我们提出了一种名为ACLNET的新型深度学习模型，用于从地面图像中分割云。ACLNET同时使用深神经网络和机器学习（ML）算法来提取互补功能。具体而言，它使用有效网络-B0作为骨干，“``trous tos blacial pyramid boming''（ASPP）在多个接受场上学习，并从图像中提取细节细节。ACLNET还使用K-均值聚类来更精确地提取云边界。ACLNET对白天和夜间图像都有效。它提供的错误率较低，较高的召回率和更高的F1得分比Art最先进的云分割模型。ACLNET的源代码可在此处获得：https：//github.com/ckmvigil/aclnet。

随着半导体晶片的整合密度和设计的复杂性的增加，它们中缺陷的幅度和复杂性也在上升。由于对晶圆缺陷的手动检查是昂贵的，因此高度需要基于自动的人工智能（AI）计算机视觉方法。先前关于缺陷分析的作品具有多个局限性，例如准确性低以及对分类和分割的单独模型的需求。为了分析混合型缺陷，一些以前的作品需要为每种缺陷类型分别训练一个模型，这是不可估计的。在本文中，我们介绍了基于编码器架构的新型网络WafersegClassnet（WSCN）。 WSCN执行单个和混合型晶圆缺陷的同时分类和分割。 WSCN使用“共享编码器”进行分类和细分，允许训练WSCN端到端。我们使用N-PAIR对比度损失首先预处理编码器，然后使用BCE-DICE损失进行分割，并进行分类的分类横向损失。使用N-PAIR对比度损失有助于更好地嵌入晶圆图的潜在维度。 WSCN的模型大小仅为0.51MB，仅执行0.2m的拖鞋。因此，它比其他最先进的型号轻得多。同样，它仅需要150个时期才能收敛，而先前的工作需要4,000个时代。我们在具有38,015张图像的混合WM38数据集上评估了我们的模型。 WSCN的平均分类精度为98.2％，骰子系数为0.9999。我们是第一个在混合WM38数据集上显示分割结果的人。可以从https://github.com/ckmvigil/wafersegclassnet获得源代码。

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels. This technique is known to improve the generalization performance in many learning paradigms and applications. In this work, we first analyze Mixup and show that it implicitly regularizes infinitely many directional derivatives of all orders. We then propose a new method to improve Mixup based on the novel insight. To demonstrate the effectiveness of the proposed method, we conduct experiments across various domains such as images, tabular data, speech, and graphs. Our results show that the proposed method improves Mixup across various datasets using a variety of architectures, for instance, exhibiting an improvement over Mixup by 0.8% in ImageNet top-1 accuracy.

Multi-Scale and U-shaped Networks are widely used in various image restoration problems, including deblurring. Keeping in mind the wide range of applications, we present a comparison of these architectures and their effects on image deblurring. We also introduce a new block called as NFResblock. It consists of a Fast Fourier Transformation layer and a series of modified Non-Linear Activation Free Blocks. Based on these architectures and additions, we introduce NFResnet and NFResnet+, which are modified multi-scale and U-Net architectures, respectively. We also use three different loss functions to train these architectures: Charbonnier Loss, Edge Loss, and Frequency Reconstruction Loss. Extensive experiments on the Deep Video Deblurring dataset, along with ablation studies for each component, have been presented in this paper. The proposed architectures achieve a considerable increase in Peak Signal to Noise (PSNR) ratio and Structural Similarity Index (SSIM) value.

A major direction in differentially private machine learning is differentially private fine-tuning: pretraining a model on a source of "public data" and transferring the extracted features to downstream tasks. This is an important setting because many industry deployments fine-tune publicly available feature extractors on proprietary data for downstream tasks. In this paper, we use features extracted from state-of-the-art open source models to solve benchmark tasks in computer vision and natural language processing using differentially private fine-tuning. Our key insight is that by accelerating training, we can quickly drive the model parameters to regions in parameter space where the impact of noise is minimized. In doing so, we recover the same performance as non-private fine-tuning for realistic values of epsilon in [0.01, 1.0] on benchmark image classification datasets including CIFAR100.

Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements and stock prices based on the US financial market, global climate trends, and daily weather records to demonstrate significant relationships between climate and stock price fluctuation. Our analysis covers short and long-term rises and dips in company stock performances. Lastly, we take four natural disasters as a case study to observe their effect on the emotional state of people and their influence on the stock market.

