In recent years, the field of intelligent transportation systems (ITS) has achieved remarkable success, which is mainly due to the large amount of available annotation data. However, obtaining these annotated data has to afford expensive costs in reality. Therefore, a more realistic strategy is to leverage semi-supervised learning (SSL) with a small amount of labeled data and a large amount of unlabeled data. Typically, semantic consistency regularization and the two-stage learning methods of decoupling feature extraction and classification have been proven effective. Nevertheless, representation learning only limited to semantic consistency regularization may not guarantee the separation or discriminability of representations of samples with different semantics; due to the inherent limitations of the two-stage learning methods, the extracted features may not match the specific downstream tasks. In order to deal with the above drawbacks, this paper proposes an end-to-end deep semi-supervised learning double contrast of semantic and feature, which extracts effective tasks specific discriminative features by contrasting the semantics/features of positive and negative augmented samples pairs. Moreover, we leverage information theory to explain the rationality of double contrast of semantics and features and slack mutual information to contrastive loss in a simpler way. Finally, the effectiveness of our method is verified in benchmark datasets.

可变形的模型对于3D面的统计建模至关重要。以前的可变形模型的作品主要集中在大规模的面部几何形状上，但忽略了面部细节。本文通过学习一种结构含义的可编辑形态模型（SEMM）来增强形象模型。 SEMM基于皱纹线的距离字段引入了细节结构表示，并以细节位移进行建模，以建立更好的对应关系并实现对皱纹结构的直观操纵。此外，SEMM还引入了两个转换模块，以将表达式的融合体权重和年龄值转化为潜在空间的变化，从而在维持身份的同时可以有效的语义细节编辑。广泛的实验表明，所提出的模型紧凑地表示面部细节，在定性和定量上表达动画中的先前方法，并实现了面部细节的有效年龄编辑和皱纹线编辑。代码和模型可在https://github.com/gerwang/facial-detail-manipulation上找到。

最近，与传统方法相比，基于网络的图像压缩传感方法可实现高重建质量和降低的计算开销。但是，现有方法仅从网络中的部分特征中获得测量结果，并仅将它们用于图像重建。他们忽略了网络\ cite {zeiler2014Visalization}中的低，中和高级特征，所有这些特征对于高质量重建至关重要。此外，仅使用一次测量可能不足以从测量中提取更丰富的信息。为了解决这些问题，我们提出了一个新颖的测量值重复使用卷积压缩感应网络（MR-CCSNET），该网络（MR-CCSNET）采用全球传感模块（GSM）收集所有级别的功能，以实现有效的感应和测量重复使用块（MRB）多次重复使用测量值在多尺度上。最后，三个基准数据集的实验结果表明，我们的模型可以显着胜过最先进的方法。

自我监督学习（SSL），作为新出现的无监督的代表性学习范式，通常遵循两阶段的学习管道：1）学习不变和歧视性表示，并具有自动宣传借口，然后是2）下游任务。这样的两个阶段通常分别实施，这使得学到的表示对下游任务的不可知论。目前，大多数作品都致力于探索第一阶段。鉴于，关于如何使用已经学习的表示形式学习有限的标记数据的如何学习下游任务的研究较少。尤其是，从不同的借口中选择性地利用互补表示来实现下游任务至关重要和具有挑战性。在本文中，我们从技术上提出了一种新的解决方案，利用注意力机制适应任务的适当表示。同时，诉诸于信息理论，我们从理论上证明，从不同借口收集代表比单个借口更有效。广泛的实验验证了我们的方案在收集知识并缓解下游任务中的负面传递方面显着超过了当前的基于借口匹配的方法。

