经过标准的横向损失训练的深度神经网络更容易记住嘈杂的标签，从而降低了其性能。当嘈杂的标签干预时，使用互补标签的负面学习更加健壮，但模型收敛速度极慢。在本文中，我们首先引入了双向学习方案，在这种方案中，积极的学习可确保收敛速度，而负面学习则可以与标签噪声保持稳健的应对。此外，提出了一种动态样本重新加权策略，以通过利用负面学习对样本概率分布的出色歧视能力来削弱噪声标记样品的影响。此外，我们结合了自我鉴定，以进一步提高模型性能。该代码可在\ url {https://github.com/chenchenzong/bldr}中获得。

宫颈异常细胞检测是一项具有挑战性的任务，因为异常细胞和正常细胞之间的形态差异通常是微妙的。为了确定宫颈细胞是正常还是异常，细胞病理学家总是将周围细胞作为参考，并进行仔细比较以鉴定其异常。为了模仿这些临床行为，我们建议探索上下文关系，以提高宫颈异常细胞检测的性能。具体而言，利用细胞和细胞到全球图像之间的上下文关系，以增强每个感兴趣区域（ROI）建议的特征。因此，开发了两个模块，称为ROI关系注意模块（RRAM）和全球ROI注意模块（GRAM），还研究了它们的组合策略。我们通过使用特征金字塔网络（FPN）使用单头或双头更快的R-CNN来设置强基础，并将我们的RRAM和革兰氏集整合到它们中以验证提出的模块的有效性。由40,000个细胞学图像组成的大宫颈细胞检测数据集进行的实验表明，RRAM和GRAM的引入都比基线方法获得了更好的平均精度（AP）。此外，当级联RRAM和GRAM时，我们的方法优于最先进的方法（SOTA）方法。此外，我们还显示了提出的功能增强方案可以促进图像级别和涂片级别的分类。代码和训练有素的模型可在https://github.com/cviu-csu/cr4cacd上公开获得。

及时调整是以参数有效的方式对预训练的预训练语言模型的新范式。在这里，我们探讨了超级核武器的使用来产生超预价：我们提出了HyperPrompt，这是一种用于迅速基于变形金刚自我注意的任务调节的新型体系结构。超预要是通过超网络通过一代人来学习的端到端。 HyperPrompt允许网络学习特定于任务的功能地图，其中超预告是要参与的查询的任务全局记忆，同时启用了任务之间的灵活信息共享。我们表明，HyperPrompt与强大的多任务学习基线具有竞争力，其额外的任务条件参数的$ 0.14 \％$ $ \％，实现了出色的参数和计算效率。通过广泛的经验实验，我们证明，超级启示可以比强大的T5多任务学习基准和参数效率高效的适配器变体获得卓越的性能，包括及时调整和SuplyFormer ++在许多模型尺寸的自然语言理解胶水和SuperGrue的基准上。

自动驾驶可以感知其周围的决策，这是视觉感知中最复杂的情​​况之一。范式创新在解决2D对象检测任务方面的成功激发了我们寻求优雅，可行和可扩展的范式，以从根本上推动该领​​域的性能边界。为此，我们在本文中贡献了BEVDET范式。 BEVDET在鸟眼视图（BEV）中执行3D对象检测，其中大多数目标值被定义并可以轻松执行路线计划。我们只是重复使用现有模块来构建其框架，但通过构建独家数据增强策略并升级非最大抑制策略来实质性地发展其性能。在实验中，BEVDET在准确性和时间效率之间提供了极好的权衡。作为快速版本，nuscenes val设置的BEVDET微小分数为31.2％的地图和39.2％的NDS。它与FCOS3D相当，但仅需要11％的计算预算为215.3 GFLOPS，并且在15.6 fps的速度中运行的速度快9.2倍。另一个称为BEVDET基本的高精度版本得分为39.3％的地图和47.2％的NDS，大大超过了所有已发布的结果。具有可比的推理速度，它超过了 +9.8％地图和 +10.0％ND的大幅度的FCOS3D。源代码可在https://github.com/huangjunjie2017/bevdet上公开研究。

由于单个RGB图像的不利低对比度和弱可见性问题，低光图像增强（LLE）仍然具有挑战性。在本文中，我们回应了有趣的学习相关问题 - 如果利用可访问的既可接近的过分配对/曝光过度的图像和高级别的语义指导，可以提高尖端LLE模型的性能？在这里，我们提出了一种有效的语义对比的学习范例（即SCL-LLE）。除了现有的LLE智慧之外，它将图像增强任务施放为多任务联合学习，其中LLE被转换为对比学习，语义亮度一致性的三个约束，同时确保曝光，纹理和颜色一致性。 SCL-LLE允许LLE模型从未配对的阳性（常灯）/否定（过度/曝光），并使其与场景语义进行互动以正规化图像增强网络，但高级语义知识的相互作用并且在以前的方法中很少地研究了低级信号。培训易于获得的开放数据，广泛的实验表明，我们的方法超越了六个独立的交叉场景数据集的最先进的LLE模型。此外，讨论了SCL-LLE在极暗条件下有益于下游语义分割的潜力。源代码：https://github.com/linglix/sclle。

由于当前语法纠错（GEC）任务中缺乏并行数据，基于序列框架的模型不能充分培训以获得更高的性能。我们提出了两个数据合成方法，可以控制误差率和合成数据对误差类型的比率。第一种方法是用固定概率损坏单声道语料库中的每个单词，包括更换，插入和删除。另一种方法是培训误差生成模型并进一步过滤模型的解码结果。对不同合成数据的实验表明，误差率为40％，误差类型的比率相同，可以提高模型性能。最后，我们综合了大约1亿数据并实现了与现有技术的可比性，它使用了我们使用的两倍。

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.