1,005 research outputs found
Hermite spectral method for the inelastic Boltzmann equation
We propose a Hermite spectral method for the inelastic Boltzmann equation,
which makes two-dimensional periodic problem computation affordable by the
hardware nowadays. The new algorithm is based on a Hermite expansion, where the
expansion coefficients for the VHS model are reduced into several summations
and can be derived exactly. Moreover, a new collision model is built with a
combination of the quadratic collision operator and a linearized collision
operator, which helps us to balance the computational cost and the accuracy.
Various numerical experiments, including spatially two-dimensional simulations,
demonstrate the accuracy and efficiency of this numerical scheme
Vision-Language Instruction Tuning: A Review and Analysis
Instruction tuning is a crucial supervised training phase in Large Language
Models (LLMs), aiming to enhance the LLM's ability to generalize instruction
execution and adapt to user preferences. With the increasing integration of
multi-modal data into LLMs, there is growing interest in Vision-Language
Instruction Tuning (VLIT), which presents more complex characteristics compared
to pure text instruction tuning. In this paper, we systematically review the
latest VLIT settings and corresponding datasets in multi-modal LLMs and provide
insights into the intrinsic motivations behind their design. For the first
time, we offer a detailed multi-perspective categorization for existing VLIT
datasets and identify the characteristics that high-quality VLIT data should
possess. By incorporating these characteristics as guiding principles into the
existing VLIT data construction process, we conduct extensive experiments and
verify their positive impact on the performance of tuned multi-modal LLMs.
Furthermore, we discuss the current challenges and future research directions
of VLIT, providing insights for the continuous development of this field. The
code and dataset related to this paper have been open-sourced at
https://github.com/palchenli/VL-Instruction-Tuning.Comment: 34 pages, 6 figure
Unsupervised CNN-Based DIC for 2D Displacement Measurement
Digital image correlation method is a non contact deformation measurement
technique. Despite years of development, it is still difficult to solve the
contradiction between calculation efficiency and seed point quantity.With the
development of deep learning, the DIC algorithm based on deep learning provides
a new solution for the problem of insufficient calculation efficiency in
DIC.All supervised learning DIC methods requires a large set of high quality
training set. However, obtaining such a dataset can be challenging and time
consuming in generating ground truth. To fix the problem,we propose an
unsupervised CNN Based DIC for 2D Displacement Measurement.The speckle image
warp model is created to transform the target speckle image to the
corresponding predicted reference speckle image by predicted 2D displacement
map, the predicted reference speckle image is compared with the original
reference speckle image to realize the unsupervised training of the CNN.The
network's parameters are optimized using a composite loss function that
incorporates both the Mean Squared Error and Pearson correlation
coefficient.Our proposed method has a significant advantage of eliminating the
need for extensive training data annotations. We conducted several experiments
to demonstrate the validity and robustness of the proposed method. The
experimental results demonstrate that our method can achieve can achieve
accuracy comparable to previous supervised methods. The PyTorch code will be
available at the following URL: https://github.com/fead1
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