arXiv subjects

Kai Xu

Publications and source records attributed to Kai Xu.

At least 397 records · Page 22Linked to original sources

arXiv2018

Hierarchical environment-assisted dynamical speedup control

We investigate the qubit in the hierarchical environment where the first level is just one lossy cavity while the second level is the N-coupled lossy cavities. In the weak coupling regime between the qubit and the first level environment, the dynamics crossovers from the original Markovian to the new non-Markovian and from no-speedup to speedup can be realized by controlling the hierarchical environment, i.e., manipulating the number of cavities or the coupling strength between two nearest-neighbor cavities in the second level environment. And we find that the coupling strength between two nearest-neighbor cavities and the number of cavities in the second level environment have the opposite effect on the non-Markovian dynamics and speedup evolution of the qubit. In addition, in the case of strong coupling between the qubit and the first level environment, we can be surprised to find that, compared with the original non-Markovian dynamics, the added second level environment cannot play a beneficial role on the speedup of the dynamics of the system.

quant-ph↗

arXiv2018

CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing

This paper addresses the real-time encoding-decoding problem for high-frame-rate video compressive sensing (CS). Unlike prior works that perform reconstruction using iterative optimization-based approaches, we propose a non-iterative model, named "CSVideoNet". CSVideoNet directly learns the inverse mapping of CS and reconstructs the original input in a single forward propagation. To overcome the limitations of existing CS cameras, we propose a multi-rate CNN and a synthesizing RNN to improve the trade-off between compression ratio (CR) and spatial-temporal resolution of the reconstructed videos. The experiment results demonstrate that CSVideoNet significantly outperforms the state-of-the-art approaches. With no pre/post-processing, we achieve 25dB PSNR recovery quality at 100x CR, with a frame rate of 125 fps on a Titan X GPU. Due to the feedforward and high-data-concurrency natures of CSVideoNet, it can take advantage of GPU acceleration to achieve three orders of magnitude speed-up over conventional iterative-based approaches. We share the source code at https://github.com/PSCLab-ASU/CSVideoNet.

cs.CV↗

arXiv2017

Test of the multiquark structure of $a_1(1420)$ in strong two-body decays

We present an analysis of strong two-body decays of the $a_1(1420)$ with $J^{PC} = 1^{++}$ recently reported by the COMPASS Collaboration at CERN. Following the interpretation of the COMPASS Collaboration that the $a_1$ is an unusual state with a four-quark $q\bar q s \bar s$ structure we consider two possible configurations for this state --- hadronic molecular and color diquark-antidiquark structures. We find that the dominant decay mode of the $a_1$ is the decay into $K$ and $K^*$. In particular, we calculate that the four decay modes $a_1 \to VP$ with $VP = K^{*\pm}K^\mp$, $K^{*0}\bar K^0$, $\bar K^{*0}K^0$ together give a dominant contribution to the measured total width of about 150 MeV. The observational mode $a_1 \to f_0(980) + π^0$ is significantly suppressed by one order of magnitude.

hep-ph↗

arXiv2017

10-qubit entanglement and parallel logic operations with a superconducting circuit

Here we report on the production and tomography of genuinely entangled Greenberger-Horne-Zeilinger states with up to 10 qubits connecting to a bus resonator in a superconducting circuit, where the resonator-mediated qubit-qubit interactions are used to controllably entangle multiple qubits and to operate on different pairs of qubits in parallel. The resulting 10-qubit density matrix is unambiguously probed, with a fidelity of $0.668 \pm 0.025$. Our results demonstrate the largest entanglement created so far in solid-state architectures, and pave the way to large-scale quantum computation.

quant-ph↗

arXiv2017

Continuous-variable geometric phase and its manipulation for quantum computation in a superconducting circuit

Geometric phase, associated with holonomy transformation in quantum state space, is an important quantum-mechanical effect. Besides fundamental interest, this effect has practical applications, among which geometric quantum computation is a paradigm, where quantum logic operations are realized through geometric phase manipulation that has some intrinsic noise-resilient advantages and may enable simplified implementation of multiqubit gates compared to the dynamical approach. Here we report observation of a continuous-variable geometric phase and demonstrate a quantum gate protocol based on this phase in a superconducting circuit, where five qubits are controllably coupled to a resonator. Our geometric approach allows for one-step implementation of $n$-qubit controlled-phase gates, which represents a remarkable advantage compared to gate decomposition methods, where the number of required steps dramatically increases with $n$. Following this approach, we realize these gates with $n$ up to 4, verifying the high efficiency of this geometric manipulation for quantum computation.

