A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest

Large Language Models (LLMs), despite their great power in language generation, often encounter challenges when dealing with intricate and knowledge-demanding queries in specific domains. This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources, and the adaptive training of a chatbot with domain-specific inquiries. Our two-step approach starts from training a knowledge miner, namely LLMiner, which autonomously extracts Question-Answer pairs from relevant documents through a chain-of-thought reasoning process. Subsequently, we blend the mined QA pairs with a conversational dataset to fine-tune the LLM as a chatbot, thereby enriching its domain-specific expertise and conversational capabilities. We also developed a new evaluation benchmark which comprises four domain-specific text corpora and associated human-crafted QA pairs for testing. Our model shows remarkable performance improvement over generally aligned LLM and surpasses domain-adapted models directly fine-tuned on domain corpus. In particular, LLMiner achieves this with minimal human intervention, requiring only 600 seed instances, thereby providing a pathway towards self-improvement of LLMs through model-synthesized training data.Comment: Work in progres

Human Amniotic Fluid Stem Cell-Derived Exosomes as a Novel Cell-Free Therapy for Cutaneous Regeneration

Adult wound healing often results in fibrotic scarring that is caused by myofibroblast aggregation. Human amniotic fluid stem cells (hAFSCs) exhibit significantly anti-fibrotic scarring properties during wound healing. However, it is little known whether hAFSCs directly or indirectly (paracrine) contribute to this process. Using the full-thickness skin-wounded rats, we investigated the therapeutic potential of hAFSC-derived exosomes (hAFSC-exo). Our results showed that hAFSC-exo accelerated the wound healing rate and improved the regeneration of hair follicles, nerves, and vessels, as well as increased proliferation of cutaneous cells and the natural distribution of collagen during wound healing. Additionally, hAFSC-exo suppressed the excessive aggregation of myofibroblasts and the extracellular matrix. We identified several miRNAs, including let-7-5p, miR-22-3p, miR-27a-3p, miR-21-5p, and miR-23a-3p, that were presented in hAFSC-exo. The functional analysis demonstrated that these hAFSC-exo-miRNAs contribute to the inhibition of the transforming growth factor-β (TGF-β) signaling pathway by targeting the TGF-β receptor type I (TGF-βR1) and TGF-β receptor type II (TGF-βR2). The reduction of TGF-βR1 and TGF-βR2 expression induced by hAFSC-exo was also confirmed in the healing tissue. Finally, using mimics of miRNAs, we found that hAFSC-exo-miRNAs were essential for myofibroblast suppression during the TGF-β1-induced human dermal fibroblast-to-myofibroblast transition in vitro. In summary, this study is the first to show that exosomal miRNAs used in hAFSC-based therapy inhibit myofibroblast differentiation. Our study suggests that hAFSC-exo may represent a strategic tool for suppressing fibrotic scarring during wound healing

A comparative study of GNN and MLP based machine learning for the diagnosis of Alzheimer’s Disease involving data synthesis

Alzheimer’s Disease (AD) is a neurodegenerative disease that commonly occurs in older people. It is characterized by both cognitive and functional impairment. However, as AD has an unclear pathological cause, it can be hard to diagnose with confidence. This is even more so in the early stage of Mild Cognitive Impairment (MCI). This paper proposes a U-Net based Generative Adversarial Network (GAN) to synthesize fluorodeoxyglucose -positron emission tomography (FDG-PET) from magnetic resonance imaging - T1 weighted imaging (MRI-T1WI) for further usage in AD diagnosis including its early-stage MCI. The experiments have displayed promising results with Structural Similarity Index Measure (SSIM) reaching 0.9714. Furthermore, three types of classifiers are developed, i.e., one Multi-Layer Perceptron (MLP) based classifier, two Graph Neural Network (GNN) based classifiers where one is for graph classification and the other is for node classification. 10-fold cross-validation has been conducted on all trials of experiments for classifier comparison. The performance of these three types of classifiers has been compared with the different input modalities setting and data fusion strategies. The results have shown that GNN based node classifier surpasses the other two types of classifiers, and has achieved the state-of-the-art (SOTA) performance with the best accuracy at 90.18% for 3-class classification, namely AD, MCI and normal control (NC) with the synthesized fluorodeoxyglucose - positron emission tomography (FDG-PET) features fused at the input level. Moreover, involving synthesized FDG-PET as part of the input with proper data fusion strategies has also proved to enhance all three types of classifiers’ performance. This work provides support for the notion that machine learning-derived image analysis may be a useful approach to improving the diagnosis of AD

MCR-ALS-based muscle synergy extraction method combined with LSTM neural network for motion intention detection

IntroductionThe time-varying and individual variability of surface electromyographic signals (sEMG) can lead to poorer motor intention detection results from different subjects and longer temporal intervals between training and testing datasets. The consistency of using muscle synergy between the same tasks may be beneficial to improve the detection accuracy over long time ranges. However, the conventional muscle synergy extraction methods, such as non-negative matrix factorization (NMF) and principal component analysis (PCA) have some limitations in the field of motor intention detection, especially in the continuous estimation of upper limb joint angles.MethodsIn this study, we proposed a reliable multivariate curve-resolved-alternating least squares (MCR-ALS) muscle synergy extraction method combined with long-short term memory neural network (LSTM) to estimate continuous elbow joint motion by using the sEMG datasets from different subjects and different days. The pre-processed sEMG signals were then decomposed into muscle synergies by MCR-ALS, NMF and PCA methods, and the decomposed muscle activation matrices were used as sEMG features. The sEMG features and elbow joint angular signals were input to LSTM to establish a neural network model. Finally, the established neural network models were tested by using sEMG dataset from different subjects and different days, and the detection accuracy was measured by correlation coefficient.ResultsThe detection accuracy of elbow joint angle was more than 85% by using the proposed method. This result was significantly higher than the detection accuracies obtained by using NMF and PCA methods. The results showed that the proposed method can improve the accuracy of motor intention detection results from different subjects and different acquisition timepoints.DiscussionThis study successfully improves the robustness of sEMG signals in neural network applications using an innovative muscle synergy extraction method. It contributes to the application of human physiological signals in human-machine interaction

