Food allergy is a major public health problem for which there is no effective treatment. We examined the immunological changes that occurred in a group of children with significant cow’s milk allergy undergoing a novel and rapid high dose oral desensitization protocol enabled by treatment with omalizumab (anti-IgE mAb). Within a week of treatment, the CD4+ T cell response to milk was nearly eliminated, suggesting anergy in, or deletion of, milk-specific CD4+ T cells. Over the following three months while the subjects remained on high doses of daily oral milk, the CD4+ T cell response returned, characterized by a shift from IL-4 to IFN-γ production. Desensitization was also associated with reduction in milk-specific IgE and a 15-fold increase in milk-specific IgG4. These studies suggest that high dose oral allergen desensitization may be associated with deletion of allergen-specific T cells, without the apparent development of allergen-specific Foxp3+ regulatory T cells.
Intelligent Transportation Systems (ITS) have become an important pillar in modern "smart city" framework which demands intelligent involvement of machines. Traffic load recognition can be categorized as an important and challenging issue for such systems. Recently, Convolutional Neural Network (CNN) models have drawn considerable amount of interest in many areas such as weather classification, human rights violation detection through images, due to its accurate prediction capabilities. This work tackles real-life traffic load recognition problem on System-On-a-Programmable-Chip (SOPC) platform and coin it as MAT-CNN-SOPC, which uses an intelligent retraining mechanism of the CNN with known environments. The proposed methodology is capable of enhancing the efficacy of the approach by 2.44x in comparison to the state-of-art and proven through experimental analysis. We have also introduced a mathematical equation, which is capable of quantifying the suitability of using different CNN models over the other for a particular application based implementation.Index Terms-Convolutional neural network (CNN), traffic analysis, traffic density, transfer learning, system-on-aprogrammable-chip (SOPC).
The thermal covert channels (TCC's) in many-core systems can cause detrimental data breaches. In this paper, we present a three-step scheme to detect and fight against such TCC attacks. Specifically, in the detection step, each core calculates the spectrum of its own CPU workload traces that are collected over a few fixed time intervals, and then it applies a frequency scanning method to detect if there exists any TCC attack. In the next positioning step, the logical cores running the transmitter threads are located. In the last step, the physical CPU cores suspiciously engaging in a TCC attack have to undertake Dynamic Voltage Frequency Scaling (DVFS) such that any possible TCC trace will be essentially wiped out. Our experiments have confirmed that on average 97% of the TCC attacks can be detected, and with the proposed defense, the packet error rate (PER) of a TCC attack can soar to more than 70%, literally shutting down the attack in practical terms. The performance penalty caused by the inclusion of the proposed DVFS countermeasures is found to be only 3% for an 8×8 many-core system.
Heterogeneous Multi-Processor Systems-on-Chips (MPSoCs) containing CPU and GPU cores are typically required to execute applications concurrently. However, as will be shown in this paper, existing approaches are not well suited for concurrent applications as they are developed either by considering only a single application or they do not exploit both CPU and GPU cores at the same time. In this paper, we propose an energy-e cient run-time mapping and thread partitioning approach for executing concurrent OpenCL applications on both GPU and GPU cores while satisfying performance requirements. Depending upon the performance requirements, for each concurrently executing application, the mapping process nds the appropriate number of CPU cores and operating frequencies of CPU and GPU cores, and the partitioning process identi es an e cient partitioning of the applications' threads between CPU and GPU cores. We validate the proposed approach experimentally on the Odroid-XU3 hardware platform with various mixes of applications from the Polybench benchmark suite. Additionally, a case-study is performed with a real-world application SLAMBench. Results show an average energy saving of 32% compared to existing approaches while still satisfying the performance requirements.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.