通用形态（UNIMORPH）项目是一项合作的努力，可为数百种世界语言实例化覆盖范围的标准化形态拐角。该项目包括两个主要的推力：一种无独立的特征架构，用于丰富的形态注释，并以各种语言意识到该模式的各种语言的带注释数据的类型级别资源。本文介绍了过去几年对几个方面的扩张和改进（自McCarthy等人（2020年）以来）。众多语言学家的合作努力增加了67种新语言，其中包括30种濒危语言。我们已经对提取管道进行了一些改进，以解决一些问题，例如缺少性别和马克龙信息。我们还修改了模式，使用了形态学现象所需的层次结构，例如多肢体协议和案例堆叠，同时添加了一些缺失的形态特征，以使模式更具包容性。鉴于上一个UniMorph版本，我们还通过16种语言的词素分割增强了数据库。最后，这个新版本通过通过代表来自metphynet的派生过程的实例丰富数据和注释模式来推动将衍生物形态纳入UniMorph中。

在有向图中跨越树的概率分布是自然语言处理，句法依赖树中依赖关系结构的基本模型。在NLP中，依赖树通常具有额外的根约束：只有一个边缘可以从根中散发出来。但是，文献中没有提出采样算法来解释这一其他约束。在本文中，我们适应了两个跨越树采样算法，以忠实地将依赖性树从图主体中采样，并将其从根约束中进行。 Wilson（1996）的采样算法的运行时间为$ \ mathcal {o}（h）$，其中$ h $是图的平均打击时间。 Colbourn（1996）的采样算法的运行时间为$ \ MATHCAL {O}（n^3）$，通常大于有向图的平均打击时间。此外，我们以Colbourn的算法为基础，并提出了一个新颖的扩展名，可以在$ \ Mathcal {o}（k n^3 + k^2 n​​）$ time中采样$ k $树。据我们所知，没有针对跨越树木的采样算法，而没有替换有向图的算法。

Humans have internal models of robots (like their physical capabilities), the world (like what will happen next), and their tasks (like a preferred goal). However, human internal models are not always perfect: for example, it is easy to underestimate a robot's inertia. Nevertheless, these models change and improve over time as humans gather more experience. Interestingly, robot actions influence what this experience is, and therefore influence how people's internal models change. In this work we take a step towards enabling robots to understand the influence they have, leverage it to better assist people, and help human models more quickly align with reality. Our key idea is to model the human's learning as a nonlinear dynamical system which evolves the human's internal model given new observations. We formulate a novel optimization problem to infer the human's learning dynamics from demonstrations that naturally exhibit human learning. We then formalize how robots can influence human learning by embedding the human's learning dynamics model into the robot planning problem. Although our formulations provide concrete problem statements, they are intractable to solve in full generality. We contribute an approximation that sacrifices the complexity of the human internal models we can represent, but enables robots to learn the nonlinear dynamics of these internal models. We evaluate our inference and planning methods in a suite of simulated environments and an in-person user study, where a 7DOF robotic arm teaches participants to be better teleoperators. While influencing human learning remains an open problem, our results demonstrate that this influence is possible and can be helpful in real human-robot interaction.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

In this paper, we present the Circular Accessible Depth (CAD), a robust traversability representation for an unmanned ground vehicle (UGV) to learn traversability in various scenarios containing irregular obstacles. To predict CAD, we propose a neural network, namely CADNet, with an attention-based multi-frame point cloud fusion module, Stability-Attention Module (SAM), to encode the spatial features from point clouds captured by LiDAR. CAD is designed based on the polar coordinate system and focuses on predicting the border of traversable area. Since it encodes the spatial information of the surrounding environment, which enables a semi-supervised learning for the CADNet, and thus desirably avoids annotating a large amount of data. Extensive experiments demonstrate that CAD outperforms baselines in terms of robustness and precision. We also implement our method on a real UGV and show that it performs well in real-world scenarios.

