|
Standard Reference Data Program |
Data Gateway |
Chemistry WebBook |
|
NIST subscription sites provide data under the NIST Standard Reference Data Program, but require an annual fee to access. The purpose of the fee is to recover costs associated with the development of data collections included in such sites. Your institution may already be a subscriber. Follow the links above to find out more about the data in these sites and their terms of usage.
Go To: Top, Constants of diatomic molecules, References, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Data evaluated as indicated in comments:
HL - E.P. Hunter and S.G. Lias
L - S.G. Lias
Data compiled as indicated in comments:
LL - S.G. Lias and J.F. Liebman
LBLHLM - S.G. Lias, J.E. Bartmess, J.F. Liebman, J.L. Holmes, R.D. Levin, and W.G. Mallard
LLK - S.G. Lias, R.D. Levin, and S.A. Kafafi
RDSH - H.M. Rosenstock, K. Draxl, B.W. Steiner, and J.T. Herron
| Quantity | Value | Units | Method | Reference | Comment |
|---|---|---|---|---|---|
| IE (evaluated) | 6.66 ± 0.18 | eV | N/A | N/A | L |
| Quantity | Value | Units | Method | Reference | Comment |
| Proton affinity (review) | 1190.6 | kJ/mol | N/A | Hunter and Lias, 1998 | HL |
| Quantity | Value | Units | Method | Reference | Comment |
| Gas basicity | 1162.3 | kJ/mol | N/A | Hunter and Lias, 1998 | HL |
| IE (eV) | Method | Reference | Comment |
|---|---|---|---|
| 6.66 ± 0.18 | END/DER | Dalleska and Armentrout, 1994 | LL |
| 6.5 ± 0.3 | EVAL | Belyaev, Gotkis, et al., 1990 | LL |
| 7. ± 1. | EI | Farber, Srivastava, et al., 1987 | LBLHLM |
| 6.9 ± 0.15 | DER | Murad, 1983 | LBLHLM |
| 7. ± 1. | EI | Farber and Srivastava, 1976 | LLK |
| 6.5 | EI | Drowart, Exsteen, et al., 1964 | RDSH |
Go To: Top, Gas phase ion energetics data, References, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Data compiled by: K.P. Huber and G. Herzberg
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Progression of absorption bands (ΔG ~ 850) in Kr and Xe matrices, 20000 - 26000 cm-1; observed for both Ca16O and Ca18O, but not definitely assigned. | ||||||||||||
| Brewer and Wang, 1972 | ||||||||||||
| (B → A') | ||||||||||||
| Lejeune, 1945; Gaydon, 1955; missing citation; Volnyets, Kovalenok, et al., 1974 | ||||||||||||
| Orange system of strong emission bands, 15700 - 16700 cm-1; no analysis. | ||||||||||||
| Lejeune, 1945; Gaydon, 1955; missing citation; missing citation | ||||||||||||
| C 1Σ+ | 28857.8 | 560.9 Z | 4.0 | 0.3731 1 | 0.0032 | (0.0000007) | 1.989 | C ↔ x 2 R | 28772.4 Z | |||
| missing citation; Veits and Gurvich, 1967 | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| B 1Π | 25991 | [574.4] Z | 0.3882 3 1 | 0.0055 | (0.0000007) | 1.950 | B ↔ X 4 R | 25913.0 Z | ||||
| Lagerqvist, 1954; Veits and Gurvich, 1967 | ||||||||||||
| A 1Σ+ | 11554.8 | 718.9 Z | 2.11 | 0.40592 5 | 0.00137 | 5.4 | 1.9067 | A → X R | 11548.84 Z | |||
| missing citation; missing citation; Brewer and Hauge, 1968; Field, 1974 | ||||||||||||
| A' 1Π | 8433 | 545.7 H | 2.54 | 0.337 | 0.0021 | 2.093 | A' → X 6 R | 8340 | ||||
| missing citation | ||||||||||||
| a 3Πi | (8313) 7 | 556 | 3.3 | 0.335 | 2.099 | |||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| X 1Σ+ | 0 | 732.11 Z | 4.81 8 | 0.44452 | 6.58 9 | 1.8221 | 10 |
| 1 | Perturbations. |
| 2 | ξ system of Lejeune and Rosen, 1945. |
| 3 | Value of BQ; BPR - BQ = +0.0005. |
| 4 | η system of Lejeune and Rosen, 1945. |
| 5 | Many rotational perturbations by A' 1Π and a 3Πi Hultin and Lagerqvist, 1951, Field, 1974. |
| 6 | Directly observed for v' > 9 only; vibrational numbering determined from the perturbations in A 1Σ+ Field, 1974. |
| 7 | A = -58, from perturbations in A 1Σ+ as are the other constants for this state; see Field, 1974. |
| 8 | The vibrational constants in the Table are derived from levels with v ≤ 4 Hultin and Lagerqvist, 1951, Lagerqvist, 1954. From band heads in the A-X system (v" ≤ 13) and accounting for head-origin separations, Brewer and Hauge, 1968 have derived ωe= 733.4 Brewer and Hauge, 1968, ωexe= 5.28 Brewer and Hauge, 1968. |
| 9 | missing note |
| 10 | The question whether this is the ground state was for long in doubt but the observation by Ault and Andrews, 1975 of a fundamental frequency of 707 cm-1 in a nitrogen matrix seems to settle it: see also Field, 1974 and the theoretical work of Carlson, Kaiser, et al., 1970. For computed 1Σ ground state properties see Yoshimine, 1968. |
| 11 | From the Ca + ClO2 chemiluminescence spectrum Engelke, Sander, et al., 1976. By contrast, the most recent mass-spectrometric Colin, Goldfinger, et al., 1964, Drowart, Exsteen, et al., 1964 and flame-photometric Kalff and Alkemade, 1973 determinations - corrected, where necessary, for a 1Σ ground state - lead to D00 = 4.04 eV Kalff and Alkemade, 1973. |
Go To: Top, Gas phase ion energetics data, Constants of diatomic molecules, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Hunter and Lias, 1998
Hunter, E.P.; Lias, S.G.,
Evaluated Gas Phase Basicities and Proton Affinities of Molecules: An Update,
J. Phys. Chem. Ref. Data, 1998, 27, 3, 413-656. [doi:10.1063/1.556018]
[all data]
Dalleska and Armentrout, 1994
Dalleska, N.F.; Armentrout, P.B.,
Guided ion beam studies of reactions of alkaline earth ions with O2,
Int. J. Mass Spectrom. Ion Processes, 1994, 134, 203. [all data]
Belyaev, Gotkis, et al., 1990
Belyaev, V.N.; Gotkis, I.S.; Lebedeva, N.L.; Krasnov, K.S.,
Ionization potentials of MX molecules (M = Ca, Sr, Ba; X = F, Cl, Br, I, OH, O),
Russ. J. Phys. Chem., 1990, 64, 773. [all data]
Farber, Srivastava, et al., 1987
Farber, M.; Srivastava, R.D.; Moyer, J.W.; Leeper, J.D.,
Effusion mass spectrometric determination of thermodynamic properties of the gaseous mono- and di-hydroxides of calcium and KCaO(g),
J. Chem. Soc. Faraday Trans. 2, 1987, 83, 3229. [all data]
Murad, 1983
Murad, E.,
Abstraction reactions of Ca+ and Sr+ ions,
J. Chem. Phys., 1983, 78, 6611. [all data]
Farber and Srivastava, 1976
Farber, M.; Srivastava, R.D.,
The dissociation energies of calcium oxide and strontium oxide,
High Temp. Sci., 1976, 8, 73. [all data]
Drowart, Exsteen, et al., 1964
Drowart, J.; Exsteen, G.; Verhaegen, G.,
Mass spectrometric determination of the dissociation energy of the molecules MgO, CaO, SrO and Sr2O,
J. Chem. Soc. Faraday Trans., 1964, 60, 1920. [all data]
Brewer and Wang, 1972
Brewer, L.; Wang, J.L.-F.,
Ground state of gaseous CaO; a study of the matrix spectra of Ca and CaO,
J. Chem. Phys., 1972, 56, 4305. [all data]
Lejeune, 1945
Lejeune, J.-M.,
Application de la methode d'explosion de fils minces a l'etude du spectre de CaO,
Bull. Soc. R. Sci. Liege, 1945, 14, 318. [all data]
Gaydon, 1955
Gaydon, A.G.,
Green and orange band spectra of CaOH, CaOD and calcium oxide,
Proc. R. Soc. London A, 1955, 231, 437. [all data]
Volnyets, Kovalenok, et al., 1974
Volnyets, G.A.; Kovalenok, G.V.; Sokolov, V.A.,
Rotational structure of the 5472-5463 Å band of calcium oxide,
Opt. Spectrosc. Engl. Transl., 1974, 36, 609, In original 1034. [all data]
Veits and Gurvich, 1967
Veits, I.V.; Gurvich, L.V.,
Investigation of the absorption spectra of molecules of difficulty volatile substances and radicals in shock waves,
Dokl. Chem. Engl. Transl., 1967, 173, 377, In original 1325. [all data]
Lagerqvist, 1954
Lagerqvist, A.,
Ultra-violet and blue bands of calcium oxide,
Ark. Fys., 1954, 8, 83. [all data]
Brewer and Hauge, 1968
Brewer, L.; Hauge, R.,
Near infrared bands of diatomic CaO and SrO,
J. Mol. Spectrosc., 1968, 25, 330. [all data]
Field, 1974
Field, R.W.,
Assignment of the lowest 3Π and 1Π states of CaO, SrO, and BaO,
J. Chem. Phys., 1974, 60, 2400. [all data]
Lejeune and Rosen, 1945
Lejeune, J.-M.; Rosen, B.,
Contribution a l'etude du spectre de CaO,
Bull. Soc. R. Sci. Liege, 1945, 14, 322. [all data]
Hultin and Lagerqvist, 1951
Hultin, M.; Lagerqvist, A.,
An infra-red band-system of calcium oxide,
Ark. Fys., 1951, 2, 471. [all data]
Ault and Andrews, 1975
Ault, B.S.; Andrews, L.,
Nitrogen matrix reactions of alkaline earth metal atoms with ozone: infrared spectra of the alkaline earth metal oxide molecules,
J. Chem. Phys., 1975, 62, 2320. [all data]
Carlson, Kaiser, et al., 1970
Carlson, K.D.; Kaiser, K.; Moser, C.; Wahl, A.C.,
Electronic structure and low-lying triplet states of CaO,
J. Chem. Phys., 1970, 52, 4678. [all data]
Yoshimine, 1968
Yoshimine, Y.,
Computed ground state properties of BeO, MgO, CaO, and SrO in molecular orbital approximation,
J. Phys. Soc. Jpn., 1968, 25, 1100. [all data]
Engelke, Sander, et al., 1976
Engelke, F.; Sander, R.K.; Zare, R.N.,
Crossed-beam chemiluminescent studies of alkaline earth atoms with ClO2,
J. Chem. Phys., 1976, 65, 1146. [all data]
Colin, Goldfinger, et al., 1964
Colin, R.; Goldfinger, P.; Jeunehomme, M.,
Mass-spectrometric studies of vaporization of the sulphides of calcium, strontium and barium. The dissociation energy of S2 and SO,
Trans. Faraday Soc., 1964, 60, 306. [all data]
Kalff and Alkemade, 1973
Kalff, P.J.; Alkemade, C.T.J.,
Determination of dissociation energies for some alkaline earth (hydro-) oxides in CO/N2O flames,
J. Chem. Phys., 1973, 59, 2572. [all data]
Go To: Top, Gas phase ion energetics data, Constants of diatomic molecules, References
| IE (evaluated) | Recommended ionization energy |
Copyright for NIST Standard Reference Data is governed by the Standard Reference Data Act.
|
Standard Reference Data Program |
Data Gateway |
Chemistry WebBook |
If you have comments or questions about this site, please contact us.


