Referring image segmentation aims at localizing all pixels of the visual objects described by a natural language sentence. Previous works learn to straightforwardly align the sentence embedding and pixel-level embedding for highlighting the referred objects, but ignore the semantic consistency of pixels within the same object, leading to incomplete masks and localization errors in predictions. To tackle this problem, we propose CoupAlign, a simple yet effective multi-level visual-semantic alignment method, to couple sentence-mask alignment with word-pixel alignment to enforce object mask constraint for achieving more accurate localization and segmentation. Specifically, the Word-Pixel Alignment (WPA) module performs early fusion of linguistic and pixel-level features in intermediate layers of the vision and language encoders. Based on the word-pixel aligned embedding, a set of mask proposals are generated to hypothesize possible objects. Then in the Sentence-Mask Alignment (SMA) module, the masks are weighted by the sentence embedding to localize the referred object, and finally projected back to aggregate the pixels for the target. To further enhance the learning of the two alignment modules, an auxiliary loss is designed to contrast the foreground and background pixels. By hierarchically aligning pixels and masks with linguistic features, our CoupAlign captures the pixel coherence at both visual and semantic levels, thus generating more accurate predictions. Extensive experiments on popular datasets (e.g., RefCOCO and G-Ref) show that our method achieves consistent improvements over state-of-the-art methods, e.g., about 2% oIoU increase on the validation and testing set of RefCOCO. Especially, CoupAlign has remarkable ability in distinguishing the target from multiple objects of the same class.

Several works have proven that finetuning is an applicable approach for debiasing contextualized word embeddings. Similarly, discrete prompts with semantic meanings have shown to be effective in debiasing tasks. With unfixed mathematical representation at the token level, continuous prompts usually surpass discrete ones at providing a pre-trained language model (PLM) with additional task-specific information. Despite this, relatively few efforts have been made to debias PLMs by prompt tuning with continuous prompts compared to its discrete counterpart. Furthermore, for most debiasing methods that alter a PLM's original parameters, a major problem is the need to not only decrease the bias in the PLM but also to ensure that the PLM does not lose its representation ability. Finetuning methods typically have a hard time maintaining this balance, as they tend to violently remove meanings of attribute words. In this paper, we propose ADEPT, a method to debias PLMs using prompt tuning while maintaining the delicate balance between removing biases and ensuring representation ability. To achieve this, we propose a new training criterion inspired by manifold learning and equip it with an explicit debiasing term to optimize prompt tuning. In addition, we conduct several experiments with regard to the reliability, quality, and quantity of a previously proposed attribute training corpus in order to obtain a clearer prototype of a certain attribute, which indicates the attribute's position and relative distances to other words on the manifold. We evaluate ADEPT on several widely acknowledged debiasing benchmarks and downstream tasks, and find that it achieves competitive results while maintaining (and in some cases even improving) the PLM's representation ability. We further visualize words' correlation before and after debiasing a PLM, and give some possible explanations for the visible effects.

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.

Deep learning (DL) methods have been widely applied to anomaly-based network intrusion detection system (NIDS) to detect malicious traffic. To expand the usage scenarios of DL-based methods, the federated learning (FL) framework allows multiple users to train a global model on the basis of respecting individual data privacy. However, it has not yet been systematically evaluated how robust FL-based NIDSs are against existing privacy attacks under existing defenses. To address this issue, we propose two privacy evaluation metrics designed for FL-based NIDSs, including (1) privacy score that evaluates the similarity between the original and recovered traffic features using reconstruction attacks, and (2) evasion rate against NIDSs using Generative Adversarial Network-based adversarial attack with the reconstructed benign traffic. We conduct experiments to show that existing defenses provide little protection that the corresponding adversarial traffic can even evade the SOTA NIDS Kitsune. To defend against such attacks and build a more robust FL-based NIDS, we further propose FedDef, a novel optimization-based input perturbation defense strategy with theoretical guarantee. It achieves both high utility by minimizing the gradient distance and strong privacy protection by maximizing the input distance. We experimentally evaluate four existing defenses on four datasets and show that our defense outperforms all the baselines in terms of privacy protection with up to 7 times higher privacy score, while maintaining model accuracy loss within 3% under optimal parameter combination.

促性腺营养蛋白释放激素受体（GNRH1R）是治疗子宫疾病的有前途的治疗靶标。迄今为止，在临床研究中可以使用几个GNRH1R拮抗剂，而不满足多个财产约束。为了填补这一空白，我们旨在开发一个基于学习的框架，以促进有效，有效地发现具有理想特性的新的口服小型分子药物靶向GNRH1R。在目前的工作中，首先通过充分利用已知活性化合物和靶蛋白的结构的信息，首先提出了配体和结构组合模型，即LS-Molgen，首先提出了分子生成的方法，该信息通过其出色的性能证明了这一点。比分别基于配体或结构方法。然后，进行了A中的计算机筛选，包括活性预测，ADMET评估，分子对接和FEP计算，其中约30,000个生成的新型分子被缩小到8，以进行实验合成和验证。体外和体内实验表明，其中三个表现出有效的抑制活性（化合物5 IC50 = 0.856 nm，化合物6 IC50 = 0.901 nm，化合物7 IC50 = 2.54 nm对GNRH1R，并且化合物5在基本PK属性中表现良好例如半衰期，口服生物利用度和PPB等。我们认为，提议的配体和结构组合结合的分子生成模型和整个计算机辅助工作流程可能会扩展到从头开始的类似任务或铅优化的类似任务。