We present DyFOS, an active perception method that Dynamically Finds Optimal States to minimize localization error while avoiding obstacles and occlusions. We consider the scenario where a ground target without any exteroceptive sensors must rely on an aerial observer for pose and uncertainty estimates to localize itself along an obstacle-filled path. The observer uses a downward-facing camera to estimate the target's pose and uncertainty. However, the pose uncertainty is a function of the states of the observer, target, and surrounding environment. To find an optimal state that minimizes the target's localization uncertainty, DyFOS uses a localization error prediction pipeline in an optimization search. Given the states mentioned above, the pipeline predicts the target's localization uncertainty with the help of a trained, complex state-dependent sensor measurement model (which is a probabilistic neural network in our case). Our pipeline also predicts target occlusion and obstacle collision to remove undesirable observer states. The output of the optimization search is an optimal observer state that minimizes target localization uncertainty while avoiding occlusion and collision. We evaluate the proposed method using numerical and simulated (Gazebo) experiments. Our results show that DyFOS is almost 100x faster than yet as good as brute force. Furthermore, DyFOS yielded lower localization errors than random and heuristic searches.

The presence of bias in deep models leads to unfair outcomes for certain demographic subgroups. Research in bias focuses primarily on facial recognition and attribute prediction with scarce emphasis on face detection. Existing studies consider face detection as binary classification into 'face' and 'non-face' classes. In this work, we investigate possible bias in the domain of face detection through facial region localization which is currently unexplored. Since facial region localization is an essential task for all face recognition pipelines, it is imperative to analyze the presence of such bias in popular deep models. Most existing face detection datasets lack suitable annotation for such analysis. Therefore, we web-curate the Fair Face Localization with Attributes (F2LA) dataset and manually annotate more than 10 attributes per face, including facial localization information. Utilizing the extensive annotations from F2LA, an experimental setup is designed to study the performance of four pre-trained face detectors. We observe (i) a high disparity in detection accuracies across gender and skin-tone, and (ii) interplay of confounding factors beyond demography. The F2LA data and associated annotations can be accessed at http://iab-rubric.org/index.php/F2LA.

Adaptive mesh refinement (AMR) is necessary for efficient finite element simulations of complex physical phenomenon, as it allocates limited computational budget based on the need for higher or lower resolution, which varies over space and time. We present a novel formulation of AMR as a fully-cooperative Markov game, in which each element is an independent agent who makes refinement and de-refinement choices based on local information. We design a novel deep multi-agent reinforcement learning (MARL) algorithm called Value Decomposition Graph Network (VDGN), which solves the two core challenges that AMR poses for MARL: posthumous credit assignment due to agent creation and deletion, and unstructured observations due to the diversity of mesh geometries. For the first time, we show that MARL enables anticipatory refinement of regions that will encounter complex features at future times, thereby unlocking entirely new regions of the error-cost objective landscape that are inaccessible by traditional methods based on local error estimators. Comprehensive experiments show that VDGN policies significantly outperform error threshold-based policies in global error and cost metrics. We show that learned policies generalize to test problems with physical features, mesh geometries, and longer simulation times that were not seen in training. We also extend VDGN with multi-objective optimization capabilities to find the Pareto front of the tradeoff between cost and error.

我们提出了一种自制算法，以从以自我为中心的视频数据中学习表示形式。最近，已经做出了重大努力，以捕捉人类在日常活动中与自己的环境进行互动。结果，已经出现了几个大型的以相互作用的多模式数据的自我为中心的数据集。但是，来自视频的学习表征可能具有挑战性。首先，鉴于长期连续视频的未经保育性质，学习有效表示需要专注于互动的时间。其次，日常活动的视觉表示应对环境状态的变化敏感。但是，当前成功的多模式学习框架鼓励随着时间的推移表示代表。为了应对这些挑战，我们利用音频信号来确定有利于更好学习的可能相互作用的时刻。我们还提出了一个新颖的自我监督目标，该目标从相互作用引起的听觉状态变化中学习。我们在两个大规模的中心数据集（Epic-Kitchens-100和最近发布的EGO4D）上广泛验证了这些贡献，并显示了几个下游任务的改进，包括行动识别，长期行动预期和对象状态变化分类。