--- author: display_name: JP email<EMAIL_ADDRESS>tags: - data - utilities - water date: 2016-07-12 18:14:11 -0700 categories: [] RetailCulinaryWater: hub: alias: Utah Retail Culinary Water Service Areas name: culinarywaterserviceareas item_id: dc62a286013f447e88fc45480077c944 org: utahDNR updates: - Annually (On DNR schedule) --- {% include abstract.html name="Retail Culinary Water Suppliers Dataset" stewards="Utah Division of Water Resources, Utah Department of Natural Resources" abstract="This dataset attempts to represent the retail culinary water suppliers service area boundaries for the State of Utah, with a small selection of suppliers in Idaho and Wyoming represented as well. Data sources include the Utah Division of Water Resources, Utah Division of Drinking Water, Utah Division of Water Rights, Utah Division of Public Utilities, and the Utah Geospatial Resource Center. Boundaries were derived from a combination of the following data layers available in the State Geographic Information Database (SGID): Counties, various county parcels, Municipalities, PLSSSections, and TaxEntities2013." %}
Utah Retail Culinary Water Service Areas represent public community water suppliers. Eventually, public
non-community (transient) and self-supplied industry (non-transient) suppliers will be added. This feature class
includes system boundaries that are historical and no longer active. It also includes some systems that are
outside the state. Historical and outside the state systems can be filtered using the ENDYEAR
and
STATE
fields, respectively.These boundaries are derived from many different sources. They include
spatial data received directly from the supplier, municipal boundaries, phone call descriptions of service areas,
printed maps that had hand drawn boundaries that were then digitized, etc.


Piers Morgan admits future on GMB isn’t certain as ITV extends show
The show will air for an extra 30 minutes each day
by Rebecca CarterTV's Piers Morgan has admitted his future on Good Morning Britain isn't set yet after ITV extended the show.
Earlier this week, it was revealed that the daytime show will run for an extra 30 minutes each day in a huge schedule shake-up.
Piers and co-star Susanna Reid celebrated the news on Wednesday's programme but he revealed he hasn't yet negotiated a contract about hosting the show longer.
Read more: Susanna Reid loses it as Piers Morgan accuses Meghan of 'playing the victim card'
Susanna said: "For some reason this programme, and probably down to you [Piers], has been extended.
"From January, we will be on air Piers Morgan until 9 o'clock in the morning," as the presenters clapped and cheered.
Piers said: "The show will yeah. The show has been extended until 9 o’clock and let’s just say I haven’t been secured for that period yet."

Piers replied: "I prefer to be honest. So, we'll see."
Charlotte Hawkins asked him: "So you might just get up at half-past eight and go?"
Piers said: "I might not be here," to which Susanna clarified: "The idea is that he might not be here at all."
Piers continued: "It just depends really if the public want me or not. I am sure viewers would like nothing more than have Adil Ray replace me full time. Wouldn't you viewers?"
Susanna hit back: "There's no problem with viewing figures when Adil Ray's on," to which Piers quipped: "Really? Well, the one's I saw absolutely tanked."
It comes after ITV announced changes to the daytime schedule.
Read more: ITV FINALLY confirms The Jeremy Kyle Show replacement
From January 6, Good Morning Britain will be extended by half an hour to run from 6am until 9am, Lorraine will move to 9am, followed at 10am by This Morning, also extended by 30 minutes, before Loose Women at 12.30pm, ending at 1.30pm.
Kevin Lygo, ITV Director of Television, said: "Live television is at the core of what ITV does and what we bring to viewers.
"Our new schedule presents the opportunity to engage with our audience throughout the first part of each day, with some of the most well-known shows on television, in a way that no other channel does."
Leave us a comment on our Facebook page @EntertainmentDailyFix and let us know what you think of this story.


MANARAT INTERNATIONAL UNIVERSITY
CSE FEST 2020
MIU CSE Club is happy to announce that we are going to arrange a
two-day long first CSE FEST 2020 for ICT enthusiasts from different
Universities, Colleges.
MIU CSE Club is happy to announce that we are going to arrange a
two-day long first CSE FEST 2020 for ICT enthusiasts from different
Universities, Colleges.
MIU CSE Club is happy to announce that we are going to arrange a
two-day long first CSE FEST 2020 for ICT enthusiasts from different
Universities, Colleges.
MIU CSE Club is happy to announce that we are going to arrange a
two-day long first CSE FEST 2020
Contact e-mail for any question or information of MIU CSE FEST
<EMAIL_ADDRESS>MIU CSE Fest 2020 Logo
MIU CSE Club is happy to announce that we are going to arrange a
two-day long first CSE FEST 2020 for ICT enthusiasts from different
Universities, Colleges. MIU CSE Club is happy to announce that we are
going to arrange a two-day long first CSE FEST 2020 for ICT
enthusiasts from different Universities, Colleges.


ngD3Timeline with date labels and line for current time
A simple ngD3Timeline
A simple ngD3Timeline without Axis
ngD3Timeline works with circles and a single date field passed in
ngD3Timeline combine circles and rectangles
ngD3Timeline with hover, click events, and scroll events
ngD3Timeline can change colors and add labels
ngD3Timeline rotate tick mark labels
ngD3Timeline with colors mapped from the data
ngD3Timeline with colors mapped from the data for individual time objects
ngD3Timeline with relative timepoints
ngD3Timeline stacked with axis on top
ngD3Timeline stacked with backgrounds
ngD3Timeline stacked with backgrounds and ticks
a complex ngD3Timeline


Transform.PositionObservation3D
object from a GPS (or similar observation).Transform.PointInTerrainObservation
from
a point cloud observation and a gridded terrain model.Tango.TangoUpdateCallback.onPoseAvailable(TangoPoseData)
Transform.OrientationObservation
from a rotationVector as given from a Rotation Vector Sensor
.


Heather Runyon
Web Designer / Front End Developer
About Me
I'm a web designer / developer based in Colorado Springs, Colorado. I have a passion for web design and love to create for web and mobile devices.
CSS
Javascript
HTML
Responsvie Web Design
Adobe Photoshop


Main figures
- Figure 1. Skewed distribution of UMIs leads to increased number of UMI collisions.
A. Skewness of UMI distributionB. Uneven nucleotide frequencies- C. Estimated number of collisions
- Figure 2. Comparison of UMI collision and sequencing error correction methods.
- Figure 3. Correcting for Cellular Barcode errors.
- A. Origin of background cells
B. Number of equidistant adjacent CBs- C. Increase in #molecules per cell
- Figure 4. Selection of the optimal size threshold for 10x BMMCs dataset.
- Figure 5. Filtration of low-quality cells for the 10x 8k PBMCs dataset.
- Figure 6. Filtration of low-quality cells for the inDrop mouse BMCs dataset.


|
App Engine Python SDK
v1.6.9 rev.445
The Python runtime is available as an experimental Preview feature.
|
Public Member Functions | |
| def | __init__ |
| def | __repr__ |
| def | reversed |
| def | to_bytes |
| def | urlsafe |
| def | advance |
| Public Member Functions inherited from google.appengine.datastore.datastore_query._BaseComponent | |
| def | __eq__ |
| def | __ne__ |
Static Public Member Functions | |
| def | from_bytes |
| def | from_websafe_string |
Static Public Attributes | |
| to_websafe_string = urlsafe | |
An immutable class that represents a relative position in a query. The position denoted by a Cursor is relative to a result in a query even if the result has been removed from the given query. Usually to position immediately after the last result returned by a batch. A cursor should only be used on a query with an identical signature to the one that produced it.
| def google.appengine.datastore.datastore_query.Cursor.__init__ | ( | self, | |
_cursor_pb = None , |
|||
urlsafe = None |
|||
| ) |
Constructor. A Cursor constructed with no arguments points the first result of any query. If such a Cursor is used as an end_cursor no results will ever be returned.
| def google.appengine.datastore.datastore_query.Cursor.advance | ( | self, | |
| offset, | |||
| query, | |||
| conn | |||
| ) |
Advances a Cursor by the given offset. Args: offset: The amount to advance the current query. query: A Query identical to the one this cursor was created from. conn: The datastore_rpc.Connection to use. Returns: A new cursor that is advanced by offset using the given query.
|
static |
Gets a Cursor given its byte string serialized form. The serialized form of a cursor may change in a non-backwards compatible way. In this case cursors must be regenerated from a new Query request. Args: cursor: A serialized cursor as returned by .to_bytes. Returns: A Cursor. Raises: datastore_errors.BadValueError if the cursor argument does not represent a serialized cursor.
|
static |
Gets a Cursor given its websafe serialized form. The serialized form of a cursor may change in a non-backwards compatible way. In this case cursors must be regenerated from a new Query request. Args: cursor: A serialized cursor as returned by .to_websafe_string. Returns: A Cursor. Raises: datastore_errors.BadValueError if the cursor argument is not a string type of does not represent a serialized cursor.
| def google.appengine.datastore.datastore_query.Cursor.reversed | ( | self | ) |
Creates a cursor for use in a query with a reversed sort order.
| def google.appengine.datastore.datastore_query.Cursor.to_bytes | ( | self | ) |
Serialize cursor as a byte string.
| def google.appengine.datastore.datastore_query.Cursor.urlsafe | ( | self | ) |
Serialize cursor as a websafe string. Returns: A base64-encoded serialized cursor.


Class Netlist
Container for parsed data
Inheritance
System.Object
Netlist
Inherited Members
System.Object.ToString()
System.Object.Equals(System.Object)
System.Object.Equals(System.Object, System.Object)
System.Object.ReferenceEquals(System.Object, System.Object)
System.Object.GetHashCode()
System.Object.GetType()
System.Object.MemberwiseClone()
Namespace: SpiceSharp
Assembly: SpiceSharpParser.dll
Syntax
public class Netlist
Constructors
| Improve this Doc View SourceNetlist(Circuit)
Constructor
Declaration
public Netlist(Circuit ckt)
Parameters
| Type | Name | Description |
|---|---|---|
| SpiceSharp.Circuit | ckt | The circuit |
Properties
| Improve this Doc View SourceCircuit
Get the circuit
Declaration
public Circuit Circuit { get; }
Property Value
| Type | Description |
|---|---|
| SpiceSharp.Circuit |
Exports
Get a list of exported quantities
Declaration
public List<Export> Exports { get; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<Export> |
Path
Get the current subcircuit path Used for parsing subcircuit definitions and instances
Declaration
public SubcircuitPath Path { get; }
Property Value
| Type | Description |
|---|---|
| SubcircuitPath |
Readers
All statement readers
Declaration
public StatementReaders Readers { get; }
Property Value
| Type | Description |
|---|---|
| StatementReaders |
Simulations
Get a list of simulations
Declaration
public List<ISimulation> Simulations { get; }
Property Value
| Type | Description |
|---|---|
| System.Collections.Generic.List<SpiceSharp.Simulations.ISimulation> |
Methods
| Improve this Doc View SourceSimulate()
Perform all simulations in the simulation queue
Declaration
public void Simulate()
StandardNetlist()
A standard netlist with the following features:
- Components: C, D, E, F, G, H, I, L, M, Q, R, S, X, W
- Control statements: .IC, .NODESET, .OPTIONS, .PARAM,
- Exporters: V(), I(), VDB(), IDB(), VP(), IP(), VR, IR, VI, RI, @dev[par]
- Subcircuit definitions
- An expression parser
Declaration
public static Netlist StandardNetlist()
Returns
| Type | Description |
|---|---|
| Netlist |
Events
| Improve this Doc View SourceAfterSimulationFinished
Event called after a simulation has finished
Declaration
public event NetlistSimulationEventHandler AfterSimulationFinished
Event Type
| Type | Description |
|---|---|
| NetlistSimulationEventHandler |
BeforeSimulationInitialized
Event called before a new simulation is started
Declaration
public event NetlistSimulationEventHandler BeforeSimulationInitialized
Event Type
| Type | Description |
|---|---|
| NetlistSimulationEventHandler |
OnExportSimulationData
Event called when a simulation exports simulation data
Declaration
public event ExportSimulationDataEventHandler OnExportSimulationData
Event Type
| Type | Description |
|---|---|
| SpiceSharp.Simulations.ExportSimulationDataEventHandler |


How a flea market under the M32 is helping transform a 'deprived' area of Bristol
Organisers say the area under the M32 was considered 'dangerous' by residents
by Alexander BrockOrganisers of a flea market underneath the M32 say it is helping uplift a derelict space once blighted by prostitution and drug pushing.
The M32 Flea Market has been running for two years in Eastville and in that time has grown to feature a variety of local crafters and artists.
The market was launched by LitterARTI, an organisation which attempts to bring communities together through events and other projects.
One of its founders, Annali Grimes, said she wanted to help transform the underpass which was considered "dangerous" by residents living nearby.
“When we started working in Eastville underneath the motorway, the only thing positive here was the DIY skatepark," she said.
"What struck me was that people lived around this area, their homes were here, the main feature is the M32 Motorway and they had this dead space right on their doorsteps, where prostitution, drug pushing and fly-tipping was happening.
"It was dark and dangerous. It's a prominent space and a lot of people would try to avoid it.
"We asked the question: What if the local community could actually benefit from this space, become guardians and look after it?
Taking inspiration from the DIY Skatepark under the same bridge, Annali began to raise funds and sought advice from residents.
"We had an initial consultation with local people and the idea of the market came from that," she said.
"It follows in the legacy of the old market which was held in the Ikea car park years ago."
Annali added the market has transformed the underpass and "reconnected" communities since it launched two years ago.
"By bringing events and people into the space, the M32 underpass is transformed into a vibrant and friendly space with the community coming together," she said.
"The M32 has cut off the communities in the area, such as Fishponds and Easton.
"These communities were once connected in the past and through the market we are reconnecting them.
"When the M32 Flea Market is on, you forget the problems in the area and for those few hours the space is filled with a bustling, happening, creative energy.
"There's friendly and colourful characters everywhere, with cool and curious stalls, ranging from pre-loved clothing to vintage and hand-made.”
The next M32 Flea Market will be held on Saturday, November 23.
For the latest news in and around Bristol, visit and bookmark Bristol Live's homepage .


Ext.NET Mobile
4.1.0
.NET Component Framework for Enterprise Level Apps
Ext.Net.Mobile.IVirtual Interface Reference
More...
Inheritance diagram for Ext.Net.Mobile.IVirtual:
Detailed Description
The documentation for this interface was generated from the following file:
Interfaci/IVirtual.cs
Ext
Net
Mobile
IVirtual
Generated by
1.8.13


 A withdrawal fee of 0.2 ARRR will be automatically deducted from the amount.
!arrrtip <@user> <amount> [private]
Tips the @ mentioned user with the desired amount, e.g.
!arrrtip @test123456#7890 1.00
!arrrtip squall <amount> [<@role>]
Rains the desired amount onto 100 random online users (optionally, within specified role), e.g.
!arrrtip squall 50.00, !arrrtip squall 50.00 @Supporters
!arrrtip soak <amount> [<@role>]
Soaks the desired amount onto 100 random online and idle users (optionally, within specified role), e.g.
!arrrtip soak 50.00, !arrrtip soak 50.00 @Supporters
!arrrtip cannon <amount> [<@role>]
Tips 2-5 random lucky online users with the amount (optionally, within specified role), e.g.
!arrrtip cannon 5.00, !arrrtip cannon 5.00 @Supporters
!arrrtip faucet
Gets an amount from the faucet (applicable every 4 hours)
!arrrtip lottery
Display information about current lottery cycle
!arrrtip lotterymanual
Displays a short lottery manual
!arrrtip lotterytickets [<@cycleNumber>]
Display information about all tickets bought in specified lottery cycle (current if left empty)
!arrrtip buyticket <amount> [private]
Buys lottery tickets for the specified amount if a lottery cycle is active, e.g.
!arrrtip buyticket 5.00
!arrrtip donate <amount> [private]
Donates to the current lottery cycle without buying a ticket, e.g.
!arrrtip donate 5.00


| Class and Description |
|---|
| BlockDeviceMapping |
| KeyPair |
| Class and Description |
|---|
| SecurityGroup |
| Class and Description |
|---|
| InstanceState
The current state of the instance..
|
| RunningInstance |
| SecurityGroup |
| Class and Description |
|---|
| Image |
| KeyPair |
| RunningInstance |
| Class and Description |
|---|
| BlockDevice |
| BlockDeviceMapping |
| Hypervisor
Hypervisor of the image.
|
| InstanceState
The current state of the instance..
|
| RootDeviceType
The root device type used by the AMI.
|
| RunningInstance |
| RunningInstance.Builder |
| Class and Description |
|---|
| KeyPair |
| Reservation |
| SecurityGroup |
| Class and Description |
|---|
| KeyPair |
| Class and Description |
|---|
| BlockDeviceMapping |
| Class and Description |
|---|
| BlockDeviceMapping.Builder |
| Reservation |
| ReservedInstancesOffering |
| RunningInstance |
| SecurityGroup |
| SecurityGroup.Builder |
| Class and Description |
|---|
| KeyPair |
| RunningInstance |
| Class and Description |
|---|
| Image.ImageState |
| InstanceState
The current state of the instance..
|
| KeyPair |
| RunningInstance |
| Class and Description |
|---|
| PasswordData
The encrypted administrator password for an instance running Windows.
|
| RootDeviceType
The root device type used by the AMI.
|
| VirtualizationType
Virtualization type of the image.
|
| Class and Description |
|---|
| SecurityGroup |
| Class and Description |
|---|
| Image |
| Image.ImageState |
| InstanceState
The current state of the instance..
|
| KeyPair |
| RunningInstance |
| SecurityGroup |
| Class and Description |
|---|
| BlockDeviceMapping |
| Class and Description |
|---|
| RootDeviceType
The root device type used by the AMI.
|
| SecurityGroup |
| Class and Description |
|---|
| Image |
| KeyPair |
| Reservation |
| RunningInstance |
| Class and Description |
|---|
| Attachment |
| AvailabilityZoneInfo |
| BlockDevice |
| BundleTask |
| Image |
| Image.EbsBlockDevice |
| InstanceStateChange |
| KeyPair |
| PasswordData
The encrypted administrator password for an instance running Windows.
|
| Permission |
| PublicIpInstanceIdPair |
| Reservation |
| RunningInstance |
| SecurityGroup |
| Snapshot |
| Subnet
Amazon EC2 VPCs contain one or more subnets.
|
| Tag
To help you manage your Amazon EC2 instances, images, and other Amazon EC2
resources, you can assign your own metadata to each resource in the form of
tags.
|
| UserIdGroupPair |
| Volume |
| Volume.InstanceInitiatedShutdownBehavior
Specifies whether the instance's Amazon EBS volumes are stopped or terminated when the
instance is shut down.
|
| Class and Description |
|---|
| BlockDeviceMapping |
| Image.Architecture |
| Class and Description |
|---|
| Attachment |
| RunningInstance |
| Snapshot |
| Volume |
| Class and Description |
|---|
| Tag
To help you manage your Amazon EC2 instances, images, and other Amazon EC2
resources, you can assign your own metadata to each resource in the form of
tags.
|
| Class and Description |
|---|
| Attachment |
| Attachment.Status |
| AvailabilityZoneInfo |
| BlockDevice |
| BundleTask |
| Image |
| InstanceStateChange |
| KeyPair |
| PasswordData
The encrypted administrator password for an instance running Windows.
|
| Permission |
| PublicIpInstanceIdPair |
| Reservation |
| RunningInstance |
| RunningInstance.Builder |
| SecurityGroup |
| SecurityGroup.Builder |
| Snapshot |
| Subnet
Amazon EC2 VPCs contain one or more subnets.
|
| Tag
To help you manage your Amazon EC2 instances, images, and other Amazon EC2
resources, you can assign your own metadata to each resource in the form of
tags.
|
| Volume |
| Volume.InstanceInitiatedShutdownBehavior
Specifies whether the instance's Amazon EBS volumes are stopped or terminated when the
instance is shut down.
|
| Volume.Status |
| Class and Description |
|---|
| KeyPair |
| Class and Description |
|---|
| Image |
| Class and Description |
|---|
| Image |
Copyright © 2009-2013 The Apache Software Foundation. All Rights Reserved.


