CROSS-LAYER CUSTOMIZATION FOR LOW POWER AND HIGH PERFORMANCE EMBEDDED MULTI-CORE PROCESSORS

Due to physical limitations and design difficulties, computer processor architecture has shifted to multi-core and even many-core based approaches in recent years. Such architectures provide potentials for sustainable performance scaling into future peta-scale/exa-scale computing platforms, at affordable power budget, design complexity, and verification efforts. To date, multi-core processor products have been replacing uni-core processors in almost every market segment, including embedded systems, general-purpose desktops and laptops, and super computers. However, many issues still remain with multi-core processor architectures that need to be addressed before their potentials could be fully realized. People in both academia and industry research community are still seeking proper ways to make efficient and effective use of these processors. The issues involve hardware architecture trade-offs, the system software service, the run-time management, and user application design, which demand more research effort into this field. Due to the architectural specialties with multi-core based computers, a Cross-Layer Customization framework is proposed in this work, which combines application specific information and system platform features, along with necessary operating system service support, to achieve exceptional power and performance efficiency for targeted multi-core platforms. Several topics are covered with specific optimization goals, including snoop cache coherence protocol, inter-core communication for producer-consumer applications, synchronization mechanisms, and off-chip memory bandwidth limitations. Analysis of benchmark program execution with conventional mechanisms is made to reveal the overheads in terms of power and performance. Specific customizations are proposed to eliminate such overheads with support from hardware, system software, compiler, and user applications. Experiments show significant improvement on system performance and power efficiency

Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification

This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available glioma datasets are relatively moderate in size,and often accompanied with incomplete MRIs in different modalities. To tackle the commonly encountered problems of insufficiently large brain tumor datasets and incomplete modality of image for deep learning, we propose to add augmented brain MR images to enlarge the training dataset by employing a pairwise Generative Adversarial Network (GAN) model. The pairwise GAN is able to generate synthetic MRIs across different modalities. To achieve the patient-level diagnostic result, we propose a post-processing strategy to combine the slice-level glioma subtype classification results by majority voting. A two-stage course-to-fine training strategy is proposed to learn the glioma feature using GAN-augmented MRIs followed by real MRIs. To evaluate the effectiveness of the proposed scheme, experiments have been conducted on a brain tumor dataset for classifying glioma molecular subtypes: isocitrate dehydrogenase 1 (IDH1) mutation and IDH1 wild-type. Our results on the dataset have shown good performance (with test accuracy 88.82%). Comparisons with several state-of-the-art methods are also included

Deep semi-supervised learning for brain tumor classification

Background: This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many available glioma datasets often contain some unlabeled brain scans, and many datasets are moderate in size. Methods: We propose to exploit deep semi-supervised learning to make full use of the unlabeled data. Deep CNN features were incorporated into a new graph-based semi-supervised learning framework for learning the labels of the unlabeled data, where a new 3D-2D consistent constraint is added to make consistent classifications for the 2D slices from the same 3D brain scan. A deep-learning classifier is then trained to classify different glioma types using both labeled and unlabeled data with estimated labels. To alleviate the overfitting caused by moderate-size datasets, synthetic MRIs generated by Generative Adversarial Networks (GANs) are added in the training of CNNs. Results: The proposed scheme has been tested on two glioma datasets, TCGA dataset for IDH-mutation prediction (molecular-based glioma subtype classification) and MICCAI dataset for glioma grading. Our results have shown good performance (with test accuracies 86.53% on TCGA dataset and 90.70% on MICCAI dataset). Conclusions: The proposed scheme is effective for glioma IDH-mutation prediction and glioma grading, and its performance is comparable to the state-of-the-art

Photochemical reaction playing a key role in particulate matter pollution over Central France:Insight from the aerosol optical properties

Atmospheric particle is one of the major air pollutants, and believed to be important for air quality, radiative forcing and climate. Measurements of aerosol optical properties, size distribution and PM10 concentration were conducted at Orleans, central France during spring (7 March to 25 April) and autumn (25 October to 5 December) 2013. The average values of aerosol scattering coefficient (b(sca)), absorption coefficient (b(abs)), single scattering albedo (SSA) at 532 nm and PM10 concentration are 54.9 +/- 58.2 Mm(-1), 10.6 +/- 10.9 Mm(-1), 0.81 +/- 0.10 and 30.6 +/- 21.6 mu g/m(3) for the spring campaign, and 35.4 +/- 36.7 Mm(-1), 3.9 +/- 4.4 Mm(-1), 0.83 +/- 0.13 and 17.4 +/- 11.8 mu g/m(3) for the autumn campaign, respectively. During the whole observation, the air parcel transported from Atlantic Ocean plays a role in cleaning up the ambient air in Orleans, while the air mass coining from the Eastern Europe induces the pollution events in Orleans. In this study, a simple approach, which based on the diurnal variation of PM10 concentration, Boundary layer depth (BLD) and the human activity factor derived from anthropogenic emission rate, was introduced to estimate the contribution of secondary aerosol to ambient aerosols. Our results show that secondary particles formation trigged by photochemical reactions and oxidations can contribute maximum of 64% and 32% for PM10 mass concentration during the spring and autumn time, respectively. These results highlight that photochemical reactions can enhance the atmospheric oxidation capacity and may faster the secondary particle formation and then play an important role in air quality. (C) 2018 Elsevier B.V. All rights reserved

