397 research outputs found
Investigation of the effects of temperature and ions on the interaction between ECG and BSA by the fluorescence quenching method
The effects of temperature and common ions on binding (-)-epicatechin gallate (ECG) to bovine serum albumin (BSA) are investigated. The binding constants (Ka) between ECG and BSA are 1.20 Ч 106 (17°C), 1.38 Ч 106 (27°C), and 5.69 x 106 L mol-1 (37°C), and the number of binding sites (n) were 1.14, 1.15, and 1.26, respectively. These results showed that the increasing temperature improves the stability of the ECG-BSA system, which results in a higher binding constant and the number of binding sites of the ECG-BSA system. The presence of Co2+ and Zn2+ ions decreased the binding constants (Ka) and the number of binding sites (n) of ECG-BSA complex. However, the presence of Cu2+ and Ni2+ increased the affinity of ECG for BSA largely. The positive ΔH and positive ΔS indicated that hydrophobic forces might play a major role in the binding between ECG and BSA
毛发宏扫描全息分析系统
The existing disease diagnosis techniques are more or less harmful to the human body, so a simple and non-invasive disease screening technology is urgently needed. This paper introduces a hair scanning technique for the diagnosis of diseases
毛发宏扫描全息分析系统
The existing disease diagnosis techniques are more or less harmful to the human body, so a simple and non-invasive disease screening technology is urgently needed. This paper introduces a hair scanning technique for the diagnosis of diseases
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent
Large Language Models (LLMs) have demonstrated a remarkable ability to
generalize zero-shot to various language-related tasks. This paper focuses on
the study of exploring generative LLMs such as ChatGPT and GPT-4 for relevance
ranking in Information Retrieval (IR). Surprisingly, our experiments reveal
that properly instructed ChatGPT and GPT-4 can deliver competitive, even
superior results than supervised methods on popular IR benchmarks. Notably,
GPT-4 outperforms the fully fine-tuned monoT5-3B on MS MARCO by an average of
2.7 nDCG on TREC datasets, an average of 2.3 nDCG on eight BEIR datasets, and
an average of 2.7 nDCG on ten low-resource languages Mr.TyDi. Subsequently, we
delve into the potential for distilling the ranking capabilities of ChatGPT
into a specialized model. Our small specialized model that trained on 10K
ChatGPT generated data outperforms monoT5 trained on 400K annotated MS MARCO
data on BEIR. The code to reproduce our results is available at
www.github.com/sunnweiwei/RankGP
SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
Monocular 3D detectors achieve remarkable performance on cars and smaller
objects. However, their performance drops on larger objects, leading to fatal
accidents. Some attribute the failures to training data scarcity or their
receptive field requirements of large objects. In this paper, we highlight this
understudied problem of generalization to large objects. We find that modern
frontal detectors struggle to generalize to large objects even on nearly
balanced datasets. We argue that the cause of failure is the sensitivity of
depth regression losses to noise of larger objects. To bridge this gap, we
comprehensively investigate regression and dice losses, examining their
robustness under varying error levels and object sizes. We mathematically prove
that the dice loss leads to superior noise-robustness and model convergence for
large objects compared to regression losses for a simplified case. Leveraging
our theoretical insights, we propose SeaBird (Segmentation in Bird's View) as
the first step towards generalizing to large objects. SeaBird effectively
integrates BEV segmentation on foreground objects for 3D detection, with the
segmentation head trained with the dice loss. SeaBird achieves SoTA results on
the KITTI-360 leaderboard and improves existing detectors on the nuScenes
leaderboard, particularly for large objects. Code and models at
https://github.com/abhi1kumar/SeaBirdComment: CVPR 202
Serial Dependence in Dermatological Judgments
This research was funded by the National Institutes of Health (NIH) grant number R01CA236793.Peer reviewedPublisher PD
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