The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

视频突出显示检测是一个至关重要但充满挑战的问题，旨在识别未修剪视频中有趣的时刻。该任务的关键在于有效的视频表示形式共同追求两个目标，即\ textit {i.e。}，跨模式表示学习和精细元素特征歧视。在本文中，这两个挑战不仅通过丰富表示建模的模式内部和跨模式关系来应对，而且还以歧视性的方式塑造了这些特征。我们提出的方法主要利用模式内编码和交叉模式共发生编码来完全表示建模。具体而言，编码的模式内模式可以增强模态特征，并通过音频和视觉信号中的模式关系学习来抑制无关的模态。同时，跨模式的共同发生编码着重于同时模式间关系，并选择性地捕获了多模式之间的有效信息。从本地上下文中抽象的全局信息进一步增强了多模式表示。此外，我们使用硬对对比度学习（HPCL）方案扩大了特征嵌入的判别能力。进一步采用了硬对采样策略来开采硬样品，以改善HPCL中的特征歧视。与其他最新方法相比，在两个基准上进行的广泛实验证明了我们提出的方法的有效性和优势。

检测到分布（OOD）样本对于在现实世界中的分类器的安全部署至关重要。但是，已知深层神经网络对异常数据过于自信。现有作品直接设计得分功能，通过挖掘分别分类器（ID）和OOD的不一致性。在本文中，我们基于以下假设，即对ID数据进行训练的自动编码器无法重建OOD和ID，我们进一步补充了这种不一致性。我们提出了一种新颖的方法，读取（重建误差聚合检测器），以统一分类器和自动编码器的不一致。具体而言，原始像素的重建误差转换为分类器的潜在空间。我们表明，转换后的重建误差桥接了语义差距，并从原始的传承了检测性能。此外，我们提出了一种调整策略，以根据OOD数据的细粒度表征来减轻自动编码器的过度自信问题。在两种情况下，我们分别提出了方法的两个变体，即仅基于预先训练的分类器和读取 - 读取器（欧几里得距离），即读取MD（Mahalanobis距离），该分类器重新训练分类器。我们的方法不需要访问测试时间数据以进行微调超参数。最后，我们通过与最先进的OOD检测算法进行了广泛的比较来证明所提出的方法的有效性。在CIFAR-10预先训练的WideresNet上，我们的方法将平均FPR@95TPR降低了9.8％，而不是先前的最新ART。

股票运动预测（SMP）旨在预测上市公司的股份量股份，由于金融市场的挥发性，这是一个具有挑战性的任务。最近的财务研究表明，动量溢出效应在股票波动中发挥着重要作用。然而，以前的研究通常只学习相关公司之间的简单连接信息，这不可避免地未能模仿真实金融市场中上市公司的复杂关系。为了解决这个问题，我们首先建立一个更全面的市场知识图（MKG），其中包含有限的公司，包括上市公司及其相关的高管，以及包括明确关系和隐性关系的混合关系。之后，我们提出了一种新颖的双重关注网络，以了解基于构造的MKG用于库存预测的势头溢出信号。对九个SOTA基线构建数据集的实证实验表明，所提出的丹林公司能够改善与构造的MKG的库存预测。

关于数据隐私和安全性的越来越多的担忧驱动了从孤立的数据源，即联合学习的隐私保留机学习的新兴领域。一类联合学习，\ Texit {垂直联合学习}，不同的各方对共同用户的不同特征，具有促进许多领域企业之间各种业务合作的潜力。在机器学习中，诸如梯度提升决策树（GBDT）和随机森林等决策树集合被广泛应用强大的型号，具有高的可解释性和建模效率。然而，最先进的垂直联合学习框架适应匿名功能以避免可能的数据泄露，使模型受到损害的可解释性。为了解决推理过程中的这个问题，在本文中，我们首先在垂直联合学习中对客场党的特征披露含义的必要性进行了问题分析。然后，我们发现树的预测结果可以表示为所有各方持有的树的子模型结果的交叉点。利用这种关键观察，我们通过隐藏决策路径来保护数据隐私并允许公开特征含义，并适应推理输出的通信有效的安全计算方法。通过理论分析和广泛的数值结果，将证明FED-EINI的优点。我们通过披露特征的含义来提高模型的可解释性，同时确保效率和准确性。

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

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.

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.

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.