In this study, we conduct a comprehensive review of smart grid security, exploring system architectures, attack methodologies, defense strategies, and future research opportunities. We provide an in-depth analysis of various attack vectors, focusing on new attack surfaces introduced by advanced components in smart grids. The review particularly includes an extensive analysis of coordinated attacks that incorporate multiple attack strategies and exploit vulnerabilities across various smart grid components to increase their adverse impact, demonstrating the complexity and potential severity of these threats. Following this, we examine innovative detection and mitigation strategies, including game theory, graph theory, blockchain, and machine learning, discussing their advancements in counteracting evolving threats and associated research challenges. In particular, our review covers a thorough examination of widely used machine learning-based mitigation strategies, analyzing their applications and research challenges spanning across supervised, unsupervised, semi-supervised, ensemble, and reinforcement learning. Further, we outline future research directions and explore new techniques and concerns. We first discuss the research opportunities for existing and emerging strategies, and then explore the potential role of new techniques, such as large language models (LLMs), and the emerging threat of adversarial machine learning in the future of smart grid security.

Training graph neural networks (GNNs) on large-scale graphs can be challenging due to the high computational expense caused by the massive number of nodes and high-dimensional nodal features. Existing graph condensation studies tackle this problem only by reducing the number of nodes in the graph. However, the resulting condensed graph data can still be cumbersome. Specifically, although the nodes of the Citeseer dataset are reduced to 0.9% (30 nodes) in training, the number of features is 3,703, severely exceeding the training sample magnitude. Faced with this challenge, we study the problem of joint condensation for both features and nodes in large-scale graphs. This task is challenging mainly due to 1) the intertwined nature of the node features and the graph structure calls for the feature condensation solver to be structure-aware; and 2) the difficulty of keeping useful information in the condensed graph. To address these challenges, we propose a novel framework TinyGraph, to condense features and nodes simultaneously in graphs. Specifically, we cast the problem as matching the gradients of GNN weights trained on the condensed graph and the gradients obtained from training over the original graph, where the feature condensation is achieved by a trainable function. The condensed graph obtained by minimizing the matching loss along the training trajectory can henceforth retain critical information in the original graph. Extensive experiments were carried out to demonstrate the effectiveness of the proposed TinyGraph. For example, a GNN trained with TinyGraph retains 98.5% and 97.5% of the original test accuracy on the Cora and Citeseer datasets, respectively, while significantly reducing the number of nodes by 97.4% and 98.2%, and the number of features by 90.0% on both datasets.

As fifth-generation (5G) and upcoming sixth-generation (6G) communications exhibit tremendous demands in providing high data throughput with a relatively low latency, millimeter-wave (mmWave) technologies manifest themselves as the key enabling components to achieve the envisioned performance and tasks. In this context, mmWave integrated circuits (IC) have attracted significant research interests over the past few decades, ranging from individual block design to complex system design. However, the highly nonlinear properties and intricate trade-offs involved render the design of analog or RF circuits a complicated process. The rapid evolution of fabrication technology also results in an increasingly long time allocated in the design process due to more stringent requirements. In this thesis, 28-GHz transceiver circuits are first investigated with detailed schematics and associated performance metrics. In this case, two target systems comprising heterogeneous individual blocks are selected and demonstrated on both the transmitter and receiver sides. Subsequently, some conventional and large-scale machine learning (ML) approaches are integrated into the design pipeline of the chosen systems to predict circuit parameters based on desired specifications, thereby circumventing the typical time-consuming iterations found in traditional methods. Finally, some potential research directions are discussed from the perspectives of circuit design and ML algorithms.

Dynamic Selection (DS), where base classifiers are chosen from a classifier's pool for each new instance at test time, has shown to be highly effective in pattern recognition. However, instability and redundancy in the classifier pools can impede computational efficiency and accuracy in dynamic ensemble selection. This paper introduces a meta-learning recommendation system (MLRS) to recommend the optimal pool generation scheme for DES methods tailored to individual datasets. The system employs a meta-model built from dataset meta-features to predict the most suitable pool generation scheme and DES method for a given dataset. Through an extensive experimental study encompassing 288 datasets, we demonstrate that this meta-learning recommendation system outperforms traditional fixed pool or DES method selection strategies, highlighting the efficacy of a meta-learning approach in refining DES method selection. The source code, datasets, and supplementary results can be found in this project's GitHub repository: https://github.com/Menelau/MLRS-PDS.

Recent developments in graph neural networks show promise for predicting the occurrence of abnormal grain growth, which has been a particularly challenging area of research due to its apparent stochastic nature. In this study, we generate a large dataset of Monte Carlo simulations of abnormal grain growth. We train simple graph convolution networks to predict which initial microstructures will exhibit abnormal grain growth, and compare the results to a standard computer vision approach for the same task. The graph neural network outperformed the computer vision method and achieved 73% prediction accuracy and fewer false positives. It also provided some physical insight into feature importance and the relevant length scale required to maximize predictive performance. Analysis of the uncertainty in the Monte Carlo simulations provides additional insights for ongoing work in this area.

