Notes on The Network Inside Out (2000)
- In the case of [Walbiri Iconography]: Munn (1973): an observer might imagine “maps” and “designs” as disparate concepts or levels of interpretation to be brought into a relationship through analysis. Munn notes that “the use of one graphic element to represent different meaning items simply reflects the premise that items are ‘the same thing’” (Munn, 1973: 171).
- This book: “an experiment in the ethnographic observation, description, and reflection on analytical phenomena for which we lack tools of description” (Riles, 2000: 1).
- The character and aesthetics of information, the manner in which information is elucidated and appreciated, its uses, and its effects (Riles, 2000: 2) -> in networks and organizations
- Network: “a set of institutions, knowledge practices, and artifacts thereof that internally generate the effects of their own reality by reflecting on themselves” (Riles, 2000: 3).
- Scott Lash: “reflexive modernity” and modernity’s “doubles” (1994: 11)
- George Marcus: no imagined “outside”
- Lyotard: “incredulity toward the metanarrative” (1984)
- The ethnographic apprehension that we’re already “inside” helps us develop tools for assessing what we already “know” (Riles, 2000: 4)
The ‘ontological turn’ and the anthropology of data
Important figures
- Roy Wagner - “The Invention of Culture”
- Marilyn Strathern - “Gender of the Gift”; “Partial Connections”
- Gilles Deleuze and Felex Guattari - “Miles Plateaux”/“A Thousand Plateaus”
- James Clifford and George Marcus - “Writing Culture”
Multiple epistemologies (knowledge of the world) to multiple ontologies (the things we’re knowing)
- One World/Many Worldviews -> Many Worlds (Naturecultures? How can we relate different worlds?)
- Pigs and animals see themselves as humans. Humans are spirits to them. -> In Amazonian cultures, all creatures have a view. Subjects (common to everything) form culture. The body is different among everyone. (Viveiros de Castro)
Perspectivism/Multinaturalism
- All subjects see things in the same way, but what they see (‘the object’) is different
Inverting the relationship between theory and ethnography
- The key of the ontological turn
- Anthropology: the application of theory -> the empirical materials should challenge the categories we’re using -> the ethnography should shape our theory
- The ontological turn as a methodology
- Challenging anthropological categories and theories - constant reinvention or conceptual innovations
Indigenous perspectivism is a theory of the equivocation, that is, of the referential alterity between homonymic concepts. Equivocation appears here as the mode of communication par excellence between different perspectival positions—and therefore as both condition of possibility and limit of the anthropological enterprise. (Viveiros de Castro, 2004:5)
Alterity, Incommensurable difference
- Amazonian ontologies sees relations based on differences rather than similarities.
- “Controlled equivocation”: gap, ambiguity
- Misunderstanding is the very thing that keeps anthropology going
Some critiques
- Essentialization of difference; reification of the “other”; exoticization
- What about shared humanity/ethics/engagement?
- Appropriation of “otherness” - another sort of colonialism?
- Just another “sub-category”
- Re-inventing the wheel - just another “culture”
What does this have to do with an anthropology of data?
- Does digital data have an ontology?
- Naively: What even is digital data if I don’t assume I already know what it is? What is knowledge? Is it material, infrastructural, abstract, concrete? What does it do? How does it work?
- How can I take seriously what people are telling me?
- How can these ethnographic engagements with ‘digital data’ shape the theories I’m using?
Issues
- Where is the radical alterity and incommensurable difference? “They” even use the word ‘ontology’!
- Can you do this sort of study critically?
Where’s the difference?
- What do you do when your informants do the same things you do, even to the point of reflexively analyzing themselves - stealing the ethnographer’s thunder?
- Annelise Riles
- Often they are not doing the same as the anthropologist
- Makes an ethnographic object out of this relation of ‘familiarity’
- Re-installing a different sort of difference through this
- The field is both ‘within and without itself’
- Shift from analyzing content to the form and aesthetics
- This is not an objective rendering; it is a recursive and transformational description.
- Neither object nor subject are the same before the process as after.
Nick Seaver
- How are we to take Big Data seriously? How can we engage in it ethnographically when it seems epitomize the opposite anthropology studies?
- Metaphors for Big Data: Oceans, Resources, Floods
- Formalism
- Hyperative kinship
- Threat to anthropology?
- Can an ethnography of Big Data be used to reshape ethnography itself?
- Can it be used to re-think the notion of difference?
Adrian Mackenzie - STS/Sociology
- Performativity of Knowledge - how do databases make/enact/perform/co-construct worlds?
- Databases as “world-making”
- ‘Agency’ of technology - socio-technical
- Databases as ontologies - particularly ‘set’ ontologies according to Badiou
- Databases as ways of “doing the multiple” - the relation between wholes and parts (like society and individual)
- access and relationality (relation to the introduction of digital anthropology)
Discussions
- Work out the relations between their definitions and our definitions
- Constant creation of concepts (Viveiros de Castro) -> nature/culture, neologisms
- Where’s politics? (Viveiros de Castro had his own political interest in trying to help the indigenous people.)
- Bruno Latour - how do you compose a world full of differences?
- Cosmopolitics - Isabelle Stengers
- Ghassan Hage - fundamentalism, universal truth
- The structure they’re moving away from is nested hierarchy
- It’s a method, not a definition of what’s out there.
Internal Otherness
- Pushing back at the idea that there’s a universal truth
- Peter Gow on Peru
- STS - the infrastructures needed to universalize knowledge
Multi-species way
- Fight against ethnocentricm


Media queries are used to make your page responsive to different size screens, windows, and devices. ## Viewports * The screen/device/window's width where your html
(styled by your css
) is being rendered. * Check it out in the inspector * There are lots of different viewports: CSS-Tricks: Media Queries for Standard Devices
@media ${media-type}, (${media-feature}: ${feature-value}) {
${css-selector} {
${css-style}: ${style-value};
...
}
}
@media
all
print
screen
speech
aspect ratio
height
width
orientation
aspect ratio
@media (aspect-ratio: 1/1) { /* selector(s) and style(s) here */ }
height
@media (min-height: 500px) { /* selector(s) and style(s) here */ }
width
@media (max-width: 750px) { /* selector(s) and style(s) here */ }
orientation
@media (orientation: landscape) { /* selector(s) and style(s) here */ }


|
Bullet Collision Detection & Physics Library
|
Go to the source code of this file.
Classes | |
| struct | btHashString |
| very basic hashable string implementation, compatible with btHashMap More... | |
| class | btHashInt |
| class | btHashPtr |
| class | btHashKeyPtr< Value > |
| class | btHashKey< Value > |
| class | btHashMap< Key, Value > |
| The btHashMap template class implements a generic and lightweight hashmap. More... | |
Variables | |
| const int | BT_HASH_NULL = 0xffffffff |
| const int BT_HASH_NULL = 0xffffffff |
Definition at line 62 of file btHashMap.h.


Iran’s Khamenei defends fuel price rises amid protests
by Richard GoodIran’s supreme leader Ayatollah Khamenei has defended petrol price rises that have prompted widespread protests across the country.
Khamenei told state television that some of those taking part in the demonstrations were enemies of the republic, carrying out sabotage and arson.
An unknown number of people are believed to have died in street clashes in dozens of cities across the country; the death of at least one policeman has been confirmed by state media.
Despite the 50 percent rise, at eight litres for one euro petrol prices in Iran are still among the cheapest in the world.
But the protests are reported to have become political, and internet observers are reporting that networks are running so slow that it amounts to a shutdown.
The government says it is raising petrol prices in order to provide additional subsidies to low income families.


