68 research outputs found
A review of enhancement of biohydrogen productions by chemical addition using a supervised machine learning method
In this work, the impact of chemical additions, especially nanoāparticles (NPs), was quan-titatively analyzed using our constructed artificial neural networks (ANNs)āresponse surface methodology (RSM) algorithm. Feābased and Niābased NPs and ions, including Mg2+, Cu2+, Na+, NH4+, and K+, behave differently towards the response of hydrogen yield (HY) and hydrogen evolution rate (HER). Manipulating the size and concentration of NPs was found to be effective in enhancing the HY for Feābased NPs and ions, but not for Niābased NPs and ions. An optimal range of particle size (86ā120 nm) and Niāion/NP concentration (81ā120 mg Lā1) existed for HER. Meanwhile, the manipulation of the size and concentration of NPs was found to be ineffective for both iron and nickel for the improvement of HER. In fact, the variation in size of NPs for the enhancement of HY and HER demonstrated an appreciable difference. The smaller (less than 42 nm) NPs were found to definitely improve the HY, whereas for the HER, the relatively bigger size of NPs (40ā50 nm) seemed to significantly increase the H2 evolution rate. It was also found that the variations in the concentration of the investigated ions only statistically influenced the HER, not the HY. The level of response (the enhanced HER) towards inputs was underpinned and the order of significance towards HER was identified as the following: Na+ \u3e Mg2+ \u3e Cu2+ \u3e NH4+ \u3e K+
Fast Model Debias with Machine Unlearning
Recent discoveries have revealed that deep neural networks might behave in a
biased manner in many real-world scenarios. For instance, deep networks trained
on a large-scale face recognition dataset CelebA tend to predict blonde hair
for females and black hair for males. Such biases not only jeopardize the
robustness of models but also perpetuate and amplify social biases, which is
especially concerning for automated decision-making processes in healthcare,
recruitment, etc., as they could exacerbate unfair economic and social
inequalities among different groups. Existing debiasing methods suffer from
high costs in bias labeling or model re-training, while also exhibiting a
deficiency in terms of elucidating the origins of biases within the model. To
this respect, we propose a fast model debiasing framework (FMD) which offers an
efficient approach to identify, evaluate and remove biases inherent in trained
models. The FMD identifies biased attributes through an explicit counterfactual
concept and quantifies the influence of data samples with influence functions.
Moreover, we design a machine unlearning-based strategy to efficiently and
effectively remove the bias in a trained model with a small counterfactual
dataset. Experiments on the Colored MNIST, CelebA, and Adult Income datasets
along with experiments with large language models demonstrate that our method
achieves superior or competing accuracies compared with state-of-the-art
methods while attaining significantly fewer biases and requiring much less
debiasing cost. Notably, our method requires only a small external dataset and
updating a minimal amount of model parameters, without the requirement of
access to training data that may be too large or unavailable in practice
A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem
The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern-based predictions of the distribution of item sizes in online bin packing. In comparison to traditional heuristics like BestFit(BF) and FirstFit(FF), as well as the more recent PatternPack(PaP) and ProfilePacking(PrP) algorithm based on online predictions, FPP demonstrates competitive and superior performance in solving various benchmark problems. Particularly, it excels in addressing problems with evolving distributions, making it a promising solution for real-world applications where the item sizes may change over time. This research unveils the promising potential of employing fuzzy logic to effectively address uncertainty in scheduling and planning problems
Bayesian Calibration of AquaCrop Model for Winter Wheat by Assimilating UAV Multi-Spectral Images
Crop growth model plays a paramount role in smart farming management, which not only provides quantitative information on crop development but also evaluates various management strategies. A reliable model is desirable but challenging due to the presence of unknown and uncertain parameters; therefore, crop model calibration is significant to achieve its potentials. This work is focused on the calibration of AquaCrop model by leveraging advanced Bayesian inference algorithms and UAV multi-spectral images at field scales. In particular, aerial images with high spatial- temporal resolutions are first applied to obtain Canopy Cover (CC) value by using machine learning based classification. The CC is then assimilated into AquaCrop model and uncertain parameters could be inferred by Markov Chain Monte Carlo (MCMC). Both simulation and experimental validation are performed. The experimental aerial images of winter wheat at Yangling district from Oct/2017 to June/2018 are applied to validate the proposed method against the conventional optimisation based approach by Simulated Annealing (SA). 100 Monte Carlo simulations show that the root mean squared error (RMSE) of Bayesian approach yields a smaller parameter estimation error than optimisation approach. While the experimental results show that: (i) a good wheat/background classification result is obtained for the accurate calculation of CC; (ii) the predicted CC values by Bayesian approach are consistent with measurements by 4-fold cross validation, where the RMSE is 0.0271 smaller than optimisation approach (0.0514); (iii) in addition to parameter estimation, their distribution information is also obtained in the developed Bayesian approach, reflecting the prediction confidence. It is believed that the Bayesian model calibration, although is developed for AquaCrop model, can find a wide range of applications to various simulation models in agriculture and forestry
Secrets of RLHF in Large Language Models Part I: PPO
Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) emerges as the pivotal technological paradigm underpinning this pursuit.
Current technical routes usually include \textbf{reward models} to measure
human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize
policy model outputs, and \textbf{process supervision} to improve step-by-step
reasoning capabilities. However, due to the challenges of reward design,
environment interaction, and agent training, coupled with huge trial and error
cost of large language models, there is a significant barrier for AI
researchers to motivate the development of technical alignment and safe landing
of LLMs. The stable training of RLHF has still been a puzzle. In the first
report, we dissect the framework of RLHF, re-evaluate the inner workings of
PPO, and explore how the parts comprising PPO algorithms impact policy agent
training. We identify policy constraints being the key factor for the effective
implementation of the PPO algorithm. Therefore, we explore the PPO-max, an
advanced version of PPO algorithm, to efficiently improve the training
stability of the policy model. Based on our main results, we perform a
comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT.
The absence of open-source implementations has posed significant challenges to
the investigation of LLMs alignment. Therefore, we are eager to release
technical reports, reward models and PPO code
Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst
The recently discovered neutron star transient Swift J0243.6+6124 has been
monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT).
Based on the obtained data, we investigate the broadband spectrum of the source
throughout the outburst. We estimate the broadband flux of the source and
search for possible cyclotron line in the broadband spectrum. No evidence of
line-like features is, however, found up to . In the absence of
any cyclotron line in its energy spectrum, we estimate the magnetic field of
the source based on the observed spin evolution of the neutron star by applying
two accretion torque models. In both cases, we get consistent results with
, and peak luminosity of which makes the source the first Galactic ultraluminous
X-ray source hosting a neutron star.Comment: publishe
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