Attentional Encoder Network for Targeted Sentiment Classification

Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term patterns. To address this issue, this paper proposes an Attentional Encoder Network (AEN) which eschews recurrence and employs attention based encoders for the modeling between context and target. We raise the label unreliability issue and introduce label smoothing regularization. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of our model.Comment: 7 page

High Resolution Nanoimprint for Nanophotonics

Nanophotonics have drawn huge attention in recent years in various applications. Surface sensing technique, including surface-enhanced Raman spectroscopy (SERS), is an important topic of nanophotonics and has been widely investigated. The capability of SERS-active device depends on two main factors: good reproducibility and high enhancement factor. Ordered metallic nanostructures with high resolution are usually preferred for SERS application. Nanoimprint lithography can provide a low-cost and high resolution fabrication technique for SERS-active devices. The objective of this research is to explore the application of nanoimprint lithography in SERS-active devices. This work begins with two issues of nanoimprint lithography: mold fabrication and throughput improvement. The potential of nanoimprint lithography depends on reliable mold fabrication. Two techniques are investigated, which are polyelectrolyte electrostatic self-assembly and controlled polymer reflow. Based on the observation of exceptional thermal stability of entangled polymer, step-and-repeat thermal nanoimprint lithography is developed. This technique significantly improves the throughput and enables the large scale application of thermal nanoimprint. Ordered metallic nanostructures have been widely used as SERS-active substrates. In order to achieve high enhancement, extremely high resolution is needed, which can be limited by lithography technique. In this work, SERS-active devices based on gap surface plasmon polaritons are fabricated by nanoimprint lithography. 17 times more enhancement is achieved compared with conventional SERS-active devices on the same structure dimensions. This technique opens up possibilities of single molecule detection in the future

Investigación sobre la debilidad por fatiga de los nodos de vigas transversales antes y después del refuerzo del puente de acero en celosía

In this research, fatigue tests on full-size specimens are conducted for a steel cross-beam joint before and after reinforcement. Combined with a three-dimensional (3D) numerical simulation, the 3D stress parameters, and their redistribution rules study anew with a web crack and new crack initiation locations and fatigue weakness details are predicted. The research results include the following: 1) The empirical formula parameter m of the Z-axis stress for the new crack tip is approximately 0.05. 2) The fatigue performance of the web’s new crack tips is significantly improved by bolting reinforced steel plates, the stress range is reduced by 60%-98.78%, and the original crack stops growing in size. The health monitoring system can choose the predicted weak details as valid monitoring points so that the fatigue damage can be intelligently perceived after the reinforcement of steel bridges.En este estudio, la prueba de fatiga de una muestra a gran escala antes y después del refuerzo se llevó a cabo en la junta de viga transversal de un puente de acero. Combinado con la simulación numérica 3D, se estudiaron los parámetros de tensión 3D y las leyes de redistribución de las redes agrietadas, y se predijeron nuevas ubicaciones de grietas y detalles débiles por fatiga. Los resultados muestran que el refuerzo de la placa de acero atornillada puede mejorar la resistencia a la fatiga de la viga, y la amplitud de tensión de la punta de grieta de la placa de banda se puede reducir en más del 60%. Los puntos de monitoreo y medición se pueden establecer en los detalles débiles de la predicción, de modo que el daño por fatiga en el funcionamiento del puente de acero en la etapa posterior del refuerzo se encuentre en un estado de percepción inteligente.

WiMorse: a contactless Morse code text input system using ambient WiFi signals

International audienceRecent years have witnessed advances of Internet of Things (IoT) technologies and their applications to enable contactless sensing and human-computer interaction in smart homes. For people with Motor Neurone Disease (MND), their motion capabilities are severely impaired and they have difficulties interacting with IoT devices and even communicating with other people. As the disease progresses, most patients lose their speech function eventually which makes the widely adopted voice-based solutions fail. In contrast, most patients can still move their fingers slightly even after they have lost the control of their arms and hands. Thus we propose to develop a Morse code based text input system, called WiMorse, which allows patients with minimal single-finger control to input and communicate with other people without attaching any sensor to their fingers. WiMorse leverages ubiquitous commodity WiFi devices to track subtle finger movements contactlessly and encode them as Morse code input. In order to sense the very subtle finger movements, we propose to employ the ratio of the Channel State Information (CSI) between two antennas to enhance the Signal to Noise Ratio. To address the severe location dependency issue in wireless sensing with accurate theoretical underpinning and experiments, we propose a signal transformation mechanism to automatically convert signals based on the input position, achieving stable sensing performance. Comprehensive experiments demonstrate that WiMorse can achieve higher than 95% recognition accuracy for finger generated Morse code, and is robust against input position, environment changes, and user diversity

Construction and experimental validation of a signature for predicting prognosis and immune infiltration analysis of glioma based on disulfidptosis-related lncRNAs

