Dialogue policy is a core module in pipeline dialogue systems as it drives conversation generation. Personalized dialogue policies aim to equip chatbots with tailored personalities, making them behave like real users, providing more accurate action responses, and improving the anthropomorphic capabilities of personal assistants. Yet existing dialogue policy approaches often overlook individual personalities because obtaining explicit user profiles is costly and time-consuming. In this article, we propose a personalized dialogue policy learning framework, named PDL. It dynamically learns implicit user profiles from successful dialogue trajectories. Specifically, we collect a lot of success histories from human–computer interactions to extract sequences of user belief states and agent actions. The extracted sequences are processed via a loop clipping operation and modeled with an autoregressive transformer to mimic human analytical behavior. After that, a new user’s latent personalized preferences are predicted based on the autoregressive transformer model. The personalized preferences are employed to implement dialogue policies via three categories of reinforcement learning algorithms, including value-based approaches, policy-based approaches, and model-based approaches. The experiments are conducted on three different task-oriented dialogue datasets, and the results show that the proposed PDL framework achieves state-of-the-art results compared to other comparative approaches.

Four-dimensional Cone-beam CT (4D-CBCT) has already been integrated into many commercial radiotherapy systems to facilitate image-guided radiotherapy (IGRT). These techniques reconstruct a sequence of three-dimensional (3D) CBCT volumes based on respiratory phases, providing motion-compensated images. Nevertheless, current 4D-CBCT methods still suffer from poor imaging quality due to the limited number of views used for phase-resolved reconstruction, and deep learning-based methods require high-quality training data, which is commonly unavailable. In this paper, we propose a self-supervised 4D-CBCT imaging method based on dynamic neural attenuation fields (D-NAF). The dynamic pulmonary CBCT images are encoded into a 4D implicit representation incorporating both spatial information and temporal information. Moreover, we employed a separable spatial-temporal encoding method to reduce the dimensionality of the solution space, leading to enhanced convergence and reconstruction quality. The results demonstrate that the proposed method yields superior imaging quality in comparison to alternative approaches, with an RMSE value of 59.82 HU, PSNR of 35.33 dB and SSIM of 0.9358. This method is expected to be a new benchmark for self-supervised 4D-CBCT imaging.

Deep Reinforcement learning (DRL) has been successfully applied to the dialogue policy of task-oriented dialogue systems. However, one challenge in the existing DRL-based dialogue policy methods is their unstructured state-action representations without the ability to learn the relationship between dialogue states and actions. To alleviate this problem, we propose a graph-structured dialogue policy framework for task-oriented dialogue systems. More specifically, we use an unsupervised approach to construct two different bipartite graphs. Then, we generate the user-related and knowledge-related subgraphs based on the matching dialogue sub-states with bipartite graph nodes. A variant of graph convolutional network is employed to encode dialogue subgraphs. After that, we use a bidirectional gated cycle unit (BGRU) and self-attention mechanism to obtain the high-level historical state representations and employ a neural network for the high-level current state representations. The two state representations are joined to learn the action value of dialogue policy. Experiments implemented with different DRL algorithms demonstrate that the proposed framework significantly improves the effectiveness and stability of dialogue policies.

A recent trend in Non-Rigid Structure-from-Motion (NRSfM) is to express local, differential constraints between pairs of images, from which the surface normal at any point can be obtained by solving a system of polynomial equations. While this approach is more successful than its counterparts relying on global constraints, the resulting methods face two main problems: First, most of the equation systems they formulate are of high degree and must be solved using computationally expensive polynomial solvers. Some methods use polynomial reduction strategies to simplify the system, but this adds some phantom solutions. In any event, an additional mechanism is employed to pick the best solution, which adds to the computation without any guarantees on the reliability of the solution. Second, these methods formulate constraints between a pair of images. Even if there is enough motion between them, they may suffer from local degeneracies that make the resulting estimates unreliable without any warning mechanism. %Unfortunately, these systems are of high degree with up to five real solutions. Hence, a computationally expensive strategy is required to select a unique solution. Furthermore, they suffer from degeneracies that make the resulting estimates unreliable, without any mechanism to identify this situation. In this paper, we solve these problems for isometric/conformal NRSfM. We show that, under widely applicable assumptions, we can derive a new system of equations in terms of the surface normals, whose two solutions can be obtained in closed-form and can easily be disambiguated locally. Our formalism also allows us to assess how reliable the estimated local normals are and to discard them if they are not. Our experiments show that our reconstructions, obtained from two or more views, are significantly more accurate than those of state-of-the-art methods, while also being faster. %In this paper, we show that, under widely applicable assumptions, we can derive a new system of equations in terms of the surface normals, whose two solutions can be obtained in closed-form and can easily be disambiguated locally. Our formalism also allows us to assess how reliable the estimated local normals are and to discard them if they are not. Our experiments show that our reconstructions, obtained from two or more views, are significantly more accurate than those of state-of-the-art methods, while also being faster.