35,974 research outputs found
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
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