Probabilistic Compute-in-Memory Design For Efficient Markov Chain Monte Carlo Sampling

Markov chain Monte Carlo (MCMC) is a widely used sampling method in modern artificial intelligence and probabilistic computing systems. It involves repetitive random number generations and thus often dominates the latency of probabilistic model computing. Hence, we propose a compute-in-memory (CIM) based MCMC design as a hardware acceleration solution. This work investigates SRAM bitcell stochasticity and proposes a novel ``pseudo-read'' operation, based on which we offer a block-wise random number generation circuit scheme for fast random number generation. Moreover, this work proposes a novel multi-stage exclusive-OR gate (MSXOR) design method to generate strictly uniformly distributed random numbers. The probability error deviating from a uniform distribution is suppressed under $10^{-5}$. Also, this work presents a novel in-memory copy circuit scheme to realize data copy inside a CIM sub-array, significantly reducing the use of R/W circuits for power saving. Evaluated in a commercial 28-nm process development kit, this CIM-based MCMC design generates 4-bit$\sim$32-bit samples with an energy efficiency of $0.53$~pJ/sample and high throughput of up to $166.7$M~samples/s. Compared to conventional processors, the overall energy efficiency improves $5.41\times10^{11}$ to $2.33\times10^{12}$ times

Modeling Single-Phase Inverter and Its Decentralized Coordinated Control by Using Feedback Linearization

It is a very crucial problem to make a microgrid operated reasonably and stably. Considering the nonlinear mathematics model of inverter established in this paper, the input-output feedback linearization method is used to transform the nonlinear mathematics model of inverters to a linear tracking synchronization and consensus regulation control problem. Based on the linear mathematics model and multiagent consensus algorithm, a decentralized coordinated controller is proposed to make amplitudes and angles of voltages from inverters be consensus and active and reactive power shared in the desired ratio. The proposed control is totally distributed because each inverter only requires local and one neighbor’s information with sparse communication structure based on multiagent system. The hybrid consensus algorithm is used to keep the amplitude of the output voltages following the leader and the angles of output voltage as consensus. Then the microgrid can be operated more efficiently and the circulating current between DGs can be effectively suppressed. The effectiveness of the proposed method is proved through simulation results of a typical microgrid system

Quarterly GDP forecast based on coupled economic and energy feature WA-LSTM model

Existing macroeconomic forecasting methods primarily focus on the characteristics of economic data, but they overlook the energy-related features concealed behind these economic characteristics, which may lead to inaccurate GDP predictions. Therefore, this paper meticulously analyzes the relationship between energy big data and economic data indicators, explores the coupling feature mining of energy big data and economic data, and constructs features coupling economic and energy data. Targeting the nonlinear variation coupling features in China’s quarterly GDP data and using the long short-term memory (LSTM) neural network model based on deep learning, we employ wavelet analysis technology (WA) to decompose selected macroeconomic variables and construct a prediction model combining LSTM and WA, which is further compared with multiple benchmark models. The research findings show that, in terms of quarterly GDP data prediction, the combined deep learning model and wavelet analysis significantly outperform other methods. When processing structurally complex, nonlinear, and multi-variable data, the LSTM and WA combined prediction model demonstrate better generalization capabilities, with its prediction accuracy generally surpassing other benchmark models

Gene editing in monogenic autism spectrum disorder: animal models and gene therapies

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disease, and its diagnosis is dependent on behavioral manifestation, such as impaired reciprocal social interactions, stereotyped repetitive behaviors, as well as restricted interests. However, ASD etiology has eluded researchers to date. In the past decades, based on strong genetic evidence including mutations in a single gene, gene editing technology has become an essential tool for exploring the pathogenetic mechanisms of ASD via constructing genetically modified animal models which validates the casual relationship between genetic risk factors and the development of ASD, thus contributing to developing ideal candidates for gene therapies. The present review discusses the progress in gene editing techniques and genetic research, animal models established by gene editing, as well as gene therapies in ASD. Future research should focus on improving the validity of animal models, and reliable DNA diagnostics and accurate prediction of the functional effects of the mutation will likely be equally crucial for the safe application of gene therapies

Ligand-dependent spatiotemporal signaling profiles of the mu-opioid receptor are controlled by distinct protein-interaction networks

Ligand-dependent differences in the regulation and internalization of the mu-opioid receptor (MOR) have been linked to the severity of adverse effects that limit opiate use in pain management. MOR activation by morphine or [D-Ala2,N-MePhe4,Gly-ol]-enkephalin (DAMGO) causes differences in spatiotemporal signaling dependent on MOR distribution at the plasma membrane. Morphine stimulation of MOR activates a Gai/o–Gbg–protein kinase C (PKC)a phosphorylation pathway that limits MOR distribution and is associated with a sustained increase in cytosolic extracellular signal–regulated kinase (ERK) activity. In contrast, DAMGO causes a redistribution of the MOR at the plasma membrane (before receptor internalization), that facilitates transient activation of cytosolic and nuclear ERK. Here, we used proximity biotinylation proteomics to dissect the different protein-interaction networks that underlie the spatiotemporal signaling of morphine and DAMGO. We found that DAMGO, but not morphine, activates Ras‐related C3 botulinum toxin substrate 1 (Rac1). Both Rac1 and nuclear ERK activity was dependent on the scaffolding proteins IQ motif–containing GTPase-activating protein-1 (IQGAP1) and Crk-like protein (CRKL). In contrast, morphine increased the proximity of the MOR to desmosomal proteins, which form specialized and highly ordered membrane domains. Knockdown of two desmosomal proteins, junction plakoglobin (JUP) or desmocolin-1 (DSC1), switched the morphine spatiotemporal signaling profile to mimic that of DAMGO, resulting in a transient increase in nuclear ERK activity. The identification of the MOR-interaction networks that control differential spatiotemporal signaling reported here is an important step towards understanding how signal compartmentalization contributes to opioid-induced responses including anti-nociception and the development of tolerance and dependence

