Data augmentation is a valuable tool for the design of deep learning systems to overcome data limitations and stabilize the training process. Especially in the medical domain, where the collection of large-scale data sets is challenging and expensive due to limited access to patient data, relevant environments, as well as strict regulations, community-curated large-scale public datasets, pretrained models, and advanced data augmentation methods are the main factors for developing reliable systems to improve patient care. However, for the development of medical acoustic sensing systems, an emerging field of research, the community lacks large-scale publicly available data sets and pretrained models. To address the problem of limited data, we propose a conditional generative adversarial neural network-based augmentation method which is able to synthesize mel spectrograms from a learned data distribution of a source data set. In contrast to previously proposed fully convolutional models, the proposed model implements residual Squeeze and Excitation modules in the generator architecture. We show that our method outperforms all classical audio augmentation techniques and previously published generative methods in terms of generated sample quality and a performance improvement of 2.84% of Macro F1-Score for a classifier trained on the augmented data set, an enhancement of $1.14\%$ in relation to previous work. By analyzing the correlation of intermediate feature spaces, we show that the residual Squeeze and Excitation modules help the model to reduce redundancy in the latent features. Therefore, the proposed model advances the state-of-the-art in the augmentation of clinical audio data and improves the data bottleneck for the design of clinical acoustic sensing systems.

在这项工作中，我们提出了一种基于有条件的WaseStein生成对抗网络的临床音频数据集的新型数据增强方法，该网络具有梯度惩罚（CWGAN-GP），并在日志频谱图上运行。为了验证我们的方法，我们创建了一个临床音频数据集，该数据集在总髋关节置换术（THA）过程中记录在现实世界手术室中，并包含典型的声音，类似于干预的不同阶段。我们证明了所提出的方法从数据集分布中生成现实的类调节样品的能力，并表明使用生成的增强样品训练在分类精度方面优于经典音频增强方法。使用RESNET-18分类器评估了性能，该分类器在使用建议的增强方法的5倍交叉验证实验中显示了平均每类准确性提高1.70％。由于临床数据通常是昂贵的，因此实际的和高质量的数据增强方法的开发对于提高基于学习的算法的鲁棒性和概括能力至关重要，这对于安全至关重要的医学应用尤其重要。因此，提出的数据增强方法是改善基于临床音频的机器学习系统的数据瓶颈的重要一步。

In the present paper, semantic parsing challenges are briefly introduced and QDMR formalism in semantic parsing is implemented using sequence to sequence model with attention but uses only part of speech(POS) as a representation of words of a sentence to make the training as simple and as fast as possible and also avoiding curse of dimensionality as well as overfitting. It is shown how semantic operator prediction could be augmented with other models like the CopyNet model or the recursive neural net model.

Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module.

This paper proposes an easy-to-compute upper bound for the overlap index between two probability distributions without requiring any knowledge of the distribution models. The computation of our bound is time-efficient and memory-efficient and only requires finite samples. The proposed bound shows its value in one-class classification and domain shift analysis. Specifically, in one-class classification, we build a novel one-class classifier by converting the bound into a confidence score function. Unlike most one-class classifiers, the training process is not needed for our classifier. Additionally, the experimental results show that our classifier \textcolor{\colorname}{can be accurate with} only a small number of in-class samples and outperforms many state-of-the-art methods on various datasets in different one-class classification scenarios. In domain shift analysis, we propose a theorem based on our bound. The theorem is useful in detecting the existence of domain shift and inferring data information. The detection and inference processes are both computation-efficient and memory-efficient. Our work shows significant promise toward broadening the applications of overlap-based metrics.

We propose a framework in which multiple entities collaborate to build a machine learning model while preserving privacy of their data. The approach utilizes feature embeddings from shared/per-entity feature extractors transforming data into a feature space for cooperation between entities. We propose two specific methods and compare them with a baseline method. In Shared Feature Extractor (SFE) Learning, the entities use a shared feature extractor to compute feature embeddings of samples. In Locally Trained Feature Extractor (LTFE) Learning, each entity uses a separate feature extractor and models are trained using concatenated features from all entities. As a baseline, in Cooperatively Trained Feature Extractor (CTFE) Learning, the entities train models by sharing raw data. Secure multi-party algorithms are utilized to train models without revealing data or features in plain text. We investigate the trade-offs among SFE, LTFE, and CTFE in regard to performance, privacy leakage (using an off-the-shelf membership inference attack), and computational cost. LTFE provides the most privacy, followed by SFE, and then CTFE. Computational cost is lowest for SFE and the relative speed of CTFE and LTFE depends on network architecture. CTFE and LTFE provide the best accuracy. We use MNIST, a synthetic dataset, and a credit card fraud detection dataset for evaluations.

