We consider the nonlinear inverse problem of learning a transition operator $\mathbf{A}$ from partial observations at different times, in particular from sparse observations of entries of its powers $\mathbf{A},\mathbf{A}^2,\cdots,\mathbf{A}^{T}$. This Spatio-Temporal Transition Operator Recovery problem is motivated by the recent interest in learning time-varying graph signals that are driven by graph operators depending on the underlying graph topology. We address the nonlinearity of the problem by embedding it into a higher-dimensional space of suitable block-Hankel matrices, where it becomes a low-rank matrix completion problem, even if $\mathbf{A}$ is of full rank. For both a uniform and an adaptive random space-time sampling model, we quantify the recoverability of the transition operator via suitable measures of incoherence of these block-Hankel embedding matrices. For graph transition operators these measures of incoherence depend on the interplay between the dynamics and the graph topology. We develop a suitable non-convex iterative reweighted least squares (IRLS) algorithm, establish its quadratic local convergence, and show that, in optimal scenarios, no more than $\mathcal{O}(rn \log(nT))$ space-time samples are sufficient to ensure accurate recovery of a rank-$r$ operator $\mathbf{A}$ of size $n \times n$. This establishes that spatial samples can be substituted by a comparable number of space-time samples. We provide an efficient implementation of the proposed IRLS algorithm with space complexity of order $O(r n T)$ and per-iteration time complexity linear in $n$. Numerical experiments for transition operators based on several graph models confirm that the theoretical findings accurately track empirical phase transitions, and illustrate the applicability and scalability of the proposed algorithm.

许多学科的动力系统被建模为相互作用的粒子或试剂，其相互作用规则取决于非常少量的变量（例如，成对距离，相位的成对差异，等等），这是代理对状态的函数。然而，这些相互作用规则可以产生自组织的动力学，并具有复杂的新兴行为（聚类，羊群，蜂群等）。我们提出了一种学习技术，鉴于沿着代理轨迹的状态和速度的观察，它以非参数方式产生了相互作用内核所依赖的变量和相互作用内核本身。这产生了有效的尺寸降低，从而避免了高维观测数据（所有试剂的状态和速度）的维度诅咒。我们证明了我们的方法对各种一阶交互系统的学习能力。

我们研究了非线性状态空间模型中对不可糊化的观察函数的无监督学习。假设观察过程的大量数据以及状态过程的分布，我们引入了一种非参数通用力矩方法，以通过约束回归来估计观察函数。主要的挑战来自观察函数的不可抑制性以及国家与观察之间缺乏数据对。我们解决了二次损失功能可识别性的基本问题，并表明可识别性的功能空间是闭合状态过程的RKHS。数值结果表明，前两个矩和时间相关以及上限和下限可以识别从分段多项式到平滑函数的功能，从而导致收敛估计器。还讨论了该方法的局限性，例如由于对称性和平稳性而引起的非识别性。

We develop the first fully dynamic algorithm that maintains a decision tree over an arbitrary sequence of insertions and deletions of labeled examples. Given $\epsilon > 0$ our algorithm guarantees that, at every point in time, every node of the decision tree uses a split with Gini gain within an additive $\epsilon$ of the optimum. For real-valued features the algorithm has an amortized running time per insertion/deletion of $O\big(\frac{d \log^3 n}{\epsilon^2}\big)$, which improves to $O\big(\frac{d \log^2 n}{\epsilon}\big)$ for binary or categorical features, while it uses space $O(n d)$, where $n$ is the maximum number of examples at any point in time and $d$ is the number of features. Our algorithm is nearly optimal, as we show that any algorithm with similar guarantees uses amortized running time $\Omega(d)$ and space $\tilde{\Omega} (n d)$. We complement our theoretical results with an extensive experimental evaluation on real-world data, showing the effectiveness of our algorithm.

Robot assistants are emerging as high-tech solutions to support people in everyday life. Following and assisting the user in the domestic environment requires flexible mobility to safely move in cluttered spaces. We introduce a new approach to person following for assistance and monitoring. Our methodology exploits an omnidirectional robotic platform to detach the computation of linear and angular velocities and navigate within the domestic environment without losing track of the assisted person. While linear velocities are managed by a conventional Dynamic Window Approach (DWA) local planner, we trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular velocities commands and maintain the orientation of the robot towards the user. We evaluate our navigation system on a real omnidirectional platform in various indoor scenarios, demonstrating the competitive advantage of our solution compared to a standard differential steering following.

