3,615 research outputs found
The relationship between exercise and cognition in diabetes mellitus
The increasing prevalence and incidence of type 2 diabetes mellitus (T2D) has been referred to as a global epidemic. This thesis aimed to synthesise the evidence base in both animal models and human studies that exercise exposure is related to better cognition in diabetes, via 2 systematic reviews. Secondly, we investigated the efficacy of a novel form of exercise, POWER training (high velocity PRT), for cognitive function in this cohort. We hypothesised that 12 months of high intensity POWER training would significantly improve cognitive function in a cohort of older adults with T2D and multiple co-morbidities. The GREAT2DO study was the first RCT to evaluate the effects of a one-year intervention of POWER training compared to a SHAM exercise control condition on insulin resistance, HbA1c, body composition, physical performance, inflammation, adipokines, cardiovascular health status, and quality of life as well as to explore relationships between these domains in response to the intervention in this cohort. In this GREAT2DO cognitive sub-study, we assessed global cognition and several cognitive domains at baseline in relation to physical and psychological health, fitness and functional performance, as well as changes over time in cognitive outcomes in response to the intervention. We found that cognitive function improved in both POWER and sham exercise groups over time, although unexpectedly without group effect. However, we showed for the first time that there were significant direct relationships between increases in skeletal muscle mass, total muscle strength, total static balance time, and total adiponectin levels and improvements in cognitive function, and that these relationships only existed in the POWER group, as hypothesised. There is need for further study, in particular exploration of the persistence, clinical relevance, and mechanisms underlying attenuation of the rate of cognitive decline and incident dementia in this high-risk cohort
SCOPE: Scalable Composite Optimization for Learning on Spark
Many machine learning models, such as logistic regression~(LR) and support
vector machine~(SVM), can be formulated as composite optimization problems.
Recently, many distributed stochastic optimization~(DSO) methods have been
proposed to solve the large-scale composite optimization problems, which have
shown better performance than traditional batch methods. However, most of these
DSO methods are not scalable enough. In this paper, we propose a novel DSO
method, called \underline{s}calable \underline{c}omposite
\underline{op}timization for l\underline{e}arning~({SCOPE}), and implement it
on the fault-tolerant distributed platform \mbox{Spark}. SCOPE is both
computation-efficient and communication-efficient. Theoretical analysis shows
that SCOPE is convergent with linear convergence rate when the objective
function is convex. Furthermore, empirical results on real datasets show that
SCOPE can outperform other state-of-the-art distributed learning methods on
Spark, including both batch learning methods and DSO methods
Measuring the similarity of PML documents with RFID-based sensors
The Electronic Product Code (EPC) Network is an important part of the
Internet of Things. The Physical Mark-Up Language (PML) is to represent and
de-scribe data related to objects in EPC Network. The PML documents of each
component to exchange data in EPC Network system are XML documents based on PML
Core schema. For managing theses huge amount of PML documents of tags captured
by Radio frequency identification (RFID) readers, it is inevitable to develop
the high-performance technol-ogy, such as filtering and integrating these tag
data. So in this paper, we propose an approach for meas-uring the similarity of
PML documents based on Bayesian Network of several sensors. With respect to the
features of PML, while measuring the similarity, we firstly reduce the
redundancy data except information of EPC. On the basis of this, the Bayesian
Network model derived from the structure of the PML documents being compared is
constructed.Comment: International Journal of Ad Hoc and Ubiquitous Computin
Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation
Past works on multimodal machine translation (MMT) elevate bilingual setup by
incorporating additional aligned vision information. However, an image-must
requirement of the multimodal dataset largely hinders MMT's development --
namely that it demands an aligned form of [image, source text, target text].
This limitation is generally troublesome during the inference phase especially
when the aligned image is not provided as in the normal NMT setup. Thus, in
this work, we introduce IKD-MMT, a novel MMT framework to support the
image-free inference phase via an inversion knowledge distillation scheme. In
particular, a multimodal feature generator is executed with a knowledge
distillation module, which directly generates the multimodal feature from
(only) source texts as the input. While there have been a few prior works
entertaining the possibility to support image-free inference for machine
translation, their performances have yet to rival the image-must translation.
In our experiments, we identify our method as the first image-free approach to
comprehensively rival or even surpass (almost) all image-must frameworks, and
achieved the state-of-the-art result on the often-used Multi30k benchmark. Our
code and data are available at: https://github.com/pengr/IKD-mmt/tree/master..Comment: Long paper accepted by EMNLP2022 main conferenc
Better Sign Language Translation with Monolingual Data
Sign language translation (SLT) systems, which are often decomposed into
video-to-gloss (V2G) recognition and gloss-to-text (G2T) translation through
the pivot gloss, heavily relies on the availability of large-scale parallel G2T
pairs. However, the manual annotation of pivot gloss, which is a sequence of
transcribed written-language words in the order in which they are signed,
further exacerbates the scarcity of data for SLT. To address this issue, this
paper proposes a simple and efficient rule transformation method to transcribe
the large-scale target monolingual data into its pseudo glosses automatically
for enhancing the SLT translation. Empirical results show that the proposed
approach can significantly improve the performance of SLT, especially achieving
state-of-the-art results on two SLT benchmark datasets PHEONIX-WEATHER 2014T
and ASLG-PC12. Our code has been released at:
https://github.com/pengr/Mono\_SLT
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