158 research outputs found
The retirement migration puzzle in China
We examine whether and how retirement affects migration decisions in China. Using a regression discontinuity (RD) design approach combined with a nationally representative sample of 228,855 adults aged between 40 and 75, we find that retirement increases the probability of migration by 12.9 percentage points. Approximately 38% of the total migration effects can be attributed to inter temporal substitution (delayed migration). Retirement-induced migrants are lower-educated and have restricted access to social security. Household-level migration decisions can reconcile different migration responses across gender. Retirees migrate for risk sharing and family protection mechnisms, reducing market production of their families in the receiving households
Can I live with you after I retire? Retirement, old age support, and internal migration of older adults in China
This study examines the causal impact of retirement on migration decisions. Using a regression discontinuity (RD) design approach, combined with a nationally representative sample of 228,855 Chinese older adults, we find that retirement increases the probability of migration by 12.9 p.p. (an 80% increase in migration). Approximately 38% of the total migration effects can be attributed to inter-temporal substitution. Retirement-induced migrants are lower-educated, have restricted access to social security, and come from origins with high living costs. Relying on old age support from adult children in migration is a likely mechanism. These findings are consistent with a simple theoretical model of migration for older adults.Series: Department of Economics Working Paper Serie
Is There Any Social Principle for LLM-Based Agents?
Focus on Large Language Model based agents should involve more than
"human-centered" alignment or application. We argue that more attention should
be paid to the agent itself and discuss the potential of social sciences for
agents.Comment: 3 pages, 1 figur
Determinants of successful disease control through voluntary quarantine dynamics on social networks
In the wake of epidemics, quarantine measures are typically recommended by
health authorities or governments to help control the spread of the disease.
Compared with mandatory quarantine, voluntary quarantine offers individuals the
liberty to decide whether to isolate themselves in case of infection exposure,
driven by their personal assessment of the trade-off between economic loss and
health risks as well as their own sense of social responsibility and concern
for public health. To better understand self-motivated health behavior choices
under these factors, here we incorporate voluntary quarantine into an endemic
disease model -- the susceptible-infected-susceptible (SIS) model -- and
perform comprehensive agent-based simulations to characterize the resulting
behavior-disease interactions in structured populations. We quantify the
conditions under which voluntary quarantine will be an effective intervention
measure to mitigate disease burden. Furthermore, we demonstrate how individual
decision-making factors, including the level of temptation to refrain from
quarantine and the degree of social compassion, impact compliance levels of
voluntary quarantines and the consequent collective disease mitigation efforts.
We find that successful disease control requires either a sufficiently low
level of temptation or a sufficiently high degree of social compassion, such
that even complete containment of the epidemic is attainable. In addition to
well-mixed populations, our simulation results are applicable to other more
realistic social networks of contacts, including spatial lattices, small-world
networks, and real social networks. Our work offers new insights into the
fundamental social dilemma aspect of disease control through non-pharmaceutical
interventions, such as voluntary quarantine and isolation, where the collective
outcome of individual decision-making is crucial.Comment: 19 pages, 5 figure
Visual Prompt Multi-Modal Tracking
Visible-modal object tracking gives rise to a series of downstream
multi-modal tracking tributaries. To inherit the powerful representations of
the foundation model, a natural modus operandi for multi-modal tracking is full
fine-tuning on the RGB-based parameters. Albeit effective, this manner is not
optimal due to the scarcity of downstream data and poor transferability, etc.
In this paper, inspired by the recent success of the prompt learning in
language models, we develop Visual Prompt multi-modal Tracking (ViPT), which
learns the modal-relevant prompts to adapt the frozen pre-trained foundation
model to various downstream multimodal tracking tasks. ViPT finds a better way
to stimulate the knowledge of the RGB-based model that is pre-trained at scale,
meanwhile only introducing a few trainable parameters (less than 1% of model
parameters). ViPT outperforms the full fine-tuning paradigm on multiple
downstream tracking tasks including RGB+Depth, RGB+Thermal, and RGB+Event
tracking. Extensive experiments show the potential of visual prompt learning
for multi-modal tracking, and ViPT can achieve state-of-the-art performance
while satisfying parameter efficiency. Code and models are available at
https://github.com/jiawen-zhu/ViPT.Comment: Accepted by CVPR202
The impact of long-term care insurance in China on beneficiaries and caregivers: A systematic review
Background
China’s long-term care insurance (LTCI) policy has been minimally evaluated. This systematic review aimed to assess the impact of China’s LTCI pilot on beneficiaries and their caregivers.
Methods
This review is based on a search of peer-reviewed studies in English (Embase, MEDLINE, Web of Science) and Chinese (China National Knowledge Infrastructure [CNKI], VIP, Wanfang) databases from January 2016 through July 2020, with all studies published in English or Chinese included. We included quantitative analyses of beneficiary-level data that assessed the impact of LTCI on beneficiaries and their caregivers, with no restriction placed on the outcomes studied.
Results
Nine studies met our inclusion criteria. One study was a randomised trial and two used quasi-experimental approaches. Four studies examined LTCI’s effect on beneficiaries’ quality of life, physical pain, and health service utilisation; one study reported the effect on beneficiaries’ healthcare expenditures; and one study evaluated the impact on caregivers’ care tasks. These studies generally found LTCI to be associated with an improvement in patients’ quality of life (including decreased physical pain), a reduction in the number of outpatient visits and hospitalisations, decreased patient-level health expenditures (e.g. one study reported a reduction in the length of stay, inpatient expenditures, and health insurance expenditures in tertiary hospitals by 41.0%, 17.7%, and 11.4%, respectively), and reduced informal care tasks for caregivers. In addition, four out of four studies that evaluated this outcome found that beneficiaries’ overall satisfaction with LTCI was high.
Conclusion
The current evidence base for the effects of LTCI in China on beneficiaries and their caregivers is sparse. Nonetheless, the existing studies suggest that LTCI has positive effects on beneficiaries and their caregivers. Further rigorous research on the impacts of LTCI in China is needed to inform the future expansion of the program
Data Upcycling Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) emerges as a challenging yet promising technique
for compressing deep learning models, characterized by the transmission of
extensive learning representations from proficient and computationally
intensive teacher models to compact student models. However, only a handful of
studies have endeavored to compress the models for single image
super-resolution (SISR) through KD, with their effects on student model
enhancement remaining marginal. In this paper, we put forth an approach from
the perspective of efficient data utilization, namely, the Data Upcycling
Knowledge Distillation (DUKD) which facilitates the student model by the prior
knowledge teacher provided via upcycled in-domain data derived from their
inputs. This upcycling process is realized through two efficient image zooming
operations and invertible data augmentations which introduce the label
consistency regularization to the field of KD for SISR and substantially boosts
student model's generalization. The DUKD, due to its versatility, can be
applied across a broad spectrum of teacher-student architectures. Comprehensive
experiments across diverse benchmarks demonstrate that our proposed DUKD method
significantly outperforms previous art, exemplified by an increase of up to
0.5dB in PSNR over baselines methods, and a 67% parameters reduced RCAN model's
performance remaining on par with that of the RCAN teacher model
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