Using NWP Analysis in Satellite Rainfall Estimation of Heavy Precipitation Events over Complex Terrain

This study investigates the use of Weather Research and Forecasting (WRF) high-resolution storm analysis in satellite rainfall estimation over complex terrains. Rainfall estimation here is based on the NOAA-Climate Prediction Center morphing (CMORPH) product. Specifically, CMORPH rainfall is adjusted by applying a power-law function whose parameter values are obtained from the comparison between WRF and CMORPH hourly rain rates. Results are presented based on the analyses of five storm cases that induced catastrophic floods in southern Europe. The WRF-based adjusted CMORPH rain rates exhibited improved error statistics against independent radar-rainfall estimates. We show that the adjustment reduces the underestimation of high rain rates thus moderating the strong rainfall magnitude dependence of CMORPH bias. The higher Heidke skill scores for all rain rate thresholds indicate that the adjustment procedure meliorates CMORPH rain rates to provide a better estimation. Results also indicate that the missed rain detection of CMORPH rainfall estimates are also identifiable in the WRF-CMORPH comparison, however, the herein adjustment procedure does not incorporate this effect on CMORPH estimates

Knowledge Encryption and Its Applications to Simulatable Protocols With Low Round-Complexity

We introduce a new notion of public key encryption, knowledge encryption, for which its ciphertexts can be reduced to the public-key, i.e., any algorithm that can break the ciphertext indistinguishability can be used to extract the (partial) secret key. We show that knowledge encryption can be built solely on any two-round oblivious transfer with game-based security, which are known based on various standard (polynomial-hardness) assumptions, such as the DDH, the Quadratic($N^{th}$) Residuosity or the LWE assumption. We use knowledge encryption to construct the first three-round (weakly) simulatable oblivious transfer. This protocol satisfies (fully) simulatable security for the receiver, and weakly simulatable security ($(T, \epsilon)$-simulatability) for the sender in the following sense: for any polynomial $T$ and any inverse polynomial $\epsilon$, there exists an efficient simulator such that the distinguishing gap of any distinguisher of size less than $T$ is at most $\epsilon$. Equipped with these tools, we construct a variety of fundamental cryptographic protocols with low round-complexity, assuming only the existence of two-round oblivious transfer with game-based security. These protocols include three-round delayed-input weak zero knowledge argument, three-round weakly secure two-party computation, three-round concurrent weak zero knowledge in the BPK model, and a two-round commitment with weak security under selective opening attack. These results improve upon the assumptions required by the previous constructions. Furthermore, all our protocols enjoy the above $(T, \epsilon)$-simulatability (stronger than the distinguisher-dependent simulatability), and are quasi-polynomial time simulatable under the same (polynomial hardness) assumption

Zero-Knowledge Functional Elementary Databases

Zero-knowledge elementary databases (ZK-EDBs) enable a prover to commit a database ${D}$ of key-value $(x,v)$ pairs and later provide a convincing answer to the query ``send me the value $D(x)$ associated with $x$\u27\u27 without revealing any extra knowledge (including the size of ${D}$). After its introduction, several works extended it to allow more expressive queries, but the expressiveness achieved so far is still limited: only a relatively simple queries--range queries over the keys and values-- can be handled by known constructions. In this paper we introduce a new notion called zero knowledge functional elementary databases (ZK-FEDBs), which allows the most general functional queries. Roughly speaking, for any Boolean circuit $f$, ZK-FEDBs allows the ZK-EDB prover to provide convincing answers to the queries of the form ``send me all records ${(x,v)}$ in ${{D}}$ satisfying $f(x,v)=1$,\u27\u27 without revealing any extra knowledge (including the size of ${D}$). We present a construction of ZK-FEDBs in the random oracle model and generic group model, whose proof size is only linear in the length of record and the size of query circuit, and is independent of the size of input database $D$. Our technical constribution is two-fold. Firstly, we introduce a new variant of zero-knowledge sets (ZKS) which supports combined operations on sets, and present a concrete construction that is based on groups with unknown order. Secondly, we develop a tranformation that tranforms the query of Boolean circuit into a query of combined operations on related sets, which may be of independent interest

Visual Representation Learning with Transformer: A Sequence-to-Sequence Perspective