Mailbeam
Send an encrypted message to a GitHub user.
Note: RSA support only for now.
Github Username
Message
Encrypt


public int result(int n) { if (n == 1) return 2; else return 2 * result(n - 1); } If n > 0, how many times will "result" be called to evaluate "result(n)"?
n > 0
n
2^n
2n
n^2
On each call, n is decremented by 1, until it reaches 1 when it hits the base case.


We are not bottons :)
We are not bottons :)
I'm so, COOL!


Cassandra E. Granade
Centre for Engineered Quantum Systems
joint work with Joshua Combes and D. G. Cory • arXiv coming soon
https://www.cgranade.com/research/talks/iqc/09-2015/
$
\newcommand{\ee}{\mathrm{e}}
\newcommand{\ii}{\mathrm{i}}
\newcommand{\dd}{\mathrm{d}}
\newcommand{\id}{𝟙}
\newcommand{\TT}{\mathrm{T}}
\newcommand{\defeq}{\mathrel{:=}}
\newcommand{\Tr}{\operatorname{Tr}}
\newcommand{\Var}{\operatorname{Var}}
\newcommand{\Cov}{\operatorname{Cov}}
\newcommand{\rank}{\operatorname{rank}}
\newcommand{\expect}{\mathbb{E}}
\newcommand{\sket}[1]{|#1\rangle\negthinspace\rangle}
\newcommand{\sbraket}[1]{\langle\negthinspace\langle#1\rangle\negthinspace\rangle}
\newcommand{\Gini}{\operatorname{Ginibre}}
\newcommand{\supp}{\operatorname{supp}}
$
This talk can be summarized in one slide.
\begin{equation} \Pr(\text{model} | \text{data}) = \frac{\Pr(\text{data} | \text{model})}{\Pr(\text{data})} \Pr(\text{model}) \end{equation}
Bayesian methods for experimental data processing work.
Quantum tomography: Characterizing quantum states and processes from experimental data.
From a statistical perspective, this is a parameter estimation problem.
We want tomographic methods that: - Provide accurate estimates. - Characterize their uncertainty. - Allow inclusion of prior knowledge. - Can track changes with time. - Come with “off-the-shelf” implementations.
We get the first two for free from taking a Bayesian perspective.
- What prior should we use? - How should we update estimates in time? - How can we efficiently implement Bayesian tomography?
In this talk, we will address all three challenges.
See also: - Jones [10/fpbsfw](https://dx.doi.org/10/fpbsfw) - Blume-Kohout [10/cn772j](https://dx.doi.org/10/cn772j)
Suppose we want to learn $x$ from data $D$. Then \[ \Pr(x | D) = \frac{\Pr(D | x)}{\Pr(D)} \Pr(x) \] is the posterior distribution over $x$.
$\Pr(x | D)$ describes what we know about $x$.
We estimate $x$ by the expectation value \[ \hat{x} = \expect[x | D] = \int x\,\Pr(x|D)\, \dd x. \]
This estimator is optimal for the mean squared error.
We estimate our error in the same way: \begin{align*} \expect[(x - \hat{x}(D))^2 | D] & = \expect[(x - \expect[x | D])^2 | D] \\ & = \Var(x | D). \end{align*} For multiple parameters, \[ \expect[(\vec{x} - \hat{\vec{x}})^\TT (\vec{x} - \hat{\vec{x}}) | D] = \Tr(\Cov(\vec{x} | D)). \]
More generally, \[ \hat{f} = \expect[f(x) | D]. \]
$\Pr(D | x)$ is a likelihood function that specifies an experimental model.
For state tomography, our likelihood is Born's rule, \[ \Pr(E | \rho) = \Tr[E \rho], \] where $\rho$ is a state and $E$ is a measurement effect.
Thus, Bayes' rule allows us to estimate $\rho$.
Same techniques as before apply to enable learning snapshots of dynamics. Choi-Jamiołkowski isomorphism lets us rewrite process tomography as (ancilla-assisted) state tomography.
\[ \Tr[E \Lambda(\rho)] = \Tr[(\id \otimes E) J(\Lambda) (\rho^\TT \otimes \id)] = \sbraket{\rho^\TT, E | J(\Lambda)} \]
We parameterize a state $\rho$ as a real vector $\vec{x}$, \[ x_i = \sbraket{B_i | \rho} = \Tr(B_i^\dagger \rho), \] where $\{B_i\}$ is a basis of Hermitian operators.
By convention, $\Tr(B_i) = \delta_{i0} / \sqrt{D}$. E.g.:
For state tomography, the BME is approximately optimal for the fidelity (Ferrie and Keung [1503.00677](https://arxiv.org/abs/1503.00677)).
The error in an observable $X$ is given by the covariance superoperator $\Sigma\rho = \Cov(\sket{\rho})$, \begin{align*} \Var(X) & = \Var_{\rho}[\langle X\rangle_{\rho}] + \langle X^2\rangle_{\expect[\rho]} - \langle X\rangle_{\expect[\rho]}^2 \\ & = \sbraket{X | \Sigma\rho | X} + \sbraket{X | X | \hat\rho} - \langle X\rangle_{\expect[\rho]}. \end{align*} (Blume-Kohout 10/cn772j)
In practice, Bayesian mean estimation is not tractable in the exact case. We thus use particle filtering to approximate.
- Huszár and Houlsby [10/s86](https://dx.doi.org/10/s86) - Ferrie [10/7nt](https://dx.doi.org/10/7nt)
Implemented by the QInfer library for Python.
\begin{align} \Pr(\rho) & \approx \sum_{p\in\text{particles}} w_p \delta(\rho - \rho_p) \\ w_p & \mapsto w_p \times \Pr(E | \rho_p) / \mathcal{N} \end{align}
Big advantage: we only need samples from the prior!
As we collect data, this becomes unstable, so we must resample.
Particle filtering is used in a range of quantum information applications.
- Hamiltonian learning: Granade *et al.* [10/s87](https://dx.doi.org/10/s87), Stenberg *et al.* [10/7nw](https://dx.doi.org/10/7nw) - Randomized benchmarking: Granade *et al.* [10/zmz](https://dx.doi.org/10/zmz) - Quantum Hamiltonian learning and bootstrapping: Wiebe *et al.* [10/tdk](https://dx.doi.org/10/tdk), Wiebe *et al.* [10/7nx](https://dx.doi.org/10/7nx) - Phase estimation: Wiebe and Granade [1508.00869](https://arxiv.org/abs/1508.00869)
- MCMC: works, but isn't streaming or time-dependent. - Rejection filtering (Wiebe and Granade [1508.00869](https://arxiv.org/abs/1508.00869)): only keeps sufficient statistics; need more expressive instrumental distribution.
Expressing as Bayesian parameter estimation / particle filtering problem affords us a few other advantages.
A credible region $R_\alpha$ for $\rho$ satisfies \[ \Pr(\rho \in R_{\alpha} | D) \ge \alpha. \]
Construct from particle approximation to posterior, covariance regions (Granade *et al* [10/s87](https://dx.doi.org/10/s87)), convex hull or minimum-volume enclosing ellipse (Ferrie [10/tb4](https://dx.doi.org/10/tb4)).
Built-in to QInfer.
- Akaike / Bayesian information criterion: Guţă *et al.* [10/7nz](https://dx.doi.org/10/7nz), van Enk and Blume-Kohout [10/7n2](https://dx.doi.org/10/7n2) - Bayes factors: Wiebe *et al.* [10/tdk](https://dx.doi.org/10/tdk) \begin{equation} \text{BF}(A : B) = \frac{\Pr(A | \text{data})}{\Pr(B | \text{data})} \end{equation} - Model averaging: Ferrie [10/7nt](https://dx.doi.org/10/7nt)
Bayes factor–based model selection built-in to QInfer.
\begin{align*} \text{Suppose } \vec{x} & \sim \Pr(\vec{x} | \vec{y}) \\ \text{Then, } \Pr(D | \vec{y}) & = \expect_{\vec{x} | \vec{y}} [\Pr(D | \vec{x}, \vec{y})] \\ & = \int \Pr(D | \vec{x}, \vec{y}) \Pr(\vec{x} | \vec{y})\,\dd\vec{x}. \end{align*} Marginalizing gives us a likelihood for the hyperparameters $\vec{y}$!
Allows us to include effects outside of Born's rule. For instance, non-Cauchy decoherence (Granade et al. 10/s87).
But how do we choose our prior? Let's get to the meat of things.
In order to be useful, a prior over states should: - Have support over all states, - Allow us to encode our assumptions, and - Be efficient to draw samples from.
It helps to consider an uninformative prior first.
$\rank(\rho) = K$. If $K = 1$, $\rho$ is pure. If $K = N$, Hilbert-Schmidt prior.
NB: Choosing $X$ to be real gives Ginibre over redits.
import qinfer as qi
basis = qi.tomography.pauli_basis(1)
prior = qi.tomography.GinibreReditDistribution(basis)
qi.tomography.plot_rebit_prior(prior, rebit_axes=[1, 3])
What makes $\Gini(N, K)$ uninformative? \[ \expect[\rho] = \id / N. \] The mean is the maximally-mixed state.
- Haar, uniform over pure states - Bures, uniform over mixed states - BCSZ, uniform over CPTP maps
How do we add information to the prior, specifically the prior estimate state $\rho_\mu$?
Big idea: Use an ensemble of amplitude damping channels to transform a uniform prior.
Let $\phi$ be a fiducial prior. Then, for scalars $\alpha,\beta$ and a state $\rho_*$, draw $\rho_{\text{sample}}$ by:
NB: $\supp \pi \supseteq \supp \phi$
Choose $\rho_*$ s. t. $\expect_{\rho\sim\pi}[\rho] = \rho_\mu$:
\[ \rho_* = \left(\frac{\alpha + \beta}{\alpha}\right) \left( \rho_\mu - \frac{\beta}{\alpha+\beta} \frac{\id}{N} \right) \]
Choose $\alpha,\beta$ s. t. $\expect[\epsilon]$ is minimized: \[ \alpha = 1 \qquad \beta = \frac{\lambda_\min}{1 - N \lambda_\min}, \] where $\lambda_\min$ is the smallest eigenvalue of $\rho_\mu$.
That is, we contract the fiducial prior as little as possible to obtain the desired mean.
This construction preserves the support of our “flat” prior, takes $\rho_\mu$ as an input and can be easily sampled.
Inherits other assumptions by convexity (e.g.: rebit, CPTP, positivity, etc.).
import qinfer as qi
import qutip as qt
I, X, Y, Z = qt.qeye(2), qt.sigmax(), qt.sigmay(), qt.sigmaz()
prior_mean = (I + (2/3) * Z + (1/3) * X) / 2
basis = qi.tomography.pauli_basis(1)
fiducial_prior = qi.tomography.GinibreReditDistribution(basis)
prior = qi.tomography.GADFLIDistribution(fiducial_prior, prior_mean)
QInfer's tomography support is backed by QuTiP.
Posterior covariance characterizes uncertainty.
Principal channels tell us which components dominate our uncertianty.
(a quick tutorial)
All models are wrong, some are useful. —Chris Ferrie
We've seen how to create bases and priors, to finish we need a model, an updater and a heuristic.
model = qi.BinomialModel(qi.tomography.TomographyModel(basis))
The sequential Monte Carlo updater performs Bayes updates using particle filtering.
updater = qi.smc.SMCUpdater(model, 2000, prior)
heuristic = qi.tomography.RandomPauliHeuristic(updater,
other_fields={'n_meas': 40}
)
This heuristic measures random Paulis 40 times each.
for idx_exp in xrange(50):
experiment = heuristic()
# This is where your data goes! 💗
# For now, we'll simulate. 💔
datum = model.simulate_experiment(true_state, experiment)
updater.update(datum, experiment)
Interlace Bayesian updates with diffusion \[ \rho(t_{k+1}) = \rho(t_k) + \Delta \eta. \] and truncation.
Draw each traceless parameter of the diffusion step $\Delta \eta$ from a Gaussian.
(Isard and Blake [10/cc76f6](https://dx.doi.org/10/cc76f6))
We don't need to assume that the "true" state follows a random walk.


