 research outputs found
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
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