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Zhigang Li

Zhigang Li appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
astro-ph.COApplicationsSystems and ControlComputer VisionMethodologyQuantitative Methodseess.SYcond-mat.mes-hallcond-mat.mtrl-scieess.SPGenomicsgr-qchep-phInformation RetrievalInformation TheoryMachine Learningmath.APmath.ITNetworking and Internet Architecturephysics.atom-phphysics.optics

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Published work

25 published item(s)

preprint2026arXiv

A Zero-Inflated Beta Mixture Model for Marginal Mediation Analysis with Compositional Microbiome Mediators

The role of the microbiome in disease pathogenesis is an emerging field with strong evidence suggesting that dysbiosis is associated with precancerous and cancerous states. Microbiome data present substantial challenges for causal mediation analysis due to sparsity, compositional constraints, and latent heterogeneity. To address these issues, we propose a zero-inflated beta mixture (ZIBM) method for mediation analysis with compositional microbiome mediators. The proposed method accommodates excess zeros through a zero-inflation component and captures heterogeneity in non-zero relative abundances using a beta mixture distribution. Within the potential-outcomes framework, the ZIBM provides estimates of marginal microbiome-mediated causal effects, and model parameters are estimated using an expectation-maximization algorithm. Simulation studies demonstrate that the ZIBM yields more accurate estimation and reliable inference under conditions commonly observed in microbiome data, compared with existing approaches. An application to a real microbiome study further illustrates its practical utility. These results indicate that the proposed method provides a more flexible and robust statistical framework for mediation analysis involving compositional microbiome data.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Tuning Thermal Conductivity of Hybrid Perovskites through Halide Alloying

Tuning the thermal transport properties of hybrid halide perovskites is critical for their applications in optoelectronics, thermoelectrics, and photovoltaics. Here, we demonstrate an effective strategy to modulate the thermal transport property of hybrid perovskites by halide alloying. A highly tunable thermal conductivity of mixed-halide hybrid perovskites is achieved due to halide-alloying and structural distortion. Our experimental measurements show that the room temperature thermal conductivity of MAPb(BrxI1-x)3 (x = 0-1) can be largely modulated from 0.27 W/mK (x = 0.5) to 0.47 W/mK (x = 1). Molecular dynamics simulations further demonstrate that the thermal conductivity reduction of hybrid halide perovskites results from the suppression of the mean free paths of the low-frequency acoustic and optical phonons. It is found that halide alloying and the induced structural distortion can largely increase the scatterings of optical and acoustic phonons, respectively. The confined diffusion of MA+ cations in the octahedra cage is found to act as an additional thermal transport channel in hybrid perovskites and can contribute around 10-20% of the total thermal conductivity. Our findings provide a strategy for tailoring the thermal transport in hybrid halide perovskites which may largely benefit their related applications.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Formation Tracking for a Multi-Auv System Based on an Adaptive Sliding Mode Method in the Water Flow Environment

In this paper, formation tracking for a multi-AUV system (MAS) using an improved adaptive sliding mode control method is studied in the Three Dimensional (3-D) underwater environment. Firstly, the kinematics model and the dynamic model of the AUVs are given as the Six Dimensions of Freedom (6-DOF) considered. Then, control law based on the mathematical model of the AUVs is proposed based on the improved sliding mode method. A second order sliding mode control method is adopted to eliminate the chatting phenomenon of the controller. Thirdly, considering the water flow in the underwater working environment of the AUVs, an adaptive module is added to the controller. With the adaptive approach, the finite disturbances caused by water flow could be handled with the controller. The proposed method achieves stability by substituting an adaptive continuous term for the switching term in the controller. At last, a robust sliding mode controller with continuous model predictive control strategy for the multi-AUV system is developed to achieve leader-follower formation tracking under the presence of bounded flow disturbances, and simulations are implemented to confirm the effectiveness of the proposed method.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

HT-MMIOW: A Hypothesis Test approach for Microbiome Mediation using Inverse Odds Weighting

