我们证明，与畴壁（DW）位置的大量随机变化的量化量（名义上是5态）突触的极低分辨率可以是节能的，并且与使用浮动精度相比，与类似尺寸的深度神经网络（DNN）相比具有相当高的测试精度。突触权重。具体地，电压控制的DW器件展示随机性的随机行为，与微磁性模拟严格，并且只能编码有限状态;但是，它们在训练和推论中都可以非常节能。我们表明，通过对学习算法实施合适的修改，我们可以解决随机行为以及减轻其低分辨率的影响，以实现高测试精度。在这项研究中，我们提出了原位和前地训练算法，基于Hubara等人提出的算法的修改。 [1]适用于突触权重的量化。我们使用2个，3和5状态DW设备作为Synapse培训Mnist DataSet上的几个5层DNN。对于原位训练，采用单独的高精度存储器单元来保护和累积重量梯度，然后被量化以编程低精密DW设备。此外，在训练期间使用尺寸的噪声公差余量来解决内部编程噪声。对于前训训练，首先基于所表征的DW设备模型和噪声公差余量进行前体DNN，其类似于原位培训。值得注意的是，对于原位推断，对设备的能量耗散装置仅是每次推断仅13页，因为在整个MNIST数据集上进行10个时期进行训练。

Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.

计算幽默检测系统很少对幽默反应的主观性进行建模，或者考虑对幽默的替代反应 - 即犯罪。我们分析了不同年龄段的男性和女性注释者的大量幽默和犯罪评级数据集。我们发现女性比男性更强烈地联系这两个概念，她们倾向于给出较低的幽默评分和更高的进攻得分。我们还发现，幽默与犯罪之间的相关性随着年龄的增长而增加。尽管幽默发现没有性别或年龄差异，但女性和较旧的注释者表示，她们比男性更频繁地理解笑话文本。我们讨论对计算幽默检测和下游任务的影响。

社交媒体用户生成的文本实际上是许多NLP任务的主要资源。但是，本文不遵循标准写作规则。此外，在书面通信中使用方言（例如摩洛哥阿拉伯语）增加了NLP任务的复杂性。方言是一种口头语言，没有标准拼字法，这会导致用户在写作时即兴拼写。因此，对于相同的词，我们可以找到多种形式的音译。随后，必须将这些不同的音译标准化为一种规范的单词形式。为了实现这一目标，我们利用了用YouTube评论生成的单词嵌入模型的强大性。此外，使用提供规范形式的摩洛哥阿拉伯方言词典，我们构建了一个规范化词典，我们称为Manorm。我们已经进行了几项实验，以证明Manorm的效率，这些实验表明其在方言归一化中有用。

随着智能干扰的出现，干扰攻击已成为无线系统性能的更严重威胁。智能化器能够更改其策略，以最大限度地减少由合法节点进行跟踪的概率。因此，需要一种能够持续调节对干扰策略的抗干扰机构来打击这种干扰物。值得注意的是，现有的抗干扰方法在这里不适用，因为它们主要关注减轻与不变的干扰政策的干扰攻击，并且很少考虑一个智能的干扰器作为对手。因此，在本文中，提出了与抗干扰技术一起工作的干扰型识别技术。所提出的识别方法采用经常性的神经网络，将Jammer的占用通道作为输入，输出干扰类型。在此方案下，首先确定实时干扰策略，然后选择最合适的对策。因此，可以通过所提出的识别技术来立即检测对干扰策略的任何改变，允许快速切换到适合新的干扰策略的新的抗干扰方法。为了评估所提出的识别方法的性能，派生检测的准确性是Jammer策略切换时间的函数。当Jammer策略切换时间为45时，仿真结果显示所有所考虑的用户数字的检测精度大于70％，当Jammer策略切换时间为45时，精度会提高到90％。

语义通信将在实现下一代无线系统中实现目标面向服务的关键作用。然而，该域中的大多数现有技术仅限于特定应用程序（例如，文本或图像），并且它不能够实现所定向的通信，其中必须与语义一起考虑发送信息的有效性，以便执行a某些任务。在本文中，提出了一种综合语义通信框架，以实现面向目标的任务执行。为了捕获扬声器和侦听器之间的语义，使用信仰的概念来定义一个通用语言，以使扬声器向听众描述环境观察。然后，提出了优化问题以选择完美描述了观察的最小信念集，同时最小化任务执行时间和传输成本。建议将课程学习（CL）和强化学习（RL）结合的新型自上而下框架来解决这个问题。仿真结果表明，在训练期间，所提出的CL方法在收敛时间，任务执行时间和传输成本方面优于传统的RL。

储层计算（RC）已经获得了最近的兴趣，因为无需培训储层权重，从而实现了极低的资源消费实施，这可能会对边缘计算和现场学习的影响有严格的限制。理想情况下，天然硬件储层应被动，最小，表现力和可行性。迄今为止，拟议的硬件水库很难满足所有这些标准。因此，我们建议通过利用偶极耦合，沮丧的纳米磁体的被动相互作用来符合所有这些标准的水库。挫败感大大增加了稳定的储层国家的数量，丰富了储层动力学，因此这些沮丧的纳米磁体满足了天然硬件储层的所有标准。同样，我们提出了具有低功率互补金属氧化物半导体（CMOS）电路的完全沮丧的纳米磁管储层计算（NMRC）系统与储层接口，并且初始实验结果证明了储层的可行性。在三个单独的任务上，通过微磁模拟对储层进行了验证。将所提出的系统与CMOS Echo-State网络（ESN）进行了比较，表明总体资源减少了10,000,000多倍，这表明，由于NMRC自然是被动的，而且最小的可能是具有极高资源效率的潜力。

Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.