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Two-dimensional (2D) materials have attracted considerable attention due to their unique physicochemical properties and significant potential in energy-related applications. Polymeric carbon nitrides (PCNs) with 2D stacked architecture show promise as photocatalysts for solar-to-fuel conversion and as versatile 2D semiconductors. However, the lack of a clear definition of the exact structural model of these materials limits our fundamental understanding of their unique properties. Here, we investigate the structure-induced optical properties of PCNs through \textit{ab initio} calculations. Our study on the electronic and optical properties of PCNs highlights the significant influence of structure on their behavior, especially near band edges. The analysis reveals that the degree of condensation and corrugation influences the electron/hole localization and the energy levels of $\pi$ electrons, which are crucial for the optical behavior. In addition, the microstructures of 2D configurations lead to divergent optical properties in 3D configurations, with characteristic peaks identified at 350 nm and interlayer interactions ranging from 400 to 500 nm, depending on the specific microstructures. Through observations over 2D and 3D structures, we elucidate exciton photophysical processes in PCN materials. This highlights the substantial differences in optical properties between actual 2D and 3D structures, while also demonstrating the potential for carrier and energy transport mechanisms to occur perpendicular to the plane. Finally, our results provide deep insights into the understanding of previously hidden microstructural, electronic, and optical properties of PCNs, paving the way for further performance and property enhancements in this class of materials.

Training of deep models for classification tasks is hindered by local minima problems and vanishing gradients, while unsupervised layer-wise pretraining does not exploit information from class labels. Here, we propose a new regularization technique, called diversifying regularization (DR), which applies a penalty on hidden units at any layer if they obtain similar features for different types of data. For generative models, DR is defined as divergence over the variational posteriori distributions and included in the maximum likelihood estimation as a prior. Thus, DR includes class label information for greedy pretraining of deep belief networks which result in a better weight initialization for fine-tuning methods. On the other hand, for discriminative training of deep neural networks, DR is defined as a distance over the features and included in the learning objective. With our experimental tests, we show that DR can help the backpropagation to cope with vanishing gradient problems and to provide faster convergence and smaller generalization errors.

Recent human activity recognition (HAR) methods, based on on-body inertial sensors, have achieved increasing performance; however, this is at the expense of longer CPU calculations and greater energy consumption. Therefore, these complex models might not be suitable for real-time prediction in mobile systems, e.g., in elder-care support and long-term health-monitoring systems. Here, we present a new method called RapidHARe for real-time human activity recognition based on modeling the distribution of a raw data in a half-second context window using dynamic Bayesian networks. Our method does not employ any dynamic-programming-based algorithms, which are notoriously slow for inference, nor does it employ feature extraction or selection methods. In our comparative tests, we show that RapidHARe is an extremely fast predictor, one and a half times faster than artificial neural networks (ANNs) methods, and more than eight times faster than recurrent neural networks (RNNs) and hidden Markov models (HMMs). Moreover, in performance, RapidHare achieves an F1 score of 94.27\% and accuracy of 98.94\%, and when compared to ANN, RNN, HMM, it reduces the F1-score error rate by 45\%, 65\%, and 63\% and the accuracy error rate by 41\%, 55\%, and 62\%, respectively. Therefore, RapidHARe is suitable for real-time recognition in mobile devices.

The accurate geo-localization of mobile devices based upon received signal strength (RSS) in an urban area is hindered by obstacles in the signal propagation path. Current localization methods have their own advantages and drawbacks. Triangular lateration (TL) is fast and scalable but employs a monotone RSS-to-distance transformation that unfortunately assumes mobile devices are on the line of sight. Radio frequency fingerprinting (RFP) methods employ a reference database, which ensures accurate localization but unfortunately hinders scalability. Here, we propose a new, simple, and robust method called lookup lateration (LL), which incorporates the advantages of TL and RFP without their drawbacks. Like RFP, LL employs a dataset of reference locations but stores them in separate lookup tables with respect to RSS and antenna towers. A query observation is localized by identifying common locations in only associating lookup tables. Due to this decentralization, LL is two orders of magnitude faster than RFP, making it particularly scalable for large cities. Moreover, we show that analytically and experimentally, LL achieves higher localization accuracy than RFP as well. For instance, using grid size 20 m, LL achieves 9.11 m and 55.66 m, while RFP achieves 72.50 m and 242.19 m localization errors at 67\% and 95\%, respectively, on the Urban Hannover Scenario dataset.

This paper presents a human gait data collection for analysis and activity recognition consisting of continues recordings of combined activities, such as walking, running, taking stairs up and down, sitting down, and so on; and the data recorded are segmented and annotated. Data were collected from a body sensor network consisting of six wearable inertial sensors (accelerometer and gyroscope) located on the right and left thighs, shins, and feet. Additionally, two electromyography sensors were used on the quadriceps (front thigh) to measure muscle activity. This database can be used not only for activity recognition but also for studying how activities are performed and how the parts of the legs move relative to each other. Therefore, the data can be used (a) to perform health-care-related studies, such as in walking rehabilitation or Parkinson's disease recognition, (b) in virtual reality and gaming for simulating humanoid motion, or (c) for humanoid robotics to model humanoid walking. This dataset is the first of its kind which provides data about human gait in great detail. The database is available free of charge https://github.com/romanchereshnev/HuGaDB., Comment: 4 Figures, 3 tables

We point out that the transition probabilities used in a recent perturbative calculation of pair creation in an external magnetic field in the expanding de Sitter space with the $in$ and $out$ fermion states defined by the Bunch-Davies modes [C. Crucean et al., Phys. Rev. D 73 044019 (20016)] are gauge dependent quantities. We examine the gauge variations of these amplitudes assuming a decoupling of the interaction at infinite times, which allows to conclude that the source of the problem lies in the nonoscillatory behavior of the fermion current in the infinite future., Comment: 13 pages; no figures; to appear in PRD