Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo

To sample from a general target distribution $p_*\propto e^{-f_*}$ beyond the isoperimetric condition, Huang et al. (2023) proposed to perform sampling through reverse diffusion, giving rise to Diffusion-based Monte Carlo (DMC). Specifically, DMC follows the reverse SDE of a diffusion process that transforms the target distribution to the standard Gaussian, utilizing a non-parametric score estimation. However, the original DMC algorithm encountered high gradient complexity, resulting in an exponential dependency on the error tolerance $\epsilon$ of the obtained samples. In this paper, we demonstrate that the high complexity of DMC originates from its redundant design of score estimation, and proposed a more efficient algorithm, called RS-DMC, based on a novel recursive score estimation method. In particular, we first divide the entire diffusion process into multiple segments and then formulate the score estimation step (at any time step) as a series of interconnected mean estimation and sampling subproblems accordingly, which are correlated in a recursive manner. Importantly, we show that with a proper design of the segment decomposition, all sampling subproblems will only need to tackle a strongly log-concave distribution, which can be very efficient to solve using the Langevin-based samplers with a provably rapid convergence rate. As a result, we prove that the gradient complexity of RS-DMC only has a quasi-polynomial dependency on $\epsilon$, which significantly improves exponential gradient complexity in Huang et al. (2023). Furthermore, under commonly used dissipative conditions, our algorithm is provably much faster than the popular Langevin-based algorithms. Our algorithm design and theoretical framework illuminate a novel direction for addressing sampling problems, which could be of broader applicability in the community.Comment: 54 page

Hydrothermal synthesis of magnetite nanoparticles as MRI contrast agents

Magnetite (Fe3O4) nanoparticles prepared using hydrothermal approach were employed to study their potential application as magnetic resonance imaging (MRI) contrast agent. The hydrothermal process involves precursors FeCl2·4H2O and FeCl3 with NaOH as reducing agent to initiate the precipitation of Fe3O4, followed by hydrothermal treatment to produce nano-sized Fe3O4. Chitosan (CTS) was coated onto the surface of the as-prepared Fe3O4 nanoparticles to enhance its stability and biocompatible properties. The size distribution of the obtained Fe3O4 nanoparticles was examined using transmission electron microscopy (TEM). The cubic inverse spinel structure of Fe3O4 nanoparticles was confirmed by X-ray diffraction technique (XRD). Fourier transform infrared (FTIR) spectrum indicated the presence of the chitosan on the surface of the Fe3O4 nanoparticles. The superparamagnetic behaviour of the produced Fe3O4 nanoparticles at room temperature was elucidated using a vibrating sample magnetometer (VSM). From the result of custom made phantom study of magnetic resonance (MR) imaging, coated Fe3O4 nanoparticles have been proved to be a promising contrast enhanced agent in MR imaging

Hydrothermal preparation of high saturation magnetization and coercivity cobalt ferrite nanocrystals without subsequent calcination

In this work, CoFe2O4 nanocrystals with high saturation magnetization (Ms) and high coercivity (Hc) have been fabricated via a simple hydrothermal method and without subsequent calcination. The resulting CoFe2O4 nanocrystals are characterized by X-ray diffraction, transmission electron microscopy, scanning electron microscopy, energy-dispersive X-ray spectrometry, differential scanning calorimetry and vibrating sample magnetometry. The results indicate that CoFe2O4 nanocrystals are single crystal and the average crystallite size is increasing with the hydrothermal temperature. The electron micrographs show that the nanocrystals are well-dispersed and possess uniform size. The shape of CoFe2O4 nanocrystals is transformed from spherical into rod by increasing the hydrothermal temperature. The nanocrystals show relatively high Ms of 74.8 emu g−1 and Hc of 2216 Oe, as compared to previous reported results. The obtained results reveal the applicability of this method for efficiently producing well crystallized and relatively high magnetic properties CoFe2O4 nanocrystals as compared to other methods. More importantly, it does not require further calcination processes

Electrochemical Oxidation of Cysteine at a Film Gold Modified Carbon Fiber Microelectrode Its Application in a Flow—Through Voltammetric Sensor

A flow-electrolytical cell containing a strand of micro Au modified carbon fiber electrodes (CFE) has been designedand characterized for use in a voltammatric detector for detecting cysteine using high-performance liquid chromatography. Cysteine is more efficiently electrochemical oxidized on a Au /CFE than a bare gold and carbon fiber electrode. The possible reaction mechanism of the oxidation process is described from the relations to scan rate, peak potentials and currents. For the pulse mode, and measurements with suitable experimental parameters, a linear concentration from 0.5 to 5.0 mg·L−1 was found. The limit of quantification for cysteine was below 60 ng·mL−1

Effects of the neurogranin variant rs12807809 on thalamocortical morphology in schizophrenia

10.1371/journal.pone.0085603PLoS ONE812-POLN

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Research on Power Demand Suppression Based on Charging Optimization and BESS Configuration for Fast-Charging Stations in Beijing

In order to reduce the recharging time of electric vehicles, the charging power and voltage are becoming higher, which has led to a huge distribution capacity demand and load fluctuation, especially in pure electric buses (PEBs) with large onboard batteries. Based on one actual direct current (DC) fast-charging station, a two-step strategy for the suppression of the peak charging power was developed in this paper, which combined charging optimization and a battery energy storage system (BESS) configuration. A novel charging strategy was proposed, with the PEBs fast-charging during operating hours and normal charging at night, based on a new charging topology. Then, a charging sequence optimization model was established, according to the operation characteristics analysis of the DC fast-charging station. The particle swarm optimization (PSO) algorithm is applied to optimize the charging sequence, which is disordered at present. Linear programming is used to configure the battery energy storage system in order to further decrease the peak charging power and satisfy the distribution capacity constraint. The two-step strategy was simulated by the dataset from the real station. The results show that the distribution capacity demand, charging load fluctuation, electricity cost, and size of the BESS were significantly decreased