Salient object detection (SOD) aims to determine the most visually attractive objects in an image. With the development of virtual reality technology, 360{\deg} omnidirectional image has been widely used, but the SOD task in 360{\deg} omnidirectional image is seldom studied due to its severe distortions and complex scenes. In this paper, we propose a Multi-Projection Fusion and Refinement Network (MPFR-Net) to detect the salient objects in 360{\deg} omnidirectional image. Different from the existing methods, the equirectangular projection image and four corresponding cube-unfolding images are embedded into the network simultaneously as inputs, where the cube-unfolding images not only provide supplementary information for equirectangular projection image, but also ensure the object integrity of the cube-map projection. In order to make full use of these two projection modes, a Dynamic Weighting Fusion (DWF) module is designed to adaptively integrate the features of different projections in a complementary and dynamic manner from the perspective of inter and intra features. Furthermore, in order to fully explore the way of interaction between encoder and decoder features, a Filtration and Refinement (FR) module is designed to suppress the redundant information between the feature itself and the feature. Experimental results on two omnidirectional datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both qualitatively and quantitatively.

网络体系结构搜索（NAS），尤其是可区分的体系结构搜索（DARTS）方法，已经显示出在特定感兴趣的特定数据集中学习出色的模型体系结构的强大力量。与使用固定的数据集相反，在这项工作中，我们关注NAS的不同但重要的方案：如何完善部署的网络模型体系结构，以增强其鲁棒性，并通过一些收集和错误分类的示例的指导来增强其鲁棒性，这些示例被某些降低了现实世界中的未知损坏具有特定的模式（例如噪声，模糊等）。为此，我们首先进行了一项实证研究，以验证模型体系结构绝对与腐败模式有关。令人惊讶的是，通过仅添加一些损坏和错误分类的示例（例如，$ 10^3 $示例）到清洁培训数据集（例如$ 5.0 \ times 10^4 $示例）中，我们可以完善模型体系结构并显着增强鲁棒性。为了使其更加实用，应仔细研究关键问题，即如何为有效的NAS指导选择适当的失败示例。然后，我们提出了一个新颖的核心失效指导飞镖，该飞镖嵌入了K-Center-Greedy算法的飞镖，以选择合适的损坏故障示例以完善模型体系结构。我们使用我们的方法在清洁和15个腐败上使用飞镖精制的DNN，并在四个特定的现实世界腐败的指导下进行了指导。与最先进的NAS以及基于数据启发的增强方法相比，我们的最终方法可以在损坏的数据集和原始清洁数据集上获得更高的精度。在某些腐败模式上，我们可以达到超过45％的绝对准确性提高。

本文提出了一种网络，称为MVSTR，用于多视图立体声（MV）。它建在变压器上，并能够用全局上下文和3D一致性提取密集的功能，这对于实现MV的可靠匹配至关重要。具体地，为了解决现有的基于CNN的MVS方法的有限接收领域的问题，首先提出全局上下文变换器模块来探索视图内的全局上下文。另外，为了进一步实现致密功能，以3D一致，通过精心设计的巧妙机制构建了3D几何变压器模块，以便于观看互联信息交互。实验结果表明，建议的MVSTR在DTU数据集中实现了最佳的整体性能，并在坦克和寺庙基准数据集上的强大泛化。

在过去的几年里，深度神经网络（DNN）取得了巨大的成功，并且在许多应用领域中不断应用。然而，在工业任务的实际部署期间，由于超容易的原因，发现DNN被发现是错误的，缺乏在实际使用过程中对现实世界腐败的鲁棒性。为了解决这些挑战，通过通过在神经级别的再试，微调或直接重量固定来通过更新权重（即，网络参数）来修复实际操作环境下的近期尝试。在这项工作中，作为第一次尝试，我们通过共同优化架构和重量，以更高（即，块）级别来修复DNN。我们首先履行实证研究，以调查整个网络级和层次修复的限制，这激励我们探索块水平的DNN修复的新修复方向。为此，我们首先提出对弱势群体定位的对抗侵犯块定位的频谱分析，其在前向和后向过程中考虑块中的神经元“状态和权重”梯度，这使得即使在几个示例下也能够修复更准确的候选块定位。然后，我们进一步提出了面向架构的基于搜索的修复，该修复将目标块放宽到更高的深度特征级别的连续修复搜索空间。通过联合优化该空间中的架构和权重，我们可以识别更好的块架构。我们实施我们提出的修复技术作为一个名为ArchRepair的工具，并进行广泛的实验以验证提出的方法。结果表明，我们的方法不仅可以修复，还可以提高准确性和稳健性，优于最先进的DNN修复技术。

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.

Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations to improve generalization by co-locating similar words in vector space. For instance, Word2Vec is a self-supervised predictive model that captures the context of words using a neural network. Similarly, GLoVe is a popular unsupervised model incorporating corpus-wide word co-occurrence statistics. Such word embedding has significantly boosted important NLP tasks, including sentiment analysis, document classification, and machine translation. However, the embeddings are dense floating-point vectors, making them expensive to compute and difficult to interpret. In this paper, we instead propose to represent the semantics of words with a few defining words that are related using propositional logic. To produce such logical embeddings, we introduce a Tsetlin Machine-based autoencoder that learns logical clauses self-supervised. The clauses consist of contextual words like "black," "cup," and "hot" to define other words like "coffee," thus being human-understandable. We evaluate our embedding approach on several intrinsic and extrinsic benchmarks, outperforming GLoVe on six classification tasks. Furthermore, we investigate the interpretability of our embedding using the logical representations acquired during training. We also visualize word clusters in vector space, demonstrating how our logical embedding co-locate similar words.

The surrogate loss of variational autoencoders (VAEs) poses various challenges to their training, inducing the imbalance between task fitting and representation inference. To avert this, the existing strategies for VAEs focus on adjusting the tradeoff by introducing hyperparameters, deriving a tighter bound under some mild assumptions, or decomposing the loss components per certain neural settings. VAEs still suffer from uncertain tradeoff learning.We propose a novel evolutionary variational autoencoder (eVAE) building on the variational information bottleneck (VIB) theory and integrative evolutionary neural learning. eVAE integrates a variational genetic algorithm into VAE with variational evolutionary operators including variational mutation, crossover, and evolution. Its inner-outer-joint training mechanism synergistically and dynamically generates and updates the uncertain tradeoff learning in the evidence lower bound (ELBO) without additional constraints. Apart from learning a lossy compression and representation of data under the VIB assumption, eVAE presents an evolutionary paradigm to tune critical factors of VAEs and deep neural networks and addresses the premature convergence and random search problem by integrating evolutionary optimization into deep learning. Experiments show that eVAE addresses the KL-vanishing problem for text generation with low reconstruction loss, generates all disentangled factors with sharp images, and improves the image generation quality,respectively. eVAE achieves better reconstruction loss, disentanglement, and generation-inference balance than its competitors.

With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.

Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.

Considering the computation complexity, we propose a Guided Hybrid Quantization with One-to-one Self-Teaching (GHOST}) framework. More concretely, we first design a structure called guided quantization self-distillation (GQSD), which is an innovative idea for realizing lightweight through the synergy of quantization and distillation. The training process of the quantization model is guided by its full-precision model, which is time-saving and cost-saving without preparing a huge pre-trained model in advance. Second, we put forward a hybrid quantization (HQ) module to obtain the optimal bit width automatically under a constrained condition where a threshold for distribution distance between the center and samples is applied in the weight value search space. Third, in order to improve information transformation, we propose a one-to-one self-teaching (OST) module to give the student network a ability of self-judgment. A switch control machine (SCM) builds a bridge between the student network and teacher network in the same location to help the teacher to reduce wrong guidance and impart vital knowledge to the student. This distillation method allows a model to learn from itself and gain substantial improvement without any additional supervision. Extensive experiments on a multimodal dataset (VEDAI) and single-modality datasets (DOTA, NWPU, and DIOR) show that object detection based on GHOST outperforms the existing detectors. The tiny parameters (<9.7 MB) and Bit-Operations (BOPs) (<2158 G) compared with any remote sensing-based, lightweight or distillation-based algorithms demonstrate the superiority in the lightweight design domain. Our code and model will be released at https://github.com/icey-zhang/GHOST.