arXiv subjects

Sungwon Kim

Publications and source records attributed to Sungwon Kim.

42 records · Page 3Linked to original sources

arXiv2021

Ultrafast carrier-lattice interactions and interlayer modulations of Bi2Se3 by X-ray free electron laser diffraction

As a 3D topological insulator, bismuth selenide (Bi2Se3) has potential applications for electrically and optically controllable magnetic and optoelectronic devices. How the carriers interact with lattice is important to understand the coupling with its topological phase. It is essential to measure with a time scale smaller than picoseconds for initial interaction. Here we use an X-ray free-electron laser to perform time-resolved diffraction to study ultrafast carrier-induced lattice contractions and interlayer modulations in Bi2Se3 thin films. The lattice contraction depends on the carrier concentration and is followed by an interlayer expansion accompanied by oscillations. Using density functional theory (DFT) and the Lifshitz model, the initial contraction can be explained by van der Waals force modulation of the confined free carrier layers. Band inversion, related to a topological phase transition, is modulated by the expansion of the interlayer distance. These results provide insight into instantaneous topological phases on ultrafast timescales.

cond-mat.mtrl-sci↗

arXiv2020

Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search

Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have been proposed to generate mel-spectrograms from text in parallel. Despite the advantage, the parallel TTS models cannot be trained without guidance from autoregressive TTS models as their external aligners. In this work, we propose Glow-TTS, a flow-based generative model for parallel TTS that does not require any external aligner. By combining the properties of flows and dynamic programming, the proposed model searches for the most probable monotonic alignment between text and the latent representation of speech on its own. We demonstrate that enforcing hard monotonic alignments enables robust TTS, which generalizes to long utterances, and employing generative flows enables fast, diverse, and controllable speech synthesis. Glow-TTS obtains an order-of-magnitude speed-up over the autoregressive model, Tacotron 2, at synthesis with comparable speech quality. We further show that our model can be easily extended to a multi-speaker setting.

eess.AS↗

arXiv2020

NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity

Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis. However, a flow-based network is considered to be inefficient in parameter complexity because of reduced expressiveness of bijective mapping, which renders the models unfeasibly expensive in terms of parameters. We present an alternative parameterization scheme called NanoFlow, which uses a single neural density estimator to model multiple transformation stages. Hence, we propose an efficient parameter decomposition method and the concept of flow indication embedding, which are key missing components that enable density estimation from a single neural network. Experiments performed on audio and image models confirm that our method provides a new parameter-efficient solution for scalable NFs with significant sublinear parameter complexity.

cs.LG↗

arXiv2020

CMOS-based cryogenic control of silicon quantum circuits

The most promising quantum algorithms require quantum processors hosting millions of quantum bits when targeting practical applications. A major challenge towards large-scale quantum computation is the interconnect complexity. In current solid-state qubit implementations, a major bottleneck appears between the quantum chip in a dilution refrigerator and the room temperature electronics. Advanced lithography supports the fabrication of both CMOS control electronics and qubits in silicon. When the electronics are designed to operate at cryogenic temperatures, it can ultimately be integrated with the qubits on the same die or package, overcoming the wiring bottleneck. Here we report a cryogenic CMOS control chip operating at 3K, which outputs tailored microwave bursts to drive silicon quantum bits cooled to 20mK. We first benchmark the control chip and find electrical performance consistent with 99.99% fidelity qubit operations, assuming ideal qubits. Next, we use it to coherently control actual silicon spin qubits and find that the cryogenic control chip achieves the same fidelity as commercial instruments. Furthermore, we highlight the extensive capabilities of the control chip by programming a number of benchmarking protocols as well as the Deutsch-Josza algorithm on a two-qubit quantum processor. These results open up the path towards a fully integrated, scalable silicon-based quantum computer.

quant-ph↗

arXiv2020

Generative Adversarial Networks for Crystal Structure Prediction

The constant demand for new functional materials calls for efficient strategies to accelerate the materials design and discovery. In addressing this challenge, machine learning generative models can offer promising opportunities since they allow for the continuous navigation of chemical space via low dimensional latent spaces. In this work, we employ a crystal representation that is inversion-free with a low memory requirement based on unit cell information and fractional atomic coordinates, and build the generative adversarial network (GAN) for crystal structures. The proposed model is then applied to the Mg-Mn-O ternary inorganic materials system to generate novel structures with application as potential water-splitting photoanodes, and combined with the evaluation of their photoanode properties for high-throughput virtual screening (HTVS). The generative-HTVS system that we built predicts 23 new crystal structures with a reasonable predicted stability and bandgap. These findings suggest that the proposed generative model can be an effective way to explore hidden portions of the chemical space, an area that is usually unreachable when conventional substitution-based discovery is employed.

cond-mat.mtrl-sci↗

arXiv2019

FloWaveNet : A Generative Flow for Raw Audio

Most modern text-to-speech architectures use a WaveNet vocoder for synthesizing high-fidelity waveform audio, but there have been limitations, such as high inference time, in its practical application due to its ancestral sampling scheme. The recently suggested Parallel WaveNet and ClariNet have achieved real-time audio synthesis capability by incorporating inverse autoregressive flow for parallel sampling. However, these approaches require a two-stage training pipeline with a well-trained teacher network and can only produce natural sound by using probability distillation along with auxiliary loss terms. We propose FloWaveNet, a flow-based generative model for raw audio synthesis. FloWaveNet requires only a single-stage training procedure and a single maximum likelihood loss, without any additional auxiliary terms, and it is inherently parallel due to the characteristics of generative flow. The model can efficiently sample raw audio in real-time, with clarity comparable to previous two-stage parallel models. The code and samples for all models, including our FloWaveNet, are publicly available.

cs.SD↗