Scalable Solutions for Automated Single Pulse Identification and Classification in Radio Astronomy

Data collection for scientific applications is increasing exponentially and is forecasted to soon reach peta- and exabyte scales. Applications which process and analyze scientific data must be scalable and focus on execution performance to keep pace. In the field of radio astronomy, in addition to increasingly large datasets, tasks such as the identification of transient radio signals from extrasolar sources are computationally expensive. We present a scalable approach to radio pulsar detection written in Scala that parallelizes candidate identification to take advantage of in-memory task processing using Apache Spark on a YARN distributed system. Furthermore, we introduce a novel automated multiclass supervised machine learning technique that we combine with feature selection to reduce the time required for candidate classification. Experimental testing on a Beowulf cluster with 15 data nodes shows that the parallel implementation of the identification algorithm offers a speedup of up to 5X that of a similar multithreaded implementation. Further, we show that the combination of automated multiclass classification and feature selection speeds up the execution performance of the RandomForest machine learning algorithm by an average of 54% with less than a 2% average reduction in the algorithm's ability to correctly classify pulsars. The generalizability of these results is demonstrated by using two real-world radio astronomy data sets.Comment: In Proceedings of the 47th International Conference on Parallel Processing (ICPP 2018). ACM, New York, NY, USA, Article 11, 11 page

Deep Learning on Smart Meter Data: Non-Intrusive Load Monitoring and Stealthy Black-Box Attacks

Climate change and environmental concerns are instigating widespread changes in modern electricity sectors due to energy policy initiatives and advances in sustainable technologies. To raise awareness of sustainable energy usage and capitalize on advanced metering infrastructure (AMI), a novel deep learning non-intrusive load monitoring (NILM) model is proposed to disaggregate smart meter readings and identify the operation of individual appliances. This model can be used by Electric power utility (EPU) companies and third party entities, and then utilized to perform active or passive consumer power demand management. Although machine learning (ML) algorithms are powerful, these remain vulnerable to adversarial attacks. In this thesis, a novel stealthy black-box attack that targets NILM models is proposed. This work sheds light on both effectiveness and vulnerabilities of ML models in the smart grid context and provides valuable insights for maintaining security especially with increasing proliferation of artificial intelligence in the power system

Design and Control of Electrically Excited Synchronous Machines for Vehicle Applications

Electrically excited synchronous machines (EESMs) are becoming an alternative to permanent magnet synchronous machines (PMSMs) in electric vehicles (EVs). This mainly attributes to the zero usage of rare-earth materials, as well as the ability to achieve high starting torque, the effectiveness to do field weakening and the flexibility to adjust power factor provided by EESMs. Furthermore, in case of converter failure at high speed, safety can be improved by shutting down the field current in EESMs. The purpose of this study is to investigate the potential application of EESMs in EVs. To achieve this aim, several topics are covered in this study. These topics are studied to confront the challenges before EESMs could become prevalent and to maximumly use the advantages of EESMs for EV applications. In control strategies, the challenge is to properly adjust the combination of stator and field currents so that high power factor and minimum copper losses can be achieved. To tackle this, control strategies are proposed so that reactive power consumption and total copper losses are minimized. With the proposed strategies, the output power is maximized along the torque-speed envelope and high efficiency in field-weakening is achieved. In dynamic current control, due to the magnetic couplings between field winding and stator winding, a current rise in one winding would induce an electromagnetic force (EMF) in the other. This introduces disturbances in dynamic current control. In this study, a current control algorithm is proposed to cancel the induced EMF and the disturbances are mitigated. In machine design, high starting torque and effective field weakening are expected to be achieved in the same EESM design. To realize this, some criteria need to be satisfied. These criteria are derived and integrated into the design procedure including multi-objective optimizations. A 48\ua0V EESM is prototyped during the study. In experimental verification, a torque density of 10 N\ub7m/L is achieved including cooling jacket. In field excitation, a contactless excitation technology is adopted, which leads to inaccessibility of the field winding. To realize precise control of field current in a closed loop, an estimation method of field current is proposed. Based on the estimation, closed-loop field current control is established. The field current reference is tracked within an error of 2% in experimental verifications. The cost of an EESM drive increases because of the additional converter used for field excitation. A technique is proposed in which the switching harmonics are extracted for field excitation. With this technique, both stator and field windings can be powered using only one inverter. From all the challenges tackled in this study, it can be concluded that the application of EESMs in EVs is feasible

