Learning robot navigation strategies among pedestrian is crucial for domain based applications. Combining perception, planning and prediction allows us to model the interactions between robots and pedestrians, resulting in impressive outcomes especially with recent approaches based on deep reinforcement learning (RL). However, these works do not consider multi-robot scenarios. In this paper, we present MultiSoc, a new method for learning multi-agent socially aware navigation strategies using RL. Inspired by recent works on multi-agent deep RL, our method leverages graph-based representation of agent interactions, combining the positions and fields of view of entities (pedestrians and agents). Each agent uses a model based on two Graph Neural Network combined with attention mechanisms. First an edge-selector produces a sparse graph, then a crowd coordinator applies node attention to produce a graph representing the influence of each entity on the others. This is incorporated into a model-free RL framework to learn multi-agent policies. We evaluate our approach on simulation and provide a series of experiments in a set of various conditions (number of agents / pedestrians). Empirical results show that our method learns faster than social navigation deep RL mono-agent techniques, and enables efficient multi-agent implicit coordination in challenging crowd navigation with multiple heterogeneous humans. Furthermore, by incorporating customizable meta-parameters, we can adjust the neighborhood density to take into account in our navigation strategy.
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In the contemporary digital landscape, the continuous generation of extensive streaming data across diverse domains has become pervasive. Yet, a significant portion of this data remains unlabeled, posing a challenge in identifying infrequent events such as anomalies. This challenge is further amplified in non-stationary environments, where the performance of models can degrade over time due to concept drift. To address these challenges, this paper introduces a new method referred to as VAE4AS (Variational Autoencoder for Anomalous Sequences). VAE4AS integrates incremental learning with dual drift detection mechanisms, employing both a statistical test and a distance-based test. The anomaly detection is facilitated by a Variational Autoencoder. To gauge the effectiveness of VAE4AS, a comprehensive experimental study is conducted using real-world and synthetic datasets characterized by anomalous rates below 10\% and recurrent drift. The results show that the proposed method surpasses both robust baselines and state-of-the-art techniques, providing compelling evidence for their efficacy in effectively addressing some of the challenges associated with anomalous sequence detection in non-stationary streaming data.
Multi-source domain adaptation aims to reduce performance degradation when applying machine learning models to unseen domains. A fundamental challenge is devising the optimal strategy for feature selection. Existing literature is somewhat paradoxical: some advocate for learning invariant features from source domains, while others favor more diverse features. To address the challenge, we propose a statistical framework that distinguishes the utilities of features based on the variance of their correlation to label $y$ across domains. Under our framework, we design and analyze a learning procedure consisting of learning approximately shared feature representation from source tasks and fine-tuning it on the target task. Our theoretical analysis necessitates the importance of learning approximately shared features instead of only the strictly invariant features and yields an improved population risk compared to previous results on both source and target tasks, thus partly resolving the paradox mentioned above. Inspired by our theory, we proposed a more practical way to isolate the content (invariant+approximately shared) from environmental features and further consolidate our theoretical findings.
Performance modeling for large-scale data analytics workloads can improve the efficiency of cluster resource allocations and job scheduling. However, the performance of these workloads is influenced by numerous factors, such as job inputs and the assigned cluster resources. As a result, performance models require significant amounts of training data. This data can be obtained by exchanging runtime metrics between collaborating organizations. Yet, not all organizations may be inclined to publicly disclose such metadata. We present a privacy-preserving approach for sharing runtime metrics based on differential privacy and data synthesis. Our evaluation on performance data from 736 Spark job executions indicates that fully anonymized training data largely maintains performance prediction accuracy, particularly when there is minimal original data available. With 30 or fewer available original data samples, the use of synthetic training data resulted only in a one percent reduction in performance model accuracy on average.
To enhance accuracy of robot state estimation, active sensing (or perception-aware) methods seek trajectories that maximize the information gathered by the sensors. To this aim, one possibility is to seek trajectories that minimize the (estimation error) covariance matrix output by an extended Kalman filter (EKF), w.r.t. its control inputs over a given horizon. However, this is computationally demanding. In this article, we derive novel backpropagation analytical formulas for the derivatives of the covariance matrices of an EKF w.r.t. all its inputs. We then leverage the obtained analytical gradients as an enabling technology to derive perception-aware optimal motion plans. Simulations validate the approach, showcasing improvements in execution time, notably over PyTorch's automatic differentiation. Experimental results on a real vehicle also support the method.
