arXiv subjects

Jian Guo

Publications and source records attributed to Jian Guo.

108 records · Page 6Linked to original sources

arXiv2022

Arbitrary coherent distributions in a programmable quantum walk

The coherent superposition of position states in a quantum walk (QW) can be precisely engineered towards the desired distributions to meet the need of quantum information applications. The coherent distribution can make full use of quantum parallel in computation and simulation. Particularly, the uniform superposition provides the robust non-locality, which has wide applications such as the generation of genuine multi-bit random numbers without post-processing. We experimentally demonstrate that the rich dynamics featured with arbitrary coherent distributions can be obtained by introducing different sets of the time- and position-dependent operations. Such a QW is realized by a resource-constant and flexible optical circuit, in which the variable operation is executed based on a Sagnac interferometer in an intrinsically stable and precisely controlled way. Our results contribute to the practical realization of quantum-walk-based quantum computation, quantum simulations and quantum information protocols.

quant-ph↗

arXiv2022

Maximal coin-walker entanglement in a ballistic quantum walk

We report the position-inhomogeneous quantum walk (IQW) can be utilized to produce the maximal high dimensional entanglement while maintaining the quadratic speedup spread of the wave-function. Our calculations show that the maximal coin-walker entanglement can be generated in any odd steps or asymptotically in even steps, and the nearly maximal entanglement can be obtained in even steps after $2$. We implement the IQW by a stable resource-saving time-bin optical network, in which a polarization Sagnac loop is employed to realize the precisely tunable phase shift. Our approach opens up an efficient way for high-dimensional entanglement engineering as well as promotes investigations on the role of coin-walker interactions in QW based applications.

quant-ph↗

arXiv2021

FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance

Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations. The development of deep reinforcement learning techniques is partially addressing these issues. Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading. In this paper, we propose an RLOps in finance paradigm and present a FinRL-Podracer framework to accelerate the development pipeline of deep reinforcement learning (DRL)-driven trading strategy and to improve both trading performance and training efficiency. FinRL-Podracer is a cloud solution that features high performance and high scalability and promises continuous training, continuous integration, and continuous delivery of DRL-driven trading strategies, facilitating a rapid transformation from algorithmic innovations into a profitable trading strategy. First, we propose a generational evolution mechanism with an ensemble strategy to improve the trading performance of a DRL agent, and schedule the training of a DRL algorithm onto a GPU cloud via multi-level mapping. Then, we carry out the training of DRL components with high-performance optimizations on GPUs. Finally, we evaluate the FinRL-Podracer framework for a stock trend prediction task on an NVIDIA DGX SuperPOD cloud. FinRL-Podracer outperforms three popular DRL libraries Ray RLlib, Stable Baseline 3 and FinRL, i.e., 12% \sim 35% improvements in annual return, 0.1 \sim 0.6 improvements in Sharpe ratio and 3 times \sim 7 times speed-up in training time. We show the high scalability by training a trading agent in 10 minutes with $80$ A100 GPUs, on NASDAQ-100 constituent stocks with minute-level data over 10 years.

q-fin.CP↗

arXiv2021

RoBin: Facilitating the Reproduction of Configuration-Related Vulnerability

Vulnerability reproduction paves a way in debugging software failures, which need intensive manual efforts. However, some key factors (e.g., software configuration, trigger method) are often missing, so we can not directly reproduce the failure without extra attempts. Even worse, highly customized configuration options of programs create a barrier for reproducing the vulnerabilities that only appear under some specific combinations of configurations. In this paper, we address the problem mentioned above -- reproducing the configuration-related vulnerability. We try to solve it by proposing a binary similarity-based method to infer the specific building configurations via the binary from crash report. The main challenges are as follows: precise compilation option inference, program configuration inference, and source-code-to-binary matching. To achieve the goal, we implement RoBin, a binary similarity-based building configuration inference tool. To demonstrate the effectiveness, we test RoBin on 21 vulnerable cases upon 4 well-known open-source programs. It shows a strong ability in pinpointing the building configurations causing the vulnerability. The result can help developers reproduce and diagnose the vulnerability, and finally, patch the programs.

cs.CR↗

arXiv2021

VisMCA: A Visual Analytics System for Misclassification Correction and Analysis. VAST Challenge 2020, Mini-Challenge 2 Award: Honorable Mention for Detailed Analysis of Patterns of Misclassification

This paper presents VisMCA, an interactive visual analytics system that supports deepening understanding in ML results, augmenting users' capabilities in correcting misclassification, and providing an analysis of underlying patterns, in response to the VAST Challenge 2020 Mini-Challenge 2. VisMCA facilitates tracking provenance and provides a comprehensive view of object detection results, easing re-labeling, and producing reliable, corrected data for future training. Our solution implements multiple analytical views on visual analysis to offer a deep insight for underlying pattern discovery.

