LSTD: A Low-Shot Transfer Detector for Object Detection

Recent advances in object detection are mainly driven by deep learning with large-scale detection benchmarks. However, the fully-annotated training set is often limited for a target detection task, which may deteriorate the performance of deep detectors. To address this challenge, we propose a novel low-shot transfer detector (LSTD) in this paper, where we leverage rich source-domain knowledge to construct an effective target-domain detector with very few training examples. The main contributions are described as follows. First, we design a flexible deep architecture of LSTD to alleviate transfer difficulties in low-shot detection. This architecture can integrate the advantages of both SSD and Faster RCNN in a unified deep framework. Second, we introduce a novel regularized transfer learning framework for low-shot detection, where the transfer knowledge (TK) and background depression (BD) regularizations are proposed to leverage object knowledge respectively from source and target domains, in order to further enhance fine-tuning with a few target images. Finally, we examine our LSTD on a number of challenging low-shot detection experiments, where LSTD outperforms other state-of-the-art approaches. The results demonstrate that LSTD is a preferable deep detector for low-shot scenarios.Comment: Accepted by AAAI201

The intersection of nuclear magnetic resonance and quantum chemistry

Nuclear Magnetic resonance and quantum chemistry have been recognized to be strong tools for probing the structure and dynamics of molecules to further solve chemistry and biological problems. Chemical shift measured by NMR experiment and chemical shielding, molecular energy and molecular structure calculated by quantum chemistry provide extensive information. Exact analytic gradients, are obtained for cavitation, dispersion and repulsion energies and time-dependent density functional theory for the continuum solvation model, which could be used to probe the structure, dynamics and properties of molecules. Copper in CuA azurin is recognized to be coordinated by a structure water molecule by comparing the experimental His120 pKa reported in literature with quantum mechanical calculation result. Accurate 13C NMR chemical shielding for small organic molecules can be obtained by quantum mechanical calculation by considering electron correlation effect, complete basis set extrapolation and vibrational correction. Basis set incompleteness is found to be the main source of inaccuracy and cannot be removed by applying any fixed correction, but is dependent on the chemical nature of the relevant group. The 13C chemical shielding of methyl, ethylene and ethyne carbon is significantly improved by vibrational correction. Trifluroacetic acid catalyzed retinoic acid isomerization is recognized to simultaneously decay to polymer by using 1H NMR method. Common intermediate occurs for the isomeration and all-trans, 9-cis and 9,13-dicis retinoic acid all first convert to 13-cis retinoic acid. Free energy changes obtained by NMR experiment compare well with the calculated result using quantum mechanical method done by Professor Harbison. Solid-State CPMAS NMR method shows that DL-aspartic acid crystalizes to racemic crystals rather than conglomerate over most of its temperature range, which is confirmed by PXRD. In contrast, glutamic acid crystalizes as a conglomerate under normal circumstances. Adviser: Gerard S. Harbiso

Understanding and Predicting Delay in Reciprocal Relations

Reciprocity in directed networks points to user's willingness to return favors in building mutual interactions. High reciprocity has been widely observed in many directed social media networks such as following relations in Twitter and Tumblr. Therefore, reciprocal relations between users are often regarded as a basic mechanism to create stable social ties and play a crucial role in the formation and evolution of networks. Each reciprocity relation is formed by two parasocial links in a back-and-forth manner with a time delay. Hence, understanding the delay can help us gain better insights into the underlying mechanisms of network dynamics. Meanwhile, the accurate prediction of delay has practical implications in advancing a variety of real-world applications such as friend recommendation and marketing campaign. For example, by knowing when will users follow back, service providers can focus on the users with a potential long reciprocal delay for effective targeted marketing. This paper presents the initial investigation of the time delay in reciprocal relations. Our study is based on a large-scale directed network from Tumblr that consists of 62.8 million users and 3.1 billion user following relations with a timespan of multiple years (from 31 Oct 2007 to 24 Jul 2013). We reveal a number of interesting patterns about the delay that motivate the development of a principled learning model to predict the delay in reciprocal relations. Experimental results on the above mentioned dynamic networks corroborate the effectiveness of the proposed delay prediction model.Comment: 10 page