Latent Space Energy-based Model for Fine-grained Open Set Recognition

Fine-grained open-set recognition (FineOSR) aims to recognize images belonging to classes with subtle appearance differences while rejecting images of unknown classes. A recent trend in OSR shows the benefit of generative models to discriminative unknown detection. As a type of generative model, energy-based models (EBM) are the potential for hybrid modeling of generative and discriminative tasks. However, most existing EBMs suffer from density estimation in high-dimensional space, which is critical to recognizing images from fine-grained classes. In this paper, we explore the low-dimensional latent space with energy-based prior distribution for OSR in a fine-grained visual world. Specifically, based on the latent space EBM, we propose an attribute-aware information bottleneck (AIB), a residual attribute feature aggregation (RAFA) module, and an uncertainty-based virtual outlier synthesis (UVOS) module to improve the expressivity, granularity, and density of the samples in fine-grained classes, respectively. Our method is flexible to take advantage of recent vision transformers for powerful visual classification and generation. The method is validated on both fine-grained and general visual classification datasets while preserving the capability of generating photo-realistic fake images with high resolution

Undrained shear strength of soft clay reinforce with single 16mm diameter encapsulated bottom ash column

Soft clay soil can be categorized as problematic soil. It consists of low shear strength, low permeability and high compressibility characteristics affect the stability and settlement of the structures constructed on this type of soil. A careful design analysis could be taken for any structure built on it. However, those characteristics could be improved through many methods and the easiest method that is being used in the construction field was stone column. On the other hand, coal is one of the world’s most important sources of energy. Disposal of bottom ash become environmental issues if it is not effectively reused or recycled for other application. This study is to present suitability in term of shear strength by using bottom ash to replace sand or granular material in column for ground improvement technique using laboratory scale model. Since sand is one of non-renewable material so by using by-product or waste material such bottom ash we can reduce the cost of construction as well as keep the non-renewable natural material in balance. Several experimental procedures are carried out to know the physical and mechanical properties of bottom ash and kaolin clay sample. Kaolin is being used as soil sample and bottom ash as the reinforced columns. The shear strength of the encapsulated bottom ash column measured by Unconfined Compression Test. A total 4 batches of kaolin sample had been tested and each batch consist of 5 specimens represent sample without bottom ash, partially penetration and fully penetration for singular bottom ash column. The specimen used were 50mm in diameter and 100mm in height. The diameter of bottom ash is 16mm and the height of the column are 60mm, 80mm and 100mm. The encapsulated bottom ash was installed at the centre of the specimen. The encapsulated bottom ash column with 10.24% area replacement ratio are 58.21%, 58.66% and 42.58% at sample penetration ratio, Hc/Hs of 0.6, 0.8 and 1.0 respectively. It can be concluded that the shear strength of soft clay could be improved by installation of encapsulated bottom ash column. However the value of shear strength of soft clay inserted with partially penetration column increased more significant compared to the fully penetration column

Ice-Water-Gas Interaction during Icebreaking by an Airgun Bubble

When an airgun releases high-pressure gas underwater below an ice plate, it is observed that a bubble is formed rapidly while the ice plate is broken fiercely. In order to study the ice-water-gas interaction during this transient and violent phenomenon, a set of laboratory-scale devices was designed and a series of icebreaking experiments were carried out. High-speed photography was used to capture the evolution of the bubble and the ice plate. It was found that the airgun bubble had a unique ‘pear’ shape compared with the spherical bubble generated by electric sparking. The pressure induced by the pulsation of the airgun bubble near a rigid wall was measured by the pressure sensor. The initial shockwave, oscillatory pressure peaks caused by the directional fast air injection, secondary shockwave, and pressure peak caused by the bubble jet impact were clearly recorded. Three damage patterns of ice plates were observed and corresponding reasons were analyzed. The influence of dimensionless parameters, such as airgun-ice distance (Formula presented.) and ice thickness (Formula presented.), was also investigated. The physical mechanism of ice-water-gas interaction was summarized

Quantum size effects on the perpendicular upper critical field in ultra-thin lead films

