AffinityNet: semi-supervised few-shot learning for disease type prediction

While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the "big p, small N" problem (i.e., a relatively small number of samples with high-dimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the AffinityNet model consists of stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not. As a new deep learning module, kNN attention pooling layers can be plugged into any neural network model just like convolutional layers. As a simple special case of kNN attention pooling layer, feature attention layer can directly select important features that are useful for classification tasks. Experiments on both synthetic data and cancer genomic data from TCGA projects show that our AffinityNet model has better generalization power than conventional neural network models with little training data. The code is freely available at https://github.com/BeautyOfWeb/AffinityNet .Comment: 14 pages, 6 figure

Understanding ESL Preservice Teachers’ Metaphorical Epistemology in the Teaching Practicum Context

This study examined twenty English as Second Language (ESL) preservice teachers’ learning-to-teach experiences amid  teaching practicums from the perspectives of social realist theory and practice architectures in the USA. Utilizing iterative discourse analysis and constant-comparative approach, this paper reveals four aspects of the participants’ metaphorical epistemology. First, the participants developed the constructivist metaphorical epistemology on ESL teaching by grappling with emergent properties and practice architectures. Second, the participants’ metaphorical epistemology changed from surface reflection to pedagogical reflection. In addition, the participants modeled the reflective disposition by activating their personal emergent properties and tackling the social-political arrangements. Lastly, the participants’ metaphorical epistemology predominantly focuses on their pedagogical reasoning and negotiation of their multiple professional identities. Implications for facilitating ESL preservice teachers’ metaphorical epistemologies in the teaching practicum context are discussed

Ion-Beam Modified Terahertz GaAs Photoconductive Antenna

Ion-implanted photoconductive GaAs terahertz (THz) antennas were demonstrated to deliver both high-efficiency and high-power THz emitters, which are attributed to excellent carrier acceleration and fast carrier trapping for THz generations by analyzing ultrafast carrier dynamics at subpicosecond scale. The implantation distance at over 2.5 μm is deep enough to make defects (Ga vacancies, As Ga + …, etc.) quite few; hence, a few with good mobility similar to bare GaAs ensures excellent carrier acceleration in shallow distance <1.0 μm as photo carriers are generated by the pump laser. The implantation dosage is carefully optimized to make carrier trapping very fast, and screen effects by photo-generated carriers are significantly suppressed, which increases the THz radiation power of SI-GaAs antennas by two orders of magnitude. Under the same photo-excitation conditions (pump laser power, bias voltage), photocurrents from GaAs antennas with optimum conditions 300 keV, 5 × 1014 cm−2 for H implantation are decreased by two orders of magnitude; meanwhile, the THz radiation is enhanced by over four times, which means that the electrical-to-THz power conversion efficiency is improved by a factor of over 1600