Graph Enabled Cross-Domain Knowledge Transfer

To leverage machine learning in any decision-making process, one must convert the given knowledge (for example, natural language, unstructured text) into representation vectors that can be understood and processed by machine learning model in their compatible language and data format. The frequently encountered difficulty is, however, the given knowledge is not rich or reliable enough in the first place. In such cases, one seeks to fuse side information from a separate domain to mitigate the gap between good representation learning and the scarce knowledge in the domain of interest. This approach is named Cross-Domain Knowledge Transfer. It is crucial to study the problem because of the commonality of scarce knowledge in many scenarios, from online healthcare platform analyses to financial market risk quantification, leaving an obstacle in front of us benefiting from automated decision making. From the machine learning perspective, the paradigm of semi-supervised learning takes advantage of large amount of data without ground truth and achieves impressive learning performance improvement. It is adopted in this dissertation for cross-domain knowledge transfer. (to be continued

Graph enabled cross-domain knowledge transfer

The world has never been more connected, led by the information technology revolution in the past decades that has fundamentally changed the way people interact with each other using social networks. Consequently, enormous human activity data are collected from the business world and machine learning techniques are widely adopted to aid our decision processes. Despite of the success of machine learning in various application scenarios, there are still many questions that need to be well answered, such as optimizing machine learning outcomes when desired knowledge cannot be extracted from the available data. This naturally drives us to ponder if one can leverage some side information to populate the knowledge domain of their interest, such that the problems within that knowledge domain can be better tackled. In this work, such problems are investigated and practical solutions are proposed. To leverage machine learning in any decision-making process, one must convert the given knowledge (for example, natural language, unstructured text) into representation vectors that can be understood and processed by machine learning model in their compatible language and data format. The frequently encountered difficulty is, however, the given knowledge is not rich or reliable enough in the first place. In such cases, one seeks to fuse side information from a separate domain to mitigate the gap between good representation learning and the scarce knowledge in the domain of interest. This approach is named Cross-Domain Knowledge Transfer. It is crucial to study the problem because of the commonality of scarce knowledge in many scenarios, from online healthcare platform analyses to financial market risk quantification, leaving an obstacle in front of us benefiting from automated decision making. From the machine learning perspective, the paradigm of semi-supervised learning takes advantage of large amount of data without ground truth and achieves impressive learning performance improvement. It is adopted in this dissertation for cross-domain knowledge transfer. Furthermore, graph learning techniques are indispensable given that networks commonly exist in real word, such as taxonomy networks and scholarly article citation networks. These networks contain additional useful knowledge and are ought to be incorporated in the learning process, which serve as an important lever in solving the problem of cross-domain knowledge transfer. This dissertation proposes graph-based learning solutions and demonstrates their practical usage via empirical studies on real-world applications. Another line of effort in this work lies in leveraging the rich capacity of neural networks to improve the learning outcomes, as we are in the era of big data. In contrast to many Graph Neural Networks that directly iterate on the graph adjacency to approximate graph convolution filters, this work also proposes an efficient Eigenvalue learning method that directly optimizes the graph convolution in the spectral space. This work articulates the importance of network spectrum and provides detailed analyses on the spectral properties in the proposed EigenLearn method, which well aligns with a series of CNN models that attempt to have meaningful spectral interpretation in designing graph neural networks. The disser-tation also addresses the efficiency, which can be categorized in two folds. First, by adopting approximate solutions it mitigates the complexity concerns for graph related algorithms, which are naturally quadratic in most cases and do not scale to large datasets. Second, it mitigates the storage and computation overhead in deep neural network, such that they can be deployed on many light-weight devices and significantly broaden the applicability. Finally, the dissertation is concluded by future endeavors

Neural Network Pruning as Spectrum Preserving Process

Neural networks have achieved remarkable performance in various application domains. Nevertheless, a large number of weights in pre-trained deep neural networks prohibit them from being deployed on smartphones and embedded systems. It is highly desirable to obtain lightweight versions of neural networks for inference in edge devices. Many cost-effective approaches were proposed to prune dense and convolutional layers that are common in deep neural networks and dominant in the parameter space. However, a unified theoretical foundation for the problem mostly is missing. In this paper, we identify the close connection between matrix spectrum learning and neural network training for dense and convolutional layers and argue that weight pruning is essentially a matrix sparsification process to preserve the spectrum. Based on the analysis, we also propose a matrix sparsification algorithm tailored for neural network pruning that yields better pruning result. We carefully design and conduct experiments to support our arguments. Hence we provide a consolidated viewpoint for neural network pruning and enhance the interpretability of deep neural networks by identifying and preserving the critical neural weights.Comment: arXiv admin note: substantial text overlap with arXiv:2304.0345