The human microbiome has an important role in determining health. Mediation analyses quantify the contribution of the microbiome in the causal path between exposure and disease; however, current mediation models cannot fully capture the high dimensional, correlated, and compositional nature of microbiome data and do not typically accommodate dichotomous outcomes. We propose a novel approach that uses inverse odds weighting to test for the mediating effect of the microbiome. We use simulation to demonstrate that our approach gains power for high dimensional mediators, and it is agnostic to the effect of interactions between the exposure and mediators. Our application to infant gut microbiome data from the New Hampshire Birth Cohort Study revealed a mediating effect of 6-week infant gut microbiome on the relationship between maternal prenatal antibiotic use during pregnancy and incidence of childhood allergy by 5 years of age.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Data-Driven Dispatchable Regions with Potentially Active Boundaries for Renewable Power Generation: Concept and Construction

The dispatchable region of volatile renewable power generation (RPG) quantifies how much uncertainty the power system can handle at a given operating point. State-of-the-art dispatchable region (DR) research has studied how system operational constraints influence the DR but has seldom considered the effect of the uncertainty features of RPG outputs. The traditional DR is generally described by a large number of boundaries, and it is computationally intensive to construct. To bridge these gaps, a novel type of DR is defined, which is enclosed by potentially active boundaries (PABs) that consider the operational constraints and uncertainty features of RPG outputs. The proposed DR is easier to construct because the PABs are only a small part of the traditional DR boundaries. The procedure for constructing the proposed DR is described in terms of the progressive search for PABs, which is formulated as a mixed-integer linear program by incorporating the discrete observed data points of RPG outputs as an approximate distribution. A parallel solution paradigm is also developed to expedite the construction procedure when using a large observed dataset. Simulation tests on the IEEE 30-bus and 118-bus systems verify the effectiveness and scalability of the proposed DR and the efficiency of the proposed algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

MarZIC: A Marginal mediation model for Zero-Inflated Compositional mediators with applications to microbiome data

The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data that is compounded by its compositional structure. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are actually not zero (i.e., false zeros). We develop a novel marginal mediation analysis method under the potential-outcomes framework to fill this gap and show the marginal model can also account for the compositional structure. The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation

6D pose estimation from a single RGB image is a challenging and vital task in computer vision. The current mainstream deep model methods resort to 2D images annotated with real-world ground-truth 6D object poses, whose collection is fairly cumbersome and expensive, even unavailable in many cases. In this work, to get rid of the burden of 6D annotations, we formulate the 6D pose refinement as a Markov Decision Process and impose on the reinforcement learning approach with only 2D image annotations as weakly-supervised 6D pose information, via a delicate reward definition and a composite reinforced optimization method for efficient and effective policy training. Experiments on LINEMOD and T-LESS datasets demonstrate that our Pose-Free approach is able to achieve state-of-the-art performance compared with the methods without using real-world ground-truth 6D pose labels.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Contention-based Grant-free Transmission with Independent Multi-pilot Scheme

Contention-based grant-free transmission is very promising for future massive machine-type communication (mMTC). In contention-based transmission, the random pilot collision is a challenging problem. To solve this problem, multiple pilots scheme is used to reduce the pilot collision probabliltiy. However, the existing work on multiple pilots relies on the low correlation of spatial channels, limiting its applicability. In this paper, an independent multi-pilot scheme is proposed, which utilizes the diversity of multiple pilots and is not limited by the spatial correlation. The receiver employs interference cancellation for both data symbols and multiple pilots to ensure the performance. The simulation results also show that the proposed independent multi-pilot scheme can significantly improve the BLER performance and increase the number of simultaneous access users.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Cosmological constraints from the redshift dependence of the Alcock-Paczynski effect: Possibility of estimateing the non-linear systematics using fast simulations

The tomographic AP method is so far the best method in separating the Alcock-Paczynski (AP) signal from the redshift space distortion (RSD) effects and deriving powerful constraints on cosmological parameters using the $\lesssim40h^{-1}\ \rm Mpc$ clustering region. To guarantee that the method can be easily applied to the future large scale structure (LSS) surveys, we study the possibility of estimating the systematics of the method using fast simulation method. The major contribution of the systematics comes from the non-zero redshift evolution of the RSD effects, which is quantified by $\hatξ_{Δs}(μ,z)$ in our analysis, and estimated using the BigMultidark exact N-body simulation and approximate COLA simulation samples. We find about 5\%/10\% evolution when comparing the $\hatξ_{Δs}(μ,z)$ measured as $z=0.5$/$z=1$ to the measurements at $z=0$. We checked the inaccuracy in the 2pCFs computed using COLA, and find it 5-10 times smaller than the intrinsic systematics of the tomographic AP method, indicating that using COLA to estimate the systematics is good enough. Finally, we test the effect of halo bias, and find $\lesssim$1.5\% change in $\hatξ_{Δs}$ when varying the halo mass within the range of $2\times 10^{12}$ to $10^{14}$ $M_{\odot}$. We will perform more studies to achieve an accurate and efficient estimation of the systematics in redshift range of $z=0-1.5$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Cosmological parameter estimation from large-scale structure deep learning

We propose a light-weight deep convolutional neural network (CNN) to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy. The training set is based on 465 realizations of a cubic box with a side length of $256\ h^{-1}\ \rm Mpc$, sampled with $128^3$ particles interpolated over a cubic grid of $128^3$ voxels. These volumes have cosmological parameters varying within the flat $Λ$CDM parameter space of $0.16 \leq Ω_m \leq 0.46$ and $2.0 \leq 10^9 A_s \leq 2.3$. The neural network takes as an input cubes with $32^3$ voxels and has three convolution layers, three dense layers, together with some batch normalization and pooling layers. In the final predictions from the network we find a $2.5\%$ bias on the primordial amplitude $σ_8$ that can not easily be resolved by continued training. We correct this bias to obtain unprecedented accuracy in the cosmological parameter estimation with statistical uncertainties of $δΩ_m$=0.0015 and $δσ_8$=0.0029, which are several times better than the results of previous CNN works. Compared with a 2-point analysis method using clustering region of 0-130 and 10-130 $h^{-1}$ Mpc, the CNN constraints are several times and an order of magnitude more precise, respectively. Finally, we conduct preliminary checks of the error-tolerance abilities of the neural network, and find that it exhibits robustness against smoothing, masking, random noise, global variation, rotation, reflection, and simulation resolution. Those effects are well understood in typical clustering analysis, but had not been tested before for the CNN approach. Our work shows that CNN can be more promising than people expected in deriving tight cosmological constraints from the cosmic large scale structure.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Optimizing AD Pruning of Sponsored Search with Reinforcement Learning

Industrial sponsored search system (SSS) can be logically divided into three modules: keywords matching, ad retrieving, and ranking. During ad retrieving, the ad candidates grow exponentially. A query with high commercial value might retrieve a great deal of ad candidates such that the ranking module could not afford. Due to limited latency and computing resources, the candidates have to be pruned earlier. Suppose we set a pruning line to cut SSS into two parts: upstream and downstream. The problem we are going to address is: how to pick out the best $K$ items from $N$ candidates provided by the upstream to maximize the total system's revenue. Since the industrial downstream is very complicated and updated quickly, a crucial restriction in this problem is that the selection scheme should get adapted to the downstream. In this paper, we propose a novel model-free reinforcement learning approach to fixing this problem. Our approach considers downstream as a black-box environment, and the agent sequentially selects items and finally feeds into the downstream, where revenue would be estimated and used as a reward to improve the selection policy. To the best of our knowledge, this is first time to consider the system optimization from a downstream adaption view. It is also the first time to use reinforcement learning techniques to tackle this problem. The idea has been successfully realized in Baidu's sponsored search system, and online long time A/B test shows remarkable improvements on revenue.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Robust RGB-based 6-DoF Pose Estimation without Real Pose Annotations