多任务学习是通过在任务中传输和利用共同知识来提高模型的性能。现有的MTL主要关注多个任务（MTS）之间标签集的场景通常是相同的，因此它们可以用于跨任务学习。虽然几乎罕见的作品探索了每个任务只有少量训练样本的情况，而其标签集只是部分重叠甚至不是。由于这些任务之间可用的相关信息，学习此类MTS更具挑战性。为此，我们提出了一个框架来通过共同利用来自学习的辅助大任务的大量信息，以足够多的类来涵盖所有这些任务的富力信息以及在部分重叠的任务中共享的信息。在我们实现使用所学习辅助任务的相同神经网络架构来学习各个任务的情况下，关键的想法是利用可用的标签信息来自适应地修剪辅助网络的隐藏层神经元，以构造每个任务的相应网络，同时伴随各个任务的联合学习。我们的实验结果表明其与最先进的方法相比其有效性。

群集广泛用于文本分析，自然语言处理，图像分割和其他数据挖掘字段。作为一个有前途的聚类算法，通过允许对象属于几个类别的若干类来提供对数据的更深层次的识别，这延长了硬，模糊和可能性聚类。但是，由于需要估计比其他基于经典分区的算法更多的参数，因此当可用数据充足并且质量良好时，它才能很好地运行。为了克服这些缺点，本文通过引入转移学习策略来提出转移证据C-Mean（TECM）算法。 TECM的目标函数是通过基于ECM的目标函数在源域中引入源域中的重心而获得的目标函数，并且使用迭代优化策略来解决客观函数。另外，TECM可以适应源域和目标域中的簇数不同的情况。所提出的算法已在合成和现实世界数据集上验证。实验结果证明了与原始ECM的TECM的有效性以及其他代表多任务或转移聚类算法。

深度学习（DL）模型为各种医学成像基准挑战提供了最先进的性能，包括脑肿瘤细分（BRATS）挑战。然而，局灶性病理多隔室分割（例如，肿瘤和病变子区）的任务特别具有挑战性，并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性，可以实现最不确定的地区的临床审查，从而建立信任并铺平临床翻译。最近，已经引入了许多不确定性估计方法，用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中，我们探索并评估在Brats 2019-2020任务期间开发的公制，以对不确定量化量化（Qu-Brats），并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制（1）奖励不确定性估计，对正确断言产生高置信度，以及在不正确的断言处分配低置信水平的估计数，（2）惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性，所有这些都参与了主要的Brats细分任务。总体而言，我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值，因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。

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.

Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight of fixed-wing and vertical takeoff and landing (VTOL) capabilities of multicopter UAVs. This paper presents the modeling, control and simulation of a new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The airframe orientation of the lifting wing needs to tilt a specific angle often within $ 45$ degrees, neither nearly $ 90$ nor approximately $ 0$ degrees. Compared with some convertiplane and tail-sitter UAVs, the lifting-wing quadcopter has a highly reliable structure, robust wind resistance, low cruise speed and reliable transition flight, making it potential to work fully-autonomous outdoor or some confined airspace indoor. In the modeling part, forces and moments generated by both lifting wing and rotors are considered. Based on the established model, a unified controller for the full flight phase is designed. The controller has the capability of uniformly treating the hovering and forward flight, and enables a continuous transition between two modes, depending on the velocity command. What is more, by taking rotor thrust and aerodynamic force under consideration simultaneously, a control allocation based on optimization is utilized to realize cooperative control for energy saving. Finally, comprehensive Hardware-In-the-Loop (HIL) simulations are performed to verify the advantages of the designed aircraft and the proposed controller.

In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries for attacking each benign example. To reduce query cost, we propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability. Specifically, by treating the attack on each benign example as one task, we develop a meta-learning framework by training a meta-generator to produce perturbations conditioned on benign examples. When attacking a new benign example, the meta generator can be quickly fine-tuned based on the feedback information of the new task as well as a few historical attacks to produce effective perturbations. Moreover, since the meta-train procedure consumes many queries to learn a generalizable generator, we utilize model-level adversarial transferability to train the meta-generator on a white-box surrogate model, then transfer it to help the attack against the target model. The proposed framework with the two types of adversarial transferability can be naturally combined with any off-the-shelf query-based attack methods to boost their performance, which is verified by extensive experiments.