Business Process Text Sketch Automation Generation Using Large Language Model

Business Process Management (BPM) is gaining increasing attention as it has the potential to cut costs while boosting output and quality. Business process document generation is a crucial stage in BPM. However, due to a shortage of datasets, data-driven deep learning techniques struggle to deliver the expected results. We propose an approach to transform Conditional Process Trees (CPTs) into Business Process Text Sketches (BPTSs) using Large Language Models (LLMs). The traditional prompting approach (Few-shot In-Context Learning) tries to get the correct answer in one go, and it can find the pattern of transforming simple CPTs into BPTSs, but for close-domain and CPTs with complex hierarchy, the traditional prompts perform weakly and with low correctness. We suggest using this technique to break down a difficult CPT into a number of basic CPTs and then solve each one in turn, drawing inspiration from the divide-and-conquer strategy. We chose 100 process trees with depths ranging from 2 to 5 at random, as well as CPTs with many nodes, many degrees of selection, and cyclic nesting. Experiments show that our method can achieve a correct rate of 93.42%, which is 45.17% better than traditional prompting methods. Our proposed method provides a solution for business process document generation in the absence of datasets, and secondly, it becomes potentially possible to provide a large number of datasets for the process model extraction (PME) domain.Comment: 10 pages, 7 figure

PEGA: Personality-Guided Preference Aggregator for Ephemeral Group Recommendation

Recently, making recommendations for ephemeral groups which contain dynamic users and few historic interactions have received an increasing number of attention. The main challenge of ephemeral group recommender is how to aggregate individual preferences to represent the group's overall preference. Score aggregation and preference aggregation are two commonly-used methods that adopt hand-craft predefined strategies and data-driven strategies, respectively. However, they neglect to take into account the importance of the individual inherent factors such as personality in the group. In addition, they fail to work well due to a small number of interactive records. To address these issues, we propose a Personality-Guided Preference Aggregator (PEGA) for ephemeral group recommendation. Concretely, we first adopt hyper-rectangle to define the concept of Group Personality. We then use the personality attention mechanism to aggregate group preferences. The role of personality in our approach is twofold: (1) To estimate individual users' importance in a group and provide explainability; (2) to alleviate the data sparsity issue that occurred in ephemeral groups. The experimental results demonstrate that our model significantly outperforms the state-of-the-art methods w.r.t. the score of both Recall and NDCG on Amazon and Yelp datasets

Microcirculatory changes identified by photoacoustic microscopy in patients with complex regional pain syndrome type I after stellate ganglion blocks

Complex regional pain syndrome (CRPS) is a chronic pain syndrome that causes intractable pain, disability, and poor quality of life for patients. The etiology and pathophysiology of CRPS are still poorly understood. Due to a lack of proper diagnostic tools, the prognosis of CRPS is primarily based on clinical observation. The objective of this work is to evaluate a new imaging modality, photoacoustic microscopy (PAM), for assisting diagnoses and monitoring the progress and treatment outcome of CRPS. Blood vasculature and oxygen saturation (sO_2) were imaged by PAM from eight adult patients with CRPS-1. Patients’ hands and cuticles were imaged both before and after stellate ganglion block (SGB) for comparison. For all patients, both vascular structure and sO_2 could be assessed by PAM. In addition, more vessels and stronger signals were observed after SGB. The results show that PAM can help diagnose and monitor CRPS

Pseudogap, Superconducting Energy Scale, and Fermi Arcs in Underdoped Cuprate Superconductors

Through the measurements of magnetic field dependence of specific heat in $La_{2-x}Sr_xCuO_4$ in zero temperature limit, we determined the nodal slope $v_\Delta$ of the quasiparticle gap. It is found that $v_\Delta$ has a very similar doping dependence of the pseudogap temperature $T^*$ or value $\Delta_p$. Meanwhile the virtual maximum gap at ($\pi,0$) derived from $v_\Delta$ is found to follow the simple relation $\Delta_q=0.46k_BT^*$ upon changing the doping concentration. This strongly suggests a close relationship between the pseudogap and superconductivity. It is further found that the superconducting transition temperature is determined by both the residual density of states of the pseudogap phase and the nodal gap slope in the zero temperature limit, namely, $T_c \approx \beta v_\Delta \gamma_n(0)$, where $\gamma_n(0)$ is the extracted zero temperature value of the normal state specific heat coefficient which is proportional to the size of the residual Fermi arc $k_{arc}$. This manifests that the superconductivity may be formed by forming a new gap on the Fermi arcs near nodes below $T_c$. These observations mimic the key predictions of the SU(2) slave boson theory based on the general resonating-valence-bond (RVB) picture.Comment: 6 pages, 6 figures, to be published in Phys. Rev.

Cascaded Detail-Preserving Networks for Super-Resolution of Document Images

The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the low-resolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image super-resolution. Our model is composed by the Detail-Preserving Networks with small magnification. The loss function with perceptual terms is designed to simultaneously preserve the original patterns and enhance the edge of the characters. These networks are trained with the same architecture and different parameters and then assembled into a pipeline model with a larger magnification. The low-resolution images can upscale gradually by passing through each Detail-Preserving Network until the final high-resolution images. Through extensive experiments on two scanning document image datasets, we demonstrate that the proposed approach outperforms recent state-of-the-art image super-resolution methods, and combining it with standard OCR system lead to signification improvements on the recognition results