Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.

动机：近年来，基于形象的生物测定稳步成为高吞吐量，引发了快速自动化方法，以提取来自数百种图像的生物学有意义的信息。从想象成的成功取得灵感，我们驯服细胞造就花，一个公开源和弱标记的显微镜图像的大规模数据集（890K图像，894级）。预先训练的细胞造黄养箱产生了对上游显微镜分类任务的想象成特征具有竞争力的功能。我们展示了CytoImAgenet的证据表明，CytoImAgenet在想象中训练有素的功能中捕获信息不可用。数据集是在https://www.kaggle.com/stanleyhua/cyaagenet中提供的。

在过去的25年中，我们目睹了机器学习在编译器领域的广泛应用。选择和相位订购问题。但是，有限的作品已在最先进的编译器（即LLVM）上游，以将前者无缝集成到编译器的优化管道中，以便由用户容易部署。 MLGO是此类项目的第一个项目之一，它仅努力使用强化学习使用基于ML的INLINER来减少二进制的代码大小。本文介绍了mlgoperf；第一个端到端框架，能够使用LLVM的ML Inliner优化性能。它采用二级ML模型来生成用于训练重新定位的增强学习代理的奖励，该辅助剂以前由MLGO用作主要模型。它通过预测分析功能的函数的速度加速来做到这一点，并为主要模型提供快速训练框架，否则将是不切实际的。实验结果表明，MLGOPERF在LLVM在O3时的优化方面的优化分别为SPEC CPU2006和CBENCH基准分别获得了1.8％和2.2％。此外，提出的方法为我们的基准测试带来了自动点守则区域的26％，可以将其转化为额外的3.7％速度值。

近期量子系统嘈杂。串扰噪声已被确定为超导噪声中间尺度量子（NISQ）设备的主要噪声来源之一。串扰源于附近Qubits上的两Q量门门的并发执行，例如\ texttt {cx}。与单独运行相比，它可能会大大提高门的错误率。可以通过调度或硬件调整来减轻串扰。然而，先前的研究在汇编的后期很晚，通常是在完成硬件映射之后的。它可能会错过优化算法逻辑，路由和串扰的巨大机会。在本文中，我们通过在早期编译阶段同时考虑所有这些因素来推动信封。我们提出了一个称为CQC的串扰感知量子程序汇编框架，该框架可以增强串扰缓解，同时实现令人满意的电路深度。此外，我们确定了从中间表示向电路转换的机会，例如，以特定的特定串扰缓解措施，例如，\ texttt {cx}梯子构造在变异的量子eigensolvers（VQE）中。通过模拟和Real IBM-Q设备进行评估表明，我们的框架可以显着将错误率降低6 $ \ times $，而与最先进的门调度相比，仅$ \ sim $ 60 \％\％的电路深度方法。特别是对于VQE，我们使用IBMQ Guadalupe证明了49 \％的回路深度减少，而对H4分子的先前ART进行了9.6 \％的保真度改善。我们的CQC框架将在GitHub上发布。

用于图像分类的最可公开的数据集是单个标签，而图像在我们的日常生活中是固有的多标记。这种注释差距使得许多预先接受的单标准分类模型在实际情况下失败。该注释问题更加关注空中图像：从传感器收集的空中数据自然地覆盖具有多个标签的相对大的陆地面积，而被广泛可用的注释空中数据集（例如，UCM，AID）是单标记的。作为手动注释的多标签空中图像将是时间/劳动，我们提出了一种新的自我校正综合域适应（SCIDA）方法，用于自动多标签学习。 SCIDA是弱监督，即，自动学习多标签图像分类模型，从使用大量的公共可用的单一标签图像。为实现这一目标，我们提出了一种新颖的标签 - 明智的自我校正（LWC）模块，以更好地探索潜在的标签相关性。该模块还使无监督的域适配（UDA）从单个到多标签数据中可能。对于模型培训，所提出的型号仅使用单一标签信息，但不需要先验知识的多标记数据;它预测了多标签空中图像的标签。在我们的实验中，用单标签的MAI-AID-S和MAI-UCM-S数据集接受培训，所提出的模型直接在收集的多场景空中图像（MAI）数据集上进行测试。

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physicochemical world, forming a special kind of sequence-structure data. With the development of Artificial Intelligence (AI) techniques, Protein Representation Learning (PRL) has recently emerged as a promising research topic for extracting informative knowledge from massive protein sequences or structures. To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications. We first briefly introduce the motivations for protein representation learning and formulate it in a general and unified framework. Next, we divide existing PRL methods into three main categories: sequence-based, structure-based, and sequence-structure co-modeling. Finally, we discuss some technical challenges and potential directions for improving protein representation learning. The latest advances in PRL methods are summarized in a GitHub repository https://github.com/LirongWu/awesome-protein-representation-learning.

This paper presents a practical global optimization algorithm for the K-center clustering problem, which aims to select K samples as the cluster centers to minimize the maximum within-cluster distance. This algorithm is based on a reduced-space branch and bound scheme and guarantees convergence to the global optimum in a finite number of steps by only branching on the regions of centers. To improve efficiency, we have designed a two-stage decomposable lower bound, the solution of which can be derived in a closed form. In addition, we also propose several acceleration techniques to narrow down the region of centers, including bounds tightening, sample reduction, and parallelization. Extensive studies on synthetic and real-world datasets have demonstrated that our algorithm can solve the K-center problems to global optimal within 4 hours for ten million samples in the serial mode and one billion samples in the parallel mode. Moreover, compared with the state-of-the-art heuristic methods, the global optimum obtained by our algorithm can averagely reduce the objective function by 25.8% on all the synthetic and real-world datasets.

Adversarial imitation learning (AIL) has become a popular alternative to supervised imitation learning that reduces the distribution shift suffered by the latter. However, AIL requires effective exploration during an online reinforcement learning phase. In this work, we show that the standard, naive approach to exploration can manifest as a suboptimal local maximum if a policy learned with AIL sufficiently matches the expert distribution without fully learning the desired task. This can be particularly catastrophic for manipulation tasks, where the difference between an expert and a non-expert state-action pair is often subtle. We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of multiple exploratory, auxiliary tasks in addition to a main task. The addition of these auxiliary tasks forces the agent to explore states and actions that standard AIL may learn to ignore. Additionally, this particular formulation allows for the reusability of expert data between main tasks. Our experimental results in a challenging multitask robotic manipulation domain indicate that LfGP significantly outperforms both AIL and behaviour cloning, while also being more expert sample efficient than these baselines. To explain this performance gap, we provide further analysis of a toy problem that highlights the coupling between a local maximum and poor exploration, and also visualize the differences between the learned models from AIL and LfGP.

Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation layer computes estimates of the agent states and provides Gaussian representations of marginal posteriors of agent positions to the control layer for approximate Bayesian FIM computations. Based on a cost function that accumulates Bayesian FIM contributions over a sliding window of discrete future timesteps, a receding horizon (RH) control is performed. Approximations that make it possible to solve the resulting tree-search problem efficiently are also discussed. A numerical case study demonstrates the intelligent behavior of a single controlled anchor in a 3-D scenario and the resulting significantly improved localization accuracy.