基于硬件的加速度是促进许多计算密集型数学操作的广泛尝试。本文提出了一个基于FPGA的体系结构来加速卷积操作 - 在许多卷积神经网络模型中出现的复杂且昂贵的计算步骤。我们将设计定为标准卷积操作，打算以边缘-AI解决方案启动产品。该项目的目的是产生一个可以一次处理卷积层的FPGA IP核心。系统开发人员可以使用Verilog HDL作为体系结构的主要设计语言来部署IP核心。实验结果表明，我们在简单的边缘计算FPGA板上合成的单个计算核心可以提供0.224 GOPS。当董事会充分利用时，可以实现4.48 GOP。

识别息肉对于在计算机辅助临床支持系统中自动分析内窥镜图像的自动分析具有挑战性。已经提出了基于卷积网络（CNN），变压器及其组合的模型，以分割息肉以有希望的结果。但是，这些方法在模拟息肉的局部外观方面存在局限性，或者在解码过程中缺乏用于空间依赖性的多层次特征。本文提出了一个新颖的网络，即结肠形式，以解决这些局限性。 Colonformer是一种编码器架构，能够在编码器和解码器分支上对远程语义信息进行建模。编码器是一种基于变压器的轻量级体系结构，用于在多尺度上建模全局语义关系。解码器是一种层次结构结构，旨在学习多层功能以丰富特征表示。此外，添加了一个新的Skip连接技术，以完善整体地图中的息肉对象的边界以进行精确分割。已经在五个流行的基准数据集上进行了广泛的实验，以进行息肉分割，包括Kvasir，CVC-Clinic DB，CVC-ColondB，CVC-T和Etis-Larib。实验结果表明，我们的结肠构造者在所有基准数据集上的表现优于其他最先进的方法。

随着人类生活中的许多实际应用，包括制造监控摄像机，分析和加工客户行为，许多研究人员都注明了对数字图像的面部检测和头部姿势估计。大量提出的深度学习模型具有最先进的准确性，如YOLO，SSD，MTCNN，解决了面部检测或HOPENET的问题，FSA-NET，用于头部姿势估计问题的速度。根据许多最先进的方法，该任务的管道由两部分组成，从面部检测到头部姿势估计。这两个步骤完全独立，不共享信息。这使得模型在设置中清除但不利用每个模型中提取的大部分特色资源。在本文中，我们提出了多任务净模型，具有利用从面部检测模型提取的特征的动机，将它们与头部姿势估计分支共享以提高精度。此外，随着各种数据，表示面部的欧拉角域大，我们的模型可以预测360欧拉角域的结果。应用多任务学习方法，多任务净模型可以同时预测人头的位置和方向。为了提高预测模型的头部方向的能力，我们将人脸从欧拉角呈现到旋转矩阵的载体。

头部姿势估计是一个具有挑战性的任务，旨在解决与预测三维向量相关的问题，这为人机互动或客户行为中的许多应用程序提供服务。以前的研究提出了一些用于收集头部姿势数据的精确方法。但这些方法需要昂贵的设备，如深度摄像机或复杂的实验室环境设置。在这项研究中，我们引入了一种新的方法，以有效的成本和易于设置，以收集头部姿势图像，即UET-HEADBETS数据集，具有顶视图头姿势数据。该方法使用绝对方向传感器而不是深度摄像机快速设置，但仍然可以确保良好的效果。通过实验，我们的数据集已显示其分发和可用数据集之间的差异，如CMU Panoptic DataSet \ Cite {CMU}。除了使用UET符号数据集和其他头部姿势数据集外，我们还介绍了称为FSANET的全范围模型，这显着优于UET-HEALPETS数据集的头部姿势估计结果，尤其是在顶视图上。此外，该模型非常重量轻，占用小尺寸图像。