While much progress has been made in 6-DoF object pose estimation from a single RGB image, the current leading approaches heavily rely on real-annotation data. As such, they remain sensitive to severe occlusions, because covering all possible occlusions with annotated data is intractable. In this paper, we introduce an approach to robustly and accurately estimate the 6-DoF pose in challenging conditions and without using any real pose annotations. To this end, we leverage the intuition that the poses predicted by a network from an image and from its counterpart synthetically altered to mimic occlusion should be consistent, and translate this to a self-supervised loss function. Our experiments on LINEMOD, Occluded-LINEMOD, YCB and new Randomization LINEMOD dataset evidence the robustness of our approach. We achieve state of the art performance on LINEMOD, and OccludedLINEMOD in without real-pose setting, even outperforming methods that rely on real annotations during training on Occluded-LINEMOD.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Cosmological constraints from the redshift dependence of the Alcock-Paczynski effect: Fourier space analysis

The tomographic Alcock-Paczynski (AP) method utilizes the redshift evolution of the AP distortion to place constraints on cosmological parameters. It has proved to be a robust method that can separate the AP signature from the redshift space distortion (RSD) effect, and deliver powerful cosmological constraints using the $\lesssim 40h^{-1}\ \rm Mpc$ clustering region. In previous works, the tomographic AP method was performed via the anisotropic 2-point correlation function statistic. In this work we consider the feasibility of conducting the analysis in the Fourier domain and examine the pros and cons of this approach. We use the integrated galaxy power spectrum (PS) as a function of direction, $\hat P_{Δk}(μ)$, to quantify the magnitude of anisotropy in the large-scale structure clustering, and use its redshift variation to do the AP test. The method is tested on the large, high resolution Big-MultiDark Planck (BigMD) simulation at redshifts $z=0-1$, using the underlying true cosmology $Ω_m=0.3071,\ w=-1$. Testing the redshift evolution of $\hat P_{Δk}(μ)$ in the true cosmology and cosmologies deviating from the truth with $δΩ_m=0.1,\ δw=0.3$, we find that the redshift evolution of the AP distortion overwhelms the effects created by the RSD by a factor of $\sim1.7-3.6$. We test the method in the range of $k\in(0.2,1.8)\ h\ \rm Mpc^{-1}$, and find that it works well throughout the entire regime. We tune the halo mass within the range $2\times 10^{13}$ to $10^{14}\ M_{\odot}$, and find that the change of halo bias results in $\lesssim 5 \%$ change in $\hat P_{Δk}(μ)$, which is less significant compared with the cosmological effect. Our work shows that it is feasible to conduct the tomographic AP analysis in the Fourier space.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Toward accurate measurement of property-dependent galaxy clustering I. Comparison of the Vmax method and the "shuffled" method

Galaxy clustering provides insightful clues to our understanding of galaxy formation and evolution, as well as the universe. The redshift assignment for the random sample is one of the key steps to measure the galaxy clustering accurately. In this paper, by virtue of the mock galaxy catalogs, we investigate the effect of two redshift assignment methods on the measurement of galaxy two-point correlation functions (hereafter 2PCFs), the Vmax method and the "shuffled" method. We found that the shuffled method significantly underestimates both of the projected 2PCFs and the two-dimensional 2PCFs in redshift space. While the Vmax method does not show any notable bias on the 2PCFs for volume-limited samples. For flux-limited samples, the bias produced by the Vmax method is less than half of the shuffled method on large scales. Therefore, we strongly recommend the Vmax method to assign redshifts to random samples in the future galaxy clustering analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

A Semiparametric Joint Model for Terminal Trend of Quality of Life and Survival in Palliative Care Research

Palliative medicine is an interdisciplinary specialty focusing on improving quality of life (QOL) for patients with serious illness and their families. Palliative care programs are available or under development at over 80% of large US hospitals (300+ beds). Palliative care clinical trials present unique analytic challenges relative to evaluating the palliative care treatment efficacy which is to improve patients diminishing QOL as disease progresses towards end of life (EOL). A unique feature of palliative care clinical trials is that patients will experience decreasing QOL during the trial despite potentially beneficial treatment. Often longitudinal QOL and survival data are highly correlated which, in the face of censoring, makes it challenging to properly analyze and interpret longitudinal QOL trajectory. To address these issues, we propose a novel semiparametric statistical approach to jointly model longitudinal QOL and survival data. There are two sub-models in our approach: a semiparametric mixed effects model for longitudinal QOL and a Cox model for survival. We use regression splines method to estimate the nonparametric curves and AIC to select knots. We assess the model through simulation and application to establish a novel modeling approach that could be applied in future palliative care treatment research trials.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Data-Driven Real-Time Power Dispatch for Maximizing Variable Renewable Generation

