Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery. However, molecule optimization, a critical task in the drug discovery pipeline, is currently an area that has seen little involvement from LLMs. Most of existing approaches focus solely on capturing the underlying patterns in chemical structures provided by the data, without taking advantage of expert feedback. These non-interactive approaches overlook the fact that the drug discovery process is actually one that requires the integration of expert experience and iterative refinement. To address this gap, we propose DrugAssist, an interactive molecule optimization model which performs optimization through human-machine dialogue by leveraging LLM's strong interactivity and generalizability. DrugAssist has achieved leading results in both single and multiple property optimization, simultaneously showcasing immense potential in transferability and iterative optimization. In addition, we publicly release a large instruction-based dataset called 'MolOpt-Instructions' for fine-tuning language models on molecule optimization tasks. We have made our code and data publicly available at https://github.com/blazerye/DrugAssist, which we hope to pave the way for future research in LLMs' application for drug discovery.

Reconfigurable Intelligent Surfaces (RISs) and Non-Orthogonal Multiple Access (NOMA) can enhance Visible Light Communication (VLC) systems by mitigating signal blockage and improving spectrum utilization. While boosting communication efficiency is crucial, maintaining high illumination quality is equally important. This paper investigates a novel approach to simultaneously improving the sum rate (SR) and illumination uniformity (IU) in a RIS-assisted NOMA-based VLC system. Communication and illumination optimization problems are formulated as a non-convex mixed-integer non-linear programming problem, considering RIS placement, LED-user association, and power allocation. To the best of our knowledge, this is the first work to jointly optimize SR and IU with explicit consideration of RIS placement. We propose a joint optimization approach that leverages a differential evolution algorithm to optimize RIS placement. During each iteration, the obtained solutions are further refined using a block coordinate descent algorithm, which iteratively solves the decomposed sub-problems of LED-user association and power allocation. Simulation results show that the approach outperforms existing methods in both SR and IU. Moreover, RIS placement optimization is shown to be crucial, as neglecting it significantly degrades performance. Finally, the impacts of noise power and total LED power are analyzed, offering practical insights for parameter selection in VLC systems.

As an effective global optimization technique, artificial bee colony (ABC) algorithm has become one of the hottest research topics in the fields of evolutionary algorithms. However, the solution search equation is not rotationally invariant, which causes the problem that the performance of ABC is sensitive to the coordinate system. Although many improved ABC variants have been developed, they rarely considered the problem. Hence, to solve the problem, we propose a new ABC variant with bi-coordinate systems (BSABC), including the original coordinate system and the eigen coordinate system. The two coordinate systems own different characteristics: (1) the former one aims to maintain the population diversity, and (2) the latter one is to adapt the search to the fitness landscape of the problems. Based on the characteristics, in the BSABC, the two coordinate systems are used in the employed bee phase and onlooker bee phase, respectively. Meanwhile, two new solution search equations are designed by utilizing the elite information, and they are respectively performed in the two coordinate systems to further improve the algorithm performance. As another contribution of this study, in the scout bee phase, the multivariate Gaussian distribution is constructed to replace the original method to generate offspring, which is helpful to save the search experience. The performance of the BSABC is verified by extensive experiments on the CEC2013 test suite and one real-world optimization problem, and four well-established ABC variants and three other evolutionary algorithms are included in the performance comparison. The comparison results confirm that the BSABC shows competitive performance by achieving better results on the majority of test functions.

Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years.Despite of the promising progress achieved by recent studies in this field, persona information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the persona can be involved via End-to-End learning.This paper proposes to adopt the personalityrelated characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, so as to guide the optimization towards the direction of generating both persona-aware and relevant responses.Besides, to reasonably evaluate the performances of various persona modeling approaches, this paper further presents three direct persona-oriented metrics from different perspectives.The experimental results have shown that our proposed methodology can notably improve the performance of persona-aware response generation, and the metrics are reasonable to evaluate the results.

Cartoon-to-photo facial translation could be widely used in different applications, such as law enforcement and anime remaking. Nevertheless, current general-purpose image-to-image models \ygyan{usually} %can only produce blurry or unrelated results in this task. In this paper, we propose a Cartoon-to-Photo facial translation with Generative Adversarial Networks (\name) for inverting cartoon faces to generate photo-realistic and related face images. In order to produce convincing faces with intact facial parts, we exploit global and local discriminators to capture global facial features and three local facial regions, respectively. Moreover, we use a specific content network to capture and preserve face characteristic and identity between cartoons and photos. As a result, the proposed approach can generate convincing high-quality faces that satisfy both the characteristic and identity constraints of input cartoon faces. Compared with recent works on unpaired image-to-image translation, our proposed method is able to generate more realistic and correlative images.