Visual representation learning is the key of solving various vision problems. Relying on the seminal grid structure priors, convolutional neural networks (CNNs) have been the de facto standard architectures of most deep vision models. For instance, classical semantic segmentation methods often adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the latest efforts have been focused on increasing the receptive field, through either dilated (i.e., atrous) convolutions or inserting attention modules. However, the FCN-based architecture remains unchanged. In this paper, we aim to provide an alternative perspective by treating visual representation learning generally as a sequence-to-sequence prediction task. Specifically, we deploy a pure Transformer to encode an image as a sequence of patches, without local convolution and resolution reduction. With the global context modeled in every layer of the Transformer, stronger visual representation can be learned for better tackling vision tasks. In particular, our segmentation model, termed as SEgmentation TRansformer (SETR), excels on ADE20K (50.28% mIoU, the first position in the test leaderboard on the day of submission), Pascal Context (55.83% mIoU) and reaches competitive results on Cityscapes. Further, we formulate a family of Hierarchical Local-Global (HLG) Transformers characterized by local attention within windows and global-attention across windows in a hierarchical and pyramidal architecture. Extensive experiments show that our method achieves appealing performance on a variety of visual recognition tasks (e.g., image classification, object detection and instance segmentation and semantic segmentation).Comment: Extended version of CVPR 2021 paper arXiv:2012.1584

Aspirin Use and Common Cancer Risk:A Meta-Analysis of Cohort Studies and Randomized Controlled Trials

BackgroundWhether aspirin use can decrease or increase cancer risk remains controversial. In this study, a meta-analysis of cohort studies and randomized controlled trials (RCTs) were conducted to evaluate the effect of aspirin use on common cancer risk.MethodMedline and Embase databases were searched to identify relevant studies. Meta-analyses of cohort studies and RCTs were performed to assess the effect of aspirin use on the risk of colorectal, gastric, breast, prostate and lung cancer. Cochran Q test and the I square metric were calculated to detect potential heterogeneity among studies. Subgroup meta-analyses according to exposure categories (frequency and duration) and timing of aspirin use (whether aspirin was used before and after cancer diagnosis) were also performed. A dose-response analysis was carried out to evaluate and quantify the association between aspirin dose and cancer risk.ResultsA total of 88 cohort studies and seven RCTs were included in the final analysis. Meta-analyses of cohort studies revealed that regular aspirin use reduced the risk of colorectal cancer (CRC) (RR=0.85, 95%CI: 0.78-0.92), gastric cancer (RR=0.67, 95%CI: 0.52-0.87), breast cancer (RR=0.93, 95%CI: 0.87-0.99) and prostate cancer (RR=0.92, 95%CI: 0.86-0.98), but showed no association with lung cancer risk. Additionally, meta-analyses of RCTs showed that aspirin use had a protective effect on CRC risk (OR=0.74, 95%CI: 0.56-0.97). When combining evidence from meta-analyses of cohorts and RCTs, consistent evidence was found for the protective effect of aspirin use on CRC risk. Subgroup analysis showed that high frequency aspirin use was associated with increased lung cancer risk (RR=1.05, 95%CI: 1.01-1.09). Dose-response analysis revealed that high-dose aspirin use may increase prostate cancer risk.ConclusionsThis study provides evidence for low-dose aspirin use for the prevention of CRC, but not other common cancers. High frequency or high dose use of aspirin should be prescribed with caution because of their associations with increased lung and prostate cancer risk, respectively. Further studies are warranted to validate these findings and to find the minimum effective dose required for cancer prevention

Genetically predicted high IGF-1 levels showed protective effects on COVID-19 susceptibility and hospitalization:a Mendelian randomisation study with data from 60 studies across 25 countries

Background: Epidemiological studies observed gender differences in COVID-19 outcomes, however, whether sex hormone plays a causal in COVID-19 risk remains unclear. This study aimed to examine associations of sex hormone, sex hormones-binding globulin (SHBG), insulin-like growth factor-1 (IGF-1), and COVID-19 risk. Methods: Two-sample Mendelian randomization (TSMR) study was performed to explore the causal associations between testosterone, estrogen, SHBG, IGF-1, and the risk of COVID-19 (susceptibility, hospitalization, and severity) using genome-wide association study (GWAS) summary level data from the COVID-19 Host Genetics Initiative (N=1,348,701). Random-effects inverse variance weighted (IVW) MR approach was used as the primary MR method and the weighted median, MR-Egger, and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test were conducted as sensitivity analyses. Results: Higher genetically predicted IGF-1 levels have nominally significant association with reduced risk of COVID-19 susceptibility and hospitalization. For one standard deviation increase in genetically predicted IGF-1 levels, the odds ratio was 0.77 (95% confidence interval [CI], 0.61-0.97, p=0.027) for COVID-19 susceptibility, 0.62 (95% CI: 0.25-0.51, p=0.018) for COVID-19 hospitalization, and 0.85 (95% CI: 0.52-1.38, p=0.513) for COVID-19 severity. There was no evidence that testosterone, estrogen, and SHBG are associated with the risk of COVID-19 susceptibility, hospitalization, and severity in either overall or sex-stratified TSMR analysis. Conclusions: Our study indicated that genetically predicted high IGF-1 levels were associated with decrease the risk of COVID-19 susceptibility and hospitalization, but these associations did not survive the Bonferroni correction of multiple testing. Further studies are needed to validate the findings and explore whether IGF-1 could be a potential intervention target to reduce COVID-19 risk