cs.HC↗

arXiv2021

A model-based framework for learning transparent swarm behaviors

This paper proposes a model-based framework to automatically and efficiently design understandable and verifiable behaviors for swarms of robots. The framework is based on the automatic extraction of two distinct models: 1) a neural network model trained to estimate the relationship between the robots' sensor readings and the global performance of the swarm, and 2) a probabilistic state transition model that explicitly models the local state transitions (i.e., transitions in observations from the perspective of a single robot in the swarm) given a policy. The models can be trained from a data set of simulated runs featuring random policies. The first model is used to automatically extract a set of local states that are expected to maximize the global performance. These local states are referred to as desired local states. The second model is used to optimize a stochastic policy so as to increase the probability that the robots in the swarm observe one of the desired local states. Following these steps, the framework proposed in this paper can efficiently lead to effective controllers. This is tested on four case studies, featuring aggregation and foraging tasks. Importantly, thanks to the models, the framework allows us to understand and inspect a swarm's behavior. To this end, we propose verification checks to identify some potential issues that may prevent the swarm from achieving the desired global objective. In addition, we explore how the framework can be used in combination with a "standard" evolutionary robotics strategy (i.e., where performance is measured via simulation), or with online learning.

cs.RO↗

arXiv2020

Photonic flywheel in a monolithic fiber resonator

We demonstrate the first compact photonic flywheel with sub-fs time jitter (averaging times up to 10 μs) at the quantum-noise limit of a monolithic fiber resonator. Such quantum-limited performance is accessed through novel two-step pumping scheme for dissipative Kerr soliton (DKS) generation. Controllable interaction between stimulated Brillouin lasing and Kerr nonlinearity enhances the DKS coherence and mitigate the thermal instability challenge, achieving a remarkable 22-Hz intrinsic comb linewidth and an unprecedented phase noise of -180 dBc/Hz at 945 MHz carrier at free running. The scheme can be generalized to various device platforms for field-deployable precision metrology.

physics.optics↗

arXiv2020

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage.

cs.LG↗

arXiv2020

Bayesian Symbolic Regression

Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machine learning method by bridging X and Y using mathematical expressions composed of some basic functions. However, the search space of all possible expressions grows exponentially with the length of the expression, making it infeasible for enumeration. Genetic programming (GP) has been traditionally and commonly used in SR to search for the optimal solution, but it suffers from several limitations, e.g. the difficulty in incorporating prior knowledge; overly-complicated output expression and reduced interpretability etc. To address these issues, we propose a new method to fit SR under a Bayesian framework. Firstly, Bayesian model can naturally incorporate prior knowledge (e.g., preference of basis functions, operators and raw features) to improve the efficiency of fitting SR. Secondly, to improve interpretability of expressions in SR, we aim to capture concise but informative signals. To this end, we assume the expected signal has an additive structure, i.e., a linear combination of several concise expressions, whose complexity is controlled by a well-designed prior distribution. In our setup, each expression is characterized by a symbolic tree, and the proposed SR model could be solved by sampling symbolic trees from the posterior distribution using an efficient Markov chain Monte Carlo (MCMC) algorithm. Finally, compared with GP, the proposed BSR(Bayesian Symbolic Regression) method saves computer memory with no need to keep an updated 'genome pool'. Numerical experiments show that, compared with GP, the solutions of BSR are closer to the ground truth and the expressions are more concise. Meanwhile we find the solution of BSR is robust to hyper-parameter specifications such as the number of trees.

stat.ME↗

arXiv2019

Mid-infrared optical frequency comb generation from a chi-2 optical superlattice box resonator

Optical frequency combs (OFCs) at Mid-Infrared (MIR) wavelengths are essential for applications in precise spectroscopy, gas sensing and molecular fingerprinting, because of its revolutionary precision in both wavelength and frequency domain. The microresonator-based OFCs make a further step towards practical applications by including such high precision in a compact and cost-effective package. However, dispersion engineering is still a challenge for the conventional chi-3 micro-ring resonators and a MIR pump laser is required. Here we develop a different platform of a chi-2 optical superlattice box resonator to generate MIR OFC by optical parametric down conversion. With near-material-limited quality factor of 2.0*10^7, broadband MIR OFC can be generated with over 250 nm span around 2060 nm, where only a common near-infrared laser is necessary as pump. The fine teeth spacing corresponds to a measurable radio frequency beat note at 1.566 GHz, and also results in a fine spectroscopy resolution. Its linewidth is measured to be 6.1 kHz, which reveals a low comb noise that agrees well with the clean temporal waveforms. With high output power of over 370 mW, such MIR OFC is capable for long distance sensing and ranging applications.

