由于监视摄像头网络的无处不在，从图像中计算的自动人士最近引起了现代智能城市的城市监测的注意。当前的计算机视觉技术依赖于基于深度学习的算法，这些算法估算了静止图像中的行人密度。只有一堆作品利用视频序列中的时间一致性。在这项工作中，我们提出了一个时空的细心神经网络，以估计监视视频中的行人数量。通过利用连续帧之间的时间相关性，我们在广泛使用的FDST基准上将最新的计数误差降低了5％，定位误差降低了7.5％。

图像文本匹配是在涉及对视觉和语言的共同理解的任务中发挥领导作用。在文献中，此任务通常被用作培训能够共同处理图像和文本的架构的预训练目标。但是，它具有直接的下游应用程序：跨模式检索，其中包括查找与给定查询文本或反之亦然相关的图像。解决此任务对于跨模式搜索引擎至关重要。许多最近的方法提出了针对图像文本匹配问题的有效解决方案，主要是使用最近的大型视觉语言（VL）变压器网络。但是，这些模型通常在计算上很昂贵，尤其是在推理时间。这样可以防止他们在大规模的跨模式检索场景中采用，几乎应该立即向用户提供结果。在本文中，我们建议通过提出对齐和提炼网络（Aladin）来填补有效性和效率之间的空白。阿拉丁首先通过在细粒度的图像和文本上对齐来产生高效的分数。然后，它通过提炼从细粒对齐方式获得的相关性分数来提炼共享的嵌入空间 - 可以进行有效的KNN搜索。我们在MS-Coco上取得了显着的结果，表明我们的方法可以与最先进的VL变形金刚竞争，同时快了近90倍。复制我们结果的代码可在https://github.com/mesnico/aladin上获得。

随着网络和在线百科全书的可访问性的增加，要管理的数据量正在不断增加。例如，在Wikipedia中，有数百万页用多种语言编写。这些页面包含通常缺乏文本上下文的图像，在概念上保持浮动，因此很难找到和管理。在这项工作中，我们介绍了我们设计的系统，用于参加Kaggle上的Wikipedia图像捕捉匹配挑战，其目的是使用与图像（URL和视觉数据）相关的数据来在大量可用图像中找到正确的标题。能够执行此任务的系统将改善大型在线百科全书上多媒体内容的可访问性和完整性。具体而言，我们提出了一个由最近的变压器模型提供支持的两个模型的级联，能够有效地推断出查询图像数据和字幕之间的相关得分。我们通过广泛的实验来验证，提出的两模型方法是处理大量图像和标题的有效方法，同时保持了推理时的整体计算复杂性。我们的方法取得了显着的结果，在Kaggle Challenge的私人排行榜上获得了0.53的归一化折扣累积增益（NDCG）值。

虽然卷积神经网络（CNNS）在许多愿景任务中显示出显着的结果，但它们仍然是通过简单但具有挑战性的视觉推理问题所紧张的。在计算机视觉中最近的变压器网络成功的启发，在本文中，我们介绍了经常性视觉变压器（RVIT）模型。由于经常性连接和空间注意在推理任务中的影响，该网络实现了来自SVRT数据集的同样不同视觉推理问题的竞争结果。空间和深度尺寸中的重量共享正规化模型，允许它使用较少的自由参数学习，仅使用28K培训样本。全面的消融研究证实了混合CNN +变压器架构的重要性和反馈连接的作用，其迭代地细化内部表示直到获得稳定的预测。最后，本研究可以更深入地了解对求解视觉抽象推理任务的注意力和经常性联系的作用。

太空探索一直是人类灵感的来源，并且由于现代望远镜，现在可以观察远离我们的天体。在网络上越来越多的空间的现实和虚构的图像，并利用现代深层学习架构，如生成的对抗网络，现在可以生成新的空间表示。在这项研究中，使用轻量级GaN，从网络获得的图像数据集，以及Galaxy动物园数据集，我们已经产生了数千个新的天体，星系，最后，最后的宇宙视图。。复制我们的结果的代码在https://github.com/davide-ccomini/ganiverse上公开提供，并且可以在https://davide-ccomini.github.io/goccomiverse/中探索生成的图像。

In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention. Many work striving for this goal have been emerging with the development of robotics, both hardware and software. Today we believe that an important underpinning of long-term robot autonomy is the ability of robots to learn on site and on-the-fly, especially when they are deployed in changing environments or need to traverse different environments. In this paper, we examine the problem of long-term autonomy from the perspective of robot learning, especially in an online way, and discuss in tandem its premise "data" and the subsequent "deployment".

