Sleep has traditionally been conceptualized as a state of cognitive disconnection, yet emerging evidence indicates that decision-making capacities persist across sleep stages. Here, we elucidate the computational mechanisms underlying real-time lexical decision-making during polysomnographically-verified sleep, using facial electromyography and hierarchical drift diffusion modeling in both healthy individuals and participants with narcolepsy. We found that lexical decision-making was preserved during N1 and lucid REM sleep, but relied on distinct computational strategies: in N1 sleep, both enhanced sensory-motor processing and increased evidence accumulation supported decisions about words, whereas in lucid REM sleep, lexical decisions were driven exclusively by evidence accumulation processes. Cross-state comparisons revealed two fundamental principles: (1) Selective preservation-during N1 sleep, lexical decisions for words were maintained while those for pseudowords were selectively impaired, indicating that cognitive resources during sleep are preferentially allocated to meaningful stimuli; (2) Parallel strategic adaptations-during lucid REM sleep, participants increased their decision thresholds, requiring more evidence before responding, which helped maintain accuracy even though the efficiency of evidence accumulation was reduced. Our findings demonstrate that, rather than a passive decline, sleep involves dynamic and state-specific reconfiguration of the computational mechanisms underlying decision-making, with important implications for understanding consciousness and cognitive flexibility.

Immunohistochemistry (IHC) staining is crucial for determining tumor subtypes, obtaining protein expression information, and developing personalized treatment plans. But compared with hematoxylin and eosin (H&E) staining, IHC staining is more complex and expensive. With the advancement of deep learning, converting H&E stained images into IHC stained images has gradually emerged as a solution for obtaining IHC staining. However, current virtual staining processes suffer from difficulties in aligning pathological semantic features, posing significant challenges for network training, which poses significant challenges for network training. To solve these issues, we propose a multi-view multi-level pathology semantic perception learning method for H&E-to-IHC virtual staining (M2PL-GAN). Unlike prior approaches, M2PL-GAN introduces a comprehensive semantic learning paradigm from three views: structural contextual relations, feature distribution, and topology-aware fine-grained semantics. These correspond to the Context-aware Correlation Mechanism (CACM), the Local-aware Distribution Alignment Mechanism (LDAM), and the Graph- aware Bidirectional Contrastive Learning Mechanism (GBCLM) respectively. Among them, CACM enhances contextual consistency by establishing semantic correlations between virtual and real IHC images at local scales. LDAM ensures alignment of semantic feature distributions between virtual and real IHC images, mitigating semantic shifts caused by HE-IHC staining. GBCLM leverages graph neural network to capture topology-aware semantic representations and optimizes semantic feature alignment through bidirectional contrastive learning. Extensive experiments on both public and private datasets demonstrate that our method outperforms state-of-the-art approaches in both quantitative metrics and qualitative evaluations. Our code is available in https://github.com/Pikachu-one/M2PL-GAN.

Linear differential microphone arrays (LDMAs) are becoming popular due to their potentially high directional gain and frequency-invariant beampattern. By increasing the number of microphones, the Maclaurin expansion-based LDMAs address the inherently poor robustness problem of the conventional LDMA at low frequencies. However, this method encounters severe beampattern distortion and the deep nulls problem in the white noise gain (WNG) and the directivity factor (DF) at high frequencies as the number of microphones increases. In this paper, we reveal that the severe beampattern distortion is attributed to the deviation term of the synthesized beampattern while the deep nulls problem in the WNG and the DF is attributed to the violation of the distortionless constraint in the desired direction. We then propose two new design methods to avoid the degraded performance of LDMAs. Compared to the Maclaurin series expansion-based method, the first method additionally imposes the distortionless constraint in the desired direction, and the deep nulls problem in the WNG and the DF can be avoided. The second method explicitly requires the response of the higher order spatial directivity pattern in the deviation term to be zero, and thus the beampattern distortion can be avoided. By choosing the frequency-wise parameter that determines the number of the considered higher order spatial directivity patterns, the second method enables a good trade-off between the WNG and the beampattern distortion. Simulations exemplify the superiority of the proposed method against existing methods in terms of the robustness and the beampattern distortion.

Moving target tracking in directional sensor networks has recently attracted attention by right of special directional sensing features. Unlike omnidirectional sensors, the directional sensor senses the target only in the direction of its orientation. It can provide quantized direction that indicates the presence or absence of the target in the sensing field, rather than just the analog measurement of sensing signal with respect to the detected target. A quantized Kalman filter (QKF) based on both quantized directions and analog ranging measurements is derived in the minimum mean-square error (MMSE) sense. Its performances of mean square estimation error (MSE) and complexity are also analyzed. Then, a reduced-complexity QKF of high-accuracy is pursued to facilitate its implementation. It is proved that the QKF yields a smaller MSE than the traditional extended Kalman filter (EKF) merely based on analog measurements. The posterior Cramér-Rao lower bound (PCRLB) is introduced as the performance measure. The performance advantages of the proposed QKF are demonstrated using Monte Carlo simulations in a target tracking application using ultrasonic ranging sensors.

A new generalised Kalman filtering algorithm using a multiplicative measurement noise model is developed for tracking moving targets in a wireless sensor network. This multiplicative error model facilitates more accurate characterisation of the distance dependence measurement errors of range‐estimating sensors. Two new formulations of extended Kalman filter (EKF) and unscented Kalman filter (UKF), called generalised EKF (GEKF) and generalised UKF (GUKF) are derived. Comparing with conventional EKF and UKF formulations, it is shown that GEKF and GUKF can achieve smaller tracking error than traditional EKF and UKF. Simulation results are also reported that demonstrated the superior performance of GEKF and GUKF over existing methods.

A novel energy-balanced task-scheduling method is proposed that extends the lifespan of wireless sensor networks (WSNs) for collaborative target tracking using an unscented Kalman filter (UKF) algorithm. It is shown that the tracking accuracy is approximately proportional to the number of active sensor nodes participating in collaborative tracking. Excessive sensor nodes thus may be put to sleep mode to conserve energy provided there are a sufficient number of active sensor nodes. It is then shown that the lifespan of a WSN is dictated by the distribution of residue energy of sensor nodes. Specifically, we have shown that an energy-balanced WSN is likely to maximize its lifespan. As such, at each step of the tracking task, the head node must judiciously select active nodes from all sensors within the sensing range to minimize residue energy variations (energy balanced) while achieving desired tracking accuracy. This is formulated as a subset selection problem, which is shown to have a complexity that is NP-hard. Several energy-balanced scheduling for tracking (EBaST) heuristic algorithms are proposed to solve this problem with polynomial execution complexities. Extensive simulations have been conducted to compare EBaST against some state-of-the-art scheduling algorithms. It is observed that EBaST is more capable of significantly extending the WSNs lifespan than competing algorithms while delivering comparable or better tracking accuracy.

A distributed extended Kalman filter (EKF) algorithm is developed for tracking moving targets in a wireless sensor network equipped with distance estimating sensors. In particular, a distance-dependent measurement error of range-estimating sensors is modeled as a multiplicative noise in the observation model. A new formulation of EKF, called generalized EKF (GEKF) based on the multiplicative noise model is developed. Compared to conventional EKF formulation, it is shown that GEKF can achieve smaller estimation error than traditional EKF. Simulation results also demonstrated superior performance of GEKF.