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Liang Yu

Publications and source records attributed to Liang Yu.

At least 55 records · Page 3Linked to original sources
arXiv2020

Deep Reinforcement Learning for Smart Grid Protection Against Coordinated Multistage Transmission Line Attacks

With the increase of connectivity in power grid, a cascading failure may be triggered by the failure of a transmission line, which can lead to substantial economic losses and serious negative social impacts. Therefore, it is very important to identify the critical lines under various types of attacks that may initiate a cascading failure and deploy defense resources to protect them. Since coordinated multistage line attacks can lead to larger negative impacts compared with a single-stage attack or a multistage attack without coordination, this paper intends to identify the critical lines under coordinated multistage attacks that may initiate a cascading failure and deploy limited defense resources optimally. To this end, we first formulate a total generation loss maximization problem with the consideration of multiple attackers and multiple stages. Due to the large size of solution space, it is very challenging to solve the formulated problem. To overcome the challenge, we reformulate the problem as a Markov game and design its components, e.g., state, action, and reward. Next, we propose a scalable algorithm to solve the Markov game based on multi-agent deep reinforcement learning and prioritized experience replay, which can determine the optimal attacking line sequences. Then, we design a defense strategy to decide the optimal defense line set. Extensive simulation results show the effectiveness of the proposed algorithm and the designed defense strategy.

eess.SY↗
arXiv2020

Photometry of 10 Million Stars from the First Two Years of TESS Full Frame Images

The Transiting Exoplanet Survey Satellite (TESS) is the first high-precision full-sky photometry survey in space. We present light curves from a magnitude limited set of stars and other stationary luminous objects from the TESS Full Frame Images, as reduced by the MIT Quick Look Pipeline (QLP). Our light curves cover the full two-year TESS Primary Mission and include $\sim$ 14,770,000 and $\sim$ 9,600,000 individual light curve segments in the Southern and Northern ecliptic hemispheres, respectively. We describe the photometry and detrending techniques we used to create the light curves, and compare the noise properties with theoretical expectations. All of the QLP light curves are available at MAST as a High Level Science Product via doi.org/10.17909/t9-r086-e880 (https://archive.stsci.edu/hlsp/qlp). This is the largest collection of TESS photometry available to the public to date.

astro-ph.EP↗
arXiv2020

Luzin's (N) and randomness reflection

We show that a computable function $f:\mathbb R\rightarrow\mathbb R$ has Luzin's property (N) if and only if it reflects $Π^1_1$-randomnes, if and only if it reflects $Δ^1_1(\mathcal O)$-randomness, and if and only if it reflects $\mathcal O$-Kurtz randomness, but reflecting Martin-Löf randomness or weak-2-randomness does not suffice. Here a function $f$ is said to reflect a randomness notion $R$ if whenever $f(x)$ is $R$-random, then $x$ is $R$-random as well. If additionally $f$ is known to have bounded variation, then we show $f$ has Luzin's (N) if and only if it reflects weak-2-randomness, and if and only if it reflects $\emptyset'$-Kurtz randomness. This links classical real analysis with algorithmic randomness.

math.LO↗
arXiv2020

A Giant Planet Candidate Transiting a White Dwarf

Astronomers have discovered thousands of planets outside the solar system, most of which orbit stars that will eventually evolve into red giants and then into white dwarfs. During the red giant phase, any close-orbiting planets will be engulfed by the star, but more distant planets can survive this phase and remain in orbit around the white dwarf. Some white dwarfs show evidence for rocky material floating in their atmospheres, in warm debris disks, or orbiting very closely, which has been interpreted as the debris of rocky planets that were scattered inward and tidally disrupted. Recently, the discovery of a gaseous debris disk with a composition similar to ice giant planets demonstrated that massive planets might also find their way into tight orbits around white dwarfs, but it is unclear whether the planets can survive the journey. So far, the detection of intact planets in close orbits around white dwarfs has remained elusive. Here, we report the discovery of a giant planet candidate transiting the white dwarf WD 1856+534 (TIC 267574918) every 1.4 days. The planet candidate is roughly the same size as Jupiter and is no more than 14 times as massive (with 95% confidence). Other cases of white dwarfs with close brown dwarf or stellar companions are explained as the consequence of common-envelope evolution, wherein the original orbit is enveloped during the red-giant phase and shrinks due to friction. In this case, though, the low mass and relatively long orbital period of the planet candidate make common-envelope evolution less likely. Instead, the WD 1856+534 system seems to demonstrate that giant planets can be scattered into tight orbits without being tidally disrupted, and motivates searches for smaller transiting planets around white dwarfs.

