114 research outputs found

    Petri net modeling and performance analysis of can fieldbus

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    The CAN FB (Controller Area Network FieldBus) has been in existence for ten years. It supports automated manufacturing and process control environments to interconnect intelligent devices such as valves, sensors, and actuators. CAN FieldBus has a high bit rate and the ability to detect errors. It is immune to noise and resistant to shock, vibration, and heat. Two recently introduced mechanisms, Distributed Priority Queue (DPQ) and Priority Promotion (PP) enable CAN FieldBus networks to share out the system bandwidth and grant ail upper bound on the transmission times so as to meet the requirements in real-time communications. Modeling and analysis of such networks are an important research area for their wide applications in manufacturing automation. This thesis presents a Petri net methodology which models and analyzes CAN FieldBus access protocol. A Reachability Graph of the Petri net model is -utilized to study the behavioral properties of the protocol. A timed Petri net simulator is used to evaluate the performance of the protocol. Performance measures include the completion time for successful events and operations. Operational parameters investigated using the Petri Net model are FieldBus speed, the length of each frame, and the number of frames in a message

    A Retrospective Paired Study: Efficacy and Safety of Nimotuzumab Combined with Radiochemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma

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    Objective: To evaluate the efficacy and safety of nimotuzumab in combination with radiochemotherapy as the primary treatment in patients with locoregionally advanced nasopharyngeal carcinoma (NPC). Methods: We retrospectively reviewed patients with locoregionally advanced nasopharyngeal carcinoma from September 2012 to December 2016. 188 newly diagnosed patients with stage III–IVB nasopharyngeal carcinoma were treated with at least 1-2 cycles of chemotherapy concurrently with planned IMRT. 88 patients received nimotuzumab 200 mg/week. Acute and late radiation-related toxicities were graded according to the Acute and Late Radiation Morbidity Scoring Criteria of Radiation Therapy Oncology Group. Results: After 3 months of treatment, the complete response rates of nasopharyngeal tumors in the study group and the control group were 78.4% and 65.5%, respectively (?2=4.070, P=0.044). The total complete response rates of cervical lymph nodes in the study group and the control group were 80.7% and 67.6% respectively (?2=4.022, P=0.045).The median cycle for nimotuzumab addition was 6.3 weeks. With a median follow-up of 36.3 months (range, 12–72 months), the estimated 3-year progression failure-free survival and overall survival rates for the study group and the control group were 85.24% vs 81.97% and 96.67% vs 90.0%, respectively. The 3-year local recurrence-free survival rates for the study group and the control group were 96.67% vs 83.60%, respectively (P=0.047). Grade 3 radiation-induced mucositis accounted for 36.4% of treated patients. No skin rash and infusion reaction were observed, distinctly from what is reported in control patients. Conclusion: Nimotuzumab plus chemoradiotherapy in the treatment of locoregionally advanced nasopharyngeal carcinoma showed promising outcomes in terms of locoregional control, without increasing the incidence of radiation-related toxicities for patients

    Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models

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    With ever increasing parameters and computation, vision-language pre-trained (VLP) models exhibit prohibitive expenditure in downstream task adaption. Recent endeavors mainly focus on parameter efficient transfer learning (PETL) for VLP models by only updating a small number of parameters. However, excessive computational overhead still plagues the application of VLPs. In this paper, we aim at parameter and computation efficient transfer learning (PCETL) for VLP models. In particular, PCETL not only needs to limit the number of trainable parameters in VLP models, but also to reduce the computational redundancy during inference, thus enabling a more efficient transfer. To approach this target, we propose a novel dynamic architecture skipping (DAS) approach towards effective PCETL. Instead of directly optimizing the intrinsic architectures of VLP models, DAS first observes the significances of their modules to downstream tasks via a reinforcement learning (RL) based process, and then skips the redundant ones with lightweight networks, i.e., adapters, according to the obtained rewards. In this case, the VLP model can well maintain the scale of trainable parameters while speeding up its inference on downstream tasks. To validate DAS, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of VL tasks. The experimental results not only show the great advantages of DAS in reducing computational complexity, e.g. -11.97% FLOPs of METER on VQA2.0, but also confirm its competitiveness against existing PETL methods in terms of parameter scale and performance. Our source code is given in our appendix

    Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting

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    Pre-trained language models (PLMs) have played an increasing role in multimedia research. In terms of vision-language (VL) tasks, they often serve as a language encoder and still require an additional fusion network for VL reasoning, resulting in excessive memory overhead. In this paper, we focus on exploring PLMs as a stand-alone model for VL reasoning tasks. Inspired by the recently popular prompt tuning, we first prove that the processed visual features can be also projected onto the semantic space of PLMs and act as prompt tokens to bridge the gap between single- and multi-modal learning. However, this solution exhibits obvious redundancy in visual information and model inference, and the placement of prompt tokens also greatly affects the final performance. Based on these observations, we further propose a novel transfer learning approach for PLMs, termed Dynamic Visual Prompting (DVP). Concretely, DVP first deploys a cross-attention module to obtain text-related and compact visual prompt tokens, thereby greatly reducing the input length of PLMs. To obtain the optimal placement, we also equip DVP with a reinforcement-learning based search algorithm, which can automatically merge DVP with PLMs for different VL tasks via a very short search process. In addition, we also experiment DVP with the recently popular adapter approach to keep the most parameters of PLMs intact when adapting to VL tasks, helping PLMs achieve a quick shift between single- and multi-modal tasks. We apply DVP to two representative PLMs, namely BERT and T5, and conduct extensive experiments on a set of VL reasoning benchmarks including VQA2.0, GQA and SNLIVE. The experimental results not only show the advantage of DVP on efficiency and performance, but also confirm its superiority in adapting pre-trained language models to VL tasks

