图形神经网络（GNNS）在学习从图形结构数据中展示了成功，其中包含欺诈检测，推荐和知识图形推理。然而，培训GNN有效地具有挑战性，因为：1）GPU存储器容量有限，对于大型数据集可能不足，而2）基于图形的数据结构导致不规则的数据访问模式。在这项工作中，我们提供了一种统计分析的方法，并确定了GNN培训前更频繁地访问的数据。我们的数据分层方法不仅利用输入图的结构，而且还从实际GNN训练过程中获得了洞察力，以实现更高的预测结果。通过我们的数据分层方法，我们还提供了一种新的数据放置和访问策略，以进一步最大限度地减少CPU-GPU通信开销。我们还考虑了多GPU GNN培训，我们也展示了我们在多GPU系统中的策略的有效性。评估结果表明，我们的工作将CPU-GPU流量降低了87-95％，并通过数亿节点和数十亿边缘的图表提高了现有解决方案的GNN训练速度。

对于大部分现实生活中的话语，其意图不能仅由它们的语义或句法特征决定。尽管并非所有的社会语言和务实信息都可以数字化，但至少语音特征在理解口语中是必不可少的。尤其是在诸如韩国人之类的头部最终语言中，句子 - 最终韵律在识别说话者的意图方面非常重要。本文提出了一个系统，该系统鉴于其笔录在某些情况下使用辅助声学特征，可以识别出口语的固有意图。这里的要点是对于歧视意图需要声学提示的情况。因此，提出的分类系统决定给定的话语是利用来自头部金属的语调依赖性的片段，陈述，问题，命令还是命令/命令。基于对参与数据注释的韩语的直观理解，我们构建了一个网络，该网络识别语音的意图，并通过测试句子验证其实用性。该系统如果与最新的语音识别器结合在一起，则预计将灵活地插入各种语言理解模块中。

Stylegan最近的成功表明，预训练的Stylegan潜在空间对现实的视频生成很有用。但是，由于难以确定stylegan潜在空间的方向和幅度，因此视频中产生的运动通常在语义上没有意义。在本文中，我们提出了一个框架来通过利用多模式（声音图像文本）嵌入空间来生成现实视频。由于声音提供了场景的时间上下文，因此我们的框架学会了生成与声音一致的视频。首先，我们的声音反演模块将音频直接映射到Stylegan潜在空间中。然后，我们结合了基于夹子的多模式嵌入空间，以进一步提供视听关系。最后，提出的帧发电机学会在潜在空间中找到轨迹，该空间与相应的声音相干，并以层次结构方式生成视频。我们为声音引导的视频生成任务提供新的高分辨率景观视频数据集（视听对）。实验表明，我们的模型在视频质量方面优于最新方法。我们进一步显示了几种应用程序，包括图像和视频编辑，以验证我们方法的有效性。

现有研究突出物体检测（SOD）对专注于提取与边缘信息的不同对象和聚合多级功能来提高SOD性能。为了实现令人满意的性能，该方法采用精细的边缘信息和低多级差异。然而，不能实现性能增益和计算效率，这有动力研究了我们研究现有编码器解码器结构中的低效率，以避免这种权衡。我们提出了示踪剂，通过结合引导的跟踪模块来检测具有显式边缘的突出物体。我们使用快速傅里叶变换在第一编码器的末尾采用掩蔽边缘注意模块，以将精细边缘信息传播到下游特征提取。在多级聚合阶段，联盟注意力模块识别互补信道和重要的空间信息。为了提高解码器性能和计算效率，我们最大限度地减少了对对象注意模块的解码器块使用。该模块从精细通道和空间表示中提取未检测到的对象和边缘信息。随后，我们提出了一种自适应像素强度损失函数来处理与传统损耗函数不同的像素相对重要的像素，其同样处理所有像素。与13现有方法的比较显示，示踪剂在五个基准数据集上实现了最先进的性能。特别地，追踪性3（TE3）优于LDF，现有方法，同时需要1.8倍的学习参数，更少的时间; TE3速度快5倍。

Many recent works on understanding deep learning try to quantify how much individual data instances influence the optimization and generalization of a model, either by analyzing the behavior of the model during training or by measuring the performance gap of the model when the instance is removed from the dataset. Such approaches reveal characteristics and importance of individual instances, which may provide useful information in diagnosing and improving deep learning. However, most of the existing works on data valuation require actual training of a model, which often demands high-computational cost. In this paper, we provide a training-free data valuation score, called complexity-gap score, which is a data-centric score to quantify the influence of individual instances in generalization of two-layer overparameterized neural networks. The proposed score can quantify irregularity of the instances and measure how much each data instance contributes in the total movement of the network parameters during training. We theoretically analyze and empirically demonstrate the effectiveness of the complexity-gap score in finding 'irregular or mislabeled' data instances, and also provide applications of the score in analyzing datasets and diagnosing training dynamics.

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

Crowdsourcing has emerged as an effective platform to label a large volume of data in a cost- and time-efficient manner. Most previous works have focused on designing an efficient algorithm to recover only the ground-truth labels of the data. In this paper, we consider multi-choice crowdsourced labeling with the goal of recovering not only the ground truth but also the most confusing answer and the confusion probability. The most confusing answer provides useful information about the task by revealing the most plausible answer other than the ground truth and how plausible it is. To theoretically analyze such scenarios, we propose a model where there are top-two plausible answers for each task, distinguished from the rest of choices. Task difficulty is quantified by the confusion probability between the top two, and worker reliability is quantified by the probability of giving an answer among the top two. Under this model, we propose a two-stage inference algorithm to infer the top-two answers as well as the confusion probability. We show that our algorithm achieves the minimax optimal convergence rate. We conduct both synthetic and real-data experiments and demonstrate that our algorithm outperforms other recent algorithms. We also show the applicability of our algorithms in inferring the difficulty of tasks and training neural networks with the soft labels composed of the top-two most plausible classes.

Affect understanding capability is essential for social robots to autonomously interact with a group of users in an intuitive and reciprocal way. However, the challenge of multi-person affect understanding comes from not only the accurate perception of each user's affective state (e.g., engagement) but also the recognition of the affect interplay between the members (e.g., joint engagement) that presents as complex, but subtle, nonverbal exchanges between them. Here we present a novel hybrid framework for identifying a parent-child dyad's joint engagement by combining a deep learning framework with various video augmentation techniques. Using a dataset of parent-child dyads reading storybooks together with a social robot at home, we first train RGB frame- and skeleton-based joint engagement recognition models with four video augmentation techniques (General Aug, DeepFake, CutOut, and Mixed) applied datasets to improve joint engagement classification performance. Second, we demonstrate experimental results on the use of trained models in the robot-parent-child interaction context. Third, we introduce a behavior-based metric for evaluating the learned representation of the models to investigate the model interpretability when recognizing joint engagement. This work serves as the first step toward fully unlocking the potential of end-to-end video understanding models pre-trained on large public datasets and augmented with data augmentation and visualization techniques for affect recognition in the multi-person human-robot interaction in the wild.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

The nonconvex formulation of matrix completion problem has received significant attention in recent years due to its affordable complexity compared to the convex formulation. Gradient descent (GD) is the simplest yet efficient baseline algorithm for solving nonconvex optimization problems. The success of GD has been witnessed in many different problems in both theory and practice when it is combined with random initialization. However, previous works on matrix completion require either careful initialization or regularizers to prove the convergence of GD. In this work, we study the rank-1 symmetric matrix completion and prove that GD converges to the ground truth when small random initialization is used. We show that in logarithmic amount of iterations, the trajectory enters the region where local convergence occurs. We provide an upper bound on the initialization size that is sufficient to guarantee the convergence and show that a larger initialization can be used as more samples are available. We observe that implicit regularization effect of GD plays a critical role in the analysis, and for the entire trajectory, it prevents each entry from becoming much larger than the others.