缺失痕迹的插值和重建是地震数据处理的关键步骤，此外，这也是一个高度不良的问题，尤其是对于复杂的情况，例如高比率随机离散丢失，连续缺失和缺失，富含断层或盐分身体调查。这些复杂的案例在当前作品中很少提及。为了应对复杂的缺失案例，我们提出了一种新型的3-D GAN框架的多维对抗GAN（MDA GAN）。它可以在3D复合物使用三个歧视器缺少重建后，保持数据的各向异性和空间连续性。该功能缝合模块的设计并嵌入到发电机中，以保留更多输入数据的信息。 TANH横熵（TCE）损失是得出的，该损失为生成器提供了最佳的重建梯度，以使生成的数据更加平滑且连续。我们通过实验验证了研究的各个组件的有效性，然后在多个可公开的数据上测试了该方法。该方法实现了多达95％的随机离散缺失和100个连续缺失的痕迹的合理重建。在断层和盐体富含调查中，MDA GAN仍然为复杂病例带来令人鼓舞的结果。在实验上，已经证明，在简单和复杂的情况下，我们的方法的性能要比其他方法更好。https：//github.com/douyimin/mda_gan

数据驱动的故障检测已被视为3D图像分割任务。从合成数据训练的模型在某些调查中很难概括。最近，使用稀疏手动2D切片的训练3D断层分割被认为会产生令人鼓舞的结果，但是手动标记具有许多假阴性标签（异常注释），这对训练有害，因此对检测性能有害。在稀疏的2D标签下训练3D断层分割网络的动机，同时抑制假阴性标签，我们分析了训练过程梯度，并提出了蒙版骰子（MD）损失。此外，故障是一个边缘功能，并且当前的编码器decoder架构广泛用于故障检测（例如，U形网络）不利于边缘表示。因此，提出了故障网络，该故障网络是为故障的特征而设计的，采用高分辨率传播特征，并嵌入多尺度压缩融合块以融合多尺度信息，从而使边缘信息在传播和融合过程中得到充分保存，从而通过几个计算资源实现高级性能。实验表明，MD损失支持将人类经验纳入训练中，并抑制其中的假阴性标签，从而使基线模型可以提高性能并推广到更多的调查。故障网络能够提供对故障的更稳定和可靠的解释，它使用极低的计算资源，并且推断的速度明显快于其他模型。我们的方法表明与几种主流方法相比，最佳性能。

As various city agencies and mobility operators navigate toward innovative mobility solutions, there is a need for strategic flexibility in well-timed investment decisions in the design and timing of mobility service regions, i.e. cast as "real options" (RO). This problem becomes increasingly challenging with multiple interacting RO in such investments. We propose a scalable machine learning based RO framework for multi-period sequential service region design & timing problem for mobility-on-demand services, framed as a Markov decision process with non-stationary stochastic variables. A value function approximation policy from literature uses multi-option least squares Monte Carlo simulation to get a policy value for a set of interdependent investment decisions as deferral options (CR policy). The goal is to determine the optimal selection and timing of a set of zones to include in a service region. However, prior work required explicit enumeration of all possible sequences of investments. To address the combinatorial complexity of such enumeration, we propose a new variant "deep" RO policy using an efficient recurrent neural network (RNN) based ML method (CR-RNN policy) to sample sequences to forego the need for enumeration, making network design & timing policy tractable for large scale implementation. Experiments on multiple service region scenarios in New York City (NYC) shows the proposed policy substantially reduces the overall computational cost (time reduction for RO evaluation of > 90% of total investment sequences is achieved), with zero to near-zero gap compared to the benchmark. A case study of sequential service region design for expansion of MoD services in Brooklyn, NYC show that using the CR-RNN policy to determine optimal RO investment strategy yields a similar performance (0.5% within CR policy value) with significantly reduced computation time (about 5.4 times faster).

