Automatic Designs in Deep Neural Networks

To train a Deep Neural Network (DNN) that performs well for a task, many design steps are taken including data designs, model designs and loss designs. Despite that remarkable progress has been made in all these domains of designing DNNs, the unexplored design space of each component is still vast. That brings the research field of developing automated techniques to lift some heavy work from human researchers when exploring the design space. The automated designs can help human researchers to make massive or challenging design choices and reduce the expertise required from human researchers. Much effort has been made towards automated designs of DNNs, including synthetic data generation, automated data augmentation, neural architecture search and so on. Despite the huge effort, the automation of DNN designs is still far from complete. This thesis contributes in two ways: identifying new problems in the DNN design pipeline that can be solved automatically, and proposing new solutions to problems that have been explored by automated designs. The first part of this thesis presents two problems that were usually solved with manual designs but can benefit from automated designs. To tackle the problem of inefficient computation due to using a static DNN architecture for different inputs, some manual efforts have been made to use different networks for different inputs as needed, such as cascade models. We propose an automated dynamic inference framework that can cut this manual effort and automatically choose different architectures for different inputs during inference. To tackle the problem of designing differentiable loss functions for non-differentiable performance metrics, researchers usually design the loss manually for each individual task. We propose an unified loss framework that reduces the amount of manual design of losses in different tasks. The second part of this thesis discusses developing new techniques in domains where the automated design has been shown effective. In the synthetic data generation domain, we propose a novel method to automatically generate synthetic data for small-data object detection. The synthetic data generated can amend the limited annotated real data of the small-data object detection tasks, such as rare disease detection. In the architecture search domain, we propose an architecture search method customized for generative adversarial networks (GANs). GANs are commonly known unstable to train where we propose this new method that can stabilize the training of GANs in the architecture search process.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163208/1/llanlan_1.pd

Dynamically Grown Generative Adversarial Networks

Recent work introduced progressive network growing as a promising way to ease the training for large GANs, but the model design and architecture-growing strategy still remain under-explored and needs manual design for different image data. In this paper, we propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator. It enjoys the benefits of both eased training because of progressive growing and improved performance because of broader architecture design space. Experimental results demonstrate new state-of-the-art of image generation. Observations in the search procedure also provide constructive insights into the GAN model design such as generator-discriminator balance and convolutional layer choices.Comment: Accepted to AAAI 202

Controlled release of paclitaxel from a self-assembling peptide hydrogel formed in situ and antitumor study in vitro

Background: A nanoscale injectable in situ-forming hydrogel drug delivery system was developed in this study. The system was based on a self-assembling peptide RADA16 solution, which can spontaneously form a hydrogel rapidly under physiological conditions. We used the RADA16 hydrogel for the controlled release of paclitaxel (PTX), a hydrophobic antitumor drug. Methods: The RADA16-PTX suspension was prepared simply by magnetic stirring, followed by atomic force microscopy, circular dichroism analysis, dynamic light scattering, rheological analysis, an in vitro release assay, and a cell viability test. Results: The results indicated that RADA16 and PTX can interact with each other and that the amphiphilic peptide was able to stabilize hydrophobic drugs in aqueous solution. The particle size of PTX was markedly decreased in the RADA16 solution compared with its size in water. The RADA16-PTX suspension could form a hydrogel in culture medium, and the elasticity of the hydrogel showed a positive correlation with peptide concentration. In vitro release measurements indicated that hydrogels with a higher peptide concentration had a longer half-release time. The RADA16-PTX hydrogel could effectively inhibit the growth of the breast cancer cell line, MDA-MB-435S, in vitro, and hydrogels with higher peptide concentrations were more effective at inhibiting tumor cell proliferation. The RADA16-PTX hydrogel was effective at controlling the release of PTX and inhibiting tumor cell growth in vitro. Conclusion: Self-assembling peptide hydrogels may work well as a system for drug delivery

Effects of magnanimous therapy on emotional, psychosomatic and immune functions of lung cancer patients

This study was a randomised controlled study on the effects of the individual computer magnanimous therapy and group computer magnanimous therapy on emotional, psychosomatic and immune function among advanced lung cancer patients. Patients were examined at baseline and 2 weeks later using the Psychosomatic Status Scale for Cancer Patients, Hospital Anxiety Depression Scale and IgA, IgG, IgM and natural killer cell functions. The results showed that individual computer magnanimous therapy and group computer magnanimous therapy were beneficial for advanced lung cancer patients in improving depression, anxiety, psychosomatic status and immune functions. The improvements of immune functions may be related to the improvements of the participants’ emotional and psychosocial status

Study on the Inhibitory Effects of Ephedra Aconite Asarum

Dendritic cells (DCs) can secrete cytokines stimulated by lipopolysaccharide (LPS), which leads to not just acute inflammatory responses but also Th1 polarization. Furtherly, chronic inflammation or autoimmune diseases could be triggered. As a classic Traditional Chinese Medicine formula, Ephedra Aconite Asarum Decoction with the main ingredients of ephedrine and hypaconitine can show effect on anti-inflammation and immunoregulation. But it remains unclear whether Ephedra Aconite Asarum Decoction controls DCs. In this study, we investigated the effects of Ephedra Aconite Asarum Decoction on LPS-induced bone marrow-derived DCs (BMDCs) in vitro. We found that Ephedra Aconite Asarum Decoction lowered surface costimulators on DCs and reduced the expression of Th1 type cytokines. Yet it is slightly beneficial for shifting to Th2. Our work reveals that the Ephedra Aconite Asarum Decoction can regulate Th1 inflammation through intervening DCs

Schur complement-based infinity norm bounds for the inverse of S-Sparse Ostrowski Brauer matrices

In this paper, we study the Schur complement problem of $S$-SOB matrices, and prove that the Schur complement of $S$-Sparse Ostrowski-Brauer ($S$-SOB) matrices is still in the same class under certain conditions. Based on the Schur complement of $S$-SOB matrices, some upper bound for the infinite norm of $S$-SOB matrices is obtained. Numerical examples are given to certify the validity of the obtained results. By using the infinity norm bound, an error bound is given for the linear complementarity problems of $S$-SOB matrices

The Effect of Different Laser Irradiation on Cyclophosphamide-Induced Leucopenia in Rats

Objective. To assess the effect of different lasers on cyclophosphamide- (CTX-) induced leucopenia in rats. Methods. 11 rats were normal control and 55 rats were injected with a dose of 80 mg/kg CTX for the first time and 40 mg/kg on the 6th and the 11th days to establish a leucopenia model. Rats of the irradiation groups received a 5-minute laser irradiation with either single 10.6 μm or 650 nm laser or alternatively 10.6 μm–650 nm laser irradiation, besides a sham treatment on acupoint Dazhui (DU 14) and acupoint Zusanli (ST 36) of both sides, 8 times for 16 days. Normal and model control group received no treatment. Results. On day 16 after the first CTX injection, the WBC counts from all the laser irradiation groups were significantly higher than those from the model control and the sham group (P<0.05), while there were no significant differences compared with the normal control (P>0.05). The TI of 10.6 μm–650 nm laser irradiation group was significantly higher than that of the model control group (P<0.05). Conclusions. The single and combined 10.6 μm and 650 nm laser irradiation on ST36 and DU14 accelerated the recovery of the WBC count in the rats with leucopenia