TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation

In this paper, we conduct a study on the state-of-the-art methods for text-to-image synthesis and propose a framework to evaluate these methods. We consider syntheses where an image contains a single or multiple objects. Our study outlines several issues in the current evaluation pipeline: (i) for image quality assessment, a commonly used metric, e.g., Inception Score (IS), is often either miscalibrated for the single-object case or misused for the multi-object case; (ii) for text relevance and object accuracy assessment, there is an overfitting phenomenon in the existing R-precision (RP) and Semantic Object Accuracy (SOA) metrics, respectively; (iii) for multi-object case, many vital factors for evaluation, e.g., object fidelity, positional alignment, counting alignment, are largely dismissed; (iv) the ranking of the methods based on current metrics is highly inconsistent with real images. To overcome these issues, we propose a combined set of existing and new metrics to systematically evaluate the methods. For existing metrics, we offer an improved version of IS named IS* by using temperature scaling to calibrate the confidence of the classifier used by IS; we also propose a solution to mitigate the overfitting issues of RP and SOA. For new metrics, we develop counting alignment, positional alignment, object-centric IS, and object-centric FID metrics for evaluating the multi-object case. We show that benchmarking with our bag of metrics results in a highly consistent ranking among existing methods that is well-aligned with human evaluation. As a by-product, we create AttnGAN++, a simple but strong baseline for the benchmark by stabilizing the training of AttnGAN using spectral normalization. We also release our toolbox, so-called TISE, for advocating fair and consistent evaluation of text-to-image models.Comment: Accepted to ECCV 2022; TISE toolbox is available at https://github.com/VinAIResearch/tise-toolbo

Expansion of Major Urban Areas in the US Great Plains from 2000 to 2009 Using Satellite Scatterometer Data

A consistent dataset delineating and characterizing changes in urban environments will be valuable for socioeconomic and environmental research and for sustainable urban development. Remotely sensed data have been long used to map urban extent and infrastructure at various spatial and spectral resolutions. Although many datasets and approaches have been tried, there is not yet a universal way to map urban extents across the world. Here we combined a microwave scatterometer (QuikSCAT) dataset at ~1 km posting with percent impervious surface area (%ISA) data from the National Land Cover Dataset (NLCD) that was generated from Landsat data, and ambient population data from the LandScan product to characterize and quantify growth in nine major urban areas in the US Great Plains from 2000 to 2009. Nonparametric Mann-Kendall trend tests on backscatter time series from urban areas show significant expanding trends in eight of nine urban areas with p-values ranging 0.032 to 0.001. The sole exception is Houston, which has a substantial non-urban backscatter at the northeastern edge of the urban core. Strong power law scaling relationships between ambient population and either urban area or backscatter power (r2 of 0.96 in either model) with sub-linear exponents (β of 0.911 and 0.866, respectively) indicate urban areas become more compact with more vertical built-up structure than lateral expansion to accommodate the increased population. Increases in backscatter and %ISA datasets between 2001 and 2006 show agreement in both magnitude and direction for all urban areas except Minneapolis-St. Paul (MSP), likely due to the presence of many lakes and ponds throughout the MSP metropolitan area. We conclude discussing complexities in the backscatter data caused by large metal structures and rainfall

Case Report: Successful Treatment of a Child With COVID-19 Reinfection-Induced Fulminant Myocarditis by Cytokine-Adsorbing oXiris® Hemofilter Continuous Veno-Venous Hemofiltration and Extracorporeal Membrane Oxygenation

