Fast Ion Gates Outside the Lamb-Dicke Regime by Robust Quantum Optimal Control

We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors. The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of the ions to create phonon-mediated entangling gates and, unlike the state of the art, requires neither weak-coupling Lamb-Dicke approximation nor perturbation treatment. With the application of gradient-based optimal control, it enables finding amplitude- and phase-modulated laser control protocols that work beyond the Lamb-Dicke regime, promising gate speed at the order of microseconds comparable to the characteristic trap frequencies. Also, robustness requirements on the temperature of the ions and initial optical phase can be conveniently included to pursue high-quality fast gates against experimental imperfections. Our approach represents a step in speeding up quantum gates to achieve larger quantum circuits for quantum computation and simulation, and thus can find applications in near-future experiments.Comment: 9 pages, 3 figure

Brake or Step On the Gas? Empirical Analyses of Credit Effects on Individual Consumption

Understanding the effects of credit on consumption is crucial for guiding users’ consumption behavior, designing financial marketing strategies, and identifying credit\u27s value in stimulating the economy. Whereas several studies have endeavored on this issue, most simply utilize observations of a single credit channel and/or focus on an overall effect without considering the potentially heterogeneous short-term and long-term consumption changes. This study, leveraging a quasi-experimental design with high-resolution transaction data, examines how people respond to credit in both short- and long-term periods. Results show that credit users’ consumption amount significantly expand by 51.74% after getting access to credit in the short term. However, they ultimately cut their consumption by 4.02% to cope with financial constraints in the long term. We also reveal and quantify the spillover effects of credit on consumption with savings channels. We draw on regulatory focus theory to rationalize the changes on consumers’ consumption behavior after credit activation

Isoperimetric Problems on the Line with Density |x|^p

On the line with density |x|^p, we prove that the best single bubble is an interval with endpoint at the origin and that the best double bubble is two adjacent intervals that meet at the origin

Review of city models and the applications on flood risk management

Cities are the place where a large portion of the population lives. Traditional urban planning models usually based on separate functions of a city or region. A coherent city model is a newly developed tool to take the interaction between each section into consideration. The city model in this paper focuses on the water system infrastructure because flood risk is becoming an increasingly challenging issue with the rapid urbanization and extreme weather under climate change. The paper aims to give a timely review of the development of city models from various originates. Then, it introduces a number of popular modelling techniques that have been demonstrated useful or may be of potential usage for city modelling purpose, such as GIS, CIM, ABM, etc. The review of model techniques provides the readers with suggestions on how to choose the technique to deal with their own research question. After that, this paper also points out the possible future directions of city models with challenges requiring further research efforts

Insights from Niche Markets: Explainable and Predictive Values of Consumption Tendency on Credit Risks

The rapid development of FinTech drives the growing popularity of digital payment transactions. This phenomenon, especially given the increasing number of offline and online transactions being recorded in a real-time manner, offers great opportunities for financial service platforms to track consumers’ consumption tendencies and dynamically monitor and evaluate their creditworthiness. In our recent research, we first theorized the value of category-level consumption tendency based on the self-regulatory theory and employed econometric methods to empirically test the relationship between category-level consumption tendency and credit behavior. Then, we proposed a Deep Hierarchical Partial Attention-based Model (DHPAM) to predict credit default risk with full employment of product category features. We provided strong experimental evidence to show that the proposed DHPAM outperforms the state-of-the-art machine learning models. This paper, based on theories, empirical analyses, and a prediction model, offers comprehensive and practical guidance on the optimal utilization of consumption information in credit risk management

