148 research outputs found
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments
With the great success of deep learning (DL) in image classification, speech
recognition, and other fields, more and more studies have applied various
neural networks (NNs) to wireless resource allocation. Generally speaking,
these artificial intelligent (AI) models are trained under some special
learning hypotheses, especially that the statistics of the training data are
static during the training stage. However, the distribution of channel state
information (CSI) is constantly changing in the real-world wireless
communication environment. Therefore, it is essential to study effective
dynamic DL technologies to solve wireless resource allocation problems. In this
paper, we propose a novel framework, named meta-gating, for solving resource
allocation problems in an episodically dynamic wireless environment, where the
CSI distribution changes over periods and remains constant within each period.
The proposed framework, consisting of an inner network and an outer network,
aims to adapt to the dynamic wireless environment by achieving three important
goals, i.e., seamlessness, quickness and continuity. Specifically, for the
former two goals, we propose a training method by combining a model-agnostic
meta-learning (MAML) algorithm with an unsupervised learning mechanism. With
this training method, the inner network is able to fast adapt to different
channel distributions because of the good initialization. As for the goal of
continuity, the outer network can learn to evaluate the importance of inner
network's parameters under different CSI distributions, and then decide which
subset of the inner network should be activated through the gating operation.
Additionally, we theoretically analyze the performance of the proposed
meta-gating framework.Comment: accepted by IEEE TCO
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits
Motivated by concerns about making online decisions that incur undue amount
of risk at each time step, in this paper, we formulate the probably
anytime-safe stochastic combinatorial semi-bandits problem. In this problem,
the agent is given the option to select a subset of size at most from a set
of ground items. Each item is associated to a certain mean reward as well
as a variance that represents its risk. To mitigate the risk that the agent
incurs, we require that with probability at least , over the entire
horizon of time , each of the choices that the agent makes should contain
items whose sum of variances does not exceed a certain variance budget. We call
this probably anytime-safe constraint. Under this constraint, we design and
analyze an algorithm {\sc PASCombUCB} that minimizes the regret over the
horizon of time . By developing accompanying information-theoretic lower
bounds, we show that under both the problem-dependent and problem-independent
paradigms, {\sc PASCombUCB} is almost asymptotically optimal. Experiments are
conducted to corroborate our theoretical findings. Our problem setup, the
proposed {\sc PASCombUCB} algorithm, and novel analyses are applicable to
domains such as recommendation systems and transportation in which an agent is
allowed to choose multiple items at a single time step and wishes to control
the risk over the whole time horizon.Comment: To be presented at ICML 2023. 57 pages, 6 figure
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning
Prompt-based learning reformulates downstream tasks as cloze problems by
combining the original input with a template. This technique is particularly
useful in few-shot learning, where a model is trained on a limited amount of
data. However, the limited templates and text used in few-shot prompt-based
learning still leave significant room for performance improvement.
Additionally, existing methods using model ensembles can constrain the model
efficiency. To address these issues, we propose an augmentation method called
MixPro, which augments both the vanilla input text and the templates through
token-level, sentence-level, and epoch-level Mixup strategies. We conduct
experiments on five few-shot datasets, and the results show that MixPro
outperforms other augmentation baselines, improving model performance by an
average of 5.08% compared to before augmentation.Comment: Under review at the Frontiers of Computer Science
(https://www.springer.com/journal/11704/); 14 pages, 4 figures, 5 table
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
Existing image augmentation methods consist of two categories:
perturbation-based methods and generative methods. Perturbation-based methods
apply pre-defined perturbations to augment an original image, but only locally
vary the image, thus lacking image diversity. In contrast, generative methods
bring more image diversity in the augmented images but may not preserve
semantic consistency, thus incorrectly changing the essential semantics of the
original image. To balance image diversity and semantic consistency in
augmented images, we propose SGID, a Semantic-guided Generative Image
augmentation method with Diffusion models for image classification.