We study energy-efficient offloading strategies in a large-scale MEC system with heterogeneous mobile users and network components. The system is considered with enabled user-task handovers that capture the mobility of various mobile users. We focus on a long-run objective and online algorithms that are applicable to realistic systems. The problem is significantly complicated by the large problem size, the heterogeneity of user tasks and network components, and the mobility of the users, for which conventional optimizers cannot reach optimum with a reasonable amount of computational and storage power. We formulate the problem in the vein of the restless multi-armed bandit process that enables the decomposition of high-dimensional state spaces and then achieves near-optimal algorithms applicable to realistically large problems in an online manner. Following the restless bandit technique, we propose two offloading policies by prioritizing the least marginal costs of selecting the corresponding computing and communication resources in the edge and cloud networks. This coincides with selecting the resources with the highest energy efficiency. Both policies are scalable to the offloading problem with a great potential to achieve proved asymptotic optimality - approach optimality as the problem size tends to infinity. With extensive numerical simulations, the proposed policies are demonstrated to clearly outperform baseline policies with respect to power conservation and robust to the tested heavy-tailed lifespan distributions of the offloaded tasks.
Differential privacy (DP) has various desirable properties, such as robustness to post-processing, group privacy, and amplification by subsampling, which can be derived independently of each other. Our goal is to determine whether stronger privacy guarantees can be obtained by considering multiple of these properties jointly. To this end, we focus on the combination of group privacy and amplification by subsampling. To provide guarantees that are amenable to machine learning algorithms, we conduct our analysis in the framework of R\'enyi-DP, which has more favorable composition properties than $(\epsilon,\delta)$-DP. As part of this analysis, we develop a unified framework for deriving amplification by subsampling guarantees for R\'enyi-DP, which represents the first such framework for a privacy accounting method and is of independent interest. We find that it not only lets us improve upon and generalize existing amplification results for R\'enyi-DP, but also derive provably tight group privacy amplification guarantees stronger than existing principles. These results establish the joint study of different DP properties as a promising research direction.
Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.
In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by labeled samples from the source domain. This could lead to disconnection between the labeled and unlabeled target samples as well as misalignment between unlabeled target samples and the source domain. In this paper, we propose a novel approach called Cross-domain Adaptive Clustering to address this problem. To achieve both inter-domain and intra-domain adaptation, we first introduce an adversarial adaptive clustering loss to group features of unlabeled target data into clusters and perform cluster-wise feature alignment across the source and target domains. We further apply pseudo labeling to unlabeled samples in the target domain and retain pseudo-labels with high confidence. Pseudo labeling expands the number of ``labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning. Extensive experiments on benchmark datasets, including DomainNet, Office-Home and Office, demonstrate that our proposed approach achieves the state-of-the-art performance in semi-supervised domain adaptation.
Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain's annotated data are unavailable. We study a novel and practical problem of Open Domain Generalization (OpenDG), which learns from different source domains to achieve high performance on an unknown target domain, where the distributions and label sets of each individual source domain and the target domain can be different. The problem can be generally applied to diverse source domains and widely applicable to real-world applications. We propose a Domain-Augmented Meta-Learning framework to learn open-domain generalizable representations. We augment domains on both feature-level by a new Dirichlet mixup and label-level by distilled soft-labeling, which complements each domain with missing classes and other domain knowledge. We conduct meta-learning over domains by designing new meta-learning tasks and losses to preserve domain unique knowledge and generalize knowledge across domains simultaneously. Experiment results on various multi-domain datasets demonstrate that the proposed Domain-Augmented Meta-Learning (DAML) outperforms prior methods for unseen domain recognition.
Recently, deep learning has achieved very promising results in visual object tracking. Deep neural networks in existing tracking methods require a lot of training data to learn a large number of parameters. However, training data is not sufficient for visual object tracking as annotations of a target object are only available in the first frame of a test sequence. In this paper, we propose to learn hierarchical features for visual object tracking by using tree structure based Recursive Neural Networks (RNN), which have fewer parameters than other deep neural networks, e.g. Convolutional Neural Networks (CNN). First, we learn RNN parameters to discriminate between the target object and background in the first frame of a test sequence. Tree structure over local patches of an exemplar region is randomly generated by using a bottom-up greedy search strategy. Given the learned RNN parameters, we create two dictionaries regarding target regions and corresponding local patches based on the learned hierarchical features from both top and leaf nodes of multiple random trees. In each of the subsequent frames, we conduct sparse dictionary coding on all candidates to select the best candidate as the new target location. In addition, we online update two dictionaries to handle appearance changes of target objects. Experimental results demonstrate that our feature learning algorithm can significantly improve tracking performance on benchmark datasets.
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