arXiv subjects

Tiantian Wang

Publications and source records attributed to Tiantian Wang.

35 records · Page 2Linked to original sources

arXiv2024

SQUID oscillations in PbTe nanowire networks

Network structures by semiconductor nanowires hold great promise for advanced quantum devices, especially for applications in topological quantum computing. In this study, we created networks of PbTe nanowires arranged in loop configurations. Using shadow-wall epitaxy, we defined superconducting quantum interference devices (SQUIDs) using the superconductor Pb. These SQUIDs exhibit oscillations in supercurrent upon the scanning of a magnetic field. Most of the oscillations can be fitted assuming a sinusoidal current-phase relation for each Josephson junction. Under certain conditions, the oscillations are found to be skewed, suggesting possible deviation from a sinusoidal behavior. Our results highlight the potential of PbTe nanowires for building complex quantum devices in the form of networks.

cond-mat.mes-hall↗

arXiv2024

Gate-tunable subband degeneracy in semiconductor nanowires

Degeneracy and symmetry have a profound relation in quantum systems. Here, we report gate-tunable subband degeneracy in PbTe nanowires with a nearly symmetric cross-sectional shape. The degeneracy is revealed in electron transport by the absence of a quantized plateau. Utilizing a dual gate design, we can apply an electric field to lift the degeneracy, reflected as emergence of the plateau. This degeneracy and its tunable lifting were challenging to observe in previous nanowire experiments, possibly due to disorder. Numerical simulations can qualitatively capture our observation, shedding light on device parameters for future applications.

cond-mat.mes-hall↗

arXiv2024

Selective-Area-Grown PbTe-Pb Planar Josephson Junctions for Quantum Devices

Planar Josephson junctions are predicted to host Majorana zero modes. The material platforms in previous studies are two dimensional electron gases (InAs, InSb, InAsSb and HgTe) coupled to a superconductor such as Al or Nb. Here, we introduce a new material platform for planar JJs, the PbTe-Pb hybrid. The semiconductor, PbTe, was grown as a thin film via selective area epitaxy. The Josephson junction was defined by a shadow wall during the deposition of the superconductor Pb. Scanning transmission electron microscopy reveals a sharp semiconductor-superconductor interface. Gate-tunable supercurrent and multiple Andreev reflections are observed. A perpendicular magnetic field causes interference patterns of the switching current, exhibiting Fraunhofer-like and SQUID-like behaviors. We further demonstrate a prototype device for Majorana detection, wherein phase bias and tunneling spectroscopy are applicable.

cond-mat.supr-con↗

arXiv2023

Discovery of a Single-Band Mott Insulator in a van der Waals Flat-Band Compound

The Mott insulator provides an excellent foundation for exploring a wide range of strongly correlated physical phenomena, such as high-temperature superconductivity, quantum spin liquid, and colossal magnetoresistance. A Mott insulator with the simplest degree of freedom is an ideal and highly desirable system for studying the fundamental physics of Mottness. In this study, we have unambiguously identified such an anticipated Mott insulator in a van der Waals layered compound Nb3Cl8. In the high-temperature phase, where interlayer coupling is negligible, density functional theory calculations for the monolayer of Nb3Cl8 suggest a half-filled flat band at the Fermi level, whereas angle-resolved photoemission spectroscopy experiments observe a large gap. This observation is perfectly reproduced by dynamical mean-field theory calculations considering strong electron correlations, indicating a correlation-driven Mott insulator state. Since this half-filled band derived from a single 2a1 orbital is isolated from all other bands, the monolayer of Nb3Cl8 is an ideal realization of the celebrated single-band Hubbard model. Upon decreasing the temperature, the bulk system undergoes a phase transition, where structural changes significantly enhance the interlayer coupling. This results in a bonding-antibonding splitting in the Hubbard bands, while the Mott gap remains dominant. Our discovery provides a simple and seminal model system for investigating Mott physics and other emerging correlated states.

