Adaptive Feature Interpolation for Low-Shot Image Generation

Training of generative models especially Generative Adversarial Networks can easily diverge in low-data setting. To mitigate this issue, we propose a novel implicit data augmentation approach which facilitates stable training and synthesize high-quality samples without need of label information. Specifically, we view the discriminator as a metric embedding of the real data manifold, which offers proper distances between real data points. We then utilize information in the feature space to develop a fully unsupervised and data-driven augmentation method. Experiments on few-shot generation tasks show the proposed method significantly improve results from strong baselines with hundreds of training samples.Comment: ECCV'22. Code available at https://github.com/dzld00/Adaptive-Feature-Interpolation-for-Low-Shot-Image-Generatio

Detecting and quantifying natural selection at two linked loci from time series data of allele frequencies with forward-in-time simulations

Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time. This improvement provides an opportunity for us to study natural selection based on time serial samples of genomes while accounting for genetic recombination effect and local linkage information. Such time series genomic data allow for more accurate estimation of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel Bayesian statistical framework for inferring natural selection at a pair of linked loci by capitalising on the temporal aspect of DNA data with the additional flexibility of modeling the sampled chromosomes that contain unknown alleles. Our approach is built on a hidden Markov model where the underlying process is a two-locus Wright-Fisher diffusion with selection, which enables us to explicitly model genetic recombination and local linkage. The posterior probability distribution for selection coefficients is computed by applying the particle marginal Metropolis-Hastings algorithm, which allows us to efficiently calculate the likelihood. We evaluate the performance of our Bayesian inference procedure through extensive simulations, showing that our approach can deliver accurate estimates of selection coefficients, and the addition of genetic recombination and local linkage brings about significant improvement in the inference of natural selection. We also illustrate the utility of our method on real data with an application to ancient DNA data associated with white spotting patterns in horses

Optimization of Low Salt Perilla Shrimp Paste and Analysis of Its Volatile Flavor Compounds

This study developed a low-salt Perilla shrimp paste by combining traditional preparation with the addition of yeast strain co-fermentation using oriental white shrimp and Perilla as raw materials. The effects of Perilla addition, salt addition, fermentation time and inoculation amount of koji seeds on the formula of low-salt Perilla shrimp paste were studied utilize the optimal process prediction obtained from Box-Behnken and make adjustments to the predicted process conditions based on actual production conditions. The results showed that the co-fermentation with yeast could effectively reduce the amount of salt added. The results indicated that the amount of shrimp added was fixed at 300 g. The sensory evaluation of the low-salt Perilla shrimp paste prepared under the conditions of 35% Perilla addition, 10% salt addition, and 3.5% yeast inoculation was 87.94 under the conditions of fermentation time of 5 d at 32 ℃. Its relative content of volatile flavor substances reached 97.82%, including 6 esters, 11 alcohols, 9 acids and 5 aldehydes. The contents of amino acid nitrogen, salt, protein, moisture and ash all met the requirements of SC/T 3602-2016 industry standard. Among them, the salt content was 106 mg/100 g, which was lower than the requirement of GB/T 23789-2009 national standard for salt content not higher than 120 mg/100 g. Compared to products in the market, it featured low salt and high protein content. The formulated products in this study are low-salt foods. It combines the freshness and flavor of shrimp paste with the slightly pungent and sweet aroma of Perilla

On converse bounds for classical communication over quantum channels

We explore several new converse bounds for classical communication over quantum channels in both the one-shot and asymptotic regimes. First, we show that the Matthews-Wehner meta-converse bound for entanglement-assisted classical communication can be achieved by activated, no-signalling assisted codes, suitably generalizing a result for classical channels. Second, we derive a new efficiently computable meta-converse on the amount of classical information unassisted codes can transmit over a single use of a quantum channel. As applications, we provide a finite resource analysis of classical communication over quantum erasure channels, including the second-order and moderate deviation asymptotics. Third, we explore the asymptotic analogue of our new meta-converse, the $\Upsilon$-information of the channel. We show that its regularization is an upper bound on the classical capacity, which is generally tighter than the entanglement-assisted capacity and other known efficiently computable strong converse bounds. For covariant channels we show that the $\Upsilon$-information is a strong converse bound.Comment: v3: published version; v2: 18 pages, presentation and results improve

