生成的对抗网络（GANS）已经促进了解决图像到图像转换问题的新方向。不同的GANS在目标函数中使用具有不同损耗的发电机和鉴别器网络。仍然存在差距来填补所生成的图像的质量并靠近地面真理图像。在这项工作中，我们介绍了一个名为循环辨别生成的对抗网络（CDGAN）的新的图像到图像转换网络，填补了上述空白。除了加速本的原始架构之外，所提出的CDGAN通过结合循环图像的附加鉴别器网络来产生高质量和更现实的图像。所提出的CDGAN在三个图像到图像转换数据集上进行测试。分析了定量和定性结果，并与最先进的方法进行了比较。在三个基线图像到图像转换数据集中，所提出的CDGAN方法优于最先进的方法。该代码可在https://github.com/kishankancharagunta/cdgan获得。

The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as it requires the ability to reason over very long movie segments. This is in contrast to most existing video recognition models, which are typically designed for short-range video analysis. This work proposes a State-Space Transformer model that can efficiently capture dependencies in long movie videos for accurate movie scene detection. Our model, dubbed TranS4mer, is built using a novel S4A building block, which combines the strengths of structured state-space sequence (S4) and self-attention (A) layers. Given a sequence of frames divided into movie shots (uninterrupted periods where the camera position does not change), the S4A block first applies self-attention to capture short-range intra-shot dependencies. Afterward, the state-space operation in the S4A block is used to aggregate long-range inter-shot cues. The final TranS4mer model, which can be trained end-to-end, is obtained by stacking the S4A blocks one after the other multiple times. Our proposed TranS4mer outperforms all prior methods in three movie scene detection datasets, including MovieNet, BBC, and OVSD, while also being $2\times$ faster and requiring $3\times$ less GPU memory than standard Transformer models. We will release our code and models.

Real-world datasets exhibit imbalances of varying types and degrees. Several techniques based on re-weighting and margin adjustment of loss are often used to enhance the performance of neural networks, particularly on minority classes. In this work, we analyze the class-imbalanced learning problem by examining the loss landscape of neural networks trained with re-weighting and margin-based techniques. Specifically, we examine the spectral density of Hessian of class-wise loss, through which we observe that the network weights converge to a saddle point in the loss landscapes of minority classes. Following this observation, we also find that optimization methods designed to escape from saddle points can be effectively used to improve generalization on minority classes. We further theoretically and empirically demonstrate that Sharpness-Aware Minimization (SAM), a recent technique that encourages convergence to a flat minima, can be effectively used to escape saddle points for minority classes. Using SAM results in a 6.2\% increase in accuracy on the minority classes over the state-of-the-art Vector Scaling Loss, leading to an overall average increase of 4\% across imbalanced datasets. The code is available at: https://github.com/val-iisc/Saddle-LongTail.

Current self-supervised learning algorithms are often modality-specific and require large amounts of computational resources. To address these issues, we increase the training efficiency of data2vec, a learning objective that generalizes across several modalities. We do not encode masked tokens, use a fast convolutional decoder and amortize the effort to build teacher representations. data2vec 2.0 benefits from the rich contextualized target representations introduced in data2vec which enable a fast self-supervised learner. Experiments on ImageNet-1K image classification show that data2vec 2.0 matches the accuracy of Masked Autoencoders in 16.4x lower pre-training time, on Librispeech speech recognition it performs as well as wav2vec 2.0 in 10.6x less time, and on GLUE natural language understanding it matches a retrained RoBERTa model in half the time. Trading some speed for accuracy results in ImageNet-1K top-1 accuracy of 86.8\% with a ViT-L model trained for 150 epochs.

In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions. Due to the scarcity of explicit user feedback, modern recommender systems typically optimize for the same fixed combination of implicit feedback signals across all users. However, this approach disregards a growing body of work highlighting that (i) implicit signals can be used by users in diverse ways, signaling anything from satisfaction to active dislike, and (ii) different users communicate preferences in different ways. We propose applying the recent Interaction Grounded Learning (IGL) paradigm to address the challenge of learning representations of diverse user communication modalities. Rather than taking a fixed, human-designed reward function, IGL is able to learn personalized reward functions for different users and then optimize directly for the latent user satisfaction. We demonstrate the success of IGL with experiments using simulations as well as with real-world production traces.

