521 research outputs found
Gender Differences in Cognition among Older Adults in China
In this paper, the authors model gender differences in cognitive ability in China using a new sample of middle-aged and older Chinese respondents. Modeled after the American Health and Retirement Survey (HRS), the CHARLS Pilot survey respondents are 45 years and older in two quite distinct provinces—Zhejiang a high growth industrialized province on the East Coast, and Gansu, a largely agricultural and poor Province in the West. Their measures of cognition in CHARLS relies on two measures that proxy for different dimensions of adult cognition—episodic memory and intact mental status. They relate both these childhood health measures to adult health and SES outcomes during the adult years. They find large cognitive differences to the detriment of women that were mitigated by large gender differences in education among these generations of Chinese people. These gender differences in cognition are especially concentrated within poorer communities in China with gender difference being more sensitive to community level attributes than to family level attributes, with economic resources. In traditional poor Chinese communities, there are strong economic incentives to favor boys at the expense of girls not only in their education outcomes, but in their nutrition and eventually their adult height. These gender cognitive differences have been steadily decreasing across birth cohorts as the economy of China grew rapidly. Among younger cohorts of young adults in China, there is no longer any gender disparity in cognitive ability.
An activity-based spatial-temporal community electricity vulnerability assessment framework
The power system is among the most important critical infrastructures in
urban cities and is getting increasingly essential in supporting people s daily
activities. However, it is also susceptible to most natural disasters such as
tsunamis, floods, or earthquakes. Electricity vulnerability, therefore, forms a
crucial basis for community resilience. This paper aims to present an
assessment framework of spatial-temporal electricity vulnerability to support
the building of community resilience against power outages. The framework
includes vulnerability indexes in terms of occupant demographics, occupant
activity patterns, and urban building characteristics. To integrate factors in
these aspects, we also proposed a process as activity
simulation-mapping-evaluation-visualization to apply the framework and
visualize results. This framework can help planners make an effective
first-time response by identifying the most vulnerable areas when a massive
power outage happens during natural disasters. It can also be integrated into
community resilience analysis models and potentially contributes to effective
disaster risk managementComment: to be published in Proceedings of the 5th International Conference on
Building Energy and Environmen
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
Transductive Few-Shot learning has gained increased attention nowadays
considering the cost of data annotations along with the increased accuracy
provided by unlabelled samples in the domain of few shot. Especially in
Few-Shot Classification (FSC), recent works explore the feature distributions
aiming at maximizing likelihoods or posteriors with respect to the unknown
parameters. Following this vein, and considering the parallel between FSC and
clustering, we seek for better taking into account the uncertainty in
estimation due to lack of data, as well as better statistical properties of the
clusters associated with each class. Therefore in this paper we propose a new
clustering method based on Variational Bayesian inference, further improved by
Adaptive Dimension Reduction based on Probabilistic Linear Discriminant
Analysis. Our proposed method significantly improves accuracy in the realistic
unbalanced transductive setting on various Few-Shot benchmarks when applied to
features used in previous studies, with a gain of up to in accuracy. In
addition, when applied to balanced setting, we obtain very competitive results
without making use of the class-balance artefact which is disputable for
practical use cases. We also provide the performance of our method on a high
performing pretrained backbone, with the reported results further surpassing
the current state-of-the-art accuracy, suggesting the genericity of the
proposed method
Community Time-Activity Trajectory Modelling based on Markov Chain Simulation and Dirichlet Regression
Accurate modeling of human time-activity trajectory is essential to support
community resilience and emergency response strategies such as daily energy
planning and urban seismic vulnerability assessment. However, existing modeling
of time-activity trajectory is only driven by socio-demographic information
with identical activity trajectories shared among the same group of people and
neglects the influence of the environment. To further improve human
time-activity trajectory modeling, this paper constructs community
time-activity trajectory and analyzes how social-demographic and built
environment influence people s activity trajectory based on Markov Chains and
Dirichlet Regression. We use the New York area as a case study and gather data
from American Time Use Survey, Policy Map, and the New York City Energy & Water
Performance Map to evaluate the proposed method. To validate the regression
model, Box s M Test and T-test are performed with 80% data training the model
and the left 20% as the test sample. The modeling results align well with the
actual human behavior trajectories, demonstrating the effectiveness of the
proposed method. It also shows that both social-demographic and built
environment factors will significantly impact a community's time-activity
trajectory. Specifically, 1) Diversity and median age both have a significant
influence on the proportion of time people assign to education activity. 2)
Transportation condition affects people s activity trajectory in the way that
longer commute time decreases the proportion of biological activity (eg.
