Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis

Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective. In recent years, with the development of deep learning, many CNN-based approaches have been widely researched in both tumor localization and cancer classification tasks. Even though previous single models achieved great performance in both tasks, these methods have some limitations in inference time, GPU requirement, and separate fine-tuning for each model. In this study, we aim to redesign and build end-to-end multi-task architecture to conduct both segmentation and classification. With our proposed approach, we achieved outstanding performance and time efficiency, with 79.8% and 86.4% in DeepLabV3+ architecture in the segmentation task.Comment: 7 pages, 3 figure

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Accumulation and distribution of zinc in the leaves and roots of the hyperaccumulator Noccaea caerulescens

Understanding the uptake mechanisms of heavy metals by hyperaccumulators is necessary for improving phytoextraction options to reduce metal toxicities in contaminated soils. In this study, the capacity of Zn uptake by the hyperaccumulator Noccaea caerulescens was investigated and compared to the non-hyperaccumulator Thlaspi arvense. The plants were grown under hydroponic conditions in a glasshouse. The distribution of Zn in the roots and leaves of these species was investigated by scanning electron microscopy with energy-dispersive X-ray analysis. Compared with the control with no Zn added, it was shown that prolonged Zn treatments decreased the biomass of both N. caerulescens and T. arvense. Since N. caerulescens requires Zn for growth, no Zn toxicity symptoms were observed, even when the Zn concentration in shoots reached 2.5% dry mass. T. arvense showed serious Zn toxicity only after two weeks of Zn treatment. Zn uptake by N. caerulescens was mainly translocated to the leaves while almost all of the Zn taken-up by T. arvense was retained in the roots. In N. caerulescens, increasing concentration of Zn in the supply decreased Ca and P concentrations in the shoots by up to 50 and 35%, respectively. Zn-containing crystals were abundant in both the upper and lower epidermal cells of the leaves and in the cortex of the roots during the later growth phase. Co-localization of Ca and Zn, P and S were found in leaf and root tissues. The results suggest that Zn-rich crystals with an abundance of the Zn ligand in the roots and shoots, and co-localization and interaction between Zn and other ions, may have functional significance with respect to conferring particular attributes to N. caerulescens that are not present in the non-hyperaccumulator counterpart. An understanding of these species-specific differences has relevance from the perspective of offering some insight into how particular species could contribute to a strategy for the detoxification of Zn-contaminated sites

Factors influencing to use of Bluezone

This study aims to understand the main factors and their influence on the behavioral intention of users about using Bluezone. Surveys are sent to users through the Google Form tool. Experimental results through analysis of exploratory factors on 224 survey subjects show that there are 4 main factors affecting user behavior. Structural equation modeling indicates that trust, performance expectations, effort expectations, and social influence have a positive impact on behavioral intention of using BluezoneComment: in Vietnamese languag

Simple Transferability Estimation for Regression Tasks

We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which received little previous attention, and propose two simple and computationally efficient approaches that estimate transferability based on the negative regularized mean squared error of a linear regression model. We prove novel theoretical results connecting our approaches to the actual transferability of the optimal target models obtained from the transfer learning process. Despite their simplicity, our approaches significantly outperform existing state-of-the-art regression transferability estimators in both accuracy and efficiency. On two large-scale keypoint regression benchmarks, our approaches yield 12% to 36% better results on average while being at least 27% faster than previous state-of-the-art methods.Comment: Paper published at The 39th Conference on Uncertainty in Artificial Intelligence (UAI) 202

Assessment of seasonal winter temperature forecast errors in the regcm model over northern Vietnam

This study verified the seasonal six-month forecasts for winter temperatures for northern Vietnam in 1998–2018 using a regional climate model (RegCM4) with the boundary conditions of the climate forecast system Version 2 (CFSv2) from the National Centers for Environmental Prediction (NCEP). First, different physical schemes (land-surface process, cumulus, and radiation parameterizations) in RegCM4 were applied to generate 12 single forecasts. Second, the simple ensemble forecasts were generated through the combinations of those different physical formulations. Three subclimate regions (R1, R2, R3) of northern Vietnam were separately tested with surface observations and a reanalysis dataset (Japanese 55-year reanalysis (JRA55)). The highest sensitivity to the mean monthly temperature forecasts was shown by the land-surface parameterizations (the biosphere−atmosphere transfer scheme (BATS) and community land model version 4.5 (CLM)). The BATS forecast groups tended to provide forecasts with lower temperatures than the actual observations, while the CLM forecast groups tended to overestimate the temperatures. The forecast errors from single forecasts could be clearly reduced with ensemble mean forecasts, but ensemble spreads were less than those root-mean-square errors (RMSEs). This indicated that the ensemble forecast was underdispersed and that the direct forecast from RegCM4 needed more postprocessing

Assessing the utilization of high-resolution 2-field HLA typing in solid organ transplantation.

HLA typing in solid organ transplantation (SOT) is necessary for determining HLA-matching status between donor-recipient pairs and assessing patients\u27 anti-HLA antibody profiles. Histocompatibility has traditionally been evaluated based on serologically defined HLA antigens. The evolution of HLA typing and antibody identification technologies, however, has revealed many limitations with using serologic equivalents for assessing compatibility in SOT. The significant improvements to HLA typing introduced by next-generation sequencing (NGS) require an assessment of the impact of this technology on SOT. We have assessed the role of high-resolution 2-field HLA typing (HR-2F) in SOT by retrospectively evaluating NGS-typed pre- and post-SOT cases. HR-2F typing was highly instructive or necessary in 41% (156/385) of the cases. Several pre- and posttransplant scenarios were identified as being better served by HR-2F typing. Five different categories are presented with specific case examples. The experience of another center (Temple University Hospital) is also included, whereby 21% of the cases required HR-2F typing by Sanger sequencing, as supported by other legacy methods, to properly address posttransplant anti-HLA antibody issues

A New Approach and Tool in Verifying Asynchronous Circuits

Research in asynchronous circuit approach has been carried out recently when asynchronous circuits are presented more widely in electronic systems. As they are more important in human life, their correctness should be considered carefully. Although there are some EDA tools for design and synthesis of asynchronous circuits, they are lack of methods for verifying the correctness of the produced circuits. In this work, we are about to propose a verification method and apply it in making a new version of the PAiD tool that can enable engineers to design, synthesize and verify asynchronous circuits. Experiments in verifying circuits have been also provided in this work