BACKGROUND: Indirect cardiomyocyte damage-related hyperinflammatory response is one of the key mechanisms in COVID-19-induced fulminant myocarditis. In addition to the clinical benefit of using cytokines absorption hemofiltration, the effectiveness of instituting veno-arterial extracorporeal membrane oxygenation (VA-ECMO) support for cardiac compromise has been reported. However, current literature enunciates a paucity of available data on the effectiveness of these novel modalities. CASE PRESENTATION: We reported a 9-year-old boy with recurrent COVID-19 infection-causing fulminant myocarditis, who was treated successfully by using novel modalities of CONCLUSION: We conclude that the novel highly-absorptive hemofilter CVVH and VA-ECMO may be effective treatment modalities in managing SARS-CoV-2-induced fulminant myocarditis. Our report highlights the need for further well-designed investigations to confirm this extrapolation

AITIA: Efficient Secure Computation of Bivariate Causal Discovery

Researchers across various fields seek to understand causal relationships but often find controlled experiments impractical. To address this, statistical tools for causal discovery from naturally observed data have become crucial. Non-linear regression models, such as Gaussian process regression, are commonly used in causal inference but have limitations due to high costs when adapted for secure computation. Support vector regression (SVR) offers an alternative but remains costly in an Multi-party computation context due to conditional branches and support vector updates. In this paper, we propose Aitia, the first two-party secure computation protocol for bivariate causal discovery. The protocol is based on optimized multi-party computation design choices and is secure in the semi-honest setting. At the core of our approach is BSGD-SVR, a new non-linear regression algorithm designed for MPC applications, achieving both high accuracy and low computation and communication costs. Specifically, we reduce the training complexity of the non-linear regression model from approximately from $\mathcal{O}(N^3)$ to $\mathcal{O}(N^2)$ where $N$ is the number of training samples. We implement Aitia using CrypTen and assess its performance across various datasets. Empirical evaluations show a significant speedup of $3.6\times$ to $340\times$ compared to the baseline approach

Role of Process Control in Improving Space Vehicle Safety A Space Shuttle External Tank Example

Developing a safe and reliable space vehicle requires good design and good manufacturing, or in other words "design it right and build it right". A great design can be hard to build or manufacture mainly due to difficulties related to quality. Specifically, process control can be a challenge. As a result, the system suffers from low quality which leads to low reliability and high system risk. The Space Shuttle has experienced some of those cases, but has overcome these difficulties through extensive redesign efforts and process enhancements. One example is the design of the hot gas temperature sensor on the Space Shuttle Main Engine (SSME), which resulted in failure of the sensor in flight and led to a redesign of the sensor. The most recent example is the Space Shuttle External Tank (ET) Thermal Protection System (TPS) reliability issues that contributed to the Columbia accident. As a result, extensive redesign and process enhancement activities have been performed over the last two years to minimize the sensitivities and difficulties of the manual TPS application process

Unsupervised deep learning-based Reconfigurable Intelligent Surface aided broadcasting communications in Industrial IoTs

This paper presents a general system framework which lays the foundation for Reconfigurable Intelligent Surface (RIS)-enhanced broadcast communications in Industrial Internet of Things (IIoTs). In our system model, we consider multiple sensor clusters co-existing in a smart factory where the direct links between these clusters and a central base station (BS) is blocked completely. In this context, an RIS is utilized to reflect signals broadcast from BS toward cluster heads (CHs) which act as a representative of clusters, where BS only has access to the statistical distribution of the channel state information (CSI). An analytical upper bound of the total ergodic spectral efficiency and an approximation of outage probability are derived. Based on these analytical results, two algorithms are introduced to control the phase shifts at RIS, which are the Riemannian conjugate gradient (RCG) method and the deep neural network (DNN) method. While the RCG algorithm operates based on the conventional iterative method, the DNN technique relies on unsupervised deep learning. Our numerical results show that the both algorithms achieve satisfactory performance based on only statistical CSI. In addition, compared to the RCG scheme, using deep learning reduces the computational latency by more than 10 times with an almost identical total ergodic spectral efficiency achieved. These numerical results reveal that while using conventional RCG method may provide unsatisfactory latency, DNN technique shows much promise for enabling RIS in ultra reliable and low latency communications (URLLC) in the context of IIoTs