124 research outputs found
A sustainable residential community that promotes urban dwellers’ well-being – new city development in Nanjing China
The process of rapid urbanization has brought not only opportunities for the development of urban areas but also challenges for the design of quality urban space, especially for residential neighborhoods and public spaces. For the past four decades, the most common housing option for urban dwellers in China has been an apartment in a high-rise concrete tower in a gated residential community. This type of housing model initially improved the living conditions for residents and helped to solve the land shortage issue; however, that model can no longer fulfill residents’ demand for well-being or maintain a sustainable way of living.
This thesis examines the current condition of the newly developed residential projects in Nanjing and identifies some of the problems of the current model of housing development. Those problems include reduced mobility of pedestrians and automobiles, land-usage issues caused by building hybrid open spaces, and a lack of places that promote social interaction.
The method for opening these gated residential communities and for creating a better housing option for the general public has been a controversial subject in the realm of urban planning and architecture design. Learning from the successful precedents of mixed-used urban projects and quality public space, as well as the theory of new urbanism and sustainable development, this thesis provides a new option for sustainable residential communities in the new urban area of Nanjing—namely, one that promotes the well-being of urban residents
Taming Gradient Variance in Federated Learning with Networked Control Variates
Federated learning, a decentralized approach to machine learning, faces
significant challenges such as extensive communication overheads, slow
convergence, and unstable improvements. These challenges primarily stem from
the gradient variance due to heterogeneous client data distributions. To
address this, we introduce a novel Networked Control Variates (FedNCV)
framework for Federated Learning. We adopt the REINFORCE Leave-One-Out (RLOO)
as a fundamental control variate unit in the FedNCV framework, implemented at
both client and server levels. At the client level, the RLOO control variate is
employed to optimize local gradient updates, mitigating the variance introduced
by data samples. Once relayed to the server, the RLOO-based estimator further
provides an unbiased and low-variance aggregated gradient, leading to robust
global updates. This dual-side application is formalized as a linear
combination of composite control variates. We provide a mathematical expression
capturing this integration of double control variates within FedNCV and present
three theoretical results with corresponding proofs. This unique dual structure
equips FedNCV to address data heterogeneity and scalability issues, thus
potentially paving the way for large-scale applications. Moreover, we tested
FedNCV on six diverse datasets under a Dirichlet distribution with {\alpha} =
0.1, and benchmarked its performance against six SOTA methods, demonstrating
its superiority.Comment: 14 page
AWTE-BERT:Attending to Wordpiece Tokenization Explicitly on BERT for Joint Intent Classification and SlotFilling
Intent classification and slot filling are two core tasks in natural language
understanding (NLU). The interaction nature of the two tasks makes the joint
models often outperform the single designs. One of the promising solutions,
called BERT (Bidirectional Encoder Representations from Transformers), achieves
the joint optimization of the two tasks. BERT adopts the wordpiece to tokenize
each input token into multiple sub-tokens, which causes a mismatch between the
tokens and the labels lengths. Previous methods utilize the hidden states
corresponding to the first sub-token as input to the classifier, which limits
performance improvement since some hidden semantic informations is discarded in
the fine-tune process. To address this issue, we propose a novel joint model
based on BERT, which explicitly models the multiple sub-tokens features after
wordpiece tokenization, thereby generating the context features that contribute
to slot filling. Specifically, we encode the hidden states corresponding to
multiple sub-tokens into a context vector via the attention mechanism. Then, we
feed each context vector into the slot filling encoder, which preserves the
integrity of the sentence. Experimental results demonstrate that our proposed
model achieves significant improvement on intent classification accuracy, slot
filling F1, and sentence-level semantic frame accuracy on two public benchmark
datasets. The F1 score of the slot filling in particular has been improved from
96.1 to 98.2 (2.1% absolute) on the ATIS dataset
Reproducible and Portable Big Data Analytics in the Cloud
Cloud computing has become a major approach to help reproduce computational
experiments because it supports on-demand hardware and software resource
provisioning. Yet there are still two main difficulties in reproducing big data
applications in the cloud. The first is how to automate end-to-end execution of
analytics including environment provisioning, analytics pipeline description,
pipeline execution, and resource termination. The second is that an application
developed for one cloud is difficult to be reproduced in another cloud, a.k.a.
