Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation

Diffusion generative modeling has become a promising approach for learning robotic manipulation tasks from stochastic human demonstrations. In this paper, we present Diffusion-EDFs, a novel SE(3)-equivariant diffusion-based approach for visual robotic manipulation tasks. We show that our proposed method achieves remarkable data efficiency, requiring only 5 to 10 human demonstrations for effective end-to-end training in less than an hour. Furthermore, our benchmark experiments demonstrate that our approach has superior generalizability and robustness compared to state-of-the-art methods. Lastly, we validate our methods with real hardware experiments. Project Website: https://sites.google.com/view/diffusion-edfs/homeComment: 31 pages, 13 figure

Data-enabled Field Experiment Planning, Management, And Research Using Cyberinfrastructure

In the spring of 2013, NASA conducted a field campaign known as Iowa Flood Studies (IFloodS) as part of the Ground Validation (GV) program for the Global Precipitation Measurement (GPM) mission. The purpose of IFloodS was to enhance the understanding of flood-related, space-based observations of precipitation processes in events that transpire worldwide. NASA used a number of scientific instruments such as ground based weather radars, rain and soil moisture gauges, stream gauges, and disdrometers to monitor rainfall events in Iowa. This article presents the cyberinfrastructure tools and systems that supported the planning, reporting, and management of the field campaign and that allow these data and models to be accessed, evaluated, and shared for research. The authors describe the collaborative informatics tools, which are suitable for the network design, that were used to select the locations in which to place the instruments. How the authors used information technology tools for instrument monitoring, data acquisition, and visualizations after deploying the instruments and how they used a different set of tools to support data analysis and modeling after the campaign are also explained. All data collected during the campaign are available through the Global Hydrology Resource Center (GHRC), a NASA Distributed Active Archive Center (DAAC)

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Risk of COVID-19 transmission in heterogeneous age groups and effective vaccination strategy in Korea: a mathematical modeling study

OBJECTIVES: This study aims to analyze the possibility and conditions of maintaining an effective reproductive number below 1 using a mathematical model.METHODS: The total population was divided into five age groups (0-17, 18-29, 30-59, 60-74, and ≥75 years). Maximum likelihood estimation (MLE) was used to estimate the transmission rate of each age group. Mathematical model simulation was conducted until December 31, 2021, by establishing various strategies for vaccination and social distancing without considering variants.RESULTS: MLE results revealed that the group aged 0-17 years had a lower risk of transmission than other age groups, and the older age group had relatively high risks of infection. If 70% of the population will be vaccinated by the end of 2021, then simulations showed that even if social distancing was eased, the effective reproductive number would remain below 1 near August if it was not at the level of the third re-spreading period. However, if social distancing was eased and it reached the level of the re-spreading period, the effective reproductive number could be below 1 at the end of 2021.CONCLUSIONS: Considering both stable and worsened situations, simulation results emphasized that sufficient vaccine supply and control of the epidemic by maintaining social distancing to prevent an outbreak at the level of the re-spreading period are necessary to minimize mortality and maintain the effective reproductive number below 1.</jats:p

A comprehensive analysis of non-pharmaceutical interventions and vaccination on Ebolavirus disease outbreak: Stochastic modeling approach.

Ebolavirus disease (EVD) outbreaks have intermittently occurred since the first documented case in the 1970s. Due to its transmission characteristics, large outbreaks have not been observed outside Africa. However, within the continent, significant outbreaks have been attributed to factors such as endemic diseases with similar symptoms and inadequate medical infrastructure, which complicate timely diagnosis. In this study, we employed a stochastic modeling approach to analyze the spread of EVD during the early stages of an outbreak, with an emphasis on inherent risks. We developed a model that considers healthcare workers and unreported cases, and assessed the effect of non-pharmaceutical interventions (NPIs) using actual data. Our results indicate that the implementation of NPIs led to a decrease in the transmission rate and infectious period by 30% and 40% respectively, following the declaration of the outbreak. We also investigated the risks associated with delayed outbreak recognition. Our simulations suggest that, when accounting for NPIs and recognition delays, prompt detection could have resulted in a similar outbreak scale, with approximately 50% of the baseline NPIs effect. Finally, we discussed the potential effects of a vaccination strategy as a follow-up measure after the outbreak declaration. Our findings suggest that a vaccination strategy can reduce both the burden of NPIs and the scale of the outbreak

