Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction. In this paper, we propose unified framework, which jointly considers the sampling and reconstruction process for image compressive sensing based on well-designed cascade neural networks. Two sub-networks, which are the sampling sub-network and the reconstruction sub-network, are included in the proposed framework. In the sampling sub-network, an adaptive full connected layer instead of the traditional random matrix is used to mimic the sampling operator. In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals. The SDA is used to solve the signal mapping problem and the signals are initially reconstructed. Furthermore, CNN is used to fully recover the structure and texture features of the image to obtain better reconstruction performance. Extensive experiments show that this framework outperforms many other state-of-the-art methods, especially at low sampling rates.

通过连续行动解决部分可观察到的马尔可夫决策过程（POMDP）是具有挑战性的，尤其是对于高维操作空间。为了减轻这一困难，我们提出了一种新的基于采样的在线POMDP求解器，称为使用Voronoi Trees（Advt）的自适应离散化。它结合使用蒙特卡洛树搜索与动作空间的自适应离散化以及乐观的优化，以有效地采样高维连续的动作空间并计算最佳动作。具体而言，我们使用称为Voronoi树的分层分区来适应每个采样信念的动作空间。 Voronoi树是一种二进制空间分区（BSP），它隐式地将单元格的分区保留为从单元中采样的两个点的伏诺图图。这种分区策略可以保持分区和估计每个细胞的大小的成本，即使在高维空间中，需要许多采样点才能覆盖空间。 Advt使用单元格的估计尺寸形成单元的动作值的上限结合，进而使用上等信心来指导蒙特卡洛树搜索扩展并进一步离散动作空间。该策略使Advt能够更好地利用动作空间中的本地信息，从而导致动作空间离散化更具适应性，因此与现有求解器相比，计算良好的POMDP解决方案的效率更高。对四种基准问题的模拟实验表明，与最新的连续作用POMDP求解器相比，ADVT优于高维连续作用空间的表现要好于高维连续的动作空间。

当仅积极（P）和未标记（U）数据可用时，正面标记（PU）学习涉及二进制分类问题。已经提出了许多基于线性模型和神经网络的PU方法。但是，仍然缺乏关于理论上增强风格算法如何使用P和U数据的研究。考虑到在某些情况下，当神经网络即使使用完全监督的数据也不能像增强算法一样好时，我们提出了一种新颖的增强PU学习算法：ADA-PU，ADA-PU与神经网络进行了比较。 ADA-PU遵循ADABOOST的一般过程，同时维护和更新了P数据的两个不同分布。在新更新的分布上学习了弱分类器后，仅使用PU数据估算最终集合的相应组合权重。我们证明，使用较小的基础分类器集，确保该方法可以保留增强算法的理论属性。在实验中，我们表明ADA-PU在基准PU数据集上优于神经网络。我们还研究了网络安全性的现实世界数据集UNSW-NB15，并证明ADA-PU在恶意活动检测方面具有出色的性能。

由B5G和6G技术驱动，多网络融合是未来通信的不可或缺的趋势。在本文中，我们专注于并分析\弹性{卫星 - 地面下行链路传输}（STDT）的\ emph {安全性能}（sp）。这里，STDT由卫星网络和具有合法移动接收器的车辆网络和移动窃听器分配来组成。为了从移动终端的角度理解分析该系统的SP，采用随机几何论，这假设两个地面车辆在卫星的一个光束中随机分布。此外，基于该理论，STDT中的两个关键和特定指标的闭合形式分析表达分别来源，秘密中断概率和遍历秘密能力。另外，讨论了限制STDT的SP的若干相关变量，并提出了特定方案以增强SP。然后，在高信噪比场景中研究了渐近性，并给出了准确和渐近的闭合形式表达式。最后，仿真结果表明，在保证STDT可靠性的前提下，渐近解决方案优于相应的准确结果，显着提高了效果。

In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The proposed benchmark provides drastic appearance variations caused by seasonal changes and diverse weather and illumination conditions. While significant progress has been made in advancing visual SLAM on small-scale datasets with similar conditions, there is still a lack of unified benchmarks representative of real-world scenarios for autonomous driving. We introduce a new unified benchmark for jointly evaluating visual odometry, global place recognition, and map-based visual localization performance which is crucial to successfully enable autonomous driving in any condition. The data has been collected for more than one year, resulting in more than 300 km of recordings in nine different environments ranging from a multi-level parking garage to urban (including tunnels) to countryside and highway. We provide globally consistent reference poses with up to centimeter-level accuracy obtained from the fusion of direct stereo-inertial odometry with RTK GNSS. We evaluate the performance of several state-of-the-art visual odometry and visual localization baseline approaches on the benchmark and analyze their properties. The experimental results provide new insights into current approaches and show promising potential for future research. Our benchmark and evaluation protocols will be available at https://www.4seasons-dataset.com/.

