SPATIAL MODELING AND ANALYSIS IN GIS

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SPATIAL ANALYSIS GIS AND REMOTE SENSING APPLICATIONS IN THE HEALTH SCIENCES

SPATIAL ANALYSIS GIS AND REMOTE SENSING APPLICATIONS IN THE HEALTH SCIENCES

Researchers have discovered over the years that it is extremely difficult to “prove”that clustering or clusters have indeed occurred. Thus Wartenberg and Greenberg(1993) and others suggest that point pattern analysis should be undertaken togenerate rather than test hypotheses. They “consider cluster studies to be preepidemiology: analytic investigations that can be done prior to more traditional, timeconsuming and costly epidemiologic designs” (Wartenberg and Greenberg, 1993, p.1764). They also emphasize the need for researchers to pay close attention to issues ofstatistical power and confounding. “Statistical power is the ability to detect an effectgiven that it is present” and “[C]onfounding is the erroneous attribution of anobservation (or cluster) to a factor which is related to both an exposure (or riskfactor) and an outcome (or disease)” (Wartenberg and Greenberg, 1993, p. 1764).Confounders include uneven population distributions, age, gender, ethnicity, andother factors.Wartenberg and Greenberg (1993) set out four steps for the researcher to takewhen examining clusters. First, one has to characterize the data, which could becounts of disease events by geographical area, point locations of cases, event times,distances between events, counts of both cases and controls, and so on. Second, onemust decide the domain from which the data come; this includes spatial, temporal,and space-time clusters. Third, one specifies a null hypothesis which is often thatdisease cases occur randomly. Fourth, one specifies an alternative hypothesis,typically that the distribution of cases deviates from a random pattern in a certainway, i.e., according to an underlying mechanism such as contagion or exposure to acontaminant.As mentioned earlier, many methods are available for analyzing point patterns ofdisease occurrence. Early entrants into the field were nearest neighbor analysis andquadrat analysis. Pisani et al. (1984) used North’s (1977) clustering method, which isbased on the distance to nearest neighbor, to determine the degree of clusteringamong dwellings reporting variola minor (smallpox) in Braganca Paulista County,Brazil. The level of spatial clustering of cases was determined for different values of“defined distances” or fixed distances between dwellings with susceptibles andpotential infective agents.In his study of the diffusion of fowl pest disease in England and Wales, Gilg (1973)
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THE MICROSOFT 2017 CONVERSATIONAL SPEECH RECOGNITION SYSTEM