Traditional power dispatch methods have difficulties in accommodating large-scale variable renewable generation (VRG) and have resulted in unnecessary VRG spillage in the practical industry. The recent dispatchable-interval-based methods have the potential to reduce VRG curtailment, but the dispatchable intervals are not allocated effectively due to the lack of exploiting historical dispatch records of VRG units. To bridge this gap, this paper proposes a novel data-driven real-time dispatch approach to maximize VRG utili-zation by using do-not-exceed (DNE) limits. This approach defines the maximum generation output ranges that the system can ac-commodate without compromising reliability. The DNE limits of VRG units and operating base points of conventional units are co-optimized by hybrid stochastic and robust optimization, and the decision models are formulated as mixed-integer linear programs by the sample average approximation technique exploiting historical VRG data. A strategy for selecting historical data samples is also proposed to capture the VRG uncertainty more accurately under variant prediction output levels. Computational experiments show the effectiveness of the proposed methods.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Multi-Period Do-Not-Exceed Limit for Variable Renewable Generation Dispatch Considering Discrete Recourse Controls

The do-not-exceed (DNE) limit method was proposed to accommodate more variable renewable generation (VRG) securely. However, the lack of involving discrete recourse control precludes this method from gaining more flexibility for better VRG integration. This letter formulates a multi-period DNE limit model considering continuous and discrete recourse controls. This model belongs to two-stage robust optimization with mixed integer recourse. A nested column-and-constraint generation approach is employed to solve this model. Case studies show the effectiveness of the proposed method.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Output Constraint Transfer for Kernelized Correlation Filter in Tracking

Kernelized Correlation Filter (KCF) is one of the state-of-the-art object trackers. However, it does not reasonably model the distribution of correlation response during tracking process, which might cause the drifting problem, especially when targets undergo significant appearance changes due to occlusion, camera shaking, and/or deformation. In this paper, we propose an Output Constraint Transfer (OCT) method that by modeling the distribution of correlation response in a Bayesian optimization framework is able to mitigate the drifting problem. OCT builds upon the reasonable assumption that the correlation response to the target image follows a Gaussian distribution, which we exploit to select training samples and reduce model uncertainty. OCT is rooted in a new theory which transfers data distribution to a constraint of the optimized variable, leading to an efficient framework to calculate correlation filters. Extensive experiments on a commonly used tracking benchmark show that the proposed method significantly improves KCF, and achieves better performance than other state-of-the-art trackers. To encourage further developments, the source code is made available https://github.com/bczhangbczhang/OCT-KCF.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Talk More Listen Less: Energy-Efficient Neighbor Discovery in Wireless Sensor Networks

Neighbor discovery is a fundamental service for initialization and managing network dynamics in wireless sensor networks and mobile sensing applications. In this paper, we present a novel design principle named Talk More Listen Less (TMLL) to reduce idle-listening in neighbor discovery protocols by learning the fact that more beacons lead to fewer wakeups. We propose an extended neighbor discovery model for analyzing wakeup schedules in which beacons are not necessarily placed in the wakeup slots. Furthermore, we are the first to consider channel occupancy rate in discovery protocols by introducing a new metric to trade off among duty-cycle, latency and channel occupancy rate. Guided by the TMLL principle, we have designed Nihao, a family of energy-efficient asynchronous neighbor discovery protocols for symmetric and asymmetric cases. We compared Nihao with existing state of the art protocols via analysis and real-world testbed experiments. The result shows that Nihao significantly outperforms the others both in theory and practice.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Nonlocal effective medium analysis in symmetric metal-dielectric multilayer metamaterials