Feedforward fully convolutional neural networks currently dominate in semantic segmentation of 3D point clouds. Despite their great success, they suffer from the loss of local information at low-level layers, posing significant challenges to accurate scene segmentation and precise object boundary delineation. Prior works either address this issue by post-processing or jointly learn object boundaries to implicitly improve feature encoding of the networks. These approaches often require additional modules which are difficult to integrate into the original architecture. To improve the segmentation near object boundaries, we propose a boundary-aware feature propagation mechanism. This mechanism is achieved by exploiting a multi-task learning framework that aims to explicitly guide the boundaries to their original locations. With one shared encoder, our network outputs (i) boundary localization, (ii) prediction of directions pointing to the object's interior, and (iii) semantic segmentation, in three parallel streams. The predicted boundaries and directions are fused to propagate the learned features to refine the segmentation. We conduct extensive experiments on the S3DIS and SensatUrban datasets against various baseline methods, demonstrating that our proposed approach yields consistent improvements by reducing boundary errors. Our code is available at https://github.com/shenglandu/PushBoundary.

Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and the dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only 1.1% more computation.

Tongue cancer is a common oral cavity malignancy that originates in the mouth and throat. Much effort has been invested in improving its diagnosis, treatment, and management. Surgical removal, chemotherapy, and radiation therapy remain the major treatment for tongue cancer. The survival of patients determines the treatment effect. Previous studies have identified certain survival and risk factors based on descriptive statistics, ignoring the complex, nonlinear relationship among clinical and demographic variables. In this study, we utilize five cutting-edge machine learning models and clinical data to predict the survival of tongue cancer patients after treatment. Five-fold cross-validation, bootstrap analysis, and permutation feature importance are applied to estimate and interpret model performance. The prognostic factors identified by our method are consistent with previous clinical studies. Our method is accurate, interpretable, and thus useable as additional evidence in tongue cancer treatment and management.

In this paper, we propose a large-scale language pre-training for text GENeration using dIffusion modEl, which is named GENIE. GENIE is a pre-training sequence-to-sequence text generation model which combines Transformer and diffusion. The diffusion model accepts the latent information from the encoder, which is used to guide the denoising of the current time step. After multiple such denoise iterations, the diffusion model can restore the Gaussian noise to the diverse output text which is controlled by the input text. Moreover, such architecture design also allows us to adopt large scale pre-training on the GENIE. We propose a novel pre-training method named continuous paragraph denoise based on the characteristics of the diffusion model. Extensive experiments on the XSum, CNN/DailyMail, and Gigaword benchmarks shows that GENIE can achieves comparable performance with various strong baselines, especially after pre-training, the generation quality of GENIE is greatly improved. We have also conduct a lot of experiments on the generation diversity and parameter impact of GENIE. The code for GENIE will be made publicly available.

We address the theoretical and practical problems related to the trajectory generation and tracking control of tail-sitter UAVs. Theoretically, we focus on the differential flatness property with full exploitation of actual UAV aerodynamic models, which lays a foundation for generating dynamically feasible trajectory and achieving high-performance tracking control. We have found that a tail-sitter is differentially flat with accurate aerodynamic models within the entire flight envelope, by specifying coordinate flight condition and choosing the vehicle position as the flat output. This fundamental property allows us to fully exploit the high-fidelity aerodynamic models in the trajectory planning and tracking control to achieve accurate tail-sitter flights. Particularly, an optimization-based trajectory planner for tail-sitters is proposed to design high-quality, smooth trajectories with consideration of kinodynamic constraints, singularity-free constraints and actuator saturation. The planned trajectory of flat output is transformed to state trajectory in real-time with consideration of wind in environments. To track the state trajectory, a global, singularity-free, and minimally-parameterized on-manifold MPC is developed, which fully leverages the accurate aerodynamic model to achieve high-accuracy trajectory tracking within the whole flight envelope. The effectiveness of the proposed framework is demonstrated through extensive real-world experiments in both indoor and outdoor field tests, including agile SE(3) flight through consecutive narrow windows requiring specific attitude and with speed up to 10m/s, typical tail-sitter maneuvers (transition, level flight and loiter) with speed up to 20m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical Eight and Cuban Eight) with acceleration up to 2.5g.