AutoHint: Automatic Prompt Optimization with Hint Generation

This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM). While LLMs have demonstrated remarkable ability in achieving high-quality annotation in various tasks, the key to applying this ability to specific tasks lies in developing high-quality prompts. Thus we propose a framework to inherit the merits of both in-context learning and zero-shot learning by incorporating enriched instructions derived from input-output demonstrations to optimize original prompt. We refer to the enrichment as the hint and propose a framework to automatically generate the hint from labeled data. More concretely, starting from an initial prompt, our method first instructs a LLM to deduce new hints for selected samples from incorrect predictions, and then summarizes from per-sample hints and adds the results back to the initial prompt to form a new, enriched instruction. The proposed method is evaluated on the BIG-Bench Instruction Induction dataset for both zero-shot and few-short prompts, where experiments demonstrate our method is able to significantly boost accuracy for multiple tasks

One Amino Acid Change of Angiotensin II Diminishes Its Effects on Abdominal Aortic Aneurysm

Angiotensin (Ang) A is formed by the decarboxylation of the N terminal residue of AngII. The present study determined whether this one amino acid change impacted effects of AngII on abdominal aortic aneurysm (AAA) formation in mice. Computational analyses implicated that AngA had comparable binding affinity to both AngII type 1 and 2 receptors as AngII. To compare effects of these two octapeptides in vivo, male low-density lipoprotein receptor (Ldlr) or apolipoprotein E (Apoe) deficient mice were infused with either AngII or AngA (1 μg/kg/min) for 4 weeks. While AngII infusion induced AAA consistently in both mouse strains, the equivalent infusion rate of AngA did not lead to AAA formation. We also determined whether co-infusion of AngA would influence AngII-induced aortic aneurysm formation in male Apoe−/− mice. Co-infusion of the same infusion rate of AngII and AngA did not change AngII-induced AAA formation. Since it was reported that a 10-fold higher concentration of AngA elicited comparable vasoconstrictive responses as AngII, we compared a 10-fold higher rate (10 μg/kg/min) of AngA infusion into male Apoe−/− mice with AngII (1 μg/kg/min). This rate of AngA led to abdominal aortic dilation in three of ten mice, but no aortic rupture, whereas the 10-fold lower rate of AngII infusion led to abdominal aortic dilation or rupture in eight of ten mice. In conclusion, AngA, despite only being one amino acid different from AngII, has diminished effects on aortic aneurysmal formation, implicating that the first amino acid of AngII has important pathophysiological functions

TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data

Anomaly detection on time series data has been successfully used in power grid operation and maintenance, flow detection, fault diagnosis, and other applications. However, anomalies in time series often lack strict definitions and labels, and existing methods often suffer from the need for rigid hypotheses, the inability to handle high-dimensional data, and highly time-consuming calculation costs. Generative Adversarial Networks (GANs) can learn the distribution pattern of normal data, detecting anomalies by comparing the reconstructed normal data with the original data. However, it is difficult for GANs to extract contextual information from time series data. In this paper, we propose a new method, Transformer-based GAN for Anomaly Detection of Time Series Data (TGAN-AD), The transformer-based generators of TGAN-AD can extract contextual features of time series data to prompt the performance. TGAN-AD’s discriminator can also assist in determining abnormal data. Anomaly scores are calculated through both the generator and the discriminator. We have conducted comprehensive experiments on three public datasets. Experimental results show that our TGAN-AD has better performance in anomaly detection than the state-of-the-art anomaly detection techniques, with the highest Recall and F1 values on all datasets. Our experiments also demonstrate the high efficiency of the model and the optimal choice of hyperparameters

TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data

Anomaly detection on time series data has been successfully used in power grid operation and maintenance, flow detection, fault diagnosis, and other applications. However, anomalies in time series often lack strict definitions and labels, and existing methods often suffer from the need for rigid hypotheses, the inability to handle high-dimensional data, and highly time-consuming calculation costs. Generative Adversarial Networks (GANs) can learn the distribution pattern of normal data, detecting anomalies by comparing the reconstructed normal data with the original data. However, it is difficult for GANs to extract contextual information from time series data. In this paper, we propose a new method, Transformer-based GAN for Anomaly Detection of Time Series Data (TGAN-AD), The transformer-based generators of TGAN-AD can extract contextual features of time series data to prompt the performance. TGAN-AD’s discriminator can also assist in determining abnormal data. Anomaly scores are calculated through both the generator and the discriminator. We have conducted comprehensive experiments on three public datasets. Experimental results show that our TGAN-AD has better performance in anomaly detection than the state-of-the-art anomaly detection techniques, with the highest Recall and F1 values on all datasets. Our experiments also demonstrate the high efficiency of the model and the optimal choice of hyperparameters

Preconditioning via angiotensin type 2 receptor activation improves therapeutic efficacy of bone marrow mononuclear cells for cardiac repair.

BACKGROUND: The therapeutic efficiency of bone marrow mononuclear cells (BMMNCs) autologous transplantation for myocardial infarction (MI) remains low. Here we developed a novel strategy to improve cardiac repair by preconditioning BMMNCs via angiotensin II type 2 receptor (AT2R) stimulation. METHODS AND RESULTS: Acute MI in rats led to a significant increase of AT2R expression in BMMNCs. Preconditioning of BMMNCs via AT2R stimulation directly with an AT2R agonist CGP42112A or indirectly with angiotensin II plus AT1R antagonist valsartan led to ERK activation and increased eNOS expression as well as subsequent nitric oxide generation, ultimately improved cardiomyocyte protection in vitro as measured by co-culture approach. Intramyocardial transplantation of BMMNCs preconditioned via AT2R stimulation improved survival of transplanted cells in ischemic region of heart tissue and reduced cardiomyocyte apoptosis and inflammation at 3 days after MI. At 4 weeks after transplantation, compared to DMEM and non-preconditioned BMMNCs group, AT2R stimulated BMMNCs group showed enhanced vessel density in peri-infarct region and attenuated infarct size, leading to global heart function improvement. CONCLUSIONS: Preconditioning of BMMNCs via AT2R stimulation exerts protective effect against MI. Stimulation of AT2R in BMMNCs may provide a new strategy to improving therapeutic efficiency of stem cells for post MI cardiac repair