最近实现了更准确的短期预测的数据驱动的空气质量预测。尽管取得了成功，但大多数目前的数据驱动解决方案都缺乏适当的模型不确定性的量化，以传达信任预测的程度。最近，在概率深度学习中已经制定了几种估计不确定性的实用工具。但是，在空气质量预测领域的域中没有经验应用和广泛的比较这些工具。因此，这项工作在空气质量预测的真实环境中应用了最先进的不确定性量化。通过广泛的实验，我们描述了培训概率模型，并根据经验性能，信心可靠性，置信度估计和实际适用性评估其预测性不确定性。我们还使用空气质量数据中固有的“自由”对抗培训和利用时间和空间相关性提出改善这些模型。我们的实验表明，所提出的模型比以前的工作更好地在量化数据驱动空气质量预测中的不确定性方面表现出。总体而言，贝叶斯神经网络提供了更可靠的不确定性估计，但可能挑战实施和规模。其他可扩展方法，如深合奏，蒙特卡罗（MC）辍学和随机重量平均-Gaussian（SWAG）可以执行良好，如果正确应用，但具有不同的权衡和性能度量的轻微变化。最后，我们的结果表明了不确定性估计的实际影响，并证明了，实际上，概率模型更适合提出知情决策。代码和数据集可用于\ url {https:/github.com/abdulmajid-murad/deep_probabilistic_forecast}

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

In this work, we propose a novel image reconstruction framework that directly learns a neural implicit representation in k-space for ECG-triggered non-Cartesian Cardiac Magnetic Resonance Imaging (CMR). While existing methods bin acquired data from neighboring time points to reconstruct one phase of the cardiac motion, our framework allows for a continuous, binning-free, and subject-specific k-space representation.We assign a unique coordinate that consists of time, coil index, and frequency domain location to each sampled k-space point. We then learn the subject-specific mapping from these unique coordinates to k-space intensities using a multi-layer perceptron with frequency domain regularization. During inference, we obtain a complete k-space for Cartesian coordinates and an arbitrary temporal resolution. A simple inverse Fourier transform recovers the image, eliminating the need for density compensation and costly non-uniform Fourier transforms for non-Cartesian data. This novel imaging framework was tested on 42 radially sampled datasets from 6 subjects. The proposed method outperforms other techniques qualitatively and quantitatively using data from four and one heartbeat(s) and 30 cardiac phases. Our results for one heartbeat reconstruction of 50 cardiac phases show improved artifact removal and spatio-temporal resolution, leveraging the potential for real-time CMR.

This article presents a survey of literature in the area of Human-Robot Interaction (HRI), specifically on systems containing more than two agents (i.e., having multiple humans and/or multiple robots). We identify three core aspects of ``Multi-agent" HRI systems that are useful for understanding how these systems differ from dyadic systems and from one another. These are the Team structure, Interaction style among agents, and the system's Computational characteristics. Under these core aspects, we present five attributes of HRI systems, namely Team size, Team composition, Interaction model, Communication modalities, and Robot control. These attributes are used to characterize and distinguish one system from another. We populate resulting categories with examples from recent literature along with a brief discussion of their applications and analyze how these attributes differ from the case of dyadic human-robot systems. We summarize key observations from the current literature, and identify challenges and promising areas for future research in this domain. In order to realize the vision of robots being part of the society and interacting seamlessly with humans, there is a need to expand research on multi-human -- multi-robot systems. Not only do these systems require coordination among several agents, they also involve multi-agent and indirect interactions which are absent from dyadic HRI systems. Adding multiple agents in HRI systems requires advanced interaction schemes, behavior understanding and control methods to allow natural interactions among humans and robots. In addition, research on human behavioral understanding in mixed human-robot teams also requires more attention. This will help formulate and implement effective robot control policies in HRI systems with large numbers of heterogeneous robots and humans; a team composition reflecting many real-world scenarios.

