TVL₁ Planarity Regularization for 3D Shape Approximation

The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within. This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy

Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper

DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines

Integrated data analysis (IDA) pipelines—that combine data management (DM) and query processing, high-performance computing (HPC), and machine learning (ML) training and scoring—become increasingly common in practice. Interestingly, systems of these areas share many compilation and runtime techniques, and the used—increasingly heterogeneous—hardware infrastructure converges as well. Yet, the programming paradigms, cluster resource management, data formats and representations, as well as execution strategies differ substantially. DAPHNE is an open and extensible system infrastructure for such IDA pipelines, including language abstractions, compilation and runtime techniques, multi-level scheduling, hardware (HW) accelerators, and computational storage for increasing productivity and eliminating unnecessary overheads. In this paper, we make a case for IDA pipelines, describe the overall DAPHNE system architecture, its key components, and the design of a vectorized execution engine for computational storage, HW accelerators, as well as local and distributed operations. Preliminary experiments that compare DAPHNE with MonetDB, Pandas, DuckDB, and TensorFlow show promising results

비정형데이터가 있는 제한적인 상품정보 제공환경에서의 검색과 구매 행동에 관한 연구

학위논문 (석사) -- 서울대학교 대학원 : 경영대학 경영학과, 2020. 8. 송인성.I develop an empirical model of search and choice in which consumers are presented with limited product information prior to the search. In the model, consumers search and click on the items listed on product listing pages. They expect to view vertical as well as horizontal attribute values that cannot be observed on product listing pages (i.e. costly attribute values) after clicking-through. Vertical costly attributes include quantified review scores of several product attributes. They reflect actual users satisfaction with the product attributes. This paper has the following contributions to the literature. First, the model reflects consumers higher uncertainty of their utility prior to search which can be reduced by obtaining information about costly attribute values. It is in line with consumer learning literature. Second, the model also reflects consumers heteroskedastic uncertainty of their utility during searching for the products without violating the parsimony of the model. Third, this paper uses a deep learning method in order to extract the structured features from reviews. The model is applied to the aggregate search and choice data from Chrome-OS laptops at Bestbuy.com. The model shows the realistic values of parameter estimates and better in-sample fit in comparison with Kim et al. (2016). With the estimated model parameters, I conduct the counterfactual experiment that shows how consumer search set size and manufacturer market share and revenue change in a full information environment. In the full information environment, consumers reduce their search set size by -3.9% and choose almost the same products as they do in the limited information environment. It leads to an increase in consumer surplus by 3.19%. For producers, most of their market share and revenue increase. Furthermore, the brands with relatively low rank in total rating and high rank in average review score shows the relative higher increase. Therefore, I want to suggest to manufacturers that they should post quantified review scores with respect to each attribute on product listing pages in order to boost their sales and revenues especially when their total rating is relatively low.본 연구에서는 제한된 상품 정보가 제공되는 환경에서 소비자가 상품을 검색하고 구매하는 행위를 설명하는 실증적 모형을 개발하였다. 본 모형에서 소비자는 상품 리스트 페이지에서 제품을 검색하는 과정에서 상품을 클릭 한 후에만 볼 수 있는 vertical attribute과 horizontal attribute에 대한 기대치를 가지고 있다(본 연구에서는 이를 costly attributes이라고 명함). Vertical costly attributes에는 상품의 몇가지 특성에 대한 리뷰 점수들이 포함되어 있다. 이 리뷰 점수들은 특정 상품 특징에 대한 실제 소비자 만족도를 계량화 한 것이다. 본 연구는 기존 연구에 다음과 같은 학문적 의의를 가진다. 첫째, 본 모형은 검색하기 전 단계에서 제품 효용에 대한 더 높은 불확실성을 가지고 있음을 반영하고 있다. 불확실성은 costly attribute에 대한 정보를 얻음으로써 어느정도 완화되는데, 이는 기존 소비자 학습 문헌과 일맥상통하는 바이다. 둘째, 본 연구는 모형을 복잡하게 만들지 않으면서, 상품을 검색하는 동안 소비자가 검색과정에서 이분산적인 효용을 가지는 것을 반영하였다. 셋째, 본 연구는 리뷰로부터 구조화된 변수를 추출하기 위해 딥러닝(deep learning) 모형을 활용하였다. 해당 모형은 베스트바이닷컴(Bestbuy.com)에 있는 크롬노트북 카테고리에 있는 검색 및 구매 데이터에 적용되었다. 그리고 현실적인 모수 추정치가 나왔고, Kim et al. (2016)와 비교했을 때, 해당 데이터에 대해 더 나은 적합도를 보여주었다. 그리고 추정된 모수를 통해 가상적인 상황에 대한 실험을 하였다. 해당 실험에서는 상품정보가 리스트 페이지에서 모두 제공되는 상황에서 소비자의 검색량과 제조업체의 시장 점유율 및 수익률이 어떻게 변하는지 보았다. 가상적인 상황에서 소비자는 -3.9%만큼 검색량을 줄이는데, 최종적으로 선택하는 상품은 거의 변화가 없었다. 그래서 변화된 정보제공 환경에서 소비자잉여 3.19%만큼 증가하였다. 그리고 대부분 제조업체의 시장 점유율과 수익률이 증가하였다. 더불어서 total rating이 비교적 낮고 리뷰 점수가 높은 브랜드가 상대적으로 더 큰 폭의 증가세를 보였다. 이 결과는 어떤 업체가 total rating이 상대적으로 낮다면, 상품 리스트 페이지에 상품 특성들에 대한 리뷰 점수를 게시하여 판매율과 수익율을 증가시킬 수 있음을 시사한다.1 Introduction 1 2 Data 8 2-1 Details of Search and Choice Data 9 2-2 Data Summary 13 2-3 Review Feature Extraction 13 2-3-1 Convolutional Neural Network for Extracting Features 17 3 Empirical Settings 24 3-1 Product Information Environment 24 3-2 Model-free Evidence 26 4 Model 31 4-1 Utility and Empirical Specification 31 4-2 Optimal Sequential Search: Reservation Utility 32 4-3 Search and Choice Probabilities 35 5 Estimation and Identification Strategy 36 5-1 Pre-estimation 36 5-2 Main Model Estimation 37 5-3 Identification 38 6 Results 40 7 Counterfactual Experiment 43 8 Conclusion 45 References 47 Appendix 50Maste

Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics

A significant portion of the effort involved in advanced process control, process analytics, and machine learning involves acquiring and preparing data. Literature often emphasizes increasingly complex modelling techniques with incremental performance improvements. However, when industrial case studies are published they often lack important details on data acquisition and preparation. Although data pre-processing is unfairly maligned as trivial and technically uninteresting, in practice it has an out-sized influence on the success of real-world artificial intelligence applications. This work describes best practices for acquiring and preparing operating data to pursue data-driven modelling and control opportunities in industrial processes. We present practical considerations for pre-processing industrial time series data to inform the efficient development of reliable soft sensors that provide valuable process insights.Comment: This work has been accepted to the 22nd IFAC World Congress 202

Simulation of the performance of complex data-intensive workflows

PhD ThesisRecently, cloud computing has been used for analytical and data-intensive processes as it offers many attractive features, including resource pooling, on-demand capability and rapid elasticity. Scientific workflows use these features to tackle the problems of complex data-intensive applications. Data-intensive workflows are composed of many tasks that may involve large input data sets and produce large amounts of data as output, which typically runs in highly dynamic environments. However, the resources should be allocated dynamically depending on the demand changes of the work flow, as over-provisioning increases the cost and under-provisioning causes Service Level Agreement (SLA) violation and poor Quality of Service (QoS). Performance prediction of complex workflows is a necessary step prior to the deployment of the workflow. Performance analysis of complex data-intensive workflows is a challenging task due to the complexity of their structure, diversity of big data, and data dependencies, in addition to the required examination to the performance and challenges associated with running their workflows in the real cloud. In this thesis, a solution is explored to address these challenges, using a Next Generation Sequencing (NGS) workflow pipeline as a case study, which may require hundreds/ thousands of CPU hours to process a terabyte of data. We propose a methodology to model, simulate and predict runtime and the number of resources used by the complex data-intensive workflows. One contribution of our simulation methodology is that it provides an ability to extract the simulation parameters (e.g., MIPs and BW values) that are required for constructing a training set and a fairly accurate prediction of the run time for input for cluster sizes much larger than ones used in training of the prediction model. The proposed methodology permits the derivation of run time prediction based on historical data from the provenance fi les. We present the run time prediction of the complex workflow by considering different cases of its running in the cloud such as execution failure and library deployment time. In case of failure, the framework can apply the prediction only partially considering the successful parts of the pipeline, in the other case the framework can predict with or without considering the time to deploy libraries. To further improve the accuracy of prediction, we propose a simulation model that handles I/O contention