A gallery of examples showcasing complications that arise in mass digitisations
Multilingual text, font sizes are different for different languages - consequently line heights for different languages also vary. Indented list inside footnote is extracted into a separate paragraph.
Japanese and German text
Italian verses with line numbers and right-aligned reference numbers; footnote references also include notes in German. Many lines in verses have been wrongly segmented into indivisual paragraphs.
Badly segmented footnote; multilingual text
German, Japanese and transliterated Japanese
Transliterated japanese (Romaji); verses very poorly segmented into new paragraphs due to artefacts in the left margin.
Multicolumn Czech text is present in the beginning of the page - but it actually belongs to the previous article. This one begins with Herr Chmel...
Contains Latin and German; latin is present as isolated blocks as well as interspresed with German text in prose, but they have distinct formats. Besides artefacts, the paragraph segmentation is poor.
Page containing German and Greek
German text, and then a mixture of German and Greek in one paragraph.
Mostly greek text with a German notes in between
Multilingual text with somewhat poor paragraph segmentation
Multilingual text. Footnote reference is misunderstood as "!"


Tiffin Café
- Designing an
E-commerce site
DESCRIPTION: Tiffin is a family run café in the center of Lisbon serving plant based food made with love. They wanted to increase their online visibility with an E-commerce site, but on their own terms making sure the style felt like them.
ROLE: We were three UX designer doing the user research, then my role was to take care of the competitive and business research as well as wireframes, ideating, branding, design, prototypes and preparing assets for the developer ..
TIMELINE: April- May 2020 using the Agile method and delivering protoypes for mobile, tablet and desktop
01 Challenges
We were three UX designers doing the research in the beginning phase. My team mates, Ines Ferreira and Frederico Dias were absolutely great to work with and together we came up with some cool ideas. For the design phase we each had a go at creating a version of the design so Tiffin had multiple options to choose from. The challenge was to make something that felt authentic to Tiffin and that they felt represented them well.
01
Problem, How can we help them increase their online precense in a way that they feel correctly represented?
02
The goal was to design a web page that had their core values of honesty, authenticity and community at heart.
03
The solution was a simple and straightforward web page reflecting all the values that were important to the café
02 The Process - - - ⌲
01
01 Research
- User research
- Desk research
- Business & Competitive research
02
02 Defining
- Affinity diagram
- User flows
- Concept sketching
- Sitemap
- Card sorting
- How might we..?
03
03 Ideating
- Brainstorm design solutions
04
04 Validating
- Low-fi prototype
- Testing
- Mid-fi prototype
- Testing
- Style
- Hi-fidelity prototype
- Testing
“Our main struggle was keeping up with the competition online. Beautiful Instagram posts are not something that comes naturally to us and we don’t feel like we fit in on Instagram. The main goal for us is to get more visibility. “
01-01 User research
In order to find out the needs, paint-points, and expectations of both the owners of Tiffin, the general coffee goer and their customers we conducted two surveys and three interviews, we got over 100 survey responses.
01-02 Desk research
Since it was very important for the owners that the website would feel authentic to their brand we had to imerge ourselves in the world of Tiffin to really understand what they were all about.
01-03 Competitive analysis
Who are the main competitors, what do they do well, and what do they do that that Tiffin doesn't?
02-01 Affinity map
Gathering all the insights gained from the research phase in one place
02-02 User flow
Understanding what steps the user will go through when they visit the website
02-03 Concept sketching
Visualising the concept
02-04 Card sorting
Testing where the users think the different menu options of the website fit into categories.
02-05 Site map
Structuring the content into a sitemap and making sure navigation is good.
03-01 Brainstorming
With all the data collected from the research phase it's time to brainstorm design opportunities
04-01 Low-fidelity prototype
Time to start putting the ideas to life in a low-fidelity prototype starting with the desktop version.
04-02 Mid-fidelity prototype
To the left is the first Mid-fi prototype.
I made a prototype on the platform Useberry, which allows you
test your design with your users and shows you a video of
where they clicked. Very useful to see what's
intuitive to the users!
Changes that were made:
- Adding a home button
- Changing 'shop' to 'order'
- Putting the food menu in order
04-03 Style
01
01 Moodboard
Taking inspiration for what the actual Tiffin café looks like inside in order to convey a feeling of authenticity
02
02 Alternatives
Coming up with a lot of alternative versions and testing which ones the owners respond to the best
03
03 Style tile
Following certain rules for font and colours to be consistent
Next steps
The assets have now been handed over to a developer to be implemented
Thank you for reading✌


With an innovative and progressive thought process,
we at Affascinante focus on providing bespoke furniture, modular
kitchens
and wardrobes. We have an in house team providing a one-stop solution for
all your designing, décor and installation needs. Our services offer a
professional experience guiding clients through each step of their journey
towards creating their desired living spaces. We at Affascinante believe in
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Welcome to the official website of siege, a community-oriented developer and designer.
I have been working with FiveM and GTA V modding for over a year, and I am always looking to expand my work. I also work with graphic design.
I personally own and develop under the name Siege Modifications (aka SiegeMods). I make liveries, clothing, and other design products. Join the SiegeMods Discord here.
I also own and manage notsiege development, our website is here. We make maps, liveries, clothing, and scripts. Come check us out!
FiveM© 2021, the CitizenFX Collective
GTA©, GTA V© and Grand Theft Auto V© 2021, Rockstar Games
siege© 2021, siege


Note
Click here to download the full example code
Benchmark ONNX conversion¶
Example Train and deploy a scikit-learn pipeline converts a simple model. This example takes a similar example but on random data and compares the processing time required by each option to compute predictions.
Training a pipeline¶
import numpy
from pandas import DataFrame
from tqdm import tqdm
from sklearn import config_context
from sklearn.datasets import make_regression
from sklearn.ensemble import (
GradientBoostingRegressor, RandomForestRegressor,
VotingRegressor)
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from mlprodict.onnxrt import OnnxInference
from onnxruntime import InferenceSession
from skl2onnx import to_onnx
from onnxcustom.utils import measure_time
N = 11000
X, y = make_regression(N, n_features=10)
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=0.01)
print("Train shape", X_train.shape)
print("Test shape", X_test.shape)
reg1 = GradientBoostingRegressor(random_state=1)
reg2 = RandomForestRegressor(random_state=1)
reg3 = LinearRegression()
ereg = VotingRegressor([('gb', reg1), ('rf', reg2), ('lr', reg3)])
ereg.fit(X_train, y_train)
Out:
Train shape (110, 10)
Test shape (10890, 10)
VotingRegressor(estimators=[('gb', GradientBoostingRegressor(random_state=1)),
('rf', RandomForestRegressor(random_state=1)),
('lr', LinearRegression())])
Measure the processing time¶
We use function measure_time
.
The page about assume_finite
may be useful if you need to optimize the prediction.
We measure the processing time per observation whether
or not an observation belongs to a batch or is a single one.
sizes = [(1, 50), (10, 50), (1000, 10), (10000, 5)]
with config_context(assume_finite=True):
obs = []
for batch_size, repeat in tqdm(sizes):
context = {"ereg": ereg, 'X': X_test[:batch_size]}
mt = measure_time(
"ereg.predict(X)", context, div_by_number=True,
number=10, repeat=repeat)
mt['size'] = context['X'].shape[0]
mt['mean_obs'] = mt['average'] / mt['size']
obs.append(mt)
df_skl = DataFrame(obs)
df_skl
Out:
0%| | 0/4 [00:00<?, ?it/s]
25%|##5 | 1/4 [00:20<01:01, 20.40s/it]
50%|##### | 2/4 [00:40<00:40, 20.32s/it]
75%|#######5 | 3/4 [00:46<00:13, 13.71s/it]
100%|##########| 4/4 [00:57<00:00, 12.51s/it]
100%|##########| 4/4 [00:57<00:00, 14.29s/it]
Graphe.
df_skl.set_index('size')[['mean_obs']].plot(
title="scikit-learn", logx=True, logy=True)
Out:
<AxesSubplot:title={'center':'scikit-learn'}, xlabel='size'>
ONNX runtime¶
The same is done with the two ONNX runtime available.
onx = to_onnx(ereg, X_train[:1].astype(numpy.float32),
target_opset={'': 14, 'ai.onnx.ml': 2})
sess = InferenceSession(onx.SerializeToString(),
providers=['CPUExecutionProvider'])
oinf = OnnxInference(onx, runtime="python_compiled")
obs = []
for batch_size, repeat in tqdm(sizes):
# scikit-learn
context = {"ereg": ereg, 'X': X_test[:batch_size].astype(numpy.float32)}
mt = measure_time(
"ereg.predict(X)", context, div_by_number=True,
number=10, repeat=repeat)
mt['size'] = context['X'].shape[0]
mt['skl'] = mt['average'] / mt['size']
# onnxruntime
context = {"sess": sess, 'X': X_test[:batch_size].astype(numpy.float32)}
mt2 = measure_time(
"sess.run(None, {'X': X})[0]", context, div_by_number=True,
number=10, repeat=repeat)
mt['ort'] = mt2['average'] / mt['size']
# mlprodict
context = {"oinf": oinf, 'X': X_test[:batch_size].astype(numpy.float32)}
mt2 = measure_time(
"oinf.run({'X': X})['variable']", context, div_by_number=True,
number=10, repeat=repeat)
mt['pyrt'] = mt2['average'] / mt['size']
# end
obs.append(mt)
df = DataFrame(obs)
df
Out:
0%| | 0/4 [00:00<?, ?it/s]
25%|##5 | 1/4 [00:20<01:02, 20.95s/it]
50%|##### | 2/4 [00:42<00:42, 21.15s/it]
75%|#######5 | 3/4 [00:51<00:15, 15.93s/it]
100%|##########| 4/4 [01:12<00:00, 17.86s/it]
100%|##########| 4/4 [01:12<00:00, 18.20s/it]
Graph.
df.set_index('size')[['skl', 'ort', 'pyrt']].plot(
title="Average prediction time per runtime",
logx=True, logy=True)
Out:
<AxesSubplot:title={'center':'Average prediction time per runtime'}, xlabel='size'>
ONNX runtimes are much faster than scikit-learn to predict one observation. scikit-learn is optimized for training, for batch prediction. That explains why scikit-learn and ONNX runtimes seem to converge for big batches. They use similar implementation, parallelization and languages (C++, openmp).
Total running time of the script: ( 2 minutes 17.003 seconds)


