arXiv subjects

Li Zeng

Publications and source records attributed to Li Zeng.

At least 73 records · Page 4Linked to original sources

arXiv2023

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

cs.LG↗

arXiv2023

State space model multiple imputation for missing data in non-stationary multivariate time series with application in digital Psychiatry

Mobile technology enables unprecedented continuous monitoring of an individual's behavior, social interactions, symptoms, and other health conditions, presenting an enormous opportunity for therapeutic advancements and scientific discoveries regarding the etiology of psychiatric illness. Continuous collection of mobile data results in the generation of a new type of data: entangled multivariate time series of outcome, exposure, and covariates. Missing data is a pervasive problem in biomedical and social science research, and the Ecological Momentary Assessment (EMA) using mobile devices in psychiatric research is no exception. However, the complex structure of multivariate time series introduces new challenges in handling missing data for proper causal inference. Data imputation is commonly recommended to enhance data utility and estimation efficiency. The majority of available imputation methods are either designed for longitudinal data with limited follow-up times or for stationary time series, which are incompatible with potentially non-stationary time series. In the field of psychiatry, non-stationary data are frequently encountered as symptoms and treatment regimens may experience dramatic changes over time. To address missing data in possibly non-stationary multivariate time series, we propose a novel multiple imputation strategy based on the state space model (SSMmp) and a more computationally efficient variant (SSMimpute). We demonstrate their advantages over other widely used missing data strategies by evaluating their theoretical properties and empirical performance in simulations of both stationary and non-stationary time series, subject to various missing mechanisms. We apply the SSMimpute to investigate the association between social network size and negative mood using a multi-year observational smartphone study of bipolar patients, controlling for confounding variables.

stat.ME↗

arXiv2022

Spectral CT Reconstruction via Low-rank Representation and Structure Preserving Regularization

With the development of computed tomography (CT) imaging technology, it is possible to acquire multi-energy data by spectral CT. Being different from conventional CT, the X-ray energy spectrum of spectral CT is cutting into several narrow bins which leads to the result that only a part of photon can be collected in each individual energy channel, which cause the image qualities to be severely degraded by noise and artifacts. To address this problem, we propose a spectral CT reconstruction algorithm based on low-rank representation and structure preserving regularization in this paper. To make full use of the prior knowledge about both the inter-channel correlation and the sparsity in gradient domain of inner-channel data, this paper combines a low-rank correlation descriptor with a structure extraction operator as priori regularization terms for spectral CT reconstruction. Furthermore, a split-Bregman based iterative algorithm is developed to solve the reconstruction model. Finally, we propose a multi-channel adaptive parameters generation strategy according to CT values of each individual energy channel. Experimental results on numerical simulations and real mouse data indicate that the proposed algorithm achieves higher accuracy on both reconstruction and material decomposition than the methods based on simultaneous algebraic reconstruction technique (SART), total variation minimization (TVM), total variation with low-rank (LRTV), and spatial-spectral cube matching frame (SSCMF). Compared with SART, our algorithm improves the feature similarity (FSIM) by 40.4% on average for numerical simulation reconstruction, whereas TVM, LRTV, and SSCMF correspond to 26.1%, 28.2%, and 29.5%, respectively.

physics.med-ph↗

arXiv2022

EOS Manual

The exoplanet field now abounds with new discoveries of planets and planetary systems. It is of great interest for the scientific community to understand the compositions and internal structures of these new planets outside our own solar system, and then infer their formation scenarios. In particular, the proper implementation of the equation-of-states of various matters into the calculation of internal structure is crucial. Thus, we have come up with this write-up that provides the detailed steps to turn an equation-of-state (EOS) into a mass-radius relation of exoplanets, and highlight the physics and chemistry involved in these calculations. It must be visualized!

astro-ph.EP↗

arXiv2022

Gradient-enhanced deep neural network approximations

We propose in this work the gradient-enhanced deep neural networks (DNNs) approach for function approximations and uncertainty quantification. More precisely, the proposed approach adopts both the function evaluations and the associated gradient information to yield enhanced approximation accuracy. In particular, the gradient information is included as a regularization term in the gradient-enhanced DNNs approach, for which we present similar posterior estimates (by the two-layer neural networks) as those in the path-norm regularized DNNs approximations. We also discuss the application of this approach to gradient-enhanced uncertainty quantification, and present several numerical experiments to show that the proposed approach can outperform the traditional DNNs approach in many cases of interests.

cs.LG↗

arXiv2022

Adaptive deep density approximation for fractional Fokker-Planck equations

In this work, we propose adaptive deep learning approaches based on normalizing flows for solving fractional Fokker-Planck equations (FPEs). The solution of a FPE is a probability density function (PDF). Traditional mesh-based methods are ineffective because of the unbounded computation domain, a large number of dimensions and the nonlocal fractional operator. To this end, we represent the solution with an explicit PDF model induced by a flow-based deep generative model, simplified KRnet, which constructs a transport map from a simple distribution to the target distribution. We consider two methods to approximate the fractional Laplacian. One method is the Monte Carlo approximation. The other method is to construct an auxiliary model with Gaussian radial basis functions (GRBFs) to approximate the solution such that we may take advantage of the fact that the fractional Laplacian of a Gaussian is known analytically. Based on these two different ways for the approximation of the fractional Laplacian, we propose two models, MCNF and GRBFNF, to approximate stationary FPEs and MCTNF to approximate time-dependent FPEs. To further improve the accuracy, we refine the training set and the approximate solution alternately. A variety of numerical examples is presented to demonstrate the effectiveness of our adaptive deep density approaches.

cs.LG↗

arXiv2022

Experimental preparation and manipulation of squeezed cat states via an all-optical in-line squeezer

The squeezed cat state, an essential quantum resource, can be used for quantum error correction and slowing decoherence of the optical cat state. However, preparing a squeezed cat state with high generation rate, and effectively manipulating it, remain challenging. In this work, a high-performance all-optical in-line squeezer is developed to prepare a squeezed cat state and manipulate the phase of the quadrature squeezing. This scheme has the advantages that the phase of the quadrature squeezing of the squeezed cat state can be manipulated by changing the working condition of the squeezer, and that a higher generation rate can be achieved via the deterministic squeezing operation of the in-line squeezer. The generation rate of squeezed cat states reaches 2 kHz, the same as that of the initial cat state. The all-optical in-line squeezer proposed here removes the requirements of electro-optic and opto-electric conversions necessary for an off-line squeezer, thus enabling high-bandwidth squeezing operations on non-Gaussian states. These results provide an efficient method to prepare and manipulate optical squeezed cat states, which makes a step closer to their applications in all-optical quantum information processing.

quant-ph↗

arXiv2022

Keck/NIRSPEC studies of He I in the atmospheres of two inflated hot gas giants orbiting K dwarfs: WASP-52b and WASP-177b

We present the detection of neutral helium at 10833A in the atmosphere of WASP-52b and tentative evidence of helium in the atmosphere of the grazing WASP-177b, using high-resolution observations acquired with the NIRSPEC instrument on the Keck II telescope. We detect excess absorption by helium in WASP-52b's atmosphere of $3.44 \pm 0.31$% ($11σ$), or equivalently $66 \pm 5$ atmospheric scale heights. This absorption is centered on the planet's rest frame ($Δv = 0.00 \pm 1.19$km s$^{-1}$). We model the planet's escape using a 1D Parker wind model and calculate its mass-loss rate to be $\sim 1.4 \times 10^{11}$g s$^{-1}$, or equivalently 0.5% of its mass per Gyr. For WASP-177b, we see evidence for red-shifted ($Δv = 6.02 +/- 1.88$km s$^{-1}$) helium-like absorption of $1.28 \pm 0.29$% (equal to $23 \pm 5$ atmospheric scale heights). However, due to residual systematics in the transmission spectrum of similar amplitude, we do not interpret this as significant evidence for He absorption in the planet's atmosphere. Using a 1D Parker wind model, we set a $3σ$ upper limit on WASP-177b's escape rate of $7.9 \times 10^{10}$ g s$^{-1}$. Our results, taken together with recent literature detections, suggest the tentative relation between XUV irradiation and He I absorption amplitude may be shallower than previously suggested. Our results highlight how metastable helium can advance our understanding of atmospheric loss and its role in shaping the exoplanet population.

astro-ph.EP↗

arXiv2022

Quantum coherence of an orbital angular momentum multiplexed continuous-variable entangled state

Orbital angular momentum (OAM) multiplexed entangled state is an important quantum resource for high dimensional quantum information processing. In this paper, we experimentally quantify quantum coherence of OAM multiplexed continuous-variable (CV) entangled state and characterize its evolution in a noisy environment. We show that the quantum coherence of the OAM multiplexed CV entangled state carrying topological charges $l=1$ and $l=2$ are the same as that of the Gaussian mode with $l=0$ in a noisy channel. Furthermore, we show that the quantum coherence of OAM multiplexed entangled state is robust to noise, even though the sudden death of entanglement is observed. Our results provide reference for applying quantum coherence of OAM multiplexed CV entangled state in a noisy environment.

quant-ph↗

arXiv2022

Deterministic distribution of orbital angular momentum multiplexed continuous-variable entanglement and quantum steering

Orbital angular momentum (OAM) multiplexing provides an efficient method to improve data-carrying capacity in various quantum communication protocols. It is a precondition to distribute OAM multiplexed quantum resources in quantum channels for implementing quantum communication. However, quantum steering of OAM multiplexed optical fields and the effect of channel noise on OAM multiplexed quantum resources remain unclear. Here, we generate OAM multiplexed continuous-variable (CV) entangled states and distribute them in lossy or noisy channels. We show that the decoherence property of entanglement and quantum steering of the OAM multiplexed states carrying topological charges $l=1$ and $l=2$ are the same as that of the Gaussian mode with $l=0$ in lossy and noisy channels. The sudden death of entanglement and quantum steering of high-order OAM multiplexed states is observed in the presence of excess noise. Our results demonstrate the feasibility to realize high data-carrying capacity quantum information processing by utilizing OAM multiplexed CV entangled states.

quant-ph↗

arXiv2022

Solving time dependent Fokker-Planck equations via temporal normalizing flow

In this work, we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck (TFP) equations. It is well known that solutions of such equations are probability density functions, and thus our approach relies on modelling the target solutions with the temporal normalizing flows. The temporal normalizing flow is then trained based on the TFP loss function, without requiring any labeled data. Being a machine learning scheme, the proposed approach is mesh-free and can be easily applied to high dimensional problems. We present a variety of test problems to show the effectiveness of the learning approach.

cs.LG↗

arXiv2022

New Perspectives on the Exoplanet Radius Gap from a Mathematica Tool and Visualized Water Equation of State

Recent astronomical observations obtained with the Kepler and TESS missions and their related ground-based follow-ups revealed an abundance of exoplanets with a size intermediate between Earth and Neptune. A low occurrence rate of planets has been identified at around twice the size of Earth, known as the exoplanet radius gap or radius valley. We explore the geometry of this gap in the mass-radius diagram, with the help of a Mathematica plotting tool developed with the capability of manipulating exoplanet data in multidimensional parameter space, and with the help of visualized water equations of state in the temperature-density graph and the entropy-pressure graph. We show that the radius valley can be explained by a compositional difference between smaller, predominantly rocky planets and larger planets that exhibit greater compositional diversity including cosmic ices (water, ammonia, methane) and gaseous envelopes. In particular, among the larger planets, when viewed from the perspective of planet equilibrium temperature, the hot ones are consistent with ice-dominated composition without significant gaseous envelopes, while the cold ones have more diverse compositions, including various amounts of gaseous envelopes.

astro-ph.EP↗

arXiv2021

Social Distancing, Gathering, Search Games: Mobile Agents on Simple Networks

During epidemics, the population is asked to Socially Distance, with pairs of individuals keeping two meters apart. We model this as a new optimization problem by considering a team of agents placed on the nodes of a network. Their common aim is to achieve pairwise graph distances of at least D, a state we call socially distanced. (If D=1, they want to be at distinct nodes; if D=2 they want to be non-adjacent.) We allow only a simple type of motion called a Lazy Random Walk: with probability p (called the laziness parameter), they remain at their current node next period; with complementary probability 1-p , they move to a random adjacent node. The team seeks the common value of p which achieves social distance in the least expected time, which is the absorption time of a Markov chain. We observe that the same Markov chain, with different goals (absorbing states), models the gathering, or multi-rendezvous problem (all agents at the same node). Allowing distinct laziness for two types of agents (searchers and hider), extends the existing literature on predator-prey search games to multiple searchers. We consider only special networks: line, cycle and grid. Keywords: epidemic, random walk, dispersion, rendezvous search

math.OC↗

arXiv2021

K2-79b and K2-222b: Mass measurements of two small exoplanets with periods beyond 10 days that overlap with periodic magnetic activity signals