Impact of ENSO events on meteorological drought in the Weihe River basin, China

El Niño–Southern Oscillation (ENSO) events influence elements of the terrestrial water cycle such as precipitation and temperature, which in turn have a significant impact on drought. This work assessed the impact of El Niño and La Niña on droughts from 1970 to 2020 in the Weihe River basin (WRB) in China. This study used a standardized precipitation index (SPI) to characterize meteorological drought. The regional drought response to extreme events in El Niño/La Niña was analyzed using principal component analysis (PCA), Wilcoxon and Mann–Whitney tests, and other methods. The results showed that, based on PCA, the WRB is divided into two regions, with the northwest region (67%) comprising more area than the southeast region (33%). El Niño/La Niña significantly impacted drought in the WRB. Droughts mainly occurred in the El Niño year and the year following La Niña. El Niño had the highest number of drought years (44%), followed by the year following La Niña (43%). The number of droughts was lowest in the year following El Niño (22%). At 1-, 3-, and 6-month timescales, significant droughts mainly occurred from July to December in El Niño years and the summer following La Niña. On a 12-month timescale, significant droughts mainly occurred from January to April in El Niño years, while no droughts occurred in La Niña years. The longer the timescale of the SPI, the more months of significant drought in El Niño years; however, the intensity of drought in the basin was reduced. In the year following La Niña, summer droughts intensified on a 6-month timescale compared to a 3-month timescale. El Niño and La Niña had greater impacts on the drought index in the northwest region of the WRB. In the northwest region, 60% of the months showed significant drought, compared to only 2% of the months in the southeast region. The drought intensity was higher in the northwest region. The results of this study provide a reference for drought management and early warning systems in the WRB and support solutions to water shortage

Genome-wide analysis of a avirulent and reveal the strain induces pro-tective immunity against challenge with virulent Streptococcus suis Serotype 2

BACKGROUND: It was previously reported in China that two recent large-scale outbreaks of Streptococcus suis serotype 2 (S. suis 2) infections in human were caused by two highly virulent S. suis 2 strains, from which a novel genomic island (GEI), associated with disease onset and progression and designated 89 K, was identified. Here, an avirulent strain, 05HAS68, was isolated from a clinically healthy pig. RESULTS: By comparing the genomes of this avirulent strain with virulent strains, it was found that massive genomic rearrangements occurred, resulting in alterations in gene expression that caused enormous single gene gain and loss. Important virulent genes were lost, such as extracellular protein factor (ef) and suilysin (sly) and larger mutants, such as muramidase-released protein (mrp). Piglets vaccinated with the avirulent strain, 05HAS68, had increased TNF-α and IFN-γ levels in the peripheral blood and were fully protected from challenge infection with the most virulent S. suis 2 strain, 05ZYH33. Transfusion of T cells and plasma from vaccinated pigs resulted in protection of recipient animals against the 05ZYH33 challenge. CONCLUSION: These results suggest that analysis genome of the avirulent strains are instrumental in the development of vaccines and for the functional characterization of important of genetic determinants

Observation of many-body Fock space dynamics in two dimensions

Quantum many-body simulation provides a straightforward way to understand fundamental physics and connect with quantum information applications. However, suffering from exponentially growing Hilbert space size, characterization in terms of few-body probes in real space is often insufficient to tackle challenging problems such as quantum critical behavior and many-body localization (MBL) in higher dimensions. Here, we experimentally employ a new paradigm on a superconducting quantum processor, exploring such elusive questions from a Fock space view: mapping the many-body system onto an unconventional Anderson model on a complex Fock space network of many-body states. By observing the wave packet propagating in Fock space and the emergence of a statistical ergodic ensemble, we reveal a fresh picture for characterizing representative many-body dynamics: thermalization, localization, and scarring. In addition, we observe a quantum critical regime of anomalously enhanced wave packet width and deduce a critical point from the maximum wave packet fluctuations, which lend support for the two-dimensional MBL transition in finite-sized systems. Our work unveils a new perspective of exploring many-body physics in Fock space, demonstrating its practical applications on contentious MBL aspects such as criticality and dimensionality. Moreover, the entire protocol is universal and scalable, paving the way to finally solve a broader range of controversial many-body problems on future larger quantum devices.Comment: 8 pages, 4 figures + supplementary informatio