Event Location:
IQS
Via Augusta, 390
Barcelona, Barcelona, Spain
Abstract:
Around 80% of the work to create a data warehouse/BI solution is spent on the ETL phase. Although building an ETL solution can be a challenge, you can break down the project into at least two separate processes for easier management. One process is strictly related to business modeling, and therefore cannot be replicated. But the other is made up of purely technical processes that are always the same, regardless of the business environment we operate in, and thus can be highly automated. In this session, we will look at well-known patterns to solving common problems and how they can be automated with the help of specific tools and techniques that use metadata to reduce development time and bugs. Using these engineering techniques, you will be able to adopt an Agile approach to your BI solution.
Speaker(s): Davide Mauri,
Abstract:
DBAs do not always have time to work with some of the amazing visual data tools that are becoming common place now for analysts. In this presentation I will show how in just a few very easy steps you can learn more than the basics of PowerView, PowerPivot and Data Mining using data you are already familiar with as a DBA, your SQL Server installation data. We will look at new and interesting way to load, transform, merge and analyze configuration and performance data for many servers simultaneously. I will also demonstrate how to best utilize that data for reports in Excel, SSRS and Visio to get the most out of automation, standardization and visualization with the new Power tools.
Speaker(s): Rodney Landrum,
Abstract:
This introductory session describes and demonstrates how to create a big data analytics solution with structured data by using Azure HDInsight and Excel 2013. This session will be of interest to those new to the concept of big data, new to self-service data modeling with Power Pivot, and for those interested to understand how big data can play a role in a self-service BI solution. Demonstrations will reinforce the introductory theory by showing how to store data files, and how to use Pig to transform data, and Hive to query data. The “big data” will then be retrieved and queried by using Power BI add-ins in Excel 2013, including Power Query, Power Pivot and Power View.
Speaker(s): Peter Myers,
Abstract:
Microsoft have released a distribution of Hadoop targeted at making big data accessible to everyone. Hadoop is different to SQL Server. Not necessarily better, just different. It is a tool for a job. if you have only ever worked with an RDBMS such as SQL Server then Hadoop is going to play with your mind a little and you are going to need to rethink a few things. This session is going to take Hadoop and break it apart. I will also look at some of the tooling Hive and Pig. Keep an open mind and let me show you that Hadoop has lots to offer.
Speaker(s): Allan Mitchell,
Abstract:
En muchas ocasiones nos planteamos una pregunta de negocio, conocemos el modelo dimensional, las herramientas y la forma de acceder a la información para responderla, pero fallamos en cómo exponemos dicha respuesta de forma visual. En esta sesión se verán múltiples ejemplos concretos de respuestas de negocio. En cada una de ellas se estudiarán diversas formas de mostrar la información (tablas, diversos tipos de gráficos, colores), pros y contras de cada una de ellas; partiendo de una mala solución que se irá comentando y mejorando, mediante técnicas y herramientas de visualización, hasta convertirla en una buena respuesta.
Speaker(s): Salvador Ramos,
Abstract:
¿Quién no ha tenido problemas de rendimiento en las soluciones de BI? Monitorizar las consultas y los recursos se convierte en algo crucial para detectar problemas. Durante la sesión construiremos un sistema de monitorización que incorpora datos de DMVs, contadores de rendimiento y los nuevos Eventos Extendidos de SSAS, y que además puede realizar acciones automatizadas como cancelar consultas o enviar alertas a los administradores. ¿Quieres convertirte en el ojo que todo lo ve? No te pierdas la sesión.
Speaker(s): Ruben Pertusa Lopez,
Abstract:
If you want to conduct advanced Business Intelligence development and Business Analysis, then it is useful to understand data visualisation and have R as a tool in your toolset. In this intensive one hour session, we will look at the latest suite of Microsoft Business Intelligence tools - Microsoft Power BI in conjunction with R. The demos will be implemented in both tools in an end-to-end hour long session so that delegates can see when to use each technology in order to determine 'what to use' and 'when to use it', as well as seeing where the technologies complement each other. We will visualise the data according to data visualisation principles set out by Stephen Few amongst others.
Speaker(s): Jen Stirrup,
Abstract:
Please join this session to see end to end exciting demos in a story format for two real world scenarios. We will use tools and technologies including Power Query, Power Pivot, Power View, Power Map, Power BI sites, QA, Windows Azure HD Insight, STORM, APS, SAP BI Connector etc. All demos are based on real world implementations including Microsoft Cybercrime center, a retail customer etc.Please join this interactive session to have an immersive and fun experience!
Speaker(s): Sanjay Soni,
Abstract:
Todo el mundo que trabaja con base de datos siempre se ha preguntado alguna vez qué son los planes de ejecución y como se leen. Saber leer un plan de ejecución nos va a dar información valiosísima de cara a mejorar el rendimiento de una consulta. En esta sesión vamos a centrarnos en aprender a leer T-SQL para interpretar lo que está haciendo SQL Server para devolvernos la información.
Speaker(s): Enrique Bañuls,
Abstract:
Reporting Services es una tecnología madura que se ha ido transformando hasta proporcionarnos diferentes controles y funcionalidades a la hora de realizar nuestros informes. Podemos crear suscripciones, gráficas, alertas de datos... pero, ¿estamos sacando todo el partido que podemos sacarle? En esta sesión veremos como llevar a Reporting Services a limites que no te imaginarias. ¡Se acabaron los informes aburridos y poco interactivos!
Speaker(s): Sergio Carrillo Vila,
Abstract:
En esta sesión veremos dos novedades que ofrece SQL Server 2014 para gestionar la concurrencia de las cargas transaccionales de los sistemas OLTP de forma muy transparente.La durabilidad diferida de transacciones permite "aligerar" el acceso al archivo de transacciones penalizando ligeramente la "transaccionalidad completa" de la operacion: Veremos pros y contras de esta operación. Las mejoras en gestión de bloqueos permitirán que operaciones de mantenimiento no se vean bloqueadas (o bloqueen) las operaciones del día a día del sistema transaccional.
Speaker(s): Eladio Rincon,
Abstract:
Toda esta revolución de SQL Server 2014 no pasa desapercibida para nadie. Sin embargo, quizá podamos hacer que pase desapercibida la llega a nuestras organizaciones de esta nueva versión, ya que plantear una migración de forme directa es muy difícil que logre cristalizar. ¿Qué hacemos entonces? Aprenderemos algunas formas de conseguir que la implantación de SQL Server 2014 sea un proceso nada disruptivo que nos lleve, como quien no quiere la cosa, a una plataforma actualizada con todas las ventajas que ello supone.
Speaker(s): Alberto López Grande,
Abstract:
Did you know that Power View is not any longer limited to getting data from Tabular models ? Now that Power View users can connect to multidimensional BI Semantic Model (Cubes) we will have a look at how to get the best out of this interactive data exploration, visualization and presentation for multidimensional models. The session we'll present - An understanding of the Multidimensional Model Objects in Power View - How to create a Shared Report Data Source Connection to a Multidimensional Model - How to create a Power View Report with a Multidimensional Model Data Source - An visual exploration of a Multidimensional Model by using Power View
Speaker(s): Regis Baccaro,
Abstract:
Ever wanted to work with a BI Tool the way you search with Google aehm... Bing? With Power BI there comes a new toolset called QA, which should enable you to do that. Is it really like this? Does it work? How does it work? This session will introduce QA, will have a look behind the scences of QA, how to build models addressing natural language questions and a will review the usability
Speaker(s): Oliver Engels,
Abstract:
DML is used in most cases without thinking about the multiple operations for the db engine. This session will give a deep dive into the internal storage engine down to record level. After finishing the theory (and inside the theory) the differen DML commands and their tremendous operational tasks for the db engine will be investigated. SEE, what a workload will be caused by a "forwarded record". What tremendous workload will occur in a page split. What happens if an existing record will be updated in fixed length attributes
Speaker(s): Uwe Ricken,
Abstract:
El tiempo entre que un dato está en el sistema transaccional y está disponible en nuestro DW puede ser un problema cuando se construye un sistema de información complejo. En la sesión veremos algunas técnicas combinadas que ayudan a minimizar estos tiempos para trabajar en tiempo semi-real.
Speaker(s): Miguel Egea,
Abstract:
On-Premise Server are still here and will surely last for a while. Buying a server isn't s complex task, but buying the server that maximize the ROI of the money you spent on it is not an easy task, since there is so much choice that finding the correct machine is really not trivial. In this session we'll see the internals of a server, learning how memory, cpu, disks and all the rest works together. Along with some basic numbers and math we'll see how to find the correct hardware needed and how to evaluate the server we're going to buy, before releasing it in production and realizing, too late, that it physically can't delivery the performance we need.
Speaker(s): Davide Mauri,
Abstract:
La gestión de grandes volúmenes de información es cada día más común, y los datos de texto no son una excepción, desde nombres de clientes en una tabla hasta documentos completos digitalizados. Veremos qué opciones nos ofrece SQL Server para tratar textos y cómo podemos aplicarlas a ámbitos como buscadores difusos, gestión documental, enriquecimiento de data warehouses y más.
Speaker(s): Pau Sempere,
Abstract:
In-Memory OLTP aka Hekaton aka XTP is without a doubt one of the key features in brand new SQL Server 2014. By placing your data in memory and using native compile stored procedures you can boost and increase you SQL Server performance in times. But it is not so easy to implement as it may seem, you need to make a lot of changes in your code and even in your application architecture. In this session you'll learn what parts of your system can benefit by using In-Memory OLTP and what you should take into consideration before starting. As a bonus, I'll share my own experience of implementing In-Memory OLTP into a real production system.
Speaker(s): Sergey Olontsev,
Abstract:
