The Effect of Government Purchase on the Professional Development of Social Work in Wuhan - A Case Study

Global Independent Study, Summer 2018 -- Wuhan, China -- Partner Agencie(s): ISEE Social Service Centerhttps://deepblue.lib.umich.edu/bitstream/2027.42/145782/1/Zheng_GIS_poster.pd

Object-oriented Neural Programming (OONP) for Document Understanding

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201

Tensor Computation: A New Framework for High-Dimensional Problems in EDA

Many critical EDA problems suffer from the curse of dimensionality, i.e. the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g. full-chip routing/placement and circuit sizing), or extensive process variations (e.g. variability/reliability analysis and design for manufacturability). The computational challenges generated by such high dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents "tensor computation" as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and System

G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X Data

Monocular depth inference is a fundamental problem for scene perception of robots. Specific robots may be equipped with a camera plus an optional depth sensor of any type and located in various scenes of different scales, whereas recent advances derived multiple individual sub-tasks. It leads to additional burdens to fine-tune models for specific robots and thereby high-cost customization in large-scale industrialization. This paper investigates a unified task of monocular depth inference, which infers high-quality depth maps from all kinds of input raw data from various robots in unseen scenes. A basic benchmark G2-MonoDepth is developed for this task, which comprises four components: (a) a unified data representation RGB+X to accommodate RGB plus raw depth with diverse scene scale/semantics, depth sparsity ([0%, 100%]) and errors (holes/noises/blurs), (b) a novel unified loss to adapt to diverse depth sparsity/errors of input raw data and diverse scales of output scenes, (c) an improved network to well propagate diverse scene scales from input to output, and (d) a data augmentation pipeline to simulate all types of real artifacts in raw depth maps for training. G2-MonoDepth is applied in three sub-tasks including depth estimation, depth completion with different sparsity, and depth enhancement in unseen scenes, and it always outperforms SOTA baselines on both real-world data and synthetic data.Comment: 18 pages, 16 figure

Effects of Anthropogenic Emission Control and Meteorology Changes on the Inter-Annual Variations of PM2.5–AOD Relationship in China

We identified controlling factors of the inter-annual variations of surface PM2.5–aerosol optical depth (AOD) relationship in China from 2006 to 2017 using a nested 3D chemical transport model—GEOS-Chem. We separated the contributions from anthropogenic emission control and meteorological changes by fixing meteorology at the 2009 level and fixing anthropogenic emissions at the 2006 level, respectively. Both observations and model show significant downward trends of PM2.5/AOD ratio (η, p < 0.01) in the North China Plain (NCP), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) in 2006–2017. The model suggests that the downward trends are mainly attributed to anthropogenic emission control. PM2.5 concentration reduces faster at the surface than aloft due to the closeness of surface PM2.5 to emission sources. The Pearson correlation coefficient of surface PM2.5 and AOD (rPM-AOD) shows strong inter-annual variations (±27%) but no statistically significant trends in the three regions. The inter-annual variations of rPM-AOD are mainly determined by meteorology changes. Except for the well-known effects from relative humidity, planetary boundary layer height and wind speed, we find that temperature, tropopause pressure, surface pressure and atmospheric instability are also important meteorological elements that have a strong correlation with inter-annual variations of rPM-AOD in different seasons. This study suggests that as the PM2.5–AOD relationship weakens with reduction of anthropogenic emissions, validity of future retrieval of surface PM2.5 using satellite AOD should be carefully evaluated

Threshold-Based Fast Successive-Cancellation Decoding of Polar Codes

Fast SC decoding overcomes the latency caused by the serial nature of the SC decoding by identifying new nodes in the upper levels of the SC decoding tree and implementing their fast parallel decoders. In this work, we first present a novel sequence repetition node corresponding to a particular class of bit sequences. Most existing special node types are special cases of the proposed sequence repetition node. Then, a fast parallel decoder is proposed for this class of node. To further speed up the decoding process of general nodes outside this class, a threshold-based hard-decision-aided scheme is introduced. The threshold value that guarantees a given error-correction performance in the proposed scheme is derived theoretically. Analysis and hardware implementation results on a polar code of length $1024$ with code rates $1/4$, $1/2$, and $3/4$ show that our proposed algorithm reduces the required clock cycles by up to $8\%$, and leads to a $10\%$ improvement in the maximum operating frequency compared to state-of-the-art decoders without tangibly altering the error-correction performance. In addition, using the proposed threshold-based hard-decision-aided scheme, the decoding latency can be further reduced by $57\%$ at $\mathrm{E_b}/\mathrm{N_0} = 5.0$~dB.Comment: 14 pages, 8 figures, 5 tables, submitted to IEEE Transactions on Communication