联邦学习（FL）是一种分散的方法，使医院能够在不共享私人患者数据进行培训的情况下协作学习模型。在FL中，参与者医院定期交换培训结果，而不是使用中央服务器培训样品。但是，访问模型参数或梯度可以暴露私人培训数据样本。为了应对这一挑战，我们采用安全的多方计算（SMC）来建立一个保护隐私的联合学习框架。在我们提出的方法中，医院分为集群。在当地培训之后，每家医院在同一集群中分解了其他医院的模型权重，因此没有一家医院可以自己检索其他医院的体重。然后，所有医院总结了收到的权重，将结果发送到中央服务器。最后，中央服务器汇总了结果，检索模型的平均权重并更新模型，而无需访问各个医院的权重。我们在公开可用的存储库《癌症基因组图集》（TCGA）上进行实验。我们将提议框架的性能与差异隐私进行比较，并将平均为基准。结果表明，与差异隐私相比，我们的框架可以实现更高的准确性，而没有隐私泄漏风险，而较高的通信开销则可以实现。

在世界各地人行道的位置，状况和可访问性上缺乏数据，这不仅会影响人们旅行的何处和方式，而且从根本上限制了交互式映射工具和城市分析。在本文中，我们使用层次多尺度注意模型从卫星图像中构建半自动性的初步工作，从卫星图像中构建人行道网络拓扑模型，从而使用基于学习的基于学习的语义细分从街道级图像从街道图像中推断出表面材料，并评估人行道条件和可访问性。使用人群+AI的功能。我们呼吁创建一个标有卫星和街景场景的数据库，以供人行道和人行道可及性问题以及标准化的基准测试。

在设计可持续和弹性的城市建造环境的同时，越来越多地促进了世界各地的，重大的数据差距对压迫可持续性问题挑战开展的研究。已知人行道具有强大的经济和环境影响;然而，由于数据收集的成本持久和耗时的性质，大多数城市缺乏它们的表面的空间目录。计算机愿景的最新进展与街道级别图像的可用性一起为城市提供了新的机会，以利用较低的实施成本和更高的准确性提取大规模建筑环境数据。在本文中，我们提出了一个基于主动学习的框架，利用计算机视觉技术来使用广泛可用的街道图像进行分类的计算机视觉技术。我们培训了来自纽约市和波士顿的图像的框架，评价结果显示了90.5％的Miou评分。此外，我们使用六个不同城市的图像评估框架，表明它可以应用于具有不同城市面料的区域，即使在培训数据的领域之外。 Citysurfaces可以为研究人员和城市代理商提供低成本，准确，可扩展的方法来收集人行道材料数据，在寻求主要可持续性问题方面发挥着关键作用，包括气候变化和地表水管理。

对行人基础设施，特别是人行道的大规模分析对人类以人为本的城市规划和设计至关重要。受益于通过纽约市开放数据门户提供的Procepetric特征和高分辨率OrthoImages的丰富数据集，我们培养计算机视觉模型来检测遥感图像的人行道，道路和建筑物，达到83％的Miou持有-out测试集。我们应用形状分析技术来研究提取的人行道的不同属性。更具体地，我们对人行道的宽度，角度和曲率进行了瓷砖明智的分析，除了它们对城市地区的可行性和可达性的一般影响，众所周知，在轮椅用户的移动性中具有重要作用。初步结果是有前途的，瞥见了不同城市采用的拟议方法的潜力，使研究人员和从业者可以获得更生动的行人领域的画面。

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.

Minimising the longest travel distance for a group of mobile robots with interchangeable goals requires knowledge of the shortest length paths between all robots and goal destinations. Determining the exact length of the shortest paths in an environment with obstacles is challenging and cannot be guaranteed in a finite time. We propose an algorithm in which the accuracy of the path planning is iteratively increased. The approach provides a certificate when the uncertainties on estimates of the shortest paths become small enough to guarantee the optimality of the goal assignment. To this end, we apply results from assignment sensitivity assuming upper and lower bounds on the length of the shortest paths. We then provide polynomial-time methods to find such bounds by applying sampling-based path planning. The upper bounds are given by feasible paths, the lower bounds are obtained by expanding the sample set and leveraging knowledge of the sample dispersion. We demonstrate the application of the proposed method with a multi-robot path-planning case study.

Recent advances in language modeling have enabled new conversational systems. In particular, it is often desirable for people to make choices among specified options when using such systems. We address the problem of reference resolution, when people use natural expressions to choose between real world entities. For example, given the choice `Should we make a Simnel cake or a Pandan cake?' a natural response from a non-expert may be indirect: `let's make the green one'. Reference resolution has been little studied with natural expressions, thus robustly understanding such language has large potential for improving naturalness in dialog, recommendation, and search systems. We create AltEntities (Alternative Entities), a new public dataset of entity pairs and utterances, and develop models for the disambiguation problem. Consisting of 42K indirect referring expressions across three domains, it enables for the first time the study of how large language models can be adapted to this task. We find they achieve 82%-87% accuracy in realistic settings, which while reasonable also invites further advances.

The problem of reversing the compilation process, decompilation, is an important tool in reverse engineering of computer software. Recently, researchers have proposed using techniques from neural machine translation to automate the process in decompilation. Although such techniques hold the promise of targeting a wider range of source and assembly languages, to date they have primarily targeted C code. In this paper we argue that existing neural decompilers have achieved higher accuracy at the cost of requiring language-specific domain knowledge such as tokenizers and parsers to build an abstract syntax tree (AST) for the source language, which increases the overhead of supporting new languages. We explore a different tradeoff that, to the extent possible, treats the assembly and source languages as plain text, and show that this allows us to build a decompiler that is easily retargetable to new languages. We evaluate our prototype decompiler, Beyond The C (BTC), on Go, Fortran, OCaml, and C, and examine the impact of parameters such as tokenization and training data selection on the quality of decompilation, finding that it achieves comparable decompilation results to prior work in neural decompilation with significantly less domain knowledge. We will release our training data, trained decompilation models, and code to help encourage future research into language-agnostic decompilation.

Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality - among other traits - by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics.

Numerous models have tried to effectively embed knowledge graphs in low dimensions. Among the state-of-the-art methods, Graph Neural Network (GNN) models provide structure-aware representations of knowledge graphs. However, they often utilize the information of relations and their interactions with entities inefficiently. Moreover, most state-of-the-art knowledge graph embedding models suffer from scalability issues because of assigning high-dimensional embeddings to entities and relations. To address the above limitations, we propose a scalable general knowledge graph encoder that adaptively involves a powerful tensor decomposition method in the aggregation function of RGCN, a well-known relational GNN model. Specifically, the parameters of a low-rank core projection tensor, used to transform neighborhood entities in the encoder, are shared across relations to benefit from multi-task learning and incorporate relations information effectively. Besides, we propose a low-rank estimation of the core tensor using CP decomposition to compress the model, which is also applicable, as a regularization method, to other similar linear models. We evaluated our model on knowledge graph completion as a common downstream task. We train our model for using a new loss function based on contrastive learning, which relieves the training limitation of the 1-N method on huge graphs. We improved RGCN performance on FB15-237 by 0.42% with considerably lower dimensionality of embeddings.