傅立叶Ptychographic显微镜(FPM)是一种成像过程,它通过计算平均值克服了传统的传统显微镜空间带宽产品(SBP)的限制。它利用使用低数值孔径(NA)物镜捕获的多个图像,并通过频域缝线实现高分辨率相成像。现有的FPM重建方法可以广泛地分为两种方法:基于迭代优化的方法,这些方法基于正向成像模型的物理学以及通常采用馈送深度学习框架的数据驱动方法。我们提出了一个混合模型驱动的残留网络,该网络将远期成像系统的知识与深度数据驱动的网络相结合。我们提出的架构LWGNET将传统的电线流优化算法展开为一种新型的神经网络设计,该设计通过复杂的卷积块增强了梯度图像。与其他传统的展开技术不同,LWGNET在PAR上执行时使用的阶段较少,甚至比现有的传统和深度学习技术更好,尤其是对于低成本和低动态范围CMOS传感器。低位深度和低成本传感器的性能提高有可能显着降低FPM成像设置的成本。最后,我们在收集到的实际数据上显示出始终提高的性能。
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We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension. The main results are: \emph{(1)} the class of solution functions of linear programming (LP) and quadratic programming (QP) is a universal approximant for the $C^k$ smooth model class or some restricted Sobolev space, and we characterize the rate-distortion, \emph{(2)} the approximation power is investigated through a viewpoint of regression error, where information about the target function is provided in terms of data observations, \emph{(3)} compositionality in the form of a deep architecture with optimization as a layer is shown to reconstruct some basic functions used in numerical analysis without error, which implies that \emph{(4)} a substantial reduction in rate-distortion can be achieved with a universal network architecture, and \emph{(5)} we discuss the statistical bounds of empirical covering numbers for LP/QP, as well as a generic optimization problem (possibly nonconvex) by exploiting tame geometry. Our results provide the \emph{first rigorous analysis of the approximation and learning-theoretic properties of solution functions} with implications for algorithmic design and performance guarantees.
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We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. Such a model can be used to produce 3D "remixes" of a given scene, by mapping spatial latent codes into a 3D volumetric representation, which can subsequently be rendered from arbitrary views using physically based volume rendering. By construction, the generated scenes remain view-consistent across arbitrary camera configurations, without any flickering or spatio-temporal artifacts. During training, we employ a combination of 2D, obtained through differentiable volume tracing, and 3D Generative Adversarial Network (GAN) losses, across multiple scales, enforcing realism on both its 3D structure and the 2D renderings. We show results on semi-stochastic scenes of varying scale and complexity, obtained from real and synthetic sources. We demonstrate, for the first time, the feasibility of learning plausible view-consistent 3D scene variations from a single exemplar scene and provide qualitative and quantitative comparisons against recent related methods.
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The increasing reliance on online communities for healthcare information by patients and caregivers has led to the increase in the spread of misinformation, or subjective, anecdotal and inaccurate or non-specific recommendations, which, if acted on, could cause serious harm to the patients. Hence, there is an urgent need to connect users with accurate and tailored health information in a timely manner to prevent such harm. This paper proposes an innovative approach to suggesting reliable information to participants in online communities as they move through different stages in their disease or treatment. We hypothesize that patients with similar histories of disease progression or course of treatment would have similar information needs at comparable stages. Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users. The result is a variant of the collaborative information filtering or recommendation system tailored to the needs of users of online health communities. We report results of our experiments on an expert curated data set which demonstrate the superiority of the proposed approach over the state of the art baselines with respect to accurate and timely prediction of topic tags (and hence information sources of interest).
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Consider two brands that want to jointly test alternate web experiences for their customers with an A/B test. Such collaborative tests are today enabled using \textit{third-party cookies}, where each brand has information on the identity of visitors to another website. With the imminent elimination of third-party cookies, such A/B tests will become untenable. We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences. Our design respects the privacy of customers. We propose an estimater of the Average Treatment Effect (ATE), show that it is unbiased and theoretically compute its variance. Our demonstration describes how a marketer for a brand can design such an experiment and analyze the results. On real and simulated data, we show that the approach provides valid estimate of the ATE with low variance and is robust to the proportion of visitors overlapping across the brands.
