448 research outputs found
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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
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 such that there is an edge coloring of by colors with no
copy of given graph in which every color appears an even number of times.
When , the question of whether 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 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 .Comment: Note added: Heath and Zerbib also proved the result on
independently. arXiv:2307.0131
Analyze the Robustness of Classifiers under Label Noise
This study explores the robustness of label noise classifiers, aiming to
enhance model resilience against noisy data in complex real-world scenarios.
Label noise in supervised learning, characterized by erroneous or imprecise
labels, significantly impairs model performance. This research focuses on the
increasingly pertinent issue of label noise's impact on practical applications.
Addressing the prevalent challenge of inaccurate training data labels, we
integrate adversarial machine learning (AML) and importance reweighting
techniques. Our approach involves employing convolutional neural networks (CNN)
as the foundational model, with an emphasis on parameter adjustment for
individual training samples. This strategy is designed to heighten the model's
focus on samples critically influencing performance.Comment: 21 pages, 11 figure
Non-Excludable Bilateral Trade Between Groups
Bilateral trade is one of the most natural and important forms of economic
interaction: A seller has a single, indivisible item for sale, and a buyer is
potentially interested. The two parties typically have different, privately
known valuations for the item, and ideally, they would like to trade if the
buyer values the item more than the seller. The celebrated impossibility result
by Myerson and Satterthwaite shows that any mechanism for this setting must
violate at least one important desideratum. In this paper, we investigate a
richer paradigm of bilateral trade, with many self-interested buyers and
sellers on both sides of a single trade who cannot be excluded from the trade.
We show that this allows for more positive results. In fact, we establish a
dichotomy in the possibility of trading efficiently. If in expectation, the
buyers value the item more, we can achieve efficiency in the limit. If this is
not the case, then efficiency cannot be achieved in general. En route, we
characterize trading mechanisms that encourage truth-telling, which may be of
independent interest. We also evaluate our trading mechanisms experimentally,
and the experiments align with our theoretical results.Comment: 14 pages, 2 figures, 1 table, aaai 202
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
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