THE MICROSOFT 2017 CONVERSATIONAL SPEECH RECOGNITION SYSTEM

ABSTRACT We describe the 2017 version of Microsoft’s conversational speech recognition system, in which we update our 2016 system with recent developments in neuralnetworkbased acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNNBLSTM acoustic model to the set of model architectures we combined previously, and includes characterbased and dialog session aware LSTM language models in rescoring. For system combination we adopt a twostage approach, whereby subsets of acoustic models are first combined at the senoneframe level, followed by a wordlevel voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1% word error rate on the 2000 Switchboard evaluation set. 1. INTRODUCTION We have witnessed steady progress in the improvement of automatic speech recognition (ASR) systems for conversational speech, a genre that was once considered among the hardest in the speech recognition community due to its unconstrained nature and intrinsic variability 1. The combination of deep networks and efficient training methods with older neural modeling concepts 2, 3, 4, 5, 6, 7, 8 have produced steady advances in both acoustic modeling 9, 10, 11, 12, 13, 14, 15 and language modeling 16, 17, 18, 19. These systems typically use deep convolutional neural network (CNN) architectures in acoustic modeling, and multilayered recurrent networks with gated memory (longshortterm memory, LSTM 8) models for both acoustic and language modeling, driving the word error rate on the benchmark Switchboard corpus 20 down from its mid2000s plateau of around 15% to well below 10%. We can attribute this progress to the neural models’ ability to learn regularities over a wide acoustic context in both time and frequency dimensions, and, in the case of language models, to condition on unlimited histories and learn representations of functional word similarity 21, 22. Given these developments, we carried out an experiment last year, to measure the accuracy of a stateoftheart conversational speech recognition system against that of professional transcribers. We were trying to answer the question whether machines had effectively caught up with humans in this, originally very challenging, speech recognition task. To measure human error on this task, we submitted the Switchboard evaluation data to our standard conversational speech transcription vendor pipeline (who was left blind to the experiment), postprocessed the output to remove text normalization discrepancies, and then applied the NIST scoring protocol. The resulting human word error was 5.9%, not statistically different from the 5.8% error rate achieved by our ASR system 23. In a followup study 24, we found that qualitatively, too, the human and machine transcriptions were remarkably similar: the same short function words account for most of the errors, the same speakers tend to be easy or hard to transcribe, and it is difficult for human subjects to tell whether an errorful transcript was produced by a human or ASR. Meanwhile, another research group carried out their own measurement of human transcription error 25, while multiple groups reported further improvements in ASR performance 25, 26. The IBM human transcription study employed a more involved transcription process with more listening passes, a pool of transcribers, and access to the conversational context of each utterance, yielding a human error rate of 5.1%. Together with a prior study by LDC 27, we can conclude that human performance, unsurprisingly, falls within a range depending on the level of effort expended. In this paper we describe a new iteration in the development of our system, pushing well past the 5.9% benchmark we measured previously. The overall gain comes from a combination of smaller improvements in all components of the recognition system. We added an additional acoustic model architecture, a CNNBLSTM, to our system. Language modeling was improved with an additional utterancelevel LSTM based on characters instead of words, as well as a dialog sessionbased LSTM that uses the entire preceding conversation as history. Our system combination approach was refined by combining predictions from multiple acoustic models at both the senoneframe and word levels. Finally, we added an LM rescoring step after confusion network creation, bringing us to an overall error rate of 5.1%, thus surpassing the human accuracy level we had measured previously. The remainder Fig. 1. LACE network architecture of the paper describes each of these enhancements in turn, followed by overall results. 2. ACOUSTIC MODELS 2.1. Convolutional Neural Nets We used two types of CNN model architectures: ResNet and LACE (VGG, a third architecture used in our previous system, was dropped). The residualnetwork (ResNet) architecture 28 is a standard CNN with added highway connections 29, i.e., a linear transform of each layer’s input to the layer’s output 29, 30. We apply batch normalization 31 before computing rectified linear unit (ReLU) activations. The LACE (layerwise context expansion with attention) model is a modified CNN architecture 32. LACE, first proposed in 32 and depicted in Figure 1, is a variant of timedelay neural network (TDNN) 4 in which each higher layer is a weighted sum of nonlinear transformations of a window of lower layer frames. Lower layers focus on extracting simple local patterns while higher layers extract complex patterns that cover broader contexts. Since not all frames in a window carry the same importance, a learned attention mask is applied, shown as the “elementwise matrix product” in Figure 1. The LACE model thus differs from the earlier TDNN models 4, 33 in this attention masking, as well as the ResNetlike linear passthrough connections. As shown in the diagram, the model is composed of four blocks, each with the same architecture. Each block starts with a convolution layer with stride two, which subsamples the input and increases the number of channels. This layer is followed by four ReLU convolution layers with jumplinks similar to those used in ResNet. As for ResNet, batch normalization 31 is used between layers. 2.2. Bidirectional LSTM For our LSTMbased acoustic models we use a bidirectional architecture (BLSTM) 34 without frameskipping 11. The core model structure is the LSTM defined in 10. We found that using networks with more than six layers did not improve the word error rate on the development set, and chose 512 hidden units, per direction, per layer; this gave a reasonable tradeoff between training time and final model accuracy. BLSTM performance was significantly enhanced using a spatial smoothing technique, first described in 23. Briefly, a twodimensional topology is imposed on each layer, and activation patterns in which neighboring units are correlated are rewarded.
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TRACING SOURCES AND SPATIAL DISTRIBUTION OF SEAGRASS SEDIMENTS, YAO YAI ISLAND, THAILAND

TRACING SOURCES AND SPATIAL DISTRIBUTION OF SEAGRASS SEDIMENTS, YAO YAI ISLAND, THAILAND

... of Yao Yai island, Thailand The following hypotheses are tested: 1) Spatial distribution of sediment deposited in the bay would have a large terrestrial signature, related to major pathways of. .. from the distribution of other sources Terrestrial and mangrove distribution is similar, as with coral and seagrass detritus distribution; both groups of sources seem to display opposite distribution. .. of sediment in catchment-coast connectivity, this thesis aims to use sediment tracing methods to identify primary sources of sediment and the spatial distribution of deposited sediment in a seagrass
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Exploring alternative restoration techniques in constraint programming

