Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning

While enabling large language models to implement function calling (known as APIs) can greatly enhance the performance of LLMs, function calling is still a challenging task due to the complicated relations between different APIs, especially in a context-learning setting without fine-tuning. This paper proposes a simple yet controllable target-driven approach called Reverse Chain to empower LLMs with capabilities to use external APIs with only prompts. Given that most open-source LLMs have limited tool-use or tool-plan capabilities, LLMs in Reverse Chain are only employed to implement simple tasks, e.g., API selection and argument completion, and a generic rule is employed to implement a controllable multiple functions calling. In this generic rule, after selecting a final API to handle a given task via LLMs, we first ask LLMs to fill the required arguments from user query and context. Some missing arguments could be further completed by letting LLMs select another API based on API description before asking user. This process continues until a given task is completed. Extensive numerical experiments indicate an impressive capability of Reverse Chain on implementing multiple function calling. Interestingly enough, the experiments also reveal that tool-use capabilities of the existing LLMs, e.g., ChatGPT, can be greatly improved via Reverse Chain

Text detection and recognition based on a lensless imaging system

Lensless cameras are characterized by several advantages (e.g., miniaturization, ease of manufacture, and low cost) as compared with conventional cameras. However, they have not been extensively employed due to their poor image clarity and low image resolution, especially for tasks that have high requirements on image quality and details such as text detection and text recognition. To address the problem, a framework of deep-learning-based pipeline structure was built to recognize text with three steps from raw data captured by employing lensless cameras. This pipeline structure consisted of the lensless imaging model U-Net, the text detection model connectionist text proposal network (CTPN), and the text recognition model convolutional recurrent neural network (CRNN). Compared with the method focusing only on image reconstruction, UNet in the pipeline was able to supplement the imaging details by enhancing factors related to character categories in the reconstruction process, so the textual information can be more effectively detected and recognized by CTPN and CRNN with fewer artifacts and high-clarity reconstructed lensless images. By performing experiments on datasets of different complexities, the applicability to text detection and recognition on lensless cameras was verified. This study reasonably demonstrates text detection and recognition tasks in the lensless camera system,and develops a basic method for novel applications

Chinese Medicinal Herbs in Relieving Perimenopausal Depression: A Randomized, Controlled Trial

Abstract Objective: To explore the effects of GengNianLe (GNL, also called perimenopausal depression relieving formula), a defined formula of Chinese medicinal herbs in relieving perimenopausal depression in Chinese women. Methods : Between September 2004 and April 2008, 47 Chinese women were randomized into a GNL group (n ϭ 21) and a control group which received tibolone (n ϭ 26) using a randomization chart. Depression was rated with the 24-item Hamilton Depression Scale (HAMD). The serum levels of follicle stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E 2 ) were detected before and after the treatment. Results: After 12 weeks of treatment, HAMD scores in both groups decreased significantly (p Ͻ 0.05) with no significant difference between the groups (p Ͼ 0.05). The levels of FSH decreased significantly and the level of E 2 increased significantly in both groups, and they changed more in the control group. No side-effect of treatment was reported in either group during treatment. Conclusions: The Chinese medicinal formula GNL showed promise in relieving perimenopausal depression and merits further study. 9

Evolution of the Groundwater Flow System since the Last Glacial Maximum in the Aksu River Basin (Northwest China)

Thoroughly investigating the evolution of groundwater circulation and its controlling mechanism in the Aksu River Basin, where human activities are intensifying and the groundwater environment is increasingly deteriorating, is highly urgent and important for promoting the theory, development and implementation of groundwater flow systems (GFSs) and protecting groundwater resources. Based on a detailed analysis of the sediment grain size distribution, chronology, electrofacies, glacial sedimentary sequence, palaeoclimate indicators and existing groundwater age, this paper systematically reconstructs the palaeosedimentary environment of the basin-scale aquifer system in the study area and scientifically reveals the evolutionary pattern and formation mechanism of the GFS. The results showed that the later period of the late Pleistocene experienced a rapid downcutting erosional event caused by tectonic uplift, and the sedimentary environment transitioned from a dry–cold deep downcutting environment in the Last Glacial Maximum (LGM) to a coarse-grained fast-filling fluvial facies sedimentary environment in the Last Glacial Deglaciation (LDP) as the temperature rose; then, it shifted to an environment of fine-grained stable alternating accumulation of fluvial facies and lacustrine facies that was dominated by the warm and arid conditions of the Holocene megathermal period (HMP); this process changed the previous river base level via erosion, glacier elongation or shortening and river level, thus resulting in a complex coupling relationship between the palaeosedimentary environment, palaeoclimate and basin GFS. Furthermore, the existing GFS pattern in the basin exhibits a vertically unconformable groundwater age distribution, which indicates that it is the outcome of the complex superposition of groundwater flow controlled by the palaeosedimentary environment in different periods. Therefore, neotectonic movement and climate fluctuation have jointly acted on the variation in the river level, resulting in the “seesaw” effect, thereby fundamentally controlling the strength of the driving force of groundwater and resulting in the gradual evolution of the GFS from the fully developed regional GFS pattern during the LGM to the current multihierarchy nested GFS pattern