近年来，空中机器人背景下的高速导航和环境互动已成为几个学术和工业研究研究的兴趣领域。特别是，由于其若干环境中的潜在可用性，因此搜索和拦截（SAI）应用程序造成引人注目的研究区域。尽管如此，SAI任务涉及有关感官权重，板载计算资源，致动设计和感知和控制算法的具有挑战性的发展。在这项工作中，已经提出了一种用于高速对象抓握的全自动空中机器人。作为一个额外的子任务，我们的系统能够自主地刺穿位于靠近表面的杆中的气球。我们的第一款贡献是在致动和感觉水平的致动和感觉水平的空中机器人的设计，包括具有额外传感器的新型夹具设计，使机器人能够高速抓住物体。第二种贡献是一种完整的软件框架，包括感知，状态估计，运动计划，运动控制和任务控制，以便快速且强大地执行自主掌握任务。我们的方法已在一个具有挑战性的国际竞争中验证，并显示出突出的结果，能够在室外环境中以6米/分来自动搜索，遵循和掌握移动物体

我们研究了从3D对象组成的场景的稀疏源观察的新型视图综合的问题。我们提出了一种简单但有效的方法，既不是持续的也不是隐含的，挑战近期观测综合的趋势。我们的方法将观察显式编码为启用摊销渲染的体积表示。我们证明，虽然由于其表现力，但由于其表现力，但由于其富有力的力量，我们的简单方法获得了与最新的基线的比较比较了与最先进的基线的相当甚至更好的新颖性重建质量，同时增加了渲染速度超过400倍。我们的模型采用类别无关方式培训，不需要特定于场景的优化。因此，它能够将新颖的视图合成概括为在训练期间未见的对象类别。此外，我们表明，通过简单的制定，我们可以使用视图综合作为自我监控信号，以便在没有明确的3D监督的情况下高效学习3D几何。

Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The development of artificial intelligence (AI) systems capable of solving math problems and proving theorems has garnered significant interest in the fields of machine learning and natural language processing. For example, mathematics serves as a testbed for aspects of reasoning that are challenging for powerful deep learning models, driving new algorithmic and modeling advances. On the other hand, recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning. In this survey paper, we review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. We also evaluate existing benchmarks and methods, and discuss future research directions in this domain.

The state-of-the-art language model-based automatic metrics, e.g. BARTScore, benefiting from large-scale contextualized pre-training, have been successfully used in a wide range of natural language generation (NLG) tasks, including machine translation, text summarization, and data-to-text. Recent studies show that considering both major errors (e.g. mistranslated tokens) and minor errors (e.g. imperfections in fluency) can produce high-quality human judgments. This inspires us to approach the final goal of the evaluation metrics (human-like evaluations) by automatic error analysis. To this end, we augment BARTScore by incorporating the human-like error analysis strategies, namely BARTScore++, where the final score consists of both the evaluations of major errors and minor errors. Experimental results show that BARTScore++ can consistently improve the performance of vanilla BARTScore and outperform existing top-scoring metrics in 20 out of 25 test settings. We hope our technique can also be extended to other pre-trained model-based metrics. We will release our code and scripts to facilitate the community.

The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them. While existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, they are insufficient for diagnosing whether metrics are: 1) consistent, i.e., decrease as errors are introduced into a summary, 2) effective on human-written texts, and 3) sensitive to different error types (as summaries can contain multiple errors). To address these needs, we present a benchmark of unfaithful minimal pairs (BUMP), a dataset of 889 human-written, minimally different summary pairs, where a single error (from an ontology of 7 types) is introduced to a summary from the CNN/DailyMail dataset to produce an unfaithful summary. We find BUMP complements existing benchmarks in a number of ways: 1) the summaries in BUMP are harder to discriminate and less probable under SOTA summarization models, 2) BUMP enables measuring the consistency of metrics, and reveals that the most discriminative metrics tend not to be the most consistent, 3) BUMP enables the measurement of metrics' performance on individual error types and highlights areas of weakness for future work.