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Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge devices. However, commercial edge platforms based on standard GPUs are not optimized to deploy SNNs, resulting in high energy and latency. While analog In-Memory Computing (IMC) platforms can serve as energy-efficient inference engines, they are accursed by the immense energy, latency, and area requirements of high-precision ADCs (HP-ADC), overshadowing the benefits of in-memory computations. We propose a hardware/software co-design methodology to deploy SNNs into an ADC-Less IMC architecture using sense-amplifiers as 1-bit ADCs replacing conventional HP-ADCs and alleviating the above issues. Our proposed framework incurs minimal accuracy degradation by performing hardware-aware training and is able to scale beyond simple image classification tasks to more complex sequential regression tasks. Experiments on complex tasks of optical flow estimation and gesture recognition show that progressively increasing the hardware awareness during SNN training allows the model to adapt and learn the errors due to the non-idealities associated with ADC-Less IMC. Also, the proposed ADC-Less IMC offers significant energy and latency improvements, $2-7\times$ and $8.9-24.6\times$, respectively, depending on the SNN model and the workload, compared to HP-ADC IMC.
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The paper describes the work that has been submitted to the 5th workshop on Challenges and Applications of Automated Extraction of socio-political events from text (CASE 2022). The work is associated with Subtask 1 of Shared Task 3 that aims to detect causality in protest news corpus. The authors used different large language models with customized cross-entropy loss functions that exploit annotation information. The experiments showed that bert-based-uncased with refined cross-entropy outperformed the others, achieving a F1 score of 0.8501 on the Causal News Corpus dataset.
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已经证明,深层合奏将典型的集体学习中看到的积极效果扩展到神经网络和增强学习(RL)。但是,要提高此类整体模型的效率仍然有很多事情要做。在这项工作中,我们介绍了在RL(feft)中快速传输的各种合奏,这是一种基于合奏的新方法,用于在高度多模式环境中进行增强学习,并改善了转移到看不见的环境。该算法分为两个主要阶段:合奏成员的培训,以及合成成员的合成(或微调)成员,以在新环境中起作用。该算法的第一阶段涉及并行培训常规的政策梯度或参与者 - 批评者,但增加了鼓励这些政策彼此不同的损失。这会导致单个单峰剂探索最佳策略的空间,并捕获与单个参与者相比,捕获环境的多模式的更多。 DEFT的第二阶段涉及将组件策略综合为新的策略,该策略以两种方式之一在修改的环境中效果很好。为了评估DEFT的性能,我们从近端策略优化(PPO)算法的基本版本开始,并通过faft的修改将其扩展。我们的结果表明,预处理阶段可有效地在多模式环境中产生各种策略。除了替代方案,faft通常会收敛到高奖励的速度要快得多,例如随机初始化而无需faft和合奏成员的微调。虽然当然还有更多的工作来分析理论上的熟练并将其扩展为更强大,但我们认为,它为在环境中捕获多模式的框架提供了一个强大的框架,同时仍将使用简单策略表示的RL方法。
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我们提出了一个新型混合动力系统(硬件和软件),该系统载有微型无人接地车辆(MiniUGV),以执行复杂的搜索和操纵任务。该系统利用异质机器人来完成使用单个机器人系统无法完成的任务。它使无人机能够探索一个隐藏的空间,并具有狭窄的开口,Miniugv可以轻松进入并逃脱。假定隐藏的空间可用于MiniUGV。 MiniUGV使用红外(IR)传感器和单眼相机在隐藏空间中搜索对象。所提出的系统利用摄像机的更广阔的视野(FOV)以及对象检测算法的随机性引导隐藏空间中的MiniUGV以找到对象。找到对象后,MiniUGV使用视觉伺服抓住它,然后返回其起点,从无人机将其缩回并将物体运送到安全的地方。如果在隐藏空间中没有发现对象,则无人机继续进行空中搜索。束缚的MiniUGV使无人机具有超出其影响力并执行搜索和操纵任务的能力,而该任务对于任何机器人都无法单独进行。该系统具有广泛的应用,我们通过重复实验证明了其可行性。
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动画字符上的现实动态服装具有许多AR/VR应用程序。在创作这种动态服装几何形状仍然是一项具有挑战性的任务时,数据驱动的模拟提供了一个有吸引力的替代方案,尤其是如果可以简单地使用基础字符的运动来控制它。在这项工作中,我们专注于动态3D服装,尤其是对于松散的服装。在数据驱动的设置中,我们首先学习了合理服装几何形状的生成空间。然后,我们学会了对该空间的映射,以捕获运动依赖的动态变形,该变形在服装的先前状态以及相对于基础体的相对位置为条件。从技术上讲,我们通过在服装的规范状态下预测富含框架依赖的皮肤重量的服装状态下的人均局部位移来对服装动力学进行建模,从而将服装带入全球空间。我们通过预测剩余的局部位移来解决所有剩余的人均碰撞。所得的服装几何形状被用作历史记录,以实现迭代推出预测。我们证明了对看不见的身体形状和运动输入的合理概括,并在多个最新的替代方案中显示出改进。
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