Exploration of WRF simulations of extreme rainfall in Egypt

This research evaluates the performance of the Weather Research and Forecasting model (WRF-ARW, version 4.0) in simulating a regional extreme rainfall event over the Alexandria region of Egypt. Different domain configurations, spin-up times and physical schemes are explored to work out appropriate settings for using WRF in the region. Alexandria is an important economic region of the West Nile Delta that faces a growing climate crisis (e.g. rising temperature, rising sea level, increasing flooding) in recent decades, whilst inadequate coverage of in-situ rainfall observations (radars and rain gauges) makes the development of a hydrological early warning system very difficult. Although some researchers have conducted many WRF studies in countries with rich hydrological data, such as the United States and the United Kingdom, there are not many studies in exploring the ability of WRF to reproduce extreme weather events in countries with insufficient data like Egypt. Therefore, we carry out WRF sensitivity studies of an extreme rainfall event (occurred on 04 November 2015) in the Alexandria region to find out the optimal model configurations for Egypt and other similar areas. In this study, WRF was tested in five scenarios with different types of configurations. The model sensitivity was evaluated for: (1) domain size, (2) number of vertical levels, (3) horizontal resolution (nesting ratio), (4) spin-up times, (5) physical parameterisation schemes (MP, PBL, CU). During the entire screening process, the best configuration identified in each scenario will be adopted as the corresponding configuration in the following scenarios. All simulations used the newly developed ERA5 reanalysis dataset as the forcing data. Model simulations were verified at high temporal and spatial resolutions against the Global Precipitation Measurement data (GPM data). Seven objective verification metrics (POD, FBI, CSI, FAR, RMSE, MBE and SD) were used to calculate the performance of WRF simulations to identify the likely optimal model configurations. The sensitivity study shows that the rainfall distribution and magnitude are most sensitive to the spin-up time and physical schemes (especially the cumulus convection scheme). It is observed that the improvement of WRF's reproducibility of rainfall intensity may be accompanied by a decrease in the reproducibility of rainfall distribution. The most recommended configurations include three-level nesting (D01 80x80; D02 112x112; D03 88X88), 58 vertical levels, 1:3:3 (31.5, 10.5 and 3.5km) grid ratio, 48h spin-up time, WSM6 microphysics scheme, MYJ planetary boundary layer scheme, and Grell-Freitas cumulus convection scheme. Its hitting rate is 0.818, the false alarm rate is 0.088 and the rainfall mean bias error is -1.639. The knowledge gained in this study provides a useful foundation for developing a flood early warning system by linking WRF with WRF-Hydro

Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis

BackgroundRadiomics have been increasingly used in the clinical management of hepatocellular carcinoma (HCC), such as markers prediction. Ki-67 and cytokeratin 19 (CK-19) are important prognostic markers of HCC. Radiomics has been introduced by many researchers in the prediction of these markers expression, but its diagnostic value remains controversial. Therefore, this review aims to assess the diagnostic value of radiomics in predicting Ki-67 and CK-19 expression in HCC.MethodsOriginal studies were systematically searched in PubMed, EMBASE, Cochrane Library, and Web of Science from inception to May 2023. All included studies were evaluated by the radiomics quality score. The C-index was used as the effect size of the performance of radiomics in predicting Ki-67and CK-19 expression, and the positive cutoff values of Ki-67 label index (LI) were determined by subgroup analysis and meta-regression.ResultsWe identified 34 eligible studies for Ki-67 (18 studies) and CK-19 (16 studies). The most common radiomics source was magnetic resonance imaging (MRI; 25/34). The pooled C-index of MRI-based models in predicting Ki-67 was 0.89 (95% CI:0.86–0.92) in the training set, and 0.87 (95% CI: 0.82–0.92) in the validation set. The pooled C-index of MRI-based models in predicting CK-19 was 0.86 (95% CI:0.81–0.90) in the training set, and 0.79 (95% CI: 0.73–0.84) in the validation set. Subgroup analysis suggested Ki-67 LI cutoff was a significant source of heterogeneity (I2 = 0.0% P>0.05), and meta-regression showed that the C-index increased as Ki-67 LI increased.ConclusionRadiomics shows promising diagnostic value in predicting positive Ki-67 or CK-19 expression. But lacks standardized guidelines, which makes the model and variables selection dependent on researcher experience, leading to study heterogeneity. Therefore, standardized guidelines are warranted for future research.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023427953

Flood inundation mapping with multi-satellite soil moisture observations

In recent decades, remote sensing has widely been used in mapping floods inundations, and many studies have explored the association between antecedent soil moisture and precipitation to assess or predict floods with quantity and intensity. However, capturing the specific flooding events is not always guaranteed because of the satellite poor revisit frequency. Moreover, little attention has been paid to retrieve historic flood inundation based on soil moisture dynamics, especially in the areas with the data scarcity both in terms of soil moisture observations and fine temporal resolution satellite data. In this study we attempt to explore this issue in two contrasting areas: one arid and one humid, which are the Nile Delta and the Mississippi River Delta, respectively. Several flooding events are selected to conduct specific flood inundation analysis. Multiple satellite microwave soil moisture products are analysed, including European Space Agency Climate Change Initiative (ESA-CCI) Soil Moisture, Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer (AMSR-2) and ESA Sentinel satellite imagery. Considering that the soil moisture decreases more slowly than the surface flooded water, the present study aims to retrieve historic flood inundation based on soil moisture dynamics from satellites, and the main objectives are: (1) to make a comparison on spatial and temporal dynamic patterns of the above-mentioned products in two study areas; (2) to investigate a method for distinguishing the flooded areas and the areas which are always fully saturated; (3) to develop an approach for detecting historic flood inundation based on soil moisture dynamics; and (4) to calibrate the soil moisture output from WRF-Hydro model using satellite soil moisture observations. Results are expected to be applicable for decision-making in flood disaster relief and flood prediction