Optical Lattices with Higher-order Exceptional Points by Non-Hermitian Coupling

Exceptional points (EPs) are degeneracies in open wave systems with coalescence of at least two energy levels and their corresponding eigenstates. In higher dimensions, more complex EP physics not found in two-state systems is observed. We consider the emergence and interaction of multiple EPs in a four coupled optical waveguides system by non-Hermitian coupling showing a unique EP formation pattern in a phase diagram. In addition, absolute phase rigidities are computed to show the mixing of the different states in definite parameter regimes. Our results could be potentially important for developing further understanding of EP physics in higher dimensions via generalized paradigm of nonHermitian coupling for a new generation of parity-time (PT) devices.Comment: To appear: Appl. Phys. Let

Solitary waves in Parity-time (PT) symmetric Bragg-grating structure and the existence of Optical Rogue Waves

In this work, we have studied the traveling wave solution in a nonlinear Bragg grating structure in which the core of the optical fiber is having Parity-time (PT) symmetric refractive index distribution. We have found bright solitary wave solution below the PT-threshold for forward wave and dark solitary wave solution above the PT-threshold for backward wave. The effects of increasing the traveling wave speed on the spatio-temporal evolutions of the analytical solutions have been shown and the emergence of the Optical Rogue Waves (ORWs) has been explored based on the system parameters

Experimental Observation of Acoustic Weyl Points and Topological Surface States

Weyl points emerge as topological monopoles of Berry flux in the three-dimensional (3D) momentum space and have been extensively studied in topological semimetals. As the underlying topological principles apply to any type of waves under periodic boundary conditions, Weyl points can also be realized in classical wave systems, which are easier to engineer compared to condensed matter materials. Here, we made an acoustic Weyl phononic crystal by breaking space inversion (P) symmetry using a combination of slanted acoustic waveguides. We conducted angle-resolved transmission measurements to characterize the acoustic Weyl points. We also experimentally confirmed the existence of acoustic "Fermi arcs" and demonstrated robust one-way acoustic transport, where the surface waves can overcome a step barrier without reflection. This work lays a solid foundation for the basic research in 3D topological acoustic effects.Comment: 17 pages, 5 figure

Deep Learning-Based Hybrid Model with Multi-Head Attention for Multi-Horizon Stock Price Prediction

The prediction of stock prices is challenging due to their volatility, irregular patterns, and complex time-series structure. Reliably forecasting stock market data plays a crucial role in minimizing financial risk and optimizing investment strategies. However, traditional models often struggle to capture temporal dependencies and extract relevant features from noisy inputs, which limits their predictive performance. To improve this, we developed an enhanced recursive feature elimination (RFE) method that blends the importance of impurity-based features from random forest and gradient boosting models with Kendall tau correlation analysis, and we applied SHapley Additive exPlanations (SHAP) analysis to externally validate the reliability of the selected features. This approach leads to more consistent and reliable feature selection for short-term stock prediction over 1-, 3-, and 7-day intervals. The proposed deep learning (DL) architecture integrates a temporal convolutional network (TCN) for long-term pattern recognition, a gated recurrent unit (GRU) for sequence capture, and multi-head attention (MHA) for focusing on critical information, thereby achieving superior predictive performance. We evaluate the proposed approach using daily stock price data from three leading companies—HDFC Bank, Tata Consultancy Services (TCS), and Tesla—and two major stock indices: Nifty 50 and S&P 500. The performance of our model is compared against five benchmark models: temporal convolutional network (TCN), long short-term memory (LSTM), GRU, Bidirectional GRU, and a hybrid TCN–GRU model. Our method consistently shows lower error rates and higher predictive accuracy across all datasets, as measured by four commonly used performance metrics