physics.optics↗

arXiv2018

Provable Emergent Pattern Formation by a Swarm of Anonymous, Homogeneous, Non-Communicating, Reactive Robots with Limited Relative Sensing and no Global Knowledge or Positioning

In this work, we explore emergent behaviors by swarms of anonymous, homogeneous, non-communicating, reactive robots that do not know their global position and have limited relative sensing. We introduce a novel method that enables such severely limited robots to autonomously arrange in a desired pattern and maintain it. The method includes an automatic proof procedure to check whether a given pattern will be achieved by the swarm from any initial configuration. An attractive feature of this proof procedure is that it is local in nature, avoiding as much as possible the computational explosion that can be expected with increasing robots, states, and action possibilities. Our approach is based on extracting the local states that constitute a global goal (in this case, a pattern). We then formally show that these local states can only coexist when the global desired pattern is achieved and that, until this occurs, there is always a sequence of actions that will lead from the current pattern to the desired pattern. Furthermore, we show that the agents will never perform actions that could a) lead to intra-swarm collisions or b) cause the swarm to separate. After an analysis of the performance of pattern formation in the discrete domain, we also test the system in continuous time and space simulations and reproduce the results using asynchronous agents operating in unbounded space. The agents successfully form the desired patterns while avoiding collisions and separation.

cs.RO↗

arXiv2017

Towards an executable semantics of automobile RTOS standard and its application to conformance verification

The automobile Real-Time Operating System (RTOS) is hard to design and implement due to its real time features and increasing complexity. Some automobile RTOS standards are released aiming at unifying the software architecture of vehicle systems. Most of the standards are presented informally in natural languages, which may lead to not only ambiguities in specifications but also difficulties in conformance verification. This paper proposes a rewriting-based approach for formalising the automobile RTOS standard. Taking the OSEK/VDX standard as an example, an executional formal semantics of the automobile RTOS kernel, which focuses on the real time features, is defined using $\mathbb{K}$, a rewriting-based framework. We also report some ambiguous definitions of the OSEK/VDX standard, which we find in the process of formalisation. The $\mathbb{K}$ semantics of the OSEK/VDX standard is applied to conformance verification, which is used to check the conformance of not only the automobile operating system kernel but the applications.

cs.SE↗

arXiv2016

Gate-induced insulator to band-like transport transition in organolead halide perovskite

Understanding the intrinsic charge transport in organolead halide perovskites is essential for the development of high-efficiency photovoltaics and other optoelectronic devices. Despite the rapid advancement of the organolead halide perovskite in photovoltaic and optoelectronic applications, the intrinsic charge carrier transport in these materials remains elusive partly due to the difficulty of fabricating electrical devices and obtaining good electrical contact. Here, we report the fabrication of organolead halide perovskite microplates with monolayer graphene as low barrier electrical contact. A systematic charge transport studies reveal an insulator to band-like transport transition. Our studies indicate that the insulator to band-like transport transition depends on the orthorhombic-to-tetragonal phase transition temperature and defect densities of the organolead halide perovskite microplates. Our findings are not only important for the fundamental understanding of charge transport behavior but also offer valuable practical implications for photovoltaics and optoelectronic applications based on the organolead halide perovskite.

cond-mat.mtrl-sci↗

arXiv2015

Estimating heterogeneous graphical models for discrete data with an application to roll call voting

We consider the problem of jointly estimating a collection of graphical models for discrete data, corresponding to several categories that share some common structure. An example for such a setting is voting records of legislators on different issues, such as defense, energy, and healthcare. We develop a Markov graphical model to characterize the heterogeneous dependence structures arising from such data. The model is fitted via a joint estimation method that preserves the underlying common graph structure, but also allows for differences between the networks. The method employs a group penalty that targets the common zero interaction effects across all the networks. We apply the method to describe the internal networks of the U.S. Senate on several important issues. Our analysis reveals individual structure for each issue, distinct from the underlying well-known bipartisan structure common to all categories which we are able to extract separately. We also establish consistency of the proposed method both for parameter estimation and model selection, and evaluate its numerical performance on a number of simulated examples.

stat.AP↗

arXiv2015

Deep CNN Ensemble with Data Augmentation for Object Detection

We report on the methods used in our recent DeepEnsembleCoco submission to the PASCAL VOC 2012 challenge, which achieves state-of-the-art performance on the object detection task. Our method is a variant of the R-CNN model proposed Girshick:CVPR14 with two key improvements to training and evaluation. First, our method constructs an ensemble of deep CNN models with different architectures that are complementary to each other. Second, we augment the PASCAL VOC training set with images from the Microsoft COCO dataset to significantly enlarge the amount training data. Importantly, we select a subset of the Microsoft COCO images to be consistent with the PASCAL VOC task. Results on the PASCAL VOC evaluation server show that our proposed method outperform all previous methods on the PASCAL VOC 2012 detection task at time of submission.

cs.CV↗

arXiv2015

Second-Harmonic Generation of Spoof Surface Plasmon Polaritons Using Nonlinear Plasmonic Metamaterials

The second harmonic generation is one of the most important applications of nonlinear effect, which has attracted great interests in nonlinear optics and microwave in the past decades. To the best of our knowledge, however, generating the second harmonics of surface plasmon polaritons (SPPs) has not been reported. Here, we propose to generate the second harmonics of spoof SPPs with high efficiency at microwave frequencies using subwavelength-scale nonlinear active device integrated on specially designed plasmonic waveguides, which are composed of two ultrathin corrugated metallic strips printed on the top and bottom surfaces of a thin dielectric slab anti-symmetrically. We show that the plasmonic waveguide supports broadband propagations of spoof SPPs with strong subwavelength effect, whose dispersion property can be controlled by changing the geometrical parameters. By loading the nonlinear device made from semiconductors to the intersection of two plasmonic waveguides with different corrugation depths, we experimentally demonstrate the efficient generation of second-harmonic SPPs in broad frequency band. The proposed second-harmonic generator can be directly used as SPP frequency multiplier, and the proposed method can be extended to achieve high-order harmonics and produce SPP mixers, which are essential to SPP integrated circuits and systems.

physics.optics↗

arXiv2014

Towards barrier free contact to MoS2 using graphene electrodes

The two-dimensional (2D) layered semiconductors such as MoS2 have attracted tremendous interest as a new class of electronic materials. However, there is considerable challenge in making reliable contacts to these atomically thin materials. Here we present a new strategy by using graphene as back electrodes to achieve Ohmic contact to MoS2. With a finite density of states, the Fermi level of graphene can be readily modified by gate potential to ensure a nearly perfect band alignment with MoS2. We demonstrate, for the first time, a transparent contact can be made to MoS2 with essentially zero contact barrier and linear output behaviour at cryogenic temperatures (down to 1.9 K) for both monolayer and multilayer MoS2. Benefiting from the barrier-free transparent contacts, we show that a metal-insulator-transition (MIT) can be observed in a two-terminal MoS2 device, a phenomenon that could be easily masked by Schottky barrier and only seen in four-terminal devices in conventional metal-contacted MoS2 system. With further passivation y born nitride encapsulation, we demonstrate a record high extrinsic (two-terminal) field effect mobility over 1300 cm2/Vs in MoS2.

cond-mat.mtrl-sci↗

arXiv2011

Astrodynamical Space Test of Relativity using Optical Devices I (ASTROD I) - A class-M fundamental physics mission proposal for Cosmic Vision 2015-2025: 2010 Update

This paper on ASTROD I is based on our 2010 proposal submitted for the ESA call for class-M mission proposals, and is a sequel and an update to our previous paper [Experimental Astronomy 23 (2009) 491-527; designated as Paper I] which was based on our last proposal submitted for the 2007 ESA call. In this paper, we present our orbit selection with one Venus swing-by together with orbit simulation. In Paper I, our orbit choice is with two Venus swing-bys. The present choice takes shorter time (about 250 days) to reach the opposite side of the Sun. We also present a preliminary design of the optical bench, and elaborate on the solar physics goals with the radiation monitor payload. We discuss telescope size, trade-offs of drag-free sensitivities, thermal issues and present an outlook. ASTROD I is a planned interplanetary space mission with multiple goals. The primary aims are: to test General Relativity with an improvement in sensitivity of over 3 orders of magnitude, improving our understanding of gravity and aiding the development of a new quantum gravity theory; to measure key solar system parameters with increased accuracy, advancing solar physics and our knowledge of the solar system; and to measure the time rate of change of the gravitational constant with an order of magnitude improvement and the anomalous Pioneer acceleration, thereby probing dark matter and dark energy gravitationally. It is envisaged as the first in a series of ASTROD missions. ASTROD I will consist of one spacecraft carrying a telescope, four lasers, two event timers and a clock. Two-way, two-wavelength laser pulse ranging will be used between the spacecraft in a solar orbit and deep space laser stations on Earth, to achieve the ASTROD I goals.

gr-qc↗