A Layer Decomposition-Recomposition Framework for Neuron Pruning towards Accurate Lightweight Networks

Neuron pruning is an efficient method to compress the network into a slimmer one for reducing the computational cost and storage overhead. Most of state-of-the-art results are obtained in a layer-by-layer optimization mode. It discards the unimportant input neurons and uses the survived ones to reconstruct the output neurons approaching to the original ones in a layer-by-layer manner. However, an unnoticed problem arises that the information loss is accumulated as layer increases since the survived neurons still do not encode the entire information as before. A better alternative is to propagate the entire useful information to reconstruct the pruned layer instead of directly discarding the less important neurons. To this end, we propose a novel Layer Decomposition-Recomposition Framework (LDRF) for neuron pruning, by which each layer's output information is recovered in an embedding space and then propagated to reconstruct the following pruned layers with useful information preserved. We mainly conduct our experiments on ILSVRC-12 benchmark with VGG-16 and ResNet-50. What should be emphasized is that our results before end-to-end fine-tuning are significantly superior owing to the information-preserving property of our proposed framework.With end-to-end fine-tuning, we achieve state-of-the-art results of 5.13x and 3x speed-up with only 0.5% and 0.65% top-5 accuracy drop respectively, which outperform the existing neuron pruning methods.Comment: accepted by AAAI19 as ora

RESIDUAL STRESS ANALYSIS OF CERAMIC COATINGS ON BIOCOMPATIBLE MAGNESIUM ALLOYS

poster abstractMagnesium and its alloys have gained special interest in medical applica-tions in recent years, as promising biodegradable metallic implant materials, due to their excellent mechanical properties and biocompatibilities. However, magnesium alloys rapidly corrode in human body. Therefore, a dense ceram-ic coating has been produced on the surface of the magnesium alloy through the method of microarc oxidation (MAO), which improves the corrosion re-sistance of the magnesium alloy. The objective of this study is to evaluate the residual stress of the ceramic coating on biocompatible AZ31 magnesium alloy. The corresponding residual stresses with different applied voltages have been examined in this study. An integrated experimental and modeling approach has been employed. Residual stresses attributed to the MgO constituent of the coatings at oxida-tion voltages between 250 V to 350 V have been evaluated by X-ray diffrac-tion (XRD) using sin2ψ method. An analytic model is also used to compute the stress distributions in the coatings. The residual stresses decreased with the increase of the applied voltage. The predicated stresses from the analytic model are in good agreement with the experimental measurements. At 350V, the coating has a uniform surface morphology and the lowest residual stress. This is the optimal voltage in the MAO process to produce the high-quality corrosion resistant coating. The measured stresses using sin2 ψ XRD method in the MgO constituent of the MAO coatings are tensile in nature. The voltage-dependent residual stress has been released during the microarc discharge process, which is attributed to the micro-pores and cracks formed in the coating

Strategies of reducing input sample volume for extracting circulating cell-free nuclear DNA and mitochondrial DNA in plasma

Background: Circulating cell-free (ccf) DNA in blood has been suggested as a potential biomarker in many conditions regarding early diagnosis and prognosis. However, misdiagnosis can result due to the limited DNA resources in Biobank's plasma samples or insufficient DNA targets from a predominant DNA background in genetic tests. This study explored several strategies for an efficient DNA extraction to increase DNA amount from limited plasma input. Methods: Ccf plasma DNA was extracted with three different methods, a phenol-chloroform-isoamylalcohol (PCI) method, a High Pure PCR Template Preparation Kit method and a method used for single cell PCR in this group. Subsequently, the total DNA was measured by Nanodrop and the genome equivalents (GE) of the GAPDH housekeeping gene and MTATP 8 gene were measured using a multiplex real-time quantitative PCR for the quantitative assessment of nDNA and mtDNA. Results: Instead of 400-800 μL (routine input in the laboratory), 50 μLof plasma input enabled the extraction of ccf DNA sufficient for quantitative analysis. Using the PCI method and the kit method, both nDNA and mtDNA could be successfully detected in plasma samples, but nDNA extracted using protocol for single cell PCR was not detectable in 25% of plasma samples. In comparison to the other two methods, the PCI method showed lower DNA purity, but higher concentrations and more GE of nDNA and mtDNA. Conclusions: The PCI method was more efficient than the other two methods in the extraction of ccf DNA in plasma. Limited plasma is available for ccf DNA analysi

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM