Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection

RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However, the modality bias problem remains unsolved as multispectral pedestrian detectors learn the statistical bias in datasets. Specifically, datasets in multispectral pedestrian detection mainly distribute between ROTO (day) and RXTO (night) data; the majority of the pedestrian labels statistically co-occur with their thermal features. As a result, multispectral pedestrian detectors show poor generalization ability on examples beyond this statistical correlation, such as ROTX data. To address this problem, we propose a novel Causal Mode Multiplexer (CMM) framework that effectively learns the causalities between multispectral inputs and predictions. Moreover, we construct a new dataset (ROTX-MP) to evaluate modality bias in multispectral pedestrian detection. ROTX-MP mainly includes ROTX examples not presented in previous datasets. Extensive experiments demonstrate that our proposed CMM framework generalizes well on existing datasets (KAIST, CVC-14, FLIR) and the new ROTX-MP. We will release our new dataset to the public for future research.Comment: CVPR202

Dzyaloshinskii-Moriya torque-driven resonance in antiferromagnetic {\alpha}-Fe2O3

We examine the high-frequency optical mode of {\alpha}-Fe2O3 and report that Dzyaloshinskii-Moriya (DM) interaction generates a new type of torque on the magnetic resonance. Using a continuous-wave terahertz interferometer, we measure the optical mode spectra, where the asymmetric absorption with a large amplitude and broad linewidth is observed near the magnetic transition point, Morin temperature (TM ~ 254.3 K). Based on the spin wave model, the spectral anomaly is attributed to the DM interaction-induced torque, enabling to extract the strength of DM interaction field of 4 T. Our work opens a new avenue to characterize the spin resonance behaviors at an antiferromagnetic singular point for next-generation and high-frequency spin-based information technologies.Comment: 4 figure

Self-supervised representation learning anomaly detection methodology based on boosting algorithms enhanced by data augmentation using StyleGAN for manufacturing imbalanced data

This study proposes a methodology for detecting anomalies in the manufacturing industry using a self-supervised representation learning approach based on deep generative models. The challenge arises from the limited availability of data on defective products compared with normal data, leading to degradation in the performance of deep learning models owing to data imbalances. To address this limitation, we propose a process that leverages the Gramian angular field to transform time-series data into images, applies StyleGAN for image augmentation of anomalous data, and utilizes a boosting algorithm for classifier selection in supervised learning. Additionally, we compared the accuracy of the classifier before and after data augmentation. In experimental cases involving CNC milling machine data and wire arc additive manufacturing data, the proposed approach outperformed the approach before augmentation, resulting in improved precision, recall, and F1-score for anomaly detection. Furthermore, Bayesian optimization of the hyperparameters of the boosting algorithm further enhanced the performance metrics. The proposed process effectively addresses the data imbalance problem, and demonstrates its applicability to various manufacturing industries.Peer reviewe

Genome-wide Association Study of Integrated Meat Quality-related Traits of the Duroc Pig Breed

The increasing importance of meat quality has implications for animal breeding programs. Research has revealed much about the genetic background of pigs, and many studies have revealed the importance of various genetic factors. Since meat quality is a complex trait which is affected by many factors, consideration of the overall phenotype is very useful to study meat quality. For integrating the phenotypes, we used principle component analysis (PCA). The significant SNPs refer to results of the GRAMMAR method against PC1, PC2 and PC3 of 14 meat quality traits of 181 Duroc pigs. The Genome-wide association study (GWAS) found 26 potential SNPs affecting various meat quality traits. The loci identified are located in or near 23 genes. The SNPs associated with meat quality are in or near five genes (ANK1, BMP6, SHH, PIP4K2A, and FOXN2) and have been reported previously. Twenty-five of the significant SNPs also located in meat quality-related QTL regions, these result supported the QTL effect indirectly. Each single gene typically affects multiple traits. Therefore, it is a useful approach to use integrated traits for the various traits at the same time. This innovative approach using integrated traits could be applied on other GWAS of complex-traits including meat-quality, and the results will contribute to improving meat-quality of pork

Genome-wide Association Study of Chicken Plumage Pigmentation

To increase plumage color uniformity and understand the genetic background of Korean chickens, we performed a genome-wide association study of different plumage color in Korean native chickens. We analyzed 60K SNP chips on 279 chickens with GEMMA methods for GWAS and estimated the genetic heritability for plumage color. The estimated heritability suggests that plumage coloration is a polygenic trait. We found new loci associated with feather pigmentation at the genome-wide level and from the results infer that there are additional genetic effect for plumage color. The results will be used for selecting and breeding chicken for plumage color uniformity

Bovine Genome-wide Association Study for Genetic Elements to Resist the Infection of Foot-and-mouth Disease in the Field

Foot-and-mouth disease (FMD) is a highly contagious disease affecting cloven-hoofed animals and causes severe economic loss and devastating effect on international trade of animal or animal products. Since FMD outbreaks have recently occurred in some Asian countries, it is important to understand the relationship between diverse immunogenomic structures of host animals and the immunity to foot-and-mouth disease virus (FMDV). We performed genome wide association study based on high-density bovine single nucleotide polymorphism (SNP) chip for identifying FMD resistant loci in Holstein cattle. Among 624532 SNP after quality control, we found that 11 SNPs on 3 chromosomes (chr17, 22, and 15) were significantly associated with the trait at the p.adjust <0.05 after PERMORY test. Most significantly associated SNPs were located on chromosome 17, around the genes Myosin XVIIIB and Seizure related 6 homolog (mouse)-like, which were associated with lung cancer. Based on the known function of the genes nearby the significant SNPs, the FMD resistant animals might have ability to improve their innate immune response to FMDV infection

A genome-wide scan for signatures of directional selection in domesticated pigs

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Background Animal domestication involved drastic phenotypic changes driven by strong artificial selection and also resulted in new populations of breeds, established by humans. This study aims to identify genes that show evidence of recent artificial selection during pig domestication. Results Whole-genome resequencing of 30 individual pigs from domesticated breeds, Landrace and Yorkshire, and 10 Asian wild boars at ~16-fold coverage was performed resulting in over 4.3 million SNPs for 19,990 genes. We constructed a comprehensive genome map of directional selection by detecting selective sweeps using an F ST-based approach that detects directional selection in lineages leading to the domesticated breeds and using a haplotype-based test that detects ongoing selective sweeps within the breeds. We show that candidate genes under selection are significantly enriched for loci implicated in quantitative traits important to pig reproduction and production. The candidate gene with the strongest signals of directional selection belongs to group III of the metabolomics glutamate receptors, known to affect brain functions associated with eating behavior, suggesting that loci under strong selection include loci involved in behaviorial traits in domesticated pigs including tameness. Conclusions We show that a significant proportion of selection signatures coincide with loci that were previously inferred to affect phenotypic variation in pigs. We further identify functional enrichment related to behavior, such as signal transduction and neuronal activities, for those targets of selection during domestication in pigs