Multi-lingual language models (LM), such as mBERT, XLM-R, mT5, mBART, have been remarkably successful in enabling natural language tasks in low-resource languages through cross-lingual transfer from high-resource ones. In this work, we try to better understand how such models, specifically mT5, transfer *any* linguistic and semantic knowledge across languages, even though no explicit cross-lingual signals are provided during pre-training. Rather, only unannotated texts from each language are presented to the model separately and independently of one another, and the model appears to implicitly learn cross-lingual connections. This raises several questions that motivate our study, such as: Are the cross-lingual connections between every language pair equally strong? What properties of source and target language impact the strength of cross-lingual transfer? Can we quantify the impact of those properties on the cross-lingual transfer? In our investigation, we analyze a pre-trained mT5 to discover the attributes of cross-lingual connections learned by the model. Through a statistical interpretation framework over 90 language pairs across three tasks, we show that transfer performance can be modeled by a few linguistic and data-derived features. These observations enable us to interpret cross-lingual understanding of the mT5 model. Through these observations, one can favorably choose the best source language for a task, and can anticipate its training data demands. A key finding of this work is that similarity of syntax, morphology and phonology are good predictors of cross-lingual transfer, significantly more than just the lexical similarity of languages. For a given language, we are able to predict zero-shot performance, that increases on a logarithmic scale with the number of few-shot target language data points.

我们为多机器人任务计划和分配问题提出了一种新的公式，该公式结合了（a）任务之间的优先关系； （b）任务的协调，允许多个机器人提高效率； （c）通过形成机器人联盟的任务合作，而单独的机器人不能执行。在我们的公式中，任务图指定任务和任务之间的关系。我们在任务图的节点和边缘上定义了一组奖励函数。这些功能对机器人联盟规模对任务绩效的影响进行建模，并结合一个任务的性能对依赖任务的影响。最佳解决此问题是NP-HARD。但是，使用任务图公式使我们能够利用最小成本的网络流量方法有效地获得近似解决方案。此外，我们还探索了一种混合整数编程方法，该方法为问题的小实例提供了最佳的解决方案，但计算上很昂贵。我们还开发了一种贪婪的启发式算法作为基准。我们的建模和解决方案方法导致任务计划，即使在与许多代理商的大型任务中，也利用任务优先关系的关系以及机器人的协调和合作来实现高级任务绩效。

对头部磁共振成像（MRI）检查的需求不断增长，以及全球放射科医生的短缺，导致在全球报告头部MRI扫描所花费的时间增加。对于许多神经系统疾病，这种延迟会导致发病率和死亡率增加。一种自动分解工具可以通过在成像时识别异常并确定这些扫描的报告优先级来减少异常检查的报告时间。在这项工作中，我们提出了一个卷积神经网络，用于检测$ \ text {t} _2 $加权的头部MRI扫描中临床上相关的异常。使用经过验证的神经放射学报告分类器，我们从两家英国两家大型医院进行了43,754张标记的数据集，以进行模型培训，并在800张测试集上证明了准确的分类（AUC下的区域（AUC）= 0.943），由800张扫描集进行了标签。神经放射学家团队。重要的是，当仅在一家医院接受扫描培训时，模型从另一家医院进行了扫描（$ \ delta $ auc $ \ leq $ 0.02）。一项模拟研究表明，我们的模型将使异常检查的平均报告时间从28天到14天，并从两家医院的9天到5天，这表明在临床分类环境中使用了可行性。

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

Parkinson's disease is marked by altered and increased firing characteristics of pathological oscillations in the brain. In other words, it causes abnormal synchronous oscillations and suppression during neurological processing. In order to examine and regulate the synchronization and pathological oscillations in motor circuits, deep brain stimulators (DBS) are used. Although machine learning methods have been applied for the investigation of suppression, these models require large amounts of training data and computational power, both of which pose challenges to resource-constrained DBS. This research proposes a novel reinforcement learning (RL) framework for suppressing the synchronization in neuronal activity during episodes of neurological disorders with less power consumption. The proposed RL algorithm comprises an ensemble of a temporal representation of stimuli and a twin-delayed deep deterministic (TD3) policy gradient algorithm. We quantify the stability of the proposed framework to noise and reduced synchrony using RL for three pathological signaling regimes: regular, chaotic, and bursting, and further eliminate the undesirable oscillations. Furthermore, metrics such as evaluation rewards, energy supplied to the ensemble, and the mean point of convergence were used and compared to other RL algorithms, specifically the Advantage actor critic (A2C), the Actor critic with Kronecker-featured trust region (ACKTR), and the Proximal policy optimization (PPO).