We present mass and radius measurements of K2-79b and K2-222b, two transiting exoplanets orbiting active G-type stars. Their respective 10.99d and 15.39d orbital periods fall near periods of signals induced by stellar magnetic activity. The two signals might therefore interfere and lead to an inaccurate estimate of exoplanet mass. We present a method to mitigate these effects when radial velocity and activity indicator observations are available over multiple observing seasons and the orbital period of the exoplanet is known. We perform correlation and periodogram analyses on sub-sets composed of each target's two observing seasons, in addition to the full data sets. For both targets, these analyses reveal an optimal season with little to no interference at the orbital period of the known exoplanet. We make a confident mass detection of each exoplanet by confirming agreement between fits to the full radial velocity set and the optimal season. For K2-79b, we measure a mass of 11.8 $\pm$ 3.6 $M_{Earth}$ and a radius of 4.09 $\pm$ 0.17 $R_{Earth}$. For K2-222b, we measure a mass of 8.0 $\pm$ 1.8 $M_{Earth}$ and a radius of 2.35 $\pm$ 0.08 $R_{Earth}$. According to model predictions, K2-79b is a highly irradiated Uranus-analog and K2-222b hosts significant amounts of water ice. We also present an RV solution for a candidate second companion orbiting K2-222 at 147.5d.

astro-ph.EP↗

arXiv2021

Deep Analysis on Subgraph Isomorphism

Subgraph isomorphism is a well-known NP-hard problem which is widely used in many applications, such as social network analysis and knowledge graph query. Its performance is often limited by the inherent hardness. Several insightful works have been done since 2012, mainly optimizing pruning rules and matching orders to accelerate enumerating all isomorphic subgraphs. Nevertheless, their correctness and performance are not well studied. First, different languages are used in implementation with different compilation flags. Second, experiments are not done on the same platform and the same datasets. Third, some ideas of different works are even complementary. Last but not least, there exist errors when applying some algorithms. In this paper, we address these problems by re-implementing seven representative subgraph isomorphism algorithms as well as their improved versions, and conducting comprehensive experiments on various graphs. The results show pros and cons of state-of-the-art solutions and explore new approaches to optimization.

cs.DB↗

arXiv2021

GSI: GPU-friendly Subgraph Isomorphism

Subgraph isomorphism is a well-known NP-hard problem that is widely used in many applications, such as social network analysis and query over the knowledge graph. Due to the inherent hardness, its performance is often a bottleneck in various real-world applications. Therefore, we address this by designing an efficient subgraph isomorphism algorithm leveraging features of GPU architecture, such as massive parallelism and memory hierarchy. Existing GPU-based solutions adopt a two-step output scheme, performing the same join process twice in order to write intermediate results concurrently. They also lack GPU architecture-aware optimizations that allow scaling to large graphs. In this paper, we propose a GPU-friendly subgraph isomorphism algorithm, GSI. Different from existing edge join-based GPU solutions, we propose a Prealloc-Combine strategy based on the vertex-oriented framework, which avoids joining-twice in existing solutions. Also, a GPU-friendly data structure (called PCSR) is proposed to represent an edge-labeled graph. Extensive experiments on both synthetic and real graphs show that GSI outperforms the state-of-the-art algorithms by up to several orders of magnitude and has good scalability with graph size scaling to hundreds of millions of edges.

cs.DB↗

arXiv2021

A general kernel boosting framework integrating pathways for predictive modeling based on genomic data

Predictive modeling based on genomic data has gained popularity in biomedical research and clinical practice by allowing researchers and clinicians to identify biomarkers and tailor treatment decisions more efficiently. Analysis incorporating pathway information can boost discovery power and better connect new findings with biological mechanisms. In this article, we propose a general framework, Pathway-based Kernel Boosting (PKB), which incorporates clinical information and prior knowledge about pathways for prediction of binary, continuous and survival outcomes. We introduce appropriate loss functions and optimization procedures for different outcome types. Our prediction algorithm incorporates pathway knowledge by constructing kernel function spaces from the pathways and use them as base learners in the boosting procedure. Through extensive simulations and case studies in drug response and cancer survival datasets, we demonstrate that PKB can substantially outperform other competing methods, better identify biological pathways related to drug response and patient survival, and provide novel insights into cancer pathogenesis and treatment response.

stat.ML↗

arXiv2020

Confirmation of WASP-107b's extended Helium atmosphere with Keck II/NIRSPEC

We present the detection of helium in the extended atmosphere of the sub-Saturn WASP-107b using high resolution ($R \approx 25000$) near-infrared spectra from Keck II/NIRSPEC. We find peak excess absorption of $7.26 \pm 0.24\%$ (30$σ$) centered on the HeI triplet at 10833A. The amplitude and shape of the helium absorption profile is in excellent agreement with previous observations of escaping helium from this planet made by CARMENES and HST. This suggests there is no significant temporal variation in the signature of escaping helium from the planet over a two year baseline. This result demonstrates Keck II/NIRSPEC's ability to detect atmospheric escape in exoplanets, making it a useful instrument to further our understanding of the evaporation of exoplanetary atmospheres via ground-based observations of HeI.

astro-ph.EP↗