When it comes to clustered columnstore indexes, you may already understand row groups, delta stores, and compression methods, but come see how clustered columnstore indexes work with locking and blocking and when using different compression methods and techniques. We will also dive deep into Dictionaries creation and different methods for ETL processes.
Speaker(s): Niko Neugebauer,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: http://www.linkedin.com/in/enriquecatala
Contact: http://www.enriquecatala.com/
Mentor en SolidQ, Microsoft SQL Server MVP, Ingeniero en inform#225;tica, Microsoft Certified Trainer (MCT) y MCSE. Centrado en el motor relacional SQL Server, tanto en la resoluci#243;n de problemas de rendimiento y escalabilidad en sistemas OLTP como la definici#243;n e implementaci#243;n de entornos de alta disponibilidad confiables, en donde ha llevado con #233;xito m#225;s de 100 proyectos en diversos pa#237;ses (EEUU, Holanda, M#233;xico, Arabia Saud#237; o Austria)
Contact: http://www.portalsql.com
SQL Server MVP desde 2001 hasta la actualidad (con algunos años fuera del programa), trabaja como mentor en SolidQ en el departamento de BI. Es ponente habitual en eventos de comunidad de habla hispana. Apasionado de la tecnolog#237;a en general y de la relacionada con base de datos en particular.
LinkedIn: http://www.linkedin.com/profile/view?id=270775013amp;trk=tab_pro
Contact: http://www.sqlmaster.de
Uwe Ricken is working with IT systems since the 90’s. The primary passion for developments with Microsoft SQL Server expanded in 2007 with his engagement as a DBA for Deutsche Bank AG in Frankfurt am Main. After 6 years of operational experiences as a DBA and over 14 years as a developer of complex database models he achieved the “Microsoft Certified Master – SQL Server 2008” certification which “was” the highest technical certification. In the same year he earned his first award as a Data Platform MVP for his support to the Microsoft SQL Server community in Germany and Europe. Uwe Ricken is a speaker on many international conferences and events and preferred topics are “Database Internals”, “Indexing” and “Development”.
LinkedIn: http://www.linkedin.com/in/davidemauri
Contact: https://medium.com/@mauridb
Data Platform MVP for almost 12 years, I love Data and Database in all their forms. Developer for passion (C# and Python), I've been working in the IT field since 1997. My focus has been databases and performance tuning since the start, focusing both on transactional and analytical workloads. I worked for more than 10 years on Business Intelligence and Data Warehousing, then moved to IoT and Big Data for a while. Now back to database space, injecting passion and innovation into Azure SQL as Microsoft Program Manager for Azure SQL Hyperscale, helping developers to re-discover SQL, which is now almost everywhere, and take advantage of all its amazing power!
LinkedIn: http://www.linkedin.com/pub/ruben-pertusa-lopez/24/ba3/b0
Contact: http://www.sqlpass.es
Ruben is a Global BI/BigData Manager and Global Data Lead Architect at DUFRY HQ, member of PASS Spanish Group and founder of SQLSaturday Barcelona and Madrid. MCP and MCSA in SQL Server 2012 and also awarded with Microsoft MVP Data Platform. With more than 9 years of experience, he has been working in top projects for leading worldwide companies and teaching in the SolidQ BI Master. He has presented at the SQL Server 2008 R2 and 2014 Spanish launch event, PASS BA Chicago, TechNet Webcasts, Microsoft Partner Program and SolidQ Summit events.
Contact: http://www.solidq.com
Pau Sempere S#225;nchez is a Computer Science graduate, working at SolidQ as Data Platform Architect and is member of PASS Spanish Group. He#39;s worked in BI and database projects for top companies in the world, in diverse areas as communications, insurance, retail, transport, banking and automotive. He has been presenting in many Microsoft and community events such as SQLSaturday Madrid and Barcelona, the 12 hours of SQL Server, SolidQ Summit or TechNet Webcasts, and teaching in the SolidQ Master for 6 years in a row
LinkedIn: https://www.linkedin.com/in/sanjay-soni-5990b36
Sr.Technical Product Manager - SQL BI/Data Warehousing at Microsoft. Sanjay Soni is a BI Platforms Evangelist in the Microsoft IT BI Engineering. He is an MCDBA, MCTS and specializes in Business Intelligence using Microsoft technologies including Microsoft SQL Server Analysis Services, SharePoint, ProClarity, PerformancePoint, etc. Sanjay has authored and presented a variety of papers at various BI conferences including TDWI, BI Conference, PASS, SQL Connections, SharePoint Conference and TechEd
LinkedIn: http://dk.linkedin.com/in/regisbaccaro
Contact: http://theblobfarm.wordpress.com
Regis has more than 15 years of experience with SQL Server, SharePoint and .Net as an architect and developer. R#233;gis is a SQL Server MVP since 2014 and a frequent speaker at SQL conferences, a PASS Regional Mentor and the founder of SQLSaturday Denmark community event. He is also a certified industry trainer on the APS/PDW platform. In his freetime he is an avid runner, cook and farmer.
LinkedIn: http://linkedin.com/in/peterjsmyers
Peter Myers is a consultant, trainer and presenter, and has worked with Microsoft database and development products since 1997. Today he specializes in all Microsoft Business Intelligence products and also authors training course content for Microsoft products and services. He has a broad business background supported by a bachelor’s degree in applied economics and accounting, and he extends this with extensive experience backed by current MCSE certifications. He has been a Data Platform MVP since 2007.
LinkedIn: http://pt.linkedin.com/in/webcaravela/
Contact: http://www.nikoport.com
Niko Neugebauer is a Data Platform Consultant. A SQL Server MVP with over 20 years of experience in IT, he is passionate about the Microsoft Data Platform and community. Founder of the Portuguese SQL Server User Group and the main organizer of the first SQLSaturday event outside of North America (#78 Portugal), Niko speaks regularly at events such as PASS Summit, SQLRally, SQLBits, and SQLSaturday events around the world. Niko loves sharing information and knowledge and has authored over 130 blog posts on Columnstore Indexes, and regularly contributes to the open-sourced CISL library focused on Columnstore Indexes.
Contact: http://www.solidq.com/blogs
Eladio es MVP en SQL Server desde el a#241;o 2002; es director de bases de datos relacionales de SolidQ en Espa#241;a donde dirige proyectos para clientes de SolidQ en todo el mundo. Ponente habitual en conferencias Microsoft MSDN/TechNet, SQLPASS o grupos de usuarios; participa con sus compa#241;eros en los blogs tecnol#243;gicos de su empresa http://blogs.solidq.com/bigdata-hadoop , http://blogs.solidq.com/elrincondeldba y http://blogs.solidq.com/bigdata-hadoop
Contact: https://onlysearch.wordpress.com/
Allan Mitchell is a SQL Server MVP and runs elastio, a small consultancy helping customers to make informed decisions about their data storage and integration. His focus is on enterprise search as well as real-time data integration.
Contact: https://www.simple-talk.com/blogs/author/2133-rodney-landrum/
Rodney Landrum has been architecting solutions for SQL Server for over 12 years. He has worked with and written about many SQL Server technologies, including DTS, Integration Services, Analysis Services, and Reporting Services. He has co-authored 4 books on Reporting Services. He is been a regular contributor to SQL Server magazine, sqlservercentral.com and Simple-talk.com. Rodney is also SQL Server MVP
LinkedIn: http://uk.linkedin.com/in/jenstirrup/
Contact: http://www.jenstirrup.com
Jen Stirrup, Microsoft Regional Director and MVP is a well-known Business Intelligence and Data Visualization expert, author, data strategist, and Master of Business Administration (MBA) student in London. Jen leads a boutique analytics consultancy, delivering strategic advice to companies large and small. Jen has presented in Africa, India, Europe and North America. Jen has presented at Ignite, Techorama, TechEd, PASS Summit, SQLBits, and SQLSaturday events in Europe and the US. She also delivers the MAPA Azure Architects course worldwide with Microsoft team members. Jen was also featured on the Best of PASS Summit 2015 DVD as one of the top 10 speakers. Jen also was awarded PASS prestigious PASSion Award in 2012
Contact: http://www.sqlserversi.com
Consultor, Formador y Mentor en Business Intelligence. SQL Server MVP. Director de Formación en SolidQ. Microsoft MCSE 2012: Business Intelligence. Autor de libro gratuito "Microsoft BI: vea el cubo medio lleno".
LinkedIn: https://www.linkedin.com/in/alberto-l%C3%B3pez-grande-b756aa9/
Contact: http://qwalgrande.com/
Data Platform MVP, sqlpass.es coordination team. But before all that, I am DBA. I write the blog quot;Diario de un DBAquot; (http://qwalgrande.com). I am currently Head of Structured Systems in Grupo Catalana Occidente. Working with SQL Server since 1998 in many areas (management, architecture, infrastructure, high availability, tunning, SSIS, SSAS, data quality,...).
LinkedIn: https://es.linkedin.com/pub/sergio-carrillo-vila/1b/a32/346
Sergio Carrillo trabaja ahora mismo en Samp;G Consulting en el departamento de Inteligencia de Negocio como Senior BI Developer. MCTS de Microsoft Dynamics AX 2009 y SQL Server, realiza labores de aruqitecto en Inteligencia de Negocio, alrededor la plataforma de datos de Microsoft. Es ingeniero inform#225;tico por la universidad de Alicante, en la que continua sus estudios de Postgrado
Contact: http://www.ssis-components.net
Oliver Engels is CEO of oh22data AG, a Microsoft Gold Partner in Germany specializing in CRM and BI. His special interests are Azure, Data Governance and Integration, Visualisation Tools like SSRS, Power BI, Tableau, R, and SharePoint. He has worked with SQL Server since version 6.5 and is a founder and Board member of PASS Germany, a PASS Regional Mentor, and runs the Frankfurt PASS Local Group. For more than seven years he is a Microsoft Data Platform MVP and a Microsoft pTSP.
LinkedIn: http://linkedin.com/in/sergeyolontsev
Contact: http://SergeyOlontsev.com/
I have been working with SQL Server since SQL Server 2000 version as a database administrator, developer, consultant and trainer, focusing on high availability and disaster recovery solutions, ETL, troubleshooting and performance tuning, developing high performance solutions . I'm a regular speaker at SQL Server user group meetings, SQLSaturday events and other conferences. An owner of MCM certification and MVP award on SQL Server.
LinkedIn: http://www.linkedin.com/in/davidemauri
Contact: https://medium.com/@mauridb
Data Platform MVP for almost 12 years, I love Data and Database in all their forms. Developer for passion (C# and Python), I've been working in the IT field since 1997. My focus has been databases and performance tuning since the start, focusing both on transactional and analytical workloads. I worked for more than 10 years on Business Intelligence and Data Warehousing, then moved to IoT and Big Data for a while. Now back to database space, injecting passion and innovation into Azure SQL as Microsoft Program Manager for Azure SQL Hyperscale, helping developers to re-discover SQL, which is now almost everywhere, and take advantage of all its amazing power!
The following is a list of sponsors that helped fund the event.


