Nanoscale Au-ZnO heterostructure developed by atomic layer deposition towards amperometric H2O2 detection

Nanoscale Au-ZnO heterostructures were fabricated on 4-in. SiO2/Si wafers by the atomic layer deposition (ALD) technique. Developed Au-ZnO heterostructures after post-deposition annealing at 250 degrees C were tested for amperometric hydrogen peroxide (H2O2) detection. The surface morphology and nanostructure of Au-ZnO heterostructures were examined by field emission scanning electron microscopy (FE-SEM), Raman spectroscopy, atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), etc. Additionally, the electrochemical behavior of Au-ZnO heterostructures towards H2O2 sensing under various conditions is assessed by chronoamperometry and electrochemical impedance spectroscopy (EIS). The results showed that ALD-fabricated Au-ZnO heterostructures exhibited one of the highest sensitivities of 0.53 mu A mu M(-1)cm(-2), the widest linear H2O2 detection range of 1.0 mu M-120mM, a low limit of detection (LOD) of 0.78 mu M, excellent selectivity under the normal operation conditions, and great long-term stability. Utilization of the ALD deposition method opens up a unique opportunity for the improvement of the various capabilities of the devices based on Au-ZnO heterostructures for amperometric detection of different chemicals

Low-Power Wireless Wearable ECG Monitoring Chestbelt Based on Ferroelectric Microprocessor

Since cadiovascular disease (CVD) posts a heavy threat to people's health, long-term electrocardiogram (ECG) monitoring is of great value for the improvement of treatment. To realize remote long-term ECG monitoring, a low-power wireless wearable ECG monitoring device is proposed in this paper. The ECG monitoring device, abbreviated as ECGM, is designed based on ferroelectric microprocessor which provides ultra-low power consumption and contains four parts-MCU, BLE, Sensors and Power. The MCU part means circuit of MSP430FR2433, the core of ECGM. The BLE part is the CC2640R2F module applied for wireless transmission of the collected bio-signal data. And the sensors part includes several sensors like BMD101 used for monitoring bio-signals and motion of the wearer, while the Power part consists of battery circuit, charging circuit and 3.3V/1.8V/4.4V power supply circuit. The ECGM first collects ECG signals from the fabric electrodes adhered to wearers' chest, preprocesses the signals to eliminate the injected noise, and then transmit the output data to wearers' hand-held mobile phones through Bluetooth low energy (BLE). The wearers are enabled to acquire ECGs and other physiological parameters on their phones as well as some corresponding suggestions. The novelty of the system lies in the combination of low-power ECG sensor chip with ferroelectric microprocessor, thus achieving ultra-low power consumption and high signal quality

Model-Free, Regret-Optimal Best Policy Identification in Online CMDPs

This paper considers the best policy identification (BPI) problem in online Constrained Markov Decision Processes (CMDPs). We are interested in algorithms that are model-free, have low regret, and identify an optimal policy with a high probability. Existing model-free algorithms for online CMDPs with sublinear regret and constraint violation do not provide any convergence guarantee to an optimal policy and provide only average performance guarantees when a policy is uniformly sampled at random from all previously used policies. In this paper, we develop a new algorithm, named Pruning-Refinement-Identification (PRI), based on a fundamental structural property of CMDPs proved in Koole(1988); Ross(1989), which we call limited stochasticity. The property says for a CMDP with $N$ constraints, there exists an optimal policy with at most $N$ stochastic decisions. The proposed algorithm first identifies at which step and in which state a stochastic decision has to be taken and then fine-tunes the distributions of these stochastic decisions. PRI achieves trio objectives: (i) PRI is a model-free algorithm; and (ii) it outputs a near-optimal policy with a high probability at the end of learning; and (iii) in the tabular setting, PRI guarantees $\tilde{\mathcal{O}}(\sqrt{K})$ regret and constraint violation, which significantly improves the best existing regret bound $\tilde{\mathcal{O}}(K^{\frac{4}{5}})$ under a model-free algorithm, where $K$ is the total number of episodes

Testing leptogenesis at the LHC and future muon colliders: a Z′Z' scenario

If the masses of at least two generations of right-handed neutrinos (RHNs) are near-degenerate, the scale of leptogenesis can be as low as $\sim$ 100 GeV. In this work, we study probing such resonant leptogenesis in the $B-L$ model at the LHC and future multi-TeV muon colliders via the process $Z'\to NN\to\ell^\pm\ell^\pm+{\rm jets}$, with $Z'$ the $U(1)_{B-L}$ gauge boson and $N$ the RHN. The same-sign dilepton feature of the signal makes it almost background-free, while the event number difference between positive and negative leptons is a hint for $CP$ violation, which is a key ingredient of leptogenesis. We found that resonant leptogenesis can be tested at the HL-LHC for $M_{Z'}$ up to 12 TeV, while at a 10 (30) TeV muon collider the reach can be up to $M_{Z'}\sim28~(100)$ TeV via the off-shell production of $Z'$.Comment: 11 pages + references, 4 figures, 2 tables. To match the PRD versio

Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization

We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both locally optimal and sufficiently different from existing ones. To encourage the learning policy to consistently converge towards a previously undiscovered local optimum, RSPO switches between extrinsic and intrinsic rewards via a trajectory-based novelty measurement during the optimization process. When a sampled trajectory is sufficiently distinct, RSPO performs standard policy optimization with extrinsic rewards. For trajectories with high likelihood under existing policies, RSPO utilizes an intrinsic diversity reward to promote exploration. Experiments show that RSPO is able to discover a wide spectrum of strategies in a variety of domains, ranging from single-agent particle-world tasks and MuJoCo continuous control to multi-agent stag-hunt games and StarCraftII challenges.Comment: 30 pages, 15 figures, published as a conference paper at ICLR 202

A Cross-Cultural Perspective on the Preference for Potential Effect: An Individual Participant Data (IPD) Meta-Analysis Approach

A recent paper [Tormala ZL, Jia JS, Norton MI (2012). The preference for potential. Journal of personality and social psychology, 103:567-583] demonstrated that persons often prefer potential rather than achievement when evaluating others, because information regarding potential evokes greater interest and processing, resulting in more favorable evaluations. This research aimed to expand on this finding by asking two questions: (a) Is the preference for potential effect replicable in other cultures? (b) Is there any other mechanism that accounts for this preference for potential? To answer these two questions, we replicated Tormala et al.'s study in multiple cities (17 studies with 1,128 participants) in China using an individual participant data (IPD) meta-analysis approach to test our hypothesis. Our results showed that the preference for potential effect found in the US is also robust in China. Moreover, we also found a pro-youth bias behind the preference for potential effect. To be specific, persons prefer a potential-oriented applicant rather than an achievement-oriented applicant, partially because they believe that the former is younger than the latter

Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency

Supervised learning has been widely used for attack categorization, requiring high-quality data and labels. However, the data is often imbalanced and it is difficult to obtain sufficient annotations. Moreover, supervised models are subject to real-world deployment issues, such as defending against unseen artificial attacks. To tackle the challenges, we propose a semi-supervised fine-grained attack categorization framework consisting of an encoder and a two-branch structure and this framework can be generalized to different supervised models. The multilayer perceptron with residual connection is used as the encoder to extract features and reduce the complexity. The Recurrent Prototype Module (RPM) is proposed to train the encoder effectively in a semi-supervised manner. To alleviate the data imbalance problem, we introduce the Weight-Task Consistency (WTC) into the iterative process of RPM by assigning larger weights to classes with fewer samples in the loss function. In addition, to cope with new attacks in real-world deployment, we propose an Active Adaption Resampling (AAR) method, which can better discover the distribution of unseen sample data and adapt the parameters of encoder. Experimental results show that our model outperforms the state-of-the-art semi-supervised attack detection methods with a 3% improvement in classification accuracy and a 90% reduction in training time.Comment: Tech repor