On the Art of Stick Pictures

Stick picture is a unique form of conveying ideas or feelings by using points, lines and planes. With a few strokes, objects depicted can be vividly displayed in front of us. On the basis of concise brushstrokes, it can convey the most incisive aesthetic taste and emotional experience, concise but not simple. It can not only present the expressive and impressionistic aesthetic characteristics but also express the intuitive and interesting aesthetic experience, which is the charm of concision

Structural diversity-guided optimization of carbazole derivatives as potential cytotoxic agents

Carbazole alkaloids, as an important class of natural products, have been widely reported to have extensive biological activities. Based on our previous three-component reaction to construct carbazole scaffolds, we introduced a methylene group to provide a rotatable bond, and designed series of carbazole derivatives with structural diversity including carbazole amide, carbazole hydrazide and carbazole hydrazone. All synthesized carbazole derivatives were evaluated for their in vitro cytotoxic activity against 7901 (gastric adenocarcinoma), A875 (human melanoma) and MARC145 (African green monkey kidney) cell lines. The preliminary results indicated that compound 14a exhibited high inhibitory activities on 7901 and A875 cancer cells with the lowest IC50 of 11.8 ± 1.26 and 9.77 ± 8.32 μM, respectively, which might be the new lead compound for discovery of novel carbazole-type anticancer agents

Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations

Inverse reinforcement learning (IRL) aims to explicitly infer an underlying reward function based on collected expert demonstrations. Considering that obtaining expert demonstrations can be costly, the focus of current IRL techniques is on learning a better-than-demonstrator policy using a reward function derived from sub-optimal demonstrations. However, existing IRL algorithms primarily tackle the challenge of trajectory ranking ambiguity when learning the reward function. They overlook the crucial role of considering the degree of difference between trajectories in terms of their returns, which is essential for further removing reward ambiguity. Additionally, it is important to note that the reward of a single transition is heavily influenced by the context information within the trajectory. To address these issues, we introduce the Distance-rank Aware Sequential Reward Learning (DRASRL) framework. Unlike existing approaches, DRASRL takes into account both the ranking of trajectories and the degrees of dissimilarity between them to collaboratively eliminate reward ambiguity when learning a sequence of contextually informed reward signals. Specifically, we leverage the distance between policies, from which the trajectories are generated, as a measure to quantify the degree of differences between traces. This distance-aware information is then used to infer embeddings in the representation space for reward learning, employing the contrastive learning technique. Meanwhile, we integrate the pairwise ranking loss function to incorporate ranking information into the latent features. Moreover, we resort to the Transformer architecture to capture the contextual dependencies within the trajectories in the latent space, leading to more accurate reward estimation. Through extensive experimentation, our DRASRL framework demonstrates significant performance improvements over previous SOTA methods

Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions

Towards intelligent Human-Vehicle Interaction systems and innovative Human-Vehicle Interaction designs, in-vehicle drivers' physiological data has been explored as an essential data source. However, equipping multiple biosensors is considered the limited extent of user-friendliness and impractical during the driving procedure. The lack of a proper approach to access physiological data has hindered wider applications of advanced biosignal-driven designs in practice (e.g. monitoring systems and etc.). Hence, the demand for a user-friendly approach to measuring drivers' body statuses has become more intense. In this Work-In-Progress, we present Face2Multi-modal, an In-vehicle multi-modal Data Streams Predictors through facial expressions only. More specifically, we have explored the estimations of Heart Rate, Skin Conductance, and Vehicle Speed of the drivers. We believe Face2Multi-modal provides a user-friendly alternative to acquiring drivers' physiological status and vehicle status, which could serve as the building block for many current or future personalized Human-Vehicle Interaction designs. More details and updates about the project Face2Multi-modal is online at https://github.com/unnc-ucc/Face2Multimodal/