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Cyclone Bulbul: Death toll rises to 24 in Bangladesh, India
Cyclone Bulbul, which pummeled coastal regions of India and Bangladesh, killed 12 each in the two South Asian nations.
The death toll from a cyclone that barrelled into the coasts of Bangladesh and India has risen to 24, authorities said, as the two nations assess the scale of devastation wreaked by the powerful storm.
Bangladesh carried out one of its biggest-ever evacuation drives, moving some 2.1 million people to cyclone shelters specially built to minimise casualties from such storms, which can claim thousands of lives.
More:
Cyclone Bulbul, packing winds of up to 120km per hour (75 miles/h) when it hit late on Saturday, killed 12 people in Bangladesh - 11 from falling trees - and 12 in India's West Bengal and Odisha states.
Five others remain missing after a fishing trawler sank in squally weather near Bangladesh's southern island of Bhola, district administrator Masud Alam Siddiqui told AFP news agency.
Bangladesh's junior minister for disaster management, Enamur Rahman, told AFP Bulbul left a trail of destruction, damaging some 10,000 homes of mud, tin and bamboo and 200,000 hectares (494,000 acres) of crops.
The cyclone weakened as it tracked inland.
Battered by cyclones
The Sundarbans - the world's largest mangrove forest which straddles the two nations and is home to several endangered species - shielded the coast from the storm's full impact, officials added.
In India, nearly 120,000 evacuated people were returning home as the cyclone weakened, authorities said. Coastal crops in Odisha were also extensively damaged, officials told the Press Trust of India news agency.
Bangladesh's low-lying coast, home to 30 million people, and India's east are regularly battered by cyclones that have killed hundreds of thousands of people in recent decades.
While the storms' frequency and intensity have increased - partly due to climate change - the death tolls have come down because of faster evacuations and the building of thousands of coastal shelters.
In India, flights in and out of Kolkata airport were suspended for 12 hours because of the storm.
The cyclone season in the Bay of Bengal can last from April to December. In 1999, a super-cyclone battered the coast of India's Odisha state for 30 hours, killing nearly 10,000 people.