sleeping and eating) and increases people s working time. 3) Residential
density affects almost all activities with a significant p-value for all
biological needs, household management, working, education, and personal
preference.Comment: to be published in Computers, Environment and Urban Syste
Clash Resolution Optimization based on Component and Clash Dependent Networks
Effective coordination across multi-disciplines is crucial to make sure that the locations of building components meet physical and functional constraints. Building information modeling (BIM) has been increasingly applied for coordination and one of its most widely used applications is automatic clash detection. The realistic visualization function of BIM helps reduce ambiguity and expedites clash detection. However, many project participants criticize automatic clash detection, as many detected clashes are irrelevant with no significant impact on design or construction work, thereby decreasing the precision of clash results and the benefits of BIM. In addition, clash detection consists of discovering problems, but it does not entail solving these clashes. Even though some studies discussed automatic clash detection, they rarely discussed the dependence relationships between building components. However, a building is an inseparable whole, and the dependent relationships among building components propagate the impact of clashes. Relocating one object to correct one clash may result in other objects violating spatial constraints, which may directly cause new clashes or indirectly cause them through relocating other components. Therefore, figuring out the dependency among clash objects with peripheral building components is useful to optimizing clash solutions by avoiding change propagation.
Algorithms are designed to automatically capture dependency relations from models to construct a component dependency network. The network is used as an input to distinguish irrelevant clashes for improving clash detection quality by analyzing the relations between clash components and the relations between clash components with their nearby components. The feasibility to harness the clash component network and graph theory are also explored to generate the clash component change list for minimizing clash change impact from a holistic perspective. In addition, this study demonstrates how to use BIM information to refine clash management, and specifically focus on designing a hybrid clash correction sequence to minimize potential iterative adjustments.
The contributions of this study exist at three levels. The most straightforward contribution is that this research proposed a method to improve clash detection quality as well as to provide decision support for clash resolution, which can help project teams to focus on important clashes and improve design coordination efficiency. In addition, this research proposes a new perspective to view clashes, switching the clash management focus and inspiring researchers to focus on finding global optimal solutions for all clashes other than a single clash. The third level is that even though this research focuses on clash management, the optimization algorithms based on graph theory can be used in other interdependent systems to improve design and construction performance.Ph.D
A Novel Real-Time Moving Target Tracking and Path Planning System for a Quadrotor UAV in Unknown Unstructured Outdoor Scenes
A quadrotor unmanned aerial vehicle (UAV) should have the ability to perform real-time target tracking and path planning simultaneously even when the target enters unstructured scenes, such as groves or forests. To accomplish this task, a novel system framework is designed and proposed to accomplish simultaneous moving target tracking and path planning by a quadrotor UAV with an onboard embedded computer, vision sensors, and a two-dimensional laser scanner. A support vector machine-based target screening algorithm is deployed to select the correct target from multiple candidates detected by single shot multibox detector. Furthermore, a new tracker named TLD-KCF is presented in this paper, in which a conditional scale adaptive algorithm is adopted to improve the tracking performance for a quadrotor UAV in cluttered outdoor environments. According to distance and position estimation for a moving target, our quadrotor UAV can acquire a control point to guide its fight. To reduce the computational burden, a fast path planning algorithm is proposed based on elliptical tangent model. A series of experiments are conducted on our quadrotor UAV platform DJI M100. Experimental video and comparison results among four kinds of target tracking algorithms are given to show the validity and practicality of the proposed approach
Reduce the rank calculation of a high-dimensional sparse matrix based on network controllability theory
Numerical computing of the rank of a matrix is a fundamental problem in
scientific computation. The datasets generated by the internet often correspond
to the analysis of high-dimensional sparse matrices. Notwithstanding recent
advances in the promotion of traditional singular value decomposition (SVD), an
efficient estimation algorithm for the rank of a high-dimensional sparse matrix
is still lacking. Inspired by the controllability theory of complex networks,
we converted the rank of a matrix into maximum matching computing. Then, we
established a fast rank estimation algorithm by using the cavity method, a
powerful approximate technique for computing the maximum matching, to estimate
the rank of a sparse matrix. In the merit of the natural low complexity of the
cavity method, we showed that the rank of a high-dimensional sparse matrix can
be estimated in a much faster way than SVD with high accuracy. Our method
offers an efficient pathway to quickly estimate the rank of the
high-dimensional sparse matrix when the time cost of computing the rank by SVD
is unacceptable.Comment: 10 pages, 4 figure
New Characterizations in Turnstile Streams with Applications
Recently, [Li, Nguyen, Woodruff, STOC 2014] showed any 1-pass constant probability streaming algorithm for computing a relation f on a vector x in {-m, -(m-1), ..., m}^n presented in the turnstile data stream model can be implemented by maintaining a linear sketch Ax mod q, where A is an r times n integer matrix and q = (q_1, ..., q_r) is a vector of positive integers. The space complexity of maintaining Ax mod q, not including the random bits used for sampling A and q, matches the space of the optimal algorithm.
