arXiv subjects

Yi-Wen Liu

Publications and source records attributed to Yi-Wen Liu.

At least 19 recordsLinked to original sources

arXiv2026

Entanglement Detection for Two-Qubit and Three-Qubit Pure States via Unitary Transformations and Ancilla State Measurements

Quantum entanglement is the fundamental hallmark of quantum mechanics and a core resource for realizing long-distance quantum communication and scalable linear quantum computing. Accordingly, the precise detection and quantitative quantification of entanglement constitute a foundational and critical problem in quantum information theory. To date, researchers have proposed numerous sufficient conditions for entanglement detection as well as a variety of entanglement measures to characterize the entanglement strength of quantum states; nevertheless, efficient and direct measurement schemes for core entanglement parameters remain underdeveloped. Based on unitary transformations and auxiliary measurements, this paper proposes a set of quantum circuit schemes capable of directly measuring the bipartite concurrence and the tripartite 3-tangle entanglement measure. By introducing auxiliary qubits and constructing specific controlled unitary operations, the proposed scheme maps the analytical expressions of the two entanglement measures onto the measurement probabilities of output states from quantum circuits. It enables efficient and direct quantitative measurement of bipartite and tripartite entanglement without performing full quantum state tomography. This work provides a feasible technical route for the experimental characterization of entanglement properties and lays a groundwork for the practical deployment of multipartite entanglement resources in quantum information processing.

quant-ph↗

arXiv2024

Agent-Driven Large Language Models for Mandarin Lyric Generation

Generative Large Language Models have shown impressive in-context learning abilities, performing well across various tasks with just a prompt. Previous melody-to-lyric research has been limited by scarce high-quality aligned data and unclear standard for creativeness. Most efforts focused on general themes or emotions, which are less valuable given current language model capabilities. In tonal contour languages like Mandarin, pitch contours are influenced by both melody and tone, leading to variations in lyric-melody fit. Our study, validated by the Mpop600 dataset, confirms that lyricists and melody writers consider this fit during their composition process. In this research, we developed a multi-agent system that decomposes the melody-to-lyric task into sub-tasks, with each agent controlling rhyme, syllable count, lyric-melody alignment, and consistency. Listening tests were conducted via a diffusion-based singing voice synthesizer to evaluate the quality of lyrics generated by different agent groups.

cs.CL↗

arXiv2024

Visualizing orbital angular momentum induced single wavefront dislocation in graphene

Phase singularities are phase-indeterminate points where wave amplitudes are zero, which manifest as phase vertices or wavefront dislocations. In the realm of optical and electron beams, the phase singularity has been extensively explored, demonstrating a profound connection to orbital angular momentum. Direct local imaging of the impact of orbital angular momentum on phase singularities at the nanoscale, however, remains a challenge and has yet to be achieved. Here, we study the role of orbital angular momentum in phase singularities in graphene, particularly at the atomic level, through scanning tunneling microscopy and spectroscopy. Our experiments demonstrate that the scatterings between different orbital angular momentum states, which are induced by local rotational symmetry-breaking potentials, can generate additional phase singularity, and result in robust single wavefront dislocation in real space. Our results pave the way for exploring the effects of orbital degree of freedom on quantum phases in quasiparticle interference processes.

cond-mat.mes-hall↗

arXiv2023

Effects of Convolutional Autoencoder Bottleneck Width on StarGAN-based Singing Technique Conversion

Singing technique conversion (STC) refers to the task of converting from one voice technique to another while leaving the original singer identity, melody, and linguistic components intact. Previous STC studies, as well as singing voice conversion research in general, have utilized convolutional autoencoders (CAEs) for conversion, but how the bottleneck width of the CAE affects the synthesis quality has not been thoroughly evaluated. To this end, we constructed a GAN-based multi-domain STC system which took advantage of the WORLD vocoder representation and the CAE architecture. We varied the bottleneck width of the CAE, and evaluated the conversion results subjectively. The model was trained on a Mandarin dataset which features four singers and four singing techniques: the chest voice, the falsetto, the raspy voice, and the whistle voice. The results show that a wider bottleneck corresponds to better articulation clarity but does not necessarily lead to higher likeness to the target technique. Among the four techniques, we also found that the whistle voice is the easiest target for conversion, while the other three techniques as a source produce more convincing conversion results than the whistle.

eess.AS↗

arXiv2023

Breakdown of self-cleaning mechanism for nanoscale interfacial substances in tiny-angle twisted bilayer graphene

