光学计算是一种新兴技术，用于下一代高效人工智能（AI），其速度和效率超高。电磁场模拟对于光子设备和电路的设计，优化和验证至关重要。但是，昂贵的数值模拟显着阻碍了光子电路设计循环中的可扩展性和转环。最近，已经提出了物理信息的神经网络来预测具有预定义参数的部分微分方程（PDE）的单个实例的光场解。它们复杂的PDE公式和缺乏有效的参数化机制限制了其在实际模拟方案中的灵活性和概括。在这项工作中，首次提出了一个被称为Neurolight的物理敏捷神经操作员框架，以学习一个频率域的麦克斯韦PDE家族，以进行超快速的参数光子设备模拟。我们通过几种新技术来平衡神经照明的效率和概括。具体而言，我们将不同的设备离散到统一域中，代表具有紧凑型波的参数PDE，并通过掩盖的源建模编码入射光。我们使用参数效率高的跨形神经块设计模型，并采用基于叠加的增强来进行数据效率学习。通过这些协同方法，神经亮像可以概括为大量的看不见的模拟设置，比数值求解器显示了2个磁性的模拟速度，并且比先前的神经网络模型优于降低54％的预测误差，而降低了约44％的参数。 。我们的代码可在https://github.com/jeremiemelo/neurolight上找到。

事实证明，丰富的用户行为数据对于点击率（CTR）预测应用程序具有很高的价值，尤其是在工业推荐，搜索或广告系统中。但是，由于在线服务时间的严格要求，现实世界系统不仅可以充分利用长期用户行为。大多数以前的作品都采用基于检索的策略，在此策略中，首先检索了少数用户行为以进行后续注意。但是，基于检索的方法是最佳的，会造成或多或少的信息损失，并且很难平衡检索算法的有效性和效率。在本文中，我们提出了SDIM（基于采样的深度兴趣建模），这是一种简单但有效的基于采样的端到端方法，用于建模长期用户行为。我们从多个哈希功能中采样，以生成候选项目和用户行为序列中的每个项目的哈希签名，并通过直接收集与具有相同哈希签名的候选项目相关的行为项来获得用户兴趣。我们在理论上和实验上表明，所提出的方法在基于标准的基于注意力的模型上对长期用户行为进行建模，同时更快。我们还介绍了系统中SDIM的部署。具体而言，我们通过设计一个名为BSE（行为序列编码）的单独模块（行为序列编码），将行为序列哈希（这是最耗时的部分）解脱出最耗时的部分。 BSE对于CTR服务器是无延迟的，使我们能够建模极长的用户行为。进行离线和在线实验，以证明SDIM的有效性。 SDIM现在已在线部署在Meituan应用程序的搜索系统中。

随着最近光学相变材料（PCM）的进步，光子内存中的神经科学大量已经证明了其在光学神经网络（ONN）设计中的优越性，具有接近零静态功耗，光时间延迟和紧凑的占地面积。然而，光子张量核心需要大量硬件重用来实现由于单核刻度有限的矩阵乘法。由此产生的大量PCM写入，导致严重的动态功率和压倒性的PCM，具有有限的写入耐久性。在这项工作中，我们提出了一种协同优化框架，努力，以最大限度地减少高效且可靠的光学内记忆中的整体写作工作。我们首先提出了写知感知培训，以鼓励重量块之间的相似性，并将其与训练后的优化方法相结合，以通过消除冗余写入来减少编程工作。实验表明，突出可以在具有可比性准确度的写入总数和动态功率的总数超过20倍。通过我们的努力，光子内记忆中的内蒙古大量将向机器学习中的可行应用前进，具有保存的准确性，级别更长的寿命和更低的编程能量。

由于深度学习在许多人工智能应用中显示了革命性的性能，其升级的计算需求需要用于巨大并行性的硬件加速器和改进的吞吐量。光学神经网络（ONN）是下一代神经关键组成的有希望的候选者，由于其高并行，低延迟和低能量消耗。在这里，我们设计了一个硬件高效的光子子空间神经网络（PSNN）架构，其针对具有比具有可比任务性能的前一个ONN架构的光学元件使用，区域成本和能量消耗。此外，提供了一种硬件感知培训框架，以最小化所需的设备编程精度，减少芯片区域，并提高噪声鲁棒性。我们在实验上展示了我们的PSNN在蝴蝶式可编程硅光子集成电路上，并在实用的图像识别任务中显示其实用性。

Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN only brings limited gain. In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation. In this work, we propose a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Overall, this framework can learn more discriminative features progressively while integrating complementary features together in each stage. Without bells and whistles, a single HTC obtains 38.4% and 1.5% improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves 48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018 Challenge Object Detection Task. Code is available at: https://github.com/ open-mmlab/mmdetection.

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.

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.

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.

Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hinders their applications due to the generalization problem. Recently, Implicit Neural Representation (INR) has appeared as a powerful DL-based tool for solving the inverse problem by characterizing the attributes of a signal as a continuous function of corresponding coordinates in an unsupervised manner. In this work, we proposed an INR-based method to improve dynamic MRI reconstruction from highly undersampled k-space data, which only takes spatiotemporal coordinates as inputs. Specifically, the proposed INR represents the dynamic MRI images as an implicit function and encodes them into neural networks. The weights of the network are learned from sparsely-acquired (k, t)-space data itself only, without external training datasets or prior images. Benefiting from the strong implicit continuity regularization of INR together with explicit regularization for low-rankness and sparsity, our proposed method outperforms the compared scan-specific methods at various acceleration factors. E.g., experiments on retrospective cardiac cine datasets show an improvement of 5.5 ~ 7.1 dB in PSNR for extremely high accelerations (up to 41.6-fold). The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.

Recent studies have shown that using an external Language Model (LM) benefits the end-to-end Automatic Speech Recognition (ASR). However, predicting tokens that appear less frequently in the training set is still quite challenging. The long-tail prediction problems have been widely studied in many applications, but only been addressed by a few studies for ASR and LMs. In this paper, we propose a new memory augmented lookup dictionary based Transformer architecture for LM. The newly introduced lookup dictionary incorporates rich contextual information in training set, which is vital to correctly predict long-tail tokens. With intensive experiments on Chinese and English data sets, our proposed method is proved to outperform the baseline Transformer LM by a great margin on both word/character error rate and tail tokens error rate. This is achieved without impact on the decoding efficiency. Overall, we demonstrate the effectiveness of our proposed method in boosting the ASR decoding performance, especially for long-tail tokens.