Gravitational Waves from Phase Transition of Accreting Neutron Stars

We propose that when neutron stars in low-mass X-ray binaries accrete sufficient mass and become millisecond pulsars, the interiors of these stars may undergo phase transitions, which excite stellar radial oscillations. We show that the radial oscillations will be mainly damped by gravitational-wave radiation instead of internal viscosity. The gravitational waves can be detected by the advanced Laser Interferometer Gravitational-Wave Observatory at a rate of about three events per year.Comment: Latex, article style, approximately 10 page

Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks

Deep neural networks (DNNs) have become a widely deployed model for numerous machine learning applications. However, their fixed architecture, substantial training cost, and significant model redundancy make it difficult to efficiently update them to accommodate previously unseen data. To solve these problems, we propose an incremental learning framework based on a grow-and-prune neural network synthesis paradigm. When new data arrive, the neural network first grows new connections based on the gradients to increase the network capacity to accommodate new data. Then, the framework iteratively prunes away connections based on the magnitude of weights to enhance network compactness, and hence recover efficiency. Finally, the model rests at a lightweight DNN that is both ready for inference and suitable for future grow-and-prune updates. The proposed framework improves accuracy, shrinks network size, and significantly reduces the additional training cost for incoming data compared to conventional approaches, such as training from scratch and network fine-tuning. For the LeNet-300-100 and LeNet-5 neural network architectures derived for the MNIST dataset, the framework reduces training cost by up to 64% (63%) and 67% (63%) compared to training from scratch (network fine-tuning), respectively. For the ResNet-18 architecture derived for the ImageNet dataset and DeepSpeech2 for the AN4 dataset, the corresponding training cost reductions against training from scratch (network fine-tunning) are 64% (60%) and 67% (62%), respectively. Our derived models contain fewer network parameters but achieve higher accuracy relative to conventional baselines

Two-Electron Linear Intersubband Light Absorption in a Biased Quantum Well

We point out a novel manifestation of many-body correlations in the linear optical response of electrons confined in a quantum well. Namely, we demonstrate that along with conventional absorption peak at frequency close to intersubband energy, there exists an additional peak at double frequency. This new peak is solely due to electron-electron interactions, and can be understood as excitation of two electrons by a single photon. The actual peak lineshape is comprised of a sharp feature, due to excitation of pairs of intersubband plasmons, on top of a broader band due to absorption by two single-particle excitations. The two-plasmon contribution allows to infer intersubband plasmon dispersion from linear absorption experiments.Comment: 4 pages, 3 figures; published versio