arXiv subjects

Ji Wang

Publications and source records attributed to Ji Wang.

At least 199 records · Page 11Linked to original sources

arXiv2020

Delay-Compensated Control of Sandwiched ODE-PDE-ODE Hyperbolic Systems for Oil Drilling and Disaster Relief

Motivated by engineering applications of subsea installation by deepwater construction vessels in oil drilling, and of aid delivery by unmanned aerial vehicles in disaster relief, we develop output-feedback boundary control of heterodirectional coupled hyperbolic PDEs sandwiched between two ODEs, where the measurement is the output state of one ODE and suffers a time delay. After rewriting the time-delay dynamics as a transport PDE of which the left boundary connects with the sandwiched system, a state observer is built to estimate the states of the overall system of ODE-heterodirectional coupled hyperbolic PDEs-ODE-transport PDE using the right boundary state of the last transport PDE. An observer-based output-feedback controller acting at the first ODE is designed to stabilize the overall system using backstepping transformations and frequency-domain designs. The exponential stability results of the closed-loop system, boundedness and exponential convergence of the control input are proved. The obtained theoretical result is applied to control of a deepwater oil drilling construction vessel as a simulation case, where the simulation results show the proposed control design reduces cable oscillations and places the oil drilling equipment to be installed in the target area on the sea floor. Performance deterioration under extreme and unmodeled disturbances is also illustrated.

math.OC↗

arXiv2020

EarthFinder Probe Mission Concept Study: Characterizing nearby stellar exoplanet systems with Earth-mass analogs for future direct imaging

EarthFinder is a NASA Astrophysics Probe mission concept selected for study as input to the 2020 Astrophysics National Academies Decadal Survey. The EarthFinder concept is based on a dramatic shift in our understanding of how PRV measurements should be made. We propose a new paradigm which brings the high precision, high cadence domain of transit photometry as demonstrated by Kepler and TESS to the challenges of PRV measurements at the cm/s level. This new paradigm takes advantage of: 1) broad wavelength coverage from the UV to NIR which is only possible from space to minimize the effects of stellar activity; 2) extremely compact, highly stable, highly efficient spectrometers (R>150,000) which require the diffraction-limited imaging possible only from space over a broad wavelength range; 3) the revolution in laser-based wavelength standards to ensure cm/s precision over many years; 4) a high cadence observing program which minimizes sampling-induced period aliases; 5) exploiting the absolute flux stability from space for continuum normalization for unprecedented line-by-line analysis not possible from the ground; and 6) focusing on the bright stars which will be the targets of future imaging missions so that EarthFinder can use a ~1.5 m telescope.

astro-ph.IM↗

arXiv2020

Keck/NIRC2 $L$'-Band Imaging of Jovian-Mass Accreting Protoplanets around PDS 70

We present $L$'-band imaging of the PDS 70 planetary system with Keck/NIRC2 using the new infrared pyramid wavefront sensor. We detected both PDS 70 b and c in our images, as well as the front rim of the circumstellar disk. After subtracting off a model of the disk, we measured the astrometry and photometry of both planets. Placing priors based on the dynamics of the system, we estimated PDS 70 b to have a semi-major axis of $20^{+3}_{-4}$~au and PDS 70 c to have a semi-major axis of $34^{+12}_{-6}$~au (95\% credible interval). We fit the spectral energy distribution (SED) of both planets. For PDS 70 b, we were able to place better constraints on the red half of its SED than previous studies and inferred the radius of the photosphere to be 2-3~$R_{Jup}$. The SED of PDS 70 c is less well constrained, with a range of total luminosities spanning an order of magnitude. With our inferred radii and luminosities, we used evolutionary models of accreting protoplanets to derive a mass of PDS 70 b between 2 and 4 $M_{\textrm{Jup}}$ and a mean mass accretion rate between $3 \times 10^{-7}$ and $8 \times 10^{-7}~M_{\textrm{Jup}}/\textrm{yr}$. For PDS 70 c, we computed a mass between 1 and 3 $M_{\textrm{Jup}}$ and mean mass accretion rate between $1 \times 10^{-7}$ and $5 \times~10^{-7} M_{\textrm{Jup}}/\textrm{yr}$. The mass accretion rates imply dust accretion timescales short enough to hide strong molecular absorption features in both planets' SEDs.