cond-mat.str-el↗

arXiv2023

Towards 4D Human Video Stylization

We present a first step towards 4D (3D and time) human video stylization, which addresses style transfer, novel view synthesis and human animation within a unified framework. While numerous video stylization methods have been developed, they are often restricted to rendering images in specific viewpoints of the input video, lacking the capability to generalize to novel views and novel poses in dynamic scenes. To overcome these limitations, we leverage Neural Radiance Fields (NeRFs) to represent videos, conducting stylization in the rendered feature space. Our innovative approach involves the simultaneous representation of both the human subject and the surrounding scene using two NeRFs. This dual representation facilitates the animation of human subjects across various poses and novel viewpoints. Specifically, we introduce a novel geometry-guided tri-plane representation, significantly enhancing feature representation robustness compared to direct tri-plane optimization. Following the video reconstruction, stylization is performed within the NeRFs' rendered feature space. Extensive experiments demonstrate that the proposed method strikes a superior balance between stylized textures and temporal coherence, surpassing existing approaches. Furthermore, our framework uniquely extends its capabilities to accommodate novel poses and viewpoints, making it a versatile tool for creative human video stylization.

cs.CV↗

arXiv2023

Neural Rendering of Humans in Novel View and Pose from Monocular Video

We introduce a new method that generates photo-realistic humans under novel views and poses given a monocular video as input. Despite the significant progress recently on this topic, with several methods exploring shared canonical neural radiance fields in dynamic scene scenarios, learning a user-controlled model for unseen poses remains a challenging task. To tackle this problem, we introduce an effective method to a) integrate observations across several frames and b) encode the appearance at each individual frame. We accomplish this by utilizing both the human pose that models the body shape as well as point clouds that partially cover the human as input. Our approach simultaneously learns a shared set of latent codes anchored to the human pose among several frames, and an appearance-dependent code anchored to incomplete point clouds generated by each frame and its predicted depth. The former human pose-based code models the shape of the performer whereas the latter point cloud-based code predicts fine-level details and reasons about missing structures at the unseen poses. To further recover non-visible regions in query frames, we employ a temporal transformer to integrate features of points in query frames and tracked body points from automatically-selected key frames. Experiments on various sequences of dynamic humans from different datasets including ZJU-MoCap show that our method significantly outperforms existing approaches under unseen poses and novel views given monocular videos as input.

cs.CV↗

arXiv2023

AMC-Net: An Effective Network for Automatic Modulation Classification

Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the subsequent decoding of the transmitted data. End-to-end deep learning methods have been recently applied to AMC, outperforming traditional feature engineering techniques. However, AMC still has limitations in low signal-to-noise ratio (SNR) environments. To address the drawback, we propose a novel AMC-Net that improves recognition by denoising the input signal in the frequency domain while performing multi-scale and effective feature extraction. Experiments on two representative datasets demonstrate that our model performs better in efficiency and effectiveness than the most current methods.

eess.SP↗

arXiv2022

Pixel2ISDF: Implicit Signed Distance Fields based Human Body Model from Multi-view and Multi-pose Images

In this report, we focus on reconstructing clothed humans in the canonical space given multiple views and poses of a human as the input. To achieve this, we utilize the geometric prior of the SMPLX model in the canonical space to learn the implicit representation for geometry reconstruction. Based on the observation that the topology between the posed mesh and the mesh in the canonical space are consistent, we propose to learn latent codes on the posed mesh by leveraging multiple input images and then assign the latent codes to the mesh in the canonical space. Specifically, we first leverage normal and geometry networks to extract the feature vector for each vertex on the SMPLX mesh. Normal maps are adopted for better generalization to unseen images compared to 2D images. Then, features for each vertex on the posed mesh from multiple images are integrated by MLPs. The integrated features acting as the latent code are anchored to the SMPLX mesh in the canonical space. Finally, latent code for each 3D point is extracted and utilized to calculate the SDF. Our work for reconstructing the human shape on canonical pose achieves 3rd performance on WCPA MVP-Human Body Challenge.

cs.CV↗

arXiv2022

Microwave Sensing of Andreev Bound States in a Gate-Defined Superconducting Quantum Point Contact

We use a superconducting microresonator as a cavity to sense absorption of microwaves by a superconducting quantum point contact defined by surface gates over a proximitized two-dimensional electron gas. Renormalization of the cavity frequency with phase difference across the point contact is consistent with adiabatic coupling to Andreev bound states. Near $π$ phase difference, we observe random fluctuations in absorption with gate voltage, related to quantum interference-induced modulations in the electron transmission. We identify features consistent with the presence of single Andreev bound states and describe the Andreev-cavity interaction using a dispersive Jaynes-Cummings model. By fitting the weak Andreev-cavity coupling, we extract ~GHz decoherence consistent with charge noise and the transmission dispersion associated with a localized state.

cond-mat.mes-hall↗

arXiv2022

Clean quantum point contacts in an InAs quantum well grown on a lattice-mismatched InP substrate

