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Integrating phase-change materials in metasurfaces has emerged as a powerful strategy to realize optical devices with tunable electromagnetic responses. Here, phase-change chiral metasurfaces based on GST-225 material with the designed trapezoid-shaped resonators are demonstrated to achieve tunable circular dichroism (CD) responses in the infrared regime. The asymmetric trapezoid-shaped resonators are designed to support two chiral plasmonic resonances with opposite CD responses for realizing switchable CD between negative and positive values using the GST phase change from amorphous to crystalline. The electromagnetic field distributions of the chiral plasmonic resonant modes are analyzed to understand the chiroptical responses of the metasurface. Furthermore, the variations in the absorption spectrum and CD value for the metasurface as a function of the baking time during the GST phase transition are analyzed to reveal the underlying thermal tuning process of the metasurface. The demonstrated phase-change metasurfaces with tunable CD responses hold significant promise in enabling many applications in the infrared regime such as chiral sensing, encrypted communication, and thermal imaging. 

A novel tandem radical cyclization/intermolecular coupling between 2-azaallyl anions and indole N-aryloxy acetamides is developed, which enables the synthesis of C3a-substituted pyrroloindolines under mild conditions. 

Abstract The multiple-try Metropolis method is an interesting extension of the classical Metropolis–Hastings algorithm. However, theoretical understanding about its usefulness and convergence behavior is still lacking. We here derive the exact convergence rate for the multiple-try Metropolis Independent sampler (MTM-IS) via an explicit eigen analysis. As a by-product, we prove that an naive application of the MTM-IS is less efficient than using the simpler approach of “thinned” independent Metropolis–Hastings method at the same computational cost. We further explore more variants and find it possible to design more efficient algorithms by applying MTM to part of the target distribution or creating correlated multiple trials. 

Optical phase-change materials exhibit tunable permittivity and switching properties during phase transition, which offers the possibility of dynamic control of optical devices. Here, a wavelength-tunable infrared chiral metasurface integrated with phase-change material GST-225 is demonstrated with the designed unit cell of parallelogram-shaped resonator. By varying the baking time at a temperature above the phase transition temperature of GST-225, the resonance wavelength of the chiral metasurface is tuned in the wavelength range of 2.33 µm to 2.58 µm, while the circular dichroism in absorption is maintained around 0.44. The chiroptical response of the designed metasurface is revealed by analyzing the electromagnetic field and displacement current distributions under left- and right-handed circularly polarized (LCP and RCP) light illumination. Moreover, the photothermal effect is simulated to investigate the large temperature difference in the chiral metasurface under LCP and RCP illumination, which allows for the possibility of circular polarization-controlled phase transition. The presented chiral metasurfaces with phase-change materials offer the potential to facilitate promising applications in the infrared regime, such as chiral thermal switching, infrared imaging, and tunable chiral photonics. 

The paper extends the recent star reachability method to verify the robustness of recurrent neural networks (RNNs) for use in safety-critical applications. RNNs are a popular machine learning method for various applications, but they are vulnerable to adversarial attacks, where slightly perturbing the input sequence can lead to an unexpected result. Recent notable techniques for verifying RNNs include unrolling, and invariant inference approaches. The first method has scaling issues since unrolling an RNN creates a large feedforward neural network. The second method, using invariant sets, has better scalability but can produce unknown results due to the accumulation of overapproximation errors over time. This paper introduces a complementary verification method for RNNs that is both sound and complete. A relaxation parameter can be used to convert the method into a fast overapproximation method that still provides soundness guarantees. The method is designed to be used with NNV, a tool for verifying deep neural networks and learning-enabled cyber-physical systems. Compared to state-of-the-art methods, the extended exact reachability method is 10 × faster, and the overapproximation method is 100 × to 5000 × faster.