Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation

This paper presents a novel cost aggregation network, called Volumetric Aggregation with Transformers (VAT), for few-shot segmentation. The use of transformers can benefit correlation map aggregation through self-attention over a global receptive field. However, the tokenization of a correlation map for transformer processing can be detrimental, because the discontinuity at token boundaries reduces the local context available near the token edges and decreases inductive bias. To address this problem, we propose a 4D Convolutional Swin Transformer, where a high-dimensional Swin Transformer is preceded by a series of small-kernel convolutions that impart local context to all pixels and introduce convolutional inductive bias. We additionally boost aggregation performance by applying transformers within a pyramidal structure, where aggregation at a coarser level guides aggregation at a finer level. Noise in the transformer output is then filtered in the subsequent decoder with the help of the query's appearance embedding. With this model, a new state-of-the-art is set for all the standard benchmarks in few-shot segmentation. It is shown that VAT attains state-of-the-art performance for semantic correspondence as well, where cost aggregation also plays a central role.Comment: Code and trained models are available at https://seokju-cho.github.io/VAT/ . This is ECCV'22 camera-ready version, which is revised from arXiv:2112.1168

Prospects of deep learning for medical imaging

Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems. Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly. This review article aims to survey deep learning literature in medical imaging and describe its potential for future medical imaging research. First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given. Third, wellknown software tools for deep learning are reviewed. Finally, conclusions with limitations and future directions of deep learning in medical imaging are provided

Identification and Expression Patterns of Three Vitellogenin Genes and Their Roles in Reproduction of the Alligatorweed Flea Beetle Agasicles hygrophila (Coleoptera: Chrysomelidae)

The alligatorweed flea beetle Agasicles hygrophila is an insect used for biological control of the aquatic weed Alternanthera philoxeroides (alligatorweed). Because these insects are oviparous, synthesis, and transportation of yolk proteins is integral to reproduction. Vitellin, the chief protein constituent in egg yolk, is mainly synthesized in the fat body and its synthesis is regulated by the transcript levels of Vitellogenin (Vg). In our study, we first cloned and characterized three Vg genes from A. hygrophila and quantified the expression levels of these Vgs in different tissues and developmental stages by RT-qPCR. Analysis of the full-length cDNA sequences of the three A. hygrophila Vg genes revealed that the open reading frames of AhVg1, AhVg2, and AhVg3 were 5175, 5346, and 5385 bp, encoding 1724, 1781, and 1794 amino acids, respectively. RT-qPCR analysis revealed that these three AhVgs have similar expression patterns; expression in the fat body was significantly higher than that in other tissues, and the highest expression was observed in the adult developmental stage. RNA interference was used to explore the functions of the AhVgs. A. hygrophila female adults injected with dsRNA targeting the AhVg genes showed decreased AhVg gene expression. Down regulation of all three AhVgs significantly affected ovary development, reduced egg laying capacity, and reduced the egg hatch rate compared with the control groups. Our findings provide the basis for further study of the functions of Vg genes in other insect species

Development of a Numerical Tablet Model in WLAN Band for SAR Study

This research proposes a numerical model for a tablet in the wireless local area network band for specific absorption rate (SAR) study. The design criteria, such as the tablet size, operating frequencies, antenna position, and target 1-g peak spatial-average SAR (psSAR) values in the flat phantom, are determined based on the SAR test reports of tablets distributed in South Korea from 2013 to 2017. An internal antenna is designed in a tablet platform to operate in dual bands of 2,450 MHz and 5,500 MHz. The numerical results illustrate that the 1-g psSAR values of the proposed numerical tablet model are within ±10% of the target values. Moreover, the return loss of the designed tablet model is larger than 10 dB, regardless of flat phantom, while its radiation efficiency is higher than 90% in free space

Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives

Polygenic risk scores are emerging as a potentially powerful tool to predict future phenotypes of target individuals, typically using unrelated individuals, thereby devaluing information from relatives. Here, for 50 traits from the UK Biobank data, we show that a design of 5,000 individuals with first-degree relatives of target individuals can achieve a prediction accuracy similar to that of around 220,000 unrelated individuals (mean prediction accuracy = 0.26 vs. 0.24, mean fold-change = 1.06 (95% CI: 0.99-1.13), P-value = 0.08), despite a 44-fold difference in sample size. For lifestyle traits, the prediction accuracy with 5,000 individuals including first-degree relatives of target individuals is significantly higher than that with 220,000 unrelated individuals (mean prediction accuracy = 0.22 vs. 0.16, mean fold-change = 1.40 (1.17-1.62), P-value = 0.025). Our findings suggest that polygenic prediction integrating family information may help to accelerate precision health and clinical intervention

Broad humoral and cellular immunity elicited by one-dose mRNA vaccination 18 months after SARS-CoV-2 infection

Practical guidance is needed regarding the vaccination of coronavirus disease 2019 (COVID-19) convalescent individuals in resource-limited countries. It includes the number of vaccine doses that should be given to unvaccinated patients who experienced COVID-19 early in the pandemic. We recruited COVID-19 convalescent individuals who received one or two doses of an mRNA vaccine within 6 or around 18 months after a diagnosis of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection. Their samples were assessed for IgG-binding or neutralizing activity and cell-mediated immune responses against SARS-CoV-2 wild-type and variants of concern. A total of 43 COVID-19 convalescent individuals were analyzed in the present study. The results showed that humoral and cellular immune responses against SARS-CoV-2 wild-type and variants of concern, including the Omicron variant, were comparable among patients vaccinated within 6 versus around 18 months. A second dose of vaccine did not significantly increase immune responses. One dose of mRNA vaccine should be considered sufficient to elicit a broad immune response even around 18 months after a COVID-19 diagnosis.This work was supported in part by the Bio & Medical Technology Develop‑ ment Program of the National Research Foundation (NRF) & funded by the Korean government (MSIT) (2021M3A9I2080496, to H.-R. Kim & W. B. Park), the Creative-Pioneering Researchers Program through Seoul National University (to C.-H. Lee), and the Seoul National University Hospital Research Fund (112021-5050 to P. G. Choe and 800-20220110 to C.-H. Lee)

The IPIN 2019 Indoor Localisation Competition—Description and Results

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks