What are the Actual Flaws in Important Smart Contracts (and How Can We Find Them)?

An important problem in smart contract security is understanding the likelihood and criticality of discovered, or potential, weaknesses in contracts. In this paper we provide a summary of Ethereum smart contract audits performed for 23 professional stakeholders, avoiding the common problem of reporting issues mostly prevalent in low-quality contracts. These audits were performed at a leading company in blockchain security, using both open-source and proprietary tools, as well as human code analysis performed by professional security engineers. We categorize 246 individual defects, making it possible to compare the severity and frequency of different vulnerability types, compare smart contract and non-smart contract flaws, and to estimate the efficacy of automated vulnerability detection approaches

AutoFocusFormer: Image Segmentation off the Grid

Real world images often have highly imbalanced content density. Some areas are very uniform, e.g., large patches of blue sky, while other areas are scattered with many small objects. Yet, the commonly used successive grid downsampling strategy in convolutional deep networks treats all areas equally. Hence, small objects are represented in very few spatial locations, leading to worse results in tasks such as segmentation. Intuitively, retaining more pixels representing small objects during downsampling helps to preserve important information. To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task. Since adaptive downsampling generates a set of pixels irregularly distributed on the image plane, we abandon the classic grid structure. Instead, we develop a novel point-based local attention block, facilitated by a balanced clustering module and a learnable neighborhood merging module, which yields representations for our point-based versions of state-of-the-art segmentation heads. Experiments show that our AutoFocusFormer (AFF) improves significantly over baseline models of similar sizes.Comment: CVPR 202

LivePose: Online 3D Reconstruction from Monocular Video with Dynamic Camera Poses

Dense 3D reconstruction from RGB images traditionally assumes static camera pose estimates. This assumption has endured, even as recent works have increasingly focused on real-time methods for mobile devices. However, the assumption of a fixed pose for each image does not hold for online execution: poses from real-time SLAM are dynamic and may be updated following events such as bundle adjustment and loop closure. This has been addressed in the RGB-D setting, by de-integrating past views and re-integrating them with updated poses, but it remains largely untreated in the RGB-only setting. We formalize this problem to define the new task of dense online reconstruction from dynamically-posed images. To support further research, we introduce a dataset called LivePose containing the dynamic poses from a SLAM system running on ScanNet. We select three recent reconstruction systems and apply a framework based on de-integration to adapt each one to the dynamic-pose setting. In addition, we propose a novel, non-linear de-integration module that learns to remove stale scene content. We show that responding to pose updates is critical for high-quality reconstruction, and that our de-integration framework is an effective solution.Comment: ICCV 202

Estimating adolescent sleep patterns: parent reports versus adolescent self-report surveys, sleep diaries, and actigraphy

In research and clinical contexts, parent reports are often used to gain information about the sleep patterns of their adolescents; however, the degree of concordance between parent reports and adolescent-derived measures is unclear. The present study compares parent estimates of adolescent sleep patterns with adolescent self-reports from surveys and sleep diaries, together with actigraphy. Methods: A total of 308 adolescents (59% male) aged 13–17 years completed a school sleep habits survey during class time at school, followed by a 7-day sleep diary and wrist actigraphy. Parents completed the Sleep, Medical, Education and Family History Survey. Results: Parents reported an idealized version of their adolescent’s sleep, estimating significantly earlier bedtimes on both school nights and weekends, significantly later wake times on weekends, and significantly more sleep than either the adolescent self-reported survey, sleep diary, or actigraphic estimates. Conclusion: Parent reports indicate that the adolescent averages a near-optimal amount of sleep on school nights and a more than optimal amount of sleep on weekends. However, adolescent-derived averages indicate patterns of greater sleep restriction. These results illustrate the importance of using adolescent-derived estimates of sleep patterns in this age group and the importance of sleep education for both adolescents and their parents

FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction

Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency. However, the reconstructed geometry, typically represented as a 3D truncated signed distance function (TSDF), is often coarse without fine geometric details. To address this problem, we propose three effective solutions for improving the fidelity of inference-based 3D reconstructions. We first present a resolution-agnostic TSDF supervision strategy to provide the network with a more accurate learning signal during training, avoiding the pitfalls of TSDF interpolation seen in previous work. We then introduce a depth guidance strategy using multi-view depth estimates to enhance the scene representation and recover more accurate surfaces. Finally, we develop a novel architecture for the final layers of the network, conditioning the output TSDF prediction on high-resolution image features in addition to coarse voxel features, enabling sharper reconstruction of fine details. Our method, FineRecon, produces smooth and highly accurate reconstructions, showing significant improvements across multiple depth and 3D reconstruction metrics.Comment: ICCV 202

Pseudo-Generalized Dynamic View Synthesis from a Video

Rendering scenes observed in a monocular video from novel viewpoints is a challenging problem. For static scenes the community has studied both scene-specific optimization techniques, which optimize on every test scene, and generalized techniques, which only run a deep net forward pass on a test scene. In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video. To answer whether generalized dynamic novel view synthesis from monocular videos is possible today, we establish an analysis framework based on existing techniques and work toward the generalized approach. We find a pseudo-generalized process without scene-specific appearance optimization is possible, but geometrically and temporally consistent depth estimates are needed. Despite no scene-specific appearance optimization, the pseudo-generalized approach improves upon some scene-specific methods.Comment: ICLR 2024; Originally titled as "Is Generalized Dynamic Novel View Synthesis from Monocular Videos Possible Today?"; Project page: https://xiaoming-zhao.github.io/projects/pgdv

Time-resolved detection and analysis of single nanoparticle electrocatalytic impacts

There is considerable interest in understanding the interaction and activity of single entities, such as (electro)catalytic nanoparticles (NPs), with (electrode) surfaces. Through the use of a high bandwidth, high signal/noise measurement system, NP impacts on an electrode surface are detected and analyzed in unprecedented detail, revealing considerable new mechanistic information on the process. Taking the electrocatalytic oxidation of H2O2 at ruthenium oxide (RuOx) NPs as an example, the rise time of current–time transients for NP impacts is consistent with a hydrodynamic trapping model for the arrival of a NP with a distance-dependent NP diffusion-coefficient. NP release from the electrode appears to be aided by propulsion from the electrocatalytic reaction at the NP. High-frequency NP impacts, orders of magnitude larger than can be accounted for by a single pass diffusive flux of NPs, are observed that indicate the repetitive trapping and release of an individual NP that has not been previously recognized. The experiments and models described could readily be applied to other systems and serve as a powerful platform for detailed analysis of NP impacts