The rapid development of remote sensing technologies have gained significant attention due to their ability to accurately localize, classify, and segment objects from aerial images. These technologies are commonly used in unmanned aerial vehicles (UAVs) equipped with high-resolution cameras or sensors to capture data over large areas. This data is useful for various applications, such as monitoring and inspecting cities, towns, and terrains. In this paper, we presented a method for classifying and segmenting city road traffic dashed lines from aerial images using deep learning models such as U-Net and SegNet. The annotated data is used to train these models, which are then used to classify and segment the aerial image into two classes: dashed lines and non-dashed lines. However, the deep learning model may not be able to identify all dashed lines due to poor painting or occlusion by trees or shadows. To address this issue, we proposed a method to add missed lines to the segmentation output. We also extracted the x and y coordinates of each dashed line from the segmentation output, which can be used by city planners to construct a CAD file for digital visualization of the roads.

Problem statement: Standardisation of AI fairness rules and benchmarks is challenging because AI fairness and other ethical requirements depend on multiple factors such as context, use case, type of the AI system, and so on. In this paper, we elaborate that the AI system is prone to biases at every stage of its lifecycle, from inception to its usage, and that all stages require due attention for mitigating AI bias. We need a standardised approach to handle AI fairness at every stage. Gap analysis: While AI fairness is a hot research topic, a holistic strategy for AI fairness is generally missing. Most researchers focus only on a few facets of AI model-building. Peer review shows excessive focus on biases in the datasets, fairness metrics, and algorithmic bias. In the process, other aspects affecting AI fairness get ignored. The solution proposed: We propose a comprehensive approach in the form of a novel seven-layer model, inspired by the Open System Interconnection (OSI) model, to standardise AI fairness handling. Despite the differences in the various aspects, most AI systems have similar model-building stages. The proposed model splits the AI system lifecycle into seven abstraction layers, each corresponding to a well-defined AI model-building or usage stage. We also provide checklists for each layer and deliberate on potential sources of bias in each layer and their mitigation methodologies. This work will facilitate layer-wise standardisation of AI fairness rules and benchmarking parameters.

Supervised approaches generally rely on majority-based labels. However, it is hard to achieve high agreement among annotators in subjective tasks such as hate speech detection. Existing neural network models principally regard labels as categorical variables, while ignoring the semantic information in diverse label texts. In this paper, we propose AnnoBERT, a first-of-its-kind architecture integrating annotator characteristics and label text with a transformer-based model to detect hate speech, with unique representations based on each annotator's characteristics via Collaborative Topic Regression (CTR) and integrate label text to enrich textual representations. During training, the model associates annotators with their label choices given a piece of text; during evaluation, when label information is not available, the model predicts the aggregated label given by the participating annotators by utilising the learnt association. The proposed approach displayed an advantage in detecting hate speech, especially in the minority class and edge cases with annotator disagreement. Improvement in the overall performance is the largest when the dataset is more label-imbalanced, suggesting its practical value in identifying real-world hate speech, as the volume of hate speech in-the-wild is extremely small on social media, when compared with normal (non-hate) speech. Through ablation studies, we show the relative contributions of annotator embeddings and label text to the model performance, and tested a range of alternative annotator embeddings and label text combinations.

As information extraction (IE) systems have grown more capable at whole-document extraction, the classic task of \emph{template filling} has seen renewed interest as a benchmark for evaluating them. In this position paper, we call into question the suitability of template filling for this purpose. We argue that the task demands definitive answers to thorny questions of \emph{event individuation} -- the problem of distinguishing distinct events -- about which even human experts disagree. We show through annotation studies and error analysis that this raises concerns about the usefulness of template filling evaluation metrics, the quality of datasets for the task, and the ability of models to learn it. Finally, we consider possible solutions.

Bike sharing systems often suffer from poor capacity management as a result of variable demand. These bike sharing systems would benefit from models to predict demand in order to moderate the number of bikes stored at each station. In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.