Heterogeneous ice nucleation correlates with bulk-like interfacial water

Establishing a direct correlation between interfacial water and heterogeneous ice nucleation (HIN) is essential for understanding the mechanism of ice nucleation. Here, we study the HIN efficiency of polyvinyl alcohol (PVA) surfaces with different densities of hydroxyl groups. We find that the HIN efficiency increases with the decrease of the hydroxyl group density. By explicitly considering that interfacial water molecules of PVA films consist of ‘tightly bound water’, ‘bound water’ and ‘bulk-like water’, we reveal that ‘bulk-like water’ can be correlated directly to the HIN efficiency of surfaces. As the density of hydroxyl groups decreases, ‘bulk-like water’ molecules can rearrange themselves with a reduced energy barrier into ice due to the diminishing constraint by the hydroxyl groups on the PVA surface. Our study not only provides a new strategy on experimentally controlling HIN efficiency but also gives another perspective in understanding the mechanism of ice nucleation, i.e., the phase change efficiency of ‘bulk-like’ interfacial water of a film is a predictor for the HIN efficiency of that film

JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning

Dialogue policy learning (DPL) is a crucial component of dialogue modelling. Its primary role is to determine the appropriate abstract response, commonly referred to as the "dialogue action". Traditional DPL methodologies have treated this as a sequential decision problem, using pre-defined action candidates extracted from a corpus. However, these incomplete candidates can significantly limit the diversity of responses and pose challenges when dealing with edge cases, which are scenarios that occur only at extreme operating parameters. To address these limitations, we introduce a novel framework, JoTR. This framework is unique as it leverages a text-to-text Transformer-based model to generate flexible dialogue actions. Unlike traditional methods, JoTR formulates a word-level policy that allows for a more dynamic and adaptable dialogue action generation, without the need for any action templates. This setting enhances the diversity of responses and improves the system's ability to handle edge cases effectively. In addition, JoTR employs reinforcement learning with a reward-shaping mechanism to efficiently finetune the word-level dialogue policy, which allows the model to learn from its interactions, improving its performance over time. We conducted an extensive evaluation of JoTR to assess its effectiveness. Our extensive evaluation shows that JoTR achieves state-of-the-art performance on two benchmark dialogue modelling tasks, as assessed by both user simulators and human evaluators.Comment: Our code, models and other related resources are publicly available at https://github.com/KwanWaiChung/JoT

Optimal trade-in strategy for advance selling with strategic consumers proportion

Purpose This study aimed to optimize the trade-in pricing strategy. To leverage market share, many sellers adopt trade-in strategy for advance selling, Customers can return their old products at a discount price when they buy new products. This can help increase the market share and decrease natural resource consumption. Design/Methodology/Approach We consider a seller who sells new-generation products over two periods: advance selling and regular selling. Based on the rational expectation equilibrium, we adopt dynamic programming to construct a two-period pricing model with three different trade-in strategies–only in period 2, in both periods, and not at all–explaining the trade-in strategy as a promotion tool used by a monopolist to discriminate for advance selling between new and old customers. Findings The results suggest that the optimal price is determined by the proportion of old customers, discount factor and product innovation level. Whether and when to give a trade-in rebate to old customers depends on these parameters. The seller’s choice of optimal trade-in strategy depends on the threshold value of the new customer demand and trade-in demand. Originality/Value Most existing literature focuses on advance selling strategies and trade-in strategies. To the best of our knowledge, this is a pioneering study that adopts trade-in as part of the advance selling strategy

Orientation-Shared Convolution Representation for CT Metal Artifact Learning

During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment. Against this metal artifact reduction (MAR) task, the existing deep-learning-based methods have gained promising reconstruction performance. Nevertheless, there is still some room for further improvement of MAR performance and generalization ability, since some important prior knowledge underlying this specific task has not been fully exploited. Hereby, in this paper, we carefully analyze the characteristics of metal artifacts and propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts, i.e., rotationally symmetrical streaking patterns. The proposed method rationally adopts Fourier-series-expansion-based filter parametrization in artifact modeling, which can better separate artifacts from anatomical tissues and boost the model generalizability. Comprehensive experiments executed on synthesized and clinical datasets show the superiority of our method in detail preservation beyond the current representative MAR methods. Code will be available at \url{https://github.com/hongwang01/OSCNet