客户端之间的非独立和相同分布（非IID）数据分布被视为降低联合学习（FL）性能的关键因素。处理非IID数据（如个性化FL和联邦多任务学习（FMTL）的几种方法对研究社区有很大兴趣。在这项工作中，首先，我们使用Laplacian正规化制定FMTL问题，明确地利用客户模型之间的关系进行多任务学习。然后，我们介绍了FMTL问题的新视图，首次表明配制的FMTL问题可用于传统的FL和个性化FL。我们还提出了两种算法FEDU和DFEDU，分别解决了通信集中和分散方案中的配制FMTL问题。从理论上讲，我们证明了两种算法的收敛速率实现了用于非凸起目标的强大凸起和载位加速的线性加速。实验，我们表明我们的算法优于FL设置的传统算法FedVG，在FMTL设置中的Mocha，以及个性化流程中的PFEDME和PER-FEDAVG。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

This paper aims to improve the Warping Planer Object Detection Network (WPOD-Net) using feature engineering to increase accuracy. What problems are solved using the Warping Object Detection Network using feature engineering? More specifically, we think that it makes sense to add knowledge about edges in the image to enhance the information for determining the license plate contour of the original WPOD-Net model. The Sobel filter has been selected experimentally and acts as a Convolutional Neural Network layer, the edge information is combined with the old information of the original network to create the final embedding vector. The proposed model was compared with the original model on a set of data that we collected for evaluation. The results are evaluated through the Quadrilateral Intersection over Union value and demonstrate that the model has a significant improvement in performance.

Protein structure prediction is a fundamental problem in computational molecular biology. Classical algorithms such as ab-initio or threading as well as many learning methods have been proposed to solve this challenging problem. However, most reinforcement learning methods tend to model the state-action pairs as discrete objects. In this paper, we develop a reinforcement learning (RL) framework in a continuous setting and based on a stochastic parametrized Hamiltonian version of the Pontryagin maximum principle (PMP) to solve the side-chain packing and protein-folding problem. For special cases our formulation can be reduced to previous work where the optimal folding trajectories are trained using an explicit use of Langevin dynamics. Optimal continuous stochastic Hamiltonian dynamics folding pathways can be derived with use of different models of molecular energetics and force fields. In our RL implementation we adopt a soft actor-critic methodology however we can replace this other RL training based on A2C, A3C or PPO.

The introduction of high-quality image generation models, particularly the StyleGAN family, provides a powerful tool to synthesize and manipulate images. However, existing models are built upon high-quality (HQ) data as desired outputs, making them unfit for in-the-wild low-quality (LQ) images, which are common inputs for manipulation. In this work, we bridge this gap by proposing a novel GAN structure that allows for generating images with controllable quality. The network can synthesize various image degradation and restore the sharp image via a quality control code. Our proposed QC-StyleGAN can directly edit LQ images without altering their quality by applying GAN inversion and manipulation techniques. It also provides for free an image restoration solution that can handle various degradations, including noise, blur, compression artifacts, and their mixtures. Finally, we demonstrate numerous other applications such as image degradation synthesis, transfer, and interpolation.

Event Detection (ED) is the task of identifying and classifying trigger words of event mentions in text. Despite considerable research efforts in recent years for English text, the task of ED in other languages has been significantly less explored. Switching to non-English languages, important research questions for ED include how well existing ED models perform on different languages, how challenging ED is in other languages, and how well ED knowledge and annotation can be transferred across languages. To answer those questions, it is crucial to obtain multilingual ED datasets that provide consistent event annotation for multiple languages. There exist some multilingual ED datasets; however, they tend to cover a handful of languages and mainly focus on popular ones. Many languages are not covered in existing multilingual ED datasets. In addition, the current datasets are often small and not accessible to the public. To overcome those shortcomings, we introduce a new large-scale multilingual dataset for ED (called MINION) that consistently annotates events for 8 different languages; 5 of them have not been supported by existing multilingual datasets. We also perform extensive experiments and analysis to demonstrate the challenges and transferability of ED across languages in MINION that in all call for more research effort in this area.