需求估计在动态定价中起着重要的作用，在动态定价中，可以通过基于需求曲线最大化收入来获得最佳价格。在在线酒店预订平台中，房间的需求或占用率随着房间类型而变化，随着时间的推移变化，因此获得准确的占用估算是一项挑战。在本文中，我们提出了一种新颖的酒店需求功能，该功能明确地模拟了对占用预测需求需求的价格弹性，并设计了价格弹性预测模型，以了解各种影响因素的动态价格弹性系数。我们的模型由精心设计的弹性学习模块组成，以减轻内生性问题，并在多任务框架中接受培训以解决数据稀疏性。我们在现实世界数据集上进行了全面的实验，并验证方法优于最先进的基准，以实现占用预测和动态定价。

该报告描述了用于在第二次DICOVA挑战中使用三种不同的声学模态（即语音，呼吸和咳嗽）来检测COVID-19阳性的系统。所提出的系统基于4种不同方法的组合，每种方法都集中在问题的一个方面上，并在呼吸，咳嗽和语音轨道上分别达到86.41、77.60和84.55的盲试AUC，并且这三个轨道的融合中的AUC为85.37。

扫描不合适是在高旋转和翻译速度的高动态环境中发光镜镜的关键模块。现有的研究线主要集中在一个通道上，这意味着在整个LIDAR-MIMU绕线管道中，每个点的不合时件仅进行一次。在本文中，我们提出了一个基于优化的紧密耦合激光胶-IMU的探光仪，以解决迭代点级的不合适。通过将LIDAR和IMU测量结果得出的成本共同最大程度地减少，我们的LIDAR-IMU检射法在高动态环境中的性能更加准确和健壮。此外，方法字符通过限制参数数量来良好的计算效率。

公正的学习排名（ULTR）旨在从有偏见的用户点击日志中训练公正的排名模型。当前的大多数超级方法基于检查假设（EH），该假设假设可以将点击概率分解为两个标量函数，一种与排名特征有关，另一个与偏见因素有关。不幸的是，在实践中，特征，偏见因素和点击之间的相互作用很复杂，通常不能以这种独立的方式分解。使用EH拟合点击数据可能会导致模型错误指定并带来近似错误。在本文中，我们提出了一个基于向量的EH，并将点击概率作为两个向量函数的点产物提出。该解决方案由于其在拟合任意点击功能方面的普遍性而完成。基于它，我们提出了一个名为Vectorization的新型模型，以通过将嵌入在基础向量上投射到基础向量上，以适应性地学习相关性嵌入和排序文档。广泛的实验表明，我们的方法在复杂的真实点击以及简单的模拟点击上大大优于最新的超级方法。

在本文中，我们提出了一种新的GPU实现了螺旋CT重建的Katsevich算法。我们的实现划分了宿函数，并通过音高来重建CT图像间距。通过利用katsevich算法参数的周期性属性，我们的方法只需要为所有音高计算这些参数一次，因此GPU-Memory负担较低，非常适合深度学习。通过将我们的实现嵌入到网络中，我们提出了一种具有稀疏探测器的高音高螺旋CT重建的端到端深网络。由于我们的网络利用了来自SINOGAGAMS和CT图像中提取的特征，因此它可以同时减少由SINOGRAMS的稀疏性引起的条纹伪像，并在CT图像中保持细节。实验表明，我们的网络在主观和客观评估中表明了相关方法。

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

Increasing research interests focus on sequential recommender systems, aiming to model dynamic sequence representation precisely. However, the most commonly used loss function in state-of-the-art sequential recommendation models has essential limitations. To name a few, Bayesian Personalized Ranking (BPR) loss suffers the vanishing gradient problem from numerous negative sampling and predictionbiases; Binary Cross-Entropy (BCE) loss subjects to negative sampling numbers, thereby it is likely to ignore valuable negative examples and reduce the training efficiency; Cross-Entropy (CE) loss only focuses on the last timestamp of the training sequence, which causes low utilization of sequence information and results in inferior user sequence representation. To avoid these limitations, in this paper, we propose to calculate Cumulative Cross-Entropy (CCE) loss over the sequence. CCE is simple and direct, which enjoys the virtues of painless deployment, no negative sampling, and effective and efficient training. We conduct extensive experiments on five benchmark datasets to demonstrate the effectiveness and efficiency of CCE. The results show that employing CCE loss on three state-of-the-art models GRU4Rec, SASRec, and S3-Rec can reach 125.63%, 69.90%, and 33.24% average improvement of full ranking NDCG@5, respectively. Using CCE, the performance curve of the models on the test data increases rapidly with the wall clock time, and is superior to that of other loss functions in almost the whole process of model training.

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations.