Developing normalization schemes for data isolated distributed deep learning

Distributed deep learning is an important and indispensable direction in the field of deep learning research. Earlier research has proposed many algorithms or techniques on accelerating distributed neural network training. This study discusses a new distributed training scenario, namely data isolated distributed deep learning. Specifically, each node has its own local data and cannot be shared for some reasons. However, in order to ensure the generalization of the model, the goal is to train a global model that required learning all the data, not just based on data from a local node. At this time, distributed training with data isolation is needed. An obvious challenge for distributed deep learning in this scenario is that the distribution of training data used by each node could be highly imbalanced because of data isolation. This brings difficulty to the normalization process in neural network training, because the traditional batch normalization (BN) method will fail under this kind of data imbalanced scenario. At this time, distributed training with data isolation is needed. Aiming at such data isolation scenarios, this study proposes a comprehensive data isolation deep learning scheme. Specifically, synchronous stochastic gradient descent algorithm is used for data exchange during training, and provides several normalization approaches to the problem of BN failure caused by data imbalance. Experimental results show the efficiency and accuracy of the proposed data isolated distributed deep learning scheme

P50, the Small Subunit of DNA Polymerase Delta, Is Required for Mediation of the Interaction of Polymerase Delta Subassemblies with PCNA

Mammalian DNA polymerase δ (pol δ), a four-subunit enzyme, plays a crucial and versatile role in DNA replication and various DNA repair processes. Its function as a chromosomal DNA polymerase is dependent on the association with proliferating cell nuclear antigen (PCNA) which functions as a molecular sliding clamp. All four of the pol δ subunits (p125, p50, p68, and p12) have been reported to bind to PCNA. However, the identity of the subunit of pol δ that directly interacts with PCNA and is therefore primarily responsible for the processivity of the enzyme still remains controversial. Previous model for the network of protein-protein interactions of the pol δ-PCNA complex showed that pol δ might be able to interact with a single molecule of PCNA homotrimer through its three subunits, p125, p68, and p12 in which the p50 was not included in. Here, we have confirmed that the small subunit p50 of human pol δ truthfully interacts with PCNA by the use of far-Western analysis, quantitative ELISA assay, and subcellular co-localization. P50 is required for mediation of the interaction between pol δ subassemblies and PCNA homotrimer. Thus, pol δ interacts with PCNA via its four subunits

Production of Recombinant Human DNA Polymerase Delta in a Bombyx mori Bioreactor

Eukaryotic DNA polymerase δ (pol δ) plays a crucial role in chromosomal DNA replication and various DNA repair processes. It is thought to consist of p125, p66 (p68), p50 and p12 subunits. However, rigorous isolation of mammalian pol δ from natural sources has usually yielded two-subunit preparations containing only p125 and p50 polypeptides. While recombinant pol δ isolated from infected insect cells have some problems of consistency in the quality of the preparations, and the yields are much lower. To address these deficiencies, we have constructed recombinant BmNPV baculoviruses using MultiBac system. This method makes the generation of recombinant forms of pol δ containing mutations in any one of the subunits or combinations thereof extremely facile. From about 350 infected larvae, we obtained as much as 4 mg of pol δ four-subunit complex. Highly purified enzyme behaved like the one of native form by rigorous characterization and comparison of its activities on poly(dA)/oligo(dT) template-primer and singly primed M13 DNA, and its homogeneity on FPLC gel filtration. In vitro base excision repair (BER) assays showed that pol δ plays a significant role in uracil-intiated BER and is more likely to mediate LP BER, while the trimer lacking p12 is more likely to mediate SN BER. It seems likely that loss of p12 modulates the rate of SN BER and LP BER during the repair process. Thus, this work provides a simple, fast, reliable and economic way for the large-scale production of human DNA polymerase δ with a high activity and purity, setting up a new platform for our further research on the biochemical properties of pol δ, its regulation and the integration of its functions, and how alterations in pol δ function could contribute to the etiology of human cancer or other diseases that can result from loss of genomic stability

Data-driven abnormal detection for utility-oriented sensor time series data

With the advancement of Internet of Things (IoT) technology, smart sensors have become extensively used in public utilities for data collection. This provides a basis for data-driven analysis methods that rely on sensor time series data. This PhD thesis focuses on utilities as the application and aims to identify anomalies in their operations through machine learning algorithms based on sensor time series data. Firstly, to address the issue of missing values in sensor time series data, a novel time series imputation method called MuSDRI is proposed. Unlike previous methods that only concentrate on short-term features, MuSDRI captures temporal dynamics from both short-term and long-term perspectives, resulting in improved performance over state-of-the-art methods for imputation tasks. Secondly, D2AE, a data distillation-enhanced autoencoder is proposed for anomaly detection of gas consumption data. D2AE is a new unsupervised anomaly detection method designed for detecting anomalous gas consumption of different types of users and with various causes. Experimental results demonstrate that D2AE outperforms the baseline method on two gas consumption datasets. Thirdly, a cross-network Contamination Source Identification (CSI) solution for Water Distribution Networks (WDNs) called GrassL is presented. It can transfer CSI knowledge across WDNs. To the best of the author’s knowledge, GrassL is the first transfer learning method proposed for the CSI problem in WDNs. Experimental results demonstrate that GrassL achieves the best performance compared to other deep learning methods that capture temporal and spatial dynamics in the CSI task across WDNs. In conclusion, this research contributes innovative technologies and solutions for abnormal detection in public utilities, laying the groundwork for further exploration and expansion

A Real-Time Structural Damage Detection method for High-Pile Wharf Foundations

As one of the most common structural forms in port engineering, the operation environment of high-pile wharf is quite harsh and complex, and its pile foundation often produces structural damage of different degrees. Until now, there is a lack of efficient, safe and economic damage detection methods. A novel and precise real-time structural damage detection (SDD) method using both finite element modelling (FEM) and 1D convolutional neural networks (CNNs) is established in this study. The results indicate that the proposed method could accurately identify the presence and location of damage in real time. The results also demonstrated that the proposed 1D CNNs based model are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure