Providing fast and accurate resolution to the student's query is an essential
solution provided by Edtech organizations. This is generally provided with a
chat-bot like interface to enable students to ask their doubts easily. One
preferred format for student queries is images, as it allows students to
capture and post questions without typing complex equations and information.
However, this format also presents difficulties, as images may contain multiple
questions or textual noise that lowers the accuracy of existing single-query
answering solutions. In this paper, we propose a method for extracting
questions from text or images using a BERT-based deep learning model and
compare it to the other rule-based and layout-based methods. Our method aims to
improve the accuracy and efficiency of student query resolution in Edtech
organizations.

通过使用基于 BERT 的深度学习模型，从文本或图像中提取问题，并将其与其他基于规则和布局的方法进行比较，我们旨在提高 Edtech 组织中学生问题解答的准确性和效率。

通过基于 BERT 的图像问题抽取来提高学术查询解决

Improve Academic Query Resolution through BERT-based Question Extraction  from Images

Chart visualizations are essential for data interpretation and communication;
however, most charts are only accessible in image format and lack the
corresponding data tables and supplementary information, making it difficult to
alter their appearance for different application scenarios. To eliminate the
need for original underlying data and information to perform chart editing, we
propose ChartReformer, a natural language-driven chart image editing solution
that directly edits the charts from the input images with the given instruction
prompts. The key in this method is that we allow the model to comprehend the
chart and reason over the prompt to generate the corresponding underlying data
table and visual attributes for new charts, enabling precise edits.
Additionally, to generalize ChartReformer, we define and standardize various
types of chart editing, covering style, layout, format, and data-centric edits.
The experiments show promising results for the natural language-driven chart
image editing.

利用自然语言的图表图像编辑解决方案 ChartReformer 可编辑图像的外观，消除了对原始数据和信息的需求，从而实现了更精确的编辑。

ChartReformer: 自然语言驱动的图表图像编辑

ChartReformer: Natural Language-Driven Chart Image Editing

The training process of deep neural networks (DNNs) is usually pipelined with
stages for data preparation on CPUs followed by gradient computation on
accelerators like GPUs. In an ideal pipeline, the end-to-end training
throughput is eventually limited by the throughput of the accelerator, not by
that of data preparation. In the past, the DNN training pipeline achieved a
near-optimal throughput by utilizing datasets encoded with a lightweight, lossy
image format like JPEG. However, as high-resolution, losslessly-encoded
datasets become more popular for applications requiring high accuracy, a
performance problem arises in the data preparation stage due to low-throughput
image decoding on the CPU. Thus, we propose L3, a custom lightweight, lossless
image format for high-resolution, high-throughput DNN training. The decoding
process of L3 is effectively parallelized on the accelerator, thus minimizing
CPU intervention for data preparation during DNN training. L3 achieves a 9.29x
higher data preparation throughput than PNG, the most popular lossless image
format, for the Cityscapes dataset on NVIDIA A100 GPU, which leads to 1.71x
higher end-to-end training throughput. Compared to JPEG and WebP, two popular
lossy image formats, L3 provides up to 1.77x and 2.87x higher end-to-end
training throughput for ImageNet, respectively, at equivalent metric
performance.

介绍一个定制化的 L3 图像格式，最大限度地减少 CPU 介入，提高 DNN 训练过程中的数据准备和整体吞吐量