The emerging value of the viroid model in understanding plant responses to foreign RNAs

RNAs play essential roles in various biological processes. Mounting evidence has demonstrated that RNA subcellular localization and intercellular trafficking govern their functions in coordinating plant growth at the organismal level. Beyond that, plants constantly encounter foreign RNAs (i.e., RNAs from pathogens including viruses and viroids). The subcellular localizations of RNAs are crucial for their function. While numerous types of RNAs (i.e., mRNAs, small RNAs, rRNAs, tRNAs, and long noncoding RNAs) have been found to traffic in a non-cell-autonomous fashion within plants, the underlying regulatory mechanism remains unclear. Viroids are single-stranded circular noncoding RNAs, which entirely rely on their RNA motifs to exploit cellular machinery for organelle entry and exit, cell-to-cell movement through plasmodesmata, and systemic trafficking. Viroids represent an excellent model to dissect the role of RNA 3-dimensional (3D) structural motifs in regulating RNA movement. Using nuclear-replicating viroids as a model, we showed that cellular Importin alpha-4 is likely involved in viroid RNA nuclear import, empirically supporting the involvement of Importin-based cellular pathway in RNA nuclear import. We also confirmed the involvement of a cellular protein (Virp1) that binds both Importin alpha-4 and viroids. Moreover, a conserved C-loop in nuclear-replicating viroids serves as a key signal for nuclear import. Disrupting C-loop impairs Virp1 binding, viroid nuclear accumulation and infectivity. Further, C-loop exists in a subviral satellite noncoding RNA that relies on Virp1 for nuclear import. On the other hand, no viroid can systemically infect the model plant Arabidopsis thaliana, suggesting the existence of non-host resistance yet to be understood. Here, we attempted to test whether a gene involved in RNA silencing, RNA-dependent RNA polymerase 6 (RDR6), plays a role in non-host resistance in Arabidopsis. I will discuss the data below in detail

Comparison of different differential expression analysis tools for rna-seq data

In molecular biology research, RNA-seq is a relatively new method for transcriptome profiling. It utilizes the next generation sequencing technology to provide huge amount information about the variety and abundance of RNA present in an organism of interest at a specific state and a given time. One of the most important tasks of RNA-seq analysis is finding genes that are expressed differently in different subject groups. A lot of differential expression analysis tools for RNA-seq have been developed, but there is no golden standard in this field. In this research, four commonly used tools (DESeq, edgeR, limma, and cuffdiff) are studied by comparing their performances in the normalization of different subject group data, and also in the sensitivity and specificity of selection of genes with differential expression. In addition, their performances on genes which only express in one condition are compared. The data used are SEQC and melanoma. The result shows that in differential expression analysis, DESeq is slightly better than other tools in normalization, while DESeq, edgeR, and limma, in general, display good sensitivity and specificity, and limma outputs less false positive predictions. In cases where genes of interest are absent in one of the conditions, limma has the best performance

Music Genre Classification with ResNet and Bi-GRU Using Visual Spectrograms

Music recommendation systems have emerged as a vital component to enhance user experience and satisfaction for the music streaming services, which dominates music consumption. The key challenge in improving these recommender systems lies in comprehending the complexity of music data, specifically for the underpinning music genre classification. The limitations of manual genre classification have highlighted the need for a more advanced system, namely the Automatic Music Genre Classification (AMGC) system. While traditional machine learning techniques have shown potential in genre classification, they heavily rely on manually engineered features and feature selection, failing to capture the full complexity of music data. On the other hand, deep learning classification architectures like the traditional Convolutional Neural Networks (CNN) are effective in capturing the spatial hierarchies but struggle to capture the temporal dynamics inherent in music data. To address these challenges, this study proposes a novel approach using visual spectrograms as input, and propose a hybrid model that combines the strength of the Residual neural Network (ResNet) and the Gated Recurrent Unit (GRU). This model is designed to provide a more comprehensive analysis of music data, offering the potential to improve the music recommender systems through achieving a more comprehensive analysis of music data and hence potentially more accurate genre classification