Geometry problem solving is a well-recognized testbed for evaluating the high-level multi-modal reasoning capability of deep models. In most existing works, two main geometry problems: calculation and proving, are usually treated as two specific tasks, hindering a deep model to unify its reasoning capability on multiple math tasks. However, in essence, these two tasks have similar problem representations and overlapped math knowledge which can improve the understanding and reasoning ability of a deep model on both two tasks. Therefore, we construct a large-scale Unified Geometry problem benchmark, UniGeo, which contains 4,998 calculation problems and 9,543 proving problems. Each proving problem is annotated with a multi-step proof with reasons and mathematical expressions. The proof can be easily reformulated as a proving sequence that shares the same formats with the annotated program sequence for calculation problems. Naturally, we also present a unified multi-task Geometric Transformer framework, Geoformer, to tackle calculation and proving problems simultaneously in the form of sequence generation, which finally shows the reasoning ability can be improved on both two tasks by unifying formulation. Furthermore, we propose a Mathematical Expression Pretraining (MEP) method that aims to predict the mathematical expressions in the problem solution, thus improving the Geoformer model. Experiments on the UniGeo demonstrate that our proposed Geoformer obtains state-of-the-art performance by outperforming task-specific model NGS with over 5.6% and 3.2% accuracies on calculation and proving problems, respectively.

Text-guided 3D object generation aims to generate 3D objects described by user-defined captions, which paves a flexible way to visualize what we imagined. Although some works have been devoted to solving this challenging task, these works either utilize some explicit 3D representations (e.g., mesh), which lack texture and require post-processing for rendering photo-realistic views; or require individual time-consuming optimization for every single case. Here, we make the first attempt to achieve generic text-guided cross-category 3D object generation via a new 3D-TOGO model, which integrates a text-to-views generation module and a views-to-3D generation module. The text-to-views generation module is designed to generate different views of the target 3D object given an input caption. prior-guidance, caption-guidance and view contrastive learning are proposed for achieving better view-consistency and caption similarity. Meanwhile, a pixelNeRF model is adopted for the views-to-3D generation module to obtain the implicit 3D neural representation from the previously-generated views. Our 3D-TOGO model generates 3D objects in the form of the neural radiance field with good texture and requires no time-cost optimization for every single caption. Besides, 3D-TOGO can control the category, color and shape of generated 3D objects with the input caption. Extensive experiments on the largest 3D object dataset (i.e., ABO) are conducted to verify that 3D-TOGO can better generate high-quality 3D objects according to the input captions across 98 different categories, in terms of PSNR, SSIM, LPIPS and CLIP-score, compared with text-NeRF and Dreamfields.

Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. Conclusions: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment.

数学推理是人类智力的核心能力，在抽象思维和逻辑推理中对机器提出了独特的挑战。最近的大型预训练的语言模型（例如GPT-3）在以文本形式（例如数学单词问题（MWP））编写的数学推理任务上取得了显着的进步。但是，未知模型是否可以处理更复杂的问题，这些问题涉及数学推理，例如表格数据。为了填补空白，我们提出了表格数学单词问题（TABMWP），这是一个包含38,431个开放域级等级问题的新数据集，这些问题需要在文本和表格数据上进行数学推理。 TABMWP中的每个问题都与表格上下文对齐，该上下文作为图像，半结构化文本和结构化表。有两种类型的问题：自由文本和多选择，每个问题都用金解决方案注释以揭示多步推理过程。我们在TABMWP上评估了不同的预训练模型，包括在几次设置中的GPT-3模型。正如先前的研究所表明的那样，由于很少有GPT-3依赖于内在的示例的选择，因此其性能是不稳定的，并且可能会降解为几乎机会。处理TABMWP等复杂问题时，不稳定的问题更为严重。为了减轻这种情况，我们进一步提出了一种新颖的方法，即PresspG，该方法利用策略梯度学习从少量培训数据中选择中文示例，然后为测试示例构造相应的提示。实验结果表明，与随机选择相比，我们的方法在准确性度量上优于最佳基线，并显着降低了预测方差，这验证了其在选择性上下文示例中的有效性。

在回答问题时，人类会利用跨不同模式可用的信息来综合一致，完整的思想链（COT）。在深度学习模型（例如大规模语言模型）的情况下，这个过程通常是黑匣子。最近，科学问题基准已用于诊断AI系统的多跳推理能力和解释性。但是，现有数据集无法为答案提供注释，或仅限于仅文本模式，小尺度和有限的域多样性。为此，我们介绍了科学问题答案（SQA），这是一个新的基准，由〜21k的多模式多种选择问题组成，其中包含各种科学主题和答案的注释，并提供相应的讲座和解释。我们进一步设计语言模型，以学习将讲座和解释作为思想链（COT），以模仿回答SQA问题时的多跳上推理过程。 SQA在语言模型中展示了COT的实用性，因为COT将问题的答案绩效提高了1.20％的GPT-3和3.99％的unifiedqa。我们还探索了模型的上限，以通过喂食输入中的那些来利用解释；我们观察到它将GPT-3的少量性能提高了18.96％。我们的分析进一步表明，与人类类似的语言模型受益于解释，从较少的数据中学习并仅使用40％的数据实现相同的性能。