astro-ph.EP↗
arXiv2020

Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

In commercial buildings, about 40%-50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic burden on building operators. In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building under dynamic pricing with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort. Due to the existence of unknown thermal dynamics models, parameter uncertainties (e.g., outdoor temperature, electricity price, and number of occupants), spatially and temporally coupled constraints associated with indoor temperature and CO2 concentration, a large discrete solution space, and a non-convex and non-separable objective function, it is very challenging to achieve the above aim. To this end, the above energy cost minimization problem is reformulated as a Markov game. Then, an HVAC control algorithm is proposed to solve the Markov game based on multi-agent deep reinforcement learning with attention mechanism. The proposed algorithm does not require any prior knowledge of uncertain parameters and can operate without knowing building thermal dynamics models. Simulation results based on real-world traces show the effectiveness, robustness and scalability of the proposed algorithm.

eess.SY↗
arXiv2020

TOI 694 b and TIC 220568520 b: Two Low-Mass Companions Near the Hydrogen Burning Mass Limit Orbiting Sun-like Stars

We report the discovery of TOI 694 b and TIC 220568520 b, two low-mass stellar companions in eccentric orbits around metal-rich Sun-like stars, first detected by the Transiting Exoplanet Survey Satellite (TESS). TOI 694 b has an orbital period of 48.05131$\pm$0.00019 days and eccentricity of 0.51946$\pm$0.00081, and we derive a mass of 89.0$\pm$5.3 $M_J$ (0.0849$\pm$0.0051 $M_\odot$) and radius of 1.111$\pm$0.017 $R_J$ (0.1142$\pm$0.0017 $R_\odot$). TIC 220568520 b has an orbital period of 18.55769$\pm$0.00039 days and eccentricity of 0.0964$\pm$0.0032, and we derive a mass of 107.2$\pm$5.2 $M_J$ (0.1023$\pm$0.0050 $M_\odot$) and radius of 1.248$\pm$0.018 $R_J$ (0.1282$\pm$0.0019 $R_\odot$). Both binary companions lie close to and above the Hydrogen burning mass threshold that separates brown dwarfs and the lowest mass stars, with TOI 694 b being 2-$σ$ above the canonical mass threshold of 0.075 $M_\odot$. The relatively long periods of the systems mean that the magnetic fields of the low-mass companions are not expected to inhibit convection and inflate the radius, which according to one leading theory is common in similar objects residing in short-period tidally-synchronized binary systems. Indeed we do not find radius inflation for these two objects when compared to theoretical isochrones. These two new objects add to the short but growing list of low-mass stars with well-measured masses and radii, and highlight the potential of the TESS mission for detecting such rare objects orbiting bright stars.

astro-ph.SR↗
arXiv2020

A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction

Motivation: Drug combination is a sensible strategy for disease treatment by improving the efficacy and reducing concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a plenty of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in performance and scalability. Results: In this paper, we proposed a Network Embedding framework in Multiplex Networks (NEMN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide quantitative importance of each network. To explain the feasibility of NEMN, we applied our framework to the data of drug-drug interactions, on which it showed better performance in terms of AUPR and ROC. For Drug combination prediction, we found seven novel drug combinations which have been validated by external sources among the top-ranked predictions of our model.

q-bio.QM↗
arXiv2020

Identification and Validation of the SNV Biomarkers Based on Multi-Dimensional Patterns