Realization of high-quality van der Waals (vdW) heterostructures with tailored properties by stacking two-dimensional (2D) layers requires atomically clean interfaces. Because of strong adhesion between the constituent layers, the vdW forces could drive trapped contaminants together into submicron-size bubbles, which leaves large interfacial areas atomically clean. Such a phenomenon is dubbed self-cleaning mechanism in 2D systems. Here, we demonstrate the breakdown of self-cleaning mechanism for nanoscale interfacial bubbles in tiny-angle twisted bilayer graphene (TBG). In the tiny-angle TBG, there is a triangular network of domain boundaries due to structural reconstruction. Our experiments indicate that the bubbles will mainly move along the triangular network of domain boundaries when the sizes of the bubbles are comparable to that of an AA-stacking region in the TBG. When the size of the bubble is smaller than that of an AA-stacking region, the bubble becomes motionless and is fixed in the AA-stacking region because of its large out-of-plane corrugation. Our results reveal a substantial influence of the moiré superlattice on the motion of nanoscale interfacial substances.

cond-mat.mes-hall↗

arXiv2022

Preservation of the harmonic overtones in the violin family beyond Stradivari

Three hundred years ago, Antonio Stradivari enjoyed the golden period of the violin making in the human history. Luthiers and researchers endeavor to study his legendary legacy. Unfortunately, a consensus that the present progress has reached his level remains lacking. Most of the instruments suffer from low sounding power and enigmatic deficit of harmonic overtones. In fact, there is significant energy dissipation in the acoustic couplings in the violin family, completely unaware to luthiers and researchers. The current acoustic mechanism is inefficient in resonance of the wooden plates and hence is the low rate of mechanical energy conversion to the desired sound. We propose a new method for the amelioration of the acoustic couplings to enhance the preservation of the overtone components up to 161% of the pristine and to reduce the instrumental dissipation intensity up to 3.8 dB off (73% off). It results in the tremendously improved sound projection, the shorter reaction time, the longer reverberation time, and consequently the enrichment of the timbre complexity. Our physical method is applicable to the violin family of all sizes and ages, including the Stradivari, and extendable to all string instruments.

physics.class-ph↗

arXiv2022

Coexistence of Reconstructed and Unreconstructed Structures in Structural Transition Regime of Twisted Bilayer Graphene

In twisted bilayer graphene (TBG), a twist-angle-dependent competition between interlayer stacking energy and intralayer elastic energy results in flat rigid layers at large twist angles and lattice reconstruction at small twist angles. Despite enormous scientific interest and effort in the TBG, however, an experimental study of evolution from the rigid lattice to the reconstructed lattice as a function of twist angle is still missing. Here we present a scanning tunneling microscopy and spectroscopy study to reveal the twist-angle-dependent lattice reconstruction in the TBG. Our experiment demonstrates that there is a transition regime between the rigid regime and the relaxed regime and, unexpectedly, the reconstructed and unreconstructed structures coexist in the transition regime. The coexistence of the two distinct structures in this regime may arise from subtle balance between the interlayer stacking energy and intralayer elastic energy in the TBG with intermediate moiré sizes.

cond-mat.mes-hall↗

arXiv2022

Mandarin Singing Voice Synthesis with Denoising Diffusion Probabilistic Wasserstein GAN

Singing voice synthesis (SVS) is the computer production of a human-like singing voice from given musical scores. To accomplish end-to-end SVS effectively and efficiently, this work adopts the acoustic model-neural vocoder architecture established for high-quality speech and singing voice synthesis. Specifically, this work aims to pursue a higher level of expressiveness in synthesized voices by combining the diffusion denoising probabilistic model (DDPM) and \emph{Wasserstein} generative adversarial network (WGAN) to construct the backbone of the acoustic model. On top of the proposed acoustic model, a HiFi-GAN neural vocoder is adopted with integrated fine-tuning to ensure optimal synthesis quality for the resulting end-to-end SVS system. This end-to-end system was evaluated with the multi-singer Mpop600 Mandarin singing voice dataset. In the experiments, the proposed system exhibits improvements over previous landmark counterparts in terms of musical expressiveness and high-frequency acoustic details. Moreover, the adversarial acoustic model converged stably without the need to enforce reconstruction objectives, indicating the convergence stability of the proposed DDPM and WGAN combined architecture over alternative GAN-based SVS systems.

eess.AS↗

arXiv2022

DDSP-based Singing Vocoders: A New Subtractive-based Synthesizer and A Comprehensive Evaluation

A vocoder is a conditional audio generation model that converts acoustic features such as mel-spectrograms into waveforms. Taking inspiration from Differentiable Digital Signal Processing (DDSP), we propose a new vocoder named SawSing for singing voices. SawSing synthesizes the harmonic part of singing voices by filtering a sawtooth source signal with a linear time-variant finite impulse response filter whose coefficients are estimated from the input mel-spectrogram by a neural network. As this approach enforces phase continuity, SawSing can generate singing voices without the phase-discontinuity glitch of many existing vocoders. Moreover, the source-filter assumption provides an inductive bias that allows SawSing to be trained on a small amount of data. Our experiments show that SawSing converges much faster and outperforms state-of-the-art generative adversarial network and diffusion-based vocoders in a resource-limited scenario with only 3 training recordings and a 3-hour training time.

