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An Analysis of Covariational Reasoning Pedagogy for the Introduction of Derivative in Selected Calculus Textbooks
Covariational reasoning is a cognitive activity that attends to two or more varying quantities and how their changes are related to each other. Previous studies indicate that covariational reasoning seems to have levels. Content analysis was used to examine the pedagogy and development of covariational reasoning levels in the sections that conceptually introduce derivatives in four calculus textbooks. One widely used calculus textbook was selected for the study in each of the four categories: U.S. college, U.S. high school, China college, and China high school. Two qualified investigators and I conducted the study. We used a framework of five developmental levels for covariational reasoning.
The conceptual analysis of four calculus textbooks found that the U.S. college and the U.S. high school textbooks emphasize the average and instantaneous rate of change. However, both lack development of the direction and magnitude of change. On the other hand, this study's Chinese high school calculus textbook has a greater degree of development in the direction and magnitude of change while having a deficit in the average rate of change. This study's Chinese college calculus textbook does not have any meaningful development regarding covariational reasoning pedagogy.
The relational analysis of the concepts previously identified in the conceptual analysis phase revealed that this study's U.S. college calculus textbooks provide abundant examples and exercises to transition between the average and instantaneous rate of change. On the other hand, all other calculus textbooks in this study lack any significant transition among passages that stimulate covariational reasoning.
The textbook analysis in this study provides insights into the current focus of calculus textbooks in both the U.S. and China. In addition, the study has implications for learning and teaching calculus at both high school and college, as well as future editions of calculus textbooks. Finally, limitations and recommendations are discussed
Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification
Speaker identification (SID) in the household scenario (e.g., for smart
speakers) is an important but challenging problem due to limited number of
labeled (enrollment) utterances, confusable voices, and demographic imbalances.
Conventional speaker recognition systems generalize from a large random sample
of speakers, causing the recognition to underperform for households drawn from
specific cohorts or otherwise exhibiting high confusability. In this work, we
propose a graph-based semi-supervised learning approach to improve
household-level SID accuracy and robustness with locally adapted graph
normalization and multi-signal fusion with multi-view graphs. Unlike other work
on household SID, fairness, and signal fusion, this work focuses on speaker
label inference (scoring) and provides a simple solution to realize
household-specific adaptation and multi-signal fusion without tuning the
embeddings or training a fusion network. Experiments on the VoxCeleb dataset
demonstrate that our approach consistently improves the performance across
households with different customer cohorts and degrees of confusability.Comment: To appear in Interspeech 2022. arXiv admin note: text overlap with
arXiv:2106.0820
Label-Free Liver Tumor Segmentation
We demonstrate that AI models can accurately segment liver tumors without the
need for manual annotation by using synthetic tumors in CT scans. Our synthetic
tumors have two intriguing advantages: (I) realistic in shape and texture,
which even medical professionals can confuse with real tumors; (II) effective
for training AI models, which can perform liver tumor segmentation similarly to
the model trained on real tumors -- this result is exciting because no existing
work, using synthetic tumors only, has thus far reached a similar or even close
performance to real tumors. This result also implies that manual efforts for
annotating tumors voxel by voxel (which took years to create) can be
significantly reduced in the future. Moreover, our synthetic tumors can
automatically generate many examples of small (or even tiny) synthetic tumors
and have the potential to improve the success rate of detecting small liver
tumors, which is critical for detecting the early stages of cancer. In addition
to enriching the training data, our synthesizing strategy also enables us to
rigorously assess the AI robustness.Comment: CVPR 202
Dual-attention Focused Module for Weakly Supervised Object Localization
The research on recognizing the most discriminative regions provides
referential information for weakly supervised object localization with only
image-level annotations. However, the most discriminative regions usually
conceal the other parts of the object, thereby impeding entire object
recognition and localization. To tackle this problem, the Dual-attention
Focused Module (DFM) is proposed to enhance object localization performance.
Specifically, we present a dual attention module for information fusion,
consisting of a position branch and a channel one. In each branch, the input
feature map is deduced into an enhancement map and a mask map, thereby
highlighting the most discriminative parts or hiding them. For the position
mask map, we introduce a focused matrix to enhance it, which utilizes the
principle that the pixels of an object are continuous. Between these two
branches, the enhancement map is integrated with the mask map, aiming at
partially compensating the lost information and diversifies the features. With
the dual-attention module and focused matrix, the entire object region could be
precisely recognized with implicit information. We demonstrate outperforming
results of DFM in experiments. In particular, DFM achieves state-of-the-art
performance in localization accuracy in ILSVRC 2016 and CUB-200-2011.Comment: 8 pages, 6 figures and 4 table
Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
Recent text-to-image generative models can generate high-fidelity images from
text inputs, but the quality of these generated images cannot be accurately
evaluated by existing evaluation metrics. To address this issue, we introduce
Human Preference Dataset v2 (HPD v2), a large-scale dataset that captures human
preferences on images from a wide range of sources. HPD v2 comprises 798,090
human preference choices on 433,760 pairs of images, making it the largest
dataset of its kind. The text prompts and images are deliberately collected to
eliminate potential bias, which is a common issue in previous datasets. By
fine-tuning CLIP on HPD v2, we obtain Human Preference Score v2 (HPS v2), a
scoring model that can more accurately predict human preferences on generated
images. Our experiments demonstrate that HPS v2 generalizes better than
previous metrics across various image distributions and is responsive to
algorithmic improvements of text-to-image generative models, making it a
preferable evaluation metric for these models. We also investigate the design
of the evaluation prompts for text-to-image generative models, to make the
evaluation stable, fair and easy-to-use. Finally, we establish a benchmark for
text-to-image generative models using HPS v2, which includes a set of recent
text-to-image models from the academic, community and industry. The code and
dataset is available at https://github.com/tgxs002/HPSv2 .Comment: Revisio
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