Dust emission reduction enhanced gas-to-particle conversion of ammonia in the North China Plain

Liu et al. found that the formation rate of particulate ammonium is slower in the atmosphere than that observed in the laboratory, while it is sped up due to an increase in aerosol acidity driven by an emission reduction of dust in North China Plain.Ammonium salt is an important component of particulate matter with aerodynamic diameter less than 2.5 mu m (PM2.5) and has significant impacts on air quality, climate, and natural ecosystems. However, a fundamental understanding of the conversion kinetics from ammonia to ammonium in unique environments of high aerosol loading is lacking. Here, we report the uptake coefficient of ammonia (gamma(NH3)) on ambient PM2.5 varying from 2.2 x 10(-4) to 6.0 x 10(-4) in the North China Plain. It is significantly lower than those on the model particles under simple conditions reported in the literature. The probability-weighted gamma(NH3) increases obviously, which is well explained by the annual decrease in aerosol pH due to the significant decline in alkali and alkali earth metal contents from the emission source of dust. Our results elaborate on the complex interactions between primary emissions and the secondary formation of aerosols and the important role of dust in atmospheric chemistry.Peer reviewe

Deposition potential of 0.003-10 mu m ambient particles in the humidified human respiratory tract : Contribution of new particle formation events in Beijing

Ultrafine particles (UFPs) usually explosive growth during new particle formation (NPF) events. However, the risk of exposure to UFPs on NPF days has been ignored due to the prevalence of mass-based air quality standards. In this study, the daily deposited doses, i.e., the daily deposited particle number dose (D-PNd), mass dose (D-PMd), and surface area dose (D-PSd), of ambient particles in the human respiratory tract in Beijing were evaluated based on the particle number size distribution (3 nm-10 mu m) from June 2018 to May 2019 utilizing a Multiple-Path Particle Dosimetry Model (MPPD) after the hygroscopic growth of particles in the respiratory tract had been accounted for. Our observations showed a high frequency (72.6%) of NPF on excellent air quality days, with daily mean PM2.5 concentrations less than 35 mu g m(-3). The daily D-PNd on excellent air quality days was com-parable with that on polluted days, although the D-PMd on excellent air quality days was as low as 15.6% of that on polluted days. The D-PNd on NPF days was similar to 1.3 times that on non-NPF days. The D-PNd in respiratory tract regions decreased in the order: tracheobronchial (TB) > pulmonary (PUL) > extrathoracic (ET) on NPF days, while it was PUL > TB > ET on non-NPF days. The number of deposited nucleation mode particles, which were deposited mainly in the TB region (45%), was 2 times higher on NPF days than that on non-NPF days. Our results demonstrated that the deposition potential due to UFPs in terms of particle number concentrations is high in Beijing regardless of the aerosol mass concentration. More toxicological studies related to UFPs on NPF days, especially those targeting tracheobronchial and pulmonary impairment, are required in the future.Peer reviewe

Tumor-derived exosomes confer antigen-specific immunosuppression in a murine delayed-type hypersensitivity model

Exosomes are endosome-derived small membrane vesicles that are secreted by most cell types including tumor cells. Tumor-derived exosomes usually contain tumor antigens and have been used as a source of tumor antigens to stimulate anti-tumor immune responses. However, many reports also suggest that tumor-derived exosomes can facilitate tumor immune evasion through different mechanisms, most of which are antigen-independent. In the present study we used a mouse model of delayed-type hypersensitivity (DTH) and demonstrated that local administration of tumor-derived exosomes carrying the model antigen chicken ovalbumin (OVA) resulted in the suppression of DTH response in an antigen-specific manner. Analysis of exosome trafficking demonstrated that following local injection, tumor-derived exosomes were internalized by CD11c+ cells and transported to the draining LN. Exosome-mediated DTH suppression is associated with increased mRNA levels of TGF-β1 and IL-4 in the draining LN. The tumor-derived exosomes examined were also found to inhibit DC maturation. Taken together, our results suggest a role for tumor-derived exosomes in inducing tumor antigen-specific immunosuppression, possibly by modulating the function of APCs. © 2011 Yang et al