There is increased interest in applying Artificial Intelligence and Machine Learning (AI/ML) within the nuclear industry and nuclear engineering community. Effective implementation of AI/ML could offer benefits to the nuclear domain, including enhanced identification of anomalies, anticipation of system failures, and operational schedule optimization. However, limited work has been done to investigate the feasibility and applicability of AI/ML tools in a functioning nuclear reactor. Here, we go beyond the development of a single model and introduce a multi-layered AI/ML architecture that integrates both information technology and operational technology data streams to identify, characterize, and differentiate (i) among diverse cybersecurity events and (ii) between cyber events and other operational anomalies. Leveraging Purdue Universitys research reactor, PUR-1, we demonstrate this architecture through a representative use case that includes multiple concurrent false data injections and denial-of-service attacks of increasing complexity under realistic reactor conditions. The use case includes 14 system states (1 normal, 13 abnormal) and over 13.8 million multi-variate operational and information technology data points. The study demonstrated the capability of AI/ML to distinguish between normal, abnormal, and cybersecurity-related events, even under challenging conditions such as denial-of-service attacks. Combining operational and information technology data improved classification accuracy but posed challenges related to synchronization and collection during certain cyber events. While results indicate significant promise for AI/ML in nuclear cybersecurity, the findings also highlight the need for further refinement in handling complex event differentiation and multi-class architectures.

Ensembling is commonly regarded as an effective way to improve the general performance of models in machine learning, while also increasing the robustness of predictions. When it comes to algorithmic fairness, heterogeneous ensembles, composed of multiple model types, have been employed to mitigate biases in terms of demographic attributes such as sex, age or ethnicity. Moreover, recent work has shown how in multi-class problems even simple homogeneous ensembles may favor performance of the worst-performing target classes. While homogeneous ensembles are simpler to implement in practice, it is not yet clear whether their benefits translate to groups defined not in terms of their target class, but in terms of demographic or protected attributes, hence improving fairness. In this work we show how this simple and straightforward method is indeed able to mitigate disparities, particularly benefiting under-performing subgroups. Interestingly, this can be achieved without sacrificing overall performance, which is a common trade-off observed in bias mitigation strategies. Moreover, we analyzed the interplay between two factors which may result in biases: sub-group under-representation and the inherent difficulty of the task for each group. These results revealed that, contrary to popular assumptions, having balanced datasets may be suboptimal if the task difficulty varies between subgroups. Indeed, we found that a perfectly balanced dataset may hurt both the overall performance and the gap between groups. This highlights the importance of considering the interaction between multiple forces at play in fairness.

Spiking Neural Networks (SNNs) are developed as a promising alternative to Artificial Neural networks (ANNs) due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus, they are useful to enable energy-efficient hardware inference. However, exploiting spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. In this work, we propose an FPGA-based convolutional SNN accelerator called Skydiver that exploits spatio-temporal workload balance. We propose the Approximate Proportional Relation Construction (APRC) method that can predict the relative workload channel-wisely and a Channel-Balanced Workload Schedule (CBWS) method to increase the hardware workload balance ratio to over 90%. Skydiver was implemented on a Xilinx XC7Z045 FPGA and verified on image segmentation and MNIST classification tasks. Results show improved throughput by 1.4X and 1.2X for the two tasks. Skydiver achieved 22.6 KFPS throughput, and 42.4 uJ/Image prediction energy on the classification task with 98.5% accuracy.

Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels --- voice call, short message, and email --- to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a "rich club" maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy.