ESTABLISHMENT OF NATIONAL STANDARD FOR INERTIAL PARAMETERS OF CHINESE ADULTS

Utilizing the database of human body measurement of Chinese adults, 128 regression equations of inertial parameters of adult human body segments were established in the study on the base of research on inertial parameters of young human body segments using the CT method. These equations includes: the binary and plural regression equations that calculate segmental mass and center of mass of adult male or a female; the binary and plural regression equations that calculate segmental and the whole body inertial moments of adult male or female. A measuring system consisting of a three-wire pendulum instrument and center of gravity plank is also devised in the study. After measuring the selected Chinese adults (80 males and 40 females), the regression equations of inertial parameters of Chinese adults are verified. A national standard was subsequently established

Spatio-temporal point processes with deep non-stationary kernels

Point process data are becoming ubiquitous in modern applications, such as social networks, health care, and finance. Despite the powerful expressiveness of the popular recurrent neural network (RNN) models for point process data, they may not successfully capture sophisticated non-stationary dependencies in the data due to their recurrent structures. Another popular type of deep model for point process data is based on representing the influence kernel (rather than the intensity function) by neural networks. We take the latter approach and develop a new deep non-stationary influence kernel that can model non-stationary spatio-temporal point processes. The main idea is to approximate the influence kernel with a novel and general low-rank decomposition, enabling efficient representation through deep neural networks and computational efficiency and better performance. We also take a new approach to maintain the non-negativity constraint of the conditional intensity by introducing a log-barrier penalty. We demonstrate our proposed method's good performance and computational efficiency compared with the state-of-the-art on simulated and real data

ANALYSIS OF VALGUS CHARACT,ERISTICS OF OSSEOUS STRUCTURE OF THE FEET WITH THREE-DIMENSIONAL RECONSTRUCTION TECHNIQUES

Using the advanced MR images scan technique combined with three-dimensional reconstruction software, the study went deep into the research of feet's osseous tissue structure. After an investigation of 37 sUbjects' 10 indexes including valgus index and rear foot angle, the study showed distinct differences between normal foot and flatfoot. The correlation modulus of the X-ray images of flatfoot with valgus index is 0.75, and the correlation modulus with rear foot angle is 0.29. The phenomenon that most people with flatfeet had anklebone moving outside illuminated ,that flatfoot resulted from monstrosity of the navicular, cuneiform and metatarsus. However, rear foot angle only embodyed the relative position between calcaneus and shankbone. It couldn't explain the structure differences between flatfoot and normal foot

NEW METHODS TO DETERMINE 3-D ROTATIONAL INERTIA

Due to the different stature of the various nationalities and the limitation of the sample or the divergent research methods, the results on human-body inertia parameters of occidentals are not suitable to be applied to oriental adults. In this study, the 3-D principal inertia of each segment and of the entire body of Chinese adults was determined and the mathematical models for calculating their parameters were constructed. The results from this study can aid in the development of the prosthetic limbs for invalids, and analysis of the movements of astronauts, etc. In addition, the regression equations for calculating 3-D principal inertia of human standard postures and for each segment were derived. Their reliability are tested and verified by threestring pendulum method

Reinforcement Learning, Bit by Bit

Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency poses an impediment to carrying this success over to real environments. The design of data-efficient agents calls for a deeper understanding of information acquisition and representation. We develop concepts and establish a regret bound that together offer principled guidance. The bound sheds light on questions of what information to seek, how to seek that information, and it what information to retain. To illustrate concepts, we design simple agents that build on them and present computational results that demonstrate improvements in data efficiency

Epistemic Neural Networks

Intelligence relies on an agent's knowledge of what it does not know. This capability can be assessed based on the quality of joint predictions of labels across multiple inputs. Conventional neural networks lack this capability and, since most research has focused on marginal predictions, this shortcoming has been largely overlooked. We introduce the epistemic neural network (ENN) as an interface for models that represent uncertainty as required to generate useful joint predictions. While prior approaches to uncertainty modeling such as Bayesian neural networks can be expressed as ENNs, this new interface facilitates comparison of joint predictions and the design of novel architectures and algorithms. In particular, we introduce the epinet: an architecture that can supplement any conventional neural network, including large pretrained models, and can be trained with modest incremental computation to estimate uncertainty. With an epinet, conventional neural networks outperform very large ensembles, consisting of hundreds or more particles, with orders of magnitude less computation. We demonstrate this efficacy across synthetic data, ImageNet, and some reinforcement learning tasks. As part of this effort we open-source experiment code

The Neural Testbed: Evaluating Joint Predictions

Predictive distributions quantify uncertainties ignored by point estimates. This paper introduces The Neural Testbed: an open-source benchmark for controlled and principled evaluation of agents that generate such predictions. Crucially, the testbed assesses agents not only on the quality of their marginal predictions per input, but also on their joint predictions across many inputs. We evaluate a range of agents using a simple neural network data generating process. Our results indicate that some popular Bayesian deep learning agents do not fare well with joint predictions, even when they can produce accurate marginal predictions. We also show that the quality of joint predictions drives performance in downstream decision tasks. We find these results are robust across choice a wide range of generative models, and highlight the practical importance of joint predictions to the community

A highly sensitive bio-barcode immunoassay for multi-residue detection of organophosphate pesticides based on fluorescence anti-quenching

Balancing the risks and benefits of organophosphate pesticides (OPs) on human and environmental health relies partly on their accurate measurement. A highly sensitive fluorescence anti-quenching multi-residue bio-barcode immunoassay was developed to detect OPs (triazophos, parathion, and chlorpyrifos) in apples, turnips, cabbages, and rice. Gold nanoparticles were functionalized with monoclonal antibodies against the tested OPs. DNA oligonucleotides were complementarily hybridized with an RNA fluorescent label for signal amplification. The detection signals were generated by DNA-RNA hybridization and ribonuclease H dissociation of the fluorophore. The resulting fluorescence signal enables multiplexed quantification of triazophos, parathion, and chlorpyrifos residues over the concentration range of 0.01–25, 0.01–50, and 0.1–50 ng/mL with limits of detection of 0.014, 0.011, and 0.126 ng/mL, respectively. The mean recovery ranged between 80.3% and 110.8% with relative standard deviations of 7.3%–17.6%, which correlate well with results obtained by LC-MS/MS. The proposed bio-barcode immunoassay is stable, reproducible and reliable, and is able to detect low residual levels of multi-residue OPs in agricultural products.This work was supported by the Central Public Interest Scientific Institution Basal Research Fund for the Chinese Academy of Agricultural Sciences (Grant No.: Y2021PT05), National Institute of Environmental Health Science Superfund Research Program (Grant No.: P42 ES004699), National Academy of Sciences (Subaward No.: 2000009144), and Ningbo Innovation Project for Agro-Products Quality and Safety (Grant No.: 2019CXGC007).Peer reviewe