Gradual transmittance controllable device via ion intercalation for spatial light modulators

To realize a hologram that is an effective method for implementing three-dimensional display, a novel spatial light modulator (SLM) that can generate the hologram by light interference and diffraction was developed based on transmittance changes. For a high-resolution hologram, pixel size of the SLM needs to be scaled down to visible light wavelength (380∼780 nm). However, conventional liquid crystal or micro-mirror-based SLM has a limitation in scaling down; few micrometers sized unit parts are required based on its operation mechanism. Herein, an ion intercalation-based SLM utilizing nano-scale ions as the unit part was investigated. Consequently, basic operations of the SLM (light interference and diffraction) are achieved based on the gradual transmittance changes, which demonstrates the feasibility of ion intercalation-based SLM.</jats:p

Can Koreans be ‘FREE’ from mask wearing?: Advanced mathematical model can suggest the idea

AbstractBackgroundIt was found that more than half of the population in Korea had a prior COVID-19 infection. In 2022, most nonpharmaceutical interventions, except mask-wearing indoors, had been lifted. Discussions about easing the indoor mask mandate are ongoing.MethodsWe developed an age-structured compartmental model that distinguishes vaccination history, prior infection, and medical staff from the rest of the population. Contact patterns among hosts were separated based on age and location. We simulated scenarios with the lifting of the mask mandate all at once or sequentially according to the locations. Furthermore, we investigated the impact of a new variant assuming that it has higher transmissibility and risk of breakthrough infection.FindingsWe found that the peak size of administered severe patients might not exceed 1,100 when the mask mandate is lifted everywhere, and 800 if the mask mandate only remains in the hospital. If the mask mandate is lifted in a sequence (except hospital), then the peak size of administered severe patients did not exceed 650. Moreover, if the new variant have both of higher transmissibility and immune reduction therefore the effective reproductive number of the new variant is approximately 3 times higher than the current variant, additional interventions may be needed to keep the administered severe patients from exceeding 2,000, which is the critical level we set.InterpretationOur findings showed that the lifting of the mask mandate, except in hospitals, would be applicable more manageable if it is implemented sequentially. Considering a new variant, we found that depending on the population immunity and transmissibility of the variant, wearing masks and other interventions may be necessary for controlling the disease.FundingThis paper is supported by the Korea National Research Foundation (NRF) grant funded by the Korean government (MEST) (NRF-2021M3E5E308120711). This paper is also supported by the Korea National Research Foundation (NRF) grant funded by the Korean government (MEST) (NRF-2021R1A2C100448711). This research was also supported by a fund (2022-03-008) by Research of Korea Disease Control and Prevention Agency.Research in contextEvidence before this studyThere are numerous studies in modelling transmission dynamics of COVID-19 variants but only a few published works tackle the lifting of mask mandate considering the omicron variant, although these studies did not consider unreported cases, variants, and waning immunity. Furthermore, there is no age-structured modeling study which investigated the effect of lifting mask mandate considering high immune state of the population, contributed by both of natural infection and vaccination.Added value of this studyOur mathematical model considered key factors such as vaccine status, age structure, medical staff, prior infection, and unreported cases to study the COVID-19 epidemic in Korea. Updated data and variant-specific parameters were used in the model. Contact patterns in the household, school, work, hospital and other places are considered separately to make the model applicable to the mask mandate issue. Seasonality and scenarios on possible future variants are also included in this study.Implications of all the available evidenceWith mask wearing as one of the remaining non-pharmaceutical interventions in Korea and other countries, this study proposes strategies for lifting the mask mandates while ensuring that cases remain manageable. A variant-dependent factor is incorporated into the model so that policymakers could prepare proactive intervention policies against future variants.</jats:sec

Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system

AbstractLately, there has been a rapid increase in the use of software-based deep learning neural networks (S-DNN) for the analysis of unstructured data consumption. For implementation of the S-DNN, synapse-device-based hardware DNN (H-DNN) has been proposed as an alternative to typical Von-Neumann structural computing systems. In the H-DNN, various numerical values such as the synaptic weight, activation function, and etc., have to be realized through electrical device or circuit. Among them, the synaptic weight that should have both positive and negative numerical values needs to be implemented in a simpler way. Because the synaptic weight has been expressed by conductance value of the synapse device, it always has a positive value. Therefore, typically, a pair of synapse devices is required to realize the negative weight values, which leads to additional hardware resources such as more devices, higher power consumption, larger area, and increased circuit complexity. Herein, we propose an alternative simpler method to realize the negative weight (named weight shifter) and its hardware implementation. To demonstrate the weight shifter, we investigated its theoretical, numerical, and circuit-related aspects, following which the H-DNN circuit was successfully implemented on a printed circuit board.</jats:p

The effect of resource loss on depression and peritraumatic distress during the early period of the COVID-19: considering the pandemic-situational and social context

Abstract Background The public experienced loss of resources, including their health and property during the COVID-19 pandemic. The Conservation of Resources (COR) theory is a useful tool to explain the effect of resource loss on mental health. This paper examines the effect of resource loss on depression and peritraumatic distress considering the situational and social context of the COVID-19 pandemic applying COR theory. Methods An online survey was conducted for Gyeonggi residents when the second wave of COVID-19 in South Korea declined (5 October to 13 October 2020); 2,548 subjects were included in the hierarchical linear regression analysis. Results COVID-19 infection-related experiences, resource losses (e.g., financial burden, deterioration of health, and decline of self-esteem), and fear of stigma were related to elevated levels of peritraumatic distress and depression. Risk perception was associated with peritraumatic distress. Reduced income or job loss were related to depression. Social support was a protective factor for mental health. Conclusions This study suggests that we need to focus on COVID-19 infection-related experiences and loss of daily resources in order to understand mental health deterioration during the COVID-19 pandemic. Moreover, it is important to monitor the mental health of medically and socially vulnerable groups and those who have lost resources due to the pandemic and to provide them with social support services

The effect of resource loss on depression and peritraumatic distress during the early period of the COVID-19: considering the pandemic-situational and social context

Abstract Background: The public experienced loss of resources, including their health and property during the COVID-19 pandemic. The Conservation of Resources (COR) Theory is a useful tool to explain the effect of resource loss on mental health. This paper examines the effect of resource loss on depression and peritraumatic distress considering the situational and social context of the COVID-19 pandemic, applying COR theory. Methods: An online survey was conducted for Gyeonggi residents over eight days (5 October to 13 October 2020) when the second wave of the COVID-19 in South Korea declined; 2,548 subjects were included in the hierarchical linear regression analysis.Results: COVID-19 related experiences (infection /isolation /quarantine), resource losses (e.g., financial burden, deterioration of health, and decline of self-esteem), and fear of stigma were related to elevated levels of peritraumatic distress and depression. A higher level of risk perception was associated with higher peritraumatic distress, and people with reduced income or job loss showed a higher level of depression. Social support was a protective factor for mental health.Conclusions: This study indicates that COVID-19-related experiences and loss of daily resources require attention when considering mental health parameters during COVID-19. Moreover, it is important to monitor the mental health of medically and socially vulnerable groups and those who have lost resources due to the pandemic and to provide these individuals with social support or financial compensation.</jats:p