1,355 research outputs found
孤立性拡張期高血圧は血管内皮機能異常に関連しない
内容の要約広島大学(Hiroshima University)博士(医学)Doctor of Philosophy in Medical Sciencedoctora
Responsibility-Shifting through Delegation: Evidence from China’s One-Child Policy
We provide evidence on how responsibility-shifting through delegation occurred in China’s implementation of the one-child policy. We show that trust in local governments was reduced when they were the primary enforcer of the policy (1979–1990), while trust in neighbors was reduced when civilians were incentivized to report neighbors’ violations of the policy to the authorities (1991–2015). This effect was more pronounced among parents of a firstborn daughter, who were more likely to violate the policy due to the deep-rooted son preference. This study provides the first set of field evidence on the responsibility-shifting effect of delegation
A unified analysis of likelihood-based estimators in the Plackett--Luce model
The Plackett--Luce model is a popular approach for ranking data analysis,
where a utility vector is employed to determine the probability of each outcome
based on Luce's choice axiom. In this paper, we investigate the asymptotic
theory of utility vector estimation by maximizing different types of
likelihood, such as the full-, marginal-, and quasi-likelihood. We provide a
rank-matching interpretation for the estimating equations of these estimators
and analyze their asymptotic behavior as the number of items being compared
tends to infinity. In particular, we establish the uniform consistency of these
estimators under conditions characterized by the topology of the underlying
comparison graph sequence and demonstrate that the proposed conditions are
sharp for common sampling scenarios such as the nonuniform random hypergraph
model and the hypergraph stochastic block model; we also obtain the asymptotic
normality of these estimators and discuss the trade-off between statistical
efficiency and computational complexity for practical uncertainty
quantification. Both results allow for nonuniform and inhomogeneous comparison
graphs with varying edge sizes and different asymptotic orders of edge
probabilities. We verify our theoretical findings by conducting detailed
numerical experiments.Comment: 42 pages, corrected typos, added the supplementary file containing
all remaining proof
CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation
The advancement of computer vision has pushed visual analysis tasks from
still images to the video domain. In recent years, video instance segmentation,
which aims to track and segment multiple objects in video frames, has drawn
much attention for its potential applications in various emerging areas such as
autonomous driving, intelligent transportation, and smart retail. In this
paper, we propose an effective framework for instance-level visual analysis on
video frames, which can simultaneously conduct object detection, instance
segmentation, and multi-object tracking. The core idea of our method is
collaborative multi-task learning which is achieved by a novel structure, named
associative connections among detection, segmentation, and tracking task heads
in an end-to-end learnable CNN. These additional connections allow information
propagation across multiple related tasks, so as to benefit these tasks
simultaneously. We evaluate the proposed method extensively on KITTI MOTS and
MOTS Challenge datasets and obtain quite encouraging results
Nonlinearity and efficiency dynamics of foreign exchange markets: evidence from multifractality and volatility of major exchange rates
This study investigates the efficiencies of the exchange markets
for four major currencies—the euro (EUR), the pound (GBP), the
Canadian dollar (CAD) and the Japanese yen (JPY)—from 2005 to
2019 by using multifractal detrended fluctuation analysis (MFDFA).
This study also investigates the causes of these efficiencies.
Significant multifractal properties are demonstrated by the four
markets, and long-range correlation and fat-tail distribution properties
are the main causes. We calculate and compare the multifractal
degrees in three subsamples, which are classified based on
their temporal relation to two economic events: the 2008 financial
crisis and the announcement by the Federal Reserve of its withdrawal
from the quantitative easing policy in 2014. Empirical
results suggest that multifractal properties exist at different levels
in the subsamples, thus showing that these events affect foreign
exchange market efficiencies in terms of statistics and the fractal
market. The JPY exchange market has the fewest multifractal
properties, thus indicating that this exchange market has the
highest market efficiency among these four exchange markets.
The empirical results have implications for the nonlinear mechanism
and efficiency in foreign exchange markets, which may help
investors effectively manage market risks and benefit a stable global
economy
Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning
While the Segment Anything Model (SAM) excels in semantic segmentation for
general-purpose images, its performance significantly deteriorates when applied
to medical images, primarily attributable to insufficient representation of
medical images in its training dataset. Nonetheless, gathering comprehensive
datasets and training models that are universally applicable is particularly
challenging due to the long-tail problem common in medical images. To address
this gap, here we present a Self-Sampling Meta SAM (SSM-SAM) framework for
few-shot medical image segmentation. Our innovation lies in the design of three
key modules: 1) An online fast gradient descent optimizer, further optimized by
a meta-learner, which ensures swift and robust adaptation to new tasks. 2) A
Self-Sampling module designed to provide well-aligned visual prompts for
improved attention allocation; and 3) A robust attention-based decoder
specifically designed for medical few-shot learning to capture relationship
between different slices. Extensive experiments on a popular abdominal CT
dataset and an MRI dataset demonstrate that the proposed method achieves
significant improvements over state-of-the-art methods in few-shot
segmentation, with an average improvements of 10.21% and 1.80% in terms of DSC,
respectively. In conclusion, we present a novel approach for rapid online
adaptation in interactive image segmentation, adapting to a new organ in just
0.83 minutes. Code is publicly available on GitHub upon acceptance
Project Renew Worcester
n The client for this capstone project is RENEW Worcester which is a fledgling solar power project whose main goals are to bring renewable energy in the form of solar power into local, primarily low-income communities and are committed to the mission of making the transition off of fossil fuels to clean, renewable power. Based in Worcester, Massachusetts, they are a local chapter of Co-op Power which is a consumer-owned sustainable energy cooperative (co-op) made up of numerous different local chapters all over the New England area as well as the state of New York. The problem that we will attempt to address is to determine what kind of organization RENEW should become: non-profit or for-profit, while taking into consideration that our recommendation should be in line with what would best be suited for their goals and mission. The purpose of this project is to provide research into the solar power industry – with special focus on the solar industry in Massachusetts – as well as provide detailed information on the different types of non-profit and for-profit organizations
Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification
Retrieval augmentation, which enhances downstream models by a knowledge
retriever and an external corpus instead of by merely increasing the number of
model parameters, has been successfully applied to many natural language
processing (NLP) tasks such as text classification, question answering and so
on. However, existing methods that separately or asynchronously train the
retriever and downstream model mainly due to the non-differentiability between
the two parts, usually lead to degraded performance compared to end-to-end
joint training. In this paper, we propose Differentiable Retrieval Augmentation
via Generative lANguage modeling(Dragan), to address this problem by a novel
differentiable reformulation. We demonstrate the effectiveness of our proposed
method on a challenging NLP task in e-commerce search, namely query intent
classification. Both the experimental results and ablation study show that the
proposed method significantly and reasonably improves the state-of-the-art
baselines on both offline evaluation and online A/B test.Comment: 5 pages, 2 figures; accepted by CIKM202
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