This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task. Our approach implements several key principles from the prior literature. At its core our GPS++ method is a hybrid MPNN/Transformer model that incorporates 3D atom positions and an auxiliary denoising task. The effectiveness of GPS++ is demonstrated by achieving 0.0719 mean absolute error on the independent test-challenge PCQM4Mv2 split. Thanks to Graphcore IPU acceleration, GPS++ scales to deep architectures (16 layers), training at 3 minutes per epoch, and large ensemble (112 models), completing the final predictions in 1 hour 32 minutes, well under the 4 hour inference budget allocated. Our implementation is publicly available at: https://github.com/graphcore/ogb-lsc-pcqm4mv2.

Neural networks can be trained to solve regression problems by using gradient-based methods to minimize the square loss. However, practitioners often prefer to reformulate regression as a classification problem, observing that training on the cross entropy loss results in better performance. By focusing on two-layer ReLU networks, which can be fully characterized by measures over their feature space, we explore how the implicit bias induced by gradient-based optimization could partly explain the above phenomenon. We provide theoretical evidence that the regression formulation yields a measure whose support can differ greatly from that for classification, in the case of one-dimensional data. Our proposed optimal supports correspond directly to the features learned by the input layer of the network. The different nature of these supports sheds light on possible optimization difficulties the square loss could encounter during training, and we present empirical results illustrating this phenomenon.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

This paper focuses on the uncertainty estimation of white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation errors cause the erroneous delineation of the lesions; on the other side, lesion-scale detection errors lead to wrong lesion counts. Both of these factors are clinically relevant for the assessment of multiple sclerosis patients. This work aims to compare the ability of different voxel- and lesion- scale uncertainty measures to capture errors related to segmentation and lesion detection respectively. Our main contributions are (i) proposing new measures of lesion-scale uncertainty that do not utilise voxel-scale uncertainties; (ii) extending an error retention curves analysis framework for evaluation of lesion-scale uncertainty measures. Our results obtained on the multi-center testing set of 58 patients demonstrate that the proposed lesion-scale measures achieves the best performance among the analysed measures. All code implementations are provided at https://github.com/NataliiaMolch/MS_WML_uncs

研究随机噪声的特性以优化复杂的非凸函数一直是机器学习领域的活跃研究领域。先前的工作表明，随机梯度下降的噪声通过克服景观中的不良障碍来改善优化。此外，注射人造高斯噪音已成为快速逃脱鞍点的流行想法。确实，在没有可靠的梯度信息的情况下，噪声用于探索景观，但目前尚不清楚哪种类型的噪声在探索能力方面是最佳的。为了在我们的知识上缩小这一差距，我们基于布朗尼运动的一般类型的连续时间非马克维亚过程，该过程允许该过程的相关性增加。这将基于布朗运动（例如Ornstein-Uhlenbeck过程）进行概括。我们演示了如何离散此类过程，从而导致新算法FPGD。该方法是已知算法PGD和抗PGD的概括。我们在理论上和经验上都研究了FPGD的特性，表明它具有勘探能力，在某些情况下，它比PGD和抗PGD有利。这些结果为利用噪声用于训练机器学习模型的新颖方式开辟了领域。

社会过程的持续数字化转化为时间序列数据的扩散，这些数据涵盖了诸如欺诈检测，入侵检测和能量管理等应用，在这种应用程序中，异常检测通常对于启用可靠性和安全性至关重要。许多最近的研究针对时间序列数据的异常检测。实际上，时间序列异常检测的特征是不同的数据，方法和评估策略，现有研究中的比较仅考虑了这种多样性的一部分，这使得很难为特定问题设置选择最佳方法。为了解决这一缺点，我们介绍了有关数据，方法和评估策略的分类法，并使用分类法提供了无监督时间序列检测的全面概述，并系统地评估和比较了最先进的传统以及深度学习技术。在使用九个公开可用数据集的实证研究中，我们将最常用的性能评估指标应用于公平实施标准下的典型方法。根据分类法提供的结构化，我们报告了经验研究，并以比较表的形式提供指南，以选择最适合特定应用程序设置的方法。最后，我们为这个动态领域提出了研究方向。