Open set anomaly detection (OSAD) is a crucial task that aims to identify abnormal patterns or behaviors in data sets, especially when the anomalies observed during training do not represent all possible classes of anomalies. The recent advances in quantum computing in handling complex data structures and improving machine learning models herald a paradigm shift in anomaly detection methodologies. This study proposes a Quantum Scoring Module (Qsco), embedding quantum variational circuits into neural networks to enhance the model's processing capabilities in handling uncertainty and unlabeled data. Extensive experiments conducted across eight real-world anomaly detection datasets demonstrate our model's superior performance in detecting anomalies across varied settings and reveal that integrating quantum simulators does not result in prohibitive time complexities. At the same time, the experimental results under different noise models also prove that Qsco is a noise-resilient algorithm. Our study validates the feasibility of quantum-enhanced anomaly detection methods in practical applications.

Unmanned Aerial Vehicles (UAVs) are widely utilized across industries, necessitating robust security measures. Radio Frequency (RF) fingerprinting based UAV identification can enhance UAV authentication and authorization by identifying unique RF signals, bypassing vulnerabilities of software-based methods. However, existing methods often rely on single-domain signal sources, limiting their robustness by environmental factors such as distance, interference, and multi-path propagation. To address the challenges of UAV identification, we propose a novel lightweight two-step learning-to-learn classification approach, FiCo, which integrates multiple data sources from diverse domains, including mechanical vibration and 5 G communications. In the first step, we employ two Extreme Gradient Boosting (XGBoost) models to separately analyze communication and vibration data from UAVs. In the second step, a Logistic Regression meta-network is utilized to jointly learn from the predictions of these two XGBoost models. Experimental results show that the FiCo method boosts the AUC to 0.9792 and the accuracy to 92.59%. This represents a 2% accuracy increase over the Data Combined method and it improves accuracy by 9.3% with communication data alone and by 5.6% with vibration data alone, raising the AUC by 0.088 and 0.029, respectively. This approach reduces computational complexity and requires fewer training samples, enabling faster and more agile UAV identification in practice.

Variational quantum Eigensolver (VQE) is a leading candidate for harnessing quantum computers to advance quantum chemistry and materials simulations, yet its training efficiency deteriorates rapidly for large Hamiltonians. Two issues underlie this bottleneck: (i) the no-cloning theorem imposes a linear growth in circuit evaluations with the number of parameters per gradient step; and (ii) deeper circuits encounter barren plateaus (BPs), leading to exponentially increasing measurement overheads. To address these challenges, here we propose a deep learning framework, dubbed Titan, which identifies and freezes inactive parameters of a given ansätze at initialization for a specific class of Hamiltonians, reducing the optimization overhead without sacrificing accuracy. The motivation of Titan starts with our empirical findings that a subset of parameters consistently has negligible influence on training dynamics. Its design combines a theoretically grounded data construction strategy, ensuring each training example is informative and BP-resilient, with an adaptive neural architecture that generalizes across ansätze of varying sizes. Across benchmark transverse-field Ising models, Heisenberg models, and multiple molecule systems up to $30$ qubits, Titan achieves up to $3\times$ faster convergence and $40$–$60\%$ fewer circuit evaluations than state-of-the-art baselines, while matching or surpassing their estimation accuracy. By proactively trimming parameter space, Titan lowers hardware demands and offers a scalable path toward utilizing VQE to advance practical quantum chemistry and materials science.

Chromosome straightening plays an important role in karyotype analysis. Common straightening methods usually adopt geometric algorithms, which tend to affect the chromosome banding patterns in the process of straightening, resulting in feature changes, loss of details, and poor generalization. To solve these problems, this paper proposes a novel straightening method based on disentanglement representation learning. Our method consists of two main components: the Disentanglement Representation Encoder (DRE) and the Straightening Generator (SG), where DRE discovers and disentangles the bent representation and the content representation in the latent space, while SG is used to generate the straightened chromosome images based on the disentangled representations. Leveraging the bent representation and the content representation disentangled by DRE, our method produces the straightened representation by reducing the bent representation while keeping the content representation unchanged, making straightening chromosome without changing its banding patterns possible. Evaluation results on both the Frechet Initiation Distance (FID) and the Downstream Classification Accuracy (DCA) metrics show that our method achieves good performance.

The reliability of 5G systems and their applications in a complex, dynamic, and heterogeneous environment requires rigorous testing and real-time detection for system vulnerabilities and unintended emergent behaviors. In this paper, fuzz testing is performed on 5G systems by randomly injecting and permuting control commands into the system under test (SUT) of the 5G radio resource control (RRC) authentication and authorization process, emulating Man-In-The-Middle (MITM) attacks to trigger potential vulnerabilities and unintended behaviors. The fuzzed system behaviors contain information that could indicate the system's health status, and potential vulnerabilities, and, more importantly, it enables the causation analysis in the SUT to detect the location and type of attacks or abnormal inputs from the profiling of the impacted behaviors. We then propose a Real-time Framework for Root Cause Analyses (RAFT) in NextG Vulnerability Detection based on analyzing the random fragments of the log file generated during the communication process. By processing the random fragments of the logging profiles captured during fuzz testing with the continuous bag-of-words (CBOW) Model, we extract the information of states and states transitions and perform causal analysis to identify the root cause for vulnerability detection in the 5G system. The novelty of our framework lies in the creation and analysis of the information extraction that does not require capturing the entire log file instead only the log file fragments to achieve high accuracy. This approach enables real-time detection and deployment to real-life scenarios where access to the entire logging profile is difficult to obtain or unavailable. The presented framework RAFT directly adapts to various machine learning (ML) models, which allow the adaptation to hardware with various computation complexity from internet-of-things (IoT) to Radio Access Network (RAN) servers. The experimental results show a significant performance gain and are thoroughly evaluated by the accuracy and area under the curve (AUC) results. In particular, we show that the proposed framework can attain a high AUC value ($0.92\leq\text{AUC} < 0.96$) by accessing only a 70% fragment of the original log file while maintaining a higher accuracy. In addition, we find that RAFT reduces the time complexity by more than 5% as the fragment size reduces to 70% of the original log file. The causation analysis nature of RAFT summarizes vulnerability information into essential root causes that can be easily transmitted within the network in real-time and turned into guidance for back-end engineers. The unique advantages of RAFT, including accurate causation with information fragments, reliable performance without large training datasets and less computation complexity guarantee a wide range of use cases and deployment environment of RAFT.