| Rank | Address | Balance | Num. Trans. | Num. Inputs | Num. Outputs | Last Seen | First Seen |
| {{info.rank}} | {{info._id}} | {{info.balance | number}} NSR {{convertCurrency(info.balance,"NSR",curr.symbol) | currency:curr.icon}} ({{(info.balance/NSRsupply)*100 | number:2}}%) | {{info.num_trans | number}} | {{info.num_inputs | number}} | {{info.num_outputs | number}} | {{info.lastSeen}} | {{info.firstSeen}} |


Miscellaneous
Some basic machine learning knowledge which are frequently applied in various research (also often asked in interview). I wrote them down for review.
Pareto optimality
It is a resource allocation state in which it is impossible that reallocate the resources so as to improve an individual situation without making other individuals worse off.
It is a minimum notion of efficiency but unnecessarily lead to desire results since it doesn’t take fairness and equality into account.
Pareto improvement
It means we can reallocate the limited resources to make some individuals better off without making any other individuals worse off.
Precision-recall estimation
False positive (FP): classify the negative class into positive category.
False negative (FN): classify the positive class into negative category.
Recall越低，很多正例被错误分类（放走了很多正例） Precision越低，很多负例被错误分类（包进了很多负例）
ROC and AUC
ROC is the curve based on TPR and FPR.
AUC is the area under the ROC curve.
AUC作为评价指标，可以有效处理样本不平衡问题。比如：在垃圾邮件分类中，垃圾邮件的label = 1，只占总样本的1%；非垃圾邮件的label = 0，占总样本99%。 如果一个naive分类器把所有的样本分成0，那么也有99%的精确度。 但是如果用AUC作为评价指标时，可以得到TPR=0（实际是垃圾邮件的，分对了多少），FPR=0（实际非垃圾邮件，分错了多少），不管分类的阈值怎么取，始终都是坐标系中的点(0, 0)，因此AUC=0 ? (需要进一步搞清楚AUC的计算公式)
Average precision (AP and mAP)
AP is a common metric in various object detection paper. It is related with recall and precision. Conceptually, it is the area of recall-precision curve. See below picture.
But for convenience, people usually approximately estimate this area by interpolation. Check here for the specific algorithm.
Intersection over union (IoU)
This metric has been widely used on object detection. It is used for measuring how accurate the predicted bounding box is.
Bias and variance
Bias is used to describe how close it is between the prediction and true value.
Variance is used to describe how concentrated the predictions are.
Overfitting usually lead to high variance since it is trained to fit the noises in training data.
Underfitting usually lead to high bias since it is unable to capture enough correlation between input and target.
Classification and regression
The training target in classification is discrete.
The training target in regression is continuous.
Supervised learning, unsupervised learning, semi-supervised learning
The training data is labeled in supervised learning like classification
The training data is unlabeled in unsupervised learning like clustering, generative adversarial network, EM algorithm, PCA and etc.
Off-policy and on policy
Off-policy is the training target of a policy is different from the behavior policy like 𝜀-greedy (more exploration)
On policy is the training target of a policy is exact the same with the behavior policy. (less exploration)
On policy can converge faster than off-policy.
Feature selection (how to select feature from massive statistical data)
Filtering: according to the feature variance
Wrapper: we can randomly pick some features to train and evaluate the result
Embedded: we can use a machine learning method to train first and then check how important those features are.
Feature dimensionality reduction
There two common methods: PCA and LDA. PCA is an unsupervised dimensionality reduction method, whereas LDA is supervised.
The main difference between them:
PCA try to extract new feature vector which has big variance. LDA try to project original space to low dimensional space so as to maximize the distance between different classes and minimize the within-class variance. Please check here for details about LDA.
Generative model
All types of generative models aims at learning the true distribution of training data so as to generate new data point with some variations. But it is not always possible to learn the exact data distribution.
It belongs to unsupervised learning (we don’t need to label the training data).
Two types of generative models:
- Variational autoencoder (VAE)
- Generative Adversarial Networks (GAN) check here and paper for detailed introducation.
- The generator network and discriminator network (evaluator).
P, NP, NP-Complete, NP-hard
P is the problem that we can find a solution which can be finished in polynomial time.
The relationship between P, NP, NP-hard and NP-Complete.
NP is the problem that we may not be able to find a polynomial time complexity solution but we can validate if a specific solution is correct or not within polynomial time.
NP-hard is a more general problem that some NP problems can reduce to within polynomial time. Once a NP-hard problem is solved, all such reducible NP problems will be solved. (Note: Sometimes, after NP reduce to NP-hard, this NP-hard problem may not be validated within polynomial time). The global composition in jigsaw puzzle solving can be seen as a SAT problem variant, but it cannot be validated within polynomial time. So it belongs to NP-hard.
NP-complete is overlap between NP-hard and NP, which means they are reduced from NP and still can be validated within polynomial time.
SAT (satisfiability) problem: if we can find a Boolean assignment that make a system output true. For example, we cannot find such assignment for , but we can find for .
Variant: 2-SAT and 3-SAT. See this intuitive example (过年了，正打算烧年夜饭 blabla…)
Problem Reduce: this means we can convert a problem into a more general (usually more difficult) problem. For example, problem A: find the minimum element in an array. Problem B: sort the whole array. We can say A can be reduce to B, since if we can solve the problem B, problem A will be solved trivially.
The advantage of max pooling layer
Reduce the number of parameters. To avoid over-fitting
Increase the perceptual field
Extract the main features.
Convolutional Neural Network
Batch normalization
Suppose input= [batch, height, width, depth]. If we use axes= [0,1,2] to calculate (e.g. mean, var = tf.nn.moments(input, axes=[0, 1, 2])), then the output of mean, var will be a 1-D vector with size=depth. If we use axes=[0] to calculate, then the output of mean, var will be a 3-D vector with size=[height, width, depth]. The picture below demonstrates this 3-D vector case.
Group normalization
The comparison between several normalization methods
Batch normalization has several defects:
- BN only works fine with large batch size, but this cannot guaranteed on complex tasks (e.g. object detection) due to insufficient memory.
- BN use mini-batch’s mean and variance to normalize during training, but use moving-average (滑动平均) mean and variance to normalize during testing. It introduces inconsistency, especially when data distribution between training and testing are different.
Unlike BN to normalize on dimension [batch, width, height], the group normalization (GN) try to normalize on dimension [width, height, channel=k] (k is a hyperparameter). It doesn’t matter with batch. So it solves the first defect. Moreover, GN always use group mean and variance during training and testing. IN, LN and GN doesn’t use batch dimension to normalize.
Recurrent Neural Network (RNN)
An tutorial and introduction about RNN
Hidden Markov Model
An specific dice example will help to understand.
The basic elements: Transition matrix, emission matrix, initial state.
Three basic problems:
- Given the HMM model, how to calculate the probability of an observation. (forward algorithm)
- Given the HMM model and a sequence of observations, how to estimate which the most possible hidden state sequences are. (Viterbi algorithm, dynamic programming)
- Given the observation data, how to estimate the model parameters. (learning problem, Baum–Welch algorithm)
Note: In the learning problem, the target of forward algorithm is to calculate the . The target of backward algorithm is to calculate the . The target of forward-backward algorithm is to calculate the .
For the details, please check here
EM (Expectation-Maximization) algorithm
An intuitive tutorial.
EM algorithm is an unsupervised learning.
Maximum likelihood vs Maximum A Posterior (MAP)
<=> .
Usually given the training data , our target is to maximize the posterior , where is the model parameters. To do that, we usually have two approaches:
-
Maximum likelihood. Since , if we can assume the prior is a constant, then . Maximum likelihood estimation is widely used in solving various machine learning models, especially when prior is unknown.
-
Maximum A Posterior (MAP). Sometimes, we may not be able to make such assumption that the prior is a constant. Instead, prior may satisfy a distribution. In this case, the target is . Since every data is generated independently, we have . If we use prior to represent the complexity of a model, then MAP is linked with structure risk minimization.
A systematic probabilistic graph model course
https://ermongroup.github.io/cs228-notes/
Bayes rule and Bayes network
Bayes rule is widely used on various machine learning problems. Some basic math/probabilistic knowledge need to be clarified.
The general Bayes rule for joint distribution:
If we have a bayes network which models the problem, we can simplify the calculation. For example, given a simple network as below, we have
This can work because (when we know variable B, the variable A and C can be seen independent. But if B is unknown, A and C are dependent. This is because we can use the medium variable B to represent the dependence between A and C.)
From this network, we can have some other property like
Because and
Generally, we also have below property without graph structure if are independent.
Because
Several concepts need to be distinguished
- Bayesian Network
- Markov Random Field
- Conditional Random Field
- Markov Chain
- Hidden Markov Model
- Markov Decision Process
Why divide n-1 to get unbiased variance estimation in sampling data
http://blog.sina.com.cn/s/blog_c96053d60101n24f.html
A good systematic Chinese tutorial for machine learning
https://nndl.github.io/
The summary of math part is a good material for reviewing the math background.
Information theory
Refer to here for detailed introducation.
Encode length for a random variable , .
Entropy: the average length for optimal encoding the whole of random variable X. . The more stochastic variable is, the larger entropy is.
Cross-entropy: the average encoding length when we use distribution to encode information whose real distribution is .
Obviously, , when . We have minimum , when .
Kullback-Leibler divergence (KL-divergence):
The meaning of KL-divergence is very similar with cross-entropy. Both of them measure how different between distribution and . So when is given, the optimization process of and is the same. Comparing with cross-entropy, KL-divergence measures the absolute difference. When , .
Logistic regression
sigmoid function : convert linear classification result to a probability.
maximize likelihood => solve the parameters in linear classification.
The key here is that sigmoid function normalize raw result into 0 and 1. Then we can construct below likelihood.
Apply logarithm on both side.
This is equivalent to cross-entropy. And then we can apply gradient descend to optimization.
Check here for details.
Support Vector Machine
Please check here1 and here2 for the details about naive SVM, SVM with kernel and SVM with soft margin.