A systematic review on machine-learning strategies for improving generalizability (cross-subjects and cross-sessions) electroencephalography (EEG) based in emotion classification was realized. In this context, the non-stationarity of EEG signals is a critical issue and can lead to the Dataset Shift problem. Several architectures and methods have been proposed to address this issue, mainly based on transfer learning methods. 418 papers were retrieved from the Scopus, IEEE Xplore and PubMed databases through a search query focusing on modern machine learning techniques for generalization in EEG-based emotion assessment. Among these papers, 75 were found eligible based on their relevance to the problem. Studies lacking a specific cross-subject and cross-session validation strategy and making use of other biosignals as support were excluded. On the basis of the selected papers' analysis, a taxonomy of the studies employing Machine Learning (ML) methods was proposed, together with a brief discussion on the different ML approaches involved. The studies with the best results in terms of average classification accuracy were identified, supporting that transfer learning methods seem to perform better than other approaches. A discussion is proposed on the impact of (i) the emotion theoretical models and (ii) psychological screening of the experimental sample on the classifier performances.

We introduce a new probabilistic temporal logic for the verification of Markov Decision Processes (MDP). Our logic is the first to include operators for causal reasoning, allowing us to express interventional and counterfactual queries. Given a path formula $\phi$, an interventional property is concerned with the satisfaction probability of $\phi$ if we apply a particular change $I$ to the MDP (e.g., switching to a different policy); a counterfactual allows us to compute, given an observed MDP path $\tau$, what the outcome of $\phi$ would have been had we applied $I$ in the past. For its ability to reason about different configurations of the MDP, our approach represents a departure from existing probabilistic temporal logics that can only reason about a fixed system configuration. From a syntactic viewpoint, we introduce a generalized counterfactual operator that subsumes both interventional and counterfactual probabilities as well as the traditional probabilistic operator found in e.g., PCTL. From a semantics viewpoint, our logic is interpreted over a structural causal model (SCM) translation of the MDP, which gives us a representation amenable to counterfactual reasoning. We provide a proof-of-concept evaluation of our logic on a reach-avoid task in a grid-world model.

Brain decoding is a field of computational neuroscience that uses measurable brain activity to infer mental states or internal representations of perceptual inputs. Therefore, we propose a novel approach to brain decoding that also relies on semantic and contextual similarity. We employ an fMRI dataset of natural image vision and create a deep learning decoding pipeline inspired by the existence of both bottom-up and top-down processes in human vision. We train a linear brain-to-feature model to map fMRI activity features to visual stimuli features, assuming that the brain projects visual information onto a space that is homeomorphic to the latent space represented by the last convolutional layer of a pretrained convolutional neural network, which typically collects a variety of semantic features that summarize and highlight similarities and differences between concepts. These features are then categorized in the latent space using a nearest-neighbor strategy, and the results are used to condition a generative latent diffusion model to create novel images. From fMRI data only, we produce reconstructions of visual stimuli that match the original content very well on a semantic level, surpassing the state of the art in previous literature. We evaluate our work and obtain good results using a quantitative semantic metric (the Wu-Palmer similarity metric over the WordNet lexicon, which had an average value of 0.57) and perform a human evaluation experiment that resulted in correct evaluation, according to the multiplicity of human criteria in evaluating image similarity, in over 80% of the test set.

Autoregressive processes naturally arise in a large variety of real-world scenarios, including e.g., stock markets, sell forecasting, weather prediction, advertising, and pricing. When addressing a sequential decision-making problem in such a context, the temporal dependence between consecutive observations should be properly accounted for converge to the optimal decision policy. In this work, we propose a novel online learning setting, named Autoregressive Bandits (ARBs), in which the observed reward follows an autoregressive process of order $k$, whose parameters depend on the action the agent chooses, within a finite set of $n$ actions. Then, we devise an optimistic regret minimization algorithm AutoRegressive Upper Confidence Bounds (AR-UCB) that suffers regret of order $\widetilde{\mathcal{O}} \left( \frac{(k+1)^{3/2}\sqrt{nT}}{(1-\Gamma)^2} \right)$, being $T$ the optimization horizon and $\Gamma < 1$ an index of the stability of the system. Finally, we present a numerical validation in several synthetic and one real-world setting, in comparison with general and specific purpose bandit baselines showing the advantages of the proposed approach.