Research and Development of Palmprint Authentication System Based on Android Smartphones

The security problems of online transactions by smartphones reveal extreme demand for reliable identity authentication systems. With a lower risk of forgery, richer texture, and more comfortable acquisition mode, compared with face, fingerprint, and iris, palmprint is rarely adopted for identity authentication. In this paper, we develop an effective and full-function palmprint authentication system regarding the application on an Android smartphone, which bridges the algorithmic study and application of palmprint authentication. In more detail, an overall system framework is designed with complete functions, including palmprint acquisition, key points location, ROI segmentation, feature extraction, and feature coding. Basically, we develop a palmprint authentication system having user-friendly interfaces and good compatibility with the Android smartphone. Particularly, on the one hand, to guarantee the effectiveness and efficiency of the system, we exploit the practical Log-Gabor filter for feature extraction and discuss the impact of filtering direction, downsampling ratio, and discriminative feature coding to propose an improved algorithm. On the other hand, after exploring the hardware components of the smartphone and the technical development of the Android system, we provide an open technology to extend the biometric methods to real-world applications. On the public PolyU databases, simulation results suggest that the improved algorithm outperforms the original one with a promising accuracy of 100% and a good speed of 0.041 seconds. In real-world authentication, the developed system achieves an accuracy of 98.40% and a speed of 0.051 seconds. All the results verify the accuracy and timeliness of the developed system

Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition

Palmprint recognition has been widely studied for security applications. However, there is a lack of in-depth investigations on robust palmprint recognition. Regression analysis being intuitively interpretable on robustness design inspires us to propose a correntropy-induced discriminative nonnegative sparse coding method for robust palmprint recognition. Specifically, we combine the correntropy metric and l1-norm to present a powerful error estimator that gains flexibility and robustness to various contaminations by cooperatively detecting and correcting errors. Furthermore, we equip the error estimator with a tailored discriminative nonnegative sparse regularizer to extract significant nonnegative features. We manage to explore an analytical optimization approach regarding this unified scheme and figure out a novel efficient method to address the challenging non-negative constraint. Finally, the proposed coding method is extended for robust multispectral palmprint recognition. Namely, we develop a constrained particle swarm optimizer to search for the feasible parameters to fuse the extracted robust features of different spectrums. Extensive experimental results on both contactless and contact-based multispectral palmprint databases verify the flexibility and robustness of our methods

The Construction of Ecological Security Patterns in Coastal Areas Based on Landscape Ecological Risk Assessment—A Case Study of Jiaodong Peninsula, China

Increasing land utilization, population aggregation and strong land–sea interaction make coastal areas an ecologically fragile environment. The construction of an ecological security pattern is important for maintaining the function of the coastal ecosystem. This paper takes Jiaodong Peninsula in China, a hilly coastal area, as an example for evaluating landscape ecological risk within a comprehensive framework of “nature–neighborhood–landscape”, based on spatial principal component analysis, and it constructs the ecological security pattern based on the minimum cumulative resistance model (MCR). The results showed that the overall level of ecological risk in the study area was medium. The connectivity between the areas of low landscape ecological risk was relatively low, and the high risk areas were concentrated in the north of the Peninsula. A total of 11 key ecological corridors of three types (water, green space and road corridors) and 105 potential corridors were constructed. According to the ecological network pattern, landscape ecological optimization suggestions were proposed: key corridors in the north and south of Jiaodong Peninsula should be connected; urban development should consider current ecological sources and corridors to prevent landscape fragmentation; and the ecological roles of potential corridors should be strengthened. This paper can provide a theoretical and practical basis for ecological planning and urban master planning in coastal areas in the future

Analysis of the impact of traction power supply system containing new energy on the power quality of the power system

The access of new energy in the traction power supply system (TPSS) can not only realize low-carbon operation of electrified railroads in the western region, but also promote elimination on the spot of new energy. However, it also brings a series of power quality problems to the power system. The objective of this study was to analyze the impact of TPSS containing new energy on the power quality of the power system. Firstly, a probabilistic model of TPSS containing new energy is established by means of the randomness of locomotive and new energy output power; Secondly, it is analyzed theoretically that the impact of new energy access to the TPSS on the voltage imbalance, power factor and voltage deviation of power system; Finally, Monte Carlo simulation is applied to calculate the probabilistic load flow to obtain the evaluation index, which is used to quantify the impact of new energy access on the power system quality. The results show that the matching degree of new energy output power and load power has a significant impact on the voltage imbalance, power factor and voltage deviation of the power system, while the access location has a smaller impact on the above indicators

Sensorless force estimation of end-effect upper limb rehabilitation robot system with friction compensation

Sensorless force estimation of end-effect upper limb rehabilitation robot system with friction compensatio