Browsing by Author "Wang, Minjie"
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Item Electrically tunable hot-silicon terahertz attenuator(AIP Publishing LLC, 2014) Wang, Minjie; Vajtai, Robert; Ajayan, Pulickel M.; Kono, JunichiroWe have developed a continuously tunable, broadband terahertz attenuator with a transmission tuning range greater than 103. Attenuation tuning is achieved electrically, by simply changing the DC voltage applied to a heating wire attached to a bulk silicon wafer, which controls its temperature between room temperature and ~550 K, with the corresponding free-carrier density adjusted between ~1011 cm−3 and ~1017 cm−3. This “hot-silicon”-based terahertz attenuator works most effectively at 450–550 K (corresponding to a DC voltage variation of only ~7 V) and completely shields terahertz radiation above 550 K in a frequency range of 0.1–2.5 THz. Both intrinsic and doped silicon wafers were tested and demonstrated to work well as a continuously tunable attenuator. All behaviors can be understood quantitatively via the free-carrier Drude model taking into account thermally activated intrinsic carriers.Item Giant Terahertz-Wave Absorption by Monolayer Graphene in a Total Internal Reflection Geometry(American Chemical Society, 2017) Harada, Yoichi; Ukhtary, Muhammad Shoufie; Wang, Minjie; Srinivasan, Sanjay K.; Hasdeo, Eddwi H.; Nugraha, Ahmad R.T.; Noe, G. Timothy II; Sakai, Yuji; Vajtai, Robert; Ajayan, Pulickel M.; Saito, Riichiro; Kono, JunichiroWe experimentally demonstrated significant enhancement of terahertz-wave absorption in monolayer graphene by simply sandwiching monolayer graphene between two dielectric media in a total internal reflection geometry. In going through this structure, the evanescent wave of the incident terahertz beam interacts with the sandwiched graphene layer multiple (up to four) times at varying incidence angles. We observed extremely large attenuation (up to ∼70% per reflection), especially for s-polarized radiation. The experimental results are quantitatively consistent with our calculations, where we modeled the experiment as an electromagnetic wave reflection process in monolayer graphene. We also derived analytical expressions for the absorptance, showing that the absorptance is proportional to the amount of Joule heating on the graphene surface induced by the terahertz radiation.Item Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data(JMLR, 2021) Wang, Minjie; Allen, Genevera I.In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that enjoys strong empirical performance and inherits the mathematical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our so-called iGecco+ approach selects features from each data view that are best for determining the groups, often leading to improved integrative clustering. To solve our problem, we develop a new type of generalized multi-block ADMM algorithm using sub-problem approximations that more efficiently fits our model for big data sets. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data.Item Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data(JMLR, 2021) Wang, Minjie; Allen, Genevera I.In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that enjoys strong empirical performance and inherits the mathematical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our so-called iGecco+ approach selects features from each data view that are best for determining the groups, often leading to improved integrative clustering. To solve our problem, we develop a new type of generalized multi-block ADMM algorithm using sub-problem approximations that more efficiently fits our model for big data sets. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data.Item Statistical Machine Learning Approaches for Data Integration and Graphical Models(2021-04-26) Wang, Minjie; Allen, Genevera I.Unsupervised learning aims to identify underlying patterns in unlabeled data. In this thesis, we develop methodologies involving two popular unsupervised learning problems: clustering with application to data integration and graphical models. As the volume and variety of data grows, data integration, which analyzes multiple sources of data simultaneously, has gained increasing popularity. We study mixed multi-view data, where multiple sets of diverse features are measured on the same set of samples. In the first project, by integrating all available data sources, we seek to uncover common group structure among the samples from unlabeled mixed multi-view data that may be hidden in individualistic cluster analyses of a single data view. To achieve this, we propose and develop a convex formalization that inherits the strong mathematical and empirical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our iGecco+ approach selects features from each data view that are best for determining the groups, often leading to improved integrative clustering. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data. In the second project, we seek to come up with more meaningful interpretations of clustering, which has often been challenging due to its unsupervised nature. Meanwhile, in many real-world scenarios, there are some noisy “supervising auxiliary variables”, for instance, subjective diagnostic opinions, that are related to the observed heterogeneity of the unlabeled data. By leveraging information from both supervising auxiliary variables and unlabeled data, we seek to uncover more scientifically interpretable group structures that may be hidden by completely unsupervised analyses. We propose and develop a new statistical pattern discovery method named Supervised Convex Clustering (SCC) that borrows strength from both unlabeled data and the so-called supervising auxiliary variable in order to find more interpretable patterns with a joint convex fusion penalty. Graphical models, statistical machine learning models defined on graphs, have been widely studied to understand conditional dependencies among a collection of random variables. In the third project, we consider graph selection in the presence of latent variables, a quite challenging problem in neuroscience where existing technologies can only record from a small subset of neurons. We propose an incredibly simple solution: apply a hard thresholding operator to existing graph selection methods, and demonstrate that thresholding the graphical Lasso, neighborhood selection, or CLIME estimators have superior theoretical properties in terms of graph selection consistency as well as stronger empirical results than existing approaches for the latent variable graphical model problem. We also demonstrate the applicability of our approach through a neuroscience case study on calcium-imaging data to estimate functional neural connections.Item Supervised convex clustering(Wiley, 2023) Wang, Minjie; Yao, Tianyi; Allen, Genevera I.Clustering has long been a popular unsupervised learning approach to identify groups of similar objects and discover patterns from unlabeled data in many applications. Yet, coming up with meaningful interpretations of the estimated clusters has often been challenging precisely due to their unsupervised nature. Meanwhile, in many real-world scenarios, there are some noisy supervising auxiliary variables, for instance, subjective diagnostic opinions, that are related to the observed heterogeneity of the unlabeled data. By leveraging information from both supervising auxiliary variables and unlabeled data, we seek to uncover more scientifically interpretable group structures that may be hidden by completely unsupervised analyses. In this work, we propose and develop a new statistical pattern discovery method named supervised convex clustering (SCC) that borrows strength from both information sources and guides towards finding more interpretable patterns via a joint convex fusion penalty. We develop several extensions of SCC to integrate different types of supervising auxiliary variables, to adjust for additional covariates, and to find biclusters. We demonstrate the practical advantages of SCC through simulations and a case study on Alzheimer's disease genomics. Specifically, we discover new candidate genes as well as new subtypes of Alzheimer's disease that can potentially lead to better understanding of the underlying genetic mechanisms responsible for the observed heterogeneity of cognitive decline in older adults.Item Synthesis and Terahertz Applications of Large-Area Monolayer Graphene(2016-11-22) Wang, Minjie; Kono, Junichiro; Ajayan, PulickelMonolayer graphene, successfully isolated in 2004 for the first time, is the first member of the class of materials called two-dimensional (2D) materials. It consists of a 2D honeycomb lattice of sp2-bonded carbon atoms, possessing extraordinary mechanical, chemical, and physical properties. The unique band structure and gate tunability of graphene are expected to result in novel high-frequency (THz) and optical phenomena. In this thesis work, we used two different ways to grow graphene on a copper foil via chemical vapor deposition (CVD). One method synthesized continuous, large-size monolayer graphene, while the other method created signal-crystal graphene with no domain boundaries. We transferred grown graphene from copper foil to SiO2/Si substrates by the wet-etch method with four types of copper etchants that are commonly used by researcher: HNO3, FeCl3, (NH4)2S2O8, and a commercial copper etchant. Further tests and analysis showed that the commercial copper etchant is the best for transfer purposes from the perspective of structural integrity, amount of residues, and doping carrier concentration. We conducted strain-dependent THz transmission measurements of graphene on a polyimide substrate (Kapton) using a strain-controllable mechanical-optical testing system. Experimental results showed that THz transmittance of graphene changes significantly with strain up to ~30%, but no reversible change of THz transmittance was observed. On the other hand, by using a recently proposed total internal reflection (TIR) geometry, we demonstrated significant enhancement of THz-wave absorption in monolayer graphene. Our scheme allowed the incident THz beam to be reflected by graphene four times at varying incidence angles, both below and above the critical angle for TIR. We observed extremely large THz absorption, especially for s-polarized radiation. The experimental results are quantitatively consistent with our calculations, incorporating realistic values of carrier scattering time and Fermi energy.