PEGA: Personality-Guided Preference Aggregator for Ephemeral Group Recommendation

Recently, making recommendations for ephemeral groups which contain dynamic users and few historic interactions have received an increasing number of attention. The main challenge of ephemeral group recommender is how to aggregate individual preferences to represent the group's overall preference. Score aggregation and preference aggregation are two commonly-used methods that adopt hand-craft predefined strategies and data-driven strategies, respectively. However, they neglect to take into account the importance of the individual inherent factors such as personality in the group. In addition, they fail to work well due to a small number of interactive records. To address these issues, we propose a Personality-Guided Preference Aggregator (PEGA) for ephemeral group recommendation. Concretely, we first adopt hyper-rectangle to define the concept of Group Personality. We then use the personality attention mechanism to aggregate group preferences. The role of personality in our approach is twofold: (1) To estimate individual users' importance in a group and provide explainability; (2) to alleviate the data sparsity issue that occurred in ephemeral groups. The experimental results demonstrate that our model significantly outperforms the state-of-the-art methods w.r.t. the score of both Recall and NDCG on Amazon and Yelp datasets

Cascaded Detail-Preserving Networks for Super-Resolution of Document Images

The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the low-resolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image super-resolution. Our model is composed by the Detail-Preserving Networks with small magnification. The loss function with perceptual terms is designed to simultaneously preserve the original patterns and enhance the edge of the characters. These networks are trained with the same architecture and different parameters and then assembled into a pipeline model with a larger magnification. The low-resolution images can upscale gradually by passing through each Detail-Preserving Network until the final high-resolution images. Through extensive experiments on two scanning document image datasets, we demonstrate that the proposed approach outperforms recent state-of-the-art image super-resolution methods, and combining it with standard OCR system lead to signification improvements on the recognition results

DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

Sequential recommendations have made great strides in accurately predicting the future behavior of users. However, seeking accuracy alone may bring side effects such as unfair and overspecialized recommendation results. In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity. On the one hand, it aims to provide fairer recommendations whose preference distributions are consistent with users' historical behaviors. On the other hand, it can improve the diversity of recommendations to a certain degree. But existing methods for calibration have mainly relied on the post-processing on the candidate lists, which require more computation time in generating recommendations. In addition, they fail to establish the relationship between accuracy and calibration, leading to the limitation of accuracy. To handle these problems, we propose an end-to-end framework to provide both accurate and calibrated recommendations for sequential recommendation. We design an objective function to calibrate the interests between recommendation lists and historical behaviors. We also provide distribution modification approaches to improve the diversity and mitigate the effect of imbalanced interests. In addition, we design a decoupled-aggregated model to improve the recommendation. The framework assigns two objectives to two individual sequence encoders, and aggregates the outputs by extracting useful information. Experiments on benchmark datasets validate the effectiveness of our proposed model

Imitation with Spatial-Temporal Heatmap: 2nd Place Solution for NuPlan Challenge

This paper presents our 2nd place solution for the NuPlan Challenge 2023. Autonomous driving in real-world scenarios is highly complex and uncertain. Achieving safe planning in the complex multimodal scenarios is a highly challenging task. Our approach, Imitation with Spatial-Temporal Heatmap, adopts the learning form of behavior cloning, innovatively predicts the future multimodal states with a heatmap representation, and uses trajectory refinement techniques to ensure final safety. The experiment shows that our method effectively balances the vehicle's progress and safety, generating safe and comfortable trajectories. In the NuPlan competition, we achieved the second highest overall score, while obtained the best scores in the ego progress and comfort metrics

Antitumor activity of celastrol nanoparticles in a xenograft retinoblastoma tumor model

Zhanrong Li,1,* Xianghua Wu,1,* Jingguo Li,2 Lin Yao,1 Limei Sun,1 Yingying Shi,1 Wenxin Zhang,1 Jianxian Lin,1 Dan Liang,1 Yongping Li1 1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, 2School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou, People's Republic of China*These authors contributed equally to this workBackground: Celastrol, a Chinese herbal medicine, has shown antitumor activity against various tumor cell lines. However, the effect of celastrol on retinoblastoma has not yet been analyzed. Additionally, the poor water solubility of celastrol restricts further therapeutic applications. The goal of this study was to evaluate the effect of celastrol nanoparticles (CNPs) on retinoblastoma and to investigate the potential mechanisms involved.Methods: Celastrol-loaded poly(ethylene glycol)-block-poly(ε-caprolactone) nanopolymeric micelles were developed to improve the hydrophilicity of celastrol. The 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulf-ophenyl)-2H tetrazolium monosodium salt (WST-8) assay was used to determine the inhibitory effect of CNPs on SO-Rb 50 cell proliferation in vitro. Immunofluorescence was used to evaluate the apoptotic effect of CNPs on nuclear morphology, and flow cytometry was used to quantify cellular apoptosis. The expression of Bcl-2, Bax, NF-κB p65, and phospo-NF-κB p65 proteins was assessed by Western blotting. A human retinoblastoma xenograft model was used to evaluate the inhibitory effects of CNPs on retinoblastoma in NOD-SCID mice. Hematoxylin and eosin staining was used to assess the apoptotic effects of CNPs on retinoblastoma.Results: CNPs inhibit the proliferation of SO-Rb 50 cells in a dose- and time-dependent manner with an IC50 of 17.733 µg/mL (celastrol-loading content: 7.36%) after exposure to CNPs for 48 hours. CNPs induce apoptosis in SO-Rb 50 cells in a dose-dependent manner. The expression of Bcl-2, NF-κB p65, and phospo-NF-κB p65 proteins decreased after exposure to CNPs 54.4 µg/mL for 48 hours. Additionally, the Bax/Bcl-2 ratio increased, whereas the expression of Bax itself was not significantly altered. CNPs inhibit the growth of retinoblastoma and induce apoptosis in retinoblastoma cells in mice.Conclusion: CNPs inhibit the growth of retinoblastoma in mouse xenograft model by inducing apoptosis in SO-Rb 50 cells, which may be related to the increased Bax/Bcl-2 ratio and the inhibition of NF-κB. CNPs may represent a potential alternative treatment for retinoblastoma.Keywords: apoptosis, SO-Rb 50 cells, poly(ethylene glycol)-block-poly(ε-caprolactone), nanopolymeric micelles, celastrol nanoparticles&nbsp

Experimental demonstration of high sensitivity refractive index sensing based on magnetic plasmons in a simple metallic deep nanogroove array

A high-performance wide-angle refractive index sensor based on a simple one-dimensional (1D) metallic deep nanogroove array with a high aspect ratio is experimentally fabricated and demonstrated. The 1D deep groove array is featured by the excitation of magnetic plasmon (MP), referring to an effective coupling of incident electromagnetic waves with a strong magnetic response induced inside the deep grooves. Utilizing the MP resonances that are extremely sensitive to the surrounding dielectric medium, we successfully achieve a refractive index sensitivity (RIS) up to ∼1300 nm/RIU, which is higher than that of most experimentally designed plasmonic sensors in the infrared region. Importantly, benefiting from angle-independent MP resonances with strong confinement of the magnetic field inside the deep grooves and strong electric field localization at the groove openings, we demonstrate wide-angle sensing capability valid in a broadband infrared region with an excellent linear dependence on the change of refractive index. Such a MP-based sensor, together with its simple 1D flat nature and ease of fabrication, has great potential for the practical design of high sensitive, cost-effective and compact sensing devices