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The message a designer wants to convey plays a pivotal role in directing the design of an infographic, yet most authoring workflows start with creating the visualizations or graphics first without gauging whether they fit the message. To address this gap, we propose Epigraphics, a web-based authoring system that treats an "epigraph" as the first-class object, and uses it to guide infographic asset creation, editing, and syncing. The system uses the text-based message to recommend visualizations, graphics, data filters, color palettes, and animations. It further supports between-asset interactions and fine-tuning such as recoloring, highlighting, and animation syncing that enhance the aesthetic cohesiveness of the assets. A gallery and case studies show that our system can produce infographics inspired by existing popular ones, and a task-based usability study with 10 designers show that a text-sourced workflow can standardize content, empower users to think more about the big picture, and facilitate rapid prototyping.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a disruptive technology with the potential to revolutionize wireless communication systems. In this paper, we present RIShield, a novel application of RIS technology specifically designed for radiation-sensitive environments. The aim of RIShield is to enable electromagnetic blackouts, preventing radiation leakage from target areas. We propose a comprehensive framework for RIShield deployment, considering the unique challenges and requirements of radiation-sensitive environments. By strategically positioning RIS panels, we create an intelligent shielding mechanism that selectively absorbs and reflects electromagnetic waves, effectively blocking radiation transmission. To achieve optimal performance, we model the corresponding channel and design a dynamic control that adjusts the RIS configuration based on real-time radiation monitoring. By leveraging the principles of reconfiguration and intelligent control, RIShield ensures adaptive and efficient protection while minimizing signal degradation. Through full-wave and ray-tracing simulations, we demonstrate the effectiveness of RIShield in achieving significant electromagnetic attenuation. Our results highlight the potential of RIS technology to address critical concerns in radiation-sensitive environments, paving the way for safer and more secure operations in industries such as healthcare, nuclear facilities, and defense.

Leveraging vast training data, multimodal large language models (MLLMs) have demonstrated formidable general visual comprehension capabilities and achieved remarkable performance across various tasks. However, their performance in visual document understanding still leaves much room for improvement. This discrepancy is primarily attributed to the fact that visual document understanding is a fine-grained prediction task. In natural scenes, MLLMs typically use low-resolution images, leading to a substantial loss of visual information. Furthermore, general-purpose MLLMs do not excel in handling document-oriented instructions. In this paper, we propose a High-Resolution Visual Document Assistant (HRVDA), which bridges the gap between MLLMs and visual document understanding. This model employs a content filtering mechanism and an instruction filtering module to separately filter out the content-agnostic visual tokens and instruction-agnostic visual tokens, thereby achieving efficient model training and inference for high-resolution images. In addition, we construct a document-oriented visual instruction tuning dataset and apply a multi-stage training strategy to enhance the model's document modeling capabilities. Extensive experiments demonstrate that our model achieves state-of-the-art performance across multiple document understanding datasets, while maintaining training efficiency and inference speed comparable to low-resolution models.

Evolutionary multitasking (EMT) has emerged as a popular topic of evolutionary computation over the past years. It aims to concurrently address multiple optimization tasks within limited computing resources, leveraging inter-task knowledge transfer techniques. Despite the abundance of multitask evolutionary algorithms (MTEAs) proposed for multitask optimization (MTO), there remains a comprehensive software platform to help researchers evaluate MTEA performance on benchmark MTO problems as well as explore real-world applications. To bridge this gap, we introduce the first open-source optimization platform, named MTO-Platform (MToP), for EMT. MToP incorporates over 40 MTEAs, more than 150 MTO problem cases with real-world applications, and over 10 performance metrics. Moreover, to facilitate comparative analyses between MTEAs and traditional evolutionary algorithms, we adapted over 40 popular single-task evolutionary algorithms to address MTO problems. MToP boasts a user-friendly graphical interface, facilitating results analysis, data export, and schematics visualization. More importantly, MToP is designed with extensibility in mind, allowing users to develop new algorithms and tackle emerging problem domains. The source code of MToP is available at //github.com/intLyc/MTO-Platform.