We report the thickness-dependent (in terms of atomic layers) oscillation behavior of the perpendicular upper critical field $H_{c2\perp}$ in the ultra-thin lead films at the reduced temperature ($t=T/T_c$). Distinct oscillations of the normal-state resistivity as a function of film thickness have also been observed. Compared with the $T_c$ oscillation, the $H_{c2\perp}$ shows a considerable large oscillation amplitude and a $\pi$ phase shift. The oscillatory mean free path caused by quantum size effect plays a role in $H_{c2\perp}$ oscillation.Comment: 4 pages, 4 figure

Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration

Whole brain segmentation with magnetic resonance imaging (MRI) enables the non-invasive measurement of brain regions, including total intracranial volume (TICV) and posterior fossa volume (PFV). Enhancing the existing whole brain segmentation methodology to incorporate intracranial measurements offers a heightened level of comprehensiveness in the analysis of brain structures. Despite its potential, the task of generalizing deep learning techniques for intracranial measurements faces data availability constraints due to limited manually annotated atlases encompassing whole brain and TICV/PFV labels. In this paper, we enhancing the hierarchical transformer UNesT for whole brain segmentation to achieve segmenting whole brain with 133 classes and TICV/PFV simultaneously. To address the problem of data scarcity, the model is first pretrained on 4859 T1-weighted (T1w) 3D volumes sourced from 8 different sites. These volumes are processed through a multi-atlas segmentation pipeline for label generation, while TICV/PFV labels are unavailable. Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available. We evaluate our method with Dice similarity coefficients(DSC). We show that our model is able to conduct precise TICV/PFV estimation while maintaining the 132 brain regions performance at a comparable level. Code and trained model are available at: https://github.com/MASILab/UNesT/wholebrainSeg

Oxytocin is implicated in social memory deficits induced by early sensory deprivation in mice

Acknowledgements We thank Miss Jia-Yin and Miss Yu-Ling Sun for their help in breading the mice. Funding This work was supported by grants from the National Natural Science Foundation of China (81200933 to N.-N. Song; 81200692 to L. Chen; 81101026 to Y. Huang; 31528011 to B. Lang; 81221001, 91232724 and 81571332 to Y-Q. Ding), Zhejiang Province Natural Science Foundation of China (LQ13C090004 to C. Zhang), China Postdoctoral Science Foundation (2016 M591714 to C.-C. Qi), and the Fundamental Research Funds for the Central Universities (2013KJ049).Peer reviewedPublisher PD

Transmission of H7N9 influenza virus in mice by different infective routes.

BackgroundOn 19 February 2013, the first patient infected with a novel influenza A H7N9 virus from an avian source showed symptoms of sickness. More than 349 laboratory-confirmed cases and 109 deaths have been reported in mainland China since then. Laboratory-confirmed, human-to-human H7N9 virus transmission has not been documented between individuals having close contact; however, this transmission route could not be excluded for three families. To control the spread of the avian influenza H7N9 virus, we must better understand its pathogenesis, transmissibility, and transmission routes in mammals. Studies have shown that this particular virus is transmitted by aerosols among ferrets.MethodsTo study potential transmission routes in animals with direct or close contact to other animals, we investigated these factors in a murine model.ResultsViable H7N9 avian influenza virus was detected in the upper and lower respiratory tracts, intestine, and brain of model mice. The virus was transmissible between mice in close contact, with a higher concentration of virus found in pharyngeal and ocular secretions, and feces. All these biological materials were contagious for naïve mice.ConclusionsOur results suggest that the possible transmission routes for the H7N9 influenza virus were through mucosal secretions and feces

Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers

The segmentation of kidney layer structures, including cortex, outer stripe, inner stripe, and inner medulla within human kidney whole slide images (WSI) plays an essential role in automated image analysis in renal pathology. However, the current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images encountered at a large scale. In response, the realm of digital renal pathology has seen the emergence of deep learning-based methodologies. However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation. Addressing this gap, this paper assesses the feasibility of performing deep learning based approaches on kidney layer structure segmetnation. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches, including Swin-Unet, Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We quantitatively evaluated six prevalent deep learning models on renal cortex layer segmentation using mice kidney WSIs. The empirical results stemming from our approach exhibit compelling advancements, as evidenced by a decent Mean Intersection over Union (mIoU) index. The results demonstrate that Transformer models generally outperform CNN-based models. By enabling a quantitative evaluation of renal cortical structures, deep learning approaches are promising to empower these medical professionals to make more informed kidney layer segmentation