Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a heterogeneous yet powerful cross-encoder. However, in contrast to passages or sentences, retrieval on long documents suffers from the scope hypothesis that a long document may cover multiple topics. This maximizes their structure heterogeneity and poses a granular-mismatch issue, leading to an inferior distillation efficacy. In this work, we propose a new learning framework, fine-grained distillation (FGD), for long-document retrievers. While preserving the conventional dense retrieval paradigm, it first produces global-consistent representations crossing different fine granularity and then applies multi-granular aligned distillation merely during training. In experiments, we evaluate our framework on two long-document retrieval benchmarks, which show state-of-the-art performance.

To improve the performance of the dual-encoder retriever, one effective approach is knowledge distillation from the cross-encoder ranker. Existing works construct the candidate passages following the supervised learning setting where a query is paired with a positive passage and a batch of negatives. However, through empirical observation, we find that even the hard negatives from advanced methods are still too trivial for the teacher to distinguish, preventing the teacher from transferring abundant dark knowledge to the student through its soft label. To alleviate this issue, we propose ADAM, a knowledge distillation framework that can better transfer the dark knowledge held in the teacher with Adaptive Dark exAMples. Different from previous works that only rely on one positive and hard negatives as candidate passages, we create dark examples that all have moderate relevance to the query through mixing-up and masking in discrete space. Furthermore, as the quality of knowledge held in different training instances varies as measured by the teacher's confidence score, we propose a self-paced distillation strategy that adaptively concentrates on a subset of high-quality instances to conduct our dark-example-based knowledge distillation to help the student learn better. We conduct experiments on two widely-used benchmarks and verify the effectiveness of our method.

基于语义空间中密集表示的检索模型已成为第一阶段检索的必不可少的分支。这些检索员受益于代表学习朝着压缩全球序列级嵌入的进步。但是，它们很容易忽略本地的显着短语和实体在文本中提到的，这些短语通常在第一阶段的检索中扮演枢轴角色。为了减轻这种弱点，我们提议使一个密集的检索器对齐一个表现出色的词典意识代表模型。对齐方式是通过弱化的知识蒸馏来实现的，以通过两个方面来启发猎犬 - 1）词汇扬声的对比目标，以挑战密集编码器和2）一个配对的等级正规化，以使密集的模型的行为倾向于其他人的行为。我们在三个公共基准上评估了我们的模型，这表明，凭借可比的词典觉得回收犬作为老师，我们提议的密集人可以带来一致而重大的改进，甚至超过教师。此外，我们发现我们对密集猎犬的改进是与标准排名蒸馏的补充，这可以进一步提高最先进的性能。

本文着重于几次NLP任务的文本数据增强。现有的数据增强算法要么使用一个小型培训集来生成新的合成数据，要么利用与任务无关的启发式规则（例如，同义词替代）或微调通用预训练的语言模型（例如GPT2）。因此，这些方法具有特定于任务的知识，并且仅限于在简单任务中为弱基线产生低质量的合成数据。为了解决这个问题，我们提出了知识混合数据增强模型（KNOWDA）：使用知识混合培训（KOMT）在不同的NLP任务的混合物上预测的编码器LM。 KOMT是一种培训程序，将各种异质NLP任务的输入示例重新定义为统一的文本到文本格式，并采用不同粒度的目标，以学习生成部分或完整的样本。在KOMT的帮助下，Knowda可以隐含地将所需的特定于任务的知识从任务的混合中隐含地结合在一起，并通过一些给定的实例迅速掌握目标任务的固有综合定律。据我们所知，我们是首次尝试将任务数量扩展到多任务共同培训以进行数据扩展。广泛的实验表明，i）Knowda成功地通过少量基准的基准成功地提高了Albert和Deberta的表现，表现优于先前的最新数据增强基线； ii）KNOWDA还可以改善少数弹药任务的模型性能，这是KOMT中未包含的固定任务类型。

排名者在事实上的“检索和rerank”管道中起着必不可少的作用，但其训练仍然落后 - 从中​​度的负面因素或/和/和/和作为回收者的辅助模块中学习。在这项工作中，我们首先确定了强大的排名者的两个主要障碍，即是由训练有素的回猎犬和非理想的负面负面的固有标签噪声，该噪声是为高能力的排名所采样的。因此，我们提出多个检索器，因为负面发电机改善了排名者的鲁棒性，其中i）涉及广泛的分发标签噪声，使排名者与每个噪声分布相对，而ii）与排名相对较接近排名负分配，导致更具挑战性的培训。为了评估我们的强大排名者（称为r $^2 $ anker），我们在各种环境中进行了有关流行通道检索基准测试的各种实验，包括BM25级，全等级，回收者蒸馏等。经验结果验证了新的州 - 新州 - 新州 - 我们模型的效果。

As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation CORGI-PM, which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. Moreover, we address three challenges for automatic textual gender bias mitigation, which requires the models to detect, classify, and mitigate textual gender bias. We also conduct experiments with state-of-the-art language models to provide baselines. To our best knowledge, CORGI-PM is the first sentence-level Chinese corpus for gender bias probing and mitigation.

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.

As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with multiple modalities like images. However, current MMEA algorithms all adopt KG-level modality fusion strategies but ignore modality differences among individual entities, hurting the robustness to potential noise involved in modalities (e.g., unidentifiable images and relations). In this paper we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, to dynamically predict the mutual correlation coefficients among modalities for instance-level feature fusion. A modal-aware hard entity replay strategy is also proposed for addressing vague entity details. Extensive experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has limited parameters, optimistic speed, and good interpretability. Our code will be available soon.

Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks. Yet imperceptible adversarial perturbations can significantly reduce CLIP's performance on new tasks. In this work, we identify and explore the problem of \emph{adapting large-scale models for zero-shot adversarial robustness}. We first identify two key factors during model adaption -- training losses and adaptation methods -- that affect the model's zero-shot adversarial robustness. We then propose a text-guided contrastive adversarial training loss, which aligns the text embeddings and the adversarial visual features with contrastive learning on a small set of training data. We apply this training loss to two adaption methods, model finetuning and visual prompt tuning. We find that visual prompt tuning is more effective in the absence of texts, while finetuning wins in the existence of text guidance. Overall, our approach significantly improves the zero-shot adversarial robustness over CLIP, seeing an average improvement of over 31 points over ImageNet and 15 zero-shot datasets. We hope this work can shed light on understanding the zero-shot adversarial robustness of large-scale models.

The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits. However, the energy consumption of 5G networks is today a concern. In recent years, the design of new methods for decreasing the RAN power consumption has attracted interest from both the research community and standardization bodies, and many energy savings solutions have been proposed. However, there is still a need to understand the power consumption behavior of state-ofthe-art base station architectures, such as multi-carrier active antenna units (AAUs), as well as the impact of different network parameters. In this paper, we present a power consumption model for 5G AAUs based on artificial neural networks. We demonstrate that this model achieves good estimation performance, and it is able to capture the benefits of energy saving when dealing with the complexity of multi-carrier base stations architectures. Importantly, multiple experiments are carried out to show the advantage of designing a general model able to capture the power consumption behaviors of different types of AAUs. Finally, we provide an analysis of the model scalability and the training data requirements.