Background: Single nucleotide variants (SNVs) are detected as different distributions of DNA samples of distinct types of cancer patients. Even though, it is an exacting task to select the appropriate method to identify cancer to the greatest extent of SNVs. Results: In this paper, we proposed a biomarker concept based on SNV patterns in different feature dimensions. Raw dataset (2761 samples) consisting of twelve different cancers was obtained from TCGA (The Cancer Genome Atlas). After preliminary screening of 562,321 DNA mutation sites in the samples, the mutation sites were extracted and characterized by cancer types in six different SNV feature dimensions. In this study, we found that the extracted features showed similar distribution in the cluster center of the disease type of the samples. After the initial processing of the raw data, the sample was more focused on the subtype distribution of the cancer or the cancer at the SNV level. We used k-nearest neighbors (KNN) to classify the extracted features and Leave-One-Out cross verified them. The accuracy of classifying is stable at around 97% and reached 97.43% at the highest. During the validation phase, we found validated oncogenes in the loci of the features with the highest importance among nine cancers. Conclusions: In summary, the samples showed consistent patterns according to the cancer in which it belongs. It is feasible to classify the cancer of the sample by the distribution of different dimensions of the SNVs and has a high accuracy. And has potential implications for the discovery of cancer-causing genes.

q-bio.QM↗
arXiv2020

Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia

We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization). The manifestations of computed tomography (CT) imaging of COVID-19 had their own characteristics, which are different from other types of viral pneumonia, such as Influenza-A viral pneumonia. Therefore, clinical doctors call for another early diagnostic criteria for this new type of pneumonia as soon as possible.This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases with pulmonary CT images using deep learning techniques. The candidate infection regions were first segmented out using a 3-dimensional deep learning model from pulmonary CT image set. These separated images were then categorized into COVID-19, Influenza-A viral pneumonia and irrelevant to infection groups, together with the corresponding confidence scores using a location-attention classification model. Finally the infection type and total confidence score of this CT case were calculated with Noisy-or Bayesian function.The experiments result of benchmark dataset showed that the overall accuracy was 86.7 % from the perspective of CT cases as a whole.The deep learning models established in this study were effective for the early screening of COVID-19 patients and demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.

physics.med-ph↗
arXiv2019

Deep Reinforcement Learning for Smart Home Energy Management

In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, non-shiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy.

eess.SY↗
arXiv2019

An Eccentric Massive Jupiter Orbiting a Sub-Giant on a 9.5 Day Period Discovered in the Transiting Exoplanet Survey Satellite Full Frame Images

We report the discovery of TOI-172 b from the Transiting Exoplanet Survey Satellite (TESS) mission, a massive hot Jupiter transiting a slightly evolved G-star with a 9.48-day orbital period. This is the first planet to be confirmed from analysis of only the TESS full frame images, because the host star was not chosen as a two minute cadence target. From a global analysis of the TESS photometry and follow-up observations carried out by the TESS Follow-up Observing Program Working Group, TOI-172 (TIC 29857954) is a slightly evolved star with an effective temperature of $T_{\rm eff}$ =$5645\pm50$ K, a mass of $M_{\star}$ = $1.128^{+0.065}_{-0.061}$ $M_{\odot}$, radius of $R_{\star}$ = $1.777^{+0.047}_{-0.044}$ $R_{\odot}$, a surface gravity of $\log$ $g_{\star}$ = $3.993^{+0.027}_{-0.028}$, and an age of $7.4^{+1.6}_{-1.5}$ Gyr. Its planetary companion (TOI-172 b) has a radius of $R_{\rm P}$ = $0.965^{+0.032}_{-0.029}$ $R_{\rm J}$, a mass of $M_{\rm P}$ = $5.42^{+0.22}_{-0.20}$ $M_{\rm J}$, and is on an eccentric orbit ($e = 0.3806^{+0.0093}_{-0.0090}$). TOI-172 b is one of the few known massive giant planets on a highly eccentric short-period orbit. Future study of the atmosphere of this planet and its system architecture offer opportunities to understand the formation and evolution of similar systems.