Main Page
Classes
Files
Class List
Class Index
Class Hierarchy
Class Members
Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
pluma::Host
pluma::PluginManager
pluma::Pluma
pluma::Provider
:: Copyright © 2011 Gil Costa, all rights reserved :: Documentation generated by
doxygen 1.7.4
::


For my project I decided to make a thermometer for
my computer exhaust. I used a poptart box as an
enclosure because I had a lot lying around from my
crippling brown sugar cinnimon addiction.
I thought of using a green, yellow, and red LED to show
the temperature and punching them and a thermistor through
to the front of the box. The system is powered by a 9V
battery. The thermistor is used as the first resistor in a
voltage divider, and the fluxation in the voltage at pin A0
is used to measure the temperature.
I used a 1k ohm resistor for the voltage divider
because 5k was being finnicky. I used 220 ohm
resistors for the LEDs because I know they work for
all LED colors.
Blue wires connect the LEDs to output pins, and black
wires all go to ground. The red wire connects the
voltage divider to power and white connects to the
input pin. The black and white extension goes to the
thermistor, yellow/green goes to the green LED,
orange/red goes to the red LED, and blue/purple goes
to the yellow LED.
/* Final Project HCDE 439 Isla Wisemore */ //declare variables for pins const int analogInPin = A0; const int redPin = 10; const int yellowPin = 11; const int greenPin = 9; // value read from the thermistor int tempValue = 0; void setup() { // initialize serial communications at 9600 bps: Serial.begin(9600); //initialize output pins pinMode(redPin, OUTPUT); pinMode(yellowPin, OUTPUT); pinMode(greenPin, OUTPUT); } void loop() { // read the analog in value tempValue = analogRead(analogInPin); //print to serial for debugging purposes Serial.println(tempValue); // if value is in lowest bracket if (tempValue < 185){ // turn on green, turn off others digitalWrite(redPin, LOW); digitalWrite(yellowPin, LOW); digitalWrite(greenPin, HIGH); } else if (tempValue < 210) { // turn on yellow, turn off others digitalWrite(redPin, LOW); digitalWrite(yellowPin, HIGH); digitalWrite(greenPin, LOW); } else { // turn on red, turn off others digitalWrite(redPin, HIGH); digitalWrite(yellowPin, LOW); digitalWrite(greenPin, LOW); } // wait 1 seconds before the next loop delay(1000); }
As the thermometer enters the oven, it goes from
green to yellow to red, then back to yellow when
taken out.
Here is all the documentation for my final project!


Katerra launches "elegantly simple" building products brand KOVA
by Dezeen staffDezeen promotion: Silicon Valley design-build firm Katerra has launched a brand called KOVA that offers "simple, timeless" building products and materials.
KOVA was founded by Katerra to offer architects, designers and trade professionals a one-stop shop for high quality but low-cost materials and products.
Its products range from large appliances and cabinetry through to lighting, window coverings, plumbing, flooring and ceramic tiles.
"As designers and builders specify and order their products, they may go through a lot of different partners for their plumbing, lighting, cabinets, countertops, etcetera," said Trevor Schick, head of the KOVA.
"KOVA is a one-stop shop where we provide all of these materials at a premium quality and great design, but at a better value and with a quicker turnaround," Schick continued.
"So, as a developer is looking at what they can do to drive cost down and simplify delivery on a project, KOVA is now one of the levers they can pull."
The brand's name, KOVA, is derived from the prefix "co" and the Spanish word "va", which translates as "go".
This is intended to symbolise "that design, quality and value can go together and strike a perfect balance".
All KOVA Select products are designed in Seattle by its in-house design team.
The items are designed to complement any style, enabling users to easily achieve "fully coordinated" and timeless finishes throughout the living spaces they are designing or building.
One of KOVA's first collections is KOVA Select, the brand's premium line of products that "offers superior design without the premium prices".
This includes modular plumbing fixtures that are designed to be as simple as possible, with classic geometric forms and minimal ornamentation.
All of KOVA's products are designed to "meet the highest environmental and quality standards" such as the Cal Green certification in all its kitchen and bath faucets and showerheads.
Find out more information about KOVA and KOVA Select on the brand's website.


mdu(1) mdu(1) Name mdu - display the amount of space occupied by an MSDOS directory Note of warning This manpage has been automatically generated from mtools's texinfo docu‐ mentation, and may not be entirely accurate or complete. See the end of this man page for details. Description Mdu is used to list the space occupied by a directory, its subdirectories and its files. It is similar to the du command on Unix. The unit used are clusters. Use the minfo command to find out the cluster size. mdu [-a] [ msdosfiles ... ] a All files. List also the space occupied for individual files. s Only list the total space, don't give details for each subdirec‐ tory. See Also Mtools' texinfo doc Viewing the texi doc This manpage has been automatically generated from mtools's texinfo docu‐ mentation. However, this process is only approximative, and some items, such as crossreferences, footnotes and indices are lost in this transla‐ tion process. Indeed, these items have no appropriate representation in the manpage format. Moreover, not all information has been translated into the manpage version. Thus I strongly advise you to use the original texinfo doc. See the end of this manpage for instructions how to view the texinfo doc. * To generate a printable copy from the texinfo doc, run the follow‐ ing commands: ./configure; make dvi; dvips mtools.dvi * To generate a html copy, run: ./configure; make html A premade html can be found at `http://www.gnu.org/software/mtools/man‐ ual/mtools.html' * To generate an info copy (browsable using emacs' info mode), run: ./configure; make info The texinfo doc looks most pretty when printed or as html. Indeed, in the info version certain examples are difficult to read due to the quot‐ ing conventions used in info. mtools-4.0.17 29Jun11 mdu(1)


