Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof traces for more accurate and robust generic attack detection. In particular, based on the adversarial learning mechanism, a two-stream disentangling network is designed to estimate spoof patterns from the RGB and depth inputs, respectively. In this case, it captures complementary spoofing clues inhering in different attacks. Furthermore, a fusion module is exploited, which recalibrates both representations at multiple stages to promote the disentanglement in each individual modality. It then performs cross-modality aggregation to deliver a more comprehensive spoof trace representation for prediction. Extensive evaluations are conducted on multiple benchmarks, demonstrating that learning polysemantic spoof traces favorably contributes to anti-spoofing with more perceptible and interpretable results.

Time series forecasting is a long-standing challenge due to the real-world information is in various scenario (e.g., energy, weather, traffic, economics, earthquake warning). However some mainstream forecasting model forecasting result is derailed dramatically from ground truth. We believe it's the reason that model's lacking ability of capturing frequency information which richly contains in real world datasets. At present, the mainstream frequency information extraction methods are Fourier transform(FT) based. However, use of FT is problematic due to Gibbs phenomenon. If the values on both sides of sequences differ significantly, oscillatory approximations are observed around both sides and high frequency noise will be introduced. Therefore We propose a novel frequency enhanced channel attention that adaptively modelling frequency interdependencies between channels based on Discrete Cosine Transform which would intrinsically avoid high frequency noise caused by problematic periodity during Fourier Transform, which is defined as Gibbs Phenomenon. We show that this network generalize extremely effectively across six real-world datasets and achieve state-of-the-art performance, we further demonstrate that frequency enhanced channel attention mechanism module can be flexibly applied to different networks. This module can improve the prediction ability of existing mainstream networks, which reduces 35.99% MSE on LSTM, 10.01% on Reformer, 8.71% on Informer, 8.29% on Autoformer, 8.06% on Transformer, etc., at a slight computational cost ,with just a few line of code. Our codes and data are available at https://github.com/Zero-coder/FECAM.

Vertical federated learning is a trending solution for multi-party collaboration in training machine learning models. Industrial frameworks adopt secure multi-party computation methods such as homomorphic encryption to guarantee data security and privacy. However, a line of work has revealed that there are still leakage risks in VFL. The leakage is caused by the correlation between the intermediate representations and the raw data. Due to the powerful approximation ability of deep neural networks, an adversary can capture the correlation precisely and reconstruct the data. To deal with the threat of the data reconstruction attack, we propose a hashing-based VFL framework, called \textit{HashVFL}, to cut off the reversibility directly. The one-way nature of hashing allows our framework to block all attempts to recover data from hash codes. However, integrating hashing also brings some challenges, e.g., the loss of information. This paper proposes and addresses three challenges to integrating hashing: learnability, bit balance, and consistency. Experimental results demonstrate \textit{HashVFL}'s efficiency in keeping the main task's performance and defending against data reconstruction attacks. Furthermore, we also analyze its potential value in detecting abnormal inputs. In addition, we conduct extensive experiments to prove \textit{HashVFL}'s generalization in various settings. In summary, \textit{HashVFL} provides a new perspective on protecting multi-party's data security and privacy in VFL. We hope our study can attract more researchers to expand the application domains of \textit{HashVFL}.

Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion. In general, these parties have a group of users in common but own different features. Existing VFL frameworks use cryptographic techniques to provide data privacy and security guarantees, leading to a line of works studying computing efficiency and fast implementation. However, the security of VFL's model remains underexplored.

RGB-Thermal（RGB-T）人群计数是一项具有挑战性的任务，它将热图像用作与RGB图像的互补信息，以应对低弹片或类似背景的场景中单峰基于RGB的方法的降低。大多数现有方法提出了精心设计的结构，用于RGB-T人群计数中的跨模式融合。但是，这些方法在编码RGB-T图像对中编码跨模式上下文语义信息方面存在困难。考虑到上述问题，我们提出了一个称为多发意见融合网络（MAFNET）的两流RGB-T人群计数网络，该网络旨在根据注意机制完全捕获RGB和热模式中的远距离上下文信息。具体而言，在编码器部分中，多发融合（MAF）模块嵌入到全球级别的两个特定于模态分支的不同阶段中。此外，引入了多模式多尺度聚合（MMA）回归头，以充分利用跨模态的多尺度和上下文信息，以生成高质量的人群密度图。在两个受欢迎的数据集上进行的广泛实验表明，拟议的MAFNET对RGB-T人群计数有效，并实现了最新的性能。