astro-ph.EP↗
arXiv2019

TOI-150b and TOI-163b: two transiting hot Jupiters, one eccentric and one inflated, revealed by TESS near and at the edge of the JWST CVZ

We present the discovery of TYC9191-519-1b (TOI-150b, TIC 271893367) and HD271181b (TOI-163b, TIC 179317684), two hot Jupiters initially detected using 30-minute cadence Transiting Exoplanet Survey Satellite TESS photometry from Sector 1 and thoroughly characterized through follow-up photometry (CHAT, Hazelwood, LCO/CTIO, El Sauce, TRAPPIST-S), high-resolution spectroscopy (FEROS, CORALIE) and speckle imaging (Gemini/DSSI), confirming the planetary nature of the two signals. A simultaneous joint fit of photometry and radial velocity using a new fitting package juliet reveals that TOI-150b is a $1.254\pm0.016\ R_J$, massive ($2.61^{+0.19}_{-0.12}\ M_J$) hot Jupiter in a $5.857$-day orbit, while TOI-163b is an inflated ($R_P$ = $1.478^{+0.022}_{-0.029} R_J$, $M_P$ = $1.219\pm0.11 M_J$) hot Jupiter on a $P$ = $4.231$-day orbit; both planets orbit F-type stars. A particularly interesting result is that TOI-150b shows an eccentric orbit ($e=0.262^{+0.045}_{-0.037}$), which is quite uncommon among hot Jupiters. We estimate that this is consistent, however, with the circularization timescale which is slightly larger than the age of the system. These two hot Jupiters are both prime candidates for further characterization --- in particular, both are excellent candidates for determining spin-orbit alignments via the Rossiter-McLaughlin (RM) effect and for characterizing atmospheric thermal structures using secondary eclipse observations considering they are both located closely to the James Webb Space Telescope (JWST) Continuous Viewing Zone (CVZ).

astro-ph.EP↗
arXiv2019

Characterizing K2 Candidate Planetary Systems Orbiting Low-Mass Stars IV: Updated Properties for 86 Cool Dwarfs Observed During Campaigns 1-17

We present revised stellar properties for 172 K2 target stars that were identified as possible hosts of transiting planets during Campaigns 1-17. Using medium-resolution near-infrared spectra acquired with the NASA Infrared Telescope Facility/SpeX and Palomar/TripleSpec, we found that 86 of our targets were bona fide cool dwarfs, 74 were hotter dwarfs, and 12 were giants. Combining our spectroscopic metallicities with Gaia parallaxes and archival photometry, we derived photometric stellar parameters and compared them to our spectroscopic estimates. Although our spectroscopic and photometric radius and temperature estimates are consistent, our photometric mass estimates are systematically 0.11 solar masses (34%) higher than our spectroscopic mass estimates for the least massive stars (photometric mass estimates < 0.4 solar masses). Adopting the photometric parameters and comparing our results to parameters reported in the Ecliptic Plane Input Catalog, our revised stellar radii are 0.15 solar radii (40%) larger and our revised stellar effective temperatures are roughly 65K cooler. Correctly determining the properties of K2 target stars is essential for characterizing any associated planet candidates, estimating the planet search sensitivity, and calculating planet occurrence rates. Even though Gaia parallaxes have increased the power of photometric surveys, spectroscopic characterization remains essential for determining stellar metallicities and investigating correlations between stellar metallicity and planetary properties.

astro-ph.SR↗
arXiv2019

Identifying Exoplanets with Deep Learning III: Automated Triage and Vetting of TESS Candidates

NASA's Transiting Exoplanet Survey Satellite (TESS) presents us with an unprecedented volume of space-based photometric observations that must be analyzed in an efficient and unbiased manner. With at least $\sim1,000,000$ new light curves generated every month from full frame images alone, automated planet candidate identification has become an attractive alternative to human vetting. Here we present a deep learning model capable of performing triage and vetting on TESS candidates. Our model is modified from an existing neural network designed to automatically classify Kepler candidates, and is the first neural network to be trained and tested on real TESS data. In triage mode, our model can distinguish transit-like signals (planet candidates and eclipsing binaries) from stellar variability and instrumental noise with an average precision (the weighted mean of precisions over all classification thresholds) of 97.0% and an accuracy of 97.4%. In vetting mode, the model is trained to identify only planet candidates with the help of newly added scientific domain knowledge, and achieves an average precision of 69.3% and an accuracy of 97.8%. We apply our model on new data from Sector 6, and present 288 new signals that received the highest scores in triage and vetting and were also identified as planet candidates by human vetters. We also provide a homogeneously classified set of TESS candidates suitable for future training.