    A surface-enhanced Raman scattering (SERS)-active optical fiber sensor based on a three-dimensional sensing layer

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    AbstractTo fabricate a new surface-enhanced Raman scattering (SERS)-active optical fiber sensor, the design and preparation of SERS-active sensing layer is one of important topics. In this study, we fabricated a highly sensitive three-dimensional (3D) SERS-active sensing layer on the optical fiber terminal via in situ polymerizing a porous polymer material on a flat optical fiber terminal through thermal-induced process, following with the photochemical silver nanoparticles growth. The polymerized polymer formed a 3D porous structure with the pore size of 0.29–0.81μm, which were afterward decorated with abundant silver nanoparticles with the size of about 100nm, allowing for higher SERS enhancement. This SERS-active optical fiber sensor was applied for the determination of 4-mercaptopyridine, crystal violet and maleic acid The enhancement factor of this SERS sensing layer can be reached as about 108. The optical fiber sensor with high sensitive SERS-active porous polymer is expected for online analysis and environment detection

    Approximated Prompt Tuning for Vision-Language Pre-trained Models

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    Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens. In terms of vision-language pre-trained (VLP) models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks, which greatly exacerbates the already high computational overhead. In this paper, we revisit the principle of prompt tuning for Transformer-based VLP models and reveal that the impact of soft prompt tokens can be actually approximated via independent information diffusion steps, thereby avoiding the expensive global attention modeling and reducing the computational complexity to a large extent. Based on this finding, we propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning. To validate APT, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of downstream tasks. Meanwhile, the generalization of APT is also validated on CLIP for image classification. The experimental results not only show the superior performance gains and computation efficiency of APT against the conventional prompt tuning methods, e.g., +6.6% accuracy and -64.62% additional computation overhead on METER, but also confirm its merits over other parameter-efficient transfer learning approaches

    Current Status of Foreign Rice Production and Processing

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    We reviewed the rice varieties, rice processing equipment and main processing technology development and research status of Japan, India, Thailand, Malaysia, the United States and several major foreign rice producers. The rice processing production situation of Iran, Brazil, Nigeria and Indonesia was illustrated. After the comparative analysis of domestic and foreign rice processing technology and equipment, we proposed the necessity of continuous research and development on moderate processing and milling process, aiming to provide reference for the rice processing industry in China

    Prenatal Diagnosis of Recurrent Distal 1q21.1 Duplication in Three Fetuses With Ultrasound Anomalies

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    Background: The phenotype of duplication of 1q21.1 region is variable, ranging from macrocephaly, autism spectrum disorder, congenital anomalies, to a normal phenotype. Few cases have been reported in the literature regarding prenatal diagnosis of 1q21.1 duplication syndrome. The current study presents prenatal diagnosis of 1q21.1 duplication syndrome in three fetuses with ultrasound anomalies.Case presentation: Three fetuses from three unrelated families were included in the study. The prenatal routine ultrasound examination showed nasal bone loss in Fetus 1 and Fetus 3, as well as duodenal atresia in Fetus 2. Chromosomal microarray analysis was performed to provide genetic analysis of amniotic fluid and parental blood samples. The CMA results revealed two de novo duplications of 1.34 and 2.69 Mb at distal 1q21.1 region in two fetuses with absent nasal bone, as well as a maternal inherited 1.35-Mb duplication at distal 1q21.1 in one fetus with duodenal atresia.Conclusions: The phenotype of 1q21.1 duplication syndrome in prenatal diagnosis is variable. The fetuses with nasal bone loss or duodenal atresia may be related to 1q21.1 duplication and chromosomal microarray analysis should be performed

    Meta-Analysis on Pharmacogenetics of Platinum-Based Chemotherapy in Non Small Cell Lung Cancer (NSCLC) Patients

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    AIM: To determine the pharmacogenetics of platinum-based chemotherapy in Non Small Cell Lung Cancer (NSCLC) patients. METHODS: Publications were selected from PubMed, Cochrane Library and ISI Web of Knowledge. A meta-analysis was conducted to determine the association between genetic polymorphisms and platinum-based chemotherapy by checking odds ratio (OR) and 95% confidence interval (CI). RESULTS: Data were extracted from 24 publications, which included 11 polymorphisms in 8 genes for meta-analysis. MDR1 C3435T (OR = 1.97, 95% CI: 1.11-3.50, P = 0.02), G2677A/T (OR = 2.61, 95% CI: 1.44-4.74, P = 0.002) and GSTP1 A313G (OR = 0.32, 95% CI: 0.17-0.58, P = 0.0002) were significantly correlated with platinum-based chemotherapy in Asian NSCLC patients. CONCLUSION: Attention should be paid to MDR1 C3435T, G2677A/T and GSTP1 A313G for personalized chemotherapy treatment for NSCLC patients in Asian population in the future

    New genetic loci link adipose and insulin biology to body fat distribution.

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    Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms
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