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To study the relationship between structural parameters and mechanical properties of endodontic instruments, the T02004B25 nickel-titanium endodontic instrument was selected for bending and torsion tests and finite element simulation analysis, which demonstrated the feasibility of simulation analysis method. Then based on the idea of parametric design, the models of the endodontic instruments with different structural parameters (cross-section, pitch, taper) were established, and the bending-torsion performance simulation analysis was completed. The results showed that the mechanical properties of endodontic instruments with different structural parameters are different. It is necessary to find the optimal parameters for different structure parameters of endodontic instruments to maximize their service life.

Background Multisite studies can considerably increase the pool of normally aging individuals with neurodegenerative disorders and thereby expedite the associated research. Understanding the reproducibility of the parameters of related brain structures—including the hippocampus, amygdala, and entorhinal cortex—in multisite studies is crucial in determining the impact of healthy aging or neurodegenerative diseases. Purpose To estimate the reproducibility of the fascinating structures by automatic (FreeSurfer) and manual segmentation methods in a well-controlled multisite dataset. Material and Methods Three traveling individuals were scanned at 10 sites, which were equipped with the same equipment (3T Prisma Siemens). They used the same scan protocol (two inversion-contrast magnetization-prepared rapid gradient echo sequences) and operators. Validity coefficients (intraclass correlations coefficient [ICC]) and spatial overlap measures (Dice Similarity Coefficient [DSC]) were used to estimate the reproducibility of multisite data. Results ICC and DSC values varied substantially among structures and segmentation methods, and values of manual tracing were relatively higher than the automated method. ICC and DSC values of structural parameters were greater than 0.80 and 0.60 across sites, as determined by manual tracing. Low reproducibility was observed in the amygdala parameters by automatic segmentation method (ICC = 0.349–0.529, DSC = 0.380–0.873). However, ICC and DSC scores of the hippocampus were higher than 0.60 and 0.65 by two segmentation methods. Conclusion This study suggests that a well-controlled multisite study could provide a reliable MRI dataset. Manual tracing of volume assessments is recommended for low reproducibility structures that require high levels of precision in multisite studies.

Target plays an essential role in stance detection of an opinionated review/claim, since the stance expressed in the text often depends on the target. In practice, we need to deal with targets unseen in the annotated training data. As such, detecting stance for an unknown or unseen target is an important research problem. This paper presents a novel approach that automatically identifies and adapts the target-dependent and target-independent roles that a word plays with respect to a specific target in stance expressions, so as to achieve cross-target stance detection. More concretely, we explore a novel solution of constructing heterogeneous target-adaptive pragmatics dependency graphs (TPDG) for each sentence towards a given target. An in-target graph is constructed to produce inherent pragmatics dependencies of words for a distinct target. In addition, another cross-target graph is constructed to develop the versatility of words across all targets for boosting the learning of dominant word-level stance expressions available to an unknown target. A novel graph-aware model with interactive Graphical Convolutional Network (GCN) blocks is developed to derive the target-adaptive graph representation of the context for stance detection. The experimental results on a number of benchmark datasets show that our proposed model outperforms state-of-the-art methods in cross-target stance detection.\ud \ud

The gray matter (GM) and white matter (WM) are structurally and functionally related in the human brain. Among the numerous neuroimaging studies, yet only a few have investigated these two structures in the same sample. So, there is limited and inconsistent information about how they are correlated in the brain of healthy adults. In this study, we combined cortical reconstruction with diffusion spectrum imaging (DSI) tractography to investigate the relationship between cortical morphology and microstructural properties of major WM tracts in 163 healthy young adults. The results showed that cortical thickness (CTh) was positively correlated with the coherent tract-wise fractional anisotropy (FA) value, and the correlation was stronger in the dorsal areas than in the ventral areas. For other diffusion parameters, CTh was positively correlated with axial diffusivity (AD) of coherent fibers in the frontal areas and negatively correlated with radial diffusivity (RD) of coherent fibers in the dorsal areas. These findings suggest that the correlation between GM and WM is inhomogeneity and could be interpreted with different mechanisms in different brain regions. We hope our research could provide new insights into the studies of diseases in which the GM and WM are both affected.

In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specificaspect. Based on it, we propose a novel graph-aware model with Interactive Graph Convolutional Networks (InterGCN) for aspect sentiment analysis. Specifically, an ordinary dependencygraph is first constructed for each sentence over the dependency tree. Then we refine the graph byconsidering the syntactical dependencies between contextual words and aspect-specific words toderive the aspect-focused graph. Subsequently, the aspect-focused graph and the correspondingembedding matrix are fed into the aspect-focused GCN to capture the key aspect and contextualwords. Besides, to interactively extract the dependencies between the aspect words and otheraspects, an inter-aspect GCN is adopted to model the representations learned by aspect-focusedGCN based on the inter-aspect graph. Hence, the model can be aware of the significant contextual and aspect words when interactively learning the sentiment features for a specific aspect.Experimental results on four benchmark datasets illustrate that our proposed model outperformsstate-of-the-art methods and substantially boosts the performance in comparison with BERT.

