442 research outputs found
Scalable Multiple Patterning Layout Decomposition Implemented by a Distribution Evolutionary Algorithm
As the feature size of semiconductor technology shrinks to 10 nm and beyond,
the multiple patterning lithography (MPL) attracts more attention from the
industry. In this paper, we model the layout decomposition of MPL as a
generalized graph coloring problem, which is addressed by a distribution
evolutionary algorithm based on a population of probabilistic model (DEA-PPM).
DEA-PPM can strike a balance between decomposition results and running time,
being scalable for varied settings of mask number and lithography resolution.
Due to its robustness of decomposition results, this could be an alternative
technique for multiple patterning layout decomposition in next-generation
technology nodes
Changes in inequality in utilization of preventive care services: evidence on China’s 2009 and 2015 health system reform
BACKGROUND:
Ensuring equal access to preventive care has always been given a priority in health system throughout world. This study aimed to decompose inequality in utilization of preventive care services into its contributing factors and then explore its changes over the period of China's 2009-2015 health system reform.
METHODS:
The concentration index (CI) and decomposition of the CI was performed to capture income-related inequalities in preventive services utilization and identify contribution of various determinants to such inequality using data from China Health and Nutrition Survey. Then, changes in inequality from 2009 to 2015 were estimated using Oaxaca-type decomposition technique.
RESULTS:
The CI for preventive services utilization dropped from 0.2240 in 2009 to 0.1825 in 2015. Residential location and household income made the biggest contributions to income-related inequalities in these two years. Oaxaca decomposition revealed changes in residential location, regions and medical insurance made positive contributions to decline in inequality. However, alternation in household income, age and medical services utilization pushed the equality toward deterioration.
CONCLUSION:
The pro-rich inequality in preventive healthcare services usage is evident in China despite a certain decline in such inequality during observation period. Policy actions on eliminating urban-rural and income disparity should be given the priority to equalize preventive healthcare
The effects of anti-sense interleukin-5 gene transferred by recombinant adeno-associated virus in allergic rats
The accumulation and infiltration of eosinophils in airways is one of the most important characteristics of asthma, and is mediated partly by secretion of IL-5 from Th2 lymphocytes. It is well known that interleukin-5 (IL-5) played an important role in the regulation of eosinophils. In this study, an anti-sense IL-5 gene transferred by recombinant adeno-associated virus (rAAV-ASIL-5) was prepared to transfect allergic rats. It was found that the expression of IL-5 protein in plasma and BALF were inhibited significantly. The rAAV-ASIL-5-mediated suppression of total cell counts in peripheral blood and BALF were also observed. Moreover, rAAV-ASIL-5 remarkably reduced the eosinophil counts in peripheral blood and BALF, as well as the expression of ECP protein in plasma and BALF. The inflammation in lungs of rAAV-ASIL-5 pretreated rats also became slighter when compared with allergic rats. Otherwise, no apparent pathological damage to vital organs of rats was found. In conclusion, recombinant adeno-associated virus-mediated delivery of anti-sense IL-5 gene inhibited the accumulation of eosinophils and the airways inflammation in rat model of allergic asthma via suppressing IL-5 expression. It suggested the feasibility of rAAV-ASIL-5 in the gene therapy for allergic asthma and other eosinophilic diseases
DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
Recommendation models trained on the user feedback collected from deployed
recommendation systems are commonly biased. User feedback is considerably
affected by the exposure mechanism, as users only provide feedback on the items
exposed to them and passively ignore the unexposed items, thus producing
numerous false negative samples. Inevitably, biases caused by such user
feedback are inherited by new models and amplified via feedback loops.
Moreover, the presence of false negative samples makes negative sampling
difficult and introduces spurious information in the user preference modeling
process of the model. Recent work has investigated the negative impact of
feedback loops and unknown exposure mechanisms on recommendation quality and
user experience, essentially treating them as independent factors and ignoring
their cross-effects. To address these issues, we deeply analyze the data
exposure mechanism from the perspective of data iteration and feedback loops
with the Missing Not At Random (\textbf{MNAR}) assumption, theoretically
demonstrating the existence of an available stabilization factor in the
transformation of the exposure mechanism under the feedback loops. We further
propose Dynamic Personalized Ranking (\textbf{DPR}), an unbiased algorithm that
uses dynamic re-weighting to mitigate the cross-effects of exposure mechanisms
and feedback loops without additional information. Furthermore, we design a
plugin named Universal Anti-False Negative (\textbf{UFN}) to mitigate the
negative impact of the false negative problem. We demonstrate theoretically
that our approach mitigates the negative effects of feedback loops and unknown
exposure mechanisms. Experimental results on real-world datasets demonstrate
that models using DPR can better handle bias accumulation and the universality
of UFN in mainstream loss methods
A Distribution Evolutionary Algorithm for Graph Coloring
Graph Coloring Problem (GCP) is a classic combinatorial optimization problem
that has a wide application in theoretical research and engineering. To address
complicated GCPs efficiently, a distribution evolutionary algorithm based on
population of probability models (DEA-PPM) is proposed. Based on a novel
representation of probability model, DEA-PPM employs a Gaussian orthogonal
search strategy to explore the probability space, by which global exploration
can be realized using a small population. With assistance of local exploitation
on a small solution population, DEA-PPM strikes a good balance between
exploration and exploitation. Numerical results demonstrate that DEA-PPM
performs well on selected complicated GCPs, which contributes to its
competitiveness to the state-of-the-art metaheuristics
Deep Generative Imputation Model for Missing Not At Random Data
Data analysis usually suffers from the Missing Not At Random (MNAR) problem,
where the cause of the value missing is not fully observed. Compared to the
naive Missing Completely At Random (MCAR) problem, it is more in line with the
realistic scenario whereas more complex and challenging. Existing statistical
methods model the MNAR mechanism by different decomposition of the joint
distribution of the complete data and the missing mask. But we empirically find
that directly incorporating these statistical methods into deep generative
models is sub-optimal. Specifically, it would neglect the confidence of the
reconstructed mask during the MNAR imputation process, which leads to
insufficient information extraction and less-guaranteed imputation quality. In
this paper, we revisit the MNAR problem from a novel perspective that the
complete data and missing mask are two modalities of incomplete data on an
equal footing. Along with this line, we put forward a generative-model-specific
joint probability decomposition method, conjunction model, to represent the
distributions of two modalities in parallel and extract sufficient information
from both complete data and missing mask. Taking a step further, we exploit a
deep generative imputation model, namely GNR, to process the real-world missing
mechanism in the latent space and concurrently impute the incomplete data and
reconstruct the missing mask. The experimental results show that our GNR
surpasses state-of-the-art MNAR baselines with significant margins (averagely
improved from 9.9% to 18.8% in RMSE) and always gives a better mask
reconstruction accuracy which makes the imputation more principle
Real-Time 262-Mb/s Visible Light Communication With Digital Predistortion Waveform Shaping
A digital predistortion waveform shaping scheme combined with a blue filter is proposed to optimize both the rise and fall times of a light-emitting diode (LED) and the optical receiver current of the signal of the real-time visible light communication (VLC) system. The proposed scheme is implemented on a field-programmable gate array (FPGA) and a digital-to-analog converter based test bed, which is flexible and reconfigurable by programming the FPGA to match different LED characteristics and varied data rates. A 262-Mb/s non-return-to-zero on-off keying modulation based real-time VLC link with a bit error rate of less than 1.0×10−6 is achieved over a transmission distance of 5.0 m, which uses a single white phosphorous LED with a limited power of 0.1 W
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