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Found 32 papers, 21 papers with code
3D Former: Monocular Scene Reconstruction with 3D SDF Transformers
In this work, we propose an SDF transformer network, which replaces the role of 3D CNN for better 3D feature aggregation.
Dense RGB SLAM with Neural Implicit Maps
To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder.
RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging
Our graph optimizer iteratively refines the global camera rotations by minimizing each node's single rotation objective function.
Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network
For boosting the clinical deployment of fundus image enhancement, this paper proposes the pyramid constraint to develop a degradation-invariant enhancement network (PCE-Net), which mitigates the demand for clinical data and stably enhances unknown data.
Protein-to-genome alignment with miniprot
Motivation: Protein-to-genome alignment is critical to annotating genes in non-model organisms.
Towards complete representation of bacterial contents in metagenomic samples
Our algorithm generates more circular MAGs and moves a step closer to the complete representation of microbiome communities.
Spacecraft Attitude Pointing Control under Pointing Forbidden Constraints with Guaranteed Accuracy
To resolve this problem, a switching controller structure is proposed in this paper based on the reduced-attitude representation, fusing the artificial potential field (APF) methodology and the Prescribed Performance Control (PPC) scheme together.
An Empirical Study on the Usage of Automated Machine Learning Tools
Machine learning (ML) practitioners use AutoML tools to automate and optimize the process of feature engineering, model training, and hyperparameter optimization and so on.
Fast sequence to graph alignment using the graph wavefront algorithm
Motivation: A pan-genome graph represents a collection of genomes and encodes sequence variations between them.
Structure-consistent Restoration Network for Cataract Fundus Image Enhancement
In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure.
Studying the Practices of Deploying Machine Learning Projects on Docker
Studies have recently explored the use of Docker for deploying general software projects with no specific focus on how Docker is used to deploy ML-based projects.
An Annotation-free Restoration Network for Cataractous Fundus Images
The restoration model is learned from the synthesized images and adapted to real cataract images.
FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on Multi-Scale Temporal Convolutional Network
In recent years, deep learning-based approaches have significantly improved the performance of single-channel speech enhancement.
A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction
An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at https://github. com/yd1008/ts_ensemble_sunspot.
Metagenome assembly of high-fidelity long reads with hifiasm-meta
Current metagenome assemblers developed for short sequence reads or noisy long readswere not optimized for accurate long reads.
Integrating Attention Feedback into the Recurrent Neural Network
The HA-LSTM structure is different from the standard LSTM structure because a scaled dot-product attention-based sliding controller is introduced to the LSTM structure.
Constructing Sub-scale Surrogate Model for Proppant Settling in Inclined Fractures from Simulation Data with Multi-fidelity Neural Network
The results demonstrate that constructing the settling surrogate with the MFNN can reduce the need for high-fidelity data and thus computational cost by 80%, while the accuracy lost is less than 5% compared to a high-fidelity surrogate.
Robust haplotype-resolved assembly of diploid individuals without parental data
Routine single-sample haplotype-resolved assembly remains an unresolved problem.
New strategies to improve minimap2 alignment accuracy
Summary: We present several recent improvements to minimap2, a versatile pairwise aligner for nucleotide sequences.
End-to-End Rotation Averaging With Multi-Source Propagation
These methods can suffer from bad initializations due to the noisy spanning tree or outliers in input relative rotations.
Twelve years of SAMtools and BCFtools
Background SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data.
BGM: Building a Dynamic Guidance Map without Visual Images for Trajectory Prediction
Visual images usually contain the informative context of the environment, thereby helping to predict agents' behaviors.
Haplotype-resolved de novo assembly with phased assembly graphs
Haplotype-resolved de novo assembly is the ultimate solution to the study of sequence variations in a genome.
Genomics Quantitative Methods
The design and construction of reference pangenome graphs
The recent advances in sequencing technologies enables the assembly of individual genomes to the reference quality.
Logram: Efficient Log Parsing Using n-Gram Dictionaries
As logs are usually very large in size, automated log analysis is needed to assist practitioners in their software operation and maintenance efforts.
Software Engineering
Deep Learning of Subsurface Flow via Theory-guided Neural Network
The TgNN can achieve higher accuracy than the ordinary Artificial Neural Network (ANN) because the former provides physically feasible predictions and can be more readily generalized beyond the regimes covered with the training data.
Identifying centromeric satellites with dna-brnn
Summary: Human alpha satellite and satellite 2/3 contribute to several percent of the human genome.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning
The first one is lack of collaboration between scenarios meaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a sub-optimal overall performance.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Minimap2: pairwise alignment for nucleotide sequences
Motivation: Recent advances in sequencing technologies promise ultra-long reads of $\sim$100 kilo bases (kb) in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 mega bases (Mb) in length.
Towards Better Understanding of Artifacts in Variant Calling from High-Coverage Samples
By investigating false heterozygous calls in the haploid genome, we identified the erroneous realignment in low-complexity regions and the incomplete reference genome with respect to the sample as the two major sources of errors, which press for continued improvements in these two areas.
Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM
Summary: BWA-MEM is a new alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human.