Advanced visual localization techniques encompass image retrieval challenges and 6 Degree-of-Freedom (DoF) camera pose estimation, such as hierarchical localization. Thus, they must extract global and local features from input images. Previous methods have achieved this through resource-intensive or accuracy-reducing means, such as combinatorial pipelines or multi-task distillation. In this study, we present a novel method called SuperGF, which effectively unifies local and global features for visual localization, leading to a higher trade-off between localization accuracy and computational efficiency. Specifically, SuperGF is a transformer-based aggregation model that operates directly on image-matching-specific local features and generates global features for retrieval. We conduct experimental evaluations of our method in terms of both accuracy and efficiency, demonstrating its advantages over other methods. We also provide implementations of SuperGF using various types of local features, including dense and sparse learning-based or hand-crafted descriptors.

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.

Graphic layout designs play an essential role in visual communication. Yet handcrafting layout designs are skill-demanding, time-consuming, and non-scalable to batch production. Although generative models emerge to make design automation no longer utopian, it remains non-trivial to customize designs that comply with designers' multimodal desires, i.e., constrained by background images and driven by foreground contents. In this study, we propose \textit{LayoutDETR} that inherits the high quality and realism from generative modeling, in the meanwhile reformulating content-aware requirements as a detection problem: we learn to detect in a background image the reasonable locations, scales, and spatial relations for multimodal elements in a layout. Experiments validate that our solution yields new state-of-the-art performance for layout generation on public benchmarks and on our newly-curated ads banner dataset. For practical usage, we build our solution into a graphical system that facilitates user studies. We demonstrate that our designs attract more subjective preference than baselines by significant margins. Our code, models, dataset, graphical system, and demos are available at https://github.com/salesforce/LayoutDETR.

Communications systems to date are primarily designed with the goal of reliable (error-free) transfer of digital sequences (bits). Next generation (NextG) communication systems are beginning to explore shifting this design paradigm of reliably decoding bits to reliably executing a given task. Task-oriented communications system design is likely to find impactful applications, for example, considering the relative importance of messages. In this paper, a wireless signal classification is considered as the task to be performed in the NextG Radio Access Network (RAN) for signal intelligence and spectrum awareness applications such as user equipment (UE) identification and authentication, and incumbent signal detection for spectrum co-existence. For that purpose, edge devices collect wireless signals and communicate with the NextG base station (gNodeB) that needs to know the signal class. Edge devices may not have sufficient processing power and may not be trusted to perform the signal classification task, whereas the transfer of the captured signals from the edge devices to the gNodeB may not be efficient or even feasible subject to stringent delay, rate, and energy restrictions. We present a task-oriented communications approach, where all the transmitter, receiver and classifier functionalities are jointly trained as two deep neural networks (DNNs), one for the edge device and another for the gNodeB. We show that this approach achieves better accuracy with smaller DNNs compared to the baselines that treat communications and signal classification as two separate tasks. Finally, we discuss how adversarial machine learning poses a major security threat for the use of DNNs for task-oriented communications. We demonstrate the major performance loss under backdoor (Trojan) attacks and adversarial (evasion) attacks that target the training and test processes of task-oriented communications.

U-shaped networks are widely used in various medical image tasks, such as segmentation, restoration and reconstruction, but most of them usually rely on centralized learning and thus ignore privacy issues. To address the privacy concerns, federated learning (FL) and split learning (SL) have attracted increasing attention. However, it is hard for both FL and SL to balance the local computational cost, model privacy and parallel training simultaneously. To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL. Previous works cannot preserve the data privacy, including the input, model parameters, label and output simultaneously. To effectively deal with all of them, we design a novel splitting method for U-shaped medical image networks, which splits the network into three parts hosted by different parties. Besides, the distributed learning methods usually suffer from a drift between local and global models caused by data heterogeneity. Based on this consideration, we propose a dynamic weight correction strategy (\textbf{DWCS}) to stabilize the training process and avoid model drift. Specifically, a weight correction loss is designed to quantify the drift between the models from two adjacent communication rounds. By minimizing this loss, a correction model is obtained. Then we treat the weighted sum of correction model and final round models as the result. The effectiveness of the proposed RoS-FL is supported by extensive experimental results on different tasks. Related codes will be released at https://github.com/Zi-YuanYang/RoS-FL.