Vertex-based Networks to Accelerate Path Planning Algorithms

Path planning plays a crucial role in various autonomy applications, and RRT* is one of the leading solutions in this field. In this paper, we propose the utilization of vertex-based networks to enhance the sampling process of RRT*, leading to more efficient path planning. Our approach focuses on critical vertices along the optimal paths, which provide essential yet sparser abstractions of the paths. We employ focal loss to address the associated data imbalance issue, and explore different masking configurations to determine practical tradeoffs in system performance. Through experiments conducted on randomly generated floor maps, our solutions demonstrate significant speed improvements, achieving over a 400% enhancement compared to the baseline model.Comment: Accepted to IEEE Workshop on Machine Learning for Signal Processing (MLSP'2023

Understanding the Effects of Colleague Participation and Public Cause Proximity on Employee Volunteering Intentions: The Moderating Role of Power Distance

Many organizations encourage their employees to participate in charitable activities as part of their corporate social responsibility strategies. As a result, there has been an increased research interest in employee volunteering behavior. However, while previous research on employee volunteering decisions has focused on both individual-level and organizational-level factors, there has been less focus on peer involvement and volunteer cause proximity. To go some way to filling this research area, this paper conducted two studies to examine the possible effects of colleague participation, colleague position and public cause proximity on employee volunteering intentions. Study 1 found that colleague participation and public cause proximity had significant effects on employee volunteering, and Study 2 found that power distance played a moderating role in the relationship between colleague position and employee volunteering. This study contributes to theoretical research on employee volunteering and provides some information to assist firms retain engaged volunteers

Essays on Inflation Targeting and Credit Markets

The first chapter of the dissertation studies quantitatively and systematically the impacts of a wide range of inflation targets on credit markets, on social welfare and on wealth inequality. To this end, I develop a model featuring market segmentation, market incompleteness and limited commitment to financial contracts. Under incomplete markets, moderate inflation alleviates frictions in credit markets and thus improves social welfare. After calibrating the model to recent U.S. data, I report four major findings. First, as the inflation target increases, endogenous debt limits follow a humped shape with a flat tail. This coincides with the empirical relationship between inflation and credit market activities. Second, social welfare also takes on a humped shape followed by a flat tail, which leads to an optimal inflation rate of 3%. The sizable welfare loss (0.61%) at the Friedman rule inflation and the negligible welfare loss (0.006%) at 2% inflation explain in some sense why central banks in some leading developed countries maintain their inflation targets at 2-3%. Third, the optimal inflation with a complete set of financial assets is lower than that with an incomplete set of financial assets. This result is consistent with the fact that developed countries tend to keep lower inflation targets than developing countries. Fourth, the calibrated model generates a well matched Gini coefficient of wealth at 0.72 and implies that wealth inequality increases slowly with inflation rates. The second chapter of the dissertation studies how inflation targeting affects the U.S. holdings of net foreign assets and explains two facts: the U.S. negative net position in bonds and positive net position in portfolio equity and FDI. I extend the model in the first chapter to a two-country open economy model consisting of the U.S. and emerging markets (EM). Facing idiosyncratic income risks, credit agents in each country hold a portfolio of risk-free bonds and risky productive assets with idiosyncratic investment shocks. Financial integration allows credit agents to trade both kinds of assets globally. The only difference between the two countries is that the U.S. maintains a lower inflation target than EM. Calibrated to recent U.S. data, the model generates a higher debt limit for the U.S., where agents can borrow more than those in EM. With zero net bond supply in the world, the U.S. borrows from EM. This explains the U.S. negative net position of bonds. On the other hand, U.S. credit agents enjoy better risk sharing due to a larger debt limit. Therefore, the U.S. credit agents\u27 consumption is less volatile than that of their foreign counterparts. This leads to a smaller covariance between the return from risky equity and tomorrow\u27s consumption, and thus the U.S. credit agents require a lower risk premium on risky equity. As a result, they value those risky assets more highly and in effect buy them from EM

CLEAR: Generative Counterfactual Explanations on Graphs

Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted label?". The comparison of this instance before and after perturbation can enhance human interpretation. Most existing studies on counterfactual explanations are limited in tabular data or image data. In this work, we study the problem of counterfactual explanation generation on graphs. A few studies have explored counterfactual explanations on graphs, but many challenges of this problem are still not well-addressed: 1) optimizing in the discrete and disorganized space of graphs; 2) generalizing on unseen graphs; and 3) maintaining the causality in the generated counterfactuals without prior knowledge of the causal model. To tackle these challenges, we propose a novel framework CLEAR which aims to generate counterfactual explanations on graphs for graph-level prediction models. Specifically, CLEAR leverages a graph variational autoencoder based mechanism to facilitate its optimization and generalization, and promotes causality by leveraging an auxiliary variable to better identify the underlying causal model. Extensive experiments on both synthetic and real-world graphs validate the superiority of CLEAR over the state-of-the-art methods in different aspects.Comment: 18 pages, 9 figure

