A General Static Binary Rewriting Framework for WebAssembly

Binary rewriting is a widely adopted technique in software analysis. WebAssembly (Wasm), as an emerging bytecode format, has attracted great attention from our community. Unfortunately, there is no general-purpose binary rewriting framework for Wasm, and existing effort on Wasm binary modification is error-prone and tedious. In this paper, we present BREWasm, the first general purpose static binary rewriting framework for Wasm, which has addressed inherent challenges of Wasm rewriting including high complicated binary structure, strict static syntax verification, and coupling among sections. We perform extensive evaluation on diverse Wasm applications to show the efficiency, correctness and effectiveness of BREWasm. We further show the promising direction of implementing a diverse set of binary rewriting tasks based on BREWasm in an effortless and user-friendly manner

Turning a CLIP Model into a Scene Text Spotter

We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP) model to enhance scene text detection and spotting tasks, transforming it into a robust backbone, FastTCM-CR50. This backbone utilizes visual prompt learning and cross-attention in CLIP to extract image and text-based prior knowledge. Using predefined and learnable prompts, FastTCM-CR50 introduces an instance-language matching process to enhance the synergy between image and text embeddings, thereby refining text regions. Our Bimodal Similarity Matching (BSM) module facilitates dynamic language prompt generation, enabling offline computations and improving performance. FastTCM-CR50 offers several advantages: 1) It can enhance existing text detectors and spotters, improving performance by an average of 1.7% and 1.5%, respectively. 2) It outperforms the previous TCM-CR50 backbone, yielding an average improvement of 0.2% and 0.56% in text detection and spotting tasks, along with a 48.5% increase in inference speed. 3) It showcases robust few-shot training capabilities. Utilizing only 10% of the supervised data, FastTCM-CR50 improves performance by an average of 26.5% and 5.5% for text detection and spotting tasks, respectively. 4) It consistently enhances performance on out-of-distribution text detection and spotting datasets, particularly the NightTime-ArT subset from ICDAR2019-ArT and the DOTA dataset for oriented object detection. The code is available at https://github.com/wenwenyu/TCM.Comment: arXiv admin note: text overlap with arXiv:2302.1433

Influence of the Martian crustal magnetic fields on the Mars-solar wind interaction and plasma transport

The plasma transport process is important for the ionosphere of Mars, which controls the structure of the ionosphere above an altitude of 200 km. Plasma transport from the dayside ionosphere is crucial for producing the nightside ionosphere on Mars. The alteration in dayside plasma transport in the presence of crustal fields may influence the distribution of Martian ionospheric plasma and plasma escape in the magnetotail. This study employed a three-dimensional multispecies magnetohydrodynamic (MHD) model to simulate Mars-solar wind interactions. We show the magnetic field distribution and plasma velocity variation on the Martian day-side. The results indicate that the ion transport from low- to high-solar-zenith-angle areas in the south is inhibited by crustal fields, leading to a reduction in the ion number density and a thinner ionosphere near the southern terminator. Many heavy ions remain in the southern dayside ionosphere rather than moving to the nightside. In addition, the maximum reduction in the tailward flux of the planetary ions calculated by the MHD simulation is more than 50% at the southern terminator, indicating an inhibitory effect of the crustal fields on day-to-night transport. These effects may lead to a reduction in ion number density in the southern nightside ionosphere. Finally, we demonstrate a decrease in the global heavy-ion loss rate

Machine Learning Prediction of Glass Transition Temperature of Conjugated Polymers From Chemical Structure

Predicting the glass transition temperature (Tg) is of critical importance as it governs the thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive modeling framework to predict Tg of CPs through the integration of machine learning (ML), molecular dynamics (MD) simulations, and experiments. With 154 Tg data collected, an ML model is developed by taking simplified “geometry” of six chemical building blocks as molecular features, where side-chain fraction, isolated rings, fused rings, and bridged rings features are identified as the dominant ones for Tg. MD simulations further unravel the fundamental roles of those chemical building blocks in dynamical heterogeneity and local mobility of CPs at a molecular level. The developed ML model is demonstrated for its capability of predicting Tg of several new high-performance solar cell materials to a good approximation. The established predictive framework facilitates the design and prediction of Tg of complex CPs, paving the way for addressing device stability issues that have hampered the field from developing stable organic electronics

Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration

We propose a novel end-to-end document understanding model called SeRum (SElective Region Understanding Model) for extracting meaningful information from document images, including document analysis, retrieval, and office automation. Unlike state-of-the-art approaches that rely on multi-stage technical schemes and are computationally expensive, SeRum converts document image understanding and recognition tasks into a local decoding process of the visual tokens of interest, using a content-aware token merge module. This mechanism enables the model to pay more attention to regions of interest generated by the query decoder, improving the model's effectiveness and speeding up the decoding speed of the generative scheme. We also designed several pre-training tasks to enhance the understanding and local awareness of the model. Experimental results demonstrate that SeRum achieves state-of-the-art performance on document understanding tasks and competitive results on text spotting tasks. SeRum represents a substantial advancement towards enabling efficient and effective end-to-end document understanding.Comment: Accepted to ICCV 2023 main conferenc

Characterizing and Detecting WebAssembly Runtime Bugs

WebAssembly (abbreviated WASM) has emerged as a promising language of the Web and also been used for a wide spectrum of software applications such as mobile applications and desktop applications. These applications, named as WASM applications, commonly run in WASM runtimes. Bugs in WASM runtimes are frequently reported by developers and cause the crash of WASM applications. However, these bugs have not been well studied. To fill in the knowledge gap, we present a systematic study to characterize and detect bugs in WASM runtimes. We first harvest a dataset of 311 real-world bugs from hundreds of related posts on GitHub. Based on the collected high-quality bug reports, we distill 31 bug categories of WASM runtimes and summarize their common fix strategies. Furthermore, we develop a pattern-based bug detection framework to automatically detect bugs in WASM runtimes. We apply the detection framework to seven popular WASM runtimes and successfully uncover 60 bugs that have never been reported previously, among which 13 have been confirmed and 9 have been fixed by runtime developers