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Vector Symbolic Architecture (VSA) is emerging in machine learning due to its efficiency, but they are hindered by issues of hyperdimensionality and accuracy. As a promising mitigation, the Low-Dimensional Computing (LDC) method significantly reduces the vector dimension by 100 times while maintaining accuracy, by employing a gradient-based optimization. Despite its potential, LDC optimization for VSA is still underexplored. Our investigation into vector updates underscores the importance of stable, adaptive dynamics in LDC training. We also reveal the overlooked yet critical roles of batch normalization (BN) and knowledge distillation (KD) in standard approaches. Besides the accuracy boost, BN does not add computational overhead during inference, and KD significantly enhances inference confidence. Through extensive experiments and ablation studies across multiple benchmarks, we provide a thorough evaluation of our approach and extend the interpretability of binary neural network optimization similar to LDC, previously unaddressed in BNN literature. 

The rampant occurrence of cybersecurity breaches imposes substantial limitations on the progress of network infras- tructures, leading to compromised data, financial losses, potential harm to individuals, and disruptions in essential services. The current security landscape demands the urgent development of a holistic security assessment solution that encompasses vul- nerability analysis and investigates the potential exploitation of these vulnerabilities as attack paths. In this paper, we propose GRAPHENE, an advanced system designed to provide a detailed analysis of the security posture of computing infrastructures. Using user-provided information, such as device details and software versions, GRAPHENE performs a comprehensive secu- rity assessment. This assessment includes identifying associated vulnerabilities and constructing potential attack graphs that adversaries can exploit. Furthermore, it evaluates the exploitabil- ity of these attack paths and quantifies the overall security posture through a scoring mechanism. The system takes a holistic approach by analyzing security layers encompassing hardware, system, network, and cryptography. Furthermore, GRAPHENE delves into the interconnections between these layers, exploring how vulnerabilities in one layer can be leveraged to exploit vulnerabilities in others. In this paper, we present the end-to-end pipeline implemented in GRAPHENE, showcasing the systematic approach adopted for conducting this thorough security analysis. 

Hierarchical Federated Learning (HFL) has shown great promise over the past few years, with significant improvements in communication efficiency and overall performance. However, current research for HFL predominantly centers on supervised learning. This focus becomes problematic when dealing with semi-supervised learning, particularly under non-IID scenarios. In order to address this gap, our paper critically assesses the performance of straightforward adaptations of current state-of-the-art semi-supervised FL (SSFL) techniques within the HFL framework. We also introduce a novel clustering mechanism for hierarchical embeddings to alleviate the challenges introduced by semi-supervised paradigms in a hierarchical setting. Our approach not only provides superior accuracy, but also converges up to 5.11× faster, while being robust to non-IID data distributions for multiple datasets with negligible communication overhead 

Existing classical optical network infrastructure cannot be immediately used for quantum network applications due to photon loss. The first step toward enabling quantum networks is the integration of quantum repeaters into optical networks. However, the expenses and intrinsic noise inherent in quantum hardware underscore the need for an efficient deployment strategy that optimizes the placement of quantum repeaters and memories. In this article, we present a comprehensive framework for network planning, aiming to efficiently distribute quantum repeaters across existing infrastructure, with the objective of maximizing quantum network utility within an entanglement distribution network. We apply our framework to several cases including a preliminary illustration of a dumbbell network topology and real-world cases of the SURFnet and ESnet. We explore the effect of quantum memory multiplexing within quantum repeaters, as well as the influence of memory coherence time on quantum network utility. We further examine the effects of different fairness assumptions on network planning, uncovering their impacts on real-time network performance.