We present a framework for ranking images within their class based on the strength of spurious cues present. By measuring the gap in accuracy on the highest and lowest ranked images (we call this spurious gap), we assess spurious feature reliance for $89$ diverse ImageNet models, finding that even the best models underperform in images with weak spurious presence. However, the effect of spurious cues varies far more dramatically across classes, emphasizing the crucial, often overlooked, class-dependence of the spurious correlation problem. While most spurious features we observe are clarifying (i.e. improving test-time accuracy when present, as is typically expected), we surprisingly find many cases of confusing spurious features, where models perform better when they are absent. We then close the spurious gap by training new classification heads on lowly ranked (i.e. without common spurious cues) images, resulting in improved effective robustness to distribution shifts (ObjectNet, ImageNet-R, ImageNet-Sketch). We also propose a second metric to assess feature reliability, finding that spurious features are generally less reliable than non-spurious (core) ones, though again, spurious features can be more reliable for certain classes. To enable our analysis, we annotated $5,000$ feature-class dependencies over {\it all} of ImageNet as core or spurious using minimal human supervision. Finally, we show the feature discovery and spuriosity ranking framework can be extended to other datasets like CelebA and WaterBirds in a lightweight fashion with only linear layer training, leading to discovering a previously unknown racial bias in the Celeb-A hair classification.

Graph representation of objects and their relations in a scene, known as a scene graph, provides a precise and discernible interface to manipulate a scene by modifying the nodes or the edges in the graph. Although existing works have shown promising results in modifying the placement and pose of objects, scene manipulation often leads to losing some visual characteristics like the appearance or identity of objects. In this work, we propose DisPositioNet, a model that learns a disentangled representation for each object for the task of image manipulation using scene graphs in a self-supervised manner. Our framework enables the disentanglement of the variational latent embeddings as well as the feature representation in the graph. In addition to producing more realistic images due to the decomposition of features like pose and identity, our method takes advantage of the probabilistic sampling in the intermediate features to generate more diverse images in object replacement or addition tasks. The results of our experiments show that disentangling the feature representations in the latent manifold of the model outperforms the previous works qualitatively and quantitatively on two public benchmarks. Project Page: https://scenegenie.github.io/DispositioNet/

现有的一些作品分别研究深神经网络的对抗或自然分布鲁棒性。但是，实际上，模型需要享受两种类型的鲁棒性，以确保可靠性。在这项工作中，我们弥合了这一差距，并表明实际上，对抗性和自然分配鲁棒性之间存在明确的权衡。我们首先考虑具有与核心和虚假功能不相交的高斯数据上的简单线性回归设置。在这种情况下，通过理论和经验分析，我们表明（i）使用$ \ ell_1 $和$ \ ell_2 $规范的对抗性培训增加了对虚假功能的模型依赖； （ii）对于$ \ ell_ \ infty $ versarial训练，仅在伪造功能的比例大于核心功能的范围时才会出现伪造的依赖； （iii）对抗训练可能会在降低分布鲁棒性方面具有意外的后果，特别是当新的测试域中更改虚假相关性时。接下来，我们使用二十个经过对抗训练的模型的测试套件提出了广泛的经验证据受过训练的对应物，验证了我们的理论结果。我们还表明，训练数据中的虚假相关性（保留在测试域中）可以改善对抗性的鲁棒性，表明先前的主张表明对抗性脆弱性植根于虚假相关性是不完整的。

建筑物的电力消耗构成了该市能源消耗的主要部分。电力消耗预测可以开发房屋能源管理系统，从而导致未来的可持续性房屋设计和总能源消耗的减少。建筑物中的能源性能受环境温度，湿度和各种电气设备等许多因素的影响。因此，多元预测方法是首选而不是单变量。选择了本田智能家庭数据集，以比较三种方法，以最大程度地减少预测错误，MAE和RMSE：人工神经网络，支持向量回归以及基于模糊规则的基于模糊规则的系统，以通过在多变量数据集上为每种方法构造许多模型在不同的时间范围内。比较表明，SVR比替代方案是一种优越的方法。