astro-ph.EP↗

arXiv2020

SIR Model-based Prediction of Infected Population of Coronavirus in Hubei Province

After the sudden outbreak of Coronavirus in Wuhan, continuous and rich data of the epidemic has been made public as the vital fact for decision support in control measures and aggressive implementation of containment strategies and plans. With the further growth and spreading of the virus, future resource allocation and planning under updated strategies and measures rely on careful study of the epidemic data and characteristics for accurate prediction and estimation. By using the SIR model and reported data, key parameters are obtained from least sum of squared errors for an accurate prediction of epidemic trend in the last four weeks.

q-bio.PE↗

arXiv2020

Towards Accurate One-Stage Object Detection with AP-Loss

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem. Due to its non-differentiability and non-convexity, the AP-loss cannot be optimized directly. For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks. We verify good convergence property of the proposed algorithm theoretically and empirically. Experimental results demonstrate notable performance improvement in state-of-the-art one-stage detectors based on AP-loss over different kinds of classification-losses on various benchmarks, without changing the network architectures. Code is available at https://github.com/cccorn/AP-loss.

cs.CV↗

arXiv2020

Combining Symbolic Execution and Model Checking to Verify MPI Programs

Message passing is the standard paradigm of programming in high-performance computing. However, verifying Message Passing Interface (MPI) programs is challenging, due to the complex program features (such as non-determinism and non-blocking operations). In this work, we present MPI symbolic verifier (MPI-SV), the first symbolic execution based tool for automatically verifying MPI programs with non-blocking operations. MPI-SV combines symbolic execution and model checking in a synergistic way to tackle the challenges in MPI program verification. The synergy improves the scalability and enlarges the scope of verifiable properties. We have implemented MPI-SV (footnote: https://mpi-sv.github.io) and evaluated it with 111 real-world MPI verification tasks. The pure symbolic execution-based technique successfully verifies 61 out of the 111 tasks (55\%) within one hour, while in comparison, MPI-SV verifies 100 tasks (90\%). On average, compared with pure symbolic execution, MPI-SV achieves 19x speedups on verifying the satisfaction of the critical property and 5x speedups on finding violations.

cs.PL↗

arXiv2019

Private Deep Learning with Teacher Ensembles

Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information. Most privacy-preserving methods lead to undesirable performance degradation. Ensemble learning is an effective way to improve model performance. In this work, we propose a new method for teacher ensembles that uses more informative network outputs under differential private stochastic gradient descent and provide provable privacy guarantees. Out method employs knowledge distillation and hint learning on intermediate representations to facilitate the training of student model. Additionally, we propose a simple weighted ensemble scheme that works more robustly across different teaching settings. Experimental results on three common image datasets benchmark (i.e., CIFAR10, MINST, and SVHN) demonstrate that our approach outperforms previous state-of-the-art methods on both performance and privacy-budget.

cs.CR↗

arXiv2019

SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning

For the dramatic increase of Android malware and low efficiency of manual check process, deep learning methods started to be an auxiliary means for Android malware detection these years. However, these models are highly dependent on the quality of datasets, and perform unsatisfactory results when the quality of training data is not good enough. In the real world, the quality of datasets without manually check cannot be guaranteed, even Google Play may contain malicious applications, which will cause the trained model failure. To address the challenge, we propose a robust Android malware detection approach based on selective ensemble learning, trying to provide an effective solution not that limited to the quality of datasets. The proposed model utilizes genetic algorithm to help find the best combination of the component learners and improve robustness of the model. Our results show that the proposed approach achieves a more robust performance than other approaches in the same area.

cs.CR↗

arXiv2019

Enabling the next generation of scientific discoveries by embracing photonic technologies

The fields of Astronomy and Astrophysics are technology limited, where the advent and application of new technologies to astronomy usher in a flood of discoveries altering our understanding of the Universe (e.g., recent cases include LIGO and the GRAVITY instrument at the VLTI). Currently, the field of astronomical spectroscopy is rapidly approaching an impasse: the size and cost of instruments, especially multi-object and integral field spectrographs for extremely large telescopes (ELTs), are pushing the limits of what is feasible, requiring optical components at the very edge of achievable size and performance. For these reasons, astronomers are increasingly looking for innovative solutions like photonic technologies that promote instrument miniaturization and simplification, while providing superior performance. Astronomers have long been aware of the potential of photonic technologies. The goal of this white paper is to draw attention to key photonic technologies and developments over the past two decades and demonstrate there is new momentum in this arena. We outline where the most critical efforts should be focused over the coming decade in order to move towards realizing a fully photonic instrument. A relatively small investment in this technology will advance astronomical photonics to a level where it can reliably be used to solve challenging instrument design limitations. For the benefit of both ground and space borne instruments alike, an endorsement from the National Academy of Sciences decadal survey will ensure that such solutions are set on a path to their full scientific exploitation, which may one day address a broad range of science cases outlined in the KSPs.

astro-ph.IM↗

arXiv2019

High-resolution Infrared Spectrograph for Exoplanet Characterization with the Keck and Thirty Meter Telescopes

HISPEC (High-resolution Infrared Spectrograph for Exoplanet Characterization) is a proposed diffraction-limited spectrograph for the W.M. Keck Observatory, and a pathfinder for the MODHIS facility project (Multi-Object Diffraction-limited High-resolution Infrared Spectrograph) on the Thirty Meter Telescope. HISPEC/MODHIS builds on diffraction-limited spectrograph designs which rely on adaptively corrected single-mode fiber feeds. Seeing-limited high-resolution spectrographs, by virtue of the conservation of beam etendue, grow in volume following a D^3 power law (D is the telescope diameter), and are subject to daunting challenges associated with their large size. Diffraction-limited spectrographs fed by single mode fibers are decoupled from the telescope input, and are orders of magnitude more compact and have intrinsically stable line spread functions. Their efficiency is directly proportional to the performance of the adaptive optics (AO) system. AO technologies have matured rapidly over the past two decades and are baselined for future extremely large telescopes. HISPEC/MODHIS will take R>100,000 spectra of a few objects in a 10" field-of-view sampled at the diffraction limit (~10-50 mas), simultaneously from 0.95 to 2.4 microns (y-K). The scientific scope ranges from exoplanet infrared precision radial velocities, spectroscopy of transiting, close-in, and directly imaged exoplanets (atmospheric composition and dynamics, RM effect, spin measurements, Doppler imaging), brown dwarf characterization, stellar physics/chemistry, proto-planetary disk kinematics/composition, Solar system, extragalactic science, and cosmology. HISPEC/MODHIS features a compact, cost-effective design optimized to fully exploit the existing Keck-AO and future TMT-NFIRAOS infrastructures and boost the scientific reach of Keck Observatory and TMT soon after first light.

astro-ph.IM↗

arXiv2019

Pretraining-Based Natural Language Generation for Text Summarization

In this paper, we propose a novel pretraining-based encoder-decoder framework, which can generate the output sequence based on the input sequence in a two-stage manner. For the encoder of our model, we encode the input sequence into context representations using BERT. For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence. In the second stage, we mask each word of the draft sequence and feed it to BERT, then by combining the input sequence and the draft representation generated by BERT, we use a Transformer-based decoder to predict the refined word for each masked position. To the best of our knowledge, our approach is the first method which applies the BERT into text generation tasks. As the first step in this direction, we evaluate our proposed method on the text summarization task. Experimental results show that our model achieves new state-of-the-art on both CNN/Daily Mail and New York Times datasets.

