Defect identification in adhesive structures using multi-Feature fusion convolutional neural network

The interface-debonding defects of adhesive bonding structures may cause a reduction in bonding strength, which in turn affects the bonding quality of adhesive bonding samples. Hence, defect recognition in adhesive bonding structures is particularly important. In this study, a terahertz (THz) wave was used to analyze bonded structure samples, and a multi-feature fusion convolutional neural network (CNN) was used to identify the defect waveforms. The pooling method of the squeeze-and-excitation (SE) attention mechanism was optimized, defect feature weights were adaptively assigned, and feature fusion was conducted using automatic label net-works to segment the THz waveforms in the adhesive bonding area with fine granularity waveforms as an input to the multi-channel CNN. The results revealed that the speed of the THz waveform labeling with the automatic labeling network was 10 times higher than that with traditional methods, and the defect-recognition accuracy of the defect-recognition network constructed in this study was up to 99.28%. The F1-score was 99.73%, and the lowest pre-embedded defect recognition error rate of the generalization experiment samples was 0.27%

Research progress on rapid determination of edible vegetable oil components

This paper summarizes the rapid detection methods of edible vegetable oil components from the aspects of analysis principle and application technology, such as simulation sensory analysis, spectral analysis, electromagnetic spectrum analysis and biochip technology. The application results of these techniques in the component mixing identification of edible vegetable were further analyzed. It was showed that the integrated online rapid detection technology of multi-channel, multi parameter and multi capacity will be the developing direction of component analysis of edible vegetable oil and will lay a technical foundation for further improving the quality supervision of edible vegetable oil and safeguarding the interests of consumers in China

Maintenance of Sorafenib following combined therapy of three-dimensional conformal radiation therapy/intensity-modulated radiation therapy and transcatheter arterial chemoembolization in patients with locally advanced hepatocellular carcinoma: a phase I/II study

BACKGROUND: Three-dimensional conformal radiation therapy (3DCRT)/intensity-modulated radiation therapy (IMRT) combined with or without transcatheter arterial chemoembolization (TACE) for locally advanced hepatocellular carcinoma (HCC) has shown favorable outcomes in local control and survival of locally advanced HCC. However, intra-hepatic spreading and metastasis are still the predominant treatment failure patterns. Sorafenib is a multikinase inhibitor with effects against tumor proliferation and angiogenesis. Maintenance Sorafenib would probably prevent or delay the intrahepatic and extrahepatic spread of HCC after radiotherapy, which provides the rationale for the combination of these treatment modalities. METHODS AND DESIGN: Patients with solitary lesion (bigger than 5 cm in diameter) histologically or cytologically confirmed HCC receive TACE (1-3 cycles) plus 3DCRT/IMRT 4-6 weeks later. Maintenance Sorafenib will be administered only for the patients with non-progression disease 4 to 6 weeks after the completion of radiotherapy. The dose will be 400 mg, p.o., twice a day. Sorafenib will be continuously given for 12 months unless intolerable toxicities and/or tumor progression. If no more than 3 patients discontinue Sorafenib treatment who experience dose-limiting toxicity after necessary dose modification and delay and/or radiation-induced liver disease in the first 15 enrolled patients, the study will recruit second fifteen patients for further evaluating safety and efficacy of treatment. Hypothesis of the current study is that Sorafenib as a maintenance therapy after combined therapy of 3DCRT/IMRT and TACE is safe and superior to radiotherapy combined with TACE alone in terms of time to progression (TTP), progression-free survival (PFS) and overall survival (OS) in comparison to historical data. DISCUSSION: A recent meta-analysis showed TACE in combination with radiotherapy, improved the survival and the tumor response of patients, and was thus more therapeutically beneficial. In this study, local therapy for HCC is the combination of TACE and radiotherapy. Radiation exposure as a kind of stress might induce the compensatory activations of multiple intracellular signaling pathway mediators, such as PI3K, MAPK, JNK and NF-kB. Vascular endothelial growth factor (VEGF) was identified as one factor that was increased in a time- and dose-dependent manner after sublethal irradiation of HCC cells in vitro, translating to enhanced intratumor angiogenesis in vivo. Therefore, Sorafenib-mediated blockade of the Raf/MAPK and VEGFR pathways might enhance the efficacy of radiation, when Sorafenib is followed sequentially as a maintenance modality. (ClinicalTrials.gov number, NCT00999843.

A general model for collaboration networks

In this paper, we propose a general model for collaboration networks. Depending on a single free parameter "{\bf preferential exponent}", this model interpolates between networks with a scale-free and an exponential degree distribution. The degree distribution in the present networks can be roughly classified into four patterns, all of which are observed in empirical data. And this model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient. More interesting, we find a peak distribution of act-size from empirical data which has not been emphasized before of some collaboration networks. Our model can produce the peak act-size distribution naturally that agrees with the empirical data well.Comment: 10 pages, 10 figure

BumbleBee: Secure Two-party Inference Framework for Large Transformers

Large transformer-based models have realized state- of-the-art performance on lots of real-world tasks such as natural language processing and computer vision. However, with the increasing sensitivity of the data and tasks they handle, privacy has become a major concern during model deployment. In this work, we focus on private inference in two-party settings, where one party holds private inputs and the other holds the model. We introduce BumbleBee, a fast and communication-friendly two-party private transformer inference system. Our contributions are three-fold: Firstly, we present optimized homomorphic encryption-based proto- cols that enable the multiplication of large matrices with 80 – 90% less communication cost than existing methods. Secondly, we offer a general method for designing efficient and accurate protocols for non-linear activation functions in transformers. Our activation protocols have demonstrated speed and reduced the communication overhead by 80 – 95% over two existing methods. Finally, we conducted intensive benchmarks on several large transformer models. Results show that BumbleBee is more than one order of magnitude faster than Iron (NeurIPS22)

Competitions of magnetism and superconductivity in FeAs-based materials

Using the numerical unrestricted Hartree-Fock approach, we study the ground state of a two-orbital model describing newly discovered FeAs-based superconductors. We observe the competition of a $(0, \pi)$ mode spin-density wave and the superconductivity as the doping concentration changes. There might be a small region in the electron-doping side where the magnetism and superconductivity coexist. The superconducting pairing is found to be spin singlet, orbital even, and mixed s$_{xy}$ + d$_{x^{2}-y^{2}}$ wave (even parity).Comment: 5 pages, 3 figure