Recent mean field interpretations of learning dynamics in over-parameterized neural networks offer theoretical insights on the empirical success of first order optimization algorithms in finding global minima of the nonconvex risk landscape. In this paper, we explore applying mean field learning dynamics as a computational algorithm, rather than as an analytical tool. Specifically, we design a Sinkhorn regularized proximal algorithm to approximate the distributional flow from the learning dynamics in the mean field regime over weighted point clouds. In this setting, a contractive fixed point recursion computes the time-varying weights, numerically realizing the interacting Wasserstein gradient flow of the parameter distribution supported over the neuronal ensemble. An appealing aspect of the proposed algorithm is that the measure-valued recursions allow meshless computation. We demonstrate the proposed computational framework of interacting weighted particle evolution on binary and multi-class classification. Our algorithm performs gradient descent of the free energy associated with the risk functional.

我们提出了在概率密度函数（PDFS）的基础变量（即订单参数）的概率密度函数（PDF）中为胶体自组装的有限的随机最佳控制问题。控制目标是根据将状态PDF从规定的初始概率指标转向最小控制工作的规定终端概率指标的提出的。为了特异性，我们使用文献中的单变量随机状态模型。本文开发的分析和对照合成的计算步骤都推广为仿制药在状态中的多元随机状态动力学，在对照模型中给出了非伴随。我们为相关的最佳控制问题得出了最佳条件。该推导产生一个由三个耦合部分微分方程的系统，以及在初始和终端时间的边界条件。最终的系统是所谓的Schr \“ {O} dinger桥问题的广义实例。然后，我们通过训练物理知识的深神经网络来确定最佳控制策略，其中“物理学”是最优化的派生条件。通过基准胶体自组装问题的数值模拟，该解决方案的性能得到了证明。

The problem of reversing the compilation process, decompilation, is an important tool in reverse engineering of computer software. Recently, researchers have proposed using techniques from neural machine translation to automate the process in decompilation. Although such techniques hold the promise of targeting a wider range of source and assembly languages, to date they have primarily targeted C code. In this paper we argue that existing neural decompilers have achieved higher accuracy at the cost of requiring language-specific domain knowledge such as tokenizers and parsers to build an abstract syntax tree (AST) for the source language, which increases the overhead of supporting new languages. We explore a different tradeoff that, to the extent possible, treats the assembly and source languages as plain text, and show that this allows us to build a decompiler that is easily retargetable to new languages. We evaluate our prototype decompiler, Beyond The C (BTC), on Go, Fortran, OCaml, and C, and examine the impact of parameters such as tokenization and training data selection on the quality of decompilation, finding that it achieves comparable decompilation results to prior work in neural decompilation with significantly less domain knowledge. We will release our training data, trained decompilation models, and code to help encourage future research into language-agnostic decompilation.

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models

Synergetic use of sensors for soil moisture retrieval is attracting considerable interest due to the different advantages of different sensors. Active, passive, and optic data integration could be a comprehensive solution for exploiting the advantages of different sensors aimed at preparing soil moisture maps. Typically, pixel-based methods are used for multi-sensor fusion. Since, different applications need different scales of soil moisture maps, pixel-based approaches are limited for this purpose. Object-based image analysis employing an image object instead of a pixel could help us to meet this need. This paper proposes a segment-based image fusion framework to evaluate the possibility of preparing a multi-scale soil moisture map through integrated Sentinel-1, Sentinel-2, and Soil Moisture Active Passive (SMAP) data. The results confirmed that the proposed methodology was able to improve soil moisture estimation in different scales up to 20% better compared to pixel-based fusion approach.

Opinion summarisation synthesises opinions expressed in a group of documents discussing the same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media posts. Such posts are noisy and have unpredictable structure, posing additional challenges for the construction of the summary distribution and the preservation of meaning compared to online reviews, which has been so far the focus of opinion summarisation. To address these challenges we present \textit{WassOS}, an unsupervised abstractive summarization model which makes use of the Wasserstein distance. A Variational Autoencoder is used to get the distribution of documents/posts, and the distributions are disentangled into separate semantic and syntactic spaces. The summary distribution is obtained using the Wasserstein barycenter of the semantic and syntactic distributions. A latent variable sampled from the summary distribution is fed into a GRU decoder with a transformer layer to produce the final summary. Our experiments on multiple datasets including Twitter clusters, Reddit threads, and reviews show that WassOS almost always outperforms the state-of-the-art on ROUGE metrics and consistently produces the best summaries with respect to meaning preservation according to human evaluations.