quant-ph↗

arXiv2017

Emulating many-body localization with a superconducting quantum processor

The law of statistical physics dictates that generic closed quantum many-body systems initialized in nonequilibrium will thermalize under their own dynamics. However, the emergence of many-body localization (MBL) owing to the interplay between interaction and disorder, which is in stark contrast to Anderson localization that only addresses noninteracting particles in the presence of disorder, greatly challenges this concept because it prevents the systems from evolving to the ergodic thermalized state. One critical evidence of MBL is the long-time logarithmic growth of entanglement entropy, and a direct observation of it is still elusive due to the experimental challenges in multiqubit single-shot measurement and quantum state tomography. Here we present an experiment of fully emulating the MBL dynamics with a 10-qubit superconducting quantum processor, which represents a spin-1/2 XY model featuring programmable disorder and long-range spin-spin interactions. We provide essential signatures of MBL, such as the imbalance due to the initial nonequilibrium, the violation of eigenstate thermalization hypothesis, and, more importantly, the direct evidence of the long-time logarithmic growth of entanglement entropy. Our results lay solid foundations for precisely simulating the intriguing physics of quantum many-body systems on the platform of large-scale multiqubit superconducting quantum processors.

quant-ph↗

arXiv2017

A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks

FPGA-based hardware accelerators for convolutional neural networks (CNNs) have obtained great attentions due to their higher energy efficiency than GPUs. However, it is challenging for FPGA-based solutions to achieve a higher throughput than GPU counterparts. In this paper, we demonstrate that FPGA acceleration can be a superior solution in terms of both throughput and energy efficiency when a CNN is trained with binary constraints on weights and activations. Specifically, we propose an optimized FPGA accelerator architecture tailored for bitwise convolution and normalization that features massive spatial parallelism with deep pipelines stages. A key advantage of the FPGA accelerator is that its performance is insensitive to data batch size, while the performance of GPU acceleration varies largely depending on the batch size of the data. Experiment results show that the proposed accelerator architecture for binary CNNs running on a Virtex-7 FPGA is 8.3x faster and 75x more energy-efficient than a Titan X GPU for processing online individual requests in small batch sizes. For processing static data in large batch sizes, the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9.5x higher energy efficiency.

cs.DC↗

arXiv2017

GRASS: Generative Recursive Autoencoders for Shape Structures

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.

cs.GR↗

arXiv2017

Surface Reconstruction with Data-driven Exemplar Priors

In this paper, we propose a framework to reconstruct 3D models from raw scanned points by learning the prior knowledge of a specific class of objects. Unlike previous work that heuristically specifies particular regularities and defines parametric models, our shape priors are learned directly from existing 3D models under a framework based on affinity propagation. Given a database of 3D models within the same class of objects, we build a comprehensive library of 3D local shape priors. We then formulate the problem to select as-few-as-possible priors from the library, referred to as exemplar priors. These priors are sufficient to represent the 3D shapes of the whole class of objects from where they are generated. By manipulating these priors, we are able to reconstruct geometrically faithful models with the same class of objects from raw point clouds. Our framework can be easily generalized to reconstruct various categories of 3D objects that have more geometrically or topologically complex structures. Comprehensive experiments exhibit the power of our exemplar priors for gracefully solving several problems in 3D shape reconstruction such as preserving sharp features, recovering fine details and so on.

cs.CG↗

arXiv2016

Harnessing Geometric Frustration to Form Band Gaps in Acoustic Channel Lattices

We demonstrate both numerically and experimentally that geometric frustration in two-dimensional periodic acoustic networks consisting of arrays of narrow air channels can be harnessed to form band gaps (ranges of frequency in which the waves cannot propagate in any direction through the system). While resonant standing wave modes and interferences are ubiquitous in all the analyzed network geometries, we show that they give rise to band gaps only in the geometrically frustrated ones (i.e. those comprising of triangles and pentagons). Our results not only reveal a new mechanism based on geometric frustration to suppress the propagation of pressure waves in specific frequency ranges, but also opens avenues for the design of a new generation of smart systems that control and manipulate sound and vibrations.

physics.class-ph↗

arXiv2016

A Data-Driven Compressive Sensing Framework for Long-Term Health Monitoring

Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. In this paper, we propose a data-driven CS framework that learns signal characteristics and individual variability from patients' data to significantly enhance CS performance and noise resilience. This is accomplished by a co-training approach that optimizes both the sensing matrix and dictionary towards improved restricted isometry property (RIP) and signal sparsity, respectively. Experimental results upon ECG signals show that our framework is able to achieve better reconstruction quality with up to 80% higher compression ratio (CP) than conventional frameworks based on random sensing matrices and overcomplete bases. In addition, our framework shows great noise resilience capability, which tolerates up to 40dB higher noise energy at a CP of 9 times.

cs.IT↗

arXiv2016

A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing

Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. However, conventional model-driven CS frameworks suffer from limited compression ratio and reconstruction quality when dealing with physiological signals due to inaccurate models and the overlook of individual variability. In this paper, we propose a data-driven CS framework that can learn signal characteristics and personalized features from any individual recording of physiologic signals to enhance CS performance with a minimized number of measurements. Such improvements are accomplished by a co-training approach that optimizes the sensing matrix and the dictionary towards improved restricted isometry property and signal sparsity, respectively. Experimental results upon ECG signals show that the proposed method, at a compression ratio of 10x, successfully reduces the isometry constant of the trained sensing matrices by 86% against random matrices and improves the overall reconstructed signal-to-noise ratio by 15dB over conventional model-driven approaches.

