Physics-based equivalent circuit model extraction for system level PDN and a novel PDN impedance measurement method

“The power distribution network (PDN) plays an important role in the power supply system, especially with the increasing of the working frequency of the integrated circuit (IC). A physics-based circuit modeling methodology is proposed in the first section. The circuit model is extracted by following the current path in the system PDN and the related parameters are calculated based on the cavity model and plane-pair PEEC methods. By extracting the equivalent circuit model, the PDN system will be transformed into RLC element-based circuit. The role of each part of the system will be easily explained and the system behavior could be changed by changing the dominance part accordingly. This methodology makes a good contribution to the system level PDN troubleshooting and layout design optimization. Compared with analytical methodologies, the measurement result is more solid and convincing. The special part of PDN is that the impedance could be as low as several milliohms, and the impedance varies during the frequency, so the accuracy of impedance measurement is challenging. Based on all these requirements, a novel PDN low impedance measurement methodology is proposed, and a probe based on I-V method is designed to support this methodology, which provides a new and practical approach of PDN impedance measurement with easy landing, simple setup, lower frequency, and less instrument quality dependent advantages. This probe could work in a wide frequency range with a relatively sufficient dynamic range”--Abstract, page iii

A FUNCTIONAL TANDEM REPEAT VNTR2-1 IN HUMAN TELOMERASE GENE REGULATION

Repetitive DNA accounts for over half of the human genome, of which the function has not been fully understand. The introduction of long-read sequencing technology significantly improved the quality of genome sequencing. This allows us to take a closer look at those long DNA repeats which are also more prone to mutation and recombinational events. We have previously identified tandem repeat of 42-bp/unit, VNTR2-1, in intron 2 of the hTERT gene as a novel regulatory element important for hTERT transcription in cancer cells. hTERT gene, encoding the catalytic subunit of human telomerase, was found to be expressed in 90% of human tumors, which synthesizes telomere DNA and helps them escape from cellular senescence. In the current study, we found that repeats of 42-bp consensus sequence of VNTR2-1 activated the luciferase gene in a reporter plasmid. Mutation of predicted cis-regulatory elements within the 42-bp repeats, including an E-box motif, resulted in a partial or complete loss of its enhancer activity. MYC family proteins c-Myc, MAX regulated hTERT gene transcription through both VNTR2-1 and E-boxes at the proximal hTERT promoter, consistent with previous data from our laboratory. Chromatin segmentation analysis of published ChIP-sequencing data from K562 cells suggested VNTR2-1 was a bivalent enhancer. The hTERT promoter became repressed in a clone of telomerase-expressing human melanoma cell line MelJuSo with genomic deletion of VNTR2-1, as suggested by increases of H3K27me3 and H3K9me3 marks. Therefore, we provided additional evidence for VNTR2-1 as a functional regulatory element that regulated hTERT expression by MYC family transcription factors. Moreover, a CRISPR-mediated chromatin purification experiment was also performed in order to discover other proteins that interacted with VNTR2-1. We utilized its repetitive nature and delivered one single guide RNA that targets most 42-bp repeats, to capture all regulatory macromolecules bound to this region. The results we obtained here may help improve our knowledge on repetitive genomic DNA functions as well as the regulatory mechanism of human telomerase gene

A Pseudo Nearest-Neighbor Approach for Missing Data Recovery on Gaussian Random Data Sets

Missing data handling is an important preparation step for most data discrimination or mining tasks. Inappropriate treatment of missing data may cause large errors or false results. In this paper, we study the effect of a missing data recovery method, namely the pseudo- nearest neighbor substitution approach, on Gaussian distributed data sets that represent typical cases in data discrimination and data mining applications. The error rate of the proposed recovery method is evaluated by comparing the clustering results of the recovered data sets to the clustering results obtained on the originally complete data sets. The results are also compared with that obtained by applying two other missing data handling methods, the constant default value substitution and the missing data ignorance (non-substitution) methods. The experiment results provided a valuable insight to the improvement of the accuracy for data discrimination and knowledge discovery on large data sets containing missing values

Microscopic Particle Manipulation via Optoelectronic Devices

The optoelectronic tweezers (or optically induced dielectrophoresis (DEP)) have showed the ability to parallelly position a large number of colloidal microparticles without any template. The microparticles can be trapped and driven by the dielectrophoretic forces induced by the optical micropatterns in OET devices. In this chapter, the design and fabrication of flat optoelectronic devices (FOD) and hybrid optoelectronic device (HOD) are described. In the typical FOD, the manipulation modes including filtering, transporting, concentrating and focusing controlling regimes are experimentally demonstrated and analyzed. The controllable rotation of self-assembled microparticle chains in FOD has also been investigated, and a method incorporating the optically induced electrorotation (OER) and AC electroosmotic (ACEO) effects is numerically and experimentally implemented for manipulating microparticle chains. Based on the above research of FOD, a hybrid DEP microdevice HOD is conceptually and experimentally proposed. The HOD integrates with metallic microelectrode layer and the underneath photoconductive layer with projected optical virtual electrodes. FOD and HOD hybrid device enables the active driving, large-scale patterning and local position adjustment of microparticles. These techniques make up the shortcoming of low flexibility of traditional metallic microelectrodes and integrate the merits of both the metal electrode-induced and microimage-induced DEP techniques

