A Novel Approach to Recovering Depth from Defocus

This paper proposes a novel approach to recovering depth from defocus, which is a deterministic approach in spatial domain. Two defocused gray images from the same scene are obtained by changing two parameters (image distance and focal length of camera) other than only parameter (image distance). The idea of this approach is to convert the gray images into the gradient images by Canny operator other than Sobel operator, then calculate the ratio of the area of region with large gradient value to that of the whole image region in each block for each defocused image by moment-preserving method, and recover depth from scene according to the ratio of the ratio of one gradient image to that of the other gradient image. The experimental results show that the proposed approach is more accurate and efficient than the traditional approach

MathAttack: Attacking Large Language Models Towards Math Solving Ability

With the boom of Large Language Models (LLMs), the research of solving Math Word Problem (MWP) has recently made great progress. However, there are few studies to examine the security of LLMs in math solving ability. Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of security in solving math problems. Compared to traditional text adversarial attack, it is essential to preserve the mathematical logic of original MWPs during the attacking. To this end, we propose logical entity recognition to identify logical entries which are then frozen. Subsequently, the remaining text are attacked by adopting a word-level attacker. Furthermore, we propose a new dataset RobustMath to evaluate the robustness of LLMs in math solving ability. Extensive experiments on our RobustMath and two another math benchmark datasets GSM8K and MultiAirth show that MathAttack could effectively attack the math solving ability of LLMs. In the experiments, we observe that (1) Our adversarial samples from higher-accuracy LLMs are also effective for attacking LLMs with lower accuracy (e.g., transfer from larger to smaller-size LLMs, or from few-shot to zero-shot prompts); (2) Complex MWPs (such as more solving steps, longer text, more numbers) are more vulnerable to attack; (3) We can improve the robustness of LLMs by using our adversarial samples in few-shot prompts. Finally, we hope our practice and observation can serve as an important attempt towards enhancing the robustness of LLMs in math solving ability. We will release our code and dataset.Comment: 11 pages, 6 figure

Free energy landscape for the binding process of Huperzine A to acetylcholinesterase

Drug-target residence time (t = 1/koff, where koff is the dissociation rate constant) has become an important index in discovering betteror best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, koff and activation free energy of dissociation (ΔG≠ off). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated.We applied this method to simulate the binding event of the anti-Alzheimer’s disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/ mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect thismethodology to be widely applicable to drug discovery and development

Ligand recognition and G-protein coupling selectivity of cholecystokinin A receptor.

Cholecystokinin A receptor (CCKAR) belongs to family A G-protein-coupled receptors and regulates nutrient homeostasis upon stimulation by cholecystokinin (CCK). It is an attractive drug target for gastrointestinal and metabolic diseases. One distinguishing feature of CCKAR is its ability to interact with a sulfated ligand and to couple with divergent G-protein subtypes, including Gs, Gi and Gq. However, the basis for G-protein coupling promiscuity and ligand recognition by CCKAR remains unknown. Here, we present three cryo-electron microscopy structures of sulfated CCK-8-activated CCKAR in complex with Gs, Gi and Gq heterotrimers, respectively. CCKAR presents a similar conformation in the three structures, whereas conformational differences in the 'wavy hook' of the Gα subunits and ICL3 of the receptor serve as determinants in G-protein coupling selectivity. Our findings provide a framework for understanding G-protein coupling promiscuity by CCKAR and uncover the mechanism of receptor recognition by sulfated CCK-8

Estimation of Terrestrial Net Primary Productivity in the Yellow River Basin of China Using Light Use Efficiency Model

The net primary productivity (NPP) of vegetation is an essential factor of ecosystem functions, including the biological geochemical carbon cycle, which is often impacted by climate change and human activities. It plays a significant role in comprehending the nature of carbon balance in an ecosystem and demonstrates the global and regional carbon cycle dynamics. The present study used an upgraded CASA model to calculate the NPP in the Yellow River Basin (YRB), China. The model’s simulation ability was improved by changing the model parameters. Further, the CASA model was validated by comparing with MODIS-NPP and in situ observed NPP, wherein the accuracy of the CASA model estimation was found satisfactory to estimate NPP changes in the study area. The simulated results of the improved CASA model showed that the mean annual NPP value of vegetation in the YRB was 283.4 gC m–2 a–1 from 2001 to 2020, with a declining trend in spatial distribution from south to north. In contrast, the NPP appeared as an increasing trend in the YRB temporally from 212 gC m–2 a–1 in 2001 to 342 gC m–2 a–1 in 2020, with a mean annual growth rate of 4.6 gC m–2 a–1. The total NPP in the YRB increased by 40,088.3 GgC between 2001 and 2020, from 226.06 TgC to 266.15 TgC. This rise can be attributed to the increase in forests. The average grassland area has reduced by 4651 km2 during the last two decades, significantly impacting the total NPP of grasslands. Although the increase in NPP in wetlands was minimal, accounting for 815.53 GgC, the highest change percentage of 79.78%, could be observed among the six vegetation types due to the anthropogenic influences and climate change. The conditions favorable for vegetation growth and a sustained environment were enhanced by the increased precipitation and temperature and the reinforced ecological protection by the government