cs.CL↗

arXiv2019

Output-Feedback Boundary Control of a Heat PDE Sandwiched Between Two ODEs

We present designs for exponential stabilization of an ODE-heat PDE-ODE coupled system where the control actuation only acts in one ODE. The combination of PDE backstepping and ODE backstepping is employed in a state-feedback control law and in an observer that estimates PDE and two ODE states only using one PDE boundary measurement. Based on the state-feedback control law and the observer, the output-feedback control law is then proposed. The exponential stability of the closed-loop system and the boundedness and exponential convergence of the control law are proved via Lyapunov analysis. Finally, numerical simulations validate the effectiveness of this method for the `sandwiched' system.

math.OC↗

arXiv2019

Detecting Earth-like Biosignatures on Rocky Exoplanets around Nearby Stars with Ground-based Extremely Large Telescopes

As we begin to discover rocky planets in the habitable zone of nearby stars with missions like TESS and CHEOPS, we will need quick advancements on instrumentation and observational techniques that will enable us to answer key science questions, such as What are the atmospheric characteristics of habitable zone rocky planets? How common are Earth-like biosignatures in rocky planets?} How similar or dissimilar are those planets to Earth? Over the next decade we expect to have discovered several Earth-analog candidates, but we will not have the tools to study the atmospheres of all of them in detail. Ground-based ELTs can identify biosignatures in the spectra of these candidate exo-Earths and understand how the planets' atmospheres compare to the Earth at different epochs. Transit spectroscopy, high-resolution spectroscopy, and reflected-light direct imaging on ELTs can identify multiple biosignatures for habitable zone, rocky planets around M stars at optical to near-infrared wavelengths. Thermal infrared direct imaging can detect habitable zone, rocky planets around AFGK stars, identifying ozone and motivating reflected-light follow-up observations with NASA missions like HabEx/LUVOIR. Therefore, we recommend that the Astro2020 Decadal Survey Committee support: (1) the search for Earth-like biosignatures on rocky planets around nearby stars as a key science case; (2) the construction over the next decade of ground-based Extremely Large Telecopes (ELTs), which will provide the large aperture and spatial resolution necessary to start revealing the atmospheres of Earth-analogues around nearby stars; (3) the development of instrumentation that optimizes the detection of biosignatures; and (4) the generation of accurate line lists for potential biosignature gases, which are needed as model templates to detect those molecules.

astro-ph.EP↗

arXiv2019

New Frontiers for Terrestrial-sized to Neptune-sized Exoplanets In the Era of Extremely Large Telescopes

Surveys reveal that terrestrial- to Neptune-sized planets (1 $< R <$ 4 R$_{\rm{Earth}}$) are the most common type of planets in our galaxy. Detecting and characterizing such small planets around nearby stars holds the key to understanding the diversity of exoplanets and will ultimately address the ubiquitousness of life in the universe. The following fundamental questions will drive research in the next decade and beyond: (1) how common are terrestrial to Neptune-sized planets within a few AU of their host star, as a function of stellar mass? (2) How does planet composition depend on planet mass, orbital radius, and host star properties? (3) What are the energy budgets, atmospheric dynamics, and climates of the nearest worlds? Addressing these questions requires: a) diffraction-limited spatial resolution; b) stability and achievable contrast delivered by adaptive optics; and c) the light-gathering power of extremely large telescopes (ELTs), as well as multi-wavelength observations and all-sky coverage enabled by a comprehensive US ELT Program. Here we provide an overview of the challenge, and promise of success, in detecting and comprehensively characterizing small worlds around the very nearest stars to the Sun with ELTs. This white paper extends and complements the material presented in the findings and recommendations published in the National Academy reports on Exoplanet Science Strategy and Astrobiology Strategy for the Search for Life in the Universe.

astro-ph.EP↗

arXiv2019

Imaging Giant Protoplanets with the ELTs

We have now accumulated a wealth of observations of the planet-formation environment and of mature planetary systems. These data allow us to test and refine theories of gas-giant planet formation by placing constraints on the conditions and timescale of this process. Yet a number of fundamental questions remain unanswered about how protoplanets accumulate material, their photospheric properties and compositions, and how they interact with protoplanetary disks. While we have begun to detect protoplanet candidates during the last several years, we are presently only sensitive to the widest separation, highest mass / accretion rate cases. Current observing facilities lack the angular resolution and inner working angle to probe the few-AU orbital separations where giant planet formation is thought to be most efficient. They also lack the contrast to detect accretion rates that would form lower mass gas giants and ice giants. Instruments and telescopes coming online over the next decade will provide high contrast in the inner giant-planet-forming regions around young stars, allowing us to build a protoplanet census and to characterize planet formation in detail for the first time.