| pgpool-II 4.0.3 Documentation | |||
|---|---|---|---|
| Prev | Up | Chapter 3. Installation of Pgpool-II | Next |
This chapter describes the installation of Pgpool-II from PRM. If you are going to install from the source code, please check Section 3.1.
Pgpool-II official RPMs can be obtained from http://www.pgpool.net/yum.
For RHEL and its derivatives do following once:
yum install http://www.pgpool.net/yum/rpms/3.6/redhat/rhel-7-x86_64/pgpool-II-release-3.6-1.noarch.rpm
Then:
yum install pgpool-II-pg96
pg96 means PostgreSQL 9.5. Pgpool-II needs PostgreSQL's library and extensions directory. Since the directory paths are different in the particular PostgreSQL versions, You must choose appropriate RPM for your PostgreSQL rpm installation. We also assume you are using PostgreSQL community rpms. Optionally you can install:
yum install pgpool-II-pg96-debuginfo
which makes it easier to retrieve debugging symbols from the core or the backtrace. We recommend to install it. There is an optional package for developers.
yum install pgpool-II-pg96-devel
This installs header files which developers are interested in
On all the PostgreSQL servers you need to install:
yum install pgpool-II-pg96-extensions
All the Pgpool-II configuration files live in /etc/pgpool-II. Please refer to Section 3.8 to see how to set up configuration files.
On RHEL7/CentOS 7, do this once.
systemctl enable pgpool.service
After this, restart the whole system or:
systemctl start pgpool.service
Please note that PostgreSQL servers must have been started before this. To stop Pgpool-II:
systemctl stop pgpool.service
After this, you can stop PostgreSQL servers.
On RHEL6/CentOS 6, do this once.
chkconfig pgpool on
After this, restart the whole system or:
service start pgpool
Please note that PostgreSQL servers must have been started before this. To stop Pgpool-II:
service stop pgpool
After this, you can stop PostgreSQL servers.


This tool will turn any song into a nightcore song. Just upload a song, and enjoy!
Find this on GitHub!


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We’ll match your current location and against your Whoop! Button address to verify instantly.
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GET /folders/{folder_id}/items
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POST /folders
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public final class TangoPositionOrientationProvider extends PositionOrientationProvider
By using data from external sensors the local Tango coordiante system can be placed and oriented in
relation to a global (geographic) coordinate system. For an initial determination of the position
and orientation parameters data from the devices compass and GPS may be added
through the handleRotationVectorObservation(float[])
and
handleLocationObservation(Location)
methods.
For further refinement manual observations may be added by pointing in a map or using the Tango point cloud sensor for matching against a terrain model
Created by runaas on 20.04.2017.
| Modifier and Type | Class and Description |
|---|---|
private class |
TangoPositionOrientationProvider.PrivPredictionMatrixUpdater |
private class |
TangoPositionOrientationProvider.TransformWorker
Worker run in the background, recomputing the transform parameters when new data is present
|
| Modifier and Type | Field and Description |
|---|---|
private float |
compass_az |
private float |
compass_dir |
private float |
declination |
(package private) static double |
EarthRadius |
private double |
geoidH_0 |
private org.ejml.data.DMatrixRMaj |
H_tango |
private KalmanFilter |
kalmanFilter
Simple Kalman filter for smoothing Tango position information and computing
speed, travel direction and predicted positions
|
private android.location.Location |
lastLocation |
private static int |
numParameters |
private java.util.concurrent.CopyOnWriteArrayList<Transform.Observation> |
observations
List of observations used in determining the transformation between TAngo and external coordiantes
|
private OriginData |
origin |
private TangoPositionOrientationProvider.PrivPredictionMatrixUpdater |
predictionMatrixUpdater |
private long |
prevCompTime
Timestamp of previous kalman filter prediction
|
private org.ejml.data.DMatrixRMaj |
R_tango |
(package private) static java.lang.String |
TAG |
private float |
tango_dir |
private double |
tangoVariance
Apriori variance of Tango device position
|
private float[] |
tmpRotA |
private float[] |
tmpRotMatrix |
private float[] |
transformMatrix |
private TangoPositionOrientationProvider.TransformWorker |
transformWorker |
private java.lang.Thread |
transformWorkerThread |
private Transform |
xform |
private boolean |
xformOk |
private org.ejml.data.DMatrixRMaj |
z_tango |
originUpdateListeners
| Constructor and Description |
|---|
TangoPositionOrientationProvider()
Create and initialize
|
| Modifier and Type | Method and Description |
|---|---|
android.location.Location |
getLocation()
Get the current position of the device
|
OriginData |
getOrigin() |
float[] |
getTransformationMatrix()
Get the current rotation of the device in the rotation matrix format
|
Transform.Observation |
handleLatLngHObservation(double latitude,
double longitude,
double height,
float horizontal_accuracy,
float vertical_accuracy)
Handle an observation of the current 3D device position.
|
Transform.Observation |
handleLatLngObservation(double latitude,
double longitude,
float accuracy)
Handle an observation of the current 2D device position.
|
Transform.Observation |
handleLocationObservation(android.location.Location location)
Create a
Transform.PositionObservation3D
object from a GPS (or similar observation). |
java.util.ArrayList<Transform.Observation> |
handlePointCloudObservation(int numPoints,
java.nio.FloatBuffer points,
DTMGrid grid,
float cloud_accuracy)
Create a set of
Transform.PointInTerrainObservation from
a point cloud observation and a gridded terrain model. |
void |
handlePoseObservation(com.google.atap.tangoservice.TangoPoseData pose)
Update the provider with the most recent pose (position and orientation) in the Tango coordinate system
Typically called from
Tango.TangoUpdateCallback.onPoseAvailable(TangoPoseData) |
Transform.Observation |
handleRotationVectorObservation(float[] rotationVector)
Create a
Transform.OrientationObservation
from a rotationVector as given from a Rotation Vector Sensor . |
void |
onDestroy() |
boolean |
removeObservation(Transform.Observation obs)
Remove an observation and initialize recompute of parameters
|
boolean |
removeObservations(java.util.Collection<Transform.Observation> obs_set)
Remove a set of observations and initialize recompute of parameters
|
void |
reset()
Initialize everything (when Tango initializes with a new origin)
|
addOriginUpdateListener, callOriginUpdateListeners, removeOriginUpdateListener
static final java.lang.String TAG
static final double EarthRadius
private volatile float[] transformMatrix
private static final int numParameters
private double tangoVariance
private long prevCompTime
private OriginData origin
private double geoidH_0
private org.ejml.data.DMatrixRMaj z_tango
private org.ejml.data.DMatrixRMaj R_tango
private org.ejml.data.DMatrixRMaj H_tango
private KalmanFilter kalmanFilter
private java.util.concurrent.CopyOnWriteArrayList<Transform.Observation> observations
private float declination
private float compass_dir
private float tango_dir
private float compass_az
private Transform xform
private volatile boolean xformOk
private volatile android.location.Location lastLocation
private TangoPositionOrientationProvider.PrivPredictionMatrixUpdater predictionMatrixUpdater
private java.lang.Thread transformWorkerThread
private TangoPositionOrientationProvider.TransformWorker transformWorker
private final float[] tmpRotA
private final float[] tmpRotMatrix
public TangoPositionOrientationProvider()
public void onDestroy()
public void reset()
PositionOrientationProvider
reset
in class PositionOrientationProvider
public float[] getTransformationMatrix()
PositionOrientationProvider
getTransformationMatrix
in class PositionOrientationProvider
public android.location.Location getLocation()
PositionOrientationProvider
getLocation
in class PositionOrientationProvider
public OriginData getOrigin()
public Transform.Observation handleLocationObservation(android.location.Location location)
Transform.PositionObservation3D
object from a GPS (or similar observation). This method is
typically called from a LocationListener
registred to a
LocationManager
.location
- location data from GPS or similar location servicesTransform.PositionObservation3D
objectpublic Transform.Observation handleLatLngObservation(double latitude, double longitude, float accuracy)
handleLocationObservation(Location)
. Can be used for manually adding a position from maplatitude
- latitude of geograpic position, in degreeslongitude
- longitude of geograpic position, in degreesaccuracy
- apriori standard deviation of position, in metersTransform.PositionObservation2D
objectpublic Transform.Observation handleLatLngHObservation(double latitude, double longitude, double height, float horizontal_accuracy, float vertical_accuracy)
handleLocationObservation(Location)
. Can be used for manually adding a position
from map, combined with an elevation observation from terrain model.latitude
- latitude of geograpic position, in degreeslongitude
- longitude of geograpic position, in degreesheight
- height of geograpic position, in metershorizontal_accuracy
- apriori standard deviation of horizontal position, in metersvertical_accuracy
- apriori standard deviation of height, in metersTransform.PositionObservation3D
objectpublic Transform.Observation handleRotationVectorObservation(float[] rotationVector)
Transform.OrientationObservation
from a rotationVector as given from a Rotation Vector Sensor
.
This method is typically called from a SensorEventListener
,
where the rotationVector is read from a SensorEvent
.rotationVector
- value from a SensorEvent
Transform.OrientationObservation
objectpublic void handlePoseObservation(com.google.atap.tangoservice.TangoPoseData pose)
Typically called from Tango.TangoUpdateCallback.onPoseAvailable(TangoPoseData)
pose
- the position orientation structure given from Tangopublic java.util.ArrayList<Transform.Observation> handlePointCloudObservation(int numPoints, java.nio.FloatBuffer points, DTMGrid grid, float cloud_accuracy)
Transform.PointInTerrainObservation
from
a point cloud observation and a gridded terrain model.
Typically called from Tango.TangoUpdateCallback.onPointCloudAvailable(com.google.atap.tangoservice.TangoPointCloudData)
numPoints
- the number of pointspoints
- a FloatBuffer of point data, with stride 4 as points are stored in the buffer as
x1, y1, z1, w1, x2, y2, z2, w2.... where w is a "reliability" measure (not used here)grid
- A gridded terrain modelcloud_accuracy
- apriori standard deviation of a point to terrain model distance observationpublic boolean removeObservation(Transform.Observation obs)
obs
- the observation to removepublic boolean removeObservations(java.util.Collection<Transform.Observation> obs_set)
obs_set
- the list of observations to remove