ANONYMOUS ROUTING FOR PRIVACY-PRESERVING DISTRIBUTED COMPUTING

Master'sMASTER OF SCIENC

Heterogeneity modeling and longitudinal clustering

Personalization has broad applications in many fields these days. Due to significant subject variations, it has become critical to incorporate subjects' heterogeneous characteristics in order to efficiently allocate personalized treatment or marketing strategies to tailor for subject specific needs.In this thesis, we develop several types of methods and theory to accommodate heterogeneity modeling in various personalization applications for longitudinal data. In the first application, we propose a personalized drug dosage recommendation scheme. Specifically, we model patients' heterogeneity using subject-specific random effects, and propose an adaptive procedure to estimate new patients' random effects and provide dosage recommendations for new patients over time. An advantage of our approach is that we do not impose any distribution assumption on estimating random effects. Moreover, the new approach can accommodate general time-varying covariates corresponding to random effects. We show that the proposed method is more efficient compared to existing approaches, especially when covariates are time-varying. In the second part of the thesis, we develop an efficient cluster analysis approach to subgroup longitudinal profiles using a penalized regression method. We utilize a pairwise-grouping penalization on the parameters corresponding to the individual nonparametric B-spline models, and thereby identify clusters based on different patterns of the predicted longitudinal curves. One advantage of the proposed method is that we approximate the longitudinal profiles and cluster trajectories into subgroups simultaneously. To implement the proposed method, we develop an alternating direction method of multipliers (ADMM) algorithm which has the desirable convergence property. In theory, we establish the consistency properties asymptotically. In addition, we show that our method outperforms the existing competitive approaches in our simulation studies and real data example. In the third part of the thesis, we are interested in marketing segmentation, where customers are clustered into different subgroups due to their heterogeneous responses to the same marketing strategy. Specifically, we propose a pairwise subgrouping approach to identify and categorize similar marketing effects into subgroups. We model customers' purchase decisions as binary responses under the generalized linear model framework and incorporate their longitudinal correlation. We impose penalization on pairwise distances of individual effects to formulate subgroups, where different subgroups are associated with different marketing effects. In theory, we establish the consistency of subgroup identification in the sense that the true underlying segmentation structure can be recovered successfully, in addition to model estimation consistency. We apply the proposed approach to a real data application using IRI marketing data on in-store display marketing effects, where the proposed method performs favorably in terms of subgrouping identification and effects estimation

LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation

Semantic map construction under bird's-eye view (BEV) plays an essential role in autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D observations to project the captured 3D features onto BEV space inherently. However, the vanilla LiDAR-based BEV feature often contains many indefinite noises, where the spatial features have little texture and semantic cues. In this paper, we propose an effective LiDAR-based method to build semantic map. Specifically, we introduce a BEV feature pyramid decoder that learns the robust multi-scale BEV features for semantic map construction, which greatly boosts the accuracy of the LiDAR-based method. To mitigate the defects caused by lacking semantic cues in LiDAR data, we present an online Camera-to-LiDAR distillation scheme to facilitate the semantic learning from image to point cloud. Our distillation scheme consists of feature-level and logit-level distillation to absorb the semantic information from camera in BEV. The experimental results on challenging nuScenes dataset demonstrate the efficacy of our proposed LiDAR2Map on semantic map construction, which significantly outperforms the previous LiDAR-based methods over 27.9% mIoU and even performs better than the state-of-the-art camera-based approaches. Source code is available at: https://github.com/songw-zju/LiDAR2Map.Comment: Accepted by CVPR202

Experiments on bright field and dark field high energy electron imaging with thick target material

Using a high energy electron beam for the imaging of high density matter with both high spatial-temporal and areal density resolution under extreme states of temperature and pressure is one of the critical challenges in high energy density physics . When a charged particle beam passes through an opaque target, the beam will be scattered with a distribution that depends on the thickness of the material. By collecting the scattered beam either near or off axis, so-called bright field or dark field images can be obtained. Here we report on an electron radiography experiment using 45 MeV electrons from an S-band photo-injector, where scattered electrons, after interacting with a sample, are collected and imaged by a quadrupole imaging system. We achieved a few micrometers (about 4 micrometers) spatial resolution and about 10 micrometers thickness resolution for a silicon target of 300-600 micron thickness. With addition of dark field images that are captured by selecting electrons with large scattering angle, we show that more useful information in determining external details such as outlines, boundaries and defects can be obtained.Comment: 7pages, 7 figure