astro-ph.EP↗
arXiv2019

TOI-216b and TOI-216c: Two warm, large exoplanets in or slightly wide of the 2:1 orbital resonance

Warm, large exoplanets with 10-100 day orbital periods pose a major challenge to our understanding of how planetary systems form and evolve. Although high eccentricity tidal migration has been invoked to explain their proximity to their host stars, a handful reside in or near orbital resonance with nearby planets, suggesting a gentler history of in situ formation or disk migration. Here we confirm and characterize a pair of warm, large exoplanets discovered by the TESS Mission orbiting K-dwarf TOI-216. Our analysis includes additional transits and transit exclusion windows observed via ground-based follow-up. We find two families of solutions, one corresponding to a sub-Saturn-mass planet accompanied by a Neptune-mass planet and the other to a Jupiter in resonance with a sub-Saturn-mass planet. We prefer the second solution based on the orbital period ratio, the planet radii, the lower free eccentricities, and libration of the 2:1 resonant argument, but cannot rule out the first. The free eccentricities and mutual inclination are compatible with stirring by other, undetected planets in the system, particularly for the second solution. We discuss prospects for better constraints on the planets' properties and orbits through follow-up, including transits observed from the ground.

astro-ph.EP↗
arXiv2019

Identifying Exoplanets with Deep Learning II: Two New Super-Earths Uncovered by a Neural Network in K2 Data

For years, scientists have used data from NASA's Kepler Space Telescope to look for and discover thousands of transiting exoplanets. In its extended K2 mission, Kepler observed stars in various regions of sky all across the ecliptic plane, and therefore in different galactic environments. Astronomers want to learn how the population of exoplanets are different in these different environments. However, this requires an automatic and unbiased way to identify the exoplanets in these regions and rule out false positive signals that mimic transiting planet signals. We present a method for classifying these exoplanet signals using deep learning, a class of machine learning algorithms that have become popular in fields ranging from medical science to linguistics. We modified a neural network previously used to identify exoplanets in the Kepler field to be able to identify exoplanets in different K2 campaigns, which range in galactic environments. We train a convolutional neural network, called AstroNet-K2, to predict whether a given possible exoplanet signal is really caused by an exoplanet or a false positive. AstroNet-K2 is highly successful at classifying exoplanets and false positives, with accuracy of 98% on our test set. It is especially efficient at identifying and culling false positives, but for now, still needs human supervision to create a complete and reliable planet candidate sample. We use AstroNet-K2 to identify and validate two previously unknown exoplanets. Our method is a step towards automatically identifying new exoplanets in K2 data and learning how exoplanet populations depend on their galactic birthplace.

astro-ph.EP↗
arXiv2018

TESS Discovery of a Transiting Super-Earth in the $π$ Mensae System

We report the detection of a transiting planet around $π$ Mensae (HD 39091), using data from the Transiting Exoplanet Survey Satellite (TESS). The solar-type host star is unusually bright (V=5.7) and was already known to host a Jovian planet on a highly eccentric, 5.7-year orbit. The newly discovered planet has a size of $2.04\pm 0.05$ $R_\oplus$ and an orbital period of 6.27 days. Radial-velocity data from the HARPS and AAT/UCLES archives also displays a 6.27-day periodicity, confirming the existence of the planet and leading to a mass determination of $4.82\pm 0.85$ $M_\oplus$. The star's proximity and brightness will facilitate further investigations, such as atmospheric spectroscopy, asteroseismology, the Rossiter--McLaughlin effect, astrometry, and direct imaging.

astro-ph.EP↗