Abstract Motivation Computational drug repositioning is an important and efficient approach towards identifying novel treatments for diseases in discovery. The emergence of large-scale, heterogeneous biological biomedical datasets has provided unprecedented opportunity developing computational methods. problem can be modeled as a recommendation system that recommends based on known drug–disease associations. formulation under this matrix completion, assuming the hidden factors contributing...

In recent years, the recommendation systems have become increasingly popular and been used in a broad variety of applications. Here, we investigate matrix completion techniques for that are based on collaborative filtering. The filtering problem can be viewed as predicting favorability user with respect to new items commodities. When rating is constructed users rows, columns, entries ratings, then modeled by filling out unknown elements matrix. This article presents comprehensive survey...

Mobile monocular 3D object detection (Mono3D) (e.g., on a vehicle, drone, or robot) is an important yet challenging task. Existing transformer-based offline Mono3D models adopt grid-based vision tokens, which suboptimal when using coarse tokens due to the limited available computational power. In this paper, we propose online framework, called MonoATT, leverages novel transformer with heterogeneous of varying shapes and sizes facilitate mobile Mono3D. The core idea MonoATT adaptively assign...

Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision collaborative autonomous vehicles, and yet challenging due small overlapping area a huge disparity between observed densities. In this paper, we propose Group-wise Contrastive Learning (GCL) scheme extract density-invariant geometric features register clouds. We mark through theoretical analysis experiments that, contrastive positives should be independent identically distributed (i.i.d.), in order train...

Learning individual-level treatment effect is a fundamental problem in causal inference and has received increasing attention many areas, especially the user growth area which concerns internet companies. Recently, disentangled representation learning methods that decompose covariates into three latent factors, including instrumental, confounding adjustment have witnessed great success estimation. However, it remains an open how to learn underlying factors precisely. Specifically, previous...

Process analytical technology (PAT) has been successfully applied in numerous chemical synthesis cases and is an important tool pharmaceutical process research development. PAT brings new methods opportunities for the real-time monitoring of processes. In multistep synthesis, complex reaction mixtures a significant challenge but provides opportunity to enhance understanding control. this study, combined multichannel spectrometer system with both near-infrared Raman spectroscopy was built,...

For many driving safety applications, it is of great importance to accurately register LiDAR point clouds generated on distant moving vehicles. However, such have extremely different density and sensor perspective the same object, making registration very hard. In this paper, we propose a novel feature extraction framework, called APR, for online cloud registration. Specifically, APR leverages an autoencoder design, where reconstructs denser aggregated with several frames instead original...

The Pareto principle states that most effects are the result of a few dominating causes. This also fits matrix completion problems. In practice, real-world data sets exhibit nonuniformly distributed observations. Unfortunately, existing algorithms designed based on uniformly this brief, we propose factorization approach to recover large-scale from submatrix is composed important rows and columns original matrix. method for evaluating importance row or column inspired by term...

Cancer is a heterogeneous disease, thus one of the central problems how to dissect resulting complex phenotypes in terms their biological building blocks. Computationally, this represent and interpret high dimensional observations through structural conceptual abstraction into most influential determinants underlying problem. The working hypothesis report consider gene interaction be largely responsible for manifestation cancer phenotypes, where representation conceptualized. Here, we...

Summary In this work, we propose a novel phase noise compensation (PNC) method aiming at mitigating impact of the laser on DAS, by deploying an auxiliary interferometer together with appropriate two-step algorithm. experimental demonstration 100 µs linear frequency modulation (LFM) pulses are injected into sensing fiber without any optical amplification under different strategies (PNC / off). Strain resolution 55 pε/√Hz 47 km measurement distance and 4.3 m spatial is realized in system. The...

Abstract In the field of biomedicine, pH and dissolved oxygen (DO) content cell metabolism fluid can reflect health provide an auxiliary reference for early detection cancer. Based on preliminary research results same group, a dual-parameter synchronous system metabolic was established. The is based fluorescence characteristics 7-Amino-4-methylcoumarin-modified CdSe quantum dots. Both DO are solved by voltage signal photodetection module. Firstly, dots were introduced. Then method briefly...

Learning individual-level treatment effect is a fundamental problem in causal inference and has received increasing attention many areas, especially the user growth area which concerns internet companies. Recently, disentangled representation learning methods that decompose covariates into three latent factors, including instrumental, confounding adjustment have witnessed great success estimation. However, it remains an open how to learn underlying factors precisely. Specifically, previous...