We give multiple strengthenings of this reduction, together with new applications. In particular, we show how to remove the following shortcomings of their reduction:
1. The Box Constraint. Their reduction applies only to algorithms that must be correct even if x_{infinity} = max_{i in [n]} |x_i| is allowed to be much larger than m at intermediate points in the stream, provided that x is in {-m, -(m-1), ..., m}^n at the end of the stream. We give a condition under which the optimal algorithm is a linear sketch even if it works only when promised that x is in {-m, -(m-1), ..., m}^n at all points in the stream. Using this, we show the first super-constant Omega(log m) bits lower bound for the problem of maintaining a counter up to an additive epsilon*m error in a turnstile stream, where epsilon is any constant in (0, 1/2). Previous lower bounds are based on communication complexity and are only for relative error approximation; interestingly, we do not know how to prove our result using communication complexity. More generally, we show the first super-constant Omega(log(m)) lower bound for additive approximation of l_p-norms; this bound is tight for p in [1, 2].
2. Negative Coordinates. Their reduction allows x_i to be negative while processing the stream. We show an equivalence between 1-pass algorithms and linear sketches Ax mod q in dynamic graph streams, or more generally, the strict turnstile model, in which for all i in [n], x_i is nonnegative at all points in the stream. Combined with [Assadi, Khanna, Li, Yaroslavtsev, SODA 2016], this resolves the 1-pass space complexity of approximating the maximum matching in a dynamic graph stream, answering a question in that work.
3. 1-Pass Restriction. Their reduction only applies to 1-pass data stream algorithms in the turnstile model, while there exist algorithms for heavy hitters and for low rank approximation which provably do better with multiple passes. We extend the reduction to algorithms which make any number of passes, showing the optimal algorithm is to choose a new linear sketch at the beginning of each pass, based on the output of previous passes
Stratified Rule-Aware Network for Abstract Visual Reasoning
Abstract reasoning refers to the ability to analyze information, discover
rules at an intangible level, and solve problems in innovative ways. Raven's
Progressive Matrices (RPM) test is typically used to examine the capability of
abstract reasoning. The subject is asked to identify the correct choice from
the answer set to fill the missing panel at the bottom right of RPM (e.g., a
33 matrix), following the underlying rules inside the matrix. Recent
studies, taking advantage of Convolutional Neural Networks (CNNs), have
achieved encouraging progress to accomplish the RPM test. However, they partly
ignore necessary inductive biases of RPM solver, such as order sensitivity
within each row/column and incremental rule induction. To address this problem,
in this paper we propose a Stratified Rule-Aware Network (SRAN) to generate the
rule embeddings for two input sequences. Our SRAN learns multiple granularity
rule embeddings at different levels, and incrementally integrates the
stratified embedding flows through a gated fusion module. With the help of
embeddings, a rule similarity metric is applied to guarantee that SRAN can not
only be trained using a tuplet loss but also infer the best answer efficiently.
We further point out the severe defects existing in the popular RAVEN dataset
for RPM test, which prevent from the fair evaluation of the abstract reasoning
ability. To fix the defects, we propose an answer set generation algorithm
called Attribute Bisection Tree (ABT), forming an improved dataset named
Impartial-RAVEN (I-RAVEN for short). Extensive experiments are conducted on
both PGM and I-RAVEN datasets, showing that our SRAN outperforms the
state-of-the-art models by a considerable margin.Comment: AAAI 2021 paper. Code: https://github.com/husheng12345/SRA
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