Unlearn What You Want to Forget: Efficient Unlearning for LLMs

Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data, however, this process might suffer from privacy issues and violations of data protection regulations. As a result, the ability to easily remove data related to individual users from such models while not deteriorating their predictive quality after the removal becomes increasingly important. To address these issues, in this work, we propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data removals, by introducing lightweight unlearning layers learned with a selective teacher-student objective into the transformers. In addition, we introduce a fusion mechanism to effectively combine different unlearning layers that learns to forget different sets of data to handle a sequence of forgetting operations. Experiments on classification and generation tasks demonstrate the effectiveness of our proposed methods compared to the state-of-the-art baselines.Comment: EMNLP 202

Vulnerability of hydropower installations to climate change : preliminary study

The climate trends observed worldwide over the past few decades appear to corroborate the concerns of the scientific community about the many threats posed by global warming. Future changes of the current climate are expected to occur on different scales all around the globe, hence modifying the environmental background on the basis of which technological installations have been designed and operated. This can potentially threat the safety of the installations as well as their. The development of suitable tools aiming to predict the impact of climate change on technological installations is then essential in the wider context of climate change mitigation. Hydropower installations play often a crucial role not only as a long-term renewable resource of energy but also for flood control and water supply in the case of droughts. All these aspects highlight the increasing importance of such installations as well as their growing vulnerability to natural hazards. It is hence essential to enlarge the current understanding of the interaction mechanisms between such installations and the changing surrounding environment in order to take adequate measures for climate change adaptation and ensure the future safety and productivity of hydropower production. The current study aims to provide a novel model for the evaluation of the impact of climate change on the safety of hydropower stations. The approach adopted allows to include in the model the uncertainty inevitably associated with the input variables and to propagate such uncertainty within the analysis. The model proposed is finally applied to a realistic case-study in order to highlight its potential and limitations

Dynamic evolution of a hydraulic-mechanical-electric system with randomly fluctuating speed based on Chebyshev polynomial approximation method

The research proposed in this paper focuses on the dynamic evolution of a hydraulic-mechanical-electric system under the effect of randomly fluctuating speed. The rapid growth of installed wind power capacity may potentially affect the stability of power grids, causing larger fluctuations of the generator speed to hydropower stations. In this work, a probabilistic component is associated to the generator speed of a deterministic hydraulic-mechanical-electric system providing a novel random model. This latter is analyzed to investigate the dynamic evolution of the system adopting the Chebyshev polynomial approximation method. A careful comparison of the numerical application results obtained by the deterministic and the probabilistic approaches is carried out. In addition, the influence of the fluctuation intensity (D) on the differential gain (kd) of the PID is investigated, proposing a law for kd as function of D. Finally, the operating ranges of the grid water hammer and of the elastic water hammer models are compared in order to validate the consistence of the law. The results of the study provide robust bases for the stable and safe operation of hydropower stations.National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesNorthwest A&F Universit

Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis

Vibration signal of rotating machinery is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. In order to improve the denoising effect of the vibration signal, an adaptive redundant second-generation wavelet (ARSGW) denoising method is proposed. In this method, a new index for denoising result evaluation (IDRE) is constructed first. Then, the maximum value of IDRE and the genetic algorithm are taken as the optimization objective and the optimization algorithm, respectively, to search for the optimal parameters of the ARSGW. The obtained optimal redundant second-generation wavelet (RSGW) is used for vibration signal denoising. After that, features are extracted from the denoised signal and then input into the support vector machine method for fault recognition. The application result indicates that the proposed ARSGW denoising method can effectively improve the accuracy of rotating machinery fault diagnosis

Experimental and numerical investigation on the influence of the clocking position on hydraulic performance of a centrifugal pump with guide vane

The investigation of the clocking effect mainly concentrates on turbines and compressors, but seldom in centrifugal pumps. In this paper, using numerical simulation and experiment, the influence of the clocking effect on the hydraulic performance of centrifugal pump with guide vane is studied. Numerical simulations with SST k-w turbulence model were applied to obtain the inner flow field of the test pump. The numerical simulations coincide with the test result, which indicates the accurate of the utilized numerical approach. The results show the clocking positions have an important effect on hydraulic performance of the centrifugal pump with guide vane. The pump demonstrates the higher efficiency and head as the tongue locate between two guide vanes. The hydraulic performance of the volute is a major factor impacting the performance of the centrifugal pump with different clocking positions. However, the clocking position has almost no effect on the performances of the impeller and diffuser. When the guide vane is close to the volute tongue, flow field of volute is more non-uniform, and the energy loss in volute appears to be larger. The results and the method of this paper can provide theoretical reference for the design and installation of guide vane in centrifugal pump

A Cheaper and Better Diffusion Language Model with Soft-Masked Noise

Diffusion models that are based on iterative denoising have been recently proposed and leveraged in various generation tasks like image generation. Whereas, as a way inherently built for continuous data, existing diffusion models still have some limitations in modeling discrete data, e.g., languages. For example, the generally used Gaussian noise can not handle the discrete corruption well, and the objectives in continuous spaces fail to be stable for textual data in the diffusion process especially when the dimension is high. To alleviate these issues, we introduce a novel diffusion model for language modeling, Masked-Diffuse LM, with lower training cost and better performances, inspired by linguistic features in languages. Specifically, we design a linguistic-informed forward process which adds corruptions to the text through strategically soft-masking to better noise the textual data. Also, we directly predict the categorical distribution with cross-entropy loss function in every diffusion step to connect the continuous space and discrete space in a more efficient and straightforward way. Through experiments on 5 controlled generation tasks, we demonstrate that our Masked-Diffuse LM can achieve better generation quality than the state-of-the-art diffusion models with better efficiency.Comment: Code is available at https://github.com/amazon-science/masked-diffusion-l

Dynamic safety assessment of a nonlinear pumped-storage generating system in a transient process

This paper focuses on a pumped-storage generating system with a reversible Francis turbine and presents an innovative framework for safety assessment in an attempt to overcome their limitations. Thus the aim is to analyze the dynamic safety process and risk probability of the above nonlinear generating system. This study is carried out based on an existing pumped-storage power station. In this paper we show the dynamic safety evaluation process and risk probability of the nonlinear generating system using Fisher discriminant method. A comparison analysis for the safety assessment is performed between two different closing laws, namely the separate mode only to include a guide vane and the linkage mode that includes a guide vane and a ball valve. We find that the most unfavorable condition of the generating system occurs in the final stage of the load rejection transient process. It is also demonstrated that there is no risk to the generating system with the linkage mode but the risk probability of the separate mode is 6 percent. The results obtained are in good agreement with the actual operation of hydropower stations. The developed framework may not only be adopted for the applications of the pumped-storage generating system with a reversible Francis turbine but serves as the basis for the safety assessment of various engineering applications.National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesScientific research funds of Northwest A&F UniversityScience Fund for Excellent Young Scholars from Northwest A&F University and Shaanxi Nova progra