Satellite Observed Widespread Decline in Mongolian Grasslands Largely Due to Overgrazing

The Mongolian Steppe is one of the largest remaining grassland ecosystems. Recent studies have reported widespread decline of vegetation across the steppe and about 70 percent of this ecosystem is now considered degraded. Among the scientific community there has been an active debate about whether the observed degradation is related to climate, or overgrazing, or both. Here, we employ a new atmospheric correction and cloud screening algorithm (MAIAC) to investigate trends in satellite observed vegetation phenology. We relate these trends to changes in climate and domestic animal populations. A series of harmonic functions is fitted to MODIS observed phenological curves to quantify seasonal and inter-annual changes in vegetation. Our results show a widespread decline (of about 12 percent on average) in MODIS observed NDVI across the country but particularly in the transition zone between grassland and the Gobi desert, where recent decline was as much as 40 percent below the 2002 mean NDVI. While we found considerable regional differences in the causes of landscape degradation, about 80 percent of the decline in NDVI could be attributed to increase in livestock. Changes in precipitation were able to explain about 30 percent of degradation across the country as a whole but up to 50 percent in areas with denser vegetation cover (p0.05). Temperature changes, while significant, played only a minor role (r20.10, p0.05). Our results suggest that the cumulative effect of overgrazing is a primary contributor to the degradation of the Mongolian steppe and is at least partially responsible for desertification reported in previous studies

Recent Widespread Tree Growth Decline Despite Increasing Atmospheric CO2

Background: The synergetic effects of recent rising atmospheric CO2 and temperature are expected to favor tree growth in boreal and temperate forests. However, recent dendrochronological studies have shown site-specific unprecedented growth enhancements or declines. The question of whether either of these trends is caused by changes in the atmosphere remains unanswered because dendrochronology alone has not been able to clarify the physiological basis of such trends. Methodology/Principal Findings: Here we combined standard dendrochronological methods with carbon isotopic analysis to investigate whether atmospheric changes enhanced water use efficiency (WUE) and growth of two deciduous and two coniferous tree species along a 9u latitudinal gradient across temperate and boreal forests in Ontario, Canada. Our results show that although trees have had around 53 % increases in WUE over the past century, growth decline (measured as a decrease in basal area increment – BAI) has been the prevalent response in recent decades irrespective of species identity and latitude. Since the 1950s, tree BAI was predominantly negatively correlated with warmer climates and/or positively correlated with precipitation, suggesting warming induced water stress. However, where growth declines were not explained by climate, WUE and BAI were linearly and positively correlated, showing that declines are not always attributable to warming induced stress and additional stressors may exist. Conclusions: Our results show an unexpected widespread tree growth decline in temperate and boreal forests due t

Uncertainty analysis of vegetation distribution in the northern high latitudes during the 21st century with a dynamic vegetation model

© The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecology and Evolution 2 (2012): 593–614, doi:10.1002/ece3.85.This study aims to assess how high-latitude vegetation may respond under various climate scenarios during the 21st century with a focus on analyzing model parameters induced uncertainty and how this uncertainty compares to the uncertainty induced by various climates. The analysis was based on a set of 10,000 Monte Carlo ensemble Lund-Potsdam-Jena (LPJ) simulations for the northern high latitudes (45oN and polewards) for the period 1900–2100. The LPJ Dynamic Global Vegetation Model (LPJ-DGVM) was run under contemporary and future climates from four Special Report Emission Scenarios (SRES), A1FI, A2, B1, and B2, based on the Hadley Centre General Circulation Model (GCM), and six climate scenarios, X901M, X902L, X903H, X904M, X905L, and X906H from the Integrated Global System Model (IGSM) at the Massachusetts Institute of Technology (MIT). In the current dynamic vegetation model, some parameters are more important than others in determining the vegetation distribution. Parameters that control plant carbon uptake and light-use efficiency have the predominant influence on the vegetation distribution of both woody and herbaceous plant functional types. The relative importance of different parameters varies temporally and spatially and is influenced by climate inputs. In addition to climate, these parameters play an important role in determining the vegetation distribution in the region. The parameter-based uncertainties contribute most to the total uncertainty. The current warming conditions lead to a complexity of vegetation responses in the region. Temperate trees will be more sensitive to climate variability, compared with boreal forest trees and C3 perennial grasses. This sensitivity would result in a unanimous northward greenness migration due to anomalous warming in the northern high latitudes. Temporally, boreal needleleaved evergreen plants are projected to decline considerably, and a large portion of C3 perennial grass is projected to disappear by the end of the 21st century. In contrast, the area of temperate trees would increase, especially under the most extreme A1FI scenario. As the warming continues, the northward greenness expansion in the Arctic region could continue.Funded by the NASA Land Use and Land Cover Change program (NASA-NNX09AI26G), Department of Energy (DE-FG0208ER64599), National Science Foundation (NSF-1028291 and NSF-0919331), and the NSF Carbon and Water in the Earth Program (NSF-0630319)