Piezoelectricity describes interconversion between electrical charge and mechanical strain.As expected for lattice ions displaced in an electric field, the proportionality constant is positive for all piezoelectric materials. The exception is poly(vinylidene-fluoride) (PVDF), which exhibits a negative longitudinal piezoelectric coefficient. Reported explanations consider exclusively contraction with applied electric field of either the crystalline or the amorphous part of this semi-crystalline polymer. To distinguish between these conflicting interpretations, we have performed in-situ dynamic X-ray diffraction measurements on P(VDF-TrFE) capacitors. We find that the piezoelectric effect is dominated by the change in lattice constant but, surprisingly, it cannot be accounted for by the polarization-biased electrostrictive contribution of the crystalline part alone. Our quantitative analysis shows that an additional contribution is operative, which we argue is due to an electromechanical coupling between the intermixed crystalline lamellae and amorphous regions. Our findings tie the counterintuitive negative piezoelectric response of PVDF and its copolymers to the dynamics of their composite microstructure. 3 Piezoelectricity describes the conversion of electrical charge to mechanical strain and vice versa. The direct piezoelectric effect is observed as a change in surface charge density of a material in response to an external mechanical stress. The effect is reversible; the thermodynamic equivalent is a change in dimension upon applying an electric field.A large piezoelectric coefficient, describing the change in spontaneous electrical polarization with applied mechanical stress, is obtained for ferroelectric materials. When an electric field is applied in the direction of the polarization most ferroelectric materials will expand. However, there is one well-known exception. The ferroelectric polymer poly(vinylidene-fluoride) (PVDF) and its copolymers with trifluoroethylene P (VDF-TrFE) show an unusual negative longitudinal piezoelectric effect. Counterintuitively, these polymers contract in the direction of an applied electric field. The two opposite behaviours are schematically represented in Fig. 1.It has been shown that the strain in PVDF varies with the polarization squared. [1] Hence the origin of piezoelectricity is electrostriction biased by the spontaneous polarization. A negative piezoelectric coefficient was extracted. Presently, two contradicting microscopic models have been proposed; the piezoelectric response is attributed to either the crystalline or the amorphous part of the semi-crystalline polymer.Quantum chemical calculations for the ferroelectric β−phase of PVDF have shown that for a single-crystal the piezoelectric effect is negative.[2] When an electric field is applied perpendicularly to the PVDF chain, the backbone stretches and its height is compressed. The lattice constant is reduced. The calculated coefficient agrees with the value experimentally determined on bulk samples, imp...

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Aiming to represent user characteristics and personal interests, the task of user profiling is playing an increasingly important role for many real-world applications, e.g., e-commerce and social networks platforms. By exploiting the data like texts and user behaviors, most existing solutions address user profiling as a classification task, where each user is formulated as an individual data instance. Nevertheless, a user's profile is not only reflected from her/his affiliated data, but also can be inferred from other users, e.g., the users that have similar co-purchase behaviors in e-commerce, the friends in social networks, etc. In this paper, we approach user profiling in a semi-supervised manner, developing a generic solution based on heterogeneous graph learning. On the graph, nodes represent the entities of interest (e.g., users, items, attributes of items, etc.), and edges represent the interactions between entities. Our heterogeneous graph attention networks (HGAT) method learns the representation for each entity by accounting for the graph structure, and exploits the attention mechanism to discriminate the importance of each neighbor entity. Through such a learning scheme, HGAT can leverage both unsupervised information and limited labels of users to build the predictor. Extensive experiments on a real-world e-commerce dataset verify the effectiveness and rationality of our HGAT for user profiling.

Airway segmentation on CT scans is critical for pulmonary disease diagnosis and endobronchial navigation. Manual extraction of airway requires strenuous efforts due to the complicated structure and various appearance of airway. For automatic airway extraction, convolutional neural networks (CNNs) based methods have recently become the stateof-the-art approach. However, there still remains a challenge for CNNs to perceive the tree-like pattern and comprehend the connectivity of airway. To address this, we propose a voxel-connectivity aware approach named AirwayNet for accurate airway segmentation. By connectivity modeling, conventional binary segmentation task is transformed into 26 tasks of connectivity prediction. Thus, our AirwayNet learns both airway structure and relationship between neighboring voxels. To take advantage of context knowledge, lung distance map and voxel coordinates are fed into AirwayNet as additional semantic information. Compared to existing approaches, AirwayNet achieved superior performance, demonstrating the effectiveness of the network's awareness of voxel connectivity.Pulmonary diseases, including chronic obstructive pulmonary diseases (COPD) and lung cancer, pose high risks to human health. The standard computed tomography (CT) helps radiologists detect pathological changes. For tracheal and bronchial surgery, airway tree modeling on CT scans is often considered a prerequisite. Meticulous efforts are required to manually segment airway due to its tree-like structure and variety in size, shape, and intensity. ⋆ Corresponding author: Jie Yang.

In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback. The most challenging problem in this scenario is how to suggest items when the user profile has not been well established, i.e., recommend for cold-start users or warm-start users with taste drifting. Existing approaches either rely on overly pessimistic linear exploration strategy or adopt meta-learning based algorithms in a full exploitation way. In this work, to quickly catch up with the user's interests, we propose to represent the exploration policy with a neural network and directly learn it from the feedback data. Specifically, the exploration policy is encoded in the weights of multi-channel stacked self-attention neural networks and trained with efficient Q-learning by maximizing users' overall satisfaction in the recommender systems. The key insight is that the satisfied recommendations triggered by the exploration recommendation can be viewed as the exploration bonus (delayed reward) for its contribution on improving the quality of the user profile. Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning. Extensive experiments and analysis conducted on three benchmark collaborative filtering datasets have demonstrated the advantage of our method over state-of-the-art methods.