We develop a Bayesian semi-parametric model for the estimating the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in the phase III AAML1031 clinical trial in which patients move through a sequence of four treatment courses. At each course, they undergo treatment that may or may not include anthracyclines (ACT). While ACT is known to be effective at treating AML, it is also cardiotoxic and can lead to early death for some patients. Our task is to estimate the potential survival probability under hypothetical dynamic ACT treatment strategies, but there are several impediments. First, since ACT was not randomized in the trial, its effect on survival is confounded over time. Second, subjects initiate the next course depending on when they recover from the previous course, making timing potentially informative of subsequent treatment and survival. Third, patients may die or drop out before ever completing the full treatment sequence. We develop a generative Bayesian semi-parametric model based on Gamma Process priors to address these complexities. At each treatment course, the model captures subjects' transition to subsequent treatment or death in continuous time under a given rule. A g-computation procedure is used to compute a posterior over potential survival probability that is adjusted for time-varying confounding. Using this approach, we conduct posterior inference for the efficacy of hypothetical treatment rules that dynamically modify ACT based on evolving cardiac function.

The binding problem is one of the fundamental challenges that prevent the artificial neural network (ANNs) from a compositional understanding of the world like human perception, because disentangled and distributed representations of generative factors can interfere and lead to ambiguity when complex data with multiple objects are presented. In this paper, we propose a brain-inspired hybrid neural network (HNN) that introduces temporal binding theory originated from neuroscience into ANNs by integrating spike timing dynamics (via spiking neural networks, SNNs) with reconstructive attention (by ANNs). Spike timing provides an additional dimension for grouping, while reconstructive feedback coordinates the spikes into temporal coherent states. Through iterative interaction of ANN and SNN, the model continuously binds multiple objects at alternative synchronous firing times in the SNN coding space. The effectiveness of the model is evaluated on synthetic datasets of binary images. By visualization and analysis, we demonstrate that the binding is explainable, soft, flexible, and hierarchical. Notably, the model is trained on single object datasets without explicit supervision on grouping, but successfully binds multiple objects on test datasets, showing its compositional generalization capability. Further results show its binding ability in dynamic situations.

Named entity recognition models (NER), are widely used for identifying named entities (e.g., individuals, locations, and other information) in text documents. Machine learning based NER models are increasingly being applied in privacy-sensitive applications that need automatic and scalable identification of sensitive information to redact text for data sharing. In this paper, we study the setting when NER models are available as a black-box service for identifying sensitive information in user documents and show that these models are vulnerable to membership inference on their training datasets. With updated pre-trained NER models from spaCy, we demonstrate two distinct membership attacks on these models. Our first attack capitalizes on unintended memorization in the NER's underlying neural network, a phenomenon NNs are known to be vulnerable to. Our second attack leverages a timing side-channel to target NER models that maintain vocabularies constructed from the training data. We show that different functional paths of words within the training dataset in contrast to words not previously seen have measurable differences in execution time. Revealing membership status of training samples has clear privacy implications, e.g., in text redaction, sensitive words or phrases to be found and removed, are at risk of being detected in the training dataset. Our experimental evaluation includes the redaction of both password and health data, presenting both security risks and privacy/regulatory issues. This is exacerbated by results that show memorization with only a single phrase. We achieved 70% AUC in our first attack on a text redaction use-case. We also show overwhelming success in the timing attack with 99.23% AUC. Finally we discuss potential mitigation approaches to realize the safe use of NER models in light of the privacy and security implications of membership inference attacks.

配备高速数字化器的前端电子设备正在使用并建议将来的核检测器。最近的文献表明，在处理来自核检测器的数字信号时，深度学习模型，尤其是一维卷积神经网络。模拟和实验证明了该领域神经网络的令人满意的准确性和其他好处。但是，仍需要研究特定的硬件加速在线操作。在这项工作中，我们介绍了Pulsedl-II，这是一种专门设计的，专门为事件功能（时间，能量等）从具有深度学习的脉冲中提取的应用。根据先前的版本，PULSEDL-II将RISC CPU纳入系统结构，以更好地功能灵活性和完整性。 SOC中的神经网络加速器采用三级（算术单元，处理元件，神经网络）层次结构，并促进数字设计的参数优化。此外，我们设计了一种量化方案和相关的实现方法（恢复和位移位），以在所选层类型的选定子集中与深度学习框架（例如Tensorflow）完全兼容。通过当前方案，支持神经网络的量化训练，并通过专用脚本自动将网络模型转换为RISC CPU软件，几乎没有准确性损失。我们在现场可编程门阵列（FPGA）上验证pulsedl-ii。最后，通过由直接数字合成（DDS）信号发生器和带有模数转换器（ADC）的FPGA开发板组成的实验设置进行系统验证。拟议的系统实现了60 PS的时间分辨率和0.40％的能量分辨率，在线神经网络推断在信号与噪声比（SNR）为47.4 dB时。

