2,118 research outputs found
Numerical Strategies of Computing the Luminosity Distance
We propose two efficient numerical methods of evaluating the luminosity
distance in the spatially flat {\Lambda}CDM universe. The first method is based
on the Carlson symmetric form of elliptic integrals, which is highly accurate
and can replace numerical quadratures. The second method, using a modified
version of Hermite interpolation, is less accurate but involves only basic
numerical operations and can be easily implemented. We compare our methods with
other numerical approximation schemes and explore their respective features and
limitations. Possible extensions of these methods to other cosmological models
are also discussed.Comment: 4 pages, 2 figures. v2: A minor error in the last equation has been
corrected (conclusions are not affected). v3: Accepted by MNRA
Masked Images Are Counterfactual Samples for Robust Fine-tuning
Deep learning models are challenged by the distribution shift between the
training data and test data. Recently, the large models pre-trained on diverse
data demonstrate unprecedented robustness to various distribution shifts.
However, fine-tuning on these models can lead to a trade-off between
in-distribution (ID) performance and out-of-distribution (OOD) robustness.
Existing methods for tackling this trade-off do not explicitly address the OOD
robustness problem. In this paper, based on causal analysis on the
aforementioned problems, we propose a novel fine-tuning method, which use
masked images as counterfactual samples that help improving the robustness of
the fine-tuning model. Specifically, we mask either the semantics-related or
semantics-unrelated patches of the images based on class activation map to
break the spurious correlation, and refill the masked patches with patches from
other images. The resulting counterfactual samples are used in feature-based
distillation with the pre-trained model. Extensive experiments verify that
regularizing the fine-tuning with the proposed masked images can achieve a
better trade-off between ID and OOD performance, surpassing previous methods on
the OOD performance. Our code will be publicly available.Comment: Accepted by CVPR 2023 (v2: improve the clarity
Zinc isotope characteristics in the biogeochemical cycle as revealed by analysis of suspended particulate matter(SMP) in Aha Lake and Hongfeng Lake, Guizhou, China
Exploring Format Consistency for Instruction Tuning
Instruction tuning has emerged as a promising approach to enhancing large
language models in following human instructions. It is shown that increasing
the diversity and number of instructions in the training data can consistently
enhance generalization performance, which facilitates a recent endeavor to
collect various instructions and integrate existing instruction tuning datasets
into larger collections. However, different users have their unique ways of
expressing instructions, and there often exist variations across different
datasets in the instruction styles and formats, i.e., format inconsistency. In
this work, we study how format inconsistency may impact the performance of
instruction tuning. We propose a framework called "Unified Instruction Tuning"
(UIT), which calls OpenAI APIs for automatic format transfer among different
instruction tuning datasets. We show that UIT successfully improves the
generalization performance on unseen instructions, which highlights the
importance of format consistency for instruction tuning. To make the UIT
framework more practical, we further propose a novel perplexity-based denoising
method to reduce the noise of automatic format transfer. We also train a
smaller offline model that achieves comparable format transfer capability than
OpenAI APIs to reduce costs in practice
Wave interaction and energy absorption from arrays of complex-shaped point absorbers
Water wave interactions with arrays of wave energy converters are numerically investigated based on the interaction theory. The converter is a heaving point absorber that can harness the ocean wave energy through up-and-down movements. A semi-analytical hybrid method is developed that combines the boundary element method and the interaction theory. The developed numerical method is verified against theoretical solutions for arrays of truncated vertical circular cylinders. Three different array layouts are studied in detail. It is found that trapped waves exist at critical wave numbers just below the cutoff values, and the peak load on the middle device increases with the number of devices in head waves. With the increase in the complexity of the array layout, significant wave force enhancement is observed, leading to a broader range of magnitude and stronger variations over the frequency band in beam waves. Moreover, variations of the q-factor show that there are some remarkable "bright spot"regions, indicating that the wave energy absorption there is locally optimized against wave conditions. By arranging the layout in a more randomized way, the optimal conditions for maximized power output can be hard to achieve, but the maximum power output can increase to a higher level
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