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{% extends "base.html" %} {% block content %}
Please enter a name for your meeting: {{ form.meeting_name(size=30, placeholder="CIFAR IMB 2017") }}
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WONDER BOYS (director: Curtis Hanson; screenwriter: Steven Kloves/novel by Michael Chabon; cinematographer: Dante Spinotti; edited by Dede Allen; cast: Michael Douglas (Grady Tripp), Robert Downey Jr. (Terry Crabtree), Katie Holmes (Hannah Green), Tobey Maguire (James Leer), Frances McDormand (Sara Gaskell), Richard Thomas (Walter Gaskell), Rip Torn (Quentin Morewood), Richard Knox (Vernon Hardapple), Jane Adams (Oola), 2000)
Reviewed by Dennis Schwartz
Curtis Hanson has come up with a lovable screwball comedy to direct, about academia in inclement Pittsburgh, for his follow up to his successful "L.A. Confidential." It is Adapted by Steve Kloves from Michael Chabon's 1995 novel. The film works as far as developing the floundering character of the unstylish middle-aged writing professor, Grady Tripp (Michael Douglas), who after success with his first novel, has developed what others on campus think is writer's block. It has been seven years since he published something new. He cannot finish the manuscript for his next book - which now numbers more than 2,600 single-spaced typewritten pages.
The film also conveys a cozy feeling for campus life for would-be writers and for faculty members who live on campus. But it suffers when its story about an aspiring writer and his faculty adviser begins to lose any sense of proportion.
The film revives an old story about the wily writer who is over-the-hill, no longer a 'wonder boy,' but someone going through the motions of teaching and writing, only to have a young student, James Leer (Tobey Maguire), breathe new life into him. It is a familiar story, but told with great charm here and with sharp observations of how these academia types talk to each other and interact.
Professor Tripp is in the midst of a hectic weekend: his wife left him; his mistress, the college chancellor, Sara Gaskell (Frances McDormand), tells him that she is pregnant by him, and can't decide whether to keep the pregnancy or not; his flamboyant New York editor, Terry Crabtree (Robert Downey Jr.), shows up for the college's annual Wordfest literary event with a tall transvestite date and wants to see Grady's new unfinished book, as he is desperate to show his New York bosses a hit novel. The manic editor steals every scene he is in. Tripp's most talented student is the allusive James Leer, who is both suicidal and a compulsive liar, who, despite having his work heavily criticized by fellow students, ends up having his manuscript published by Crabtree and also has a gay fling with him. Then, there's a dead dog in the professor's car trunk that belongs to the chancellor's husband, the head of the English department, Walter (Richard Thomas); and, the professor's nerves are so shot, that he is constantly smoking marijuana. A lot went on during the professor's weekend, a most memorable one for him, as he went around Pittsburgh with James and Crabtree. But, what he was really doing, was searching to find out if he found true love and what was wrong with his writing.
The theme of "Wonder Boys" -- is conventional -- it is about the author's self-development. The script is snappy, the actors are pleasing to behold, the photography, mostly shots of Pittsburgh in the midst of snow or a torrential downpour, is bleak-- but it interestingly sets the tone for the film's playful story. The idea for the film was a good one, but something gets stuck here, preventing the film from taking a chance and becoming a bold story, one that it could have easily become if it made that choice and knew what to edit and what to add.
Off campus, Grady Tripp has a young attractive student of his, renting a room in his house, Hannah Green (Katie Holmes). She is distinguished by her red cowboy boots which she always wears, the obvious crush she has on Tripp, and that she is a stereotype for the attractive girl in class who will try anything to further her career. Here, her attentions are flirted with but not fulfilled.
Quentin Morewood (Rip Torn) is the popular writer who is a guest of the college's for the Wordfest. He is known by his friends as Q, who cranks out books on a regular basis, is rich and famous, and is the envy of more talented writers who are not as successful as he is.
Also dragged into the story are an interracial couple who add some slapstick scenes, enlarging the quirky flavor of the film. Oola (Jane Adams) is a sweet waitress in a bar that the college people frequent. Her husband, a James Brown look-alike, who is falsely called Vernon (Knox) by Grady, turns out to be the real owner of the car driven by Grady. It is taken back by Vernon unceremoniously from in front of Tripp's house. But Grady returns to the car to retrieve the knapsack left there by Leer, because it contains the black, fur-collared sweater that Marilyn Monroe wore when she married Joe DiMaggio, which Leer impulsively stole from the bedroom vault of Walter's (Walter's a collector of such memorabilia for his upcoming book), that was the night Leer shot Walter's blind dog as it was attacking Grady. Those scenes were amusing in the way those old-fashioned comedies of the 1930s and 1940s were.
The film had a warm and fuzzy feeling to it. It had some good Bob Dylan tunes in the background. It had a tolerant feeling for odd characters, potheads, thieves, writing students, gays, interracial couples, and adulterers. It was a return to liberalism movie, bucking a trend of a lot of new films to be more conservative or more extreme in their unconventionality. In any case, it was good to see a film about some older people with a sense of humor, a bit of romance in them, who are able to loosen up a bit. This does not make it a great film, but it was diverting and had a certain sophistication to it that should attract an older audience, one more perceptive about the advantages of a higher education. To the film's credit, it keeps its focus on Tripp as someone who is mixed-up, but who is still trying to be a decent teacher and help those under his tutelage.
REVIEWED ON 3/5/2000 GRADE: C+
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
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Welcome
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Bad Argument
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Argument '{0}' is {1}
AngularJS often asserts that certain values will be present and truthy using a helper function. If the assertion fails, this error is thrown. To fix this problem, make sure that the value the assertion expects is defined and truthy.