Strong spin-orbit coupling, the resulting large $g$ factor, and small effective mass make InAs an attractive material platform for inducing topological superconductivity. The surface Fermi level pinning in the conduction band enables highly transparent ohmic contact without excessive doping. We investigate electrostatically defined quantum point contacts (QPCs) in a deep-well InAs two-dimensional electron gas. Despite the 3.3% lattice mismatch between the InAs quantum well and the InP substrate, we report clean QPCs with up to eight pronounced quantized conductance plateaus at zero magnetic field. Source-drain dc bias spectroscopy reveals a harmonic confinement potential with a nearly $5$ meV subband spacing. We find a many-body exchange interaction enhancement for the out-of-plane $g$ factor $|g_{\perp}^*| = 27 \pm 1$, whereas the in-plane $g$ factor is isotropic $|g^*_{x}| = |g^*_{y}| = 12 \pm 2$, close to the bulk value for InAs.

cond-mat.mes-hall↗

arXiv2020

Anodic Oxidation of Epitaxial Superconductor-Semiconductor Hybrids

We demonstrate a new fabrication process for hybrid semiconductor-superconductor heterostructures based on anodic oxidation (AO), allowing controlled thinning of epitaxial Al films. Structural and transport studies of oxidized epitaxial Al films grown on insulating GaAs substrates reveal spatial non-uniformity and enhanced critical temperature and magnetic fields. Oxidation of epitaxial Al on hybrid InAs heterostructures with a conducting quantum well show similarly enhanced superconducting properties transferred to the two-dimensional electron gas (2DEG) by proximity effect, with critical perpendicular magnetic fields up to 3.5 T. An insulating AlOx film, that passivates the heterostructure from exposure to air, is obtained by complete oxidation of the Al. It simultaneously removes the need to strip Al which damages the underlying semiconductor. AO passivation yielded 2DEG mobilities two times higher than similar devices with Al removed by wet etching. An AO-passivated Hall bar showed quantum Hall features emerging at a transverse field of 2.5 T, below the critical transverse field of thinned films, eventually allowing transparent coupling of quantum Hall effect and superconductivity. AO thinning and passivation are compatible with standard lithographic techniques, giving lateral resolution below <50 nm. We demonstrate local patterning of AO by realizing a semiconductor-based Josephson junction operating up to 0.3 T perpendicular.

cond-mat.mtrl-sci↗

arXiv2020

Beetle Swarm Optimization Algorithm:Theory and Application

In this paper, a new meta-heuristic algorithm, called beetle swarm optimization algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization algorithm, genetic algorithm (GA) and grasshopper optimization algorithm . Numerical experiments show that the beetle swarm optimization algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblaus optimization problem, are also considered and the proposed beetle swarm optimization algorithm is shown to be competitive in those applications.

cs.NE↗

arXiv2019

Referring Expression Object Segmentation with Caption-Aware Consistency

Referring expressions are natural language descriptions that identify a particular object within a scene and are widely used in our daily conversations. In this work, we focus on segmenting the object in an image specified by a referring expression. To this end, we propose an end-to-end trainable comprehension network that consists of the language and visual encoders to extract feature representations from both domains. We introduce the spatial-aware dynamic filters to transfer knowledge from text to image, and effectively capture the spatial information of the specified object. To better communicate between the language and visual modules, we employ a caption generation network that takes features shared across both domains as input, and improves both representations via a consistency that enforces the generated sentence to be similar to the given referring expression. We evaluate the proposed framework on two referring expression datasets and show that our method performs favorably against the state-of-the-art algorithms.

cs.CV↗

arXiv2019

Toward durable Al-InSb hybrid heterostructures via epitaxy of 2ML interfacial InAs screening layers

The large Landé g-factor, high spin-orbit coupling, and low effective mass of the two-dimensional electron gas in InSb quantum wells combined with proximal superconductivity may realize a scalable platform for topological quantum computation. Aluminum thin films directly deposited on top of InSb planar structures result in the formation of a reactive AlInSb layer at the interface. This interlayer progressively consumes the whole Al film, resulting in a disordered AlInSb layer after few months at room temperature. We report on a heterostructure design that results in a significant increase of the durability of these hybrid Al-InSb heterostructures with the preservation of a pure Al film and sharp superconductor-semiconductor interface for more than one year. Two monolayers of epitaxial InAs at the superconductor-semiconductor interface prevent interfacial reactivity as evidenced by X-ray reflectivity and energy dispersive spectroscopy measurements. Structural characterizations of the Al films by transmission electron microscopy reveal the presence of tens of nanometers wide grains predominantly oriented with Al(110) parallel to InSb(001).