With the increasing demand for predictable and accountable Artificial Intelligence, the ability to explain or justify recommender systems results by specifying how items are suggested, or why they are relevant, has become a primary goal. However, current models do not explicitly represent the services and actors that the user might encounter during the overall interaction with an item, from its selection to its usage. Thus, they cannot assess their impact on the user's experience. To address this issue, we propose a novel justification approach that uses service models to (i) extract experience data from reviews concerning all the stages of interaction with items, at different granularity levels, and (ii) organize the justification of recommendations around those stages. In a user study, we compared our approach with baselines reflecting the state of the art in the justification of recommender systems results. The participants evaluated the Perceived User Awareness Support provided by our service-based justification models higher than the one offered by the baselines. Moreover, our models received higher Interface Adequacy and Satisfaction evaluations by users having different levels of Curiosity or low Need for Cognition (NfC). Differently, high NfC participants preferred a direct inspection of item reviews. These findings encourage the adoption of service models to justify recommender systems results but suggest the investigation of personalization strategies to suit diverse interaction needs.

A backdoor attack places triggers in victims' deep learning models to enable a targeted misclassification at testing time. In general, triggers are fixed artifacts attached to samples, making backdoor attacks easy to spot. Only recently, a new trigger generation harder to detect has been proposed: the stylistic triggers that apply stylistic transformations to the input samples (e.g., a specific writing style). Currently, stylistic backdoor literature lacks a proper formalization of the attack, which is established in this paper. Moreover, most studies of stylistic triggers focus on text and images, while there is no understanding of whether they can work in sound. This work fills this gap. We propose JingleBack, the first stylistic backdoor attack based on audio transformations such as chorus and gain. Using 444 models in a speech classification task, we confirm the feasibility of stylistic triggers in audio, achieving 96% attack success.

Did you know that over 70 million of Dota2 players have their in-game data freely accessible? What if such data is used in malicious ways? This paper is the first to investigate such a problem. Motivated by the widespread popularity of video games, we propose the first threat model for Attribute Inference Attacks (AIA) in the Dota2 context. We explain how (and why) attackers can exploit the abundant public data in the Dota2 ecosystem to infer private information about its players. Due to lack of concrete evidence on the efficacy of our AIA, we empirically prove and assess their impact in reality. By conducting an extensive survey on $\sim$500 Dota2 players spanning over 26k matches, we verify whether a correlation exists between a player's Dota2 activity and their real-life. Then, after finding such a link ($p\!<\!0.01$ and $\rho>0.3$), we ethically perform diverse AIA. We leverage the capabilities of machine learning to infer real-life attributes of the respondents of our survey by using their publicly available in-game data. Our results show that, by applying domain expertise, some AIA can reach up to 98% precision and over 90% accuracy. This paper hence raises the alarm on a subtle, but concrete threat that can potentially affect the entire competitive gaming landscape. We alerted the developers of Dota2.

自动副标题是将视听产品的语音自动转化为短文本的任务，换句话说，字幕及其相应的时间戳。生成的字幕需要符合多个空间和时间要求（长度，阅读速度），同时与语音同步并以促进理解的方式进行分割。鉴于其相当大的复杂性，迄今为止，通过分别处理转录，翻译，分割为字幕并预测时间戳的元素来解决自动字幕。在本文中，我们提出了第一个直接自动字幕模型，该模型在单个解决方案中从源语音中生成目标语言字幕及其时间戳。与经过内外数据和外域数据训练的最先进的级联模型的比较表明，我们的系统提供了高质量的字幕，同时在整合性方面也具有竞争力，并具有维护单个模型的所有优势。

在过去的几年中，卷积神经网络（CNN）在各种现实世界的网络安全应用程序（例如网络和多媒体安全）中表现出了有希望的性能。但是，CNN结构的潜在脆弱性构成了主要的安全问题，因此不适合用于以安全为导向的应用程序，包括此类计算机网络。保护这些体系结构免受对抗性攻击，需要使用挑战性攻击的安全体系结构。在这项研究中，我们提出了一种基于合奏分类器的新型体系结构，该结构将1级分类（称为1C）的增强安全性与在没有攻击的情况下的传统2级分类（称为2C）的高性能结合在一起。我们的体系结构称为1.5级（Spritz-1.5c）分类器，并使用最终密度分类器，一个2C分类器（即CNNS）和两个并行1C分类器（即自动编码器）构造。在我们的实验中，我们通过在各种情况下考虑八次可能的对抗性攻击来评估我们提出的架构的鲁棒性。我们分别对2C和Spritz-1.5c体系结构进行了这些攻击。我们研究的实验结果表明，I-FGSM攻击对2C分类器的攻击成功率（ASR）是N-Baiot数据集训练的2C分类器的0.9900。相反，Spritz-1.5C分类器的ASR为0.0000。