cs.SD↗

arXiv2022

Domestic sound event detection by shift consistency mean-teacher training and adversarial domain adaptation

Semi-supervised learning and domain adaptation techniques have drawn increasing attention in the field of domestic sound event detection thanks to the availability of large amounts of unlabeled data and the relative ease to generate synthetic strongly-labeled data. In a previous work, several semi-supervised learning strategies were designed to boost the performance of a mean-teacher model. Namely, these strategies include shift consistency training (SCT), interpolation consistency training (ICT), and pseudo-labeling. However, adversarial domain adaptation (ADA) did not seem to improve the event detection accuracy further when we attempt to compensate for the domain gap between synthetic and real data. In this research, we empirically found that ICT tends to pull apart the distributions of synthetic and real data in t-SNE plots. Therefore, ICT is abandoned while SCT, in contrast, is applied to train both the student and the teacher models. With these modifications, the system successfully integrates with an ADA network, and we achieve 47.2% in the F1 score on the DCASE 2020 task 4 dataset, which is 2.1% higher than what was reported in the previous work.

cs.SD↗

arXiv2021

Direct Imaging Strain-field Vortex Networks in Twisted Bilayer Graphene Magnified by Moiré Superlattices

In two-dimensional (2D) twisted bilayers, the van der Waals (vdW) interlayer interaction introduces atomic-scale reconstruction at interface by locally rotating lattice to form strain-field vortex networks in their moiré superlattice. However, direct imaging the tiny local lattice rotation of the strain-field vortex requires extremely high spatial resolution and is an outstanding challenge in experiment. Here, a topmost small-period graphene moiré pattern is introduced to magnify sub-Angstrom distortions of the lattice and tiny local lattice rotation in underlying twisted bilayer graphene (TBG). The local periods and low-energy van Hove singularities of the topmost graphene moiré patterns are spatially modified by the atomic-scale reconstruction of the underlying TBG, thus enabling real-space imaging of the strain-field vortex networks. Our results indicate that structure-reconstructed vdW systems can provide a unique substrate to spatially engineer supported two-dimensional materials both in structures and electronic properties.

cond-mat.mes-hall↗

arXiv2021

Realizing One-dimensional Metallic States in Graphene via Periodically Coupled Zeroth Pseudo-Landau Levels

Strain-induced pseudo-magnetic fields can mimic real magnetic fields to generate a zero-magnetic-field analogue of the Landau levels (LLs), i.e., the pseudo-LLs, in graphene. The distinct nature of the pseudo-LLs enables one to realize novel electronic states beyond that can be feasible with real LLs. Here, we report the realization of one-dimensional (1D) metallic states, which can be described well by the Su-Schrieffer-Heeger model, in graphene via periodically coupled zeroth pseudo-LLs. In our experiment, nanoscale strained structures embedded with pseudo-LLs are generated periodically along 1D channel of suspended graphene monolayer. Our experiments demonstrate that the zeroth pseudo-LLs of these strained structures are coupled to form metallic states, exhibiting a serpentine pattern that snakes back and forth along the 1D suspended graphene monolayer. These results are verified theoretically by large-scale tight-binding calculations of the strained samples. Our result provides a new pathway to realize novel quantum states and engineer the electronic properties of graphene by using the localized pseudo-LLs as building blocks.

cond-mat.mes-hall↗

arXiv2021

Electronic confinement in quantum dots of twisted bilayer graphene

Electronic properties of quantum dots (QDs) depend sensitively on their parent materials. Therefore, confined electronic states in graphene QDs (GQDs) of monolayer and Bernal-stacked bilayer graphene are quite different. Twisted bilayer graphene (TBG) is distinct from monolayer and Bernal-stacked bilayer graphene because of the new degree of freedom: twist angle. In the past few years, numerous efforts have been made to realize the GQDs of monolayer and Bernal-stacked bilayer graphene and achieved great success. Thus far, however, strategies for realizing GQDs of TBG have been elusive. Here, we demonstrate a general approach for fabricating stationary GQDs of TBG by introducing nanoscale p-n junctions with sharp boundaries in the TBG. We verify the confinement of low-energy massless Dirac fermions via whispering-gallery modes in the GQDs of TBG. Unexpectedly, electronic states around van Hove singularities of the TBG are also strongly modified around the GQDs. Such a feature has never been reported and is attributed to spatial variation of the interlayer coupling in the TBG induced by the GQDs.

