For solving a broad class of nonconvex programming problems on an unbounded constraint set, we provide a self-adaptive step-size strategy that does not include line-search techniques and establishes the convergence of a generic approach under mild assumptions. Specifically, the objective function may not satisfy the convexity condition. Unlike descent line-search algorithms, it does not need a known Lipschitz constant to figure out how big the first step should be. The crucial feature of this process is the steady reduction of the step size until a certain condition is fulfilled. In particular, it can provide a new gradient projection approach to optimization problems with an unbounded constrained set. The correctness of the proposed method is verified by preliminary results from some computational examples. To demonstrate the effectiveness of the proposed technique for large-scale problems, we apply it to some experiments on machine learning, such as supervised feature selection, multi-variable logistic regressions and neural networks for classification.

我们提出了一个数据收集和注释管道，该数据从越南放射学报告中提取信息，以提供胸部X射线（CXR）图像的准确标签。这可以通过注释与其特有诊断类别的数据相匹配，这些数据可能因国家而异。为了评估所提出的标签技术的功效，我们构建了一个包含9,752项研究的CXR数据集，并使用该数据集的子集评估了我们的管道。以F1得分为至少0.9923，评估表明，我们的标签工具在所有类别中都精确而始终如一。构建数据集后，我们训练深度学习模型，以利用从大型公共CXR数据集传输的知识。我们采用各种损失功能来克服不平衡的多标签数据集的诅咒，并使用各种模型体系结构进行实验，以选择提供最佳性能的诅咒。我们的最佳模型（CHEXPERT-FRECTER EDIDENENET-B2）的F1得分为0.6989（95％CI 0.6740，0.7240），AUC为0.7912，敏感性为0.7064，特异性为0.8760，普遍诊断为0.8760。最后，我们证明了我们的粗分类（基于五个特定的异常位置）在基准CHEXPERT数据集上获得了可比的结果（十二个病理），以进行一般异常检测，同时在所有类别的平均表现方面提供更好的性能。

在快速增长的世界中，分散学习算法的设计很重要，在这个世界中，数据分布在有限的本地计算资源和通信的参与者上。在这个方向上，我们提出了一种在线算法最小化从网络上分布的单个数据/模型汇总的非凸损失函数。我们提供算法的理论性能保证，并在现实生活中展示其实用性。

多项研究表明，从孕妇中期超声检查（USG）检查获得标准化的胎儿脑生物特征？获得这些测量值是高度主观的，专业驱动的，需要多年的培训经验，从而限制了所有怀孕母亲的优质产前护理。在这项研究中，我们提出了一种深度学习方法（DL）方法，以通过准确和自动化的卡钳放置（每次生物测量法）将其作为地标建模，从而从跨炉平面（TC）的2D USG图像（TC）计算3个关键的胎儿脑生物特征。检测问题。我们利用了临床相关的生物识别约束（卡尺点之间的关系）和与域相关的数据增强，以提高U-NET DL模型的准确性（经过训练/测试：596张图像，473个受试者/143张图像，143个受试者）。我们进行了多个实验，证明了DL主链，数据增强，推广性和基准测试，通过广泛的临床验证（DL与7位经验丰富的临床医生）对最新的最新方法进行了测试。在所有情况下，单个卡尺点和计算生物特征的放置的平均误差都与临床医生之间的错误率相当。所提出的框架的临床翻译可以帮助新手用户在可靠和标准化的胎儿大脑超声图评估中的新手使用者。

We present a new dataset for 6-DoF pose estimation of known objects, with a focus on robotic manipulation research. We propose a set of toy grocery objects, whose physical instantiations are readily available for purchase and are appropriately sized for robotic grasping and manipulation. We provide 3D scanned textured models of these objects, suitable for generating synthetic training data, as well as RGBD images of the objects in challenging, cluttered scenes exhibiting partial occlusion, extreme lighting variations, multiple instances per image, and a large variety of poses. Using semi-automated RGBD-to-model texture correspondences, the images are annotated with ground truth poses accurate within a few millimeters. We also propose a new pose evaluation metric called ADD-H based on the Hungarian assignment algorithm that is robust to symmetries in object geometry without requiring their explicit enumeration. We share pre-trained pose estimators for all the toy grocery objects, along with their baseline performance on both validation and test sets. We offer this dataset to the community to help connect the efforts of computer vision researchers with the needs of roboticists.

数十种归因方法背后的一个原理是在输入功能（此处，令牌）作为其归属中删除之前和之后的预测差异。流行的输入边缘化方法（IM）方法（Kim等，2020）使用BERT代替令牌，从而产生更合理的反事实。而Kim等人。 （2020）报道IM是有效的，我们发现这个结论并不令人信服，因为论文中使用的Deletionbert指标对IM有偏见。重要的是，这种偏见存在于基于缺失的指标中，包括插入，充分性和全面性。此外，我们使用6个指标和3个数据集的严格评估没有发现IM比剩余的（LOO）基线更好的证据。我们发现IM不比LOO更好的两个原因：（1）从输入中删除单个单词仅略微降低了分类器的精度； （2）一个高度可预测的词总是给出接近零的归因，无论其对分类器的真正重要性。相比之下，通过BERT使石灰样品更加自然可在几种咆哮指标下始终提高酸橙精度。

Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers. We combined multi-task learning, multi-inputs, and Graph Attention Network to build a model capable of predicting reactivity ratios based on the monomers chemical structures.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.