EXPLORING ALTERNATIVE RESTORATION TECHNIQUES IN CONSTRAINT PROGRAMMING

... Prolog, Constraint Logic Programming (CLP) [15] In fact, most of today’s constraint programming systems are constraint logic programming sys16 Algorithm Trailing-based State Restoration Input:... search efforts, making it is possible to solve some hard problems 1.2 Constraint Programming in a Nutshell Constraint programming includes two phases: modeling and solving The modeling step is to... Chapter Constraint Programming Constraint programming systems provide the facilities to model and solve CSPs In this chapter, we briefly explain the techniques and terms that are referred to in constraint
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Phần mềm mã nguồn mở QGIS

Phần mềm mã nguồn mở QGIS

Phần mềm Mã Nguồn Mở QGIS. Tổng quan về phần mềm mã nguồn mở. Code up map lên web. Các cách hướng dẫn sử dụng phần mềm và phiên bản phần mềm. •Hệ thống thông tin địa lý còn gọi là GIS, theo tiếng Anh là viết tắt của các từ: “Geographic Information Systems”. Có thể nói chuyên từ GIS đã đang trở nên quen thuộc với nhiều người trên toàn thế giới.GIS được hình thành từ các ngành khoa học: Địa lý, Bản đồ, Tin học và Toán học.•GIS là một hệ thống thông tin (trên hệ máy tính) được thiết kế để thu thập, cập nhật, lưu trữ, tích hợp và xử lý, tra cứu, phân tích và hiển thị mọi dạng dữ liệu địa lý (có vị trí trên Trái đất). •Một hệ thống được gọi là GIS nếu nó có các công cụ hỗ trợ cho việc thao tác với dữ liệu không gian. Cơ sở dữ liệu GIS là sự tổng hợp có cấu trúc các dữ liệu số hóa không gian và phi không gian về các đối tượng bản đồ, mỗi liên hệ giữa các đối tượng không gian và các tính chất của một vùng tượng. •Như vậy, hệ thống thông tin địa lý là: một hệ thống phần mềm máy tính được sử dụng trong việc vẽ bản đồ, phân tích các vật thể, hiện tượng tồn tại trên trái đất. Công nghệ GIS tổng hợp các chức năng chung về quản lý dữ liệu như truy vấn (query) và phân tích thống kê (Statistical analysis) với sự thể hiện trực quan (visialization) và sự phân tích các vật thể hiện tượng không gian (geographic analysis) trong bản đồ. Sự khác biệt giữa GIS và các hệ thống thông tin thông thường là tính ứng dụng của nó rất rộng trong việc giải thích hiện tượng, dự báo và quy hoạch chiến lược.
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An enhance framework on hair modeling and real time animation

AN ENHANCE FRAMEWORK ON HAIR MODELING AND REAL TIME ANIMATION

... and between scalp and hair strips to provide a realistic animation A model of collision detection, response and avoidance is also specified Collision of hair strips against scalp and collision... the framework so that real- time animation of hairstyle with proper collision detection, respond and avoidance is enabled In order to produce a realistic hair animation, the movement of the animated... produce realistic animated synthetic actors with hair: hair modeling and creation, hair motion, hair rendering, and collision detection and response [Dald93] The modeling of hair specifies the

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Lecture Physical modeling in MATLAB

Lecture Physical modeling in MATLAB

Lecture Physical modeling in MATLAB has contents: Variables and values, scripts, loops, vectors, functions, zerofinding, functions of vectors, ordinary differential equations, systems of ODEs, secondorder systems, optimization and interpolation,...and other contents.

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Modeling on economic feasibility analysis of biodiesel production from microalgae in china

MODELING ON ECONOMIC FEASIBILITY ANALYSIS OF BIODIESEL PRODUCTION FROM MICROALGAE IN CHINA

.. .MODELING ON ECONOMIC FEASIBILITY ANALYSIS OF BIODIESEL PRODUCTION FROM MICROALGAE IN CHINA JIA ZONGCHAO (M.Sc., Peking University) A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF SCIENCE... extracted from the microalgae biomass, the next step is the conversion of lipid to biodiesel The common methods used for biodiesel production from microalgae consist of transesterification, either in. .. selection The selection of microalgae strains plays a crucial role for the success of microalgae based biodiesel production [84,85,86] The ideal microalgae strain for biodiesel production should:

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Altered ARID1A expression in colorectal cancer

Altered ARID1A expression in colorectal cancer

ARID1A has been described as a tumor suppressor gene, participating in chromatin re-modeling, epithelial-mesenchymal-transition and many other cellular and molecular processes. It has been cited as a contribute in tumorigenesis. The role of ARID1A in CRC is not yet defined.

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proceedings of soil specific crop management

PROCEEDINGS OF SOIL SPECIFIC CROP MANAGEMENT

Historic agronomic practices have been developed with the farm or field as the area of management. The advent of soil conservation began to lead soil management toward topographic and soilspecific features. Even so, agronomic practices and recommendations have largely been made on a field basis rather than on soilspecific properties that might influence tillage, seeding, fertilizing and weed control practices. The near completion of detailed soil surveys nationwide, particularly in the intensive agricultural areas, has provided a database of great magnitude. The advent of computer processed spatial data together with geostatistical analysis enables the display of those soil, hydrologic, and microclimate features relevant to agronomic practices. With the further development of positioning systems suitable to onsite applications, the capability now exists, or can be feasibly developed to deliver realtime, realspace changes in almost any agronomic procedures. There is also much current research in sensor technology applicable to the soil condition or property, such as organic matter content, moisture content, tilth, nitrate content, and crop yields.
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Discrete event system simulation jerry banks

Discrete event system simulation jerry banks

A simulation is the imitation of the operation of a realworld process or system over time. Whether done by hand or on a computer, simulation involves the generation of an artificial history of a system and the observation of that artificial history to draw inferences concerning the operating characteristics of the real system. The behavior of a system as it evolves over time is studied by developing a simulation model. This model usually takes the form of a set of assumptions concerning the operation of the system. These assumptions are expressed in mathematical, logical, and symbolic relationships between the entities, or objects of interest, of the system. Once developed and validated, a model can be used to investigate a wide variety of what if questions about the realworld system. Potential changes to the system can first be simulated, in order to predict their impact on system performance. Simulation can also be used to study systems in the design stage, before such systems are built. Thus, simulation modeling can be used both as an analysis tool for predicting the effect of changes to existing systems and as a design tool to predict the performance of new systems under varying sets of circumstances. In some instances, a model can be developed which
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At the heart of student migration education, mobility, and the time space production of everyday life

AT THE HEART OF STUDENT MIGRATION EDUCATION, MOBILITY, AND THE TIME SPACE PRODUCTION OF EVERYDAY LIFE

... spatial patterns and temporal rhythms of intimate relationships 23 2.3.4 The Social (Re )production of Everyday Life In centering students at the heart of the everyday, quotidian, and transnational... variety of spaces and times participate in the making, routinizing, and/ or disrupting of these elements Building on the perspective of space and ‘place’ in examining the geographies of student migration, ... actions and narratives But it is also, as they argue, exactly the attendance to negotiation and coordination of everyday life across multiple time- spaces that will expose the asymmetry of power relations
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Lecture Introduction to systems analysis and design Chapter 4 Whitten, Bentley

Lecture Introduction to systems analysis and design Chapter 4 Whitten, Bentley

Chapter 4 Systems analysis. After studying this chapter you will be able to Define systems analysis and relate it to the scope definition, problem analysis, requirements analysis, logical design, and decision analysis phases; describe a number of systems analysis approaches for solving business system problems; describe scope definition, problem analysis, requirements analysis, logical design, and decision analysis phases in terms of information system building blocks;...

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SOFTWARE ENGINEERING Chapter 5 – System Modelling

SOFTWARE ENGINEERING CHAPTER 5 – SYSTEM MODELLING

Topics covered • Context models • Interaction models • Structural models • Behavioral models Modeldriven engineering System modeling • the process of developing abstract models of a system, with each model presenting a different view or perspective of that system. • representing a system using some kind of graphical notation, which is now almost always based on notations Jul 2013 Chapter 5. System modeling 3 notation, which is now almost always based on notations in the Unified Modeling Language (UML). • helps the analyst to understand the functionality of the system and models are used to communicate with customers
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bản tiến anh môn kế toán tài chính (MBM)

BẢN TIẾN ANH MÔN KẾ TOÁN TÀI CHÍNH (MBM)

TOPIC: ANALYSIS OF FINANCIAL ACCOUNTING DOMESCO MEDICAL IMPORTEXPORT JOINT STOCK COMPANY Comparison of financial statements between January 1st, 2010 with December 31st, 2010 DOMESCO MEDICAL IMPORT EXPORT JOINT STOCK COMPANY I. Financial statement analysis: In general meaning, financial statement analysis mentions the art of analysis and the explanation of financial statements. It applies tool and analytical technique to financial statements and relevant documents in order to draw useful estimations and conclusions for business decisions. It is a selective tool to chose “candidates” for investment or merger. It is also a tool to foresee conditions and consequences of the finance in the future
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Measuring and modelling spatial variation of temperature and thermal comfort in a low density neighbourhood in singapore

MEASURING AND MODELLING SPATIAL VARIATION OF TEMPERATURE AND THERMAL COMFORT IN A LOW DENSITY NEIGHBOURHOOD IN SINGAPORE

... 2-m air temperature (Ta-2m) for SIM (BASE) at timings of peak and minimum Ta-2m 186 Appendix F Spatial variation of absolute mean radiant temperature (MRT) for Sim (BASE) at timing of peak MRT... Government of Singapore is aware of the concomitant needs of environmental management and economic (and urban) growth, and has the explicit goal of developing a “Sustainable Singapore using efficient,... variations in thermal comfort in Singapore To evaluate seasonality in thermal comfort conditions, he calculated the hourly standard effective temperature (SET) for a hypothetical person standing in an
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A Guide to the Business Analysis Body of Knowledge v2

A Guide to the Business Analysis Body of Knowledge v2

A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide) is a globally recognized standard for the practice of business analysis. The BABOK® Guide describes business analysis areas of knowledge, their associated activities and tasks, and the skills necessary to be effective in their execution. The primary purpose of the BABOK® Guide is to define the profession of business analysis. It serves as a baseline that practitioners can agree upon in order to discuss the work they do and to ensure that they have the skills they need to effectively perform the role, and defines the skills and knowledge that people who work with and employ business analysts should expect a skilled practitioner to demonstrate. It is a framework that describes the business analysis tasks that must be performed in order to understand how a solution will deliver value to the sponsoring organization. The form those tasks take, the order they are performed in, the relative importance of the tasks, and other things may vary, but each task contributes in some fashion, directly or indirectly, to that overall goal.
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SOFTWARE ENGINEERINGCourse outline

SOFTWARE ENGINEERINGCOURSE OUTLINE

Objectives• Help the student understand • theories, methods, and technologies • applied for professional software development. • A general introduction to the field of software engineering • an understanding of what it means to do software engineering Jul2013 SE. Course outline 2• an understanding of what it means to do software engineering • alternative methods and approaches. • Teamproject practice • project management and modeling • use tools and approaches to process analysis and improvement

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AN INTELLIGENT TUTORING SYSTEM FOR THAI WRITING USING CONSTRAINT BASED MODELING

AN INTELLIGENT TUTORING SYSTEM FOR THAI WRITING USING CONSTRAINT BASED MODELING

... iteratively refine and test our student model and enhancements.     We  introduce  Thairator,  an ITS  developed  in  JESS,  which  teaches  Thai language  transcription using our new findings. The student is modeled using Constraint Based Modeling ... like a personal tutor would. Student Modeling is a sub‐branch of User Modeling and  here  we  focus  on  the  domain  of  Thai language  teaching  and  develop  a  system to  iteratively refine and test our student model and enhancements. ... student  modeling module has been produced to teach Thai or  any script based language.  1.2 Research Objectives    Our research aims to develop an enhanced Constraint Based Student Model for the 
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ANALYSIS AND COMPUTATIONAL MODELLING OF DROP FORMATION FOR PIEZO ACTUATED DOD MICRO DISPENSER

ANALYSIS AND COMPUTATIONAL MODELLING OF DROP FORMATION FOR PIEZO ACTUATED DOD MICRO DISPENSER

... contribution for our understanding of dynamics of drop formation, they have not been able to capture - 24 - Analysis and Computational Modeling of Drop Formation for Piezo- actuated DOD Micro- dispenser. .. describe the drop formation, ejection and impact - 22 - Analysis and Computational Modeling of Drop Formation for Piezo- actuated DOD Micro- dispenser Another numerical method of calculation of the pressure... given to my parents and grandmother for their support and love forever -I- Analysis and Computational Modeling of Drop Formation for Piezo- actuated DOD Micro- dispenser Table of Contents Acknowledgements
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