MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph

MEGAHIT is a NGS de novo assembler for assembling large and complex metagenomics data in a time- and cost-efficient manner. It finished assembling a soil metagenomics dataset with 252Gbps in 44.1 hours and 99.6 hours on a single computing node with and without a GPU, respectively. MEGAHIT assembles the data as a whole, i.e., it avoids pre-processing like partitioning and normalization, which might compromise on result integrity. MEGAHIT generates 3 times larger assembly, with longer contig N50 and average contig length than the previous assembly. 55.8% of the reads were aligned to the assembly, which is 4 times higher than the previous. The source code of MEGAHIT is freely available at https://github.com/voutcn/megahit under GPLv3 license.Comment: 2 pages, 2 tables, 1 figure, submitted to Oxford Bioinformatics as an Application Not

A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201

Methods and compositions for enhanced resistance to abiotic stress in plants

The present invention provides transgenic plants having increased tolerance to abiotic stress comprising a recombinant nucleic acid molecule, said recombinant nucleic acid molecule comprising a nucleotide sequence encoding miR319 operatively associated with a promoter, a nucleotide sequence that is antisense to a portion of consecutive nucleotides of a nucleotide sequence encoding PCF5, and/or a nucleotide sequence that encodes a portion of consecutive nucleotides of a nucleotide sequence encoding PCF5, which when expressed produces an antisense nucleotide sequence, wherein expression of the nucleotide sequence confers increased tolerance to abiotic stress. Also provided are methods and compositions for making said transgenic plants

DocDeshadower: Frequency-aware Transformer for Document Shadow Removal

The presence of shadows significantly impacts the visual quality of scanned documents. However, the existing traditional techniques and deep learning methods used for shadow removal have several limitations. These methods either rely heavily on heuristics, resulting in suboptimal performance, or require large datasets to learn shadow-related features. In this study, we propose the DocDeshadower, a multi-frequency Transformer-based model built on Laplacian Pyramid. DocDeshadower is designed to remove shadows at different frequencies in a coarse-to-fine manner. To achieve this, we decompose the shadow image into different frequency bands using Laplacian Pyramid. In addition, we introduce two novel components to this model: the Attention-Aggregation Network and the Gated Multi-scale Fusion Transformer. The Attention-Aggregation Network is designed to remove shadows in the low-frequency part of the image, whereas the Gated Multi-scale Fusion Transformer refines the entire image at a global scale with its large perceptive field. Our extensive experiments demonstrate that DocDeshadower outperforms the current state-of-the-art methods in both qualitative and quantitative terms

ShaDocFormer: A Shadow-attentive Threshold Detector with Cascaded Fusion Refiner for document shadow removal

Document shadow is a common issue that arise when capturing documents using mobile devices, which significantly impacts the readability. Current methods encounter various challenges including inaccurate detection of shadow masks and estimation of illumination. In this paper, we propose ShaDocFormer, a Transformer-based architecture that integrates traditional methodologies and deep learning techniques to tackle the problem of document shadow removal. The ShaDocFormer architecture comprises two components: the Shadow-attentive Threshold Detector (STD) and the Cascaded Fusion Refiner (CFR). The STD module employs a traditional thresholding technique and leverages the attention mechanism of the Transformer to gather global information, thereby enabling precise detection of shadow masks. The cascaded and aggregative structure of the CFR module facilitates a coarse-to-fine restoration process for the entire image. As a result, ShaDocFormer excels in accurately detecting and capturing variations in both shadow and illumination, thereby enabling effective removal of shadows. Extensive experiments demonstrate that ShaDocFormer outperforms current state-of-the-art methods in both qualitative and quantitative measurements

UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer

Underwater images often exhibit poor quality, imbalanced coloration, and low contrast due to the complex and intricate interaction of light, water, and objects. Despite the significant contributions of previous underwater enhancement techniques, there exist several problems that demand further improvement: (i) Current deep learning methodologies depend on Convolutional Neural Networks (CNNs) that lack multi-scale enhancement and also have limited global perception fields. (ii) The scarcity of paired real-world underwater datasets poses a considerable challenge, and the utilization of synthetic image pairs risks overfitting. To address the aforementioned issues, this paper presents a Multi-scale Transformer-based Network called UWFormer for enhancing images at multiple frequencies via semi-supervised learning, in which we propose a Nonlinear Frequency-aware Attention mechanism and a Multi-Scale Fusion Feed-forward Network for low-frequency enhancement. Additionally, we introduce a specialized underwater semi-supervised training strategy, proposing a Subaqueous Perceptual Loss function to generate reliable pseudo labels. Experiments using full-reference and non-reference underwater benchmarks demonstrate that our method outperforms state-of-the-art methods in terms of both quantity and visual quality

Impact of Uniaxial Pressure on Structural and Magnetic Phase Transitions in Electron-Doped Iron Pnictides

We use neutron resonance spin echo and Larmor diffraction to study the effect of uniaxial pressure on the tetragonal-to-orthorhombic structural ($T_s$) and antiferromagnetic (AF) phase transitions in iron pnictides BaFe$_{2-x}$Ni$_{x}$As$_{2}$ ($x=0,0.03,0.12$), SrFe$_{1.97}$Ni$_{0.03}$As$_2$, and BaFe$_2$(As$_{0.7}$P$_{0.3}$)$_2$. In antiferromagnetically ordered BaFe$_{2-x}$Ni$_{x}$As$_{2}$ and SrFe$_{1.97}$Ni$_{0.03}$As$_2$ with $T_N$ and $T_s$ ($T_N\leq T_s$), a uniaxial pressure necessary to detwin the sample also increases $T_N$, smears out the structural transition, and induces an orthorhombic lattice distortion at all temperatures. By comparing temperature and doping dependence of the pressure induced lattice parameter changes with the elastoresistance and nematic susceptibility obtained from transport and ultrasonic measurements, we conclude that the in-plane resistivity anisotropy found in the paramagnetic state of electron underdoped iron pnictides depends sensitively on the nature of the magnetic phase transition and a strong coupling between the uniaxial pressure induced lattice distortion and electronic nematic susceptibility.Comment: 18 pages, 15 figure

Salting Up of Proteins at the Air/Water Interface.

Vibrational sum frequency generation (VSFG) spectroscopy and surface pressure measurements are used to investigate the adsorption of a globular protein, bovine serum albumin (BSA), at the air/water interface with and without the presence of salts. We find at low (2 to 5 ppm) protein concentrations, which is relevant to environmental conditions, both VSFG and surface pressure measurements of BSA behave drastically different from at higher concentrations. Instead of emerging to the surface immediately, as observed at 1000 ppm, protein adsorption kinetics is on the order of tens of minutes at lower concentrations. Most importantly, salts strongly enhance the presence of BSA at the interface. This "salting up" effect differs from the well-known "salting out" effect as it occurs at protein concentrations well-below where "salting out" occurs. The dependence on salt concentration suggests this effect relates to a large extent electrostatic interactions and volume exclusion. Additionally, results from other proteins and the pH dependence of the kinetics indicate that salting up depends on the flexibility of proteins. This initial report demonstrates "salting up" as a new type of salt-driven interfacial phenomenon, which is worthy of continued investigation given the importance of salts in biological and environmental aqueous systems