A biological system is a complex network of heterogeneous molecular entities and their interactions contributing to various biological characteristics of the system. However, current biological networks are noisy, sparse, and incomplete, limiting our ability to create a holistic view of the biological system and understand the biological phenomena. Experimental identification of such interactions is both time-consuming and expensive. With the recent advancements in high-throughput data generation and significant improvement in computational power, various computational methods have been developed to predict novel interactions in the noisy network. Recently, deep learning methods such as graph neural networks have shown their effectiveness in modeling graph-structured data and achieved good performance in biomedical interaction prediction. However, graph neural networks-based methods require human expertise and experimentation to design the appropriate complexity of the model and significantly impact the performance of the model. Furthermore, deep graph neural networks face overfitting problems and tend to be poorly calibrated with high confidence on incorrect predictions. To address these challenges, we propose Bayesian model selection for graph convolutional networks to jointly infer the most plausible number of graph convolution layers (depth) warranted by data and perform dropout regularization simultaneously. Experiments on four interaction datasets show that our proposed method achieves accurate and calibrated predictions. Our proposed method enables the graph convolutional networks to dynamically adapt their depths to accommodate an increasing number of interactions.

相干显微镜技术提供了跨科学和技术领域的材料的无与伦比的多尺度视图，从结构材料到量子设备，从综合电路到生物细胞。在构造更明亮的来源和高速探测器的驱动下，连贯的X射线显微镜方法（如Ptychography）有望彻底改变纳米级材料的特征。但是，相关的数据和计算需求显着增加意味着，常规方法不再足以从高速相干成像实验实时恢复样品图像。在这里，我们演示了一个工作流程，该工作流利用边缘的人工智能和高性能计算，以实现直接从检测器直接从检测器流出的X射线ptychography数据实时反演。拟议的AI支持的工作流程消除了传统的Ptychography施加的采样约束，从而使用比传统方法所需的数据较少的数据级允许低剂量成像。

在此评论中，我们为模糊C均值问题的“迭代重新加权算法”中提出了一个简单的替代推导。我们表明，对于IRW-FCM算法而得出的迭代步骤不过是流行的多数化最小化（MM）算法的步骤。本说明中提出的推导更简单明了，与IRW-FCM的推导不同，此处的推导不涉及引入任何辅助变量。此外，通过将IRW-FCM的步骤显示为MM算法，可以消除IRW-FCM算法的内环，并且可以有效地作为“单个环”算法运行算法。更确切地说，新的基于MM的推导推论IRW-FCM的单个内部环足够降低模糊C均值的目标函数，从而加快了IRW-FCM算法的速度。

自然界中多元化的生态学在许多物种中具有各种形式的群体行为。蝴蝶物种是随机飞行的突出物种之一，有点有见地，并将其转化为人造隐喻将导致巨大的可能性。本文认为一种这种隐喻称为蝴蝶交配优化（BMO）。在BMO中，BFLE遵循巡逻的交配现象，并同时捕获了多模式函数的所有局部优势。为了模仿该算法，设计了一个移动机器人（BFlyBot），以满足BMO算法中BFLE的功能。此外，多Bflybot群的设计旨在像蝴蝶本质上的作用，并遵循该算法的规则。实时实验是在多动物领域的BMO算法上进行的，并将信号源视为光源。实验结果表明，BMO算法适用于检测多个信号源，其运动的变化显着，即静态和动态。在静态信号源的情况下，随着BFlybot的初始位置的不同，收敛性在时间和平稳性方面受到影响。而具有不同阶梯尺寸的实验会导致它们在机器人的执行时间和速度方面的变化。在这项工作中，在动态环境中进行了实验，在该环境中，信号源在操纵和非操作场景中的运动。 Bflybot群能够检测到单个和多信号源，在两个固定点之间在两个固定点之间进行线性移动，以圆形，向上和向下运动。评估BMO现象，各种正在进行的和前瞻性的作品，例如中海船舶检测，讨论了空中搜索应用和地震预测。

最近的波能转化器（WEC）配备了多个腿和发电机，以最大程度地发电。传统控制器显示出捕获复杂波形模式的局限性，并且控制器必须有效地最大化能量捕获。本文介绍了多项式增强学习控制器（MARL），该控制器的表现优于传统使用的弹簧减震器控制器。我们的最初研究表明，问题的复杂性质使训练很难融合。因此，我们提出了一种新颖的跳过训练方法，使MARL训练能够克服性能饱和，并与默认的MARL训练相比，融合到最佳控制器，从而增强发电。我们还提出了另一种新型的混合训练初始化（STHTI）方法，其中最初可以单独针对基线弹簧减震器（SD）控制器对MARL控制器的个别代理进行训练，然后在将来一次或将来培训一个代理商或全部培训加速收敛。我们使用异步参与者-Critic（A3C）算法在基线弹簧减震器控制器上实现了基线弹簧减震器控制器的能源效率的两位数提高。