样本分配在现代对象检测方法中起着重要的作用。但是，大多数现有的方法都依靠手动设计来分配正 /负样本，这些样本并未明确建立样本分配和对象检测性能之间的关系。在这项工作中，我们提出了一种基于高参数搜索的新型动态样本分配方案。我们首先将分配给每个地面真理的正样本的数量定义为超参数，并采用替代优化算法来得出最佳选择。然后，我们设计一个动态的样本分配过程，以动态选择每个训练迭代中的最佳阳性数量。实验表明，所得的HPS-DET在不同对象检测基线的基线上带来了改善的性能。此外，我们分析了在不同数据集之间和不同骨架之间转移的高参数可重复使用性，以进行对象检测，这表现出我们方法的优势和多功能性。

在本文中，我们介绍了在集中差异隐私（ZCDP）下进行协方差估计的两种新算法。第一个算法达到了$ \ tilde {o}（d^{1/4} \ sqrt {\ mathrm {tr}}/\ sqrt {n} + \ sqrt {d}/n）$的frobenius错误\ mathrm {tr} $是协方差矩阵的跟踪。通过服用$ \ mathrm {tr} = 1 $，这也意味着$ \ tilde {o}的最坏情况错误绑定（d^{1/4}/\ sqrt {n}）$，它改善了标准高斯机制的$ \ tilde {o}（d/n）$用于制度$ d> \ widetilde {\ omega}（n^{2/3}）$。我们的第二个算法提供了一种对尾敏感的界限，在偏斜的数据上可能会更好。相应的算法也很简单有效。实验结果表明，它们对先前的工作提供了重大改进。

全球和本地环境显着有助于显着对象检测（SOD）中预测的完整性。不幸的是，现有的方法仍然难以生成完整的预测，并提供细节。常规方法中有两个主要问题：首先，对于全球环境，高级CNN的编码器功能无法有效地捕获长期依赖性，从而导致不完整的预测。其次，将地面真相的采样降低以适应预测的规模，因为在插值或合并过程中丢失了地面真相细节，因此会引起不准确性。因此，在这项工作中，我们开发了一个基于变压器的网络，并构成了分支机构的监督任务，以明确学习全局上下文信息。此外，我们采用从超级分辨率（SR）的像素随机散发，将预测重塑为地面真理的大小，而不是反向。因此，地面真理中的细节没有触及。此外，我们开发了一个两阶段的上下文改进模块（CRM）来融合全局上下文，并自动在预测中找到和完善本地细节。拟议的网络可以根据生成的全局和本地上下文（因此被命名为自我精制的变压器）（自我改革）指导和纠正自身。五个基准数据集的广泛实验和评估结果证明了网络的出色性能，我们实现了最新的技术。

分布强化学习〜（RL）是一类最先进的算法，可估计总回报的整个分布，而不仅仅是其期望。分布RL的经验成功取决于回报分布的表示和分布差异的选择。在本文中，我们提出了一类新类\ textit {sindhorn Distributional rl〜（sindhorndrl）}算法，该算法学习了一组有限的统计数据，即确定性样本，从每个返回分布中，然后使用sinkhorn迭代来评估sindhorn迭代之间的距离当前和目标铃铛分布。 sindhorn的差异特征是瓦斯汀距离与最大平均差异〜（MMD）之间的插值。 Sindhorndrl通过利用基于最佳传输距离的几何形状和MMD的无偏梯度估计特性，从而找到了一个甜蜜点。最后，与最先进的算法相比，Sinkhorndrl的竞争性能在55场Atari游戏中得到了证明。

分布强化学习〜（RL）是一类最先进的算法，可估计总回报的全部分布，而不仅仅是其期望。尽管分销RL的表现出色，但对基于预期的RL的优势的理论理解仍然难以捉摸。在本文中，我们将分布RL的优越性归因于其正规化效果，无论其预期如何，其价值分布信息。首先，通过稳健统计数据中总误差模型的变体的杠杆作用，我们将值分布分解为其预期和其余分布部分。因此，与基于期望的RL相比，分布RL的额外好处主要解释为在神经拟合Z-材料框架中\ textit {风险敏感的熵正则化}的影响。同时，我们在最大熵RL中的分布RL的风险敏感熵正则和香草熵之间建立了一个桥梁，专门针对参与者 - 批评算法。它揭示了分布RL诱导校正后的奖励函数，从而促进了针对环境内在不确定性的风险敏感探索。最后，广泛的实验证实了分布RL的正则化作用和不同熵正则化的相互影响的作用。我们的研究铺平了一种更好地解释分布RL算法的功效，尤其是通过正则化的镜头的方法。