EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models

Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to the outdated/noisy data. To this end, many knowledge editing approaches for LLMs have emerged -- aiming to subtly inject/edit updated knowledge or adjust undesired behavior while minimizing the impact on unrelated inputs. Nevertheless, due to significant differences among various knowledge editing methods and the variations in task setups, there is no standard implementation framework available for the community, which hinders practitioners to apply knowledge editing to applications. To address these issues, we propose EasyEdit, an easy-to-use knowledge editing framework for LLMs. It supports various cutting-edge knowledge editing approaches and can be readily apply to many well-known LLMs such as T5, GPT-J, LlaMA, etc. Empirically, we report the knowledge editing results on LlaMA-2 with EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning in terms of reliability and generalization. We have released the source code on GitHub at https://github.com/zjunlp/EasyEdit, along with Google Colab tutorials and comprehensive documentation for beginners to get started. Besides, we present an online system for real-time knowledge editing, and a demo video at http://knowlm.zjukg.cn/easyedit.mp4.Comment: The project website is https://github.com/zjunlp/EasyEdi

CFD analysis and optimization of axial flow fans

The axial fan plays a vital role in the safe production of the mine, and in this paper, a mine axial flow ventilator is designed through numerical simulation to meet the demand of air exchange inside and outside of the mine, so as to maintain the oxygen supply of the mine and discharge the harmful gases. Finite element analysis of four structural factors of axial fan blade installation angle, number of blades, deflector plate, rotational speed, drawing fan wind pressure and rotational speed cloud diagram, calculation of axial power, by analyzing the distribution of the cloud diagram to design the shape of the fan blade, and derive the change rule of the wind pressure when changing the structure of the fan. By using gradient descent method to control the percentage of imported mass flow rate, the P-Q performance curve of the fan is obtained, which optimizes its aerodynamic performance, improves efficiency, and extends its service life

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients

High modulation efficiency electro-optic modulator: Material and Design

Data explosion generates huge data traffic within the data center. The bottleneck of the data transportation is the optical transceiver. We study the critical component in the optical transceiver- optical modulator. The Figure-of-Merit (FOM), 3dB bandwidth/ V_π defines the modulation efficiency. In this dissertation, two approaches will be presented: 1. Utilizing GaN nano-pillars as electro optic material. GaN nano-pillars exhibits high second harmonic susceptibility, χ^((2)), which is proportional to the electro-optic coefficient. 2. A new design of the optical modulator: nano-slot conductive waveguide directional coupler modulator (DCM). The DCM modulation performance evaluation is based on Beam Prop mode simulation analysis. Using the 50Ω terminated lumped-element to calculate the 3dB bandwidth, the DCM shows a FOM of 202 GHz/V where the state-of-the-art silicon-organic hybrid (SOH) Mach-Zehnder modulator (MZM) is 150.3 GHz/V. The advantage of applying DCM as the modulator platform for high EO coefficient polymer is not only the DCM has higher modulation FOM, but also the ease of the modulator design and fabrication. The DCM does not require two phase shifters, Si waveguide to polymer slot waveguide couplers, and multi-mode interferometer (MMI). To prove the principle of the strong cross-coupling characteristic in DCM, we design and fabricate an air-slot, non-slab DCM, which includes the input/output mode size converter, adiabatic S-bend, and the air-slot waveguides. By scanning the central wavelength of the incident light, the output power collecting from each of the two ports exhibits wavelength dependence. The extinction ratio is ~10 dB and the peak-to-valley separation is ~1 nm. The measurement result matches with the Beam Prop simulation

Lithium-ion batteries fault diagnostic for electric vehicles using sample entropy analysis method

Fault detection plays a vital role in the operation of lithium-ion batteries in electric vehicles. Typically, during the operation of battery systems, voltage signals are susceptible to noise interference. In this paper, a novel fault detection method based on the Empirical Mode Decomposition and Sample Entropy is proposed to identify battery faults under various operating conditions. Firstly, effective fault features are extracted through the proposed Empirical Mode Decomposition method by decomposing battery voltage signals and removing the noise interference during the voltage sampling process. Experiments are conducted to quantitatively illustrate the fault features extracted by the Empirical Mode Decomposition. Then, based on these extracted fault features, the Sample Entropy values are calculated to help accurately detect and locate the battery faults. Moreover, an evaluation strategy of the detected faults is designed to indicate the battery fault level. Finally, the effectiveness of the proposed approach is verified against real-world data measured from electric vehicles in the presence of regular and sudden faults

Mesoscopic Failure Behavior of Strip Footing on Geosynthetic-Reinforced Granular Soil Foundations Using PIV Technology

Two-dimensional model tests combined with PIV technology were conducted to study the failure behavior of strip footing on geosynthetic-reinforced granular soil foundations on a mesoscale. The results showed that geosynthetic reinforcements improve the bearing capacity of granular soil foundations; however, the effectiveness of the reinforcement was affected by the position, length, and number of geosynthetics. The mesoscale factor affecting the reinforcement effectiveness was the size of the sliding wedge in the foundation, which was changed by the embedded geosynthetics. As the depth, length, number, and vertical spacing of the reinforcements varied, three possible failure modes occurred in the reinforced foundations: failure above the top reinforcement layer, failure between reinforcement layers, and failure similar to footings on the unreinforced foundation

Research Progress on Stability Analysis Methods of Granite Residual Soil Slope

Based on the instability and failure of granite residual soil slope, this paper will introduce the stability of granite residual soil slope from theoretical research, indoor and outdoor simulation tests, and numerical analysis. Based on the review of previous work and the latest research results, the stability analysis of granite residual soil slope is discussed, and the main influencing factors of granite residual soil slope instability are summarized. The main factors are: its own disintegration property; The degree of microcracks in its internal structure