Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e.g., clear edges, smooth color shading, abstract fine structures, etc.). Existing advanced models try to enhance cartoonization effect by learning to promote edges adversarially, introducing style transfer loss, or learning to align style from multiple representation space. This paper demonstrates that more distinct and vivid cartoonization effect could be easily achieved with only basic adversarial loss. Observing that cartoon style is more evident in cartoon-texture-salient local image regions, we build a region-level adversarial learning branch in parallel with the normal image-level one, which constrains adversarial learning on cartoon-texture-salient local patches for better perceiving and transferring cartoon texture features. To this end, a novel cartoon-texture-saliency-sampler (CTSS) module is proposed to dynamically sample cartoon-texture-salient patches from training data. With extensive experiments, we demonstrate that texture saliency adaptive attention in adversarial learning, as a missing ingredient of related methods in image cartoonization, is of significant importance in facilitating and enhancing image cartoon stylization, especially for high-resolution input pictures.Comment: Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7183-7207, 202

Levels and patterns of genetic diversity in wild Chrysichthys nigrodigitatus in the Lagos Lagoon complex

Mitochondrial DNA control region sequences were used to investigate the genetic diversity of populations of Chrysichthys nigrodigitatus and Chrysichthys walkeri in the Lagos Lagoon complex. A total of 34 haplotypes were detected. The genetic diversity among C. nigrodigitatus as determined by haplotype and nucleotide diversities were 0.879 ± 0.033 and 0.0131 ± 0.003, respectively and the values were 0.93 ± 0.04 and 0.010 ± 0.0020 for a population of C. walkeri. The largest genetic distance was 7.01% between C. walkeri from Lagos Lagoon (WAK) and control region sequences of Chrysichthys nigrodigitatus samples obtained from different parts of the lagoon complex in 2008 (PRE). Within population differences accounted for 80.41% of total genetic variance in C. nigrodigitatus. There was no evidence of decreased genetic diversity in the populations. The mismatch distribution and neutrality test suggest that the effective size of C. nigrodigitatus population has been large and stable for a long period.Key words: Chrysichthys nigrodigitatus, Chrysichthys walkeri, Lagos Lagoon complex, mtDNA control region

Alkali burn induced corneal spontaneous pain and activated neuropathic pain matrix in the central nerve system in mice

Purpose: To explore whether alkali burn causes corneal neuropathic pain and activates neuropathic pain matrix in the central nerve system in mice. Methods: A corneal alkali burn mouse model (grade II) was used. Mechanical threshold in the cauterized area was tested using Von Frey hairs. Spontaneous pain behavior was investigated with conditioned place preference (CPP). Phosphor extracellular signal-regulated kinase (ERK), which is a marker for neuronal activation in chronic pain processing, was investigated in several representative areas of the neuropathic pain matrix: the two regions of the spinal trigeminal nucleus (subnucleus interpolaris/caudalis ,Vi/Vc; subnucleus caudalis/upper cervical cord , Vc/C1), insular cortex, anterior cingulated cortex (ACC), and the rostroventral medulla (RVM). Further, pharmacologically blocking pERK activation in ACC of alkali burn mice was performed in a separate study. Results: Corneal alkali burn caused long lasting damage to the corneal subbasal nerve fibers and mice exhibited spontaneous pain behavior. By testing in several representative areas of neuropathic pain matrix in the higher nerve system, phosphor extracellular signal-regulated kinase (ERK) was significantly activated in Vc/C1, but not in Vi/Vc. Also, ERK was activated in the insular cortex, ACC, and RVM. Furthermore, pharmacologically blocking ERK activation in ACC abolished alkali burn induced corneal spontaneous pain. Conclusion: Alkali burn could cause corneal spontaneous pain and activate neuropathic pain matrix in the central nerve system. Furthermore, activation of ERK in ACC is required for alkali burn induced corneal spontaneous pain

Unfavorable Associations Between Serum Trimethylamine N-Oxide and L-Carnitine Levels With Components of Metabolic Syndrome in the Newfoundland Population

Background: We aimed to study the relationships between serum Trimethylamine N-oxide (TMAO) and L-carnitine levels with metabolic syndrome profiles, including obesity, blood pressure, serum lipids, serum glucose and insulin resistance (IR)-related index in humans.Methods: Cross-sectional study was performed in 1,081 subjects from the CODING study in Newfoundland. Serum TMAO and L-carnitine levels were quantified by LC-MS/MS. Metabolic markers were measured in all subjects using fasting blood samples. Partial correlation and linear regression analysis were employed after systematically controlling the major confounding factors, such as age, gender, calorie intake and physical activity level.Results: Serum L-carnitine level was positively correlated with serum triglyceride (TG), serum insulin, IR in males with normal fasting glucose (p < 0.05 for all) and positively correlated with only serum TG (p < 0.05) in those with hyperglycemia. In females, significant positive correlations were identified between serum L-carnitine level with obesity, serum total cholesterol, glucose, insulin, and IR in those with normal fasting glucose level (p < 0.05 for all), while none was found in those with hyperglycemia. Serum TMAO level was only identified to be positively correlated with serum insulin level and IR in hyperglycemic males (p < 0.05 for all).Conclusions: Serum L-carnitine level was significantly associated with an unfavorable metabolic syndrome (MS) profile mainly in subjects with normal serum glucose level, while serum TMAO level was associated with an unfavorable MS profile in subjects with hyperglycemia. The gender difference warrants further investigations

Magnetic Borophenes from an Evolutionary Search

A computational methodology based on ab initio evolutionary algorithms and spin-polarized density functional theory was developed to predict two-dimensional magnetic materials. Its application to a model system borophene reveals an unexpected rich magnetism and polymorphism. A metastable borophene with nonzero thickness is an antiferromagnetic semiconductor from first-principles calculations, and can be further tuned into a half-metal by finite electron doping. In this borophene, the buckling and coupling among three atomic layers are not only responsible for magnetism, but also result in an out-of-plane negative Poisson\u27s ratio under uniaxial tension, making it the first elemental material possessing auxetic and magnetic properties simultaneously

A Unified Query-based Paradigm for Camouflaged Instance Segmentation

Due to the high similarity between camouflaged instances and the background, the recently proposed camouflaged instance segmentation (CIS) faces challenges in accurate localization and instance segmentation. To this end, inspired by query-based transformers, we propose a unified query-based multi-task learning framework for camouflaged instance segmentation, termed UQFormer, which builds a set of mask queries and a set of boundary queries to learn a shared composed query representation and efficiently integrates global camouflaged object region and boundary cues, for simultaneous instance segmentation and instance boundary detection in camouflaged scenarios. Specifically, we design a composed query learning paradigm that learns a shared representation to capture object region and boundary features by the cross-attention interaction of mask queries and boundary queries in the designed multi-scale unified learning transformer decoder. Then, we present a transformer-based multi-task learning framework for simultaneous camouflaged instance segmentation and camouflaged instance boundary detection based on the learned composed query representation, which also forces the model to learn a strong instance-level query representation. Notably, our model views the instance segmentation as a query-based direct set prediction problem, without other post-processing such as non-maximal suppression. Compared with 14 state-of-the-art approaches, our UQFormer significantly improves the performance of camouflaged instance segmentation. Our code will be available at https://github.com/dongbo811/UQFormer.Comment: This paper has been accepted by ACM MM202