The optical nonlocality in symmetric metal-dielectric multilayer metamaterials is theoretically and experimentally investigated with respect to transverse-magnetic-polarized incident light. A nonlocal effective medium theory is derived from the transfer-matrix method to determine the nonlocal effective permittivity depending on both the frequency and wave vector in a symmetric metal-dielectric multilayer stack. In contrast to the local effective medium theory, our proposed nonlocal effective medium theory can accurately predict measured incident angle-dependent reflection spectra from a fabricated multilayer stack and provide nonlocal dispersion relations. Moreover, the bulk plasmon polaritons with large wave vectors supported in the multilayer stack are also investigated with the nonlocal effective medium theory through the analysis of the dispersion relation and eigenmode.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Principal component gene set enrichment (PCGSE)

Motivation: Although principal component analysis (PCA) is widely used for the dimensional reduction of biomedical data, interpretation of PCA results remains daunting. Most existing methods attempt to explain each principal component (PC) in terms of a small number of variables by generating approximate PCs with few non-zero loadings. Although useful when just a few variables dominate the population PCs, these methods are often inadequate for characterizing the PCs of high-dimensional genomic data. For genomic data, reproducible and biologically meaningful PC interpretation requires methods based on the combined signal of functionally related sets of genes. While gene set testing methods have been widely used in supervised settings to quantify the association of groups of genes with clinical outcomes, these methods have seen only limited application for testing the enrichment of gene sets relative to sample PCs. Results: We describe a novel approach, principal component gene set enrichment (PCGSE), for computing the statistical association between gene sets and the PCs of genomic data. The PCGSE method performs a two-stage competitive gene set test using the correlation between each gene and each PC as the gene-level test statistic with flexible choice of both the gene set test statistic and the method used to compute the null distribution of the gene set statistic. Using simulated data with simulated gene sets and real gene expression data with curated gene sets, we demonstrate that biologically meaningful and computationally efficient results can be obtained from a simple parametric version of the PCGSE method that performs a correlation-adjusted two-sample t-test between the gene-level test statistics for gene set members and genes not in the set. Availability: http://cran.r-project.org/web/packages/PCGSE/index.html Contact: rob.frost@dartmouth.edu or jason.h.moore@dartmouth.edu

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preprint2014arXiv

Spectral gene set enrichment (SGSE)

Motivation: Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. Results: We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracey-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Availability: http://cran.r-project.org/web/packages/PCGSE/index.html Contact: rob.frost@dartmouth.edu or jason.h.moore@dartmouth.edu

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Can Large Extra Dimensions Solve the Proton Radius Puzzle?

The proton charge radius extracted from the recent muonic hydrogen spectroscopy [Antognini et al. 2013; Pohl et al. 2010] differs from the CODATA 2010 recommended value [Mohr et al. 2012] by more than 4% or $4.4 σ$. This discrepancy, dubbed as the "Proton Radius Puzzle", is a big challenge to the Standard Model of particle physics, and has triggered a number of works on the quantum electrodynamic calculations recently. The proton radius puzzle may indicate the presence of an extra correction which enlarges the 2S-2P energy gap in muonic hydrogen. Here we explore the possibility of large extra dimensions which could modify the Newtonian gravity at small scales and lower the 2S state energy while leaving the 2P state nearly unchanged. We find that such effect could be produced by four or more large extra dimensions which are allowed by the current constraints from low energy physics.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Cosmic Tides

We apply CMB lensing techniques to large scale structure and solve for the 3-D cosmic tidal field. We use small scale filamentary structures to solve for the large scale tidal shear and gravitational potential. By comparing this to the redshift space density field, one can measure the gravitational growth factor on large scales without cosmic variance. This potentially enables accurate measurements of neutrino masses and reconstruction of radial modes lost in 21 cm intensity mapping, which are essential for CMB and other cross correlations. We relate the tidal fields to the squeezed limit bispectrum, and present initial results from simulations and data from the SDSS.

0 citations0 reviewsNo code linkedOpen access