In its simplest form, patent landscaping is the process of identifying all patents that are related to a particular technology or technology area. Patent landscapes are useful for a number of activities: it is important for assessing the coverage, value, or context of particular pieces of intellectual property, or for understanding the direction, speed, or concentration of innovation in a particular industry Hunt et al. [2007]. For example, companies create patent landscapes to evaluate the risks posed by competitors in a particular technology space, or to decide whether and how much to invest in pursuing particular innovations. Patent offices and economic monitoring organizations use patent landscapes to evaluate how a particular technology is affecting or might affect the economy, for example, how much economic investment is underway in a technology, how much economic value has been generated, or how many industries or companies are supported by a particular technology. Governments, in turn, can use that information to implement technology policies, for example, deciding whether to steer investment or tax incentives to companies working in particular areas (e.g., AI or green technologies). While the simplest form of patent landscaping merely identifies which patents are related to a particular area, other more sophisticated forms of patent landscaping can seek to identify how different subareas of a technology area are related, which companies or inventor groups are the most prolific, what regions are involved, or what specific types of innovations are the focus of current development.

This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The MLM is obtained by leveraging softmax outputs and clustering techniques to measure the distances between the predictions of a trained neural network and class centroids. By analyzing these distances, the MLM provides a comprehensive view of the model's misclassification tendencies, enabling decision-makers to identify the most common and critical sources of errors. The MLM allows for the prioritization of model improvements and the establishment of decision thresholds based on acceptable risk levels. The approach is evaluated on the MNIST dataset using a Convolutional Neural Network (CNN) and a perturbed version of the dataset to simulate distribution shifts. The results demonstrate the effectiveness of the MLM in assessing the reliability of predictions and highlight its potential in enhancing the interpretability and risk mitigation capabilities of neural networks. The implications of this work extend beyond image classification, with ongoing applications in autonomous systems, such as self-driving cars, to improve the safety and reliability of decision-making in complex, real-world environments.

Large Deep Neural Networks (DNNs) are often data hungry and need high-quality labeled data in copious amounts for learning to converge. This is a challenge in the field of medicine since high quality labeled data is often scarce. Data programming has been the ray of hope in this regard, since it allows us to label unlabeled data using multiple weak labeling functions. Such functions are often supplied by a domain expert. Data-programming can combine multiple weak labeling functions and suggest labels better than simple majority voting over the different functions. However, it is not straightforward to express such weak labeling functions, especially in high-dimensional settings such as images and time-series data. What we propose in this paper is a way to bypass this issue, using distance functions. In high-dimensional spaces, it is easier to find meaningful distance metrics which can generalize across different labeling tasks. We propose an algorithm that queries an expert for labels of a few representative samples of the dataset. These samples are carefully chosen by the algorithm to capture the distribution of the dataset. The labels assigned by the expert on the representative subset induce a labeling on the full dataset, thereby generating weak labels to be used in the data programming pipeline. In our medical time series case study, labeling a subset of 50 to 130 out of 3,265 samples showed 17-28% improvement in accuracy and 13-28% improvement in F1 over the baseline using clinician-defined labeling functions. In our medical image case study, labeling a subset of about 50 to 120 images from 6,293 unlabeled medical images using our approach showed significant improvement over the baseline method, Snuba, with an increase of approximately 5-15% in accuracy and 12-19% in F1 score.

Modeling and analysis for event series generated by heterogeneous users of various behavioral patterns are closely involved in our daily lives, including credit card fraud detection, online platform user recommendation, and social network analysis. The most commonly adopted approach to this task is to classify users into behavior-based categories and analyze each of them separately. However, this approach requires extensive data to fully understand user behavior, presenting challenges in modeling newcomers without historical knowledge. In this paper, we propose a novel discrete event prediction framework for new users through the lens of causal inference. Our method offers an unbiased prediction for new users without needing to know their categories. We treat the user event history as the ''treatment'' for future events and the user category as the key confounder. Thus, the prediction problem can be framed as counterfactual outcome estimation, with the new user model trained on an adjusted dataset where each event is re-weighted by its inverse propensity score. We demonstrate the superior performance of the proposed framework with a numerical simulation study and two real-world applications, including Netflix rating prediction and seller contact prediction for customer support at Amazon.

In this paper, we address a recent trend in robotic home appliances to include vision systems on personal devices, capable of personalizing the appliances on the fly. In particular, we formulate and address an important technical task of personal object search, which involves localization and identification of personal items of interest on images captured by robotic appliances, with each item referenced only by a few annotated images. The task is crucial for robotic home appliances and mobile systems, which need to process personal visual scenes or to operate with particular personal objects (e.g., for grasping or navigation). In practice, personal object search presents two main technical challenges. First, a robot vision system needs to be able to distinguish between many fine-grained classes, in the presence of occlusions and clutter. Second, the strict resource requirements for the on-device system restrict the usage of most state-of-the-art methods for few-shot learning and often prevent on-device adaptation. In this work, we propose Swiss DINO: a simple yet effective framework for one-shot personal object search based on the recent DINOv2 transformer model, which was shown to have strong zero-shot generalization properties. Swiss DINO handles challenging on-device personalized scene understanding requirements and does not require any adaptation training. We show significant improvement (up to 55%) in segmentation and recognition accuracy compared to the common lightweight solutions, and significant footprint reduction of backbone inference time (up to 100x) and GPU consumption (up to 10x) compared to the heavy transformer-based solutions.