Adversarial Attacks on Fairness of Graph Neural Networks

Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e.g., female) in graph-based applications. Although these methods greatly improve the algorithmic fairness of GNNs, the fairness can be easily corrupted by carefully designed adversarial attacks. In this paper, we investigate the problem of adversarial attacks on fairness of GNNs and propose G-FairAttack, a general framework for attacking various types of fairness-aware GNNs in terms of fairness with an unnoticeable effect on prediction utility. In addition, we propose a fast computation technique to reduce the time complexity of G-FairAttack. The experimental study demonstrates that G-FairAttack successfully corrupts the fairness of different types of GNNs while keeping the attack unnoticeable. Our study on fairness attacks sheds light on potential vulnerabilities in fairness-aware GNNs and guides further research on the robustness of GNNs in terms of fairness. The open-source code is available at https://github.com/zhangbinchi/G-FairAttack.Comment: 32 pages, 5 figure

KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media

Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. In light of these limitations, we propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning and incorporate textual cues as paragraph-level labels. Specifically, we firstly generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles. Finally, we adopt relational graph neural networks for graph-level representation learning and conduct political perspective detection. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on two benchmark datasets. We further examine the effect of knowledge walks and textual cues and how they contribute to our approach's data efficiency.Comment: accepted at NAACL 2022 main conferenc

Collaborative Graph Neural Networks for Attributed Network Embedding

Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. However, existing efforts mainly focus on exploiting network structures, while the exploitation of node attributes is rather limited as they only serve as node features at the initial layer. This simple strategy impedes the potential of node attributes in augmenting node connections, leading to limited receptive field for inactive nodes with few or even no neighbors. Furthermore, the training objectives (i.e., reconstructing network structures) of most GNNs also do not include node attributes, although studies have shown that reconstructing node attributes is beneficial. Thus, it is encouraging to deeply involve node attributes in the key components of GNNs, including graph convolution operations and training objectives. However, this is a nontrivial task since an appropriate way of integration is required to maintain the merits of GNNs. To bridge the gap, in this paper, we propose COllaborative graph Neural Networks--CONN, a tailored GNN architecture for attribute network embedding. It improves model capacity by 1) selectively diffusing messages from neighboring nodes and involved attribute categories, and 2) jointly reconstructing node-to-node and node-to-attribute-category interactions via cross-correlation. Experiments on real-world networks demonstrate that CONN excels state-of-the-art embedding algorithms with a great margin

Editorial : Theoretical advances and practical applications of spiking neural networks

Neuromorphic engineering has experienced a significant growth in popularity over the last 10 years, going from being a niche academic research area, often confused with deep learning and mostly unknown to the wider industrial community, to being the main focus of many funding calls, significant industrial endeavours, and national and international initiatives. The advent to market of neuromorphic sensors, with a related widening understanding of the event-based sensing paradigm, combined with the development of the first neuromorphic processors, has steered the wider academic community and industry toward the investigation and use of Spiking Neural Networks (SNN). Very often overlooked in favour of the now extremely popular Deep Neural Networks (DNN), SNNs have become a serious alternative to DNNs, in application domains where size, weight and power are key limiting factors to the deployment of AI systems, such in Space applications, Security and Defence, Automotive, and more generally AI at the Edge. Nonetheless, there are many aspects of SNNs that still require significant investigation, as there are many unexplored avenues in this regard. To this aim, the articles accepted in this special topic present novel research works that focus on methodologies for training of SNNs and on the use of SNN in real life applications

Dilated FCN: Listening Longer to Hear Better

Deep neural network solutions have emerged as a new and powerful paradigm for speech enhancement (SE). The capabilities to capture long context and extract multi-scale patterns are crucial to design effective SE networks. Such capabilities, however, are often in conflict with the goal of maintaining compact networks to ensure good system generalization. In this paper, we explore dilation operations and apply them to fully convolutional networks (FCNs) to address this issue. Dilations equip the networks with greatly expanded receptive fields, without increasing the number of parameters. Different strategies to fuse multi-scale dilations, as well as to install the dilation modules are explored in this work. Using Noisy VCTK and AzBio sentences datasets, we demonstrate that the proposed dilation models significantly improve over the baseline FCN and outperform the state-of-the-art SE solutions.Comment: 5 pages; will appear in WASPAA conferenc