A Tungsten Deep Potential with High Accuracy and Generalization Ability based on a Newly Designed Three-body Embedding Formalism

Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD)simulations reveal the atomisttic scale mechanisms, so they are crucial for the understanding ofthe macroscopic property deterioration of tungsten under harsh and complex service environment.The interatomic potential used in the MD simulations is required to accurately describe a widespectrum of relevant defect properties, which is by far challenging to the existing interatomicpotentials. In this paper, we propose a new three-body embedding descriptor and hybridize it intothe Deep-Potential (DP) framework, an end-to-end deep learning interatomic potential model.Trained with the dataset generated by a concurrent learning method, the potential model fortungsten, named by DP-HYB, is able to accurately predict a wide range of properties includingelastic constants, the formation energies of free surfaces, grain boundaries, point defects and defectclusters, stacking fault energies, the core structure of screw dislocation, the energy barrier and thetransition path of the screw dislocation migration. Since most of the properties are not explicitlyincluded in the training dataset, the strong generalizability of the DP-HYB model indicates thatit is a good candidate for the atomistic simulations of tungsten property deterioration, especiallythose involving the mechanical property changing under the harsh service environment

Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur

Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of large-scale scenes from in-the-wild images with inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid neural rendering model that makes image-based representation and neural 3D representation join forces to render high-quality, view-consistent images. Besides, images captured in the wild inevitably contain artifacts, such as motion blur, which deteriorates the quality of rendered images. Accordingly, we propose strategies to simulate blur effects on the rendered images to mitigate the negative influence of blurriness images and reduce their importance during training based on precomputed quality-aware weights. Extensive experiments on real and synthetic data demonstrate our model surpasses state-of-the-art point-based methods for novel view synthesis. The code is available at https://daipengwa.github.io/Hybrid-Rendering-ProjectPage

A New Pixels Flipping Method for Huge Watermarking Capacity of the Invoice Font Image

Invoice printing just has two-color printing, so invoice font image can be seen as binary image. To embed watermarks into invoice image, the pixels need to be flipped. The more huge the watermark is, the more the pixels need to be flipped. We proposed a new pixels flipping method in invoice image for huge watermarking capacity. The pixels flipping method includes one novel interpolation method for binary image, one flippable pixels evaluation mechanism, and one denoising method based on gravity center and chaos degree. The proposed interpolation method ensures that the invoice image keeps features well after scaling. The flippable pixels evaluation mechanism ensures that the pixels keep better connectivity and smoothness and the pattern has highest structural similarity after flipping. The proposed denoising method makes invoice font image smoother and fiter for human vision. Experiments show that the proposed flipping method not only keeps the invoice font structure well but also improves watermarking capacity

IGF2BP3 promotes mRNA degradation through internal m⁷G modification

Recent studies have suggested that mRNA internal m7G and its writer protein METTL1 are closely related to cell metabolism and cancer regulation. Here, we identify that IGF2BP family proteins IGF2BP1-3 can preferentially bind internal mRNA m7G. Such interactions, especially IGF2BP3 with m7G, could promote the degradation of m7G target transcripts in cancer cells. IGF2BP3 is more responsive to changes of m7G modification, while IGF2BP1 prefers m6A to stabilize the bound transcripts. We also demonstrate that p53 transcript, TP53, is m7G-modified at its 3’UTR in cancer cells. In glioblastoma, the methylation level and the half lifetime of the modified transcript could be modulated by tuning IGF2BP3, or by site-specific targeting of m7G through a dCas13b-guided system, resulting in modulation of cancer progression and chemosensitivity