MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation

Although two-stage Vector Quantized (VQ) generative models allow for synthesizing high-fidelity and high-resolution images, their quantization operator encodes similar patches within an image into the same index, resulting in a repeated artifact for similar adjacent regions using existing decoder architectures. To address this issue, we propose to incorporate the spatially conditional normalization to modulate the quantized vectors so as to insert spatially variant information to the embedded index maps, encouraging the decoder to generate more photorealistic images. Moreover, we use multichannel quantization to increase the recombination capability of the discrete codes without increasing the cost of model and codebook. Additionally, to generate discrete tokens at the second stage, we adopt a Masked Generative Image Transformer (MaskGIT) to learn an underlying prior distribution in the compressed latent space, which is much faster than the conventional autoregressive model. Experiments on two benchmark datasets demonstrate that our proposed modulated VQGAN is able to greatly improve the reconstructed image quality as well as provide high-fidelity image generation

Learning Directed Graphical Models with Optimal Transport

Estimating the parameters of a probabilistic directed graphical model from incomplete data remains a long-standing challenge. This is because, in the presence of latent variables, both the likelihood function and posterior distribution are intractable without further assumptions about structural dependencies or model classes. While existing learning methods are fundamentally based on likelihood maximization, here we offer a new view of the parameter learning problem through the lens of optimal transport. This perspective licenses a general framework that operates on any directed graphs without making unrealistic assumptions on the posterior over the latent variables or resorting to black-box variational approximations. We develop a theoretical framework and support it with extensive empirical evidence demonstrating the flexibility and versatility of our approach. Across experiments, we show that not only can our method recover the ground-truth parameters but it also performs comparably or better on downstream applications, notably the non-trivial task of discrete representation learning

Flat Seeking Bayesian Neural Networks

Bayesian Neural Networks (BNNs) provide a probabilistic interpretation for deep learning models by imposing a prior distribution over model parameters and inferring a posterior distribution based on observed data. The model sampled from the posterior distribution can be used for providing ensemble predictions and quantifying prediction uncertainty. It is well-known that deep learning models with lower sharpness have better generalization ability. However, existing posterior inferences are not aware of sharpness/flatness in terms of formulation, possibly leading to high sharpness for the models sampled from them. In this paper, we develop theories, the Bayesian setting, and the variational inference approach for the sharpness-aware posterior. Specifically, the models sampled from our sharpness-aware posterior, and the optimal approximate posterior estimating this sharpness-aware posterior, have better flatness, hence possibly possessing higher generalization ability. We conduct experiments by leveraging the sharpness-aware posterior with state-of-the-art Bayesian Neural Networks, showing that the flat-seeking counterparts outperform their baselines in all metrics of interest.Comment: Accepted at NeurIPS 202

Vector Quantized Wasserstein Auto-Encoder

Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks. Inspired by the seminal Vector Quantized Variational Auto-Encoder (VQ-VAE), most of work in learning deep discrete representations has mainly focused on improving the original VQ-VAE form and none of them has studied learning deep discrete representations from the generative viewpoint. In this work, we study learning deep discrete representations from the generative viewpoint. Specifically, we endow discrete distributions over sequences of codewords and learn a deterministic decoder that transports the distribution over the sequences of codewords to the data distribution via minimizing a WS distance between them. We develop further theories to connect it with the clustering viewpoint of WS distance, allowing us to have a better and more controllable clustering solution. Finally, we empirically evaluate our method on several well-known benchmarks, where it achieves better qualitative and quantitative performances than the other VQ-VAE variants in terms of the codebook utilization and image reconstruction/generation

A Systematic and Critical Review on the Research Landscape of Finance in Vietnam from 2008 to 2020

This paper endeavors to understand the research landscape of finance research in Vietnam during the period 2008 to 2020 and predict the key defining future research directions. Using the comprehensive database of Vietnam’s international publications in social sciences and humanities, we extract a dataset of 314 papers on finance topics in Vietnam from 2008 to 2020. Then, we apply a systematic approach to analyze four important themes: Structural issues, Banking system, Firm issues, and Financial psychology and behavior. Overall, there have been three noticeable trends within finance research in Vietnam: (1) assessment of financial policies or financial regulation, (2) deciphering the correlates of firms’ financial performances, and (3) opportunities and challenges in adopting innovations and ideas from foreign financial market systems. Our analysis identifies several fertile areas for future research, including financial market analysis in the post-COVID-19 eras, fintech, and green finance

