1,894 research outputs found
Monitoring responsibility and measures of the recycling company in water pollution prevention during the process of ship recycling
Echoes: Unsupervised Debiasing via Pseudo-bias Labeling in an Echo Chamber
Neural networks often learn spurious correlations when exposed to biased
training data, leading to poor performance on out-of-distribution data. A
biased dataset can be divided, according to biased features, into bias-aligned
samples (i.e., with biased features) and bias-conflicting samples (i.e.,
without biased features). Recent debiasing works typically assume that no bias
label is available during the training phase, as obtaining such information is
challenging and labor-intensive. Following this unsupervised assumption,
existing methods usually train two models: a biased model specialized to learn
biased features and a target model that uses information from the biased model
for debiasing. This paper first presents experimental analyses revealing that
the existing biased models overfit to bias-conflicting samples in the training
data, which negatively impacts the debiasing performance of the target models.
To address this issue, we propose a straightforward and effective method called
Echoes, which trains a biased model and a target model with a different
strategy. We construct an "echo chamber" environment by reducing the weights of
samples which are misclassified by the biased model, to ensure the biased model
fully learns the biased features without overfitting to the bias-conflicting
samples. The biased model then assigns lower weights on the bias-conflicting
samples. Subsequently, we use the inverse of the sample weights of the biased
model for training the target model. Experiments show that our approach
achieves superior debiasing results compared to the existing baselines on both
synthetic and real-world datasets. Our code is available at
https://github.com/isruihu/Echoes.Comment: Accepted by ACM Multimedia 202
Simulation of Bio-Inspired Porous Battery Electrodes
Advancement of technology has led to the increase in use of electronic devices. However, longer life of the rechargeable battery used in electronic devices is one of the biggest issue and demand in the world of electronic devices at present. Battery\u27s performance is affected by the orientation, arrangement, shape and size, and porosity of the materials out of which battery electrodes are made. The goal of this project is to develop a set of numerical libraries that allow developing material micro structures that will allow increasing the performance of rechargeable batteries. We focused on the development of an algorithm that generated porous particle electrodes of controlled particle size and polydispersity. The algorithm intends to develop the simulation tool that captures the randomness and polydispersity of spherical particles, as they occur in real commercial. This application provides very flexible VKML user interface to simulate the generation of particles live. Compared to the existing tools, this application can simulate about 12 times faster for single sized particles and provide a porosity of 56 percent or lower, about 4 times faster for random sized particles to provide the porosity as low as 47 percent or lower, about 2 times faster for 3 different sized particles and provide a porosity of about 43 percent or better, and about 5 times faster for 4 different sized particles to provide porosity as low as 42 percent
Reduced-Order Aggregate Model for Large-scale Converters with Inhomogeneous Initial Conditions in DC Microgrids
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment
Language models trained on large-scale corpus often generate content that is
harmful, toxic, or contrary to human preferences, making their alignment with
human values a critical concern. Reinforcement learning from human feedback
(RLHF) with algorithms like PPO is a prevalent approach for alignment but is
often complex, unstable, and resource-intensive. Recently, ranking-based
alignment methods have emerged, offering stability and effectiveness by
replacing the RL framework with supervised fine-tuning, but they are costly due
to the need for annotated data. Considering that existing large language models
(LLMs) like ChatGPT are already relatively well-aligned and cost-friendly,
researchers have begun to align the language model with human preference from
AI feedback. The common practices, which unidirectionally distill the
instruction-following responses from LLMs, are constrained by their bottleneck.
Thus we introduce CycleAlign to distill alignment capabilities from
parameter-invisible LLMs (black-box) to a parameter-visible model (white-box)
in an iterative manner. With in-context learning (ICL) as the core of the
cycle, the black-box models are able to rank the model-generated responses
guided by human-craft instruction and demonstrations about their preferences.
During iterative interaction, the white-box models also have a judgment about
responses generated by them. Consequently, the agreement ranking could be
viewed as a pseudo label to dynamically update the in-context demonstrations
and improve the preference ranking ability of black-box models. Through
multiple interactions, the CycleAlign framework could align the white-box model
with the black-box model effectively in a low-resource way. Empirical results
illustrate that the model fine-tuned by CycleAlign remarkably exceeds existing
methods, and achieves the state-of-the-art performance in alignment with human
value
Automatic Calibration of Process Noise Matrix and Measurement Noise Covariance for Multi-GNSS Precise Point Positioning
The Expectation-Maximization algorithm is adapted to the extended Kalman filter to multiple GNSS Precise Point Positioning (PPP), named EM-PPP. EM-PPP considers better the compatibility of multiple GNSS data processing and characteristics of receiver motion, targeting to calibrate the process noise matrix Qt and observation matrix Rt, having influence on PPP convergence time and precision, with other parameters. It is possibly a feasible way to estimate a large number of parameters to a certain extent for its simplicity and easy implementation. We also compare EM-algorithm with other methods like least-squares (co)variance component estimation (LS-VCE), maximum likelihood estimation (MLE), showing that EM-algorithm from restricted maximum likelihood (REML) will be identical to LS-VCE if certain weight matrix is chosen for LS-VCE. To assess the performance of the approach, daily observations from a network of 14 globally distributed International GNSS Service (IGS) multi-GNSS stations were processed using ionosphere-free combinations. The stations were assumed to be in kinematic motion with initial random walk noise of 1 mm every 30 s. The initial standard deviations for ionosphere-free code and carrier phase measurements are set to 3 m and 0.03 m, respectively, independent of the satellite elevation angle. It is shown that the calibrated Rt agrees well with observation residuals, reflecting effects of the accuracy of different satellite precise product and receiver-satellite geometry variations, and effectively resisting outliers. The calibrated Qt converges to its true value after about 50 iterations in our case. A kinematic test was also performed to derive 1 Hz GPS displacements, showing the RMSs and STDs w.r.t. real-time kinematic (RTK) are improved and the proper Qt is found out at the same time. According to our analysis despite the criticism that EM-PPP is very time-consuming because a large number of parameters are calculated and the first-order convergence of EM-algorithm, it is a numerically stable and simple approach to consider the temporal nature of state-space model of PPP, in particular when Qt and Rt are not known well, its performance without fixing ambiguities can even parallel to traditional PPP-RTK
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