BackgroundsDisulfidptosis, a newly discovered mechanism of programmed cell death, is believed to have a unique role in elucidating cancer progression and guiding cancer therapy strategies. However, no studies have yet explored this mechanism in glioma.MethodsWe downloaded data on glioma patients from online databases to address this gap. Subsequently, we identified disulfidptosis-related genes from published literature and verified the associated lncRNAs.ResultsThrough univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) regression algorithms analyses, we identified 10 lncRNAs. These were then utilized to construct prognostic prediction models, culminating in a risk-scoring signature. Reliability and validity tests demonstrated that the model effectively discerns glioma patients’ prognosis outcomes. We also analyzed the relationship between the risk score and immune characteristics, and identified several drugs that may be effective for high-risk patients. In vitro experiments revealed that LINC02525 could enhances glioma cells’ migration and invasion capacities. Additionally, knocking down LINC02525 was observed to promote glioma cell disulfidptosis.ConclusionThis study delves into disulfidptosis-related lncRNAs in glioma, offering novel insights into glioma therapeutic strategies

A Research of Methamphetamine Induced Psychosis in 1,430 Individuals With Methamphetamine Use Disorder: Clinical Features and Possible Risk Factors

Background and Aims: Methamphetamine (MA) abuse is commonly associated with the development of psychotic symptoms. The predictors and related risk factors of MA induced psychosis (MIP) are poorly understood. We investigated the occurrence of MIP, and analyzed the clinical features and possible risk factors among individuals with MA use disorderMethod: One thousand four hundred and thirty participants with MA use disorder were recruited from compulsory rehabilitation centers in Shanghai. A structured questionnaire including demographic characteristics, drug use history, visual analog scales, Beck Depression Inventory-13 (BDI-13), and Hamilton anxiety scale-14 (HAMA-14) were used to collect clinical related information. Fifty-six participants had accomplished the test of CogState Battery.Results: Among the 1430 individuals with MA use disorder, 37.1% were diagnosed as MIP according DSM-IV. There were significant differences in age, marital status, age of drug use onset, MA use years, Average MA use dose, interval of MA use, maximum dose, concurrent use of alcohol, and other drugs, VAS score, MA dependence, BDI-13 scores, HAMA-14 scores, verbal learning memory, and visual learning memory between the MIP group and the none MIP group (P < 0.05). The age of drug use onset (OR = 0.978, p = 0.011), average drug use dose (OR = 1.800, p = 0.015), craving score (OR = 1.069, p = 0.031), MA dependence (OR = 2.214, p < 0.001), and HAMA scores (OR = 1.028, p < 0.001) were associated to MIP.Conclusion: Individuals with MIP had more severe drug use problems, emotional symptoms and cognitive impairment. Earlier onset of drug use, higher quantity of drug use, higher craving, middle or severe drug use disorder and more anxiety symptoms may be related risk factors of MIP

Amorphous In–Ga–Zn–O Powder with High Gas Selectivity towards Wide Range Concentration of C2H5OH

Amorphous indium gallium zinc oxide (a-IGZO) powder was prepared by typical solution-based process and post-annealing process. The sample was used as sensor for detecting C2H5OH, H2, and CO. Gas-sensing performance was found to be highly sensitive to C2H5OH gas in a wide range of concentration (0.5–1250 ppm) with the response of 2.0 towards 0.5 ppm and 89.2 towards 1250 ppm. Obvious difference of response towards C2H5OH, H2, and CO was found that the response e.g., was 33.20, 6.64, and 2.84 respectively at the concentration of 200 ppm. The response time and recovery time of was 32 s and 14 s respectively towards 200 ppm concentration of C2H5OH gas under heating voltage of 6.5 V

Accuracy Analysis of Feature-Based Automatic Modulation Classification via Deep Neural Network

A feature-based automatic modulation classification (FB-AMC) algorithm has been widely investigated because of its better performance and lower complexity. In this study, a deep learning model was designed to analyze the classification performance of FB-AMC among the most commonly used features, including higher-order cumulants (HOC), features-based fuzzy c-means clustering (FCM), grid-like constellation diagram (GCD), cumulative distribution function (CDF), and raw IQ data. A novel end-to-end modulation classifier based on deep learning, named CCT classifier, which can automatically identify unknown modulation schemes from extracted features using a general architecture, was proposed. Features except GCD are first converted into two-dimensional representations. Then, each feature is fed into the CCT classifier for modulation classification. In addition, Gaussian channel, phase offset, frequency offset, non-Gaussian channel, and flat-fading channel are also introduced to compare the performance of different features. Additionally, transfer learning is introduced to reduce training time. Experimental results showed that the features HOC, raw IQ data, and GCD obtained better classification performance than CDF and FCM under Gaussian channel, while CDF and FCM were less sensitive to the given phase offset and frequency offset. Moreover, CDF was an effective feature for AMC under non-Gaussian and flat-fading channels, and the raw IQ data can be applied to different channels’ conditions. Finally, it showed that compared with the existing CNN and K-S classifiers, the proposed CCT classifier significantly improved the classification performance for MQAM at N = 512, reaching about 3.2% and 2.1% under Gaussian channel, respectively