Density-invariant Features for Distant Point Cloud Registration

Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision of collaborative autonomous vehicles, and yet is challenging due to small overlapping area and a huge disparity between observed point densities. In this paper, we propose Group-wise Contrastive Learning (GCL) scheme to extract density-invariant geometric features to register distant outdoor LiDAR point clouds. We mark through theoretical analysis and experiments that, contrastive positives should be independent and identically distributed (i.i.d.), in order to train densityinvariant feature extractors. We propose upon the conclusion a simple yet effective training scheme to force the feature of multiple point clouds in the same spatial location (referred to as positive groups) to be similar, which naturally avoids the sampling bias introduced by a pair of point clouds to conform with the i.i.d. principle. The resulting fully-convolutional feature extractor is more powerful and density-invariant than state-of-the-art methods, improving the registration recall of distant scenarios on KITTI and nuScenes benchmarks by 40.9% and 26.9%, respectively. Code is available at https://github.com/liuQuan98/GCL.Comment: In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 202

Construction of cardiovascular information extraction corpus based on electronic medical records

Cardiovascular disease has a significant impact on both society and patients, making it necessary to conduct knowledge-based research such as research that utilizes knowledge graphs and automated question answering. However, the existing research on corpus construction for cardiovascular disease is relatively limited, which has hindered further knowledge-based research on this disease. Electronic medical records contain patient data that span the entire diagnosis and treatment process and include a large amount of reliable medical information. Therefore, we collected electronic medical record data related to cardiovascular disease, combined the data with relevant work experience and developed a standard for labeling cardiovascular electronic medical record entities and entity relations. By building a sentence-level labeling result dictionary through the use of a rule-based semi-automatic method, a cardiovascular electronic medical record entity and entity relationship labeling corpus (CVDEMRC) was constructed. The CVDEMRC contains 7691 entities and 11,185 entity relation triples, and the results of consistency examination were 93.51% and 84.02% for entities and entity-relationship annotations, respectively, demonstrating good consistency results. The CVDEMRC constructed in this study is expected to provide a database for information extraction research related to cardiovascular diseases

How to achieve bidirectional zero-knowledge authentication?

Due to the completeness, reliability and zero-knowledge nature, the zero-knowledge proof is widely used to designed various protocols, including zero-knowledge authentication protocols. However, the existing zero-knowledge proof scheme cannot realize bidirectional authentication. In this paper, we design a series of bidirectional zero-knowledge protocols based on two new flavors of operations applicable to multiplicative cyclic group. The two notions are formally defined in this paper. We also provide some formal definitions and properties for the two notions. According to our definitions, any bounded polynomial function defined on multiplicative cyclic group has duality and mirror. Based on the two operations, we introduce and formally define dual commitment scheme and mirror commitment scheme. Besides, we provide two efficient constructions for dual commitment and mirror commitment respectively based on CDH assumption and RSA assumption, and named DCCDH, DCRSA, MCCDH and MCRSA respectively. We also provide the extended version supporting multiple messages in the appendix. Then, we design some efficient non-interactive as well as interactive zero-knowledge authentication protocols based on these commitments. The protocols allow two participants to submit commitments to each other so that they can achieve mutual zero-knowledge authentication only a communication initialization needed. Moreovere , similar to other commitment schemes, our schemes also can be widely used to construction of other schemes for cryptography, such as, verifiable secret sharing, zero-knowledge sets, credentials and content extraction signatures

The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research

The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds

Subspace recovery from noisy or even corrupted data is critical for various applications in machine learning and data analysis. To detect outliers, Robust PCA (R PCA) via Outlier Pursuit was proposed and had found many successful applications. However, the current theoretical analysis on Outlier Pursuit only shows that it succeeds when the sparsity of the corruption matrix is of O(n/r), where n is the number of the samples and r is the rank of the intrinsic matrix which may be comparable to n. Moreover, the regularization parameter is suggested as 3/(7 squareroot gamma n}, where gamma is a parameter that is not known a priori. In this paper, with incoherence condition and proposed ambiguity condition we prove that Outlier Pursuit succeeds when the rank of the intrinsic matrix is of O(n log n) and the sparsity of the corruption matrix is of O(n). We further show that the orders of both bounds are tight. Thus R-PCA via Outlier Pursuit is able to recover intrinsic matrix of higher rank and identify much denser corruptions than what the existing results could predict. Moreover, we suggest that the regularization parameter be chosen as 1 squareroot{log n}, which is definite. Our analysis waives the necessity of tuning the regularization parameter and also significantly extends the working range of the Outlier Pursuit. Experiments on synthetic and real data verify our theories

Corrosion Resistance of Li-Al LDHs Film Modified by Methionine for 6063 Al Alloy in 3.5 wt.% NaCl Solution

Methionine (Met) was introduced to modify the Li-Al layered double hydroxides (LDHs) film prepared on 6063 aluminum alloy by in situ method for the first time. Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, Scanning electron microscopy, and X-ray diffraction confirmed the successful insertion of Met into LDHs film and revealed that the introduction of Met could make the LDHs film much denser. Electrochemical tests illustrated that the corrosion rate of the Met modified LDHs film was reduced by more than an order of magnitude compared with the bare Al alloy. Moreover, the corrosion rate of the modified LDHs film after immersion in 3.5 wt.% NaCl solution for 21 days was almost the same as that without immersion, which indicates that the modified film has good corrosion durability. The corrosion resistance of the scratched modified film could recover to the level without a scratch on the 14th day based on the scratch test results, meaning the modified film has a good self-healing property. Finally, the anti-corrosion mechanism of the Met was proved by molecular dynamic simulations and found that the enhanced corrosion resistance may be attributed to the addition of Met that slowed the diffusion of the corrosive medium Cl− and water molecules