辅助机器人技术是一类机器人技术，涉及帮助人类在日常护理任务中，由于残疾或年龄，它们可能无法抑制这些任务。尽管研究表明，经典控制方法可用于设计政策以完成这些任务，但这些方法可能很难推广到任务的各种实例化。强化学习可以为此问题提供解决方案，在该问题中，在模拟中训练了机器人，并将其政策转移到现实世界中。在这项工作中，我们复制了公开的基线，用于培训辅助健身房环境中三个任务的机器人，并探讨了复发性神经网络和阶段性政策梯度学习的用法，以增强原始工作。我们的基线实施符合或超过原始工作的基线，但是，我们发现我们对新方法的探索并不像我们预期的那样有效。我们讨论了我们的基线结果，以及关于为什么我们的新方法不成功的一些想法。

由于异质访问点（APS）的性质，负载平衡（LB）是混合灯保真度（LIFI）和无线保真度（WIFI）网络（HLWNETS）的挑战性问题。机器学习有可能以近乎最佳的网络性能为培训过程提供复杂性的LB解决方案。但是，当网络环境（尤其是用户数量）更改时，需要进行最先进的（SOTA）学习辅助LB方法，这大大限制了其实用性。在本文中，提出了一个新颖的深神经网络（DNN）结构，称为自适应目标条件神经网络（A-TCNN），该结构在其他用户的条件下为一个目标用户进行AP选择。此外，开发了一种自适应机制，可以通过分配数据速率要求将较大数量的用户映射到较大的数字，而不会影响目标用户的AP选择结果。这使提出的方法可以处理不同数量的用户，而无需再进行重新培训。结果表明，A-TCNN实现了非常接近测试数据集的网络吞吐量，差距小于3％。还证明，A-TCNN可以获得与两个SOTA基准相当的网络吞吐量，同时最多将运行时降低了三个数量级。

我们介绍了微博观点摘要（MOS）的任务，并共享3100个金标准意见摘要的数据集，以促进该领域的研究。该数据集包含跨越2年期的推文的摘要，并且涵盖了比任何其他公共Twitter摘要数据集更多的主题。摘要本质上是抽象的，是由熟练的记者创建的，这些记者在将事实信息（主要故事）与作者观点分开的模板之后，总结了新闻文章。我们的方法不同于以前在社交媒体中生成金标准摘要的工作，这些摘要通常涉及选择代表性帖子，从而有利于提取性摘要模型。为了展示数据集的实用性和挑战，我们基准了一系列抽象性和提取性的最先进的摘要模型，并实现良好的性能，前者的表现优于后者。我们还表明，微调对于提高性能和研究使用不同样本量的好处是必要的。

信息传播是网络科学研究的一个有趣的主题，该主题研究了信息，影响或传染的方式如何通过网络传播。图形燃烧是一个简化的确定性模型，用于信息如何在网络中传播。该问题的复杂NP完整性质使使用精确算法在计算上很难求解。因此，在文献中为图形燃烧问题提出了许多启发式方法和近似算法。在本文中，我们提出了一种有效的遗传算法，称为基于中心性的遗传过偏（CBAG）来解决图燃烧问题。考虑到图形燃烧问题的独特特征，我们介绍了新颖的遗传操作员，染色体表示和评估方法。在拟议的算法中，众所周知的中心性用作我们染色体初始化程序的骨干。实施了所提出的算法并将其与15个不同尺寸基准图上的先前的启发式和近似算法进行了比较。根据结果​​，可以看出，与先前的最新启发式方法相比，所提出的算法取得了更好的性能。完整的源代码可在线获得，可用于为图形燃烧问题找到最佳或近乎最佳的解决方案。