cs.LG↗

arXiv2016

An Energy-Efficient Compressive Sensing Framework Incorporating Online Dictionary Learning for Long-term Wireless Health Monitoring

Wireless body area network (WBAN) is emerging in the mobile healthcare area to replace the traditional wire-connected monitoring devices. As wireless data transmission dominates power cost of sensor nodes, it is beneficial to reduce the data size without much information loss. Compressive sensing (CS) is a perfect candidate to achieve this goal compared to existing compression techniques. In this paper, we proposed a general framework that utilize CS and online dictionary learning (ODL) together. The learned dictionary carries individual characteristics of the original signal, under which the signal has an even sparser representation compared to pre-determined dictionaries. As a consequence, the compression ratio is effectively improved by 2-4x comparing to prior works. Besides, the proposed framework offloads pre-processing from sensor nodes to the server node prior to dictionary learning, providing further reduction in hardware costs. As it is data driven, the proposed framework has the potential to be used with a wide range of physiological signals.

cs.IT↗

arXiv2015

Quantum delayed-choice experiment with a beam splitter in a quantum superposition

A quantum system can behave as a wave or as a particle, depending on the experimental arrangement. When for example measuring a photon using a Mach-Zehnder interferometer, the photon acts as a wave if the second beam-splitter is inserted, but as a particle if this beam-splitter is omitted. The decision of whether or not to insert this beam-splitter can be made after the photon has entered the interferometer, as in Wheeler's famous delayed-choice thought experiment. In recent quantum versions of this experiment, this decision is controlled by a quantum ancilla, while the beam splitter is itself still a classical object. Here we propose and realize a variant of the quantum delayed-choice experiment. We configure a superconducting quantum circuit as a Ramsey interferometer, where the element that acts as the first beam-splitter can be put in a quantum superposition of its active and inactive states, as verified by the negative values of its Wigner function. We show that this enables the wave and particle aspects of the system to be observed with a single setup, without involving an ancilla that is not itself a part of the interferometer. We also study the transition of this quantum beam-splitter from a quantum to a classical object due to decoherence, as observed by monitoring the interferometer output.

quant-ph↗

arXiv2015

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.

cs.GR↗

arXiv2014

Studies of LL-type 500MHz 5-cell superconducting cavity at SINAP

A low loss (LL) type 500 MHz 5-cell superconducting niobium prototype cavity with large beam aperture has been developed successfully including the optimization, the deep drawing and electron beam welding, the surface treatment and the vertical testing. The performance of the fundamental mode was optimized and the higher order modes were damped by adopting an enlarged beam pipe for propagation. Surface preparation or treatment including mechanical polishing, buffered chemical polishing and high pressure rinsing with ultra-pure water and so on was carried out carefully to promise a perfect inner surface condition. The vertical testing results show that the accelerating voltage higher than 7.5 MV was obtained while the quality factor was better than 1E9 at 4.2 K. No obvious multipacting or field emission was found during the test. However, a quench happened while increasing the field a little higher than 7.5 MV that at present limited the cavity performance.

physics.acc-ph↗

arXiv2014

Charge-density wave, superconductivity and $f$-electron valence instability in EuBiS$_2$F

Superconductivity (SC) and charge-density wave (CDW) are two contrasting yet relevant collective electronic states which have received sustained interest for decades. Here we report that, in a layered europium bismuth sulfofluoride, EuBiS$_2$F, a CDW-like transition occurs at 280 K, below which SC emerges at 0.3 K, without any extrinsic doping. The Eu ions were found to exhibit an anomalously temperature-independent mixed valence of about +2.2, associated with the formation of CDW. The mixed valence of Eu gives rise to self electron doping into the conduction bands mainly consisting of the in-plane Bi-6$p$ states, which in turn brings about the CDW and SC. In particular, the electronic specific-heat coefficient is enhanced by ~ 50 times, owing to the significant hybridizations between Eu-4$f$ and Bi-6$p$ electrons, as verified by band-structure calculations. Thus, EuBiS$_2$F manifests itself as an unprecedented material that simultaneously accommodates SC, CDW and $f$-electron valence instability.

cond-mat.supr-con↗

arXiv2014

Controllable synthesis of single-, double- and triple-walled carbon nanotubes from asphalt

Single-, double- and triple-walled carbon nanotubes (SWNTs, DWNTs and TWNTs) have been controlla- bly synthesized by an arc discharge method using asphalt as carbon source. The morphology and struc- ture of three kinds of products synthesized with Fe as catalyst were characterized by scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), Raman spectroscopy and energy dispersive X-ray spectroscopy (EDX). It was found that different buffer gases strongly affect the number layers in the synthesized nanotubes. In the He gas atmosphere only SWNTs were found to be synthesized by arc discharge in contrast to the case in Ar gas atmosphere in which only TWNTs were formed. In N2 atmosphere both SWNTs and DWNTs were synthesized. Our findings indicate that asphalt is a suitable industrial carbon source for the growth of different type nanotubes.

cond-mat.mtrl-sci↗