HyperAdam: A Learnable Task-Adaptive Adam for Network Training

Deep neural networks are traditionally trained using human-designed stochastic optimization algorithms, such as SGD and Adam. Recently, the approach of learning to optimize network parameters has emerged as a promising research topic. However, these learned black-box optimizers sometimes do not fully utilize the experience in human-designed optimizers, therefore have limitation in generalization ability. In this paper, a new optimizer, dubbed as \textit{HyperAdam}, is proposed that combines the idea of "learning to optimize" and traditional Adam optimizer. Given a network for training, its parameter update in each iteration generated by HyperAdam is an adaptive combination of multiple updates generated by Adam with varying decay rates. The combination weights and decay rates in HyperAdam are adaptively learned depending on the task. HyperAdam is modeled as a recurrent neural network with AdamCell, WeightCell and StateCell. It is justified to be state-of-the-art for various network training, such as multilayer perceptron, CNN and LSTM

Effects of Stroma on ER+ Breast Cancer Cell Metastasis

Breast cancer is one of the most wide-spread diseases among women in America. If the cancer is local, it is easily controlled by surgical resection. However, if the cancer cells metastasize, patient survival is significantly reduced. 70% of breast cancers can be targeted through estrogen receptors (ER) on the membrane, with compounds such as tamoxifen. However, tamoxifen shows unreliable outcomes on different patients and it is believed that the ineffectiveness of tamoxifen is related to the epithetical-mesenchymal transition (EMT) of cancer cells. To address this problem, we are designing a system that stimulates metastasis activation with the aim of incorporating the results in target identification for drugs. To mimic the cellular microenvironment in both 3D and 2D, scaffold and plate seeding of T47D cell line, an ER+ breast cancer cell line, and human mammary fibroblast (HMF) has been utilized. To understand the activation state of secondary metastasis and identify the presence of drug-targeted receptors, IHC staining has been used. Only co-cultured samples of T47D and HMF in 3D showed EMT. The morphology of both cells, when kept isolated from each other, did not change, but epithelial markers appeared at the cytoplasm instead of the membrane after 7-days of co-culture. Our results suggest that communication of breast cancer cells with fibroblasts in 3D initiates secondary metastasis. We hypothesize that the EMT leads to loss of estrogen receptors and reduce medicines efficacy

Optimized Cartesian KK-Means

Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two data points can be approximated efficiently from their codes by the precomputed lookup tables. Traditionally, to encode a subvector of a data point in a subspace, only one sub codeword in the corresponding sub codebook is selected, which may impose strict restrictions on the search accuracy. In this paper, we propose a novel approach, named Optimized Cartesian $K$-Means (OCKM), to better encode the data points for more accurate approximate nearest neighbor search. In OCKM, multiple sub codewords are used to encode the subvector of a data point in a subspace. Each sub codeword stems from different sub codebooks in each subspace, which are optimally generated with regards to the minimization of the distortion errors. The high-dimensional data point is then encoded as the concatenation of the indices of multiple sub codewords from all the subspaces. This can provide more flexibility and lower distortion errors than traditional methods. Experimental results on the standard real-life datasets demonstrate the superiority over state-of-the-art approaches for approximate nearest neighbor search.Comment: to appear in IEEE TKDE, accepted in Apr. 201

Enhanced gas barrier properties of graphene oxide/rubber composites with strong interfaces constructed by graphene oxide and sulfur

Constructing strong interfacial interactions and complex filler networks is crucial to establishing high gas barrier properties in rubber composites. In this research, sulfur-graphene oxide (S-GO) hybrids were prepared by in situ growth of sulfur on the surfaces of GO sheets. The S-GO hybrids were also introduced into butadiene styrene rubber (SBR) using a green method of latex compounding. Results showed that sulfur could melt and spread on the surface of the GO during the crosslinking process at high temperatures. This process prevented the aggregation of GO and resulted in a fine dispersion of GO and complex filler networks in S-GO/SBR composites. More importantly, the sulfur particles on the GO surface not only aided the crosslinking of rubber molecules, but also chemically reacted with the GO radicals generated at high temperatures. This occurred by the homolytic cleavage of oxygen-containing groups, which thereby constructed covalent interfaces between the GO and SBR molecules. Due to these strong interfaces and complex filler networks, the tensile and tear strength of S-GO/SBR composites increased by 66.2% and 26.6%, respectively, when compared with conventional GO/SBR composites. The gas permeability coefficient of S-GO/SBR composites was decreased dramatically by 50.7% and 23.3% by comparison with that of pure SBR and GO/SBR composites, respectively. The apparent improvement demonstrated that the facile and effective method used in this research may open up new opportunities for the development of multifunctional rubber crosslinking agent as well as the fabrication of rubber composites with high performance