Anti-HIV-1 activity of cellulose acetate phthalate: Synergy with soluble CD4 and induction of "dead-end" gp41 six-helix bundles

BACKGROUND: Cellulose acetate phthalate (CAP), a promising candidate microbicide for prevention of sexual transmission of the human immunodeficiency virus type 1 (HIV-1) and other sexually transmitted disease (STD) pathogens, was shown to inactivate HIV-1 and to block the coreceptor binding site on the virus envelope glycoprotein gp120. It did not interfere with virus binding to CD4. Since CD4 is the primary cellular receptor for HIV-1, it was of interest to study CAP binding to HIV-1 complexes with soluble CD4 (sCD4) and its consequences, including changes in the conformation of the envelope glycoprotein gp41 within virus particles. METHODS: Enzyme-linked immunosorbent assays (ELISA) were used to study CAP binding to HIV-1-sCD4 complexes and to detect gp41 six-helix bundles accessible on virus particles using antibodies specific for the α-helical core domain of gp41. RESULTS: 1) Pretreatment of HIV-1 with sCD4 augments subsequent binding of CAP; 2) there is synergism between CAP and sCD4 for inhibition of HIV-1 infection; 3) treatment of HIV-1 with CAP induced the formation of gp41 six-helix bundles. CONCLUSIONS: CAP and sCD4 bind to distinct sites on HIV-1 IIIB and BaL virions and their simultaneous binding has profound effects on virus structure and infectivity. The formation of gp41 six-helical bundles, induced by CAP, is known to render the virus incompetent for fusion with target cells thus preventing infection

Search for light dark matter from atmosphere in PandaX-4T

We report a search for light dark matter produced through the cascading decay of $\eta$ mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne$\cdot$year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at $5.9 \times 10^{-37}{\rm cm^2}$ for dark matter mass of $0.1$ MeV$/c^2$ and mediator mass of 300 MeV$/c^2$. The lowest upper limit of $\eta$ to dark matter decay branching ratio is $1.6 \times 10^{-7}$

Incentive-aware Electric Vehicle Routing Problem: a Bi-level Model and a Joint Solution Algorithm

Fixed pickup and delivery times can strongly limit the performance of freight transportation. Against this backdrop, fleet operators can use compensation mechanisms such as monetary incentives to buy delay time from their customers, in order to improve the fleet efficiency and ultimately minimize the costs of operation. To make the most of such an operational model, the fleet activities and the incentives should be jointly optimized accounting for the customers' reactions. Against this backdrop, this paper presents an incentive-aware electric vehicle routing scheme in which the fleet operator actively provides incentives to the customers in exchange of pickup or delivery time flexibility. Specifically, we first devise a bi-level model whereby the fleet operator optimizes the routes and charging schedules of the fleet jointly with an incentive rate to reimburse the delivery delays experienced by the customers. At the same time, the customers choose the admissible delays by minimizing a monetarily-weighted combination of the delays minus the reimbursement offered by the operator. Second, we tackle the complexity resulting from the bi-level and nonlinear problem structure with an equivalent transformation method, reformulating the problem as a single-level optimization problem that can be solved with standard mixed-integer linear programming algorithms. We demonstrate the effectiveness of our framework via extensive numerical experiments using VRP-REP data from Belgium. Our results show that by jointly optimizing routes and incentives subject to the customers' preferences, the operational costs can be reduced by up to 5%, whilst customers can save more than 30% in total delivery fees

Moisture sources of summer heavy precipitation in two spatial patterns over Northeast China during 1979–2021

Abstract This study classifies the spatial distribution of heavy precipitation in summer (June–August) from 1979 to 2021 in the three provinces of Northeast China (TPNC) into two patterns by using the self‐organizing maps (SOM) neural network, and then quantitatively analyzes their moisture transport channels and sources using the Lagrangian model. The results show that the summer heavy precipitation in TPNC can be divided into the northern and southern patterns according to the distribution of the heavy precipitation. Both patterns of heavy precipitation are affected by the low‐level vortex west of TPNC, but the strength and shape of the low vortex are different. The northern pattern is mainly influenced by the westerly flow in the vortex in the mid‐high latitudes, which transports moisture from the upstream westerly region into TPNC. The southern pattern is mainly affected by the southerly jet stream southeast of TPNC, which conveys a large amount of moisture from the East Asian summer monsoon region into TPNC. In terms of the summer climatological mean, the northern pattern has a higher precipitation recycling rate, while the southern pattern has a lower recycling rate