Identification of potential sialic acid binding proteins on cell membranes by proximity chemical labeling.

The cell membrane contains a highly interactive glycan surface on a scaffold of proteins and lipids. Sialic acids are negatively charged monosaccharides, and the proteins that bind to sialic acids play an important role in maintaining the integrity and collective functions of this interactive space. Sialic acid binding proteins are not readily identified and have nearly all been discovered empirically. In this research, we developed a proximity labeling method to characterize proteins with oxidation by localized radicals produced in situ. The sites of oxidation were identified and quantified using a standard proteomic workflow. In this method, a clickable probe was synthesized and attached to modified sialic acids on the cell membrane, which functioned as a catalyst for the localized formation of radicals from hydrogen peroxide. The proteins in the sialic acid environment were labeled through amino acid oxidation, and were categorized into three groups including sialylated proteins, non-sialylated proteins with transmembrane domains, and proteins that are associated with the membrane with neither sialylated nor transmembrane domains. The analysis of the last group of proteins showed that they were associated with binding functions including carbohydrate binding, anion binding, and cation binding, thereby revealing the nature of the sialic acid-protein interaction. This new tool identified potential sialic acid-binding proteins in the extracellular space and proteins that were organized around sialylated glycans in cells

An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text Generation

In the study, we empirically compare the two recently proposed decoding methods, i.e. Contrastive Search (CS) and Contrastive Decoding (CD), for open-ended text generation. The automatic evaluation results suggest that, while CS performs worse than CD on the MAUVE metric, it substantially surpasses CD on the diversity and coherence metrics. More notably, extensive human evaluations across three different domains demonstrate that human annotators are universally more in favor of CS over CD with substantial margins. The contradicted results between MAUVE and human evaluations reveal that MAUVE does not accurately reflect human preferences. Therefore, we call upon the research community to develop better evaluation metrics for open-ended text generation. To ensure the reproducibility of our work, we have open-sourced all our code, evaluation results, as well as human annotations at https://github.com/yxuansu/Contrastive_Search_versus_Contrastive_Decoding.Comment: Technical report with 9 pages, 5 tables, and 6 figure

A new variant of the Erd\H{o}s-Gy\'{a}rf\'{a}s problem on K5K_{5}

Motivated by an extremal problem on graph-codes that links coding theory and graph theory, Alon recently proposed a question aiming to find the smallest number $t$ such that there is an edge coloring of $K_{n}$ by $t$ colors with no copy of given graph $H$ in which every color appears an even number of times. When $H=K_{4}$, the question of whether $n^{o(1)}$ colors are enough, was initially emphasized by Alon. Through modifications to the coloring functions originally designed by Mubayi, and Conlon, Fox, Lee and Sudakov, the question of $K_{4}$ has already been addressed. Expanding on this line of inquiry, we further study this new variant of the generalized Ramsey problem and provide a conclusively affirmative answer to Alon's question concerning $K_{5}$.Comment: Note added: Heath and Zerbib also proved the result on $K_{5}$ independently. arXiv:2307.0131

ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition

Class imbalance is a common challenge in real-world recognition tasks, where the majority of classes have few samples, also known as tail classes. We address this challenge with the perspective of generalization and empirically find that the promising Sharpness-Aware Minimization (SAM) fails to address generalization issues under the class-imbalanced setting. Through investigating this specific type of task, we identify that its generalization bottleneck primarily lies in the severe overfitting for tail classes with limited training data. To overcome this bottleneck, we leverage class priors to restrict the generalization scope of the class-agnostic SAM and propose a class-aware smoothness optimization algorithm named Imbalanced-SAM (ImbSAM). With the guidance of class priors, our ImbSAM specifically improves generalization targeting tail classes. We also verify the efficacy of ImbSAM on two prototypical applications of class-imbalanced recognition: long-tailed classification and semi-supervised anomaly detection, where our ImbSAM demonstrates remarkable performance improvements for tail classes and anomaly. Our code implementation is available at https://github.com/cool-xuan/Imbalanced_SAM.Comment: Accepted by International Conference on Computer Vision (ICCV) 202

TDLE: 2-D LiDAR Exploration With Hierarchical Planning Using Regional Division

Exploration systems are critical for enhancing the autonomy of robots. Due to the unpredictability of the future planning space, existing methods either adopt an inefficient greedy strategy or require a lot of resources to obtain a global solution. In this work, we address the challenge of obtaining global exploration routes with minimal computing resources. A hierarchical planning framework dynamically divides the planning space into subregions and arranges their orders to provide global guidance for exploration. Indicators that are compatible with the subregion order are used to choose specific exploration targets, thereby considering estimates of spatial structure and extending the planning space to unknown regions. Extensive simulations and field tests demonstrate the efficacy of our method in comparison to existing 2D LiDAR-based approaches. Our code has been made public for further investigation.Comment: Accepted in IEEE International Conference on Automation Science and Engineering (CASE) 202

A One-Size-Fits-All Approach to Improving Randomness in Paper Assignment

The assignment of papers to reviewers is a crucial part of the peer review processes of large publication venues, where organizers (e.g., conference program chairs) rely on algorithms to perform automated paper assignment. As such, a major challenge for the organizers of these processes is to specify paper assignment algorithms that find appropriate assignments with respect to various desiderata. Although the main objective when choosing a good paper assignment is to maximize the expertise of each reviewer for their assigned papers, several other considerations make introducing randomization into the paper assignment desirable: robustness to malicious behavior, the ability to evaluate alternative paper assignments, reviewer diversity, and reviewer anonymity. However, it is unclear in what way one should randomize the paper assignment in order to best satisfy all of these considerations simultaneously. In this work, we present a practical, one-size-fits-all method for randomized paper assignment intended to perform well across different motivations for randomness. We show theoretically and experimentally that our method outperforms currently-deployed methods for randomized paper assignment on several intuitive randomness metrics, demonstrating that the randomized assignments produced by our method are general-purpose.Comment: 24 pages, 8 figures, 3 tables, neurips 2023 spotligh

Image classification of Chinese medicinal flowers based on convolutional neural network

Background and objective: Traditional Chinese medicine has used many herbs on the prevention and treatment of diseases for thousands of years. However, many flowers are poisonous and only few herbs have medicinal properties. Relying on experts for herbs identification is time consuming. An efficient and fast identification method is proposed in this study. Methods: This study proposes ResNet101 models by combining SENet and ResNet101, adding convolutional block attention module or using Bayesian optimization on Chinese medicinal flower classification. The performances of the proposed ResNet101 models were compared. Results: The best performance for accuracy, precision, recall, F1-score and PR-AUC are coming from ResNet101 model with Bayesian optimization which are 97.64%, 97.99%, 97.86%, 97.82% and 99.72%, respectively. Conclusions: The proposed ResNet101 model provides a better solution on the image classification of Chinese medical flowers with favourable accuracy