In SVM, we want to find a hyperplane (w, b are the parameters to learn), so that this hyperplane can correctly divide two classes (the labels are -1, 1) data points.
We define that the data points which are the nearest ones to hyperplane should locate on (for positive datapoint) and (for negative datapoint). The reason why this definition makes sense is that we can always adjust the learned hyperplane by multiple a factor (e.g. k) without change the hyperplane in space (i.e. <=> ).
So our goal is to maximize the distance between and . Obviously, the distance is . We can write the objective function as below
To solve this objective, we apply Lagrangian multipler to convert original constrained problem to
Then calculate the partial gradients w.r.t. first, then . This is the naive SVM
Sometimes, the datasets cannot be divided by linear plane. So we usually use kernel tricks, which introduce nonlinear property in classifier. The main idea of kernel trick is to find a mapping to map original space to a higher space. The picture below gives an example. This is the SVM with kernel. Check here for the reasons why kernel can map to a higher dimensions.
The P mapping in above picture is the . You should distinguish the kernel function and dimensional mapping function.
However, sometimes, kernel can still fail. So people introduce the soft margin, which allows misclassification on some data points. The objective is This is soft margin.
The can be seen as hinge loss: . And SVM can be explained in hinge loss.
Kalman filter
It is a algorithm for accurately estimating or predicting based on multiple observed data. See here for an intuitive explanation and example.
A classic example is SLAM in which we have multiple data collecting sensors like odometry, IMU and visual features. The Kalman filter is to solve how to reliably combine all sensor data and estimate a accurate pose.
In linear case, the Kalman filter problem can be formulated as
The first equation is motion equation where is the state at -th moment. is motion measurement with noise which is satisfied a gauss distribution .
The second equation is observation equation where is the observation measurement with noise which is also satisfied a gauss distribution .
Since the motion and observation measurements are noisy (subject to gauss distribution) which means that the current state estimation is noisy too, our goal now is to find an optimal state estimation which has the minimum uncertainty (i.e. minimum covariance).
The way is to calculate Kalman Gain which is the optimal weight between motion and observation. I ignore the derivation of Kalman Gain, since it is a little bit complicated. You can search online about this.
The complete algorithm is:
First, predict currect state from previous state based on motion equation. (The previous state can be represented by gauss distribution )
This equation is easy to understand according to the properties of gauss distribution (The new mean and new covariance from two gauss distribution).
Second, calculate the Kalman Gain
Finally, correct/modify the rough estimation from motion equation.
If you derivate a little, you can find that if (no covariance in observation), then , which means the final estimation is totally depended on observation equation. There is a similar conclusion when .
Now we know the idea of Kalman filter in linear system.
In reality, the system is usually nonlinear (e.g. SLAM problem). To apply above idea, we need to extend KF to nonlinear case. The Taylor expansion can do this.
Cross-entropy instead of mean square error (MSE) as the loss function in classification?
when we do classification, we usually apply softmax (this is an important premise) to normalize the output value to 0-1. In this case, the gradient of MSE loss will be prone to 0 when the prediction is closed to 0 or 1. This will lead to slow convergence. On the contrary, the gradient of cross-entropy is linear with the prediction changing. When the prediction is closed to the label, the gradient will be small and vice versa.
* Cross-entropy
where is the objective cross-entropy function. To minimize it, we calculate the gradient w.r.t.
So we have
When is closed to 1, the gradient will be prone to 0. (Remember that we use one-hot encoding to represent )
* MSE
So we have the gradient
When is closed to 0 and 1, the gradient will be closed to 0. This is not desirable and will slow down the learning process. Because if , we hope the learning step is large, but the gradient is 0.
The property of softmax
- property: the big value will take a large portion of the probability.
- potential drawback: exponential problem.
Covariance and correlation coefficients
Covariance is a measure of how two variables change together, but its magnitude is unbounded, so it is difficult to interpret. By dividing covariance by the product of the two standard deviations, one can calculate the normalized version of the statistic. This is the correlation coefficient.
- Covariance: represent the unnormalized correlation.
- Correlation coefficient: represent the normalized correlation.
Refer to here for details.
Normal distribution and multivariate normal distribution
- One-variable
- Multi-variables
where is the determinant of covariance matrix C(X). By the way, C(X) is usually denoted as in many literatures.
Refer to here for details.
Some important concepts in machine learning (statistic learning)
1. Hypothesis space
Hypothesis space is a set of models which map the input to output. When hypothesis space is given, the range of learning space is determined. For example, if we plan to use a neural network as the model, the hypothesis space consists of all possible parameters of this neural network.
2. Expected risk, Empirical risk, Structure risk
In machine learning, we usually suppose all data are i.i.d (independently drawn from identical distribution). The expected risk is defined as
where are training data. is loss function. is joint probability distribution. The ideal learning procedure is to find a best which can minimize . However, it is impossible to know joint probability distribution beforehand. Because there is no need to learn if we already know . Therefore, machine learning is usually an ill-posed problem. About the ill-posed problem, you can check here and here
In practice, we minimize empirical risk instead of expected risk.
Obviously, is the loss w.r.t. joint probability distribution which is absolutely accurate. is the loss w.r.t. the average of training data which is an approximation. According to law of large numbers, this approximation could be accurate when giving enough training data.
Structure risk is similar with empirical risk. The only difference is that it introduces regularization term to penalize complex model to relieve overfitting.
3. Cross validation, k-fold cross validation
Cross validation is a strategy to select a model with best generalization ability. When we have enough labeled data, we can simply split them into training, validation, testing sets and use validation set to select model. K-fold cross validation is also a common strategy when data are relatively insufficient.
The general procedure is as follows:
- Shuffle the dataset randomly.
- Split the dataset into k groups
- For each unique group:
- Take the group as a hold out or test data set
- Take the remaining groups as a training data set
- Fit a model on the training set and evaluate it on the test set
- Retain the evaluation score and model
- Select a model with best score
4. Generative model, Discriminative model
If a model try to learn joint probability distribution , it is a generative model. If a model learn conditional probability distribution (i.e. decision boundary), it is a discriminative model.
These two concepts here are different from they are in GAN (generative adversarial network).
5. Learning Bias (Inductive Bias)
The introduction from wiki
In machine learning, models or algorithms are trained on training dataset and make predictions on testing datasets which are unseen data. Without any additional assumptions, this problem cannot be solved exactly since unseen situations might have an arbitrary output value. The kind of necessary assumptions about the nature of the target function are subsumed in the phrase inductive bias
The difference between bagging and boosting, the advantage of assembly learning
The difference see here.
I make a brief summary here.
Bagging (usually used in random forest)
- Bootstrap Sampling from training data (random sampling without replacement).
- All samples with same weights in training.
- K seperate models with same importance. So all models can be trained parallelly
Boosting (e.g. adaptive boosting method)
- Training on all data with weights (misclassified data will be more important in next training round)
- K seperate models with different importance.
Why Bagging improve performance?
There are two explanations:
- It can reduce the variance of single decision tree since model may fit some minor features if we use all training data. Minor feature fitting leads to high variance (over-fitting).
- From probabilistic perspective, the probability of wrong prediction from multiple models is low.
Why Boosting improve performance?
The goal of optimization is to minimize the misclassified data.


| Stake ~ Introduction |
| Stake is a Windows-based level editor for the NES ROM, Castlevania. It was developed
using Delphi 6, on the Windows XP operating system. It supports the many versions of
Castlevania, such as the european ROM, the american ROM, and even Akumajou Dracula -
the special rerelease of the Japanese CV. It doesn't however, support the Famicom Disk
System original of CV. (Yet :)) Here's what features Stake has:
- Graphics are read from the ROM. It doesn't support the following important things yet:
- Hidden items editing |


{% extends 'base.html' %} {% block header_title %}Ops, Error 404{% endblock header_title %} {% block content %}
Error 404, page not found.
The page you are looking for isn't here.
You can come back to the home page, if you wish.
Or you can go another way, as long as you know where you want to go ;)
“Would you tell me, please, which way I ought to go from here?"
"That depends a good deal on where you want to get to."
"I don't much care where –"
"Then it doesn't matter which way you go.”


---
layout: default
---
View the workshops, exercises and assignments to be done on your own and with your own research.Workshops
View the training decks for the workshops right in your browser.
To view the Speaker notes press "s" once the presentation is open.
View the guides, closely guided lessons which contain all information needed to follow them. They provide a low barrier to entry the world of Ansible.


Weekly Feminist Jobs Digest: 8/5/2013 - 8/9/2013
The Weekly Feminist Jobs Digest is a service of the Feminist Majority Foundation, made possible through the support of individuals like you. Your contribution is vital to the continued success of our empowering work. [ https://org.salsalabs.com/o/1400/shop/custom.jsp?donate_page_KEY=2117 ]
Donate Today [ https://org.salsalabs.com/o/1400/shop/custom.jsp?donate_page_KEY=2117 ]!
New Feminist Jobs
(DC Metro Region - 8/7/2013)
Title: Community Organizer
Organization: NARAL Pro-Choice Virginia Foundation
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30446 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30446 ]
(DC Metro Region - 8/9/2013)
Title: Fundraising Specialist
Organization: Avon Walk for Breast Cancer
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30453 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30453 ]
(DC Metro Region - 8/9/2013)
Title: VTC Engineer SHORT TERM
Organization: NetCentrics
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30452 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30452 ]
(DC Metro Region - 8/9/2013)
Title: Development Manager
Organization: She Should Run/Womens Campaign Fund PAC
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30449 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30449 ]
(East Coast - 8/6/2013)
Title: Major Gifts Officer
Organization: Center for Reproductive Rights
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30439 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30439 ]
(East Coast - 8/6/2013)
Title: State Legislative Counsel
Organization: Center for Reproductive Rights
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30440 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30440 ]
(East Coast - 8/9/2013)
Title: Architect
Organization: Cannon Design
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30455 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30455 ]
(East Coast - 8/9/2013)
Title: Director of Organizing
Organization: Girls for Gender Equity
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30454 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30454 ]
(Midwest - 8/7/2013)
Title: Community Educator-TOP Facilitator
Organization: Planned Parenthood of Illinois
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30443 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30443 ]
(Midwest - 8/7/2013)
Title: Medical Assistant
Organization: Planned Parenthood of Illinois
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30442 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30442 ]
(Nationwide (US) - 8/9/2013)
Title: Staff Attorney
Organization: ACLU Foundation
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30461 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30461 ]
(Nationwide (US) - 8/9/2013)
Title: Manager, Research and Support Regional Desk
Organization: Leadership for Educational Equity Job
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30448 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30448 ]
(West Coast - 8/9/2013)
Title: Field Researcher
Organization: Service Employees International Union
http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30460 [ http://www.feminist.org/911/jobs/jobdescription.asp?jobID=30460 ]


