257 research outputs found
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
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
Linking Microbial Decomposition to Dissolved Organic Matter Composition in the Revegetation of the Red Soil Erosion Area
Studying the changes and linkages between dissolved organic matter (DOM) and microorganisms in soils during vegetation restoration will help to understand the role of vegetation restoration in soil carbon sequestration and thus improve the understanding of the global soil carbon cycle. Soil DOM molecules were characterized by Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and the results showed that the soil DOM consisted mainly of lignin/carboxylic rich alicyclic molecule (CRAM)-like structures, while the ratios of lipids and aliphatic/protein decreased in sequence with recovery time. Lipids and aliphatic/proteins with high H/C DOM (labile DOM) degrade preferentially, while lignin/CRAM-like structures and tannins with low H/C DOM (recalcitrant DOM) are recalcitrant during vegetation restoration. With the restoration of vegetation, DOM molecules tend to be diversified and complicated, and DOM compounds with low double bond equivalent (DBE), low aromatic, and low alkyl structures will be converted into persistent organic matter with high carbon numbers and high DBE. The diversity of soil microorganisms was determined by high-throughput sequencing. The results showed that the abundance and diversity of soil bacteria increased significantly after revegetation, while the abundance and diversity of soil fungi began to increase when the ecosystem became a more mature coniferous forest. The soil microbial community exhibited complex connectivity and strong interaction with DOM molecules during vegetation restoration. As most of the DOM molecules are recalcitrant, vegetation restoration facilitates C sequestration in the soil, thereby contributing to climate change mitigation
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