Enhanced covalent interface, crosslinked network and gas barrier property of functionalized graphene oxide/styrene-butadiene rubber composites triggered by thiol-ene click reaction

The high gas barrier property of a rubber composite is of great significance for reducing the exhaust gas emissions due to tire rolling resistance and hence the contribution this factor makes to environmental protection. Enhanced covalent interfaces and crosslinked networks are crucial to the gas barrier property of rubber composites. In this research, γ-mercaptopropyltriethoxysilane (MPS) modified GO (MGO)/styrene-butadiene rubber (SBR) composites were prepared by a synergetic strategy of latex compounding method and thiol-ene click reaction. It was found that the mercapto groups in MGO reacted with the vinyl groups in SBR molecules through thiol-ene click reaction during the crosslinking process at 150 °C, thus forming strong chemical interactions at the interface in the form of GO-MPS-rubber and enhanced crosslinked networks. Meanwhile, the strong interface promoted the dispersion of MGO in SBR. The uniform dispersion of MGO, strong interface between MGO and SBR molecules and enhanced crosslinked networks resulted in improved mechanical and gas barrier properties. When filling 5 phr fillers, the tensile strength and gas barrier properties of an MGO/SBR composite improved by 19.0% and 37.5%, respectively, relative to the comparing GO/SBR composite

Achieving Strong Chemical Interface and Superior Energy-Saving Capability at the Crosslinks of Rubber Composites Containing Graphene Oxide Using Thiol-Vinyl Click Chemistry

Rapidly developments in international transportation inevitably lead to an increase in the consumption of energy and resources. Minimizing the rolling resistance of tires in this scenario is a pressing challenge. To lower the rolling resistance of tires, enhancing the interaction between fillers and rubber molecules while improving the dispersion of fillers are required to reduce the internal mutual friction and viscous loss of rubber composites. In this study, graphene oxide (GO) was modified using γ-mercaptopropyltrimethoxysilane (MPTMS) with thiol groups. A modified GO/natural rubber (MGO/NR) masterbatch with a fine dispersion of MGO was then introduced into solution-polymerized styrene butadiene rubber (SSBR) to create an MGO/SiO2/SSBR composite. During the crosslinking process at high temperatures, a strong chemical interface interaction between the MGO and rubber molecules was formed by the thiol-vinyl click reaction. The MGO sheets also act as crosslinks to enhance the crosslinking network. The results showed that the rolling resistance of the MGO SiO2/SSBR composite was superior by 19.4% and the energy loss was reduced by 15.7% compared with that of the base SiO2/SSBR composite. Strikingly, the wear performance and wet skid resistance improved by 19% and 17.3%, respectively. These results showed a strong interface that not only improved rolling resistance performance but also contributed to balancing the “magic triangle” (the combination of wear resistance, fuel efficiency, and traction) properties of tires

Enhanced Fatigue and Durability of Carbon Black/natural rubber Composites Reinforced with Graphene Oxide and Carbon Nanotubes

Graphene oxide (GO) sheets and carbon nanotubes (CNTs) are of nanometer size and offer large shape factors which are beneficial in reducing crack propagation rates of composites when used in carbon black (CB) reinforced natural rubber (NR), thereby prolonging the service lives of rubber composites. In this research, CNT-CB/NR and GO-CB/NR composites were prepared by partially replacing CB with one-dimensional CNTs and two-dimensional flaky graphene oxide GO, respectively. The results showed that the complex filler dispersion in NR matrices was improved due to the isolation effect between the different fillers. The strain-induced crystallization (SIC) ability of CB/NR was effectively enhanced by the addition of both GO and CNT. The modulus at 100% strain and tear strength of the composites were also improved. More branching and deflections were observed at the crack tips of the composites and both effectively hindered crack propagation in the materials. Under uniaxial and multi-axial cyclic loading, the fatigue lives of CNT-CB/NR and GO-CB/NR composites greatly increased when compared with the fatigue lives of CB/NR composites. The GO-CB/NR composites exhibited evident advantages in respect of fatigue resistance and durability among the three composites