本文在移动平台上介绍了四摩托车的自动起飞和着陆系统。设计的系统解决了三个具有挑战性的问题：快速姿势估计，受限的外部定位和有效避免障碍物。具体而言，首先，我们基于Aruco标记设计了着陆识别和定位系统，以帮助四极管快速计算相对姿势。其次，我们利用基于梯度的本地运动计划者快速生成无冲突的参考轨迹；第三，我们构建了一台自主状态机器，使四极管能够完全自治完成其起飞，跟踪和着陆任务；最后，我们在模拟，现实世界和室外环境中进行实验，以验证系统的有效性并证明其潜力。

这项工作考虑了在属性关系图（ARG）上表示表示的任务。 ARG中的节点和边缘都与属性/功能相关联，允许ARG编码在实际应用中广泛观察到的丰富结构信息。现有的图形神经网络提供了有限的能力，可以在局部结构环境中捕获复杂的相互作用，从而阻碍他们利用ARG的表达能力。我们提出了Motif卷积模块（MCM），这是一种新的基于基线的图表表示技术，以更好地利用本地结构信息。处理连续边缘和节点功能的能力是MCM比现有基于基础图案的模型的优势之一。 MCM以无监督的方式构建了一个主题词汇，并部署了一种新型的主题卷积操作，以提取单个节点的局部结构上下文，然后将其用于通过多层perceptron学习高级节点表示，并在图神经网络中传递消息。与其他图形学习方法进行分类的合成图相比，我们的方法在捕获结构环境方面要好得多。我们还通过将其应用于几个分子基准来证明我们方法的性能和解释性优势。

Dose verification based on proton-induced positron emitters is a promising quality assurance tool and may leverage the strength of artificial intelligence. To move a step closer towards practical application, the sensitivity analysis of two factors needs to be performed: biological washout and depth selection. selection. A bi-directional recurrent neural network (RNN) model was developed. The training dataset was generated based upon a CT image-based phantom (abdomen region) and multiple beam energies/pathways, using Monte-Carlo simulation (1 mm spatial resolution, no biological washout). For the modeling of biological washout, a simplified analytical model was applied to change raw activity profiles over a period of 5 minutes, incorporating both physical decay and biological washout. For the study of depth selection (a challenge linked to multi field/angle irradiation), truncations were applied at different window lengths (100, 125, 150 mm) to raw activity profiles. Finally, the performance of a worst-case scenario was examined by combining both factors (depth selection: 125 mm, biological washout: 5 mins). The accuracy was quantitatively evaluated in terms of range uncertainty, mean absolute error (MAE) and mean relative errors (MRE). Our proposed AI framework shows good immunity to the perturbation associated with two factors. The detection of proton-induced positron emitters, combined with machine learning, has great potential to implement online patient-specific verification in proton therapy.

近年来，在运输电气化方面取得了重大进展。作为主要的储能设备，锂离子电池（LIB）已受到广泛关注。准确地预测健康状况（SOH）不仅可以缓解用户对电池寿命的焦虑，而且还可以为电池管理提供重要信息。本文提出了一种基于视觉变压器（VIT）模型的SOH的预测方法。首先，预定义电压范围的离散充电数据用作输入数据矩阵。然后，电池的循环特征是由VIT捕获的，可以获得可以获得全局特征，并且通过将循环特征与完整连接（FC）层相结合来获得SOH。同时，引入了转移学习（TL），并根据目标任务电池的早期周期数据进一步微调基于源任务电池训练的预测模型，以提供准确的预测。实验表明，与现有的深度学习方法相比，我们的方法可以获得更好的特征表达，从而可以实现更好的预测效果和传递效果。

虽然在矩阵完成文献中广泛研究了均匀的采样，但CUR采样近似于通过行样品和列样品近似矩阵。不幸的是，在现实世界应用中，这两种采样模型在各种情况下都缺乏灵活性。在这项工作中，我们提出了一种新颖且易于实现的采样策略，即跨浓缩采样（CCS）。通过桥接统一的采样和CUR采样，CCS提供了额外的灵活性，可以节省应用程序中的采样成本。此外，我们还为基于CCS的矩阵完成提供了足够的条件。此外，我们建议针对拟议的CCS模型，提出了一种高效的非凸算法，称为迭代CUR完成（ICURC）。数值实验验证了CCS和ICURC针对均匀采样及其基线算法的经验优势，这些实验在合成数据集和实际数据集上都验证了基线算法。