cond-mat.mtrl-sci↗

arXiv2019

Ballistic superconductivity and tunable $π$-junctions in InSb quantum wells

Two-dimensional electron gases (2DEGs) coupled to superconductors offer the opportunity to explore a variety of quantum phenomena. These include the study of novel Josephson effects, superconducting correlations in quantum (spin) Hall systems, hybrid superconducting qubits and emergent topological states in semiconductors with spin-orbit interaction (SOI). InSb is a well-known example of such a strong SOI semiconductor, however hybrid superconducting devices in InSb quantum wells remain unexplored. Here, we interface InSb 2DEGs with a superconductor (NbTiN) to create Josephson junctions (JJs), thus providing the first evidence of induced superconductivity in high quality InSb quantum wells. The JJs support supercurrent transport over several microns and display clear signatures of ballistic superconductivity. Furthermore, we exploit the large Landé g-factor and gate tunability of the junctions to control the current-phase relation, and drive transitions between the $0$ and $π$-states. This control over the free energy landscape allows us to construct a phase diagram identifying these $0$ and $π$-regions, in agreement with theory. Our results establish InSb quantum wells as a promising new material platform to study the interplay between superconductivity, SOI and magnetism.

cond-mat.mes-hall↗

arXiv2018

PingAn: An Insurance Scheme for Job Acceleration in Geo-distributed Big Data Analytics System

Geo-distributed data analysis in a cloud-edge system is emerging as a daily demand. Out of saving time in wide area data transfer, some tasks are dispersed to the edges. However, due to limited computing, overload interference and cluster-level unreachable troubles, efficient execution in the edges is hard, which obstructs the guarantee on the efficiency and reliability of jobs. Launching copies across clusters can be an insurance on a task's completion. Considering cluster heterogeneity and accompanying remote data fetch, cluster selection of copies affects execution quality, as different insuring plans drive different revenues. For providing On-Line-Real-Time analysis results, a system needs to insure the geo-distributed resource for the arriving jobs. Our challenge is to achieve the optimal revenue by dynamically weighing the gains due to insurance against the loss of occupying extra resource for insuring. To this end, we design PingAn, an online insurance algorithm promising $(1+\varepsilon)\!\!-\!speed \ o(\frac{1}{\varepsilon^2+\varepsilon})\!\!-\!competitive$ in sum of the job flowtimes via cross-cluster copying for tasks. PingAn shares resource among the anterior fraction of jobs with the least unprocessed datasize and the fraction is adjustable to fit the system load condition. After sharing, PingAn concretely insures for tasks following efficiency-first reliability-aware principle to optimize the revenue of copies on jobs' performance. Trace-driven simulations demonstrate that PingAn can reduce the average job flowtimes by at least $14\%$ than the state-of-the-art speculation mechanisms. We also build PingAn in Spark on Yarn System to verify its practicality and generality. Experiments show that PingAn can reduce the average job flowtimes by up to $40\%$ comparing to the default Spark execution.

cs.DC↗

arXiv2017

Superconducting Gatemon Qubit based on a Proximitized Two-Dimensional Electron Gas

The coherent tunnelling of Cooper pairs across Josephson junctions (JJs) generates a nonlinear inductance that is used extensively in quantum information processors based on superconducting circuits, from setting qubit transition frequencies and interqubit coupling strengths, to the gain of parametric amplifiers for quantum-limited readout. The inductance is either set by tailoring the metal-oxide dimensions of single JJs, or magnetically tuned by parallelizing multiple JJs in superconducting quantum interference devices (SQUIDs) with local current-biased flux lines. JJs based on superconductor-semiconductor hybrids represent a tantalizing all-electric alternative. The gatemon is a recently developed transmon variant which employs locally gated nanowire (NW) superconductor-semiconductor JJs for qubit control. Here, we go beyond proof-of-concept and demonstrate that semiconducting channels etched from a wafer-scale two-dimensional electron gas (2DEG) are a suitable platform for building a scalable gatemon-based quantum computer. We show 2DEG gatemons meet the requirements by performing voltage-controlled single qubit rotations and two-qubit swap operations. We measure qubit coherence times up to ~2 us, limited by dielectric loss in the 2DEG host substrate.

cond-mat.mes-hall↗