A Bibliometric Analysis of the Global Research Trend in Child Maltreatment

Child maltreatment remains a major health threat globally that requires the understanding of socioeconomic and cultural contexts to craft effective interventions. However, little is known about research agendas globally and the development of knowledge-producing networks in this field of study. This study aims to explore the bibliometric overview on child maltreatment publications to understand their growth from 1916 to 2018. Data from the Web of Science Core Collection were collected in May 2018. Only research articles and reviews written in the English language were included, with no restrictions by publication date. We analyzed publication years, number of papers, journals, authors, keywords and countries, and presented the countries collaboration and co-occurrence keywords analysis. From 1916 to 2018, 47, 090 papers (53.0% in 2010–2018) were published in 9442 journals. Child Abuse & Neglect (2576 papers; 5.5%); Children and Youth Services Review (1130 papers; 2.4%) and Pediatrics (793 papers, 1.7%) published the most papers. The most common research areas were Psychology (16, 049 papers, 34.1%), Family Studies (8225 papers, 17.5%), and Social Work (7367 papers, 15.6%). Among 192 countries with research publications, the most prolific countries were the United States (26, 367 papers), England (4676 papers), Canada (3282 papers) and Australia (2664 papers). We identified 17 authors who had more than 60 scientific items. The most cited papers (with at least 600 citations) were published in 29 journals, headed by the Journal of the American Medical Association (JAMA) (7 papers) and the Lancet (5 papers). This overview of global research in child maltreatment indicated an increasing trend in this topic, with the world’s leading centers located in the Western countries led by the United States. We called for interdisciplinary research approaches to evaluating and intervening on child maltreatment, with a focus on low-middle income countries (LMICs) settings and specific contexts

Spatiotemporal evolution of SARS-CoV-2 Alpha and Delta variants during large nationwide outbreak of COVID-19, Vietnam, 2021

We analyzed 1,303 SARS-CoV-2 whole-genome sequences from Vietnam, and found the Alpha and Delta variants were responsible for a large nationwide outbreak of COVID-19 in 2021. The Delta variant was confined to the AY.57 lineage and caused >1.7 million infections and >32,000 deaths. Viral transmission was strongly affected by nonpharmaceutical interventions

Vector Quantized Wasserstein Auto-Encoder

Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks. Inspired by the seminal Vector Quantized Variational Auto-Encoder (VQ-VAE), most of work in learning deep discrete representations has mainly focused on improving the original VQ-VAE form and none of them has studied learning deep discrete representations from the generative viewpoint. In this work, we study learning deep discrete representations from the generative viewpoint. Specifically, we endow discrete distributions over sequences of codewords and learn a deterministic decoder that transports the distribution over the sequences of codewords to the data distribution via minimizing a WS distance between them. We develop further theories to connect it with the clustering viewpoint of WS distance, allowing us to have a better and more controllable clustering solution. Finally, we empirically evaluate our method on several well-known benchmarks, where it achieves better qualitative and quantitative performances than the other VQ-VAE variants in terms of the codebook utilization and image reconstruction/generation.</p

A stochastic network-based model to simulate farm-level transmission of African swine fever virus in Vietnam

African swine fever virus is highly contagious, and mortality rates reach up to 100% depending on the host, virus dose, and the transmission routes. The main objective of this study was to develop a network-based simulation model for the farm-level transmission of ASF virus to evaluate the impact of changes in farm connectivity on ASF spread in Vietnam. A hypothetical population of 1,000 pig farms was created and used for the network-based simulation, where each farm represented a node, and the connection between farms represented an edge. The three scenarios modelled in this way (baseline, low, and high) evaluated the impact of connectivity on disease transmission. The median number of infected farms was higher as the connectivity increased (low: 659, baseline: 968 and high: 993). In addition, we evaluated the impact of the culling strategy on the number of infected farms. A total of four scenarios were simulated depending on the timing of culling after a farm was infected. We found that the timing of culling at 16, 12, 8, and 6 weeks had resulted in a reduction of the number of median infected farms by 81.92%, 91.63%, 100%, and 100%, respectively. Finally, our evaluation of the implication of stability of ties between farms indicated that if the farms were to have the same trading partners for at least six months could significantly reduce the median number of infected farms to two (95th percentile: 413) than in the basic model. Our study showed that pig movements among farms had a significant influence on the transmission dynamics of ASF virus. In addition, we found that the either timing of culling, reduction in the number of trading partners each farm had, or decreased mean contact rate during the outbreaks were essential to prevent or stop further outbreaks