L0 Regularized Stationary Time Estimation for Crowd Group Analysis

Jul 17, 2014

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L0 Regularized Stationary Time Estimation for Crowd Group Analysis

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L0 Regularized Stationary Time Estimation for Crowd Group Analysis
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Crowd Size Estimation

Crowd Size Estimation. Luis Huang 12-3-08 ECE 172A - UCSD. Background and Motivation. September 27, 2007, 9:19 pm Obama Rallies Huge Crowd in New York By Jeff Zeleny

400 views • 16 slides

L0. Introduction

L0. Introduction. Rocky K. C. Chang , January 2013. The Internet is inherently insecure. Internet backbone infrastructure: DoS , worm Routing protocols (BGP): route hijacking DNS: poisoning, DoS

337 views • 23 slides

Cumulative Attribute Space for Age and Crowd Density Estimation

Cumulative Attribute Space for Age and Crowd Density Estimation. Ke Chen 1 , Shaogang Gong 1 , Tao Xiang 1 , Chen Change Loy 2 1. Queen Mary, University of London 2. The Chinese University of Hong Kong. CVPR 2013, Portland, Oregon. Problems. How old are they?.

476 views • 21 slides

MODELS FOR STATIONARY TIME SERIES

MODELS FOR STATIONARY TIME SERIES. Eni Sumarminingsih , SSi , MM. General Linear Processes. Moving Average Processes.

514 views • 26 slides

Time Series Model Estimation

Time Series Model Estimation. Materials for this lecture Read Chapter 15 pages 30 to 37 Lecture 7 Time Series.XLS Lecture 7 Vector Autoregression.XLS. Time Series Model Estimation. Outline for this lecture Review the first times series lecture Discuss model estimation

372 views • 18 slides

Understanding Time Estimation

Understanding Time Estimation. Imagine that “Real Time” is measured by a physical clock, that ticks once a second. We can plot psychological time (on the ordinate) as a function of physical time (on the abscissa). Psychophysics! A psychophysical function. Some Vocabulary.

322 views • 15 slides

Regularized Adaptation for Discriminative Classifiers

Regularized Adaptation for Discriminative Classifiers. Xiao Li and Jeff Bilmes University of Washington, Seattle. This work …. Investigates links between a number discriminative classifiers Presents a general adaptation strategy – “regularized adaptation”. Adaptation for generative models.

368 views • 16 slides

Time Series Model Estimation

Time Series Model Estimation. Materials for lecture 6 Read Chapter 15 pages 30 to 37 Lecture 6 Time Series.XLSX Lecture 6 Vector Autoregression.XLSX. Time Series Model Estimation. Outline for this lecture Review stationarity and no. of lags lecture Discuss model estimation

444 views • 27 slides

ARIMA-models for non-stationary time series

ARIMA-models for non-stationary time series Consider again the data material from Exercise 8.8 in the textbook (weekly sales figures of thermostats). This series is obviously non-stationary as it possesses a trend. SAC and SPAC.

712 views • 41 slides

Stationary Time Series

Stationary Time Series. AMS 586. Let { X t | t  T } be defined by the equation. The Moving Average Time series of order q, MA(q). where { Z t | t  T } denote a white noise time series with variance s 2.

1.13k views • 76 slides

L0 processor for NA62

L0 processor for NA62. Marian Krivda 1) , Cristina Lazzeroni 1) , Roman Lietava 1)2) 1) University of Birmingham, UK 2) Comenius University, Bratislava, Slovakia. Content. Requirements Block diagram Board layout and front panel Trigger message from each sub-detector

469 views • 16 slides

Traffic Matrix Estimation in Non-Stationary Environments

Traffic Matrix Estimation in Non-Stationary Environments. Presented by R. L. Cruz Department of Electrical & Computer Engineering University of California, San Diego Joint work with Antonio Nucci Nina Taft Christophe Diot NISS Affiliates Technology Day on Internet Tomography

566 views • 28 slides

SOS: Saving Time in Dynamic Race Detection with Stationary Analysis

SOS: Saving Time in Dynamic Race Detection with Stationary Analysis. Du Li , Witawas Srisa -an, Matthew B. Dwyer. D ata Race. T wo concurrent accesses t o the same data, and at least one access is a write O ne of the most common concurrency bugs. T hread 1. T hread 2. Release L.

407 views • 28 slides

Estimation versus Analysis

Estimation versus Analysis. A discussion of similarities and differences in approach. Estimation Vs Analysis. Presenter: Chloe Kempster. What is Estimation - What is Analysis.

409 views • 22 slides

L0 LKr trigger

L0 LKr trigger. Pixel based trigger processor (2x8 or 4x4 calorimeter cells tiles) Low granularity readout independent from the full granularity readout Fast readout after L0 for software triggers Region of Interest for Lkr full granularity readout. L0 LKr Trigger: architecture.

227 views • 8 slides

Time Series Model Estimation

Time Series Model Estimation. Materials for this lecture Read Chapter 15 pages 30 to 37 Lecture 6 Time Series.XLSX Lecture 6 Vector Autoregression.XLSX. Time Series Model Estimation. Outline for this lecture Review the first times series lecture Discuss model estimation

340 views • 19 slides

Running Time: Estimation Rules

Running Time: Estimation Rules. Running time is proportional to the most significant term in T( n ) Once a problem size becomes large, the most significant term is that which has the largest power of n This term increases faster than other terms which reduce in significance.

397 views • 18 slides

A noise-estimation algorithm for highly non-stationary environments

A noise-estimation algorithm for highly non-stationary environments. Sundarrajan Rangachari, Philipos C. Loizou Department of Electrical Engineering, University of Texas at Dallas, P.O. Box 830688, EC 33 Richardson, TX 75083-0688, USA Presenter: Shih-Hsiang( 士翔 ).

460 views • 29 slides

Models for Non-Stationary Time Series

Models for Non-Stationary Time Series. The ARIMA( p,d,q ) time series. The ARIMA( p,d,q ) time series. Many non-stationary time series can be converted to a stationary time series by taking d th order differences. Let { x t |t  T } denote a time series such that

700 views • 46 slides

A-CCNN: ADAPTIVE CCNN FOR DENSITY ESTIMATION AND CROWD COUNTING

A-CCNN: ADAPTIVE CCNN FOR DENSITY ESTIMATION AND CROWD COUNTING. Saeed Amirgholipour 1 , Xiangjian He 1 ; Wenjing Jia 1 , Dadong Wang 2 , Michelle Zeibots 1 1 Global Big Data Technologies Centre, University of Technology Sydney, Australia 2 Quantitative Imaging, CSIRO Data61, Australia.

314 views • 26 slides

Running Time: Estimation Rules

194 views • 18 slides

Multiplicative updates for L1-regularized regression

Multiplicative updates for L1-regularized regression. Prof. Lawrence Saul Dept of Computer Science & Engineering UC San Diego (Joint work with Fei Sha & Albert Park ). Trends in data analysis. Larger data sets In 1990s : thousands of examples In 2000+ : millions or billions

262 views • 25 slides

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