分布式能源的广泛采用以及智能电网技术的出现使传统上被动的电力系统用户得以积极参与能源交易。认识到传统的集中式网格驱动能源市场为这些用户提供最低盈利能力的事实，最近的研究已将重点转移到分散的点对点（P2P）能源市场上。在这些市场中，用户彼此交易能源，比买卖网格的收益更高。但是，假设持续的可用性，参与和完全合规性，大多数P2P能源交易中的研究很大程度上忽略了交易过程中用户的看法。结果，这些方法可能会导致负面态度和随着时间的推移参与度的减少。在本文中，我们设计了一个自动化的P2P能源市场，该市场将用户感知考虑在内。我们采用前景理论来对用户的看法进行建模并制定优化框架，以最大程度地提高买方的感知，同时匹配需求和生产。鉴于优化问题的非线性和非凸性性质，我们提出了基于差分进化的算法，用于交易能源，称为辩论。此外，我们推出了一种具有风险敏感的Q学习算法，该算法名为“ Q-学习和风险敏感性”（PQR），该算法（PQR）得知考虑其感知到的实用程序的卖家的最佳价格。基于真正的能耗和生产的真实痕迹以及现实的前景理论的功能，表明我们的方法可为买家带来26％的感知价值，并为卖方产生7％的奖励，与最新的最新状态相比。

Linguists distinguish between novel and conventional metaphor, a distinction which the metaphor detection task in NLP does not take into account. Instead, metaphoricity is formulated as a property of a token in a sentence, regardless of metaphor type. In this paper, we investigate the limitations of treating conventional metaphors in this way, and advocate for an alternative which we name 'metaphorical polysemy detection' (MPD). In MPD, only conventional metaphoricity is treated, and it is formulated as a property of word senses in a lexicon. We develop the first MPD model, which learns to identify conventional metaphors in the English WordNet. To train it, we present a novel training procedure that combines metaphor detection with word sense disambiguation (WSD). For evaluation, we manually annotate metaphor in two subsets of WordNet. Our model significantly outperforms a strong baseline based on a state-of-the-art metaphor detection model, attaining an ROC-AUC score of .78 (compared to .65) on one of the sets. Additionally, when paired with a WSD model, our approach outperforms a state-of-the-art metaphor detection model at identifying conventional metaphors in text (.659 F1 compared to .626).

A widely acknowledged shortcoming of WordNet is that it lacks a distinction between word meanings which are systematically related (polysemy), and those which are coincidental (homonymy). Several previous works have attempted to fill this gap, by inferring this information using computational methods. We revisit this task, and exploit recent advances in language modelling to synthesise homonymy annotation for Princeton WordNet. Previous approaches treat the problem using clustering methods; by contrast, our method works by linking WordNet to the Oxford English Dictionary, which contains the information we need. To perform this alignment, we pair definitions based on their proximity in an embedding space produced by a Transformer model. Despite the simplicity of this approach, our best model attains an F1 of .97 on an evaluation set that we annotate. The outcome of our work is a high-quality homonymy annotation layer for Princeton WordNet, which we release.

Binarized Neural Networks (BNNs) are receiving increasing attention due to their lightweight architecture and ability to run on low-power devices. The state-of-the-art for training classification BNNs restricted to few-shot learning is based on a Mixed Integer Programming (MIP) approach. This paper proposes the BeMi ensemble, a structured architecture of BNNs based on training a single BNN for each possible pair of classes and applying a majority voting scheme to predict the final output. The training of a single BNN discriminating between two classes is achieved by a MIP model that optimizes a lexicographic multi-objective function according to robustness and simplicity principles. This approach results in training networks whose output is not affected by small perturbations on the input and whose number of active weights is as small as possible, while good accuracy is preserved. We computationally validate our model using the MNIST and Fashion-MNIST datasets using up to 40 training images per class. Our structured ensemble outperforms both BNNs trained by stochastic gradient descent and state-of-the-art MIP-based approaches. While the previous approaches achieve an average accuracy of 51.1% on the MNIST dataset, the BeMi ensemble achieves an average accuracy of 61.7% when trained with 10 images per class and 76.4% when trained with 40 images per class.

One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments. However, we argue this need not be the case. In this paper, we present an approach that leverages Definition Modeling to introduce a generalized formulation of SRL as the task of describing predicate-argument structures using natural language definitions instead of discrete labels. Our novel formulation takes a first step towards placing interpretability and flexibility foremost, and yet our experiments and analyses on PropBank-style and FrameNet-style, dependency-based and span-based SRL also demonstrate that a flexible model with an interpretable output does not necessarily come at the expense of performance. We release our software for research purposes at https://github.com/SapienzaNLP/dsrl.

In this new computing paradigm, named quantum computing, researchers from all over the world are taking their first steps in designing quantum circuits for image processing, through a difficult process of knowledge transfer. This effort is named Quantum Image Processing, an emerging research field pushed by powerful parallel computing capabilities of quantum computers. This work goes in this direction and proposes the challenging development of a powerful method of image denoising, such as the Total Variation (TV) model, in a quantum environment. The proposed Quantum TV is described and its sub-components are analysed. Despite the natural limitations of the current capabilities of quantum devices, the experimental results show a competitive denoising performance compared to the classical variational TV counterpart.

In this paper, we introduced the novel concept of advisor network to address the problem of noisy labels in image classification. Deep neural networks (DNN) are prone to performance reduction and overfitting problems on training data with noisy annotations. Weighting loss methods aim to mitigate the influence of noisy labels during the training, completely removing their contribution. This discarding process prevents DNNs from learning wrong associations between images and their correct labels but reduces the amount of data used, especially when most of the samples have noisy labels. Differently, our method weighs the feature extracted directly from the classifier without altering the loss value of each data. The advisor helps to focus only on some part of the information present in mislabeled examples, allowing the classifier to leverage that data as well. We trained it with a meta-learning strategy so that it can adapt throughout the training of the main model. We tested our method on CIFAR10 and CIFAR100 with synthetic noise, and on Clothing1M which contains real-world noise, reporting state-of-the-art results.

In this paper, we present PARTIME, a software library written in Python and based on PyTorch, designed specifically to speed up neural networks whenever data is continuously streamed over time, for both learning and inference. Existing libraries are designed to exploit data-level parallelism, assuming that samples are batched, a condition that is not naturally met in applications that are based on streamed data. Differently, PARTIME starts processing each data sample at the time in which it becomes available from the stream. PARTIME wraps the code that implements a feed-forward multi-layer network and it distributes the layer-wise processing among multiple devices, such as Graphics Processing Units (GPUs). Thanks to its pipeline-based computational scheme, PARTIME allows the devices to perform computations in parallel. At inference time this results in scaling capabilities that are theoretically linear with respect to the number of devices. During the learning stage, PARTIME can leverage the non-i.i.d. nature of the streamed data with samples that are smoothly evolving over time for efficient gradient computations. Experiments are performed in order to empirically compare PARTIME with classic non-parallel neural computations in online learning, distributing operations on up to 8 NVIDIA GPUs, showing significant speedups that are almost linear in the number of devices, mitigating the impact of the data transfer overhead.

随着大规模分析的越来越重视，我们面临着整合来自多个来源的数据的需求。问题在于这些数据不可能重复使用。最终结果是高成本，进一步的缺点是，所得的集成数据将再次被重复使用。Itelos是一种通用方法，旨在最大程度地减少此过程的影响。直觉是，数据将根据其受欢迎程度而受到不同的处理：重复使用的一组数据越多，将其重复使用越多，并且在重复使用中更改的数据越少，从而降低了整体数据预处理成本，而这些成本却降低了。增加向后兼容性和未来共享

事实证明，图形神经网络（GNN）在图形结构数据的几个预测建模任务中已被证明。在这些任务中，链接预测是许多现实世界应用（例如推荐系统）的基本问题之一。但是，GNN不能免疫对抗攻击，即精心制作的恶意例子，旨在欺骗预测模型。在这项工作中，我们专注于对基于GNN的链接预测模型进行特定的白盒攻击，其中恶意节点的目的是出现在给定目标受害者的推荐节点列表中。为了实现这一目标，攻击者节点还可以指望它直接控制的其他现有同伴的合作，即在网络中注入许多``vicious''节点的能力。具体而言，所有这些恶意节点都可以添加新的边缘或删除现有的节点，从而扰乱原始图。因此，我们提出了野蛮人，一种新颖的框架和一种安装这种链接预测攻击的方法。野蛮人将对手的目标制定为一项优化任务，从而达到了攻击的有效性与所需的恶意资源的稀疏之间的平衡。在现实世界和合成数据集上进行的广泛实验表明，通过野蛮人实施的对抗性攻击确实达到了很高的攻击成功率，但使用少量恶性节点。最后，尽管这些攻击需要完全了解目标模型，但我们表明它们可以成功地转移到其他黑框方法以进行链接预测。