Fashionable Laptop Bags for Women at Reduced Prices
by Review TeamMost working women these days are required to carry a laptop to their place of work and back home. The bag to carry the laptop or notebook reflects their tastes and choices. There are fashionable bags available for this purpose in different sizes and colors. Make your choice and buy the one you need, like or both. When there are also deals going on these bags, the purchase is worth every cent spent on them.
Laptop Bag-15.6-Inch for Women
This laptop bag for women measures 15.7×11.8×11.8 inches and is suitable for laptops in sizes up to 15.6 inches. The material of the bag is a combination of PU leather & canvas and can protect your laptop from bumps, shocks, and dust. The shoulder strap has padding to give comfort while carrying it. The laptop bag has additional space to carry your accessories and other gadgets like a mouse, your mobile phone, iPod, data cable, etc.
This laptop bag for women will cost you $29.99 down from $33.88.
15.6-Inch Waterproof Laptop Tote Bag for Women
This 15.6-inch waterproof laptop tote bag for women has an outer surface made from waterproof PU leather and has a polyester lining inside. You may be able to choose from a range of colors. It has four compartments with zippers and a laptop of size 13 inches to 15.6 inches can be accommodated comfortably. You can carry some documents as well in addition to the laptop and accessories. It has a removable shoulder strap. You can use this tote bag for other purposes too. The bag’s dimensions are 16.1×6.3×11.8 inches. You could also order this bag as a gift.
This 15.6-inch waterproof laptop tote bag for women is now going at $36.99, a discount of $13.
Computer Case for Women for 14-Inch Laptop
This computer case for women is a compact bag that can carry any brand laptop or notebook of 14-inches or lower. It can accommodate the usual accessories and a bunch of documents. This bag is made of PU leather with foam inside to protect the machine from getting damaged because of a fall. The manufacturer offers a one-year warranty.
This computer case for women carries a discounted price tag of $29.99 against its earlier price of $35.68, 16% off.


echo "Hello, World!" | nms- Decrypt output immediately, without waiting for a keystroke:
ls -la | nms -a- Decrypt the content of a file, with a custom output color:
cat path/to/file | nms -a -f blue|white|yellow|black|magenta|green|red- Clear the screen before decrypting:
command | nms -a -c


--- layout: documentation title: ExternalIDType ---
The ExternalIDType provides a reference to an ID of an incident in a remote system.
Specifies the source of the External ID.


Exactly what it says on the label
Extremely brief practice getting started with HTML
More HTML practice
I landed in tech entirely by accident. I fell into service/retail industry management for about a decade, less because I wanted to and more because I happened to be good at it. When I moved to Portland, I figured it was an opportunity to find a role that made a real impact on the world and started looking for non-profit work. I didn't find a non-profit to work for, but I did find Epicodus. I've been handling various aspects of the school's career services department ever since. As much as I love having a real, tangible impact on people's lives, somewhere along the way, I fell in love with tech too. I've messed around with HTML and CSS off an on for years and had loads of fun with it, but it's only been in the last couple of years I considered it anything more serious than a haphazard hobby. I try to always be learning something, and since I'm now done with my MBA, it seems like as good a time as any to go through the curriculum.
When I'm not being a serious businessy person, I'm a pretty unrepentant geek. In my downtime I can usually be found:


Oops!
404 Page Not Found
Sorry, an error has occured, Requested page not found!
Financial data is being provided by
YAHOO FINANCE FREE Services
LAGS / DELAYS AND GAPS MAY OCCUR DUE TO THE HIGH VOLUME OF YAHOO FINANCE SERVER TRAFFIC
Please try RELOADING this page, or COMING BACK to it in a FEW SECONDS
In case of INTERNAL SERVER ERROR message RELOAD the page or simply click the below button