Adopting big data to accelerate discovery of 2D TMDCs materials via CVR method for the potential application in urban airborne Hg0 sensor

Airborne Hg0 has significant negative effect on cities and urban systems. The development of effect airborne Hg0 sensor is rather important for both urban atmospheric Hg0 detection and the evaluation of Hg0 capture materials. Previous research showed MoS2 as a typical TMDCs materials had excellent performance to capture Hg0. In this study, the other 2D TMDCs materials via CVR method in big data was initially studied for the potential urban airborne Hg0 sensor application. The combinations of Pymatgen initial screening, Factsage thermochemical screening and Aflow structural screening were developed for accelerating discovery of the 2D TMDCs in big data. The results from Pymatgen showed that except elements Cd, Sc, Y, Zn, and the other elements have the potential to form TMDC. Furthermore, elements such as Co, Ni, Mo, Ru, W and Ir have the ability forming pure TMDC and Ti, Mn, Zr and Pd can only form partial TMDC. However, other elements such as Sc, V, Cr, Fe, Cu, Zn, Y, Rh and Cd have no possibility to form TMDC. Finally, TiS2, NiS2, ZrS2, MoS2, PdS2 and WS2 were found with 2D structure, which are possible to be prepared by the S-CVR method as the airborne Hg0 sensor materials

Numerical Weather Model based Adjustment of Satellite Precipitation Products and Hydrologic Evaluations

The quantification of heavy precipitation events over mountainous regions has been a challenge for all types of satellite precipitation products. This research developed a numerical weather model-based adjustment technique to correct satellite precipitation estimates for HPEs. To successfully apply the technique, there are two prerequisites: i) the raw satellite data captures the relative spatial and temporal variabilities of precipitation (i.e. no significant surface contamination effects on satellite precipitation detection), and ii) the model provides relatively accurate precipitation outputs in terms of overall magnitude (not necessarily location). The technique was demonstrated over mountainous areas all over the world representing varying terrain complexity and climatic conditions. Results show that model-based adjustment outperforms, or at least is comparable to, the gauge-based adjustment for all high-resolution satellite products examined. In addition, the model-based adjustment requires no in situ observations and much less processing time. The results are promising for future satellite precipitation applications over mountainous areas lacking ground observations. Furthermore, the model-adjusted satellite products were used in a distributed hydrological model to evaluate the error propagation on flood simulations. Results showed that the basin outlet runoff derived from model-adjusted satellite precipitation was comparable to the one with gauge-adjusted satellite precipitation, and both of them outperformed the runoff derived from raw satellite

NWP-Based Adjustment of IMERG Precipitation for Flood-Inducing Complex Terrain Storms: Evaluation over CONUS

This paper evaluates the use of precipitation forecasts from a numerical weather prediction (NWP) model for near-real-time satellite precipitation adjustment based on 81 flood-inducing heavy precipitation events in seven mountainous regions over the conterminous United States. The study is facilitated by the National Center for Atmospheric Research (NCAR) real-time ensemble forecasts (called model), the Integrated Multi-satellitE Retrievals for GPM (IMERG) near-real-time precipitation product (called raw IMERG) and the Stage IV multi-radar/multi-sensor precipitation product (called Stage IV) used as a reference. We evaluated four precipitation datasets (the model forecasts, raw IMERG, gauge-adjusted IMERG and model-adjusted IMERG) through comparisons against Stage IV at six-hourly and event length scales. The raw IMERG product consistently underestimated heavy precipitation in all study regions, while the domain average rainfall magnitudes exhibited by the model were fairly accurate. The model exhibited error in the locations of intense precipitation over inland regions, however, while the IMERG product generally showed correct spatial precipitation patterns. Overall, the model-adjusted IMERG product performed best over inland regions by taking advantage of the more accurate rainfall magnitude from NWP and the spatial distribution from IMERG. In coastal regions, although model-based adjustment effectively improved the performance of the raw IMERG product, the model forecast performed even better. The IMERG product could benefit from gauge-based adjustment, as well, but the improvement from model-based adjustment was consistently more significant