Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having $O(1)$ global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also propose novel applications to genomics data.

Big Bird: Transformers for Longer Sequences

Pseudouridine is the most abundant RNA modification, yet except for a few well-studied cases, little is known about the modified positions and their function(s). Here, we develop Ψ-seq for transcriptome-wide quantitative mapping of pseudouridine. We validate Ψ-seq with spike-ins and de novo identification of previously reported positions and discover hundreds of unique sites in human and yeast mRNAs and snoRNAs. Perturbing pseudouridine synthases (PUS) uncovers which pseudouridine synthase modifies each site and their target sequence features. mRNA pseudouridinylation depends on both site-specific and snoRNA-guided pseudouridine synthases. Upon heat shock in yeast, Pus7p-mediated pseudouridylation is induced at >200 sites, and PUS7 deletion decreases the levels of otherwise pseudouridylated mRNA, suggesting a role in enhancing transcript stability. rRNA pseudouridine stoichiometries are conserved but reduced in cells from dyskeratosis congenita patients, where the PUS DKC1 is mutated. Our work identifies an enhanced, transcriptome-wide scope for pseudouridine and methods to dissect its underlying mechanisms and function.

Transcriptome-wide Mapping Reveals Widespread Dynamic-Regulated Pseudouridylation of ncRNA and mRNA

Chromatin state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type specific activity patterns, and for interpreting disease-association studies1-5. However, the computational challenge of learning chromatin state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise making it inaccessible to the wider scientific community. To address this challenge, we have developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large-scale functional datasets, and visualizing the resulting genome-wide maps of chromatin state annotations.

ChromHMM: automating chromatin-state discovery and characterization

Alternative Splicing Regulatory Networks: Functions, Mechanisms, and Evolution

Vehicle detection and counting in aerial images have become an interesting research focus since the last decade. It is important for a wide range of applications, such as urban planning and traffic management. However, this task is a challenging one due to the small size of the vehicles, their different types and orientations, and similarity in their visual appearance, and some other objects, such as air conditioning units on buildings, trash bins, and road marks. Many methods have been introduced in the literature for solving this problem. These methods are either based on shallow learning or deep learning approaches. However, these methods suffer from relatively low precision and recall rate. This paper introduces an automated vehicle detection and counting system in aerial images. The proposed system utilizes convolution neural network to regress a vehicle spatial density map across the aerial image. It has been evaluated on two publicly available data sets, namely, Munich and Overhead Imagery Research Data Set. The experimental results show that our proposed system is efficient and effective, and produces higher precision and recall rate than the comparative methods.

Vehicle Detection and Counting in High-Resolution Aerial Images Using Convolutional Regression Neural Network

The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm.

/pdf/deepromoter-robust-promoter-predictor-using-deep-learning-5aetq08btp.pdf

DeePromoter: Robust Promoter Predictor Using Deep Learning.

iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components.

Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time.

Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network.

Pseudouridine is the most prevalent RNA modification and has been found in both eukaryotes and prokaryotes. Currently, pseudouridine has been demonstrated in several kinds of RNAs, such as small nuclear RNA, rRNA, tRNA, mRNA, and small nucleolar RNA. Therefore, its significance to academic research and drug development is understandable. Through biochemical experiments, the pseudouridine site identification has produced good outcomes, but these lab exploratory methods and biochemical processes are expensive and time consuming. Therefore, it is important to introduce efficient methods for identification of pseudouridine sites. In this study, an intelligent method for pseudouridine sites using the deep-learning approach was developed. The proposed prediction model is called iPseU-CNN (identifying pseudouridine by convolutional neural networks). The existing methods used handcrafted features and machine-learning approaches to identify pseudouridine sites. However, the proposed predictor extracts the features of the pseudouridine sites automatically using a convolution neural network model. The iPseU-CNN model yields better outcomes than the current state-of-the-art models in all evaluation parameters. It is thus highly projected that the iPseU-CNN predictor will become a helpful tool for academic research on pseudouridine site prediction of RNA, as well as in drug discovery.

Hilal Tayara

Papers

Vehicle Detection and Counting in High-Resolution Aerial Images Using Convolutional Regression Neural Network

DeePromoter: Robust Promoter Predictor Using Deep Learning.

iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components.

Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network.

iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks.