This paper addresses the challenges of extending cellular network coverage into low-altitude airspace by deploying reconfigurable intelligent surfaces (RISs) near base stations (BSs) to serve unmanned aerial vehicles (UAVs). Existing approaches typically assume the channel is temporally and spatially correlated and the UAV location is known. Without requiring the linearity or continuity of the channel state information (CSI) transition process. we propose to recover the CSI evolution by leveraging a channel covariance-embeded radio map. At each time slot, one pilot is used to observe the high dimensional RISUAV channel. By exploiting both the radio map and the mobility model, we develop a strategy to use sequential one-pilot CSI measurements to jointly estimate the UAV location and CSI. Additionally, the phase shift vector of RIS is adaptively designed with an entropy-minimizing approach, enhancing observation quality under stochastic channel conditions. To initialize phase shifts effectively, a Random-and-Tracking strategy is introduced, combining early-stage random sensing with refined CSI tracking via eigenvector-based sensing. Numerical results, using ray-traced channel data over real city maps, demonstrate that our proposed framework achieves over 94% of the capacity attained with perfect channel knowledge at 15 dB signal-to-noise ratio (SNR), with a UAV speed of$50 ~\text{km} / \mathrm{h}$.

Electronic health records (EHRs) store patient medical history in the structured data format, which facilitates automatic healthcare risk prediction, thereby improving personalized healthcare management and treatment. There are two main categories of methods for automatic healthcare risk prediction. The first models time-series information or relationships between visits for enhanced patient representations. However, given the high dimensionality nature of the EHR data, it often obtains compromise results due to the lack of training data. The second exploits external knowledge, e.g., knowledge graphs (KGs), to augment the training data, but less attention has been paid to distinguishing the importance of features and filtering out irrelevant external knowledge, leading to overwhelming noise and inefficiency. Additionally, the joint relationships between patient features were not emphasized, which are highlighted in clinical practice. In this paper, we propose an efficient Path Filtering with Causal Analysis (PFCA) approach for enhanced healthcare risk prediction to address these challenges. PFCA first extracts personalized knowledge graphs (PKGs) consisting of paths linking the patient's features to targets and then devises a fine-grained filtering method based on path messages to remove irrelevant paths for better efficiency. Then we develop an effective similarity-based method to model different features' joint interactions with targets to learn augmented representations for each feature. Furthermore, we design a causal analysis method that includes a novel causal intervention mechanism to mine and prioritize causal features for improved predictive performance. Finally, by exploiting the attention weights of paths in the PKGs, PFCA provides target-oriented interpretations, showing how patients' features lead to targets through significant paths. Experimental results on three public real-world datasets and four healthcare risk prediction tasks confirm PFCA's effectiveness in improving predictive performance compared to ten state-of-the-art baselines, demonstrate its efficiency of path filtering and interpretability.

The class MIP* of quantum multiprover interactive proof systems with entanglement is much more powerful than its classical counterpart MIP [ 8 , 31 , 32 ]: while MIP = NEXP, the quantum class MIP * is equal to RE, a class including the halting problem. This is because the provers in MIP * can share unbounded quantum entanglement. However, recent works [ 53 , 54 ] have shown that this advantage is significantly reduced if the provers’ shared state contains noise. This article attempts to exactly characterize the effect of noise on the computational power of quantum multiprover interactive proof systems. We investigate the quantum two-prover one-round interactive system MIP * [poly, O (1)], where the verifier sends polynomially many bits to the provers and the provers send back constantly many bits. We show that noise completely destroys the computational advantage given by shared entanglement in this model. Specifically, we show that if the provers are allowed to share arbitrarily many EPR states, where each EPR state is affected by an arbitrarily small constant amount of noise, the resulting complexity class is equivalent to NEXP = MIP. This improves significantly on the previous best-known bound of NEEEXP (nondeterministic triply exponential time) [ 53 ]. We also show that this collapse in power is due to noise, rather than the O (1) answer size, by showing that allowing for noiseless EPR states gives the class the full power of RE = MIP * [poly, poly]. Along the way, we develop two technical tools of independent interest. First, we give a new, deterministic tester for the positivity of an exponentially large matrix, provided that it has a low-degree Fourier decomposition in terms of Pauli matrices. Secondly, we develop a new invariance principle for smooth matrix functions having bounded third-order Fréchet derivatives or which are Lipschitz continuous.

Quantum computing holds the promise of solving problems difficult for classical computers. However, we are still in the era of Noisy Intermediate-Scale Quantum (NISQ) computers, necessary to establish effective distributed quantum communication protocols to distribute complex quantum computing tasks across different quantum computers for execution. Significant progress has been made in quantum communication technology, particularly in quantum path creation and resource scheduling. The establishment of quantum paths relies on quantum entanglement and quantum relay technologies, achieving long-distance, high-fidelity quantum state transmission through entanglement swapping between multiple relays. However, resources in quantum communication networks are limited and expensive, making efficient resource scheduling strategies crucial for improving overall network efficiency. To address these issues, we design a network protocol that includes the Historical Weighted Fidelity Routing (HWFR) algorithm and the Dynamic Multi-Priority Quantum Scheduling (DMPQS) algorithm to enhance communication reliability across quantum computers. Both algorithms aim to enhance the reliability of quantum links, optimize resource utilization, and adapt to dynamic changes in the links. The former algorithm dynamically selects the optimal path by considering factors such as link length, noise level, entanglement success rate, and quantum relay resource constraints, ensuring high-fidelity and reliable quantum communication. The latter dynamically adjusts request priorities based on the urgency of quantum service requests and fidelity requirements, optimizing resource utilization. Experimental results show that the proposed protocol performs excellently in terms of an average response time of requests and link utilization, effectively improving the utilization efficiency of network resources and the overall performance of the system.

Hyperparameter tuning is an important task of machine learning, which can be formulated as a bilevel program (BLP). However, most existing algorithms are not applicable for BLP with non-smooth lower-level problems. To address this, we propose a single-level reformulation of the BLP based on lower-level duality without involving any implicit value function. To solve the reformulation, we propose a majorization minimization algorithm that marjorizes the constraint in each iteration. Furthermore, we show that the subproblems of the proposed algorithm for several widely-used hyperparameter turning models can be reformulated into conic programs that can be efficiently solved by the off-the-shelf solvers. We theoretically prove the convergence of the proposed algorithm and demonstrate its superiority through numerical experiments.

Quantum multiprover interactive proof systems with entanglement MIP* are much more powerful than its classical counterpart MIP (Babai et al. '91, Ji et al. '20): while MIP = NEXP, the quantum class MIP* is equal to RE, a class including the halting problem. This is because the provers in MIP* can share unbounded quantum entanglement. However, recent works of Qin and Yao '21 and '23 have shown that this advantage is significantly reduced if the provers' shared state contains noise. This paper attempts to exactly characterize the effect of noise on the computational power of quantum multiprover interactive proof systems. We investigate the quantum two-prover one-round interactive system MIP*[poly, O(1)], where the verifier sends polynomially many bits to the provers and the provers send back constantly many bits. We show noise completely destroys the computational advantage given by shared entanglement in this model. Specifically, we show that if the provers are allowed to share arbitrarily many noisy EPR states, where each EPR state is affected by an arbitrarily small constant amount of noise, the resulting complexity class is equivalent to NEXP = MIP. This improves significantly on the previous best-known bound of NEEEXP (nondeterministic triply exponential time) by Qin and Yao '21. We also show that this collapse in power is due to the noise, rather than the O(1) answer size, by showing that allowing for noiseless EPR states gives the class the full power of RE = MIP*[poly, poly]. Along the way, we develop two technical tools of independent interest. First, we give a new, deterministic tester for the positivity of an exponentially large matrix, provided it has a low-degree Fourier decomposition in terms of Pauli matrices. Secondly, we develop a new invariance principle for smooth matrix functions having bounded third-order Fréchet derivatives or which are Lipschitz continous.

Abstract Internal network attacks pose a serious security threat to enterprises and organizations, potentially leading to critical information leaks and network system damage. Hosts, as the core data and service bearers, are often primary targets of cyber attacks. Therefore, accurately identifying hosts with malicious behavior in the network is crucial. However, detecting malicious hosts on this intranet presents several challenges. Firstly, the network state is unstructured data that dynamically changes in real-time. Secondly, the large amount of normal traffic in the network drowns out the traces generated by malicious behaviors, leading to the problem of category imbalance. Lastly, the traditional graph neural network model has limitations in processing edge information and is unable to directly learn the information in netflow. To overcome these challenges, this paper proposes a malicious host detection system. The system extracts the Host Communication Graph by time slicing and uses a random undersampling method to balance samples. For malicious host detection, this paper proposes the Relational-Edge Graph Convolutional Network (RE-GCN) model, which can directly aggregate and learn features on edges and use them to accurately classify nodes, compared to other GNN models. Comparative experiments were conducted on various netflow datasets, demonstrating the effectiveness of our approach. Our approach outperformed other common GNN models in detecting malicious hosts.

In this paper, we performed a measurement campaign at 3.5 GHz using two ±45° polarized antenna arrays in two typical industrial scenarios, the manufacturing district and workshop corridor. We obtain averaged power delay profiles (APDP) at a number of positions and fit the APDPs by power-decaying curves. We have found that the dense metallic facilities cause large excess delay of the multipath component arrival. On the other hand, we model the small-scale fading by Lognormal distribution. We also obtain root-mean-square delay spread (RMS-DS) and model it by Nakagami distribution. Futhermore, the positive correlation between RMS-DS and spatial separation distance between the transmitter and receiver is modeled by a linear function. Dense metallic facilities in workshops leads to complicated propagation environments. Channel coherent bandwidth and energy dispersion should be considered carefully in the design of wireless communication systems in industrial environments.