Specifically, SGID employs diffusion models to generate augmented images with
good image diversity. More importantly, SGID takes image labels and captions as
guidance to maintain semantic consistency between the augmented and original
images. Experimental results show that SGID outperforms the best augmentation
baseline by 1.72% on ResNet-50 (from scratch), 0.33% on ViT (ImageNet-21k), and
0.14% on CLIP-ViT (LAION-2B). Moreover, SGID can be combined with other image
augmentation baselines and further improves the overall performance. We
demonstrate the semantic consistency and image diversity of SGID through
quantitative human and automated evaluations, as well as qualitative case
studies.Comment: AAAI 202
Bioactivity-guided fractionation of the triglyceride-lowering component and in vivo and in vitro evaluation of hypolipidemic effects of Calyx seu Fructus Physalis
<p>Abstract</p> <p>Background</p> <p>In folklore, some people take the decoction of <it>Calyx seu Fructus Physalis </it>(CSFP) for lowering blood lipids. The present study is designed to evaluate the lipid-lowering activities of CSFP, and search for its pharmacodynamical material.</p> <p>Methods</p> <p>CSFP was extracted by water and 75% ethanol, respectively. The extracts of CSFP for reducing serum lipid levels were evaluated on mouse model of hyperlipidemia. The optimized extract was subjected to the bioactivity-guided fractionation in which the liquid-liquid extraction, collumn chromatography, the <it>in vivo </it>and <it>in vitro </it>models of hyperlipidemia were utilized. The structure of active component was determined by <sup>13 </sup>C-NMR and <sup>1</sup>H-NMR.</p> <p>Results</p> <p>The 75% ethanol extract of CSFP decreased the serum total cholesterol (TC) and triglyceride (TG) levels in mouse model of hyperlipidemia. Followed a separation process for the 75% ethanol extract of CSFP, the fraction B was proved to be an active fraction for lowering lipid <it>in vivo </it>and <it>in vitro </it>experiments, which could significantly decrease the serum TC and TG levels in mouse model of hyperlipidemia, and remarkably decrease the increase of TG in primary mouse hepatocytes induced by high glucose and the increase of TG in HepG2 cells induced by oleic acid. The fraction B2, isolated from B on bioactivity-guided fractionation, could significantly decrease TG level in HepG2 cells. One compound with the highest content in B2 was isolated and determined as luteolin-7-O-beta-D-glucopyranoside by NMR spectra. It could significantly reduce the TG level in HepG2 cells, and inhibited the accumulation of lipids by oil red O stain.</p> <p>Conclusion</p> <p>Our results demonstrated that the 75% ethanol extract of CSFP could improve <it>in vitro </it>and <it>in vivo </it>lipid accumulation. Luteolin-7-O-beta-D-glucopyranoside might be a leading pharmacodynamical material of CSFP for lowering lipids.</p
In-situ synthesis of ultra-fine ZrB2–ZrC–SiC nanopowders by sol-gel method
© 2019 Elsevier Ltd and Techna Group S.r.l. ZrB2–ZrC–SiC nanopowders with uniform phase distribution were prepared from cost-effective ZrOCl2·8H2O by a simple sol-gel method. The synthesis route, ceramization mechanism and morphology evolution of the nanopowders were investigated. ZrB2–ZrC–SiC ceramic precursor can be successfully obtained through hydrolysis and condensation reactions between the raw materials. Pyrolysis of the precursor was completed at 650 °C, and it produced ZrO2, SiO2, B2O3 and amorphous carbon with a yield of 39% at 1300 °C. By heat-treated at 1500 °C for 2 h, highly crystallized ZrB2–ZrC–SiC ceramics with narrow size distribution were obtained. With the holding time of 2 h, both the crystal size and the particle size can be refined. Further prolonging the holding time can lead to serious particles coarsening. Studies on the microstructure evolution of the generated carbon during the ceramic conversion demonstrates the negative effect of the ceramic formation on the structure order improvement of the carbon, due to the large amount of defects generated in it by the boro/carbothermal reduction reactions
Diagnostic and prognostic value of serum C-reactive protein in heart failure with preserved ejection fraction:a systematic review and meta-analysis
Heart failure (HF) is a major epidemic with rising morbidity and mortality rates that encumber global healthcare systems. While some studies have demonstrated the value of CRP in predicting (i) the development of HFpEF and (ii) long-term clinical outcomes in HFpEF patients, others have shown no such correlation. As a result, we conducted the following systematic review and meta-analysis to assess both the diagnostic and prognostic role of CRP in HFpEF. PubMed and Embase were searched for studies that assess the relationship between CRP and HFpEF using the following search terms: (((C-reactive protein) AND ((preserved ejection fraction) OR (diastolic heart failure))). The search period was from the start of database to August 6, 2019, with no language restrictions. A total of 312 and 233 studies were obtained from PubMed and Embase respectively, from which 19 studies were included. Our meta-analysis demonstrated the value of a high CRP in predicting the development of not only new onset HFpEF (HR: 1.08; 95% CI: 1.00–1.16; P = 0.04; I 2 = 22%), but also an increased risk of cardiovascular mortality when used as a categorical (HR: 2.52; 95% CI: 1.61–3.96; P < 0.0001; I 2 = 19%) or a continuous variable (HR: 1.24; 95% CI: 1.04–1.47; P = 0.01; I 2 = 28%), as well as all-cause mortality when used as a categorical (HR: 1.78; 95% CI: 1.53–2.06; P < 0.00001; I 2 = 0%) or a continuous variable: (HR: 1.06; 95% CI: 1.02–1.06; P = 0.003; I 2 = 61%) in HFpEF patients. CRP can be used as a biomarker to predict the development of HFpEF and long-term clinical outcomes in HFpEF patients, in turn justifying its use as a simple, accessible parameter to guide clinical management in this patient population. However, more prospective studies are still required to not only explore the utility and dynamicity of CRP in HFpEF but also to determine whether risk stratification algorithms incorporating CRP actually provide a material benefit in improving patient prognosis
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