Exponential State Estimation for Neural Networks with Leakage, Discrete and Distributed Delays

In this paper, the design problem of state estimator for neural networks with the mixed time-varying delays are investigated by constructing appropriate Lyapunov-Krasovskii functionals and using some effective mathematical techniques. In order to derive several conditions to guarantee the estimation error systems to be globally exponential stable, we transform the considered systems into the neural-type time-delay systems. Then with a set of linear inequalities(LMIs), we can obtain the stable criteria. Finally, three numerical examples are given to show the effectiveness and less conservatism of the proposed criterion

Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence

Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world. For this purpose, a desirable solution should properly balance memory stability with learning plasticity, and acquire sufficient compatibility to capture the observed distributions. Existing advances mainly focus on preserving memory stability to overcome catastrophic forgetting, but remain difficult to flexibly accommodate incremental changes as biological intelligence (BI) does. By modeling a robust Drosophila learning system that actively regulates forgetting with multiple learning modules, here we propose a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity, and accordingly coordinates a multi-learner architecture to ensure solution compatibility. Through extensive theoretical and empirical validation, our approach not only clearly enhances the performance of continual learning, especially over synaptic regularization methods in task-incremental settings, but also potentially advances the understanding of neurological adaptive mechanisms, serving as a novel paradigm to progress AI and BI together

Observation of a thermoelectric Hall plateau in the extreme quantum limit

The thermoelectric Hall effect is the generation of a transverse heat current upon applying an electric field in the presence of a magnetic field. Here we demonstrate that the thermoelectric Hall conductivity $\alpha_{xy}$ in the three-dimensional Dirac semimetal ZrTe$_5$ acquires a robust plateau in the extreme quantum limit of magnetic field. The plateau value is independent of the field strength, disorder strength, carrier concentration, or carrier sign. We explain this plateau theoretically and show that it is a unique signature of three-dimensional Dirac or Weyl electrons in the extreme quantum limit. We further find that other thermoelectric coefficients, such as the thermopower and Nernst coefficient, are greatly enhanced over their zero-field values even at relatively low fields.Comment: 17+21 pages, 3+14 figures; published versio

DualTeacher: Bridging Coexistence of Unlabelled Classes for Semi-supervised Incremental Object Detection

In real-world applications, an object detector often encounters object instances from new classes and needs to accommodate them effectively. Previous work formulated this critical problem as incremental object detection (IOD), which assumes the object instances of new classes to be fully annotated in incremental data. However, as supervisory signals are usually rare and expensive, the supervised IOD may not be practical for implementation. In this work, we consider a more realistic setting named semi-supervised IOD (SSIOD), where the object detector needs to learn new classes incrementally from a few labelled data and massive unlabelled data without catastrophic forgetting of old classes. A commonly-used strategy for supervised IOD is to encourage the current model (as a student) to mimic the behavior of the old model (as a teacher), but it generally fails in SSIOD because a dominant number of object instances from old and new classes are coexisting and unlabelled, with the teacher only recognizing a fraction of them. Observing that learning only the classes of interest tends to preclude detection of other classes, we propose to bridge the coexistence of unlabelled classes by constructing two teacher models respectively for old and new classes, and using the concatenation of their predictions to instruct the student. This approach is referred to as DualTeacher, which can serve as a strong baseline for SSIOD with limited resource overhead and no extra hyperparameters. We build various benchmarks for SSIOD and perform extensive experiments to demonstrate the superiority of our approach (e.g., the performance lead is up to 18.28 AP on MS-COCO). Our code is available at \url{https://github.com/chuxiuhong/DualTeacher}

State Estimation for Discrete-Time Stochastic Neural Networks with Mixed Delays

This paper investigates the analysis problem for stability of discrete-time neural networks (NNs) with discrete- and distribute-time delay. Stability theory and a linear matrix inequality (LMI) approach are developed to establish sufficient conditions for the NNs to be globally asymptotically stable and to design a state estimator for the discrete-time neural networks. Both the discrete delay and distribute delays employ decomposing the delay interval approach, and the Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals, such that a new stability criterion is proposed in terms of linear matrix inequalities (LMIs). Numerical examples are given to demonstrate the effectiveness of the proposed method and the applicability of the proposed method

Patterns of de novo metastasis and survival outcomes by age in breast cancer patients: a SEER population-based study

BackgroundThe role of age in metastatic disease, including breast cancer, remains obscure. This study was conducted to determine the role of age in patients with de novo metastatic breast cancer.MethodsBreast cancer patients diagnosed with distant metastases between 2010 and 2019 were retrieved from the Surveillance, Epidemiology, and End Results database. Comparisons were performed between young (aged ≤ 40 years), middle-aged (41–60 years), older (61–80 years), and the oldest old (> 80 years) patients. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were estimated using multivariate Cox proportional hazard models. Survival analysis was performed by the Kaplan–Meier method.ResultsThis study included 24155 (4.4% of all patients) de novo metastatic breast cancer patients. The number of young, middle-aged, older, and the oldest old patients were 195 (8.3%), 9397 (38.9%), 10224 (42.3%), and 2539 (10.5%), respectively. The 5-year OS rate was highest in the young (42.1%), followed by middle-aged (34.8%), older (28.3%), and the oldest old patients (11.8%). Multivariable Cox regression analysis showed that middle-aged (aHR, 1.18; 95% CI, 1.10–1.27), older (aHR, 1.42; 95% CI, 1.32–1.52), and the oldest old patients (aHR, 2.15; 95% CI, 1.98–2.33) had worse OS than young patients. Consistently, middle-aged (aHR, 1.16; 95% CI, 1.08–1.25), older (aHR, 1.32; 95% CI, 1.23–1.43), and the oldest old patients (aHR, 1.86; 95% CI, 1.71–2.03) had worse BCSS than young patients.ConclusionThis study provided clear evidence that de novo metastatic breast cancer had an age-specific pattern. Age was an independent risk factor for mortality in patients with de novo metastatic breast cancer