Efficient Representation Learning With Graph Neural Networks

Graph neural networks (GNNs) have emerged as the dominant paradigm for graph representation learning, igniting widespread interest in utilizing sophisticated GNNs for diverse computer vision tasks in various domains, including visual SLAM, 3D object recognition and segmentation, as well as visual perception with event cameras. However, the applications of these GNNs often rely on cumbersome GNN architectures for favorable performance, posing challenges for real-time interaction, particularly in edge computing scenarios. This is particularly relevant in cases such as autonomous driving, where timely responses are crucial for handling complex traffic conditions. The objective of this thesis is to contribute to the advancement of learning efficient representations using lightweight GNNs, enabling their effective deployment in resource-constrained environments. To achieve this goal, the thesis explores various efficient learning schemes, focusing on four key aspects: the data side, the model side, the data-model side, and the application side. In terms of data-driven efficient learning, the thesis proposes an adaptive data modification scheme that allows a pre-trained model to be repurposed for multiple designated downstream tasks in a resource-efficient manner, without the need for re-training or fine-tuning. For model-centric efficiency, the thesis introduces a multi-talented and lightweight architecture, without accessing human annotations, that can integrate the expertise of the pre-trained complex GNNs specializing in different tasks. Furthermore, the thesis explores a dedicated binarization scheme on the data-model side that converts both input data and model parameters into 1-bit representations, resulting in lightweight 1-bit architectures. Finally, the thesis investigates an application-specific efficient learning scheme that models the style transfer process as message passing in GNNs, enabling efficient semi-parametric stylization

Six Top Messages of New Physics at the LHC

Six top signatures provide a novel probe of new physics. We discuss production of six top quarks as the decay products of a pair of top partners in the setting of a composite Higgs model, and argue that the six top signal may generically provide one of the first final states to show a discrepancy. We construct an analysis based on quantities such as $H_T$ and the numbers of jets which are tagged as boosted tops, $W$s, or containing $b$-tags, and show that the LHC with 3~ab$^{-1}$ can discover top partners with masses up to around 2.5 TeV in the six top signature.Comment: 15 pages, 6 figures, and 2 table

An Approach to Developing Benchmark Datasets for Protein Secondary Structure Segmentation from Cryo-EM Density Maps

More and more deep learning approaches have been proposed to segment secondary structures from cryo-electron density maps at medium resolution range (5--10Å). Although the deep learning approaches show great potential, only a few small experimental data sets have been used to test the approaches. There is limited understanding about potential factors, in data, that affect the performance of segmentation. We propose an approach to generate data sets with desired specifications in three potential factors - the protein sequence identity, structural contents, and data quality. The approach was implemented and has generated a test set and various training sets to study the effect of secondary structure content and data quality on the performance of DeepSSETracer, a deep learning method that segments regions of protein secondary structures from cryo-EM map components. Results show that various content levels in the secondary structure and data quality influence the performance of segmentation for DeepSSETracer

A Tool for Segmentation of Secondary Structures in 3D Cryo-EM Density Map Components Using Deep Convolutional Neural Networks

Although cryo-electron microscopy (cryo-EM) has been successfully used to derive atomic structures for many proteins, it is still challenging to derive atomic structures when the resolution of cryo-EM density maps is in the medium resolution range, such as 5–10 Å. Detection of protein secondary structures, such as helices and β-sheets, from cryo-EM density maps provides constraints for deriving atomic structures from such maps. As more deep learning methodologies are being developed for solving various molecular problems, effective tools are needed for users to access them. We have developed an effective software bundle, DeepSSETracer, for the detection of protein secondary structure from cryo-EM component maps in medium resolution. The bundle contains the network architecture and a U-Net model trained with a curriculum and gradient of episodic memory (GEM). The bundle integrates the deep neural network with the visualization capacity provided in ChimeraX. Using a Linux server that is remotely accessed by Windows users, it takes about 6 s on one CPU and one GPU for the trained deep neural network to detect secondary structures in a cryo-EM component map containing 446 amino acids. A test using 28 chain components of cryo-EM maps shows overall residue-level F1 scores of 0.72 and 0.65 to detect helices and β-sheets, respectively. Although deep learning applications are built on software frameworks, such as PyTorch and Tensorflow, our pioneer work here shows that integration of deep learning applications with ChimeraX is a promising and effective approach. Our experiments show that the F1 score measured at the residue level is an effective evaluation of secondary structure detection for individual classes. The test using 28 cryo-EM component maps shows that DeepSSETracer detects β-sheets more accurately than Emap2sec+, with a weighted average residue-level F1 score of 0.65 and 0.42, respectively. It also shows that Emap2sec+ detects helices more accurately than DeepSSETracer with a weighted average residue-level F1 score of 0.77 and 0.72 respectively

Room-temperature van der Waals 2D ferromagnet switching by spin-orbit torques

Emerging wide varieties of the two-dimensional (2D) van der Waals (vdW) magnets with atomically thin and smooth interfaces holds great promise for next-generation spintronic devices. However, due to the lower Curie temperature of the vdW 2D ferromagnets than room temperature, electrically manipulating its magnetization at room temperature has not been realized. In this work, we demonstrate the perpendicular magnetization of 2D vdW ferromagnet Fe3GaTe2 can be effectively switched at room temperature in Fe3GaTe2/Pt bilayer by spin-orbit torques (SOTs) with a relatively low current density of 1.3 10^7A/cm2. Moreover, the high SOT efficiency of \xi_{DL}~0.22 is quantitatively determined by harmonic measurements, which is higher than those in Pt-based heavy metal/conventional ferromagnet devices. Our findings of room-temperature vdW 2D ferromagnet switching by SOTs provide a significant basis for the development of vdW-ferromagnet-based spintronic applications

Studies on Synthesis and Structure-Activity Relationship (SAR) of Derivatives of a New Natural Product from Marine Fungi as Inhibitors of Influenza Virus Neuraminidase

Based on the natural isoprenyl phenyl ether from a mangrove-derived fungus, 32 analogues were synthesized and evaluated for inhibitory activity against influenza H1N1 neuraminidase. Compound 15 (3-(allyloxy)-4-hydroxybenzaldehyde) exhibited the most potent inhibitory activity, with IC50 values of 26.96 μM for A/GuangdongSB/01/2009 (H1N1), 27.73 μM for A/Guangdong/03/2009 (H1N1), and 25.13 μM for A/Guangdong/ 05/2009 (H1N1), respectively, which is stronger than the benzoic acid derivatives (~mM level). These are a new kind of non-nitrogenous aromatic ether Neuraminidase (NA) inhibitors. Their structures are simple and the synthesis routes are not complex. The structure-activity relationship (SAR) analysis revealed that the aryl aldehyde and unsubstituted hydroxyl were important to NA inhibitory activities. Molecular docking studies were carried out to explain the SAR of the compounds, and provided valuable information for further structure modification