We introduce Gull, a generative multifunctional audio codec. Gull is a general purpose neural audio compression and decompression model which can be applied to a wide range of tasks and applications such as real-time communication, audio super-resolution, and codec language models. The key components of Gull include (1) universal-sample-rate modeling via subband modeling schemes motivated by recent progress in audio source separation, (2) gain-shape representations motivated by traditional audio codecs, (3) improved residual vector quantization modules for simpler training, (4) elastic decoder network that enables user-defined model size and complexity during inference time, (5) built-in ability for audio super-resolution without the increase of bitrate. We compare Gull with existing traditional and neural audio codecs and show that Gull is able to achieve on par or better performance across various sample rates, bitrates and model complexities in both subjective and objective evaluation metrics.

While real-world anime super-resolution (SR) has gained increasing attention in the SR community, existing methods still adopt techniques from the photorealistic domain. In this paper, we analyze the anime production workflow and rethink how to use characteristics of it for the sake of the real-world anime SR. First, we argue that video networks and datasets are not necessary for anime SR due to the repetition use of hand-drawing frames. Instead, we propose an anime image collection pipeline by choosing the least compressed and the most informative frames from the video sources. Based on this pipeline, we introduce the Anime Production-oriented Image (API) dataset. In addition, we identify two anime-specific challenges of distorted and faint hand-drawn lines and unwanted color artifacts. We address the first issue by introducing a prediction-oriented compression module in the image degradation model and a pseudo-ground truth preparation with enhanced hand-drawn lines. In addition, we introduce the balanced twin perceptual loss combining both anime and photorealistic high-level features to mitigate unwanted color artifacts and increase visual clarity. We evaluate our method through extensive experiments on the public benchmark, showing our method outperforms state-of-the-art anime dataset-trained approaches.

3D stylization, which entails the application of specific styles to three-dimensional objects, holds significant commercial potential as it enables the creation of diverse 3D objects with distinct moods and styles, tailored to specific demands of different scenes. With recent advancements in text-driven methods and artificial intelligence, the stylization process is increasingly intuitive and automated, thereby diminishing the reliance on manual labor and expertise. However, existing methods have predominantly focused on holistic stylization, thereby leaving the application of styles to individual components of a 3D object unexplored. In response, we introduce 3DStyleGLIP, a novel framework specifically designed for text-driven, part-tailored 3D stylization. Given a 3D mesh and a text prompt, 3DStyleGLIP leverages the vision-language embedding space of the Grounded Language-Image Pre-training (GLIP) model to localize the individual parts of the 3D mesh and modify their colors and local geometries to align them with the desired styles specified in the text prompt. 3DStyleGLIP is effectively trained for 3D stylization tasks through a part-level style loss working in GLIP's embedding space, supplemented by two complementary learning techniques. Extensive experimental validation confirms that our method achieves significant part-wise stylization capabilities, demonstrating promising potential in advancing the field of 3D stylization.

Text simplification aims to make the text easier to understand by applying rewriting transformations. There has been very little research on Chinese text simplification for a long time. The lack of generic evaluation data is an essential reason for this phenomenon. In this paper, we introduce MCTS, a multi-reference Chinese text simplification dataset. We describe the annotation process of the dataset and provide a detailed analysis. Furthermore, we evaluate the performance of several unsupervised methods and advanced large language models. We additionally provide Chinese text simplification parallel data that can be used for training, acquired by utilizing machine translation and English text simplification. We hope to build a basic understanding of Chinese text simplification through the foundational work and provide references for future research. All of the code and data are released at //github.com/blcuicall/mcts/.

The surge of artificial intelligence, specifically large language models, has led to a rapid advent towards the development of large-scale machine learning training clusters. Collective communications within these clusters tend to be heavily bandwidth-bound, necessitating techniques to optimally utilize the available network bandwidth. This puts the routing algorithm for the collective at the forefront of determining the performance. Unfortunately, communication libraries used in distributed machine learning today are limited by a fixed set of routing algorithms. This constraints collective performance within the domain of next-generation training clusters that employ intricate, heterogeneous, and asymmetric, large-scale topologies. Further, the emergence of irregular topologies attributed to runtime phenomena such as device failures serves to compound the complexity of the challenge. To this end, this paper introduces TACOS, an automated synthesizer that generates topology-aware collective algorithms for common distributed machine learning collectives across arbitrary input network topologies. TACOS was able to synthesize All-Reduce algorithm for a heterogeneous 512-NPU system in just 6.09 minutes while achieving performance improvement up to 4.27x over state-of-the-art prior work. TACOS exhibits high scalability, with synthesis time scaling quadratically with the number of NPUs. In contrast to prior works' NP-hard approaches, TACOS with 40K NPUs completes in 2.52 hours.