Reinforcement learning for personalized dialogue management

Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalize the dialogue, e.g. take the personal context of users into account. These works, however, are limited to personalization to a single user with whom they require multiple interactions and do not generalize the usage of context across users. This work introduces a problem where a generalized usage of context is relevant and proposes two Reinforcement Learning (RL)-based approaches to this problem. The first approach uses a single learner and extends the traditional POMDP formulation of dialogue state with features that describe the user context. The second approach segments users by context and then employs a learner per context. We compare these approaches in a benchmark of existing non-RL and RL-based methods in three established and one novel application domain of financial product recommendation. We compare the influence of context and training experiences on performance and find that learning approaches generally outperform a handcrafted gold standard

Highly Efficient Visible-Light-Driven Schottky Catalyst MoN/2D g‑C₃N₄ for Hydrogen Production and Organic Pollutants Degradation

Charge separation efficiency is vital both in photocatalytic hydrogen production and pollutants degradation, which can be enhanced by loading cocatalysts. Unfortunately, the vast majority of high active and stable cocatalysts is noble-metal (such as platinum), which greatly impedes the commercialization of photocatalysis technology. In this work, we reported a non-noble-metal Schottky catalyst MoN/2D g-C₃N₄ based on metal–semiconductor junction principles. MoN can serve as the acceptor and transporter of photogenerated electrons. For photocatalytic performance, the best one achieved much higher efficiency for hydrogen (H₂) generation (265.1 times) and Rh B degradation (1.4 times) over bare g-C₃N₄ due to the improved charge separation and transportation. The advantages of MoN can be summarized as (i) non-noble-metal; (ii) superior conductivity; and (iii) abundant adsorption and active sites

Tibial cortex transverse transport promotes ischemic diabetic foot ulcer healing via enhanced angiogenesis and inflammation modulation in a novel rat model

Abstract Background Tibial Cortex Transverse Transport (TTT) represents an innovative surgical method for treating lower extremity diabetic foot ulcers (DFUs), yet its underlying mechanisms remain elusive. Establishing an animal model that closely mirrors clinical scenarios is both critical and novel for elucidating the mechanisms of TTT. Methods We established a diabetic rat model with induced hindlimb ischemia to mimic the clinical manifestation of DFUs. TTT was applied using an external fixator for regulated bone movement. Treatment efficacy was evaluated through wound healing assessments, histological analyses, and immunohistochemical techniques to elucidate biological processes. Results The TTT group demonstrated expedited wound healing, improved skin tissue regeneration, and diminished inflammation relative to controls. Marked neovascularization and upregulation of angiogenic factors were observed, with the HIF-1α/SDF-1/CXCR4 pathway and an increase in EPCs being pivotal in these processes. A transition toward anti-inflammatory M2 macrophages indicated TTT's immunomodulatory capacity. Conclusion Our innovative rat model effectively demonstrates the therapeutic potential of TTT in treating DFUs. We identified TTT's roles in promoting angiogenesis and modulating the immune system. This paves the way for further in-depth research and potential clinical applications to improve DFU management strategies