In most previous studies, information about aspects in sentences is considered important for the Aspect-based Sentiment Analysis (ABSA task and therefore various attention mechanisms have been explored to leverage interactions between aspects and context. However, some sentiment expressions carry the same polarity regardless of the aspects they are associated with. In such cases, it is not necessary to incorporate aspect information for ABSA. In fact, in our experiments, we find that blindly leveraging interactions between aspects and context as features may introduce noise when analyzing those aspect-invariant sentiment expressions, especially when facing with limited aspect-related annotated data. Hence, in this paper, we propose an Adversarial Multi-task Learning Framework to identify the aspect-invariant/dependent sentiment expressions automatically without requiring extra annotations. In addition, we use a gating mechanism to control the contribution of representations derived from aspect-invariant and aspect-dependent hidden states when generating the final contextual sentiment representations for the given aspect. This essentially allows the exploitation of aspect-invariant sentiment features for better ABSA results. Experimental results on two benchmark datasets show that extending existing neural models using our proposed framework achieves superior performance. In addition, the aspect-invariant data extracted by our framework can be considered as pivot features for better transfer learning of the ABSA models on unseen aspects.&nbsp

Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification.

Neural attention mechanism has achieved many successes in various tasks in natural language processing. However, existing neural attention models based on a densely connected network are loosely related to the attention mechanism found in psychology and neuroscience. Motivated by the finding in neuroscience that human possesses the template-searching attention mechanism, we propose to use convolution operation to simulate attentions and give a mathematical explanation of our neural attention model. We then introduce a new network architecture, which combines a recurrent neural network with our convolution-based attention model and further stacks an attention-based neural model to build a hierarchical sentiment classification model. The experimental results show that our proposed models can capture salient parts of the text to improve the performance of sentiment classification at both the sentence level and the document level.

Supplemental material, sj-pdf-1-acr-10.1177_0284185120963919 for Reproducibility of volume and asymmetry measurements of hippocampus, amygdala, and entorhinal cortex on traveling volunteers: a multisite MP2RAGE prospective study by Jiachen Du, Peipeng Liang, Hongjian He, Qiqi Tong, Ting Gong, Tianyi Qian, Yi Sun, Jianhui Zhong and Kuncheng Li in Acta Radiologica

Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topicin sentiment analysis. A variety of neuralnetwork models have been proposed recently,however, these previous models mostly focuson the learning architecture with local textual information, ignoring the discourse andprior knowledge, which play crucial roles inhuman text comprehension. In this paper,we propose a new method to extract emotion cause with a hierarchical neural modeland knowledge-based regularizations, whichaims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. Theexperimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in differentlanguages (Chinese and English), outperforming a number of competitive baselines by atleast 2.08% in F-measure.

Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspect-based sentiment analysis (TABSA). However, existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. This may result in targets or aspects having the same vector representations in different contexts and losing the context-dependent information. To address this problem, we propose a novel method to refine the embeddings of targets and aspects. Such pivotal embedding refinement utilizes a sparse coefficient vector to adjust the embeddings of target and aspect from the context. Hence the embeddings of targets and aspects can be refined from the highly correlative words instead of using context-independent or randomly initialized vectors. Experiment results on two benchmark datasets show that our approach yields the state-of-the-art performance in TABSA task.

A very wideband uniplanar quasi-Vivaldi antenna is presented. Based on its easy fabrication, low cost, and wideband high gain performance, it appears suitable for imaging and other useful applications. It consists of a coplanar waveguide feed, and a pair Vivaldi patches. It exhibits a 10 dB reflection coefficient bandwidth of 1.9 GHz - 20 GHz. Its radiation pattern is also stable over a wide range of frequencies.

In this study, a new method was proposed to identify the coal-rock interface based on infrared thermal image characteristics during cutting process, which overcomes the problem of low identification accuracy. The cutting signals change significantly with the coal-rock proportion, thus, seven coal-rock mixture test specimens with different proportions were poured. Then, the infrared thermal images of peak were tested while cutting coal-rock specimens with different proportions. Furthermore, by analyzing the flash temperature characteristics of picks, a temperature characteristic database of peaks while cutting different coal-rock specimens was built. Finally, a dynamic recognition model for coal-rock interface identification was established based on minimum fuzzy entropy. The experimental results show that, the total recognition error was merely 3.24%, which proved that the proposed method improved the identification accuracy effectively and provided the theoretical foundation and technical premise to realize automatic and intelligent mining.

Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure.

Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation. However, existing CVAE-based models often generate responses from a single latent variable which may not be sufficient to model high variability in responses. To solve this problem, we propose a novel model that sequentially introduces a series of latent variables to condition the generation of each word in the response sequence. In addition, the approximate posteriors of these latent variables are augmented with a backward Recurrent Neural Network (RNN), which allows the latent variables to capture long-term dependencies of future tokens in generation. To facilitate training, we supplement our model with an auxiliary objective that predicts the subsequent bag of words. Empirical experiments conducted on Opensubtitle and Reddit datasets show that the proposed model leads to significant improvement on both relevance and diversity over state-of-the-art baselines.