arXiv subjects

Jianguo Lu

Publications and source records attributed to Jianguo Lu.

7 recordsLinked to original sources

arXiv2025

Intrinsic static/dynamic triboelectric pressure sensor for continuous and event-triggered control

Conventional pressure sensors often integrate two distinct mechanisms to detect static and dynamic stimuli, hindering the development of high fidelity human-machine interfaces. Here, we present an intrinsic static/dynamic triboelectric sensor (iSD Sensor) capable of reliably perceiving both continuous static pressure and transient mechanical shocks through a DC/AC signal decoupling strategy. By pairing hydrophobic expanded polytetrafluoroethylene (ePTFE) with elastic conductive sponge, a pressure-adaptive triboelectric interface is formed, where microscale and large-scale separations enable static and dynamic pressure sensing, respectively. Furthermore, by employing a charge excitation strategy, the device delivers enhanced voltage outputs over 25X in static and 15X in dynamic modes. Combined with a 3D gradient conductive sponge structure, the sensor achieves multi-region sensitivities of 34.7 V/kPa (static) and 48.4 V/kPa (dynamic) under low pressure (less than 1.8 kPa), and a detection limit as low as 6.13 Pa. By perceiving continuous static pressure and transient shocks applied by the human hand, the iSD Sensor enables robotic arm control via proportional grasping and dynamic, trigger-based sign language communication. This work advances high-sensitivity, self-powered pressure sensors toward intelligent, closed-loop human-machine interaction.

cs.RO↗

arXiv2024

Two points are enough

Prognosis and diagnosis play an important role in accelerating the development of lithium-ion batteries, as well as reliable and long-life operation. In this work, we answer an important question: What is the minimum amount of data required to extract features for accurate battery prognosis and diagnosis? Based on the first principle, we successfully extracted the best two-point feature (BTPF) for accurate battery prognosis and diagnosis using the fewest data points (only two) and the simplest feature selection method (Pearson correlation coefficient). The BTPF extraction method is tested on 820 cells from 6 open-source datasets (covering five different chemistry types, seven manufacturers, and three data types). It achieves comparable accuracy to state-of-the-art features in both prognosis and diagnosis tasks. This work challenges the cognition of existing studies on the difficulty of battery prognosis and diagnosis tasks, subverts the fixed pattern of establishing prognosis and diagnosis methods for complex dynamic systems through deliberate feature engineering, highlights the promise of data-driven methods for field battery prognosis and diagnosis applications, and provides a new benchmark for future studies.

cond-mat.mtrl-sci↗

arXiv2024

Whitening Not Recommended for Classification Tasks in LLMs

Sentence embedding is a cornerstone in NLP. Whitening has been claimed to be an effective operation to improve embedding quality obtained from Large Language Models (LLMs). However, we find that the efficacy of whitening is model-dependent and task-dependent. In particular, whitening degenerates embeddings for classification tasks. The conclusion is supported by extensive experiments. We also explored a variety of whitening operations, including PCA, ZCA, PCA-Cor, ZCA-Cor and Cholesky whitenings. A by-product of our research is embedding evaluation platform for LLMs called SentEval+.

cs.CL↗

arXiv2024

Can Large Language Models Understand Molecules?

Purpose: Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer) from OpenAI and LLaMA (Large Language Model Meta AI) from Meta AI are increasingly recognized for their potential in the field of cheminformatics, particularly in understanding Simplified Molecular Input Line Entry System (SMILES), a standard method for representing chemical structures. These LLMs also have the ability to decode SMILES strings into vector representations. Method: We investigate the performance of GPT and LLaMA compared to pre-trained models on SMILES in embedding SMILES strings on downstream tasks, focusing on two key applications: molecular property prediction and drug-drug interaction prediction. Results: We find that SMILES embeddings generated using LLaMA outperform those from GPT in both molecular property and DDI prediction tasks. Notably, LLaMA-based SMILES embeddings show results comparable to pre-trained models on SMILES in molecular prediction tasks and outperform the pre-trained models for the DDI prediction tasks. Conclusion: The performance of LLMs in generating SMILES embeddings shows great potential for further investigation of these models for molecular embedding. We hope our study bridges the gap between LLMs and molecular embedding, motivating additional research into the potential of LLMs in the molecular representation field. GitHub: https://github.com/sshaghayeghs/LLaMA-VS-GPT

q-bio.BM↗

arXiv2020

PES: Priority Edge Sampling in Streaming Triangle Estimation

The number of triangles (hereafter denoted by $Δ$) is an important metric to analyze massive graphs. It is also used to compute clustering coefficient in networks. This paper proposes a new algorithm called PES (Priority Edge Sampling) to estimate the number of triangles in the streaming model where we need to minimize the memory window. PES combines edge sampling and reservoir sampling. Compared with the state-of-the-art streaming algorithms, PES outperforms consistently. The results are verified extensively in 48 large real-world networks in different domains and structures. The performance ratio can be as large as 11. More importantly, the ratio grows with data size almost exponentially. This is especially important in the era of big data--while we can tolerate existing algorithms for smaller datasets, our method is indispensable when sampling very large data. In addition to empirical comparisons, we also proved that the estimator is unbiased, and derived the variance.

cs.SI↗

arXiv2019

Dual-branch residual network for lung nodule segmentation

An accurate segmentation of lung nodules in computed tomography (CT) images is critical to lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the similarity of visual characteristics between nodules and their surroundings, a robust segmentation of nodules becomes a challenging problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet) which is a data-driven model. Our approach integrates two new schemes to improve the generalization capability of the model: 1) the proposed model can simultaneously capture multi-view and multi-scale features of different nodules in CT images; 2) we combine the features of the intensity and the convolution neural networks (CNN). We propose a pooling method, called the central intensity-pooling layer (CIP), to extract the intensity features of the center voxel of the block, and then use the CNN to obtain the convolutional features of the center voxel of the block. In addition, we designed a weighted sampling strategy based on the boundary of nodules for the selection of those voxels using the weighting score, to increase the accuracy of the model. The proposed method has been extensively evaluated on the LIDC dataset containing 986 nodules. Experimental results show that the DB-ResNet achieves superior segmentation performance with an average dice score of 82.74% on the dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison showed that our average dice score was 0.49% higher than that of human experts. This proves that our proposed method is as good as the experienced radiologist.

cs.CV↗

arXiv2018

The estimation of bias and variance in clustering coefficient streaming algorithms

Clustering coefficient is one of the most important metrics to understand the complex structure of networks. This paper addresses the estimation of clustering coefficient in network streams. There have been substantial work in this area, most of conducting empirical comparisons of various algorithms. The variance and the bias of the estimators have not been quantified. Starting with a simple yet powerful streaming algorithm, we derived the variance and bias for the estimator, and the estimators for the variances and bias. More importantly, we simplify the estimators so that it can be used in practice. The variance and bias estimators are verified extensively on 49 real networks.

cs.SI↗