Uma rede de passes é apenas a aplicação da teoria de redes e dos seus instrumentos de análise para compreender o desempenho de um time de futebol em uma partida.
Cada jogador é representado por um vértice. Cada vértice está conectado com muitos outros por linhas, que representam os passes trocados entre os jogadores.
Quando representamos a rede de passes de um time de futebol como um grafo , podemos utilizar diversos instrumentos matemáticos para medir a centralidade de cada um dos seus vértices (jodagores). Veja alguns deles:
Centralidade de grau é definida como o número de ligações incidentes de um vértice. O grau pode ser interpretado como a probabilidade que o vértice tem de receber alguma informação da rede.
No caso de uma rede orientada (onde os laços tem direção), define-se duas medidas distintas de centralidade de grau: indegree e outdegree. Indegree é uma contagem do número de ligações direcionadas para o nó e outdegree é o número de ligações que o nó encaminha para outros.
A centralidade de Katz é uma generalização da centralidade de grau. Como a centralidade de grau mede o número de vizinhos diretos, a centralidade Katz mede o número de todos os nós que podem ser conectados através de um caminho, enquanto a contribuição de um nó distante é penalizado por um fator de atenuação.
Representa o número de passes feitos e recebidos por um jogador.
Perhaps the simplest centrality measure in a network is just the degree of a vertex, the number of edges connected to it (see Section 6.9). Degree is sometimes called degree centrality in the social networks literature, to emphasize its use as a centrality measure. In directed networks, vertices have both an in-degree and an out-degree, and both may be useful as measures of centrality in the appropriate circumstances.
Although degree centrality is a simple centrality measure, it can be very illuminating. In a social network, for instance, it seems reasonable to suppose that individuals who have connections to many others might have more influence, more access to information, or more prestige than those who have fewer connections. A non-social network example is the use of citation counts in the evaluation of scientific papers. The number of citations a paper receives from other papers, which is simply its in-degree in the citation network, gives a crude measure of whether the paper has been influential or not and is widely used as a metric for judging the impact of scientific research. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 168
Centralidade de vetor próprio é uma medida da influência de um nó numa rede. Ele atribui pontuações relativas a todos os nós da rede, baseada no conceito de que as ligações para os nós de alta pontuação contribuem mais para a pontuação do nó em questão do que ligações iguais a nós baixa pontuação.
O sistema de PageRank do Google é uma variante da medida de centralidade de vetor próprio. Outra medida de centralidade relacionada é a centralidade de Katz.
A medida de importância de um jogador está relacionada com o fato dele estar ou não conectado com outros jogadores que também são importantes.
A natural extension of the simple degree centrality is eigenvector centrality. We can think of degree centrality as awarding one “centrality point” for every network neighbor a vertex has. But not all neighbors are equivalent. In many circumstances a vertex’s importance in a network is increased by having connections to other vertices that are themselves important. This is the concept behind eigenvector centrality. Instead of awarding vertices just one point for each neighbor, eigenvector centrality gives each vertex a score proportional to the sum of the scores of its neighbors. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 169
In theory eigenvector centrality can be calculated for either undirected or directed networks. It works best however for the undirected case. In the directed case other complications arise. First of all, a directed network has an adjacency matrix that is, in general, asymmetric (see Section 6.4). This means that it has two sets of eigenvectors, the left eigenvectors and the right eigenvectors, and hence two leading eigenvectors. So which of the two should we use to define the centrality? In most cases the correct answer is to use the right eigenvector. The reason is that centrality in directed networks is usually bestowed by other vertices pointing towards you, rather than by you pointing to others. On the World Wide Web, for instance, the number and stature of web pages that point to your page can give a reasonable indication of how important or useful your page is. On the other hand, the fact that your page might point to other important pages is neither here nor there. Anyone can set up a page that points to a thousand others, but that does not make the page important. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 171
Em grafos conectados existe uma distância natural métrica entre todos os pares de nós, definido pelo comprimento de seus caminhos mais curtos. O afastamento de um nó s é definido como a soma de suas distâncias para todos os outros nós, e sua proximidade é definida como o inverso do afastamento. Assim, quanto mais central é o nó, menor é a distância do seu total para todos os outros nós.
Proximidade pode ser considerada como uma medida de rapidez, para determinar a velocidade que ela necessitará para difundir informações de s a todos os outros nós sequencialmente.
Na definição clássica de centralidade de proximidade, a disseminação de informações é modelada através da utilização dos caminhos mais curtos. Este modelo pode não ser o mais realista de todos os tipos de cenários de comunicação. Assim, as definições relacionadas foram discutidas para medir proximidade, como a centralidade de proximidade de caminhos aleatórios introduzida por Noh e Rieger (2004). Mede a velocidade com que as mensagens aleatórias chegam a um vértice de fora da rede, digamos que é uma espécie de versão aleatória das mensagens da centralidade de proximidade.
Este índice mede a proximidade de um jogador em relação a todos os outros do seu time.
An entirely different measure of centrality is provided by the closeness centrality, which measures the mean distance from a vertex to other vertices. (...) Such vertices might have better access to information at other vertices or more direct influence on other vertices. In a social network, for instance, a person with lower mean distance to others might find that their opinions reach others in the community more quickly than the opinions of someone with higher mean distance. (...) The mean distance li is not a centrality measure in the same sense as the others in this chapter, since it gives low values for more central vertices and high values for less central ones, which is the opposite of our other measures. In the social networks literature, therefore, researchers commonly calculate the inverse of li rather than li itself. This inverse is called the closeness centrality (...) Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 183
Closeness centrality is a very natural measure of centrality and is often used in social and other network studies. But it has some problems. One issue is that its values tend to span a rather small dynamic range from largest to smallest. (...) What this means in practice is that it is difficult to distinguish between central and less central vertices using this measure: the values tend to be cramped together with the differences between adjacent values showing up only when you examine the trailing digits. This means that even small fluctuations in the structure of the network can change the order of the values substantially. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 184
(...) closeness, measuring the minimum number of steps that the ball has to under go from one player to reach any other in the team. Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Javier M. Buldú, Javier Busquets, [...], and Jordi Luque
A closeness score indicates how easy it is for a player to be connected with teammates (by passing relation) and, therefore, that player is requested by the team as a target to pass the ball. Thus, it quantifies the proximity of how close is such player to his peers. Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football. Bruno Gonçalves, Sara Santos, Diogo Coutinho, [...], and Carlos Lago Peñas
Centralidade de intermediação quantifica o número de vezes que um nó age como ponte ao longo do caminho mais curto entre dois outros nós. Foi introduzido por Linton Freeman como uma medida para quantificar o controle de um ser humano sobre a comunicação entre outros seres humanos numa rede social.
Mede quantas vezes um jogador aparece como conector na cadeia de passes realizada entre dois jogadores do time.
A very different concept of centrality is betweenness centrality, which measures the extent to which a vertex lies on paths between other vertices. (...) Vertices with high betweenness centrality may have considerable influence within a network by virtue of their control over information passing between others. (...) The vertices with highest betweenness are also the ones whose removal from the network will most disrupt communications between other vertices because they lie on the largest number of paths taken by messages. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 187
Betweenness centrality differs from the other centrality measures we have considered in being not principally a measure of how well-connected a vertex is. Instead it measures how much a vertex falls “between” others. Indeed a vertex can have quite low degree, be connected to others that have low degree, even be a long way from others on average, and still have high betweenness. (...) Betweenness centrality also has another interesting property: its values are typically distributed over a wide range. The maximum possible value for the betweenness of a vertex occurs when thevertex lies on the shortest path between every other pair of vertices. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 189-190
Some other variations on the betweenness centrality idea are worth mentioning. Betweenness gets at an important idea in network analysis, that of the flow of information or other traffic and of the influence vertices might have over that flow. However, betweenness as defined by Freeman is based on counting only the shortest paths between vertex pairs, effectively assuming that all or at least most traffic passes along those shortest paths. In reality traffic flows along paths other than the shortest in many networks. Most of us, for instance, will have had the experience of hearing news about one of our friends not from that friend directly but from another mutual acquaintance—the message has passed along a path of length two via the mutual acquaintance, rather than along the direct (geodesic) path of length one. A version of betweenness centrality that makes some allowance for effects like this is the flow betweenness, which was proposed by Freeman et al. and is based on the idea of maximum flow. Imagine each edge in a network as a pipe that can carry a unit flow of some fluid. We can ask what the maximum possible flow then is between a given source vertex s and target vertex t through these pipes. In general the answer is that more than a single unit of flow can be carried between source and target by making simultaneous use of several different paths through the network. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 191
In terms of our information analogy, one can think of flow betweenness as measuring the betweenness of vertices in a network in which a maximal amount of information is continuously pumped between all sources and targets. Flow betweenness takes account of more than just the geodesic paths between vertices, since flow can go along non-geodesic paths as well as geodesic ones. (For example, the paths through vertices A and B in the example above are not geodesic.) Indeed, in some cases none of the paths that appear in the solution of the maximum flow problem are geodesic paths, so geodesic paths may not be counted at all by this measure. But this point highlights a problem with flow betweenness: although it typically counts more paths than the standard shortest-path betweenness, flow betweenness still only counts a subset of possible paths, and some important ones (such as geodesic paths) may be missed out altogether. One way to look at the issue is that both shortest-path betweenness and flow betweenness assume flows that are optimal in some sense—passing only along shortest paths in the first case and maximizing total flow in the second. Just as there is no reason to suppose that information or other traffic always takes the shortest path, there is no reason in general to suppose it should act to maximize flow (although of course there may be special cases in which it does). (...) A betweenness variant that does count all paths is the random-walk betweenness. In this variant traffic between vertices s and t is thought of as performing an (absorbing) random walk that starts at vertex s and continues un til it reaches vertex t. (...) Random walk betweenness would be an appropriate betweenness measure for traffic that traverses a network with no idea of where it is going—it simply wanders around at random until it reaches its destination. Shortest-path betweenness is the exact opposite. It is the appropriate measure for information that knows exactly where it is going and takes the most direct path to get there. It seems likely that most real-world situations fall somewhere in between these two extremes. However, it is found in practice that the two measures often give quite similar results, in which case one can with reasonable justification assume that the “correct” answer, the one lying between the limits set by the shortest-path and random-walk measures, is similar to both. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 192-193
(...) betweenness centrality, which accounts how many times a given player is necessary for completing the routes (made by the ball) connecting any other two players of its team Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Javier M. Buldú, Javier Busquets, [...], and Jordi Luque
A player with higher betweenness scores is crucial to maintainteam passing connections by acting as a connecting bridge. Also, low scores and spread across certain players may be related with well-balanced passing strategy and less specific players dependence. Betweenness centrality quantifies the occurrences that a node acts as a bridge along the geodesic path between other nodes. Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football. Bruno Gonçalves, Sara Santos, Diogo Coutinho, [...], and Carlos Lago Peñas
O coeficiente de agrupamento local de um vértice (nó) num grafo mede o quão perto os seus vizinhos estão de serem um clique (grafo completo).
Em outras palavras, pode dizer-se que o coeficiente de agrupamento local mede o grau da densidade de ligações da vizinhança de um determinado nó, isto é, corresponde ao grau com que os vizinhos de um nó se interligam. Esta medida foi introduzida por Duncan J. Watts e Steven Strogatz em 1998 para determinar se um grafo constitui uma rede de pequeno mundo.
Mede a frequência de triangulação de passes entre jogadores. Jogadores com alto índice neste indicador são mais necessários para manter a triangulação de passes entre os jogadores ao seu redor. Baixos índices neste indicador revelam uma rede de passe bem balenceada.
We can quantify the level of transitivity in a network as follows.  If u also knows w, we say that the path is closed—it forms a loop of length three, or a triangle, in the network. In the social network jargon, u, v, and w are said to form a closed triad. We define the clustering coefficient to be the fraction of paths of length two in the network that are closed. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 200
Some social networks, such as the email network above, are directed networks. In calculating clustering coefficients for direct networks, scientists have typically just ignored their directed nature (...) as if the edges were undirected. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 203
(...) clustering coefficient (...) represents the average probability that a pair of i′s friends are friends of one another. Local clustering is interesting for several reasons. First, in many networks it is found empirically to have a rough dependence on degree, vertices with higher degree having a lower local clustering coefficient on average. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 204
(...) local clustering can be used as a probe for the existence of so-called 'structural holes' in a network. While it is common in many networks, especially social networks, for the neighbors of a vertex to be connected among themselves, it happens sometimes that these expected connections between neighbors are missing. The missing links are called structural holes and were first studied in this context by Burt. If we are interested in efficient spread of information or other traffic around a network, as we were in Section 7.7, then structural holes are a bad thing— they reduce the number of alternative routes information can take through the network. On the other hand structural holes can be a good thing for the central vertex i whose friends lack connections, because they give i power over information flow between those friends. If two friends of i are not connected directly and their information about one another comes instead via their mutual connection with i then i can control the flow of that information. The local clustering coefficient measures how influential i is in this sense, taking lower values the more structural holes there are in the network around i. Thus local clustering can be regarded as a type of centrality measure, albeit one that takes small values for powerful individuals rather than large ones. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 204
In this sense, local clustering can also be thought of as akin to the betweenness centrality of Section 7.7. Where betweenness measures a vertex’s control over information flowing between all pairs of vertices in its component, local clustering is like a local version of betweenness that measures control over flows between just the immediate neighbors of a vertex. One measure is not necessarily better than another. There may be cases in which we want to take all vertices into account and others where we want to consider only immediate neighbors—the choice will depend on the particular questions we want to answer. It is worth pointing out however that betweenness is much more computationally intensive to calculate than local clustering (see Section 10.3.6), and that in practice betweenness and local clustering are strongly correlated Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 205
The clustering coefficient (...) measures the frequency with which loops of length three—triangles—appear in a network. Networks: An Introduction. M. E. J. Newman, Oxford University Press, 2010, p. 207
Other metrics, such as the clustering coefficient, which measures the number of 'neighbors' of a player that also have passed the ball between them (i.e., the number of triangles around a player), has also been quantified to evaluate the contribution of a given player to the local robustness of the passing network. Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Javier M. Buldú, Javier Busquets, [...], and Jordi Luque
In Fig. 3A, we plot the clustering coefficient C, which is related to the amount of triangles created between any triplet of players. Clustering coefficient is an indicator of the local robustness of networks, since when a triangle connecting three nodes (i.e. players) exists, and a link (i.e., pass) between two nodes is lost (i.e., not possible to make the pass), there is an alternative way of reaching the other node passing through the other two edges of the triangle. In football, the clustering coefficient mesures the triangulation between three players. Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona J. M. Buldú, J. Busquets, I. Echegoyen & F. Seirul.lo
Vamos ver alguns grafos de jogos do Brasil na última Copa do Mundo. Abaixo é possível selecionar diversas métricas diferentes para comparar o desempenho dos jogadores:


A minimal forum application for cat discussion. Demo application is deployed on AWS EC2.
Coding By ColorsRemade a well known game by javascript and canvas. You get to choose single-player mode or two-player online.
Down StairsA minimal forum application for cat discussion. Demo application is deployed on AWS EC2.
Cat CrushAdvance front-end skills continuously, especially interactive animation, user experience and cross browser compatibility.
Strive for a balance between UI/UX, engineering and the product itself.
Prefer B2C or C2C product development.


Africa's burgeoning animation scene got a boost this week with the announcement of an ambitious new partnership that will pair rising talents from across the continent ...
Magnolia Pictures has acquired U.S. and international rights to the comedic drama Lucky John Carroll Lynch’s directorial debut. Lynch is an in-demand character actor who ...
New Line’s remake of “Going in Style” launched with a moderate $600,000 on Thursday night, while Sony’s animated “Smurfs: The Lost Village” debuted with $375,000 ...
Although it sporadically errs on the side of sentimentality and simplification, "The Case for Christ" sustains interest, and even generates mild suspense ...
King Kong is still a box office giant. “Kong: Skull Island” has crossed the $500 million mark at the worldwide box office. It’s the third 2017 title to hit the milestone after Disney’s ...
"Aftermath" is a plane crash movie without a plane crash. Instead, the closest we get is a scene set in the control tower, where a computer screen shows two ...


Log In
My Wishlist
My Account
Welcome
Search
Cart
Categories |
Information |
Health store |
Pharmacy |
About Us


Though often called the koala “bear”, this cuddly animal is not a bear at all; it is a marsupial, or pouched mammal. After giving birth, a female koala carries her baby in her pouch for about six months. When the infant emerges, it rides on its mother's back or clings to her belly, accompanying her everywhere until it is about a year old.
Koalas live in eastern Australia, where the eucalyptus trees they love are most plentiful. In fact, they rarely leave these trees, and their sharp claws and opposable digits easily keep them aloft. During the day they doze, tucked into forks or nooks in the trees, sleeping for up to 18 hours.
When not asleep a koala feeds on eucalyptus leaves, especially at night. Koalas do not drink much water and they get most of their moisture from these leaves. Each animal eats a tremendous amount for its size about two and a half pounds (one kilogram) of leaves a day. Koalas even store snacks of leaves in pouches in their cheeks. A special digestive system a long gut allows koalas to break down the tough eucalyptus leaves and remain unharmed by their poison. Koalas eat so many of these leaves that they take on a distinctive odor from their oil, reminiscent of cough drops.
These plump, fuzzy mammals were widely hunted during the 1920s and 1930s, and their populations plunged. Helped by reintroduction, they have reappeared over much of their former range, but their populations are smaller and scattered. Koalas need a lot of space. About a hundred trees per animal is the pressing problem as Australia's woodlands continue to shrink.