vendor lock-in problem. To tackle these problems, we leverage serverless
computing and containerization techniques for automated scalable execution and
reproducibility, and utilize the adapter design pattern to enable application
portability and reproducibility across different clouds. We propose and develop
an open-source toolkit that supports 1) fully automated end-to-end execution
and reproduction via a single command, 2) automated data and configuration
storage for each execution, 3) flexible client modes based on user preferences,
4) execution history query, and 5) simple reproduction of existing executions
in the same environment or a different environment. We did extensive
experiments on both AWS and Azure using four big data analytics applications
that run on virtual CPU/GPU clusters. The experiments show our toolkit can
achieve good execution performance, scalability, and efficient reproducibility
for cloud-based big data analytics
Effects of brain–Computer interface combined with mindfulness therapy on rehabilitation of hemiplegic patients with stroke: a randomized controlled trial
AimTo explore the effects of brain–computer interface training combined with mindfulness therapy on Hemiplegic Patients with Stroke.BackgroundThe prevention and treatment of stroke still faces great challenges. Maximizing the improvement of patients’ ability to perform activities of daily living, limb motor function, and reducing anxiety, depression, and other social and psychological problems to improve patients’ overall quality of life is the focus and difficulty of clinical rehabilitation work.MethodsPatients were recruited from December 2021 to November 2022, and assigned to either the intervention or control group following a simple randomization procedure (computer-generated random numbers). Both groups received conventional rehabilitation treatment, while patients in the intervention group additionally received brain–computer interface training and mindfulness therapy. The continuous treatment duration was 5 days per week for 8 weeks. Limb motor function, activities of daily living, mindfulness attention awareness level, sleep quality, and quality of life of the patients were measured (in T0, T1, and T2). Generalized estimated equation (GEE) were used to evaluate the effects. The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2300070382).ResultsA total of 128 participants were randomized and 64 each were assigned to the intervention and control groups (of these, eight patients were lost to follow-up). At 6 months, compared with the control group, intervention group showed statistically significant improvements in limb motor function, mindful attention awareness, activities of daily living, sleep quality, and quality of life.ConclusionBrain–computer interface combined with mindfulness therapy training can improve limb motor function, activities of daily living, mindful attention awareness, sleep quality, and quality of life in hemiplegic patients with stroke.ImpactThis study provides valuable insights into post-stroke care. It may help improve the effect of rehabilitation nursing to improve the comprehensive ability and quality of life of patients after stroke.Clinical review registrationhttps://www.chictr.org.cn/, identifier ChiCTR2300070382
Genetic diversity fuels gene discovery for tobacco and alcohol use
Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1,2,3,4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction
Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing
Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction
Highly-stable, flexible delivery of microjoule-level ultrafast pulses in vacuumized anti-resonant hollow-core fibers for active synchronization
We demonstrate the stable and flexible light delivery of multi-{\mu}J,
sub-200-fs pulses over a ~10-m-long vacuumized anti-resonant hollow-core fiber
(AR-HCF), which was successfully used for high-performance pulse
synchronization. Compared with the pulse train launched into the AR-HCF, the
transmitted pulse train out of the fiber exhibits excellent stabilities in
pulse power and spectrum, with pointing stability largely improved. The
walk-off between the fiber-delivery and the other free-space-propagation pulse
trains, in an open loop, was measured to be <6 fs root-mean-square (RMS) over
90 minutes, corresponding to a relative optical-path variation of <2x10-7. This
walk-off can be further suppressed to ~2 fs RMS simply using an active control
loop, highlighting the great application potentials of this AR-HCF set-up in
large-scale laser and accelerator facilities
Discovery of potential biomarkers for osteoporosis using LC/GC−MS metabolomic methods
PurposeFor early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC−MS in a Chinese elderly population to find possible diagnostic biomarkers.MethodsA total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC−MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways.ResultsWe screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women.ConclusionThese findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals
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