cond-mat.mes-hall↗

arXiv2021

A Survey on Recent Deep Learning-driven Singing Voice Synthesis Systems

Singing voice synthesis (SVS) is a task that aims to generate audio signals according to musical scores and lyrics. With its multifaceted nature concerning music and language, producing singing voices indistinguishable from that of human singers has always remained an unfulfilled pursuit. Nonetheless, the advancements of deep learning techniques have brought about a substantial leap in the quality and naturalness of synthesized singing voice. This paper aims to review some of the state-of-the-art deep learning-driven SVS systems. We intend to summarize their deployed model architectures and identify the strengths and limitations for each of the introduced systems. Thereby, we picture the recent advancement trajectory of this field and conclude the challenges left to be resolved both in commercial applications and academic research.

eess.AS↗

arXiv2021

Tailoring energy landscape of graphene nanostructures on graphene and realizing atomically precise graphene origami using tilt grain boundaries

In two-dimensional van der Waals (vdWs) materials, the relative twist angle between adjacent layers not only controls their electronic properties, but also determines their stacking energy. This effect makes it much easier to realize energetically favorable configurations of the vdWs materials, for example, Bernal-stacked structure of bilayer graphene. Here we demonstrate that we can controllably tailor adhesive energy landscape of graphene nanostructures on graphene and stabilize the system with tunable twist angle by using a one-dimensional tilt grain boundary (GB). The area ratio with different stacking orders separated by tilt GB is continuously tuned, which provides a new degree of freedom to tailor the energy landscape of the system. Due to the different stacking orders separated by the tilt GB, we can repeatedly fold and unfold the graphene nanostructure exactly along the one-dimensional boundary, demonstrating the ability to realize atomically precise graphene origami.

cond-mat.mes-hall↗

arXiv2021

Direct observation of magneto-electric Aharonov-Bohm effect in moiré-scale quantum paths of minimally twisted bilayer graphene

Aharonov-Bohm (AB) effect, the well-known archetype of electron-wave interference phenomena, has been explored extensively through transport measurements. However, these techniques lack spatial resolution that would be indispensable for studying the magnetic and electrostatic AB oscillations at the nanometer scale. Here, we demonstrated that scanning tunneling microscopy (STM) can be used as an AB interferometer operating on nanometer length scales and the magneto-electric Aharonov-Bohm effect in minimally twisted bilayer graphene (TBG) was directly measured by using STM. In the minimally TBG, there is a triangular network of chiral one-dimensional states hosted by domain boundaries due to structural reconstruction. Taking advantage of the high spatial resolution of the STM, both the magnetic and electrostatic AB oscillations arising from electron interference along moiré-scale triangular quantum paths in the minimally TBG were measured. Our work enables measure and control of the AB effect and other electron-wave interference at the nanoscale.

cond-mat.mes-hall↗

arXiv2020

Denoising Click-evoked Otoacoustic Emission Signals by Optimal Shrinkage

Click-evoked otoacoustic emissions (CEOAEs) are clinically used as an objective way to infer whether cochlear functions are normal. However, because the sound pressure level of CEOAEs is typically much lower than the background noise, it usually takes hundreds, if not thousands of repetitions to estimate the signal with sufficient accuracy. In this paper, we propose to improve the signal-to-noise ratio (SNR) of CEOAE signals within limited measurement time by optimal shrinkage (OS) in two different settings: the covariance-based OS (cOS) and the singular value decomposition (SVD)-based OS (sOS). By simulation and analyzing human CEOAE data, the cOS consistently reduced the noise and enhanced the SNR by 1 to 2 dB from a baseline method (BM) that is based on calculating the median. The sOS achieved an SNR enhancement of 2 to 3 dB in simulation, and demonstrated capability to enhance the SNR in real recordings when the SNR achieved by the BM was below 0 dB. An appealing property of OS is that it produces an estimate of every individual column of the signal matrix. This property makes it possible to investigate CEOAE dynamics across a longer period of time when the cochlear conditions are not strictly stationary.

eess.SP↗

arXiv2020

Twistronics in graphene-based van der Waals structures

The electronic properties of van der Waals (vdW) structures can be substantially modified by the moire superlattice potential, which strongly depends on the twist angle among the compounds. In twisted bilayer graphene (TBG), two low-energy Van Hove singularities (VHSs) move closer with decreasing twist angles and finally become highly non-dispersive flat bands at the magic angle (~ 1.1 degree). When the Fermi level lies within the flat bands of the TBG near the magic angle, Coulomb interaction is supposed to exceed the kinetic energy of the electrons, which can drive the system into various strongly correlated phases. Moreover, the strongly correlated states of flat bands are also realized in other graphene-based vdW structures with an interlayer twist. In this article, we mainly review the recent experimental advances on the strongly correlated physics of the magic-angle TBG (MATBG) and the small-angle twisted multilayer graphene. Lastly we will give out a perspective of this field.

cond-mat.mtrl-sci↗