We consider the inverse acoustic obstacle problem for sound-soft star-shaped obstacles in two dimensions wherein the boundary of the obstacle is determined from measurements of the scattered field at a collection of receivers outside the object. One of the standard approaches for solving this problem is to reformulate it as an optimization problem: finding the boundary of the domain that minimizes the $L^2$ distance between computed values of the scattered field and the given measurement data. The optimization problem is computationally challenging since the local set of convexity shrinks with increasing frequency and results in an increasing number of local minima in the vicinity of the true solution. In many practical experimental settings, low frequency measurements are unavailable due to limitations of the experimental setup or the sensors used for measurement. Thus, obtaining a good initial guess for the optimization problem plays a vital role in this environment. We present a neural network warm-start approach for solving the inverse scattering problem, where an initial guess for the optimization problem is obtained using a trained neural network. We demonstrate the effectiveness of our method with several numerical examples. For high frequency problems, this approach outperforms traditional iterative methods such as Gauss-Newton initialized without any prior (i.e., initialized using a unit circle), or initialized using the solution of a direct method such as the linear sampling method. The algorithm remains robust to noise in the scattered field measurements and also converges to the true solution for limited aperture data. However, the number of training samples required to train the neural network scales exponentially in frequency and the complexity of the obstacles considered. We conclude with a discussion of this phenomenon and potential directions for future research.

When reading a story, humans can rapidly understand new fictional characters with a few observations, mainly by drawing analogy to fictional and real people they met before in their lives. This reflects the few-shot and meta-learning essence of humans' inference of characters' mental states, i.e., humans' theory-of-mind (ToM), which is largely ignored in existing research. We fill this gap with a novel NLP benchmark, TOM-IN-AMC, the first assessment of models' ability of meta-learning of ToM in a realistic narrative understanding scenario. Our benchmark consists of $\sim$1,000 parsed movie scripts for this purpose, each corresponding to a few-shot character understanding task; and requires models to mimic humans' ability of fast digesting characters with a few starting scenes in a new movie. Our human study verified that humans can solve our problem by inferring characters' mental states based on their previously seen movies; while the state-of-the-art metric-learning and meta-learning approaches adapted to our task lags 30% behind.

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

超声检查是乳腺癌诊断的重要常规检查，这是由于其无创，无辐射和低成本的特性。但是，由于其固有的局限性，乳腺癌的诊断准确性仍然受到限制。如果我们可以通过乳房超声图像（BUS）精确诊断乳腺癌，那将是一个巨大的成功。已经提出了许多基于学习的计算机辅助诊断方法来实现乳腺癌诊断/病变分类。但是，其中大多数需要预定的ROI，然后对ROI内的病变进行分类。常规的分类骨架，例如VGG16和RESNET50，可以在没有ROI要求的情况下获得有希望的分类结果。但是这些模型缺乏解释性，因此限制了它们在临床实践中的使用。在这项研究中，我们提出了一种具有可解释特征表示的超声图像中乳腺癌诊断的新型无ROI模型。我们利用解剖学的先验知识，即恶性肿瘤和良性肿瘤在不同的组织层之间具有不同的空间关系，并提出了悬停转换器来提出这种先验知识。提出的悬停式跨界块水平和垂直地提取层间和层内空间信息。我们进行并释放一个开放的数据集GDPH＆SYSUCC，以用于公共汽车中的乳腺癌诊断。通过与四个基于CNN的模型和两个Vision Transformer模型进行比较，通过五倍的交叉验证来评估所提出的模型。它通过最佳模型可解释性实现最新的分类性能。同时，我们提出的模型在仅给出一张公交图像时，在乳腺癌诊断方面优于两名高级超声检查员。

为了在商店中充分利用计算机视觉技术，需要考虑适合零售场景特征的实际需求。为了实现这一目标，我们介绍了联合零售数据集（Unitail），这是针对检测，阅读和匹配算法的产品的基本视觉任务的大规模基准。凭借注释的180万个四边形实例，该Unitail提供了一个检测数据集，以更好地对齐产品外观。此外，它提供了一个包含1454个产品类别，30k文本区域和21k转录的画廊风格的OCR数据集，以实现对产品的强大阅读并激励增强的产品匹配。除了使用各种最新技术对数据集进行基准测试外，我们还定制了一个新的检测器以进行产品检测，并提供了一个简单的基于OCR的匹配解决方案，以验证其有效性。

数据增强是自然语言处理（NLP）模型的鲁棒性评估的重要组成部分，以及增强他们培训的数据的多样性。在本文中，我们呈现NL-Cogmenter，这是一种新的参与式Python的自然语言增强框架，它支持创建两个转换（对数据的修改）和过滤器（根据特定功能的数据拆分）。我们描述了框架和初始的117个变换和23个过滤器，用于各种自然语言任务。我们通过使用其几个转换来分析流行自然语言模型的鲁棒性来证明NL-Upmenter的功效。基础架构，Datacards和稳健性分析结果在NL-Augmenter存储库上公开可用（\ url {https://github.com/gem-benchmark/nl-augmenter}）。

肌电图（EMG）数据已被广泛采用作为指导人类机器人协作的直观界面。实时检测人类掌握意图的主要挑战是从手动运动中识别动态EMG。先前的研究主要实施了稳态EMG分类，并在动态情况下具有少量的掌握模式，这些模式不足以产生有关实践中肌肉活动变化的差异化控制。为了更好地检测动态运动，可以将更多的EMG变异性集成到模型中。但是，只有有限的研究集中于这种动态抓紧运动的检测，而对非静态EMG分类的大多数现有评估要么需要监督运动状态的地面真相，要么仅包含有限的运动学变化。在这项研究中，我们提出了一个将动态EMG信号分类为手势的框架，并使用一种无​​监督的方法来检查不同运动阶段的影响，以细分和标记动作转变。我们从大型手势词汇中收集和利用了具有多种动态动作的大型手势词汇的数据，以基于掌握动作的常见序列编码从一个抓握意图到另一个掌握的过渡。随后根据动态EMG信号构建了用于识别手势标签的分类器，不需要对运动运动的监督注释。最后，我们使用来自不同运动阶段的EMG数据评估了多种培训策略的性能，并探讨了每个阶段揭示的信息。所有实验均以实时样式进行评估，并随着时间的推移的性能过渡。

目的：对于下臂截肢者，机器人假肢承诺将重新获得日常生活活动的能力。基于生理信号（例如肌电图（EMG））的当前控制方法容易由于运动伪影，肌肉疲劳等导致不良的推理结果。视觉传感器是有关环境状态的主要信息来源，可以在推断可行和预期的手势中发挥至关重要的作用。但是，视觉证据也容易受到其自身的伪像，最常由于对象阻塞，照明变化等。使用生理和视觉传感器测量的多模式证据融合是一种自然方法，这是由于这些模态的互补优势。方法：在本文中，我们提出了一个贝叶斯证据融合框架，用于使用眼部视频，眼睛凝视和来自神经网络模型处理前臂的EMG的掌握意图推理。当手接近对象以掌握对象时，我们将个人和融合性能分析为时间的函数。为此，我们还开发了新颖的数据处理和增强技术来训练神经网络组件。结果：我们的结果表明，相对于EMG和视觉证据，平均而言，融合会提高即将到来的GRASP类型分类准确性，而在触及阶段则提高了13.66％和14.8％的融合，从而单独地和视觉证据，总体融合精度为95.3％。结论：我们的实验数据分析表明，EMG和视觉证据表明互补的强度，因此，多模式证据的融合可以在任何给定时间胜过每个单独的证据方式。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.