在解决复杂的现实世界任务方面的最新深度学习（DL）进步导致其在实际应用中广泛采用。但是，这个机会具有重大的潜在风险，因为这些模型中的许多模型都依赖于对各种应用程序进行培训的隐私敏感数据，这使它们成为侵犯隐私的过度暴露威胁表面。此外，基于云的机器学习-AS-A-Service（MLAAS）在其强大的基础架构支持方面的广泛使用扩大了威胁表面，以包括各种远程侧渠道攻击。在本文中，我们首先在DL实现中识别并报告了一个新颖的数据依赖性计时侧通道泄漏（称为类泄漏），该实现源自广泛使用的DL Framework Pytorch中的非恒定时间分支操作。我们进一步展示了一个实用的推理时间攻击，其中具有用户特权和硬标签黑盒访问MLAA的对手可以利用类泄漏来损害MLAAS用户的隐私。 DL模型容易受到会员推理攻击（MIA）的攻击，其中对手的目标是推断在训练模型时是否使用过任何特定数据。在本文中，作为一个单独的案例研究，我们证明了具有差异隐私保护的DL模型（对MIA的流行对策）仍然容易受到MIA的影响，而不是针对对手开发的漏洞泄漏。我们通过进行恒定的分支操作来减轻班级泄漏并有助于减轻MIA，从而开发出易于实施的对策。我们选择了两个标准基准图像分类数据集CIFAR-10和CIFAR-100来训练五个最先进的预训练的DL模型，这是在具有Intel Xeon和Intel Xeon和Intel I7处理器的两个不同的计算环境中，以验证我们的方法。

我们开发了数据驱动的模型，以预测机器人在社交就餐场景中何时应进食。能够与朋友和家人独立饮食被认为是具有行动不便的人的最令人难忘，最重要的活动之一。机器人可以潜在地帮助这项活动，但是由机器人辅助的喂养是一个多方面的问题，在咬合，咬合时机和咬合转移方面面临挑战。特别是在社交就餐场景中，特别是由于在社交用餐场景中变得唯一挑战性，因为可能会中断社交人类机器人群体的互动。我们的关键见解是，考虑到社交线索的微妙平衡的咬合时序策略可能会导致在社交用餐场景中在机器人辅助喂养过程中进行无缝互动。我们通过收集一个包含30组三人共同饮食的多模式人类尊贵数据集（HHCD）来解决这个问题。我们使用此数据集分析人类人类的赋形行为，并在社交用餐场景中开发咬合时正时预测模型。我们还将这些模型转移到人类机器人的态度方案中。我们的用户研究表明，当我们的算法使用食客之间的多模式社交信号线索来建模时，预测会有所改善。 HHCD数据集，用户研究的视频和代码将在接受后公开发布。

车辆路由问题是文献中众所周知的NP-HARD组合优化问题。传统的解决方案方法涉及精心设计的启发式方法或耗时的元启发术。强化学习的最新工作一直是一种有希望的替代方法，但发现在解决方案质量方面很难与传统方法竞争。本文提出了一种混合方法，结合了加强学习，政策推出和可满足性的求解器，以实现计算时间和解决方案质量之间的可调整权衡。在流行的公共数据集中的结果表明，该算法能够比现有基于学习的方法更接近最佳水平，而计算时间较短。该方法需要最少的设计工作，并且能够在没有额外培训的情况下解决看不见的任意规模问题。此外，该方法可以推广到其他组合优化问题。