astro-ph.EP↗

arXiv2019

The need for single-mode fiber-fed spectrographs

Precise Doppler radial-velocity (RV) instruments will continue to play an essential role in advancing our holistic understanding of exoplanetary systems. The combination of orbital parameters from transit surveys and follow-up RV measurements is vital to unlock mass and density estimates of detected planets, giving us insight into their environment and structure. However, the exoplanet field is reaching a critical juncture: the measurement sensitivity of existing radial-velocity instruments is becoming the limiting factor in further increasing our knowledge. Without improvement in delivered RV measurement precision, we will not be able to provide dynamical mass and density estimates for some of the most exciting (and consequently most challenging) discoveries expected from new transit missions including the Transiting Exoplanet Survey Satellite (TESS) (Plavchan et al. 2015, Ricker et al. 2014). RV precisions at the 10cm/s level are required to fully confirm earth-like analogues, provide masses and measure density to the 1-5% level from these missions. Additionally, this RV capability will also be important to allow for efficient target selection for facilities such as the James Webb Space Telescope (JWST). A promising way forward to achieve this goal is to use single-mode fibers to inject light to a spectrograph. This mitigates many of the error terms facing current generation seeing-limited RV instruments while simultaneously offering the capability of high resolution spectroscopy within a small optical footprint (Schwab et al. 2012, Crepp 2014, Jovanovic et al. 2016a). We discuss the benefits of this technique, its challenges, and the current status of development.

astro-ph.IM↗

arXiv2019

Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud

The increasing demand for on-device deep learning services calls for a highly efficient manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity. The cloud-based solution is a promising approach to enabling deep learning applications on mobile devices where the large portions of a DNN are offloaded to the cloud. However, revealing data to the cloud leads to potential privacy risk. To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers. A simple data transformation is performed on the mobile device, while the resource-hungry training and the complex inference rely on the cloud data center. To protect the sensitive information, a lightweight privacy-preserving mechanism consisting of arbitrary data nullification and random noise addition is introduced, which provides strong privacy guarantee. A rigorous privacy budget analysis is given. Nonetheless, the private perturbation to the original data inevitably has a negative impact on the performance of further inference on the cloud side. To mitigate this influence, we propose a noisy training method to enhance the cloud-side network robustness to perturbed data. Through the sophisticated design, ARDEN can not only preserve privacy but also improve the inference performance. To validate the proposed ARDEN, a series of experiments based on three image datasets and a real mobile application are conducted. The experimental results demonstrate the effectiveness of ARDEN. Finally, we implement ARDEN on a demo system to verify its practicality.

cs.LG↗

arXiv2018

Dynamic Sounding for Multi-User MIMO in Wireless LANs

Consumer electronic (CE) devices increasingly rely on wireless local area networks (WLANs). Next generation WLANs will continue to exploit multiple antenna systems to satisfy the growing need for WLAN system capacity. Multiple-input multiple-output (MIMO) antenna systems improve the spectral efficiency and single user throughput. Multi-user MIMO (MU-MIMO) systems exploit the spatial separation of users for increasing the sum-throughput. In an MU-MIMO system, efficient channel sounding is essential for achieving optimal performance. The system analysis in this paper provides insights into the rate at which to perform channel sounding. This paper shows that optimal sounding intervals exist for single user transmit beamforming (SU-TxBF) and MU-MIMO, and proposes a low-complexity dynamic sounding approach for practical MU-MIMO WLAN deployments. The proposed approach adjusts the sounding interval adaptively based on the real-time learning outcomes in the given radio environment. Using real over-the-air channel measurements, significant throughput improvements (up to 